Ecology of Phytoplankton, Acartia Tonsa, and Microzooplankton in Apalachicola Bay, Florida Jennifer Nancy Putland

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2005 Ecology of Phytoplankton, Acartia Tonsa, and Microzooplankton in Apalachicola Bay, Florida Jennifer Nancy Putland

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COLLEGE OF ARTS AND SCIENCES

ECOLOGY OF PHYTOPLANKTON, ACARTIA TONSA, AND MICROZOOPLANKTON IN

APALACHICOLA BAY, FLORIDA

JENNIFER NANCY PUTLAND

A Dissertation submitted to the Department of Oceanography in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Fall Semester, 2005

______Richard Iverson Professor Directing Dissertation

______Sherwood Wise Outside Committee Member

______Nancy Marcus Committee Member

______Joel Kostka Committee Member

______Behzad Mortazavi Committee Member

Approved:

______William Dewar, Chair, Department of Oceanography

The Office of Graduate Studies has verified and approved the above named committee members.

Dedicated to my parents,

Charles Douglas Sauer 1923-1998

and

Joan Agnes Cassells Sauer 1937-2000

iii ACKNOWLEDGMENTS

This research was supported with a Graduate Research Fellowship from the Estuarine Reserves Division, Office of Ocean and Coastal Resource Management, National Ocean Service, National Oceanic and Atmospheric Administration and a Dissertation Research Grant from the Office of Graduate Studies at FSU. I am very thankful to the Department of Statistics, FSU for providing courteous statistical consultation and the Department of Biology, FSU for use of their epifluorescence microscope. Many other people helped me during the course of this research and I am indebted to them. Dave Oliff masterfully designed and built the penthouses, a.k.a. incubators, to house the plankton for my experiments. They worked beautifully and, to this day, show virtually no signs of corrosion. Alan Michels and Jeff Wilcox were my trench mates at the marine lab and were always gentlemen to me. Together with John Hitron, Mark Daniels, and Dennis Tinsley, they made me feel that the marine lab was my home away from home. Lauren Levi, Jessica Stewart, Jena Wanat, and Kim Wren from the Apalachicola Bay National Research Reserve spent countless hours at-sea with me towing nets and schlepping water to and fro, sometimes in miserable weather. They did a great job and always had great attitudes. They were gold. While they did tease me, I am truly thankful to Cris Oppert and Chris Sedlacek for their help in the lab and discussions about copepods. I am also thankful to Henrieta Dulaiova for her help learning how to use Surfer and Mike Dollhopf for his help learning how to analyze nutrient samples. I value the discussions I had with Lee Edmiston and Graham Lewis. This thesis benefited greatly from their pragmatism. There are several friends I have made during my time as a graduate student at FSU. All of them, with some convincing, have helped me practice my motto, “work hard, play hard” and I thank them (Peter Lazarevich, Lou St. Laurent, Evan Hunter, Heath Mills, Joanna Carter, Chris Hunt, Shawn Steadham, Greg Burke, Raoul Lavin). I also thank Bill Dewar for the bantering which made my days at the OSB so much fun. My committee, Nancy Marcus, Sherwood Wise, Joel Kostka, Behzad Mortazavi, and Richard Iverson, has been incredibly positive and supportive of my work. Their guidance has surely improved my understanding of nature. I am particularly indebted to my advisor, Richard Iverson. He has taught me more than I ever thought I would learn as a graduate student, and not

iv just about science. He is a remarkable man and I feel lucky to have spent these last four years as his student. Last, but not least, I would like to thank my husband, Mike Putland. He has been the one- man cheerleading squad for many years now. I am sure that has not been an easy task. After all, the tights alone are enough ;)! Joking aside, I am forever grateful for his patience, his words of wisdom, and that he has always believed in me and my dream.

TABLE OF CONTENTS

List of Tables ...... vi List of Figures...... vii Abstract...... x

INTRODUCTION ...... 1

1. TEMPORAL AND SPATIAL DISTRIBUTION OF PHYTOPLANKTON IN APALACHICOLA BAY, FLORIDA...... 11

Introduction...... 11 Materials and Methods...... 13 Results...... 17 Discussion...... 21

2. ECOLOGY OF ACARTIA TONSA IN APALACHICOLA BAY, FLORIDA ...... 47

Introduction...... 47 Materials and Methods...... 49 Results...... 57 Discussion...... 61

3. MICROZOOPLANKTON: MAJOR HERBIVORES IN APALACHICOLA BAY, FLORIDA ...... 81

Introduction...... 81 Materials and Methods...... 82 Results...... 87 Discussion...... 90

CONCLUSIONS...... 115

REFERENCES ...... 120

BIOGRAPHICAL SKETCH ...... 138

vi LIST OF TABLES

1.1. Well- or partially mixed estuaries that may be classified as Type LNI or HNI ...... 30

3.1. Rates of phytoplankton growth and microzooplankton herbivory ...... 97

vii LIST OF FIGURES

I.1. Apalachicola Bay, Florida...... 9

I.2. Proposed planktonic food web structure in Apalachicola Bay ...... 10

1.2. A. Average sea surface temperature during sampling in Apalachicola Bay. B. Average monthly river discharge for the Apalachicola River...... 31

1.3. Sea surface salinity (psu) distribution during 2003 and 2004...... 32

1.4. A. Attenuation coefficient (k) and average 1% light depth relative to sea surface salinity during 2003 and 2004. B. Average mixed layer light energy relative to sea surface salinity ...... 33

1.5. A. Daily incident light energy relative to sea surface temperature during 2004. B. Average bay DIN (Dissolved Inorganic Nitrogen) (+ S.D.) relative to sea surface temperature during 2003 and 2004...... 34

1.6. A. Dissolved Inorganic Nitrogen (DIN) concentration relative to sea surface salinity. B. Soluble Reactive Phosphate (SRP) concentration relative to sea surface salinity...... 35

1.7. A. Total chlorophyll concentration during winter relative to sea surface salinity. B. Total chlorophyll concentration during summer relative to sea surface salinity ...... 36

1.8. A. Abundance of picocyanobacteria relative to sea surface salinity during summer. B. Abundance of diatoms >20 µm relative to sea surface salinity during summer. C. Abundance of dinoflagellates >20 µm relative to sea surface salinity during summer ...... 38

1.9. Temporal and spatial distribution of carbon: chlorophyll a ratios during 2004...... 39

1.10.A. Phytoplankton growth during 2003 and 2004 relative to sea surface temperature. B. Phytoplankton mean biomass relative to sea surface temperature during 2004. C. Phytoplankton productivity relative to sea surface temperature during 2004 ...... 41

1.11.A. Phytoplankton growth during winter 2003 and winter 2004 relative to sea surface salinity. B. Phytoplankton carbon relative to sea surface salinity during winter 2004. Percent of total phytoplankton carbon composed of pico-, nano- and microphytoplankton relative to sea surface salinity during winter 2004. Phytoplankton productivity relative to sea surface salinity during winter 2004...... 43

1.12.A. Phytoplankton growth during summer 2003 and summer 2004 relative to sea surface salinity. B. Phytoplankton carbon relative to sea surface salinity during summer 2004. C. Percent of total phytoplankton carbon composed of pico-, nano- and microphytoplankton relative to sea surface salinity during summer 2004. D. Phytoplankton productivity relative to sea surface salinity during summer 2003 and 2004...... 45

viii 1.13. Two conceptual models proposed to describe the relationship between phytoplankton growth (µ), biomass (B), productivity (PP), size composition (Pico-,Nano- and Micro- refer to pico-, nano- and microphytoplankton, respectively) and salinity in well-mixed and partially- mixed estuaries ...... 46

2.2.A. Total prey carbon (phytoplankton carbon >5 µm plus microzooplankton carbon 5 to 20 µm) with respect to sea surface temperature. B. Percent of total prey carbon composed of phytoplankton carbon >5 µm with respect to sea surface temperature ...... 67

2.3. Summer abundance of adult Acartia tonsa with respect to sea surface salinity...... 68

2.4. Percent of phytoplankton productivity ingested by Acartia tonsa with respect to surface salinity...... 69

2.5. Clearance rate on phytoplankton by Acartia tonsa with respect to phytoplankton carbon >5 µm ...... 70

2.6.A. Total per capita ingestion rate of Acartia tonsa with respect to sea surface temperature. B. Total per capita ingestion rate of Acartia tonsa relative to predicted Acartia tonsa body carbon with respect to sea surface temperature. C. Percent of Acartia tonsa diet composed of phytoplankton with respect to sea surface temperature. D. Acartia tonsa Relative Preference Indices for microzooplankton and phytoplankton with respect to sea surface temperature...... 72

2.7. Functional feeding response for Acartia tonsa in Apalachicola Bay ...... 73

2.8. Egg production rate of Acartia tonsa with respect to sea surface temperature ...... 74

2.9. Summer egg production rate of Acartia tonsa with respect to surface salinity...... 75

2.10. Egg production efficiency of Acartia tonsa during winter and summer with respect to surface salinity...... 76

2.11.A. Percent of total carbon fixed allocated towards protein synthesis with respect to surface salinity. B. Percent of total carbon fixed allocated towards lipid synthesis with respect to surface salinity. Asterisk denotes data not included in regression. C. Percent of total carbon fixed allocated towards polysaccharide synthesis with respect to surface salinity...... 78

2.12. Total per capita ingestion rate of Acartia tonsa with respect to sea surface temperature in other estuaries ...... 79

2.13.A. Summer abundance of adult Anchoa mitchilli with respect to surface salinity. B. Summer abundance of Anchoa mitchilli eggs with respect to surface salinity ...... 80

3.2. Averaged surface salinity (psu) distribution in Apalachicola Bay during summer 2003 (A) and summer 2004 (B)...... 99

ix 3.3.A. Total prey carbon relative to sea surface temperature during 2003 and 2004. B. Total prey carbon relative to sea surface salinity during 2003 and 2004. C. Percent of total prey carbon composed of phytoplankton carbon relative to sea surface temperature during 2003 and 2004. D. Percent of total prey carbon composed of phytoplankton carbon relative to sea surface salinity during 2003 and 2004 ...... 101

3.4. Microzooplankton herbivory relative to log heterotrophic flagellate (5 to 20 µm) and ciliate abundance ...... 102

3.5.A. Total carbon ingested relative to sea surface temperature during 2003. B. Total carbon ingested relative to sea surface salinity during 2003. C. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface temperature during 2003. D. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface salinity during 2003 ...... 104

3.6.A. Total carbon ingested relative to sea surface temperature during 2004. B. Total carbon ingested relative to sea surface salinity during 2004. C. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface temperature during 2004. D. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface salinity during 2004 ...... 106

3.7.A. Calculated microzooplankton production relative to sea surface temperature in 2003. B. Calculated microzooplankton production relative to sea surface salinity in 2003. C. Calculated microzooplankton production relative to sea surface temperature in 2004. D. Calculated microzooplankton production relative to sea surface salinity in 2004 ...... 108

3.8.A. Rates of microzooplankton herbivory relative to sea surface temperature in 2003. B. Rates of microzooplankton herbivory relative to sea surface salinity in 2003.C. Rates of microzooplankton herbivory relative to sea surface temperature in 2004. D. Rates of microzooplankton herbivory relative to sea surface salinity in 2004...... 110

3.9. Coupling of microzooplankton herbivory and phytoplankton growth during 2003 and 2004 ...111

3.10. A. Percent of potential phytoplankton production ingested (%Pp) by microzooplankton relative to sea surface temperature in 2003 and 2004. B. %Pp by microzooplankton relative to sea surface salinity during 2003 and 2004...... 112

3.11. Percentage of potential phytoplankton production ingested by microzooplankton (%Pp) relative to sea surface temperature in various subtropical and tropical estuarine, coastal, and oceanic waters...... 114

C.1. Proposed estuarine planktonic food web structure throughout Apalachicola Bay ...... 119

x ABSTRACT

Apalachicola Bay is a productive estuary located in the northern Gulf of Mexico. The high productivity is, in part, the result of the Apalachicola River delivering freshwater and nutrients to the Bay. Freshwater moderates salinity in the Bay, which creates habitat for estuarine flora and fauna. Nutrient input supports high levels of phytoplankton productivity, which supports the Bay’s secondary productivity. Diversion of water from headwaters of the Apalachicola River during summer has been proposed to satisfy upstream freshwater requirements for recreation and agriculture. Natural droughts in Apalachicola Bay have led to reductions in higher trophic level productivity. The mechanisms through which water diversion are expected to reduce higher trophic level productivity in Apalachicola Bay are, for the most part, unknown. A major pathway of mass and energy flow in the Bay is through the planktonic food web. The objective of this research was to test the hypothesis that a classical diatom-copepod-fish food chain and a microbial food web characterize planktonic food web structure in low and high salinity waters, respectively in Apalachicola Bay. To achieve these objectives, data were collected within various salinity regimes in Apalachicola Bay during 2003 and 2004. The temporal and spatial distribution of phytoplankton growth, biomass, productivity, and size composition were determined in Apalachicola Bay. Growth, biomass, and productivity were highest between 26 and 27oC and lowest at temperature minima and maxima. Two conceptual models are proposed to describe the spatial distribution of phytoplankton growth, biomass, and productivity in well- and partially-mixed river dominated estuaries. Apalachicola Bay appears to fit both of these models, but during different time periods. During summer, growth, biomass, and productivity were highest between about 5 and 20 psu, and the phytoplankton community was primarily composed of picophytoplankton. During winter, growth, biomass, and productivity were highest between about 20 and 26 psu, but less than during summer. Picophytoplankton were the main component of the phytoplankton community in low salinity waters during winter. Microphytoplankton, however, were the main component of the phytoplankton community in mid to high salinity waters during winter. Reduced river discharge during summer 2004 led to a large reduction (up to 80% compared to summer 2003) in the areal extent of lower (<20 psu) salinity water where phytoplankton growth, biomass and productivity peaked. Lower river discharge during summer 2004 also led to lower nutrient

xi concentrations, phytoplankton growth and productivity at a particular salinity. Reduced river discharge during summer can be expected to reduce higher trophic level productivity in Apalachicola Bay not only because of the reduced areal extent of lower salinity-higher nutrient water where phytoplankton growth, biomass and productivity peak, but also because of reduced phytoplankton growth and productivity at a given salinity. Acartia tonsa herbivory, carnivory, egg production rate, egg production efficiency, and abundance were examined within various salinity regimes in Apalachicola Bay throughout 2003 and 2004. Acartia tonsa’s diet was mixed, albeit primarily (>50%) composed of phytoplankton. Phytoplankton were ingested in proportion to their availability in situ, while microzooplankton were preferred. Per capita total ingestion rate averaged (+ S.D.) 1.1 + 1.3 µg C Adult-1 d-1 during winter and 2.2 + 1.8 µg C Adult-1 d-1 during summer. The critical prey carbon concentration for A. tonsa was about 250 µg C L-1. As total prey carbon was <250 µg C L-1 during winter and generally >250 µg C L-1 during summer, A. tonsa was satiated during summer and food limited during winter. On average (+ S.D.), A. tonsa ingested the equivalent of 36 + 36% and 113 + 92% of their body carbon during winter and summer, respectively. The percent of phytoplankton

productivity ingested (%Pp) by Acartia tonsa ranged from 0 to 24% (median 0.4%). Average (+ S.D.) egg production rate (EPR) was lower during winter (13 + 15 eggs female-1 d-1) than during the summer (28 + 24 eggs female-1 d-1). During summer, EPR peaked to about 50 eggs female-1 d-1 between about 4 and 10 psu; whereas during winter EPR was not significantly related to salinity. During winter and summer, egg production efficiency (EPE) peaked (~99%) between about 6 and 18 psu. EPE may be influenced by salinity tolerance of A. tonsa and biochemical composition of phytoplankton ingested. Biochemical fractionation of primary productivity indicated that phytoplankton allocated more carbon to protein and lipid synthesis in lower salinity water than in higher salinity water. Acartia tonsa are the main prey of Anchoa mitchilli and A. mitchilli appear to exert significant top-down control on A. tonsa in lower salinity waters where A. tonsa EPR peaks. Upstream water diversion, leading to a reduction in the area where A. tonsa EPR is greatest, can be expected to lead to a reduction in fish productivity in Apalachicola Bay. Microzooplankton, on average, ingested ten times more of the phytoplankton productivity than Acartia tonsa, which dominates the mesozooplankton community in Apalachicola Bay. Microzooplankton ingested 75% of the phytoplankton productivity below 26oC. Above 26oC,

xii %Pp (percent of phytoplankton productivity ingested) was >75% and positively related to temperature. Ciliates, particularly oligotrichs, were abundant in all samples. Average (+ S.D.) ciliate abundances were 10.5 + 8.4 x 103 cells L-1 during winter and 23 + 11 x 103 cells L-1 during summer. Calculated microzooplankton growth rates averaged (+ S.D.) 8.5 + 5.1 d-1 during summer 2003 and 3.8 + 2.5 d-1 during summer 2004. Phytoplankton were the main (>50%) component of the microzooplankton diet, except during winter in lower (<10 psu) salinity waters when bacterioplankton were an important component of their diet. Because microzooplankton are prey for organisms occupying higher trophic levels, such as copepods, fish larvae, and oysters, energy and mass flows mainly from phytoplankton through microzooplankton to higher trophic levels in Apalachicola Bay. A reduction in discharge from the Apalachicola River during the study period led to reductions in rates of grazing, ingestion, and production of microzooplankton at a particular salinity. Reduced river discharge also decreased the areal extent of lower (<20 psu) salinity water where ingestion and production of microzooplankton were relatively high. Because microzooplankton are key constituents of the estuarine food web in Apalachicola Bay, lower nutrient input to, and higher salinity values in the Bay, expected from upstream water diversion can be expected to lead to reduced microzooplankton production and therefore higher trophic level productivity in the Bay. The hypothesis that, a classical diatom-copepod-fish food chain characterizes the planktonic food web structure in low salinity waters and a microbial food web characterizes the planktonic food web structure in high salinity waters in Apalachicola Bay, was rejected. A microbial food web depicted the transfer of energy and mass through the planktonic food web throughout Apalachicola Bay. Implications of this result are discussed with respect to our understanding of estuarine planktonic food web structure.

xiii INTRODUCTION

Of the total volume of water on the surface of the Earth, 3% is freshwater and most freshwater is frozen in polar ice caps and continental glaciers. Only 0.1% of the total volume of freshwater on the surface of the Earth occurs in rivers (Schlesinger 1997); yet, this water is important to the physical, chemical, and biological properties of the ocean. River water influences the ocean’s salinity, temperature, pH, oxygen and is a major source of ocean’s sediments and dissolved and particulate inorganic and organic matter (Libes 1992, Schlesinger 1997, Gillanders & Kingsford 2002). Where river water meets the ocean, gradients in physical and chemical properties are strongest and biological productivity is intense (McLusky & Elliot 2004). In these regions, estuaries can be formed. An estuary is, “a partially enclosed coastal body of water which is either permanently or periodically open to the sea and within which there is a measurable variation of salinity due to the mixture of sea water with fresh water derived from land drainage,” (Day 1981). Estuaries are critical habitats and nurseries to fish, shellfish, and waterfowl and provide recreational opportunities for humans. Agriculture is the main consumer of the world’s freshwater supply (Postel 2000). With human population growth and concomitant agricultural needs, the demand for freshwater is increasing and as a result, river flow to estuaries is decreasing. Most of the world’s river flow is regulated by dams and weirs (Postel & Richter 2003). In cases where minimum flows are implemented, the ecological needs of estuaries for freshwater are generally not known and minimum flows are often determined arbitrarily (Loneragan & Bunn 1999). The benefits derived from estuaries have not been adequately quantified compared with the benefits derived from irrigation and therefore estuaries are given little consideration in policy decisions (Gillanders and Kingsford 2002). Reduction in river flow is threatening the existence of estuarine habitats (Schlacher & Woolridge 1996). Freshwater diversions and droughts have led to reductions in biomass and/or changes in the distribution of wetlands, seagrasses, phytoplankton, zooplankton, fish, birds, reptiles, and marine mammals (Nichols et al. 1986, Humborg et al. 1997, Kingsford 1999, Froneman 2000, Grange et al. 2000, Snow et al. 2000, Gillanders and Kingsford 2002). Positive correlations between river discharge and landings of invertebrates and vertebrates also indicate that reductions in river flow will lead to reduced higher trophic

1 level production (Sutcliffe 1972, Sutcliffe 1973, Meeter et al. 1979, Loneragan & Bunn 1999). According to Kingsford (1999), “over exploitation of (fresh)water is fast becoming one of the worse environmental legacies for future generations.” Currently, in the southern United States there is a tri-state water war between Georgia, Alabama, and Florida. Diversion of water from the Apalachicola- Chattahoochee-Flint watershed is proposed to support upstream freshwater demands. However, freshwater from this river system flows to Apalachicola Bay, a productive estuary located in the northern Gulf of Mexico. Low river discharge to the Apalachicola Bay system is expected to lead to reduced higher trophic level production (Livingston et al. 1997). The mechanisms through which water diversion may lead to reduced higher trophic level productivity in Apalachicola Bay are unknown.

Apalachicola Bay: Physical environment The physical attributes of Apalachicola Bay (Fig. I.1) have been described (Livingston 1984). The Bay represents the largest area of the Apalachicola Bay system. The Bay is bound by barrier islands (St. Vincent, St. George, and Dog Island) to the south and west and separated from St. George Sound by a submerged shoal. Exchange between Bay and gulf water occurs through St. George Sound and openings (East Pass, Sikes Cut, West Pass, Indian Pass) through the barrier islands. Water moves predominantly from the east into St. George Sound and out to the gulf through West Pass. Residence time of water ranges from 2 to 12 days (Mortazavi et al. 2000a). Average depth of the Bay is between 2 and 3 m and as a result of tides, which are diurnal to semidiurnal with a 0.1 to 0.2 m range, and wind the Bay is generally well mixed. However, freshwater derived from the local rainfall and the Apalachicola River can lead to instances of Bay water column stratification. The Apalachicola River is, in terms of discharge, the largest in Florida and the main source of freshwater to the Bay. Headwaters of the Apalachicola River are formed at Lake Seminole where waters from the Flint and Chattahoochee Rivers meet. River flow typically peaks during spring and is minimal during summer. Annual average flow is about 665 m3 s-1. River flow is closely related to precipitation in Georgia and is considered the main determinant of salinity structure in the estuary.

2 Apalachicola Bay: Chemical environment Apalachicola Bay is considered one of the least polluted estuaries in the United States (Livingston 1984). Mean dissolved organic phosphorus (DOP), soluble reactive phosphorus (SRP), and dissolved inorganic nitrogen (DIN) input to the Bay from 1972 to 1995 has not changed significantly (Mortazavi et al. 2000a, 2000b). However, a recent study demonstrated that levels of organochlorines in the bonnethead shark, Sphyrna tiburo, in Apalachicola Bay are relatively high compared to those residing in other Florida estuaries (Gelsleichter et al. 2005). Other recent studies indicate the presence of organochlorine pesticides and heavy metals in striped bass from the Apalachicola River (Brim et al. 2001) and polychlorinated biphenyls, polycyclic aromatic hydrocarbons, and metals in loggerhead sea turtle eggs from Apalachicola Bay (Alam & Brim 2000). The nitrogen and phosphorus chemistry of Apalachicola Bay has been described (Mortazavi et al. 2000a, 2000b, 2001). Dissolved inorganic and organic nitrogen plus particulate nitrogen are the main forms of nitrogen that enter the Bay from the Apalachicola River and passes. Dissolved organic nitrogen (DON) and DIN are the main forms of nitrogen delivered by the passes and river, respectively. The river supplies most DIN, DON, and particulate nitrogen to the Bay. Export of nitrogen to the Gulf of Mexico primarily occurs through West Pass. There is virtually no retention of DON in the Bay. In contrast, during winter and summer about 44% and 83%, respectively of DIN input is retained in the Bay. Peak input of nitrogen occurs during winter. During winter, 6% of total dissolved nitrogen (TDN) input is denitrified in the sediments. About 74% and 26% of winter phytoplankton nitrogen demand is supported by new and water column regenerated nitrogen, respectively. During summer, about 22% of total dissolved nitrogen (TDN) input is denitrified in the sediments. New and water column regenerated nitrogen supply 11 and 73% of summer phytoplankton nitrogen demand. Benthic efflux of ammonium supports 16% of summer phytoplankton nitrogen demand. The Apalachicola River is also the main source of phosphorus to the Bay and export of phosphorus primarily occurs through West Pass. Particulate phosphorus, followed by DOP and SRP, are the main forms of phosphorus in the river, from the passes, and in the Bay, and exported to the gulf. Unlike DOP and SRP, there is virtually no net retention of particulate phosphorus in the Bay. About 40% of DOP plus SRP input

3 is retained in the Bay. DOP and SRP support about 10% of phytoplankton phosphorus demand. Benthic and water column remineralization of phosphorus support about 15 and 75%, respectively of phytoplankton phosphorus demand.

Apalachicola Bay: Biological environment Apalachicola Bay serves as a habitat and nursery to commercial and sport fish species such as oyster, red drum, spot, gulf menhaden, croaker, seatrout, shrimp, and blue crab and is an important forage area for migratory birds, fish, and manatees (Livingston 1983, 1984). Autochthonous and allochthonous derived organic carbon support estuarine food webs. Apalachicola Bay receives a steady supply of dissolved and particulate organic matter from freshwater wetlands, coastal marshes, seagrass beds and phytoplankton. However, phytoplankton is the main source of carbon in the Bay (Livingston 1984). Relative to other estuaries in the Gulf of Mexico, phytoplankton productivity is high in Apalachicola Bay (Pennock et al. 1999). Phytoplankton productivity peaks during summer and is primarily grazed by zooplankton (Mortazavi et al. 2000c). Stable isotope studies indicate that secondary producers in Apalachicola Bay are primarily supported by estuarine phytoplankton productivity (Chanton & Lewis 2002).

Marine planktonic food web structure Over the past 30 years our understanding of marine planktonic food web structure has changed. The flow of matter and energy between marine planktonic organisms was initially depicted as a linear food chain where diatoms were consumed by copepods which, in turn, were consumed by fish. This food chain is referred to as the herbivorous, traditional, diatom-copepod-fish, or classical food chain. However, the significance of the classical food chain was questioned when new methods and technologies revealed that small autotrophs and heterotrophs are frequently the major producers and consumers in the sea (Pomeroy 1974). Ryther (1969) and Pomeroy (1974) were among the first to suggest that the main pathways of energy transfer are different between productive and unproductive regions of the ocean. Azam et al. (1983) were the first to integrate the significance of small autotrophs and heterotrophs into marine planktonic food web

4 structure. They introduced the microbial loop wherein bacteria and cyanobacteria are ingested by flagellates and, in turn, flagellates are grazed by ciliates who are ingested by mesozooplankton. It was hypothesized that dissolved organic matter, released from phytoplankton, is utilized by bacteria and returned to the classical food chain via the microbial loop. The microbial loop was generally considered separate from the classical food chain (Fenchel 1988) and the significance of the microbial loop as a source of organic matter to higher trophic levels was doubted (Ducklow et al. 1986). Sherr & Sherr (1988) suggested that a microbial food web, including all heterotrophs and autotrophs, supports the metazoan food web. They pointed out that because of the linkages between autotrophs and heterotrophs, the microbial loop could not be considered separate from the microbial food web. The main linkage between the microbial food web and metazoan food web depends on phytoplankton size structure. The bulk of energy and mass flows from the microbial to the metazoan food web via the phytoplankton-mesozooplankton linkage during periods when phytoplankton are predominantly large in size. In contrast, during periods when phytoplankton are predominantly small in size, the bulk of energy and mass flows from the microbial to the metazoan food web via the microzooplankton-mesozooplankton linkage. Whereas high import rates of the controlling factor(s) (nutrients or light) lead to the predominance of large phytoplankton, low import rates of the controlling factor(s) lead to the predominance of small phytoplankton (Riegman et al. 1993). Legendre & Rassoulzadegan (1995) proposed that the classical food chain and microbial loop are extremes of a trophic continuum. They proposed a trophic continuum which proceeds from the classical food chain to a multivorous food web to a microbial food web to a microbial loop. High input of nitrate leads to a system dominated by a classical food chain. Systems are dominated by a multivorous food web when the input of nitrate is lower, the phytoplankton community is dominated by large and small cells, and there is active grazing by both micro- and mesozooplankton. In systems dominated by a microbial food web, nitrate input is low, phytoplankton are predominantly small in size, and microzooplankton are the main grazers. At extremely low nitrate input rates and DON concentrations, bacteria and phytoplankton compete for ammonium, large

5 phytoplankton and zooplankton are virtually non-existent, and systems are dominated by a microbial loop. A consensus is emerging that marine water with high nutrient concentrations and adequate light supports the growth of large phytoplankton and energy and matter primarily flows through the classical food chain. In contrast, in marine water with low nutrient concentrations and/or low light, phytoplankton biomass and productivity are relatively low and predominated by small cells, and energy and matter primarily flows through a microbial food web. Because microbial food webs have more trophic levels and less phytoplankton productivity, they support lower magnitudes of higher trophic level productivity than do classical food chains (Ryther 1969, Sommer et al. 2002).

Estuarine planktonic food web structure Because estuarine waters are nutrient rich and support production of higher trophic levels, a classical food chain is thought to represent estuarine planktonic food web structure (Mallin & Paerl 1994, Ning et al. 2000). In Apalachicola Bay, nutrient concentrations are high, at least in lower salinity waters (Mortazavi et al. 2001), and diatoms (Estabrook 1973) and copepods (Edmiston 1979, Marcus 1991) are abundant. Therefore, a classical food chain should describe planktonic food web structure in Apalachicola Bay. However, as noted by Pomeroy (1974), small phytoplankton can be an important component of phytoplankton biomass and productivity in estuaries (for example, Moreton Bay (O’Donohue & Dennison 1997), Urdaibai Estuary (Ansotegui et al. 2003), Pensacola Bay (Murrell & Lores 2004), York River Estuary (Ray et al. 1989, Sin et al. 1999, Sin et al. 2000), and Chesapeake Bay (McCarthy et al. 1974)). Microzooplankton are abundant in estuarine waters (Gifford and Caron 2000, Tillman 2004). The syntheses of Calbet (2001) and Calbet & Landry (2004) also indicate that mesozooplankton (primarily copepods) ingest about 10% of phytoplankton productivity in productive waters and that microzooplankton ingest about 60% of phytoplankton productivity in estuaries. The size composition of phytoplankton and the relative magnitude of herbivory by copepods and microzooplankton, however, may vary across the nutrient concentration gradient in estuaries. A classical food chain may characterize the planktonic food web structure in high nutrient waters while a microbial food web may

6 characterize the planktonic food web structure in low nutrient waters (Fig. I. 1). A gradient in planktonic food web structure such as this might explain why water diversion leads to reduced higher trophic level production. Water diversion that increases the areal extent of higher salinity water where nutrient concentrations are low (Mortazavi et al. 2001) and where a microbial food web may predominate, would lead to reduced higher trophic level productivity.

Objectives The objective of this research was to test the hypothesis that a classical food chain characterizes the planktonic food web structure in high nutrient (low salinity) waters and a microbial food web characterizes the planktonic food web structure in low nutrient (high salinity) waters in Apalachicola Bay (Fig. I. 2). To achieve this objective, data were collected within various salinity regimes in Apalachicola Bay during 2003 and 2004. Parameters measured included salinity, temperature, light attenuation coefficient, concentrations of soluble reactive phosphate and dissolved inorganic nitrogen, phytoplankton growth, biomass, productivity, and size composition, bacterioplankton abundance, microzooplankton herbivory and bacterivory, and Acartia tonsa abundance, herbivory, carnivory, egg production, and egg production efficiency. This study was unique in that the temporal and spatial distribution of these parameters were simultaneously measured in one estuary over a two year period by one investigator. To date, no previous studies have simultaneously examined the temporal and spatial distribution of phytoplankton growth, biomass (as carbon), productivity, and size composition in an estuary. Relative to open ocean studies, there are few studies of microzooplankton grazing in eutrophic marine waters (Dolan et al. 2000) and none that have simultaneously examined the temporal and spatial patterns of microzooplankton herbivory and bacterivory in an estuary. There are few published field studies that have simultaneously examined the spatial and temporal patterns of A. tonsa total ingestion and egg production rates, and thus egg production efficiency, in an estuary (Kleppel & Hazzard 2000) and none have been conducted throughout the entire seasonal cycle. Relationships between phyto- and zooplankton parameters and salinity were examined to predict the effect of water diversion on higher trophic level production.

7 Reduced freshwater discharge from the Apalachicola River will increase the areal extent of higher salinity water in the Bay (Mortazavi et al. 2001). Relationships between phytoplankton growth, biomass, productivity, size composition and salinity and zooplankton productivity and salinity, particularly during summer when water diversion is most likely to occur, in combination with areal distribution of salinity within the estuary would be valuable for predicting the effect of water diversion on higher trophic level production. Relationships between phyto- and zooplankton parameters and salinity were also examined to test the hypothesis that a classical food chain characterizes the planktonic food web structure in high nutrient (low salinity) waters and a microbial food web characterizes the planktonic food web structure in low nutrient (high salinity) waters. If (1) phytoplankton biomass is primarily composed of large (>20 µm) cells and small (<20 µm) cells in lower and higher salinity waters, respectively; (2) phytoplankton growth, biomass, productivity are highest in lower salinity waters; (3) copepods and microzooplankton are the main herbivores in lower and higher salinity waters, respectively, then the hypothesis would be supported. Dominance of a microbial food web in low nutrient/high salinity waters would help to explain why water diversion leads to reduced higher trophic level production. Relative to open ocean research, there has been a lack of funding for basic estuarine research. Most estuarine research has focused on temperate estuaries. Because estuaries are dynamic, complex, and difficult to study, scientists have generally concentrated on studying single estuaries, treating each as if it will be unique (Hobbie 2000). Single estuary studies have also been favored because of the need to solve local management problems. Because long-term, intensive, and comparative studies of estuaries have not been conducted, there is a lack of understanding of interactions and mechanisms that are common to all estuaries. This lack of understanding impedes the ability to manage estuaries for sustainable use. A secondary objective of this research was to improve our understanding of estuarine planktonic food web structure by comparing the data collected from Apalachicola Bay to that from other estuaries.

8 30.0

Florida 29.9 Study area Apalachicola River

29.8 N) o d oun East e S East rg Pass eo Bay . G St Latitude ( 29.7 St. Vincent Sound

Indian Pass Apalachicola Bay

West 29.6 Pass Sike's Cut Gulf of Mexico

29.5

-85.2 -85.1 -85.0 -84.9 -84.8 -84.7 -84.6 o Longitude ( W)

Fig. I.1. Apalachicola Bay, Florida 9 Mesozooplankton Mesozooplankton

Microzoo- Microzoo-

Phyto- Phyto- Heterotrophic DOM Heterotrophic DOM Bacteria Nutrients Bacteria Nutrients

High Nutrient-Low Salinity Low Nutrient-High Salinity

CLASSICAL FOOD CHAIN MICROBIAL FOOD WEB

Fig. I. 2. Proposed planktonic food web structure in Apalachicola Bay. Width of solid arrows denotes the predicted relative magnitude of carbon transfer. Dashed arrows denote other known pathways that have not been quantified. Microzoo- and phyto- refer to microzooplankton and phytoplankton, respectively. DOM refers to dissolved organic matter.

10 CHAPTER 1

TEMPORAL AND SPATIAL DISTRIBUTION OF PHYTOPLANKTON IN APALACHICOLA BAY, FLORIDA

Introduction

Phytoplankton are important constituents of estuarine food webs. Most research concerning estuarine phytoplankton ecology has focused on light and/or nutrient limitation status, primary productivity, and biomass as chlorophyll. A few estuarine studies have reported phytoplankton organic carbon (Wienke & Cloern 1987, Ray et al. 1989), growth rates (McManus & Cantrell 1992, Pinckney et al. 1997, Ruiz et al. 1998, Lehrter et al. 1999, Murrell & Hollibaugh 1998, Murrell et al. 2002a), or size composition (McCarthy et al. 1974, Turner et al. 1990, Iriarte 1993, Iriarte & Purdie 1994, Wasmund et al. 1999, Sin et al. 2000, Murrell & Lores 2004). Data were generally related to time and to distance along an estuary axis. In some cases, data were related to salinity (Fisher et al. 1988, Xiuren et al. 1988, Lohrenz et al. 1990, Kromkamp & Peene 1995, Humborg 1997, Ruiz et al. 1998, Sin et al. 1999, Yin et al. 2000). However, to date simultaneous estimates of phytoplankton community organic carbon, growth rates, primary productivity, and phytoplankton community size composition with respect to salinity in an estuary have not been reported. Models based upon synthesized data sets are needed to predict the consequences of future changes, such as eutrophication, water diversion, or climate change, and are especially important for estuaries that serve as fishery nursery areas. Because long-term intensive studies of estuaries that provide comparable data sets have rarely been conducted, few general models exist that were developed from synthesized data sets (Hobbie 2000). Most estuarine research has been short-term and site specific, without attempts to compare results with those obtained from other estuaries. A general model (Riley et a. 1949), that contained terms for advection, diffusion, and biological processes, has been highly modified and applied to different estuaries for specific purposes. For example, the simulation model of Sin et al. (2002), based on a hydrodynamic model, identified mechanisms controlling size-structured phytoplankton dynamics in the York River estuary. Robson & Hamilton (2003) used a combined hydrodynamic and

11 biogeochemical simulation model to identify conditions that led to a bloom of Microcystis aeruginosa in the Swan estuary. Korpinien et al. (2003) developed a biogeochemical simulation model, running with a hydrodynamical model that predicts the temporal and spatial variation of phytoplankton biomass in Neva Bay. However, a model, applicable to many estuaries, was developed by Cloern (1999) to predict light and nutrient limitation effects on estuarine phytoplankton growth. Apalachicola Bay is a shallow, well mixed, turbid, subtropical estuary, located in the northern Gulf of Mexico, with a gradient of relatively high nutrient concentrations near the river mouth and lower nutrient concentrations in higher salinity waters (Mortazavi et al. 2001). Chanton & Lewis (2002) demonstrated that secondary producers in Apalachicola Bay are mainly supported by estuarine phytoplankton productivity. Phytoplankton productivity is greatest during summer (Mortazavi et al. 2000c). Phytoplankton are frequently nitrogen limited in mid to high salinity waters and periodically phosphorus limited in low salinity waters (Iverson et al., submitted). Factors that influence higher trophic level production, such as phytoplankton size composition and organic carbon (Ryther 1969), and phytoplankton growth rates (Goldman et al. 1979, Breteler et al. 2005), have not been determined from data obtained in situ from Apalachicola Bay. Reduced freshwater discharge from the Apalachicola River, as a result of upstream water diversion, will increase the areal extent of higher salinity water in the Bay (Mortazavi et al. 2001). Relationships between phytoplankton growth, biomass, productivity, size composition and salinity, in combination with areal distribution of salinity within Apalachicola Bay, would be useful for predicting the effect of water diversion on higher trophic level production. The objectives of this study were to (1) determine the distribution of phytoplankton biomass, growth rates, productivity, and size composition in Apalachicola Bay; (2) propose conceptual models to describe the relationship between phytoplankton size composition, biomass, growth rates, productivity and salinity in well-mixed and partially-mixed, river-dominated estuaries; (3) determine if data acquired from Apalachicola Bay and other well-mixed and partially-mixed estuaries fit these models; (4) to predict the effect of possible upstream water diversion on the phytoplankton community in Apalachicola Bay. To achieve these objectives, samples were collected

12 within various salinity regimes in Apalachicola Bay during summer 2003 and throughout 2004.

Materials and Methods

Physical environment The physical attributes of Apalachicola Bay (Fig.I. 1) have been described (Livingston 1984). The Bay represents the largest area of the Apalachicola Bay estuary system. The Bay is bound by barrier islands (St. Vincent, St. George, and Dog Island) to the south and west and separated from St. George Sound by a submerged shoal. Exchange between Bay and gulf water occurs through St. George Sound and openings (East Pass, Sikes Cut, West Pass, Indian Pass) through the barrier islands. Water moves predominantly from the east into St. George Sound and out to the gulf through West Pass. Residence time of water ranges from 2 to 12 days (Mortazavi et al. 2000a). Average depth of the Bay is between 2 and 3 m and as a result of wind and tides, which are diurnal to semidiurnal with about 0.1 to 0.2 m range, the Bay is generally well mixed. However, freshwater derived from the local rainfall and the Apalachicola River can lead to instances of Bay water column stratification. The Apalachicola River is, in terms of discharge, the largest in Florida and the main source of freshwater to the Bay. Headwaters of the Apalachicola River are formed at Lake Seminole where waters from the Flint and Chattahoochee Rivers meet. River flow typically peaks during spring and is minimal during summer. Annual average flow is about 665 m3 s-1. River flow is closely related to precipitation in Georgia and is considered the main determinant of salinity structure in the estuary.

Study sites, physical measurements, and sample collection Samples from Apalachicola Bay were generally collected on a monthly basis throughout 2003 and 2004. We sampled oligohaline, mesohaline, and euryhaline portions of the Bay. Temperature and salinity were measured at 0.5 m depth intervals throughout the water column with a YSI® salinometer. Submarine light energy was measured at 0.5 m depth intervals throughout the water column with a Li-Cor® model

13 192SA underwater quanta sensor attached to a handheld meter. The attenuation coefficient (k) for PAR was estimated as the slope of underwater irradiance versus depth. Samples for nutrient and phytoplankton analyses were collected from 0.5 m depth below sea surface.

Chemical parameters Seawater collected for nutrient analyses was stored on ice in polyethylene bottles for <4 hours prior to being filtered through a 0.2 µm surfactant free cellulose acetate filter. The filtrate was stored at -20oC and within 4 months of sample collection thawed and analyzed for nitrate, nitrite, ammonium, and soluble reactive phosphate. The concentration of ammonium and soluble reactive phosphate concentration was determined by colorimetry with the method of Bower & Holm-Hansen (1980) and Murphy & Riley (1962), respectively. Colorimetric measurements were made with a Cary 1 Bio UV/Vis spectrophotometer. The concentration of nitrite plus nitrate was determined with the Vanadium (III) reduction chemiluminescence detection method (Braman & Hendrix 1989). The resulting chemiluminescence was measured with a ® ® Thermo Environmental Model 42 chemiluminescence NOx analyzer connected to a HP 3396 Series II integrator. Hereafter, the sum of nitrate, nitrite, and ammonium is referred to as DIN (Dissolved Inorganic Nitrogen).

Chlorophyll a Seawater for chlorophyll analysis was stored on ice in polyethylene bottles for <4 hours prior to being filtered through 47 mm GF/F filters at <117 mm Hg vacuum. Filtered samples were stored in darkness at -20oC and analyzed within 1 week of sample collection. Chlorophyll a was extracted from filters in 90% acetone for about 24 hours in darkness at -20oC. The concentration of chlorophyll a was measured fluorometrically with a Model 10 Turner Designs® fluorometer equipped with filter sets for optimal sensitivity of chlorophyll a in the presence of chlorophyll b (Welschmeyer 1994).

14 Phytoplankton abundance Seawater samples for the analysis of phytoplankton <20 µm in size were preserved with glutaraldehyde (about 2 % final concentration), stored in darkness at 4oC (Sherr et al. 1993), and enumerated within 1 week of sample collection. Samples were filtered (<117 mm Hg vacuum) onto 0.4 µm black Poretics polycarbonate filters and filters were mounted with Cargille’s type B immersion oil onto glass slides. A Nikon® Microphot-FX epifluorescence microscope equipped with a green excitation filter set (excitation: 510 to 560 nm; emission: 578 to 632 nm) was used to visualize phycoerythrin and phycocyanin containing cyanobacteria (MacIsaac & Stockner 1993). Phycoerythrin and phycocyanin containing cells were identified as orange and red, respectively fluorescing coccoid cells with a diameter of about 1 µm. A BH Olympus® epifluorescence microscope equipped with a blue/UV excitation filter set (U-M546, excitation 400 to 410 nm; emission 455 to 700 nm) was used to visualize eukaryotic phytoplankton <20 µm. The number of picoeukaryotes was determined by multiplying the total abundance of eukaryotic phytoplankton <20 µm by the percent of eukaryotic phytoplankton <20 µm that were <2 µm. The proportion of eukaryotic cells <20 µm that were <2 µm was estimated by taking random measurements of cells counted. Cells were counted at a total magnification of x1000 on the Nikon and x1875 on the Olympus. For each sample, at least 100 cells of each phytoplankton group (for example, phycocyanin containing cyanobacteria or eukaryotic phytoplankton <20 µm in size) were counted (Hobro & Willen 1977). Cells were counted from filters in either transects or in a minimum of 10 random fields. Samples for the analysis of phytoplankton >20 µm in size were preserved in acid Lugol’s (2% final concentration), stored in darkness at 4oC (Gifford and Caron 2000), and enumerated within about 1 month of sample collection. Samples (about 10 to 50 mL) were settled for 24 hours with Utermohl settling chambers. Cells were viewed at a total magnification of x200 through phase contrast light microscopy with an inverted Wild® microscope. For each settled sample, at least 100 cells of the most abundant phytoplankton species or genera were counted in transects (Hobro & Willen 1977). Phytoplankton species were counted, when they could be identified. Otherwise, phytoplankton genera were counted.

15 Phytoplankton biomass (organic carbon) Abundances of phycoerythrin and phycocyanin containing cyanobacteria and eukaryotic phytoplankton were converted to carbon with estimates of cell volume and carbon: volume formulae for diatoms and non-diatoms (Menden-Deuer & Lessard 2000). About ten measurements of cell dimensions were taken for each abundant phytoplankton species or genera or group per sample. Cell volumes were estimated with simple geometric volume formulae (Wetzel & Likens 1990). Total phytoplankton carbon was estimated as the sum of carbon from cyanobacteria and eukaryotic phytoplankton.

Phytoplankton productivity An analysis by Calbet & Landry (2004) found that the product of phytoplankton growth rates estimated from dilution assays and mean phytoplankton carbon was well correlated with simultaneous estimates of phytoplankton productivity determined by the 14C-uptake method. Therefore, we estimated phytoplankton productivity (PP, µg C L-1 d-1) with our estimates of daily phytoplankton growth (µ, d-1) derived from dilution -1 assays (Chapter 3) and mean phytoplankton carbon (Cm, µg C L ),

PP = µ Cm (1)

µ-g Cm = Co (e –1)/(µ-g) (2)

where Co is the initial phytoplankton carbon and g is the daily rate of phytoplankton mortality due to microzooplankton grazing (Chapter 3). Phytoplankton productivity for 2003 was estimated with phytoplankton growth and grazing rates from 2003 (Chapter 3) and empirically determined carbon: chlorophyll ratios estimated from 2004.

Statistical analyses Analysis of Covariance (ANCOVA) tests were used to determine if significant differences existed between dates with respect to relationships examined. If no significant difference was found, then data were pooled and a common regression equation was determined. Non-linear regressions or logarithmic transformation of data

16 were used when a linear regression model did not adequately explain the relation between variables (for example, low r2, variance heteroscedastic). Non-linear relationships were analyzed by dividing the data into 2 components: (1) the initial increasing segment and (2) the latter decreasing segment. ANCOVA tests were performed on each segment. Relationships were considered significant if the p-value was <0.05 (Sokal & Rohlf 1995).

Results

Physical environment Temperature ranged between about 11 to 31 oC during 2003 and 2004 (Fig.1. 2a). The lowest temperatures were observed during January and February while peak temperatures occurred between June and August. ANCOVA tests between temperature and salinity indicated that the mean temperature during summer of 2003 was not significantly different from that during summer of 2004, and the mean temperature during winter of 2003 was not significantly different from that during winter of 2004. Temperature was not significantly related to salinity during summer or winter in either 2003 or 2004. The 24 year average Apalachicola River discharge (Fig.1. 2b) peaks in about March at about 1300 m3 s-1 (http://waterdata.usgs.gov). Discharge declines thereafter and reaches a minimum of about 400 m3 s-1 in the fall. In 2003 river discharge also peaked in March (at about 1600 m3 s-1). However, river discharge remained high (about twice the 24 year average discharge), at about 1100 m3 s-1, throughout the summer months before declining to about 400 m3 s-1 in the fall. In contrast, in 2004 river discharge peaked in about February at about 1000 m3 s-1 and declined throughout the summer months. Discharge during the summer months was about 64% of the 24 year average summer discharge. However, during the fall discharge increased to about 800 m3 s-1, about twice the 24 year average discharge for the fall. We examined the salinity at about 0.5 m below sea surface during summer 2003 and summer 2004 when river discharge was above and below average, respectively (Fig.1. 3). On the dates examined, the areal extent of lower (<20 psu) salinity water was, on average, greater during summer 2003 than during summer 2004. Between about 57 to

17 100% of the Bay surface water was <20 psu during summer 2003 (Fig.1. 3a, 3b, 3c). In contrast, during summer 2004 about 18 to 50% of the Bay surface water was <20 psu (Fig.1. 3d, 3e, 3f). There was a weak but significant inverse relationship between attenuation coefficient, k, and salinity (Fig.1. 4a). The relationship between k and salinity was the same during winter and summer and during 2003 and 2004. The k ranged from 0.3 to 5.3 m-1. The average 1% light depth increased from 2.3 m at 0 psu to 4.7 m at 35 psu. The average mixed layer light energy (Fig.1. 4b), expressed as percent of incident light energy, increased from 25% Io at 0 psu to 45% Io at 35 psu. Daily incident light energy during 2004 was positively related to temperature and ranged from 27 to 55 E m-2 d-1 (Fig.1. 5a).

Chemical environment Average Bay dissolved inorganic nitrogen (DIN) was inversely related to temperature during 2003 and 2004 (Fig.1. 5b). Average Bay DIN was higher below 26oC, hereafter referred to as winter, than above 26oC, hereafter referred to as summer, and, on average, higher during 2003. DIN and salinity were inversely related (Fig.1. 6a). Within each year, the slope and intercept of the relation between DIN and salinity were significantly greater in winter than in summer. The slope and intercept of the relations between DIN and salinity in 2003 were significantly greater than those in 2004. There was a progressive decline in intercepts and slopes from winter 2003 to summer 2004. For example, the intercepts were 1996, 982, 425, and 325 µg N L-1 during winter 2003, summer 2003, winter 2004, and summer 2004, respectively. Soluble reactive phosphate (SRP) was also inversely related to salinity (Fig.1. 6b). There was no significant difference in the relation between SRP and salinity among winter 2003 and summer 2003. Therefore, the data were pooled. Likewise, data for 2004 were pooled as there was no significant difference in the relation between SRP and salinity among winter 2004 and summer 2004. In both years, the r2 for the relation between SRP and salinity was low. The slope and intercept of the relation between SRP and salinity in 2003 were significantly greater than in 2004. During 2003, SRP ranged

18 from 0 to 15 µg P L-1 and averaged 4.6 + 3.7 µg P L-1. During 2004, SRP ranged from 0 to 12 µg P L-1 and averaged 2.3 + 2.4 µg P L-1 .

Total chlorophyll a Total chlorophyll a was inversely, albeit weakly (r2 < 20%), related to salinity during the winter and summer of 2003 and 2004 (Fig.1. 7). The relationship between total chlorophyll and salinity during winter 2003 was the same as during winter 2004 (Fig.1. 7a). The relationship between total chlorophyll and salinity during summer 2003 was the same as during summer 2004 (Fig.1. 7b). During winter, total chlorophyll ranged from 2 to 16 µg L-1 and averaged 4 + 2 µg L-1. During summer, total chlorophyll ranged from 2 to 11 µg L-1 and averaged 5 + 2 µg L-1.

Phytoplankton summer abundances Picocyanobacteria abundance was non-linearly related to salinity (Fig.1. 8a). The relationship during summer 2003 was not significantly different from that during summer 2004. Peak abundance was about 1000 x 106 cells L-1 and occurred between about 14 and 22 psu. Abundances of eukaryotic phytoplankton <20 µm in size were not counted in 2003. Abundances of diatoms >20 µm in size were positively related to salinity (Fig.1. 8b). The slope of the relationship between diatom abundance and salinity during summer 2003 was not significantly different from that during summer 2004. However, the intercepts were significantly different. Average abundances were 0.13 (+ 0.12) x 106 cells L-1 during summer 2003 and 0.04 (+ 0.05) x 106 cells L-1 during summer 2004. Abundances of dinoflagellates >20 µm in size were positively related to salinity (Fig.1. 8c). The slope of the relationship between abundance and salinity during summer 2003 was not significantly different from that during summer 2004; however, intercepts were significantly different. Average abundances during summer 2003 were 0.01 (+ 0.01) x 106 cells L-1 and during summer 2004 were 0.005 (+ 0.01) x 106 cells L-1.

19 Carbon: chlorophyll a Carbon: chlorophyll a ratios were related to salinity and temperature (Fig.1. 9). At the lowest temperatures, between about 16 and 20oC, carbon: chlorophyll a ratios were positively related to salinity and ranged from 4 to 26. In contrast, at the highest temperatures, between about 28 and 30oC, carbon: chlorophyll a ratios ranged from 20 to 250 with minima occurring in waters with salinity <5 psu and >20 psu and maxima between about 10 and 20 psu.

Seasonal trends of phytoplankton growth, biomass, and productivity Phytoplankton growth, biomass (mean organic carbon), and productivity were all highest between 26 and 27oC and lowest at temperature minima and maxima (Fig.1. 10). Growth ranged from -0.6 to 1.2 d-1 during 2004 and from 0.4 to 1.8 d-1 during 2003. Biomass ranged from 0 to 1850 µg C L-1. Phytoplankton productivity ranged from 0 to 1273 µg C L-1 d-1.

Spatial trends of phytoplankton growth, biomass, size productivity, and size composition during winter Phytoplankton growth rates and productivity during winter 2004 were highest between about 20 and 26 psu and decreased below 20 psu and above 26 psu (Fig.1. 11a, 11d). Growth rates during 2003 followed the same pattern; however, were higher than during 2004. For example, growth ranged from 0.5 to 1.2 d-1 during winter 2003 as opposed to 0.2 to 0.8 d-1 during winter 2004. Phytoplankton productivity ranged from 0 to 62 µg C L-1 d-1 during winter 2004. Phytoplankton biomass during winter was low (<100 µg C L-1) below 20 psu, increased to about 250 µg C L-1 between about 20 and 26 psu, and then declined in higher salinity waters (Fig.1. 11b). Below 20 psu, >40% and <40% of phytoplankton biomass was composed of pico- and microphytoplankton, respectively (Fig.1. 11c). Above 20 psu, <40% and >40% of phytoplankton biomass was composed of pico- and microphytoplankton, respectively. However, there was one sample at about 33 psu (denoted as an outlier with an asterisk) where about 60% and 10% of biomass was composed of pico- and microphytoplankton, respectively.

20 Spatial trends of phytoplankton growth, biomass, productivity, and size composition during summer Phytoplankton growth rates and productivity during summer 2003 and 2004 were highest between about 5 and 20 psu (Fig.1. 12a, 12d). Growth and productivity was lower in 2004 than in 2003. For example, growth ranged from 0.4 to 1.8 d-1 during summer 2003 as opposed to -0.6 to 1.2 d-1 during summer 2004. Phytoplankton productivity ranged from 170 to 2543 µg C L-1 d-1 during summer 2003 as opposed to 0 to 1273 µg C L-1 d-1 during summer 2004. Phytoplankton biomass peaked between about 5 and 20 psu during summer (Fig.1. 12b). Below 34 psu, phytoplankton biomass was primarily composed of picophytoplankton (50 to 70%) and nanophytoplankton (0 to 30%) (Fig.1. 12c).

Discussion

Phytoplankton carbon: chlorophyll a ratios There was considerable spatial and temporal variation in carbon: chlorophyll ratios in Apalachicola Bay (Fig.1. 9). The factors that influence carbon: chlorophyll (C:Chl) ratios, such as light, temperature, and nutrient concentrations (Geider et al. 1997), varied spatially and temporally in Apalachicola Bay. For example, average light energy was highest in high salinity waters (Fig.1. 4) and during summer (Fig.1. 5a). Temperature varied throughout the year (Fig.1. 2a) while nutrient concentrations were highest during winter (Fig.1. 5b) and in lower salinity waters (Fig.1. 6). Estimates of C:Chl ratios generally range from 0 to 200 in oceanic (Booth et al. 1993, Arin et al. 2002, Veldhuis & Kraay 2004), coastal (Lohrenz et al. 1991, Chang et al. 2003, Garibotti et al. 2003), and estuarine waters (Cloern et al. 1985, Wienke & Cloern 1987, Ray et al. 1989, Humborg 1997, Verity 2002). C:Chl ratios from the field tend to be highest when phytoplankton are predominantly small in size (Arin et al. 2002, Veldhuis & Kraay 2004, Chang et al. 2003). In laboratory growth conditions, C:Chl ratios tend to be highest when temperature is high, when light is low, and when nutrients are high (Geider et al. 1997). Our estimates of C:Chl ratios for Apalachicola Bay fit these paradigms as C:Chl ratios

21 were highest (116 + 75) during summer in lower salinity water that had lower light energy in the euphotic zone (Fig.1. 4), higher nutrient concentrations (Fig.1. 6), and where phytoplankton were primarily <20 µm in size (Fig.1. 12c). C:Chl ratios are typically examined at various locations at a specific time or at a specific location over time (Ray et al. 1989, Booth et al. 1993, Verity 2002, Chang et al. 2003, Veldhuis & Kraay 2004). There are few studies where the spatial and temporal variability of carbon: chlorophyll ratios in an estuary are available for comparison with our data. Cloern et al. (1985) and Wienke & Cloern (1987) used different methods to estimate the C:Chl ratio, and found that a constant C:Chl ratio can be applied to various locations and times in San Francisco Bay. In contrast, our study indicates the spatial and temporal variability of carbon: chlorophyll ratios that can occur in an estuary. Therefore, applying a constant C:Chl ratio to chlorophyll data obtained from an estuary ought to be avoided, unless shown to be appropriate for the system (Cloern et al. 1985, Wienke & Cloern 1987).

Temporal variation of phytoplankton growth, biomass, and productivity in Apalachicola Bay Frequently, estuarine chlorophyll and primary productivity are reported with respect to time, irrespective of salinity. In Apalachicola Bay, phytoplankton growth, biomass, and primary productivity were highest during summer (Fig.1. 10). Temperature (Eppley 1972), light energy and nutrient concentration limit phytoplankton growth in estuaries (Boyton et al. 1982, Grobbelaar 1985, Monbet 1992, Cloern 1999). Low temperature and/or low light energy might explain the low growth rates observed during winter (Fig.1. 5a, Fig.1. 10a). Higher temperature, higher light energy, and adequate nutrient concentrations might explain the peak growth rates at 26oC. The low growth rates at high temperature (>26oC), however, are probably the result of limiting nutrient concentrations as temperature and light energy were maximal (Fig.1. 5a, 5b, Fig.1. 10a). A synthesis of data from a broad spectrum of estuaries showed that chlorophyll is highest during the warm periods of the year (Boynton et al. 1982). Although chlorophyll did not vary substantially throughout the year (Fig.1. 7), phytoplankton carbon was highest during summer in Apalachicola Bay (Fig.1. 10). Our estimates of phytoplankton biomass during 2004 are similar to those reported in other estuaries. For example, during

22 summer in the York River estuary, phytoplankton carbon ranged from 140 to 1640 µg L-1 (Ray et al. 1989). In San Francisco Bay, phytoplankton carbon ranged from 0 to 2500 µg C L-1 throughout the year (Wienke & Cloern 1987). In Apalachicola Bay, phytoplankton growth, zooplankton grazing, and export from the estuary determine phytoplankton biomass during winter. During summer, phytoplankton biomass is primarily determined by phytoplankton growth and zooplankton grazing (Mortazavi et al. 2000c). Although grazing does not balance phytoplankton growth during winter (Chapter 3) lower growth rates (Fig.1. 10a) and export (Mortazavi et al. 2000c) probably led to the relatively low winter stocks of phytoplankton during our study. In contrast, phytoplankton biomass peaked during early summer probably because phytoplankton growth peaked (Fig.1. 10a) and was not balanced by zooplankton grazing (Chapter 3). At temperatures above 26oC, zooplankton grazing exceeded phytoplankton growth (Chapter 3) and probably caused the mid to late summer decline of phytoplankton biomass (Fig.1. 10b). Phytoplankton productivity is highest during the warm periods of the year in a broad spectrum of estuaries (Boynton et al. 1982). Mortazavi et al. (2000c) also found that phytoplankton productivity peaks during summer in Apalachicola Bay. Assuming that most productivity occurred within the top 1 m of the 2.2 m average Apalachicola Bay water column, average summer phytoplankton productivity estimated in this study was 727 mg C m2 d-1 and, therefore, was similar to average summer productivity (about 1000 mg C m2 d-1) measured by Mortazavi et al. (2000c). Here we show that phytoplankton productivity peaks during summer because both phytoplankton growth and biomass peak (Fig.1. 10a, 10b). This differs from other systems, such as San Francisco Bay, where peaks in phytoplankton productivity and biomass do not co-occur (Cloern 1987). We suspect that peaks in phytoplankton growth, biomass, and productivity co-occur in Apalachicola Bay because advective losses are generally low relative to zooplankton grazing losses (Mortazavi et al. 2000c) and zooplankton grazing is generally proportional to phytoplankton growth (Chapter 3). Actively growing phytoplankton are often rich in lipids and proteins (Goldman et al. 1979, Breteler et al. 2005). Phytoplankton allocated more carbon to the synthesis of lipids and proteins when phytoplankton growth rates

23 were high in Apalachicola Bay (Chapter 2). Summer in Apalachicola Bay is therefore a period of high quality and quantity of phytoplankton productivity.

Models for spatial variation of phytoplankton parameters in well-mixed and partially-mixed river-dominated estuaries Nested within the temporal variation of phytoplankton growth, biomass, and primary productivity, is the spatial variation of these parameters. Most models of estuarine phytoplankton dynamics are computer simulation and analytical models. With the exception of some models, for example, Cloern’s non-spatial nutrient and light limitation model (Cloern 1999), estuarine phytoplankton models are generally only applicable to the estuary where data were acquired for model parameterization. Here we propose two conceptual models (Fig.1. 13) to describe the relationship between phytoplankton growth, biomass, productivity, community size composition, and salinity in specific estuarine classes. The models are based on physics with respect to light energy as affected by turbidity and water depth, and current understanding about phytoplankton physiology, in terms of size-related Michaelis half-saturation constants, and loss caused by zooplankton grazing. The proposed models apply to well-mixed and partially-mixed, river-dominated estuaries that have simple hydrodynamics. The models are not intended to apply to estuaries with complex hydrodynamics, such as San Francisco Bay. In well-mixed and partially-mixed, river-dominated estuaries, river water, containing nutrients and sediment, mixes with higher salinity coastal water that, compared to river water, has relatively lower nutrient concentrations and lower quantities of suspended sediments. As a result, low salinity estuarine waters tend to be more turbid and contain higher nutrient concentrations, while higher salinity estuarine waters tend to be relatively clearer and have lower nutrient concentrations. Assumptions and models of basic phytoplankton physiology that were used to derive the models were:

(1) Because attenuation of light energy, caused primarily by sediment delivered by the river (Cloern 1987), is inversely related to salinity and the mixed layer

24 depth is either constant or shoaling with increasing salinity, average light within the mixed layer increases with salinity; (2) In the absence of biotic influences, nutrient rich freshwater mixes conservatively with nutrient poor seawater (Liss 1976). Therefore, nutrient concentrations are inversely related to salinity; (3) Relative to small phytoplankton cells, large phytoplankton cells have higher half saturation constants for nitrogen uptake and light saturation and higher maximum growth rates when light and nutrients are adequate (Eppley et al. 1969, Parsons & Takahashi 1973, Banse 1982). Growth of larger phytoplankton is not tightly coupled to grazing (Riegman et al. 1993) and large phytoplankton dominate the phytoplankton community biomass when light energy and nutrient availability do not limit their growth (Agawin et al. 2000). Therefore, large phytoplankton dominate phytoplankton community biomass when light energy and nutrient concentrations are relatively high. Small phytoplankton dominate phytoplankton community biomass when either light energy or nutrient availability limit growth rates of large phytoplankton cells; (4) Picophytoplankton are a small (<20%) component of total phytoplankton biomass in temperate estuaries (Ray et al. 1989, Malone et al. 1991, Iriarte & Purdie 1994, Pinckney et al. 1998, Ning et al. 2000, Agawin et al. 2000, Liu et al. 2004). Studies of picophytoplankton in low latitude turbid estuaries are scarce (Murrell & Lores 2004, Murrell & Caffrey 2005), but show that picophytoplankton are an important component of total phytoplankton biomass. The synthesis by Agawin et al. (2000) shows that picophytoplankton are an important component of phytoplankton biomass and productivity in warm waters when growth rates of larger phytoplankton are limited (Agawin et al. 2000). Therefore, picophytoplankton predominate phytoplankton biomass in warm waters when light and/or nutrients are limiting to larger phytoplankton; (5) Growth rates of phytoplankton communities are the composite of growth from various taxa and therefore growth estimates represent the growth

25 characteristics of the group that dominates phytoplankton biomass (Furnas 1990); (6) Sinking and advective losses from the phytoplankton community are assumed constant across the salinity gradient or are low relative to grazing losses, which average 70 to 80% of phytoplankton growth in marine environments (Calbet 2001, Calbet & Landry 2004). Therefore, phytoplankton biomass and productivity are related to salinity in the same way that phytoplankton growth is related to salinity.

The LNI estuarine model applies to well-mixed and partially-mixed estuaries, when there is low nutrient input. Microphytoplankton are limited by light energy in lower salinity waters, where average mixed layer light intensity is low. Because nutrient input from the river to the estuary is relatively low, nutrient concentrations are low in mid to high salinity waters and limit microphytoplankton growth. Pico- or nanophytoplankton, depending on temperature, are the main component of the phytoplankton community across the salinity gradient. This occurs because relatively small phytoplankton can grow faster than microphytoplankton at low light energy levels and low nutrient concentrations. Phytoplankton growth rates, biomass, and productivity are lowest in low and high salinity waters where light energy levels and where nutrient concentrations are lowest, respectively. Phytoplankton growth rates, biomass, and productivity peak in low to mid salinity waters where light levels and nutrients concentrations are at intermediate values. The second model, which we refer to as the high nutrient input (HNI) estuarine model, also applies to well-mixed and partially-mixed estuaries, but when nutrient input is higher. Like LNI estuaries, light energy limits microphytoplankton growth in lower salinity waters. As a result, pico- or nanophytoplankton, depending on temperature, are the main component of the phytoplankton community in low salinity waters. Unlike LNI estuaries, nutrient input from the river in HNI models is relatively high and nutrient concentrations are non-limiting to microphytoplankton growth in mid to high salinity waters. Microphytoplankton are the main component of the phytoplankton community in mid to high salinity waters. This occurs because microphytoplankton have faster growth

26 rates at higher light levels and nutrient concentrations than smaller phytoplankton. Phytoplankton growth, biomass, and productivity peak within mid to high salinity waters, where microphytoplankton predominate, and are at a minimum in low salinity waters, where pico- or nanophytoplankton predominate. We used “distance downstream or upstream” as a proxy for salinity, and chlorophyll as a proxy for biomass and found that several estuaries may be classified as LNI estuaries (Fig.1. 13, Table 1.1). For example, Moreton Bay (reference 1 in table: O’Donohue & Dennison 1997) and Pensacola Bay (reference 7 in table: Murrell et al. 2002a, Murrell et al. 2002b, Murrell & Lores 2004) appear to be LNI estuaries all year. Other estuaries, such as the Urdaibai Estuary (reference 2 in table: Ruiz et al. 1998, Revilla et al. 2000, Ansotegui et al. 2001, Revilla et al. 2002, Iriarte et al. 2003, Ansotegui et al. 2003), the Neuse River Estuary (reference 3 in table: Rudek et al. 1991, Pinckney et al. 1997, Pinckney et al. 1998, Richardson et al. 2001), Delaware Bay (reference 6 in table: Watling et al. 1979, Pennock 1985, Pennock & Sharp 1986, Fisher et al. 1988, Pennock & Sharp 1994), the York River Estuary (reference 5 in table: Ray et al. 1989, Sin et al. 1999, Sin et al. 2000), and the Chesapeake Bay (reference 4 in table: McCarthy et al. 1974, Sellner 1983, Fisher et al. 1988, McManus & Cantrell 1992, Fisher et al. 1999) appear to be LNI estuaries during summer. In HNI estuaries, phytoplankton growth, biomass, and productivity values peak in mid- to high salinity waters, and are lowest in low salinity waters. Pico- or nanophytoplankton, depending on temperature, predominate phytoplankton biomass in low salinity waters. Microphytoplankton predominate biomass in mid- to high salinity waters (Fig.1. 13). This model appears applicable during winter/early spring in estuaries such as the Southampton Estuary (reference 8 in table: Iriarte 1993), the Urdaibai Estuary (Ansotegui et al. 2003) the Neuse River Estuary (Pinckney et al. 1998), Delaware Bay (Watling et al. 1979, Pennock 1985, Pennock & Sharp 1986, Fisher et al. 1988, Pennock & Sharp 1994), the York River Estuary (Sin et al. 1999, Sin et al. 2000) and Chesapeake Bay (McCarthy et al. 1974, Sellner 1983, Fisher et al. 1988, Fisher et al. 1999) (Table 1.1). The spatial distribution of phytoplankton growth, biomass, and productivity suggest that Apalachicola Bay is a LNI estuary during summer. During summer,

27 picophytoplankton were the main component of the phytoplankton biomass (Fig.1. 12c). The lower light energy levels that occurred in lower salinity waters (Fig.1. 4b) and lower nutrient concentrations that occurred in higher salinity waters (Fig.1. 6) probably allowed picophytoplankton to outcompete larger phytoplankton as picophytoplankton have relatively low half saturation constants for light and nutrients. Phytoplankton growth rates were highest between about 5 and 20 psu during summer (Fig.1. 12a). We suspect this is because average mixed layer light energy levels (Fig.1. 4b) and nutrient concentrations (Fig.1. 6) were sub-optimal in low and high salinity waters, respectively, yet adequate to support higher growth rates between about 5 and 20 psu. Biomass and productivity were highest between about 5 and 20 psu during summer (Fig.1. 12b, 12d) probably as a result of negligible export from the estuary during summer (Mortazavi et al. 2000c) and microzooplankton grazing that is generally proportional to phytoplankton growth (Chapter 3). In contrast to summer, Apalachicola Bay appears to be an HNI estuary during winter. Picophytoplankton dominated phytoplankton biomass in low salinity waters; while microphytoplankton dominated phytoplankton biomass in mid- to high salinity waters (Fig.1. 11c). The lower light levels in low salinity waters (Fig.1. 4b) probably limited growth of microphytoplankton and led to the dominance of picophytoplankton. Nitrogen concentrations in mid- to high salinity waters were higher during winter than summer (Fig.1. 5b, Fig.1. 6a). The combination of high nutrients and high light energy levels in mid- to high salinity waters probably led to active growth of microphytoplankton. When growing optimally, smaller phytoplankton, such as picophytoplankton, have lower growth rates than microphytoplankton (Parsons & Takahashi 1973). This explains the lower phytoplankton growth rates in low salinity waters, where picophytoplankton dominated the community, and higher growth rates in mid- to high salinity waters where microphytoplankton predominated (Fig.1. 11a). Because export is relatively low compared to grazing (Mortazavi et al. 2000c) and grazing is proportional to growth (Chapter 3), biomass and productivity (Fig.1. 11b, 11d) were highest in the same waters where growth was highest (between 20 and 26 psu) (Fig.1. 11a). Above about 30 psu, there was a reduction in growth, biomass, productivity, and one sample indicated that picophytoplankton were the main component

28 of phytoplankton biomass (Fig.1. 11c). We suspect that low nutrient concentrations in waters above 30 psu (Fig.1. 6), indicative of oligotrophic gulf waters, led to the nutrient limitation of microphytoplankton and dominance of picophytoplankton in the community. In the Southampton estuary, phytoplankton biomass and percent of phytoplankton biomass composed of large cells also declined in oligotrophic waters above about 32 psu (Iriarte 1993).

Management implications Upstream water diversion from the Flint and Chattahoochee Rivers, that provide water to the Apalachicola River, is proposed for the summer when recreational and agricultural requirements for freshwater typically increase. Compared to summer 2003, reduced river discharge during summer 2004 decreased the areal extent of lower (<20 psu) salinity water in the Bay (Fig.1. 3). During summer, phytoplankton growth, biomass and productivity were highest in lower (<20 psu) salinity waters (Fig.1. 12). Therefore, reduced river discharge during summer 2004 decreased the area where peak phytoplankton growth, biomass and productivity occurred. Reduced river discharge during summer 2004 also led to reduced nutrients (Fig.1. 5, Fig.1. 6) which, in turn, led to reduced phytoplankton growth and productivity at a particular salinity (Fig.1. 12a, 12c). Although phytoplankton biomass was not estimated during 2003, it is likely that phytoplankton biomass at a particular salinity was the same between summer 2003 and summer 2004. Relationships between chlorophyll and salinity during summer were not significantly different between years. Furthermore, although abundances of diatoms and dinoflagellates >20 µm in size were lower during 2004 (Fig.1. 8b, 8c), picophytoplankton were the major component of total phytoplankton biomass during summer (Fig.1. 12c) and abundances of picocyanobacteria were not significantly different between summer 2003 and summer 2004 (Fig.1. 8a). Overall, reduced river discharge in 2004 led to less phytoplankton biomass and reduced rates of phytoplankton growth and productivity in Apalachicola Bay. Since phytoplankton support secondary producers in Apalachicola Bay (Chanton & Lewis 2002), we predict that upstream water diversion will reduce higher trophic level productivity through reduced phytoplankton growth, biomass and productivity.

29 Table 1. 1. Well- or partially mixed estuaries that may be classified as Type LNI or HNI (see text for details). Limitation status (light, P- phosphorus, N-nitrogen), S (salinity), Low S (<10 psu), Mid S (10-25 psu), High S (25-30 psu), Size composition (Size), Pico, Nano, Micro (pico-, nano-, microphytoplankton), and salinity ranges where µ (growth), B (biomass), PP (primary productivity) peak.

Type References Estuary Season Limitation Size Salinity Low S High S Low S High S µ B PP LNI Temperate

1 Morteon All P, light N ? ? ? mid mid

2 Urdaibai Summer ? N Nano Nano low, mid mid mid

3 Neuse Summer ? N ? ? low, mid low, mid low, mid

4 Chesapeake Summer P, light N Nano Nano mid mid ?

5 York Summer P, light N Nano Nano ? mid mid

6 Delaware Summer light light, N ? Nano ? low, mid low, mid

Sub-tropical

7 Pensacola All P N, P Pico Pico mid mid ? Iverson et al.; this study Apalachicola Summer P N Pico Pico mid mid mid HNI Temperate

2 Urdaibai Winter/Sprg ? ? ? Micro ? high high

3 Neuse Winter/Sprg ? N ? ? ? high High

4 Chesapeake Winter/Sprg P, light N Nano ? ? high ?

5 York Winter/Sprg P, light ? Nano Micro ? high High

6 Delaware Winter/Sprg light light ? Micro ? high high

8 Southampton Fall ? ? Nano Micro ? high ?

Sub-tropical Iverson et al.; this study Apalachicola Winter/Sprg P N Pico Micro high high high 30 35

30 A C)

o 25

15 temperature ( temperature 2004 10 Average bay sea surface sea surface bay Average 2003

1600 * 24 year average ) 2003 -1 2004 1200 sec 3 * 800 * **

Average monthly 400

river discharge (m discharge river B

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

Fig. 1.2. A. Average sea surface temperature during sampling in Apalachicola Bay. B. Average monthly river discharge for the Apalachicola River (source: http://waterdata.usgs.gov). Asterisks denote months when samples were not collected.

Fig. 1.3. Sea surface salinity (psu) distribution during May (A), June (B), and July (C) of 2003 and May (D), June (E), and August (F) of 2004. Data courtesy of the Apalachicola National Estuarine Research Reserve, System Wide Monitoring Program.

32 B.Averagemixed layerlightenergy relativetoseasurfacesalinity. I

Regression for salinity during2003and2004. Fig. 1.4.A.Attenuationcoefficient( avg -1 =(I Average mixed layer light energy k (m ) (% surface light energy) o /kD)(1-e 20 30 40 50 60 70 0 2 4 6 8 1 0 k versussalinity:y=1.9-0.03x,r (-kD) ) whereDis2m (averagedepthofApalachicolaBay). 02 k ) andaverage1%lightdepthrelativetoseasurface Salinity (psu) 33 2003 2004 03 2 =0.15, p<0.01 04 B A 0 0 2 4 6 8

1% Light depth (m)

r surface temperature during2003and2004.Regressions: Dotted linedemarks winter(<25.5 Fig. 1.5.A.Dailyincidentlightenergyrelativetoseasurfacetemperature during2004.

2 =0.52, p<0.05. 2003 Average bay dissolved inorganic nitrogen -1 2004 Daily incident light energy 2004: y=520e 2003: y=2108e B.AveragebayDIN(DissolvedInorganicNitrogen) ( (µg N L ) 2 -1 1000 1500 2000

-500 (E m d ) 500 20 30 40 50 60 0 01 02 035 30 25 20 15 10

A 0.05x -0.06x , r , r 2 =0.88, p<0.01 2 =0.65, p<0.05 o C) from summer (>25.5 Temperature ( Temperature 2004 2003 34 o C) o C). Regression:y=16.7e + S.D.)relativetosea B -200 0 200 400 600 800

2004 Average bay 0.03x dissolved inorganic nitrogen , (µg N L-1)

1600 Summer 2003 Winter 2003 A 1200 Winter 2004 ) Summer 2004 -1 W03

800 S03

DIN (µg N L W04 400

S04 0 20 2003 B 2004 15 ) -1

SRP (µg P L (µg SRP 5 2003 2004 0 0 10203040

Salinity (psu)

Fig. 1.6. A. Dissolved Inorganic Nitrogen (DIN) concentration relative to sea surface salinity. Regressions: Winter 2003: y=-16+1996e-0.1x, r2=0.91, p<0.01 Summer 2003: y=-159+982e-0.1x, r2=0.84, p<0.01 Winter 2004: y=425-12x, r2=0.54, p<0.01 Summer 2004: y=-0.29+325e-0.1x, r2=0.67, p<0.01

B. Soluble Reactive Phosphate (SRP) concentration relative to sea surface salinity. Regressions: 2003: y=3.4+8e-0.3x, r2=0.33, p<0.01 2004: y=0.4+4e-0.1x, r2=0.13, p<0.05

35 20 2003 A 2004 15 A

) 10 -1 Winter (µg L 5 total chlorophyll a chlorophyll total

0 20 B 15 ) -1 10 (µg L Summer

total chlorophyll a chlorophyll total 5

0 0 10203040

Salinity (ppt)

Fig. 1.7. A. Total chlorophyll concentration during winter relative to sea surface salinity. Regression: y= 5.5-0.08x, r2=0.09, p<0.05

B. Total chlorophyll concentration during summer relative to sea surface salinity. Regression: y= 6.5-0.08x, r2=0.19, p<0.05

Fig. 1.8. A. Abundance of picocyanobacteria relative to sea surface salinity during summer. Regression: y= 137 + 75x – 2.1x2, r2=0.57, p<0.01

B. Abundance of diatoms >20 µm relative to sea surface salinity during summer. Regressions: 2003: y= 0.05 + 0.01x, r2=0.20, p<0.05 2004: y= -0.003 + 0.003x, r2=0.37, p<0.01

C. Abundance of dinoflagellates >20 µm relative to sea surface salinity during summer. Regressions: 2003: log y= -3.6 + 0.04x, r2=0.30, p<0.05 2004: log y= -3.0 + 0.04x, r2=0.29, p<0.01

37 Dinoflagellates >20 µm Diatoms >20 µm Picocyanobacteria 6 6 -1 6 -1 log (cells x 10 L-1) (cells x 10 L ) (cells x 10 L ) 1050 1400 350 700 0.0 0.2 0.4 0.6 -4 -3 -2 -1 0 0 1 0 2004 2003 2004 2003 2004 2003 02 38 Salinity (psu) 03 04 A C B 0

300

250

ll y

h 200

4 o

l 150

200

: n

o 100 0 rb

a ) C 50 10 u ps ( y t 0 20 i in l a 30 S 25 30 20 Te 15 mpe o rature ( C)

Fig. 1.9. Temporal and spatial distribution of carbon: chlorophyll a ratios during 2004. Regression: z=a+b/(1+((x-c)/d)2)+e/(1+((y-f)/g)2)+h/((1+((x-c)/d)2)*(1+((y-f)/g)2)), a=23, b=-15, c=15, d=18, e=-169, f=30, g=2, h=388, r2=0.77, p<0.05.

Fig. 1.10.A. Phytoplankton growth during 2003 and 2004 relative to sea surface temperature. Dotted line denotes approximate temperature when phytoplankton growth, biomass, and productivity peaked and also denotes winter (<25.5oC) from summer (>25.5oC). Regressions: 2003 below 26oC: y=-0.15+0.06x, r2=0.69, p<0.01 2003 above 26oC: y=5.8-0.16x, r2=0.44, p<0.05 2004 below 26oC: y=0.06+0.02x, r2=0.29, p<0.05 2004 above 26oC: y=6.4-0.2x, r2=0.63, p<0.05

B. Phytoplankton mean biomass (for calculation, see methods) relative to sea surface temperature during 2004. Regression: y=1517e(-0.5*((x-27)/3)^2), r2=0.85, p<0.01

C. Phytoplankton productivity relative to sea surface temperature during 2004. Regression: y=1281e(-0.5*((x-27)/1.5)^2), r2=0.97, p<0.01

40 2.0

1.5 A

1.0 )

-1 0.5 (d

0.0 2004

Phytoplankton growth Phytoplankton -0.5 2003

-1.0 2000 B 1500 ) 1 - 1000 2004 (µg C L

500 Mean phytoplankton biomass 0 2000 C 1500 ) -1 d -1 1000 2004 (µg L C 500 Phytoplankton productivity

0 10 15 20 25 30 35 o Temperature ( C)

Fig. 1.11.A. Phytoplankton growth during winter 2003 and winter 2004 relative to sea surface salinity. Regressions: 2003: y=-0.05+0.1x-0.002x2, r2=0.89, p<0.05 2004: y=0.22+0.03x-0.001x2, r2=0.43, p<0.10

B. Phytoplankton carbon relative to sea surface salinity during winter 2004. Regression: y=199e(-0.5*((x-28)/3)^2), r2=0.54, p<0.01

C. Percent of total phytoplankton carbon composed of pico-, nano- and microphytoplankton relative to sea surface salinity during winter 2004. Outliers not included in regressions are denoted as large symbols with asterisks. Regressions: Pico-: y=51-0.9x, r2=0.19, p<0.10 Nano-: y=32-0.8x, r2=0.40, p<0.01 Micro-: y=17+1.7x, r2=0.28, p<0.05

D. Phytoplankton productivity relative to sea surface salinity during winter 2004. Regression: y=-6.5+3.7x-0.1 x2, r2=0.56, p<0.05

2.0 2003 1.5 2004 A )

-1 1.0 (d Winter 0.5

phytoplankton growth phytoplankton growth 0.0 400

300 B ) -1 200 carbon (µg C L

Winter 2004 100 total phytoplankton 0 120 Pico Nano Micro 100 C 80 60 ** 40 Winter 2004

% composition of 20 *

phytoplankton carbon carbon phytoplankton 0 -20 100

) D -1

d 60 -1

40 Winter 2004 (µg C L 20

0 phytoplankton productivity 0 10203040

Salinity (psu)

Fig. 1.12.A. Phytoplankton growth during summer 2003 and summer 2004 relative to sea surface salinity. Regressions: 2003: y=0.6+0.1x-0.005x2, r2=0.56, p<0.05 2004: y=0.6-0.03x, r2=0.38, p<0.05

B. Phytoplankton carbon relative to sea surface salinity during summer 2004. Regression: y=1141(-0.5*((x-13)/8)^2), r2=0.74, p<0.01

C. Percent of total phytoplankton carbon composed of pico-, nano- and microphytoplankton relative to sea surface salinity during summer 2004. Regressions: Pico-: y= 67 + 1.2x – 0.05x2, r2=0.54, p<0.01 Nano-: y= 24 + 0.6x – 0.03x2, r2=0.89, p<0.01 Micro-: y= 8 – 1.8x + 0.08x2, r2=0.74, p<0.01

D. Phytoplankton productivity relative to sea surface salinity during summer 2003 and summer 2004. Regression for 2003: y=153+224x-8x2, r2=0.49, p<0.10

44 2.0 1.5 A 1.0 2003 )

-1 2004 0.5 (d

Summer 0.0 -0.5

phytoplankton growth -1.0 1600

1200 B ) -1 800 carbon (µg C L

Summer 2004 400 total phytoplankton 0 120 Pico Nano Micro 100 C 80 60 40 20 Summer 2004

% composition of 0 phytoplankton carbon -20 3200

) 2400 D -1

d 2003 -1 1600 2004 Summer

(µg C L 800

0 phytoplankton productivity 0 10203040 Salinity (psu)

45 Nutrient limited

TYPE LNI Pico- OR Nano- Lower Nutrient µ Input [Nut.] B PP

Salinity Salinity

Light limited

I av

Salinity

TYPE HNI Pico- OR Micro- Nano- Higher Nutrient µ [Nut.] B Input PP

Salinity Salinity

Fig. 1.13. Two conceptual models proposed to describe the relationship between phytoplankton growth (µ), biomass (B), productivity (PP), size composition (Pico-,Nano- and Micro- refer to pico-, nano- and microphytoplankton, respectively) and salinity in well-mixed and partially-mixed estuaries. Iav is average mixed layer light intensity and [Nut.] is nutrient concentration. See text for details.

46 CHAPTER 2

ECOLOGY OF ACARTIA TONSA IN APALACHICOLA BAY, FLORIDA

Introduction

Apalachicola Bay is a productive estuary located in the northern Gulf of Mexico. The Bay is a habitat and nursery for a wide variety of commercial and sport fish species such as croaker, seatrout, shrimp, blue crab, and oysters (Livingston 1984). The high productivity is a result of the Apalachicola River delivering freshwater and nutrients to the Bay (Livingston 1984, Mortazavi et al. 2000a, 2000b, 2000c, 2001). Freshwater moderates salinity in the Bay which creates habitat for estuarine flora and fauna. Nutrient input supports high levels of phytoplankton productivity (Mortazavi et al. 2000c), which supports the Bay’s secondary productivity (Chanton & Lewis 2002). A major pathway of energy flow in the Bay is through the planktonic food web. Diversion of water from headwaters of the Apalachicola River during summer has been proposed to satisfy upstream freshwater requirements for recreation and agriculture (Lewis 1997). Freshwater diversions and droughts in estuaries have led to changes in phytoplankton species composition (Humborg et al. 1997) and reductions in phytoplankton and zooplankton productivity and biomass (Nichols et al. 1986, Froneman 2000, Grange et al. 2000, Snow et al. 2000), and fish biomass (Nichols et al. 1986, Livingston et al. 1997, Grange et al. 2000, Gillanders and Kingsford 2002). The mechanisms through which water diversion may lead to reduced higher trophic level productivity in Apalachicola Bay are unknown. Marine water with high concentrations of new nitrogen favors the growth of large phytoplankton and leads to a classical diatom-copepod-fish food chain. In contrast, marine water with low concentrations of new nitrogen leads to a microbial food web, with low phytoplankton biomass and productivity and the predominance of small phytoplankton (Riegman et al. 1993, Legendre & Rassoulzadegan 1995, Sommer et al. 2002). Microbial food webs are thought to support lower magnitudes of higher trophic level productivity than do classical food chains (Sommer et al. 2002). In Apalachicola Bay, nutrient concentrations are high, at least in lower salinity waters (Mortazavi et al. 2001), and diatoms (Estabrook 1973) and copepods (Edmiston 1979, Marcus 1991) are

47 abundant. Therefore, a classical diatom-copepod-fish food chain might describe planktonic food web structure in Apalachicola Bay. However, the syntheses of Calbet (2001) and Calbet & Landry (2004) indicate that mesozooplankton (primarily copepods) ingest about 10% of phytoplankton productivity in productive waters and that microzooplankton ingest about 60% of phytoplankton productivity in estuaries. The relative magnitude of herbivory by copepods and microzooplankton, however, may vary across gradients of nutrient concentration in estuaries. We hypothesize that a classical diatom-copepod-fish food chain characterizes the planktonic food web structure in high nutrient waters, while a microbial food web characterizes the planktonic food web structure in low nutrient waters in Apalachicola Bay. Water diversion that increases the areal extent of higher salinity water where nutrient concentrations are low (Mortazavi et al. 2001, Chapter 1) and where a microbial food web may predominate, would lead to reduced higher trophic level productivity. The first objective of this study was to test the hypothesis that copepods are the primary herbivores in high nutrient waters in Apalachicola Bay. We specifically examined grazing by Acartia tonsa because it is the most abundant copepod and the main constituent of the mesozooplankton community in the Bay (Edmiston 1979, Marcus 1991). If A. tonsa are the main herbivores in high nutrient waters, then the percent of phytoplankton production ingested by A. tonsa will be greater than that ingested by microzooplankton (Chapter 3). Another study examined grazing by microzooplankton in Apalachicola Bay (Chapter 3). A second objective of the study was to determine the spatial and temporal patterns of ingestion, diet, egg production, and abundance of A. tonsa in Apalachicola Bay. We were specifically interested in determining the salinity range where peak egg production and abundance occurs and identifying factors influencing egg production. To achieve these objectives, grazing (herbivory and carnivory), egg production, and abundance of A. tonsa were examined within various salinity regimes in Apalachicola Bay throughout a 2 year period (2003 through 2004).

48 Materials and Methods Sampling for Acartia tonsa Samples were collected from Apalachicola Bay during 2003 and 2004 (Fig.I. 1). Locations sampled represented oligo-, meso-, and euryhaline portions of the Bay. At each location, temperature and salinity were measured at 0.5 m depth intervals throughout the water column with a YSI® salinometer. Copepods were collected with a conical 202 µm nylon mesh net equipped with a closed cod end. The net had a 6:1 filtering area to mouth area. The net was towed horizontally for short periods (<2 min) and filtered water from surface to 0.5 m depth. Volume filtered was estimated using a General Oceanics flowmeter that was mounted on the inside of the net. Another General Oceanics flowmeter was mounted on the outside of the net to estimate net filtration efficiency. Animals from one tow were used for grazing and/or egg production experiments and were gently rinsed into a cooler containing seawater collected from ~0.5 m. Animals from another tow were used to estimate the abundance of Acartia tonsa and were preserved in acid Lugol’s. Abundances of adult A. tonsa were only estimated for tows where filtration efficiency was >50%. Lugol’s samples were split with a Folsom plankton splitter between 2 to 5 times, after which about 300 adult A. tonsa were counted per sample. A. tonsa were counted with an Olympus stereomicroscope. The abundance was calculated as:

−1 Number i () fraction of original sample (1) Volume filtered (L)

Acartia tonsa grazing Prior to conducting grazing (and egg production) studies, all equipment that would contact seawater was soaked for a few days in 10% hydrochloric acid. Afterward, equipment was thoroughly rinsed and then soaked for several days with Nanopure water. Nitrile gloves were worn during all water handling procedures. The particle removal method (Frost 1972) was used to assess Acartia tonsa grazing. Grazing experiments were performed with seawater collected with a darkened polycarbonate carboy from a depth of about 0.5 m below sea surface. Within 4 hours of

49 collection, carboys containing the seawater and coolers with A. tonsa were transported to the Florida State University Edward Ball Marine Laboratory. Upon arrival, seawater (<202 µm) was added to 2L incubation bottles through silicon tubing that was equipped with 202 µm Nitex screening. The silicon tubing was kept submerged below the waterline in the bottles to reduce damage to delicate protists. A total of eight 2L bottles were filled with seawater per station. Two bottles were used to acclimate copepods, two bottles were used for initial samples, four bottles were used for final samples (two for controls and two for treatments). About 5 to 30 adult copepods (males and females) were added to each acclimation bottle. To facilitate capture of live copepods, copepods were anesthetized with small amounts (<0.5 mL per 75 mL seawater) of MS222 (3- aminobenzoic acid ethyl ester). Since nitrogen limits phytoplankton productivity throughout most of Apalachicola Bay during summer (Iverson et al. submitted) inorganic nitrogen (as ammonium chloride) was added to all incubation bottles to ensure that prey growth did not become nitrogen limited and to avoid negative grazing rates due to nutrient enrichment from copepod excretion during the course of the incubations. Enough ammonium chloride was added to bottles to elevate ammonium concentrations in bottles to those found in the river. Since nutrient analyses were not synchronous with sampling, we determined the enrichment based on the difference between ammonium concentrations in the river and that at a station with similar salinity for the previous month. All bottles were placed on a plankton wheel (~0.5 rpm) and incubated in an outdoor incubator flushed continuously with seawater from Apalachicola Bay. Incident irradiance was attenuated to 67% of ambient with neutral density screening. After an acclimation period of ~18 hours, copepods were transferred from the acclimation bottles to the treatment bottles. To transfer the copepods, the contents of the acclimation bottles were filtered through a 202 µm Nitex screen. Filtered copepods were washed into a dish of filtered seawater and examined for viability. Viable copepods were transferred to treatment bottles using a wide bore pipette and a minimal amount of MS222. Treatment bottles were placed back on the plankton wheel and incubated for ~24 hours. Samples were taken for chlorophyll a and heterotrophic protists after the acclimation period

50 (“Time zero samples”) from the initial bottles and ~24 hours later (“Time final samples”) from the control and treatment bottles. Seawater for chlorophyll analysis was filtered through 47 mm GF/F filters at <117 mm Hg vacuum. Seawater was also filtered through 47 mm 5µm Poretics® polycarbonate filters at <117 mm Hg vacuum to estimate the portion of total chlorophyll that was >5 µm. Filters were stored in darkness at -20oC and analyzed within 1 week of sample collection. Chlorophyll a was extracted from filters in 90% acetone for about 24 hours at -20oC. The concentration of chlorophyll a was measured fluorometrically with a Model 10 Turner Designs® fluorometer equipped with filter sets for optimal sensitivity of chlorophyll a in the presence of chlorophyll b (Welschmeyer 1994). Samples for the analysis of heterotrophic flagellates 5 to 20 µm in size were preserved in glutaraldehyde (2% final concentration) and stored in darkness at 4oC (Gifford and Caron 2000). Flagellates were enumerated within 1 month of sample collection. Samples were filtered (<117 mm Hg vacuum) onto 5µm Poretics® polycarbonate filters and stained with Acridine Orange (1% final concentration) (Sherr et al. 1993). Filters were mounted with Cargille’s type B immersion oil onto glass slides. A BH Olympus® epifluorescence microscope equipped with a blue/UV excitation filter set (U-M546, excitation 400 to 410 nm; emission 455 to 700 nm) was used to visualize cells. Cells that lacked red autofluorescence, indicative of chlorophyll a, were counted at a total magnification of x1875. For each filter, at least 100 cells were counted in either transects or in a minimum of 10 random fields (Hobro & Willen 1977). Samples for the analysis of heterotrophic flagellates >20 µm in size and total heterotrophic ciliates were preserved in acid Lugol’s (2% final concentration) and stored in darkness at 4oC (Gifford and Caron 2000). Samples were enumerated within 1 month of sample collection. Samples (10 to 50 mL) were settled for 24 hours with Utermohl settling chambers. Cells were counted through phase contrast light microscopy at a total magnification of x200 with an inverted Wild® microscope. If protists could not be identified in Lugol’s samples, then their trophic mode (i.e. auto- or heterotrophic) was determined by examining glutaraldehyde samples with epifluorescence microscopy. For each settled sample, transects were counted until at least 100 cells were counted (Hobro & Willen 1977).

Grazing calculations Prey were assumed to grow exponentially and thus prey growth rates in control bottles were calculated as,

1 P µl = (n t ) (2) t Po

-1 where µ (d ) is prey growth, t is incubation length (days), Po and Pt are initial and final prey densities, respectively. Prey net growth rates (NGR, d-1) in treatment bottles were estimated as,

1 P NGR = (ln t ) (3) tPo

Prey NGR in treatment bottles was assumed to be the difference between µ and grazing from Acartia tonsa (g, d-1). Thus, A. tonsa grazing in treatment bottles was calculated as,

g = µ - NGR (4)

Negative values for herbivory were corrected with the general method of Nejstgaard et al. (2001). Adult A. tonsa clearance rates (F, mL Adult-1 d-1) were calculated as,

V F = g ( ) (5) N

where N is the number of copepods added to the treatment bottle and V is the volume of the bottle (1000 mL). The percentage of phytoplankton potential production (%Pp) ingested by A. tonsa, or herbivory, was calculated as,

52 µt (µ-G)t ((Pooo e -P )-(P e -P o )) %Pp =µt x 100 (6) (Poo e -P ) where µ is phytoplankton growth determined from dilution assays (Chapter 3) and G (d-1) was calculated as,

I G = Fphyto (N ) (7)

-1 -1 I where Fphyto is clearance rate on phytoplankton (mL Adult d ) and N is the in situ abundance of A. tonsa (Adults mL-1). Per capita ingestion rate (I, µg C Adult -1 d-1) was calculated as,

I = F(Co ) (8)

-1 where Co is initial prey carbon (µg C mL ). Total per capita ingestion rate (Itot, µg C Adult-1 d-1) was calculated as,

Itot = Iphyto + Imzp (9)

where Iphyto and Imzp represent A. tonsa per capita ingestion rates of phytoplankton and microzooplankton, respectively.

Relative Preference Indices (RPI) were calculated to assess prey preference of A. tonsa. RPI’s were calculated as,

⎛⎞Iprey ⎜⎟ I RPI = ⎝⎠total (10) ⎛Cprey ⎞ ⎜⎟ ⎝⎠Ctotal

53 where Iprey is the amount of prey carbon ingested; Cprey is prey carbon in situ; Ctotal is total prey carbon in situ. RPI’s >1 indicate preference for the prey item, <1 indicate avoidance of the prey item, and values of unity indicate that prey are ingested in proportion to their availability in situ.

Conversion factors Phytoplankton carbon was estimated with measured chlorophyll and carbon: chlorophyll ratios determined for Apalachicola Bay (Chapter 1). For protists (flagellates, ciliates, dinoflagellates), cell volumes were converted to carbon with carbon-volume relationships for non-diatoms (Menden-Deuer & Lessard 2000). For each sample, about 10 randomly chosen protist cells were measured and volumes estimated with suitable formula (Wetzel and Likens 1991). Body carbon of adult Acartia tonsa was estimated from prosome length and formulae to convert prosome length to body carbon (Berggreen et al. 1988).

Egg production rate The method of Runge and Roff (2000) was used to determine the rate of Acartia tonsa egg production. Seawater was collected with a darkened polycarbonate carboy from a depth of about 0.5 m below sea surface. Upon arrival at the FSU marine lab, seawater (<20 µm) was added to 1L polyethylene incubation bottles through silicon tubing that was equipped with 20 µm Nitex screening. Three bottles were filled per station. About 5 to 30 adult copepods (males and females) were added to each bottle. To facilitate capture of live copepods, copepods were anesthetized with small amounts (<0.5 mL per 75 mL seawater) of MS222. All bottles were incubated in an outdoor incubator flushed continuously with seawater from Apalachicola Bay. Incident irradiance was attenuated to 67% of ambient using neutral density screening. After an incubation period of ~24 hours during summer and ~48 hours during winter, copepods were removed from the bottles by filtering the contents of the bottles through a 202 µm Nitex screen. Filtered copepods were washed into a dish of filtered seawater and examined for viability. Once the number and identification of dead copepods was determined, copepods were preserved in acid Lugol’s. Bottles containing

54 the <202 µm filtrate were re-incubated in the outdoor incubator. Eggs and nauplii were removed from the bottles after ~24 and 48 hours for summer and winter samples, respectively by filtering the contents of the bottles through a 20 µm Nitex screen. Filtered eggs and nauplii were gently washed into a bottle and preserved with acid Lugol’s. Adult females, eggs, and nauplii were counted with an Olympus stereomicroscope. Egg production rate (EPR) was calculated as,

1 No. Nauplii EPR = ( ) (11) tNo. Females

where t is in the initial incubation period, Nauplii is the number of nauplii that hatched during the second incubation period, and Females is the number of viable adult female A. tonsa counted at the end of the first incubation period.

Egg production efficiency Egg production efficiency (EPE) was calculated for Acartia tonsa. EPR was converted to carbon assuming eggs contained 0.14 x 10-6 µg C µm-3 (Kiorboe et al. 1985). Average egg diameter in Apalachicola Bay was 65 µm. EPE was calculated as,

1 µg egg C female-1 d -1 EPE = ( ) (12) tItot

The C:N ratio of food ingested by Acartia tonsa was also calculated by dividing total per capita carbon ingested by nitrogen ingested. We calculated the total nitrogen ingested by assuming a GGE for nitrogen of 36% (Checkley 1980, Kiorboe 1989) and by converting EPR to nitrogen by assuming 0.007 µg N egg-1 (Ambler 1985).

Products of photosynthesis To assess the quality of the phytoplankton available, the percent of fixed carbon allocated to the synthesis of lipid, protein, polysaccharides, and low molecular weight

55 (LMW) compounds was examined across the nutrient/salinity gradient during August, September, October, December 2004, and February 2005. Seawater was collected from ~0.5 m with a darkened polycarbonate carboy and within 4 hours of collection was transported to the Florida State University Edward Ball Marine Laboratory. For each station, three clear 125 mL glass bottles were filled with seawater and then inoculated 14 with ~10 µCi of NaH CO3. Preliminary experiments verified that the rate of photosynthesis in glass bottles was not significantly different from that in polycarbonate bottles. Bottles were incubated in an outdoor incubator flushed continuously with seawater from Apalachicola Bay. Incident irradiance was attenuated to 67% of ambient with neutral density screening. After an incubation period of ~24 hours samples were filtered through 25 mm GF/F filters at <117 mm Hg vacuum. Filtered samples were stored in darkness at -20oC and analyzed within 1 month of sample collection. Incorporation of 14C into lipid, protein, polysaccharide, and low molecular weight (LMW) compounds was determined following procedures modified from Li et al. (1980) and Rivkin (1985). Filters were vortex mixed with 3 mL of chloroform and 1.5 mL of a 99:1 mixture of methanol/acetic acid and then stored at 4oC for 30 minutes. The filter was vortex mixed again and rinsed with 1.5 mL of chloroform before being filtered through a 25 mm GF/F filter at <117 mm Hg vacuum into a connecting tube. Distilled water (1 mL) was added to the filtrate. The filtrate was vortexed and then centrifuged at 4000 rpm for 10 min. Aliquots (1 mL) were sampled from each phase and transferred to a liquid scintillation vial. The aliquot from the chloroform phase (containing lipids) was air dried overnight. Liquid scintillation cocktail (10 mL) was added to each vial. The filter was placed into a glass vial with 3 mL of 5% trichloroacetic acid (TCA). The suspension was heated to 95oC in a sand bath for 45 min. The slurry was then filtered through a 25 mm GF/F filter at <117 mm Hg vacuum into a connecting tube. The filter was rinsed with 2 mL of ice chilled 5% TCA. Filtrate (1 mL), containing polysaccharides, was placed into a liquid scintillation vial. The filter, containing proteins, was placed into another liquid scintillation vial. Liquid scintillation cocktail (10 mL) was added to each vial. Samples were counted with a Wallac liquid scintillation counter. Quench curves for each solvent/treatment were determined and used to calculate disintegrations per

56 minute. Counts for the lipid fraction were multiplied by 4.5 mL to estimate total 14C incorporated into lipid compounds. Likewise, counts for the low molecular weight and polysaccharide fraction were multiplied by 2.5 mL and 5 mL, respectively to estimate total 14C incorporated into low molecular weight and polysaccharide compounds. These factors correspond to the total extract volume for the respective fractions.

Statistical analyses Analysis of Covariance tests were used to determine if significant differences existed between years, seasons, or days for relationships between variables. If no significant difference was found, then data were pooled and a common regression equation was determined. Relationships were considered significant if the p-value was <0.05 (Sokal & Rohlf 1995). Non-linear regressions were used when a linear regression model did not adequately explain the relation between variables (for example, low r2, variance heteroscedastic). Non-linear relationships were analyzed by dividing the data into 2 components: (1) the initial increasing segment and (2) the latter decreasing segment. ANCOVA tests were performed on each segment.

Results

Prey carbon Total prey carbon, that is phytoplankton carbon >5 µm plus microzooplankton 5 to 20 µm, was related to temperature and was not significantly different between years (Fig.2. 2a). Below 26oC, hereafter referred to as winter, average (+ S.D.) total prey carbon was 74 + 25 µg C L-1. Above 26oC, hereafter referred to as summer, average (+ S.D.) prey carbon was 410 + 219 µg C L-1. Total prey carbon peaked to ~500 µg C L-1 between ~26 and 30oC. Microzooplankton carbon was not related to temperature and averaged (+ S.D.) 48 + 30 µg C L-1. The percent of total prey carbon composed of phytoplankton >5 µm was related to temperature and was not significantly different between years (Fig.2. 2b). The average (+ S.D.) percent of prey carbon composed of

57 phytoplankton >5 µm was 55 + 20% and 81 + 18% during winter and summer, respectively.

Acartia tonsa abundance Abundance averaged (+ S.D.) 2 + 0.9 L-1 and 0.4 + 0.5 L-1 during winter and summer 2003, respectively. In contrast, abundance averaged (+ S.D.) 1.1 + 1.2 L-1 and 4.3 + 3.8 L-1 during winter and summer 2004, respectively. There were no significant relationships between abundance and salinity during winter 2003, winter 2004, or summer 2003. We compared our summer 2004 data, when average river discharge rates into the Bay were about 400 m3 s-1, to abundance data collected by Edmiston (1979) and Marcus (1991), when river discharge into the Bay was similar (Fig.2. 3). Together these data sets indicate that during summer, when average discharge is about 400 m3 s-1, abundance of adult A. tonsa peaks to about 12 L-1 between about 12 and 22 psu.

Acartia tonsa grazing Despite application of the general method of Njestgaard et al. (2001) to correct rates of herbivory, Acartia tonsa did not ingest enough phytoplankton to appreciably affect phytoplankton growth on any of the 22 dates when we estimated herbivory (Fig.2. 4). In addition, there were no relationships between the percent of phytoplankton productivity ingested (%Pp) and salinity. The %Pp was lowest during summer 2003 when it ranged from ~0 to 1%. During winter 2003, %Pp ranged from ~0 to 8%. During winter

2004, %Pp ranged from ~0 to 3% and during summer 2004, %Pp ranged from ~0 to 24%.

The average %Pp was 2.2 + 5.3% (median 0.4%). Clearance rates on phytoplankton were non-linearly related to phytoplankton carbon <5 µm. Clearance rates peaked at about 100 µg C L-1 and ranged from 0 to 45 mL Adult-1 d-1 (Fig.2. 5). Total per capita ingestion rate was not significantly related to salinity during winter or summer in either year and ingestion rates did not differ between years. Total per capita ingestion rate was related to temperature and peaked between ~26 and 30oC (Fig.2. 6a). Average (+ S.D.) ingestion was 1.1 + 1.3 µg C Adult-1 d-1 and 2.2 + 1.8 µg C Adult-1 d-1 during winter and summer, respectively.

58 The amount of carbon ingested relative to A. tonsa body carbon was not related to salinity during winter or summer in either year and did not differ between years. The amount of carbon ingested relative to body carbon was related to temperature and peaked between ~26 and 30oC (Fig.2. 6b). On average (+ S.D.), A. tonsa ingested the equivalent of 38 + 48% and 120 + 93% body carbon during winter and summer, respectively. Body carbon averaged (+ S.D.) 2.0 + 0.9 (n=108) and 2.3 + 0.9 (n=152) µg C adult-1 during 2003 and 2004, respectively. The percent of diet composed of phytoplankton was not significantly related to salinity during winter or summer in either year and did not differ between years. In addition, the percent of diet composed of phytoplankton was not significantly related to temperature and averaged (+ S.D.) 48 + 28% during winter and 57 + 32% during summer (Fig.2. 6c). Relative Preference Indices (RPI) were not related to salinity during winter or summer in either year and did not differ between years. The RPI were not significantly related to temperature. RPI for phytoplankton averaged (+ S.D.) 0.8 + 0.4 and 0.7 + 0.4 during winter and summer, respectively. RPI for microzooplankton averaged (+ S.D.) 1.2 + 0.5 and 3.9 + 5.3 during winter and summer, respectively (Fig.2. 6d). A model I functional feeding response (Holling 1959) was assumed and fit to our data by the method of Bamstedt et al. (2000). Total per capita ingestion rate was linearly related to total prey carbon below 250 µg C L-1 and ranged from 0 and 6 µg C Adult-1 d-1 (Fig.2. 7). Above 250 µg C L-1, total per capita ingestion rate was not related to total prey carbon and average (+ S.D.) total per capita ingestion was 2.1 + 1.8 µg C Adult d-1.

Egg production rate and efficiency On average (+ S.D.), egg production rate (EPR) was lower during winter (13 + 15 eggs female-1 d-1) than during the summer (28 + 24 eggs female-1 d-1) (Fig.2. 8). EPR was not significantly related to salinity during winter 2003 or winter 2004 and rates were not different between years. During summer, EPR was significantly related to salinity and the relationship between EPR and salinity during summer 2003 was not significantly different from that during summer 2004 (Fig.2. 9). EPR peaked to about 50 eggs female-1 d-1 between about 4 and 10 psu during summer.

59 EPE peaked (~99%) between 6 and 18 psu during summer and winter (Fig.2. 10). Four data points that occurred between 10 and 12 psu were removed from the regression. Three of these data points (circled in Fig.2. 10) were from samples where Thalassiosira spp. represented a high (>50%) percentage of the abundance of diatoms >20 µm. Thalassiosira spp., specifically T. rotula, can lead to reduced copepod reproduction (Ianora et al. 1999). The fourth data point (denoted with an asterisk in Fig.2. 10) was not included in the regression. The composition of the phytoplankton community in this sample is not known. In addition to the 4 points not included in the regression, there were 3 data points that had values >100%. Two of the data points were from winter and had EPE values of 725% and 390%. One of the data points was from summer and had an EPE value of 2424%. These high values occurred at about 11, 12, and 23 psu and were considered erroneous and were removed from the data set. Between 5 and 20 psu, the calculated C:N of food ingested averaged (+ S.D.) 6 + 13. In lower (<5 psu) and higher salinity (>20 psu) water, the C:N of food ingested averaged (+ S.D.) 48 + 57 and 12 + 11, respectively (Fig.2. 10).

Products of photosynthesis The method that was used to estimate the fraction of carbon productivity relegated to the synthesis of protein, lipid, and carbohydrate did not lead to loss of fixed carbon. On average (+ S.D.), total radioactivity from split samples was 103 + 20 % of total radioactivity from un-split samples. Relationships between percent of carbon productivity synthesized into different compounds and salinity were not significantly different on the dates sampled. Therefore, data were pooled for each fraction (Fig.2. 11). Of total carbon fixed, <20% was allocated toward the synthesis of low molecular weight compounds. The percent of carbon allocated toward the synthesis of protein and lipid compounds was inversely related to salinity (Fig.2. 11a, Fig.2. 11b). In contrast, the percent of carbon allocated toward the synthesis of polysaccharide compounds was positively related to salinity (Fig.2. 11c).

60 Discussion

Herbivory Clearance rates estimated from the particle removal experiments (Fig.2. 5) are within the same range as those previously reported for Acartia tonsa feeding on phytoplankton (Gifford & Dagg 1988, Berggreen et al. 1988, Froneman 2001). Furthermore, the functional feeding response curve (Fig.2. 5) is similar to the theoretical functional response curve for zooplankton feeding kinetics (Miller 2004). Patchiness (Wiebe 1972), filtration efficiency (Smith et al. 1968) and splitting of samples (Postel et al. 2000) are some of the most important sources of error associated with estimates of copepod abundance. To address error associated with patchiness, we increased the length that the net was towed to improve precision (Wiebe 1972). However, this led to sub- optimal filtration efficiencies. Smith et al. (1968) suggest a filtration efficiency of >85%. The average (+ S.D.) filtration efficiency for our samples was 67% (+ 17%). Nevertheless, our estimates of A. tonsa abundance (Fig.2. 3) are within the same range as previously reported for Apalachicola Bay (Edmiston 1979, Marcus 1991). We estimated that the percent of phytoplankton production ingested by A. tonsa ranged from 0 to 24%, with median value of 0.4% (Fig.2. 4). When compared to the percent of production ingested by microzooplankton (>75%) (Chapter 3), it is clear that A. tonsa was not the primary herbivore in Apalachicola Bay. The grazing impact of A. tonsa on phytoplankton in Apalachicola Bay was similar to that found for adult copepods or mesozooplankton in other estuaries (Dagg 1995, Sautour et al. 2000, Froneman 2000, Froneman 2001, Liu & Dagg 2003, Froneman 2004) and in productive marine systems (Calbet 2001, Laws 2003). Our hypothesis that A. tonsa is the main herbivore in high nutrient/low salinity water in Apalachicola Bay is rejected. The classical diatom-copepod-fish food chain is not an accurate representation of energy flow through the planktonic food web at any salinity in Apalachicola Bay.

Ingestion of phytoplankton and microzooplankton Acartia tonsa is omnivorous in estuarine waters (Gifford & Dagg 1988, White & Roman 1992, Dam et al. 1994, Bollens & Penry 2003, Liu et al. 2005). In Apalachicola

61 Bay, A. tonsa had a mixed diet consisting of phytoplankton and microzooplankton. On average, phytoplankton contributed 53% to the diet of A. tonsa (Fig.2. 6c). At peak ingestion, on average (+ S.D.) 57 + 32% of carbon ingested was phytoplankton (Fig.2. 6c). The RPI for phytoplankton (Fig.2. 6d) averaged ~1, indicating that phytoplankton were ingested in proportion to their availability in situ. In contrast, the RPI for microzooplankton (Fig.2. 6d) averaged >1, indicating that the proportion of microzooplankton carbon in A. tonsa’s diet was greater than that occurring in situ. Relatively high RPI for microzooplankton is a result of high clearance rates on microzooplankton (Stoecker & Egloff 1987, Fessenden & Cowles 1994). Relatively high clearance rates on microzooplankton may be related to microzooplankton motility, size and quality. Unlike non-motile prey, motile prey, such as microzooplankton, have increased encounter rates with A. tonsa. In addition, motile prey should be more easily detected because their swimming generates a hydrodynamic signal detectable by A. tonsa (Jonsson & Tiselius 1990). In Apalachicola Bay a large portion of the phytoplankton biomass is <5 µm (Chapter 1) and therefore relatively inaccessible to adult A. tonsa (Berggreen et al. 1988). Microzooplankton may be a more optimal size for capture and retention by A. tonsa (Frost 1972, Berggreen et al. 1988, Tiselius 1989). A. tonsa may also preferentially ingest microzooplankton to meet dietary requirements (Kleppel 1993, Tang & Taal 2005, Jones & Flynn 2005). That A. tonsa had a diverse diet in this productive estuary supports the concept that planktonic food webs are complex and rarely simple diatom based chains as modeled by classical trophic dynamics (Kleppel 1993). To date, no studies have examined the spatial and temporal patterns of the total ingestion rates of Acartia tonsa in an estuary throughout an entire seasonal cycle. In Apalachicola Bay, total ingestion rate was not related to salinity during summer or winter. However, because total prey carbon had a seasonal cycle (Fig.2. 2a), total ingestion rates were related to temperature (Fig.2. 6a). Other field estimates of total ingestion by Acartia spp.(Gifford & Dagg 1988, White & Roman 1992, Kleppel & Hazzard 2000, Bollens & Penry 2003) are similar and appear to follow the same trend with temperature as for Apalachicola Bay (Fig.2. 12). Few studies have estimated in situ total prey carbon and corresponding total ingestion rate of Acartia spp. in estuaries (Gifford & Dagg 1988, Kleppel & Hazzard 2000) and none have been conducted

62 throughout the entire seasonal cycle to estimate total ingestion rates across the range of in situ total prey carbon. In Apalachicola Bay, the critical prey concentration for A. tonsa was about 250 µg C L-1 (Fig.2. 7). As total prey carbon during summer was generally >250 µg C L-1 (Fig.2. 2a), A. tonsa were satiated throughout the estuary during summer. In contrast, during winter total prey carbon was <250 µg C L-1 (Fig.2. 2a) and, as a result, A. tonsa were food limited throughout the estuary during winter. Total ingestion with respect to body carbon also suggests that A. tonsa were satiated during summer and not winter. On average (+ S.D.), A. tonsa ingested the equivalent of 38 + 48% and 120 + 93% of their body carbon during winter and summer, respectively (Fig.2. 6b).

Egg production efficiency There are few published field studies that have simultaneously examined the spatial and temporal patterns of Acartia tonsa total ingestion and egg production rates, and thus egg production efficiency, in an estuary (Kleppel & Hazzard 2000). Similar to results from other coastal and estuarine systems, EPR in Apalachicola Bay was highest during summer (Fig.2. 8) (Ambler 1985, Bellantoni & Peterson 1987, White & Roman 1992, McManus & Foster 1998) probably because of higher temperature (Ambler 1985, White & Roman 1992) and higher ingestion rates during summer (Kiorboe et al. 1985) (Fig.2. 6a). Ambler et al. (1985) observed that EPR was greatest in lower salinity water near Galveston Bay. Similarly, in Apalachicola Bay EPR was highest in lower salinity waters during summer (Fig.2. 9). Since total ingestion rate was not related to salinity during summer or winter, the pattern in EPR with respect to salinity (Fig.2. 9) is the result of the pattern in egg production efficiency with respect to salinity (Fig.2. 10). EPE peaked between about 6 and 18 psu during both winter and summer (Fig.2. 10). Peak EPE (~99%) was much greater than the average gross growth efficiency (~30%) found for planktonic crustaceans in the laboratory (Straile 1997), but similar to estimates when A. tonsa were fed optimal quality prey in the laboratory (Tang & Taal 2005). That the peak EPE occurred near the optimal salinity (15 to 22 psu) for A. tonsa (Cervetto et al. 1999) suggests that the pattern in EPE is related to the salinity tolerance of A. tonsa. Optimal enzyme activity (Caron et al. 1991) and reduced energy to maintain osmotic balance

63 (Mauchline 1998) are some of the factors which may lead to higher EPE in lower salinity waters. However, the efficiency with which ingested carbon is converted into growth may also depend on food quality (Checkley 1980, Kiorboe 1989).

Phytoplankton biochemical composition and egg production efficiency Laboratory studies show that the efficiency with which ingested carbon is converted into zooplankton biomass is influenced by food quality. Foods high in protein (Checkley 1980, Kiorboe 1989, Kleppel et al. 1998) and/or lipid (Brett & Muller-Navarra 1997, Broglio et al. 2003, Tang & Taal 2005) generally lead to high EPE and EPR. In Apalachicola Bay, phytoplankton carbon was a large fraction of the diet of Acartia tonsa (Fig.2. 6c). There are several lines of evidence to suggest that phytoplankton were relatively rich in protein and lipid in lower salinity waters of Apalachicola Bay, where maximum EPE occurred, compared to higher salinity waters. Nitrogen replete phytoplankton have higher protein content than nitrogen- deficient phytoplankton (Breteler et al. 2005). Phytoplankton typically do not respond to nitrogen enrichment in lower salinity waters of Apalachicola Bay (Iverson et al., submitted), suggesting they are nitrogen replete and therefore protein rich (Breteler et al. 2005). Measurements of the relative production of proteins, lipids, and carbohydrates with 14C incubation and biochemical fractionation procedures indicated that phytoplankton allocated more carbon to protein and lipid synthesis, and less to polysaccharide synthesis in lower salinity waters of Apalachicola Bay (Fig.2. 11). Calculated C:N ratios of the ration ingested by A. tonsa were predominantly below the Redfield Ratio in lower salinity waters (Fig.2. 10), suggesting that food ingested was nitrogen rich, and therefore protein rich, in lower salinity waters of Apalachicola Bay. High (>30 psu) salinity waters near Apalachicola Bay are nitrogen depleted (Chapter 1) and during summer phytoplankton growth is low (Chapter 1) and nitrogen limited (Iverson et al., submitted). Under natural conditions, EPR and EPE of A. tonsa are low in high salinity waters (Fig.2. 9, Fig.2. 10). However, when collected from high (~30 psu) salinity waters near Apalachicola Bay during summer A. tonsa attained an EPR of about 100 eggs female-1 d-1 when fed a diet of mixed dinoflagellates that were grown under nitrogen replete conditions (Marcus et al. 2004). This EPR is similar to that found

64 in lower salinity water and about 10 times greater than the EPR which naturally occurs in higher salinity water (Fig.2. 9). The relatively high EPR reported by Marcus et al. (2004) was the result of increased EPE, because A. tonsa appears to be satiated during summer (Fig.2. 2a, Fig.2. 7) and the dinoflagellate diet probably did not increase ingestion rates. Similarly, Jones and Flynn (2005) found that EPE increased when A. tonsa were fed a mixed diatom/dinoflagellate diet grown under nitrogen replete conditions. Therefore, the relatively high EPR reported by Marcus et al. (2004) was probably a response to improved food quality owing to the mixed dinoflagellate diet that was grown under nutrient replete conditions. We conclude that phytoplankton biochemical composition influenced A. tonsa EPE across the salinity gradient in Apalachicola Bay. However, because phytoplankton biochemical composition is correlated with salinity (Fig.2. 11), it was beyond the scope of the present study to separate the effects of salinity from the effects of phytoplankton biochemical “quality” in controlling A. tonsa EPE.

Anchoa mitchilli Like other Gulf of Mexico estuaries, Anchoa mitchilli is the most abundant fish and is present all year in Apalachicola Bay (Sheridan 1978, Blanchet 1979, Shipp 1986). A. mitchilli appear to be significant predators of A. tonsa in Apalachicola Bay. Stomach content analyses indicate that calanoid copepods, principally Acartia tonsa, are the main prey of adult A. mitchilli (Sheridan 1978). Abundances of A. tonsa peak somewhat downstream (14 to 22 psu) (Fig.2. 13a) of where their peak egg production occurs (4 to 10 psu) (Fig.2. 9) and where abundances of A. mitchilli peak (8 to 14 psu) (Fig.2. 13a). A. tonsa lipid content is highly dependant on lipids occurring in their food (Ederington et al. 1994, McManus & Foster 1998). It is possible that A. tonsa contained more lipid, or essential fatty acids, in lower salinity water where phytoplankton synthesized more lipid (Fig.2. 11b) and where their egg production efficiency peaked (Fig.2. 10). This might explain why A. mitchilli abundances peak in lower salinity water. A. mitchilli may aggregate in lower salinity waters because they prefer to ingest A. tonsa that have a relatively high lipid content. A. mitchilli egg production rate may also be greater in lower salinity waters because they ingest A. tonsa that have a relatively high lipid content. Abundances of A. mitchilli eggs (Fig.2. 13b) do not peak in the same waters

65 where adult A. mitchilli abundances peak, probably because eggs float and flow downstream from where they are produced.

Management implications Although Acartia tonsa was not the primary herbivore in Apalachicola Bay (Fig.2. 4), this species is the main prey of Anchoa mitchilli which support higher trophic levels such as seatrout, flounder, and redfish (Shipp 1986). As a result, determining the effect of reduced river discharge on A. tonsa has practical management implications. In the present study, summer egg production of A. tonsa peaked in lower (4 to 10 psu) salinity water. The areal extent of lower (e.g. <20 psu) salinity water decreases in the Bay during periods when river discharge is low (Mortazavi et al. 2001, Chapter 1). Therefore, upstream water diversion during summer will decrease the areal extent of lower salinity water where peak egg production of A. tonsa occurs. We predict that upstream water diversion will lead to reduced fish production in higher trophic levels of Apalachicola Bay because of reduced production of A. tonsa.

1000 A 800 )

-1 600

400 (µg C L Total prey carbon prey Total 200

0 120

100 B

20 % Phytoplankton carbon

0 5 101520253035 o Temperature ( C)

Fig. 2.2.A. Total prey carbon (phytoplankton carbon >5 µm plus microzooplankton carbon 5 to 20 µm) with respect to sea surface temperature. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC.

B. Percent of total prey carbon composed of phytoplankton carbon >5 µm with respect to sea surface temperature.

67 16

12 ) -1

Acartia tonsa 8 Summer (No. Adults L Adults (No. 4 abundance of * * 0 0 10203040

Salinity (psu)

Fig. 2.3. Summer abundance of adult Acartia tonsa with respect to sea surface salinity. Asterisks denote data not included in regression. Regression: y = 1 + 11e(-0.5*((x-18)/7)^2), r2 = 0.68, p<0.01. Edmiston (1979) Marcus (1991) Present study, 2004

68 25

10 Acartia tonsa

5 ingested by % Phytoplankton productivity % Phytoplankton

0 0 10203040

Salinity (psu)

Fig. 2.4. Percent of phytoplankton productivity ingested by Acartia tonsa with respect to surface salinity. Summer 2003 Summer 2004 Winter 2003 Winter 2004

69 50

40 ) -1

d 30 -1

20 (mL Adult

10 Clearance rate on phytoplankton on rate Clearance

0 0 200 400 600 800 1000 Phytoplankton >5 µm -1 (µg C L )

Fig. 2.5. Clearance rate on phytoplankton by Acartia tonsa with respect to phytoplankton carbon >5 µm.

Fig. 2.6.A. Total (phytoplankton carbon >5 µm plus microzooplankton carbon 5 to 20 µm) per capita ingestion rate of Acartia tonsa with respect to sea surface temperature. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC.

B. Total per capita ingestion rate of Acartia tonsa relative to predicted Acartia tonsa body carbon with respect to sea surface temperature.

C. Percent of Acartia tonsa diet composed of phytoplankton with respect to sea surface temperature.

D. Acartia tonsa Relative Preference Indices for microzooplankton and phytoplankton with respect to sea surface temperature. Summer Microzooplankton Winter Microzooplankton Summer Phytoplankton Winter Phytoplankton

71 6

) 5 A -1

d 4 -1 3 2

(µg Adult 1 Total ingestion rate ingestion Total 0 400

300 B

200

100 Total ingestion Total as % body carbonas % body

0 120 100 C 80 60 40 of phytoplankton

% Diet composed composed Diet % 20

0 10

8 D

Relative Preference Index Preference Relative 0 5 101520253035 o Temperature ( C)

6 )

-1 4 d -1

2 (µg C Adult Total ingestion rate ingestion Total

0 0 200 400 600 800 1000 Total prey carbon -1 (µg C L )

Fig. 2.7. Functional feeding response for Acartia tonsa in Apalachicola Bay. Total prey carbon is phytoplankton carbon >5 µm plus microzooplankton carbon 5 to 20 µm. Total per capita ingestion rate is ingestion of phytoplankton carbon >5 µm plus microzooplankton carbon 5 to 20 µm. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Regressions: Below 250 µg C L-1: y = -0.3 + 0.02x, r2=0.49, p<0.01. Above 250 µg C L-1: y = 2.9 – 0.0x, r2=0.03, p>0.05.

73 100

80 ) -1 d -1 60

40 Egg production rate

(viable eggs female 20

0 5 101520253035

o Temperature ( C)

Fig. 2.8. Egg production rate of Acartia tonsa with respect to sea surface temperature. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC.

74 100 ) -1 d -1 75

Summer 50 Egg production rate Egg production (viable eggs female eggs (viable 25

0 0 10203040

Salinity (psu)

Fig. 2.9. Summer egg production rate of Acartia tonsa with respect to surface salinity. Regression: y = 51e(-0.5*(ln(x/6)/1)^2), r2 = 0.35, p<0.05.

75 48+57 6+13 12+11 C:N ingested ration 120

100

% Egg production efficiency production Egg % 20

0 * 0 10203040

Salinity (psu)

Fig. 2.10. Egg production efficiency of Acartia tonsa during winter and summer with respect to surface salinity. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Circled data points indicate samples where Thalassiosira spp. represented a high (>50%) percentage of the abundance of diatoms >20 µm. Circled data and data with asterisk denotes data not included in regression. Regression: y=108e(-0.5*((x-13)/5)^2), r2=0.77, p<0.01. Average (+ S.D.) C:N of ingested ration below 5 psu, 5 to 20 psu, and above 20 psu denoted on top axis.

Fig. 2.11.A. Percent of total carbon fixed allocated towards protein synthesis with respect to surface salinity. Regression: y = 45 – 1x, r2 = 0.50, p<0.01.

B. Percent of total carbon fixed allocated towards lipid synthesis with respect to surface salinity. Asterisk denotes data not included in regression. Regression: y = 23 – 0.1x, r2 = 0.27, p<0.01.

C. Percent of total carbon fixed allocated towards polysaccharide synthesis with respect to surface salinity. Regression: y = 29 + 1x, r2 = 0.30, p<0.01.

77 60 A 50

Protein synthesis 30

20 30 B

20 Lipid synthesis *

10 60 C 50

Polysaccharide synthesis Polysaccharide 20 0 10203040

Salinity (psu)

78 12

) 8 -1 d -1 6

(µg Adult 4 Total ingestion rate ingestion Total

0 5 101520253035 o Temperature ( C)

Fig. 2.12. Total per capita ingestion rate of Acartia tonsa with respect to sea surface temperature in other estuaries. White symbols are winter and black symbols are summer. Dashed line denotes 25.5oC.

Gifford & Dagg (1988) Bollen & Penry (2003) Kleppel & Hazzard (2000) White & Roman (1992)

160 14 ) L) L) -1 adults 12 6 A 120 10

Summer 8

80 Acartia tonsa (No. Adults L (no. Adults/10 (no. 6 Predicted Summer Anchoa mitchilli 4 40 2

0 0 6 B eggs

4 Summer

2 Anchoa mitchilli log ((eggs/1000 L) + 1) L) ((eggs/1000 log

0 0 10203040 Salinity (psu)

Fig. 2.13.A. Summer abundance of adult Anchoa mitchilli with respect to surface salinity. Data courtesy of the Apalachicola Bay National Estuarine Research Reserve. Regression: y = 4 + 74e(-0.5*((x-12)/8)^2), r2 = 0.27, p<0.01. Dashed line is predicted abundance of Acartia tonsa adults from Fig. 2. 3.

B. Summer abundance of Anchoa mitchilli eggs with respect to surface salinity. Data from Blanchet (1979). Regression: y = 0 + 4e(-0.5*((x-20)/8)^2), r2 = 0.64, p<0.01.

80 CHAPTER 3

MICROZOOPLANKTON: MAJOR HERBIVORES IN APALACHICOLA BAY, FLORIDA

Introduction

The west coast of Florida was ranked as the seventh largest contributor to the total U.S. commercial fishing revenue during 2002 and 2003 (www.st.nmfs.gov). Commercial fishing revenue in Florida was primarily derived from shrimp (24%), crab (20%), spiny lobster (12%), grouper (15%), and snapper (5%). Florida is also the number one recreational fishing destination in the U.S. In 2002 and 2003, the recreational fishery harvested more finfish and had more angler trips than any other state. Most commercially and recreationally harvested species in Florida use estuaries as nurseries (Helfman et al. 1997). Apalachicola Bay is a highly productive estuary located in the northern Gulf of Mexico. The Bay serves as a habitat and nursery to a wide variety of commercial and sport fish species such as oyster, croaker, seatrout, shrimp, and blue crab (Livingston 1984). The high productivity is the result of the Apalachicola River delivering freshwater and nutrients to the Bay (Livingston 1984, Mortazavi et al. 2000a, Mortazavi et al. 2000b, Mortazavi et al. 2001). Freshwater moderates salinity in the Bay to create a suitable habitat for estuarine flora and fauna. Nutrients delivered to the Bay support high levels of phytoplankton productivity (Mortazavi et al. 2000c) which, in turn, supports secondary productivity (Chanton & Lewis 2002). The classical diatom-copepod-fish food chain describes the transfer of phytoplankton productivity to higher trophic levels in productive ocean waters (Riegman et al. 1993, Legendre & Rassoulzadegan 1995, Sommer et al. 2002). In Apalachicola Bay, nutrient concentrations are relatively high (Mortazavi et al. 2000a, Mortazavi et al. 2000b, Mortazavi et al. 2001), zooplankton grazing is the primary loss process controlling phytoplankton productivity and biomass in the Bay (Mortazavi et al. 2000c), and diatoms (Estabrook 1973) and copepods (Edmiston 1979, Marcus 1991) are abundant. Therefore, a classical diatom-copepod-fish food chain may describe planktonic food web structure in Apalachicola Bay. However, microzooplankton are abundant in

81 estuarine waters (Gifford and Caron 2000, Tillman 2004). Furthermore, recent studies indicate that microzooplankton are the primary herbivores in eutrophic ocean waters (Dolan et al. 2000, Calbet 2001, Calbet & Landry 2004). Microzooplankton are nutritious and an appropriate size for first-feeding larvae (Stoecker and Capuzzo 1990). Therefore, the classical diatom-copepod-fish food chain may not be an accurate representation of energy flow through the planktonic food web in Apalachicola Bay or other estuaries. Microzooplankton may be the main secondary producers and prey item of larval fish. The first objective of this study was to test the hypothesis that microzooplankton are the primary herbivores in Apalachicola Bay. If microzooplankton are the main herbivores, then the percent of phytoplankton production ingested by microzooplankton will be greater than that ingested by mesozooplankton (Chapter 2). Data obtained from a concomitant study of mesozooplankton grazing were used to test this hypothesis (Chapter 2). A second objective was to examine the spatial and temporal distribution of ingestion and production of microzooplankton. Information from this analysis yields insight into how diversion of Apalachicola River water will affect higher trophic level productivity in Apalachicola Bay. To accomplish these objective, we examined microzooplankton bacterivory and herbivory as well as microzooplankton total ingestion and calculated microzooplankton production within various salinity regimes in Apalachicola Bay throughout a 2 year period (2003 through 2004).

Materials and Methods

Study site and sample collection Samples were collected from Apalachicola Bay at 3 to 4 stations during the winter and summer of 2003 and 2004 (Fig.I. 1). Stations sampled represented oligo-, meso-, and euryhaline portions of the Bay. Temperature and salinity were measured at 0.5 m depth intervals throughout the water column with a YSI® salinometer. Samples were collected from 0.5 m depth below sea surface for phytoplankton, bacterioplankton, and protist analyses. Grazing and growth experiments were performed with water collected from 0.5 m depth below sea surface.

82 Chlorophyll and bacterial abundance Seawater for chlorophyll analysis was stored on ice in polyethylene bottles for <4 hours prior to being filtered through 47 mm GF/F filters at <117 mm Hg vacuum. Filtered samples were stored in darkness at -20oC and analyzed within 1 week of sample collection. Chlorophyll a was extracted from filters in 90% acetone for about 24 hours at -20oC. The concentration of chlorophyll a was measured fluorometrically with a Model 10 Turner Designs® fluorometer equipped with filter sets for optimal sensitivity of chlorophyll a in the presence of chlorophyll b (Welschmeyer 1994). Seawater samples for bacterioplankton analyses were preserved with glutaraldehyde (2 % final concentration) and stored in darkness at 4oC (Sherr & Sherr 1993). Bacteria were enumerated within 2 weeks of sample collection. Samples were filtered (<117 mm Hg vacuum) onto 0.2 µm black Poretics polycarbonate filters and stained with Acridine Orange (Hobbie et al. 1977). Filters were mounted with Cargille’s type B immersion oil onto glass slides. A BH Olympus® epifluorescence microscope equipped with a blue excitation filter set (U-MWIB, excitation 460 to 490 nm; emission 515 to 700 nm) was used to visualize bacteria. Cells were counted at a total magnification of x1875. At least 100 cells were counted per sample in a minimum of 10 random fields.

Microzooplankton abundance Samples for the analysis of heterotrophic flagellates 5 to 20 µm in size were preserved in glutaraldehyde (2% final concentration) and stored in darkness at 4oC (Gifford and Caron 2000). Flagellates were enumerated within 1 month of sample collection. Samples were filtered (<117 mm Hg vacuum) onto 5µm Poretics® polycarbonate filters and stained with Acridine Orange (1% final concentration) (Sherr et al. 1993). Filters were mounted with Cargille’s type B immersion oil onto glass slides. A BH Olympus® epifluorescence microscope equipped with a blue/UV excitation filter set (U-M546, excitation 400 to 410 nm; emission 455 to 700 nm) was used to visualize cells. Cells that lacked red autofluorescence, indicative of chlorophyll a, were counted at a total magnification of x1875. For each filter, at least 100 cells were counted in either transects or in a minimum of 10 random fields.

83 Samples for the analysis of ciliates and >20 µm flagellates were preserved in acid Lugol’s (2% final concentration) and stored in darkness at 4oC (Gifford and Caron 2000). Samples were enumerated within 1 month of sample collection. Samples (10 to 50 mL) were settled for 24 hours with Utermohl settling chambers. Cells were counted through phase contrast light microscopy at a total magnification of x200 with an inverted Wild® microscope. For each settled sample, transects were counted until at least 100 cells were counted (Hobro & Willen 1977).

Microzooplankton grazing measurements The dilution technique (Landry & Hassett 1982) was used to estimate microzooplankton herbivory and bacterivory. Prior to conducting microzooplankton grazing studies, all equipment that would contact seawater was soaked for a few days in 10% hydrochloric acid. Afterward, equipment was thoroughly rinsed and then soaked for several days with Nanopure water. Nitrile gloves were worn during all water handling procedures. Seawater for the size fractionated (<202 µm) predator and prey populations was collected with a darkened polycarbonate carboy from a depth of about 0.5 m below sea surface. Water for preparing the particle-free diluent was collected from the seawater intake system at the Florida State University Edward Ball Marine Laboratory. During low river flow periods, seawater near the marine laboratory typically has a salinity greater than about 28 psu. We collected freshwater from the river and combined it with seawater collected from the intake system to match the salinity of the particle-free water with that of the seawater collected for the size fractionated predator and prey populations. Nitrogen limits phytoplankton productivity throughout most of Apalachicola Bay during summer (Iverson et al. submitted). Thus, inorganic nitrogen (as ammonium chloride) was added to all incubation bottles to ensure that prey growth did not become nitrogen limited during the course of the incubations. Enough ammonium chloride was added to bottles to elevate ammonium concentrations in bottles to those found in the river. Since nutrient analyses were not synchronous with sampling, we determined the enrichment based on the difference between ammonium concentrations in the river and

84 that at a station with similar salinity for the previous month. One bottle per dilution assay (0% dilution factor) served as a control. To reduce preparation time of diluent, seawater was serially gravity filtered through 25 µm and 5 µm Sears drinking quality grade filters. The <5 µm filtrate was then filtered through 0.2 µm Pall-Gelman capsule filters. Our target dilutions per dilution assay were 95, 85, 75, 65, 55, 35, and 0% diluent (i.e. diluent: <202 µm size fractionated seawater). Appropriate volumes of diluent were added to 2L polycarbonate incubation bottles. The <202 µm seawater was then added to bottles by dispensing 0.5 m collected seawater through silicon tubing that was equipped with 202 µm Nitex screen. The silicon tubing was kept submerged below the waterline in the bottles to reduce damage to microzooplankton. Bottles were placed on a plankton wheel (~0.5 rpm) and incubated in an outdoor incubator flushed continuously with seawater from Apalachicola Bay. Incident irradiance was attenuated to 67% of ambient with neutral density screening. Samples were taken for chlorophyll a and bacterioplankton immediately after preparation of dilution treatments and again after ~24 hours. Samples for bacterioplankton and chlorophyll a were preserved, stored, and analyzed with the procedures previously mentioned.

Microzooplankton grazing calculations Rates of growth and grazing mortality for chlorophyll a and bacterioplankton were estimated with Model I linear regressions of Apparent Growth Rate (AGR) versus Actual Dilution Factor (ADF). The ADF for each bottle was calculated as,

T chl a (X) ADF = oi (1) Too chl a (X)

where To chl a (Xi) is the time zero chlorophyll a concentration at target dilution factor Xi and To chl a (Xo) is the time zero chlorophyll a concentration of the 0% dilution treatment. The prey AGR (d-1) in each incubation bottle was calculated as,

85 1 P AGR = ln ( t ) (2) tPo

where t is the duration of the incubation (days) and Po and Pt are the initial and final prey densities, respectively. The ordinal intercept of the regression is the rate of prey growth (µ, d-1) in the absence of grazing while the absolute value of the negative slope is grazing mortality (g, d-1). In cases where the relationship between ADF and AGR was non-linear, piecewise linear regression was used to estimate prey growth and grazing mortality (Redden et al. 2002). The prey concentration beyond which ingestion rate is constant (i.e. critical food concentration, Cc) was estimated with equation 6 from Redden et al. (2002). The impact of microzooplankton grazing on phytoplankton was assessed by calculating the percentage of prey potential productivity ingested (%Pp) and the amount -1 -1 of prey carbon ingested (Ic, µg C L d ),

µt (µ-g)t ((Pooo e -P )-(P e -P o )) %Pp =µt x 100 (3) (Poo e -P )

Ic = g Cm (4)

-1 where t is 1 day and Cm is the mean prey carbon (µg C L ) in the 0% diluent treatment calculated as,

C(e(µ-g)t -1) C = o (5) m (µ-g)t

-1 where Co is the initial prey carbon (µg C L ) in the 0% diluent treatment.

86 Conversion factors Phytoplankton carbon was estimated from measured chlorophyll and carbon: chlorophyll ratios determined for Apalachicola Bay (Chapter 1). For bacterioplankton, 20 fg C cell-1 was assumed (Ducklow 2000).

Statistical analyses Non-linear regressions were used when a linear regression model did not adequately explain the relation between variables (for example, low r2, variances heteroscedastic). Relationships were considered significant if the p-value was <0.05 (Sokal & Rohlf 1995). Analysis of Covariance tests were used to determine if significant differences existed between years or seasons for relationships between variables. If no significant difference was found, then data were pooled and a common regression equation was determined. Non-linear relationships were analyzed by dividing the data into 2 components: (1) the initial increasing segment and (2) the latter decreasing segment. ANCOVA tests were performed on each segment.

Results

Surface salinity The areal extent of lower (<20 psu) salinity water was, on average, greater during summer 2003 (Fig.3. 2a) than during summer 2004 (Fig.3. 2b). On average, 100% of the Bay was <20 psu during summer 2003. In contrast, during summer 2004 about 50% of the Bay was <20 psu.

Total prey carbon Total prey carbon, that is total phytoplankton plus bacterial carbon, was related to temperature and this relationship was not significantly different between years (Fig.3. 3a). During winter average (+ S.D.) total prey carbon was 75 + 29 µg C L-1. During summer average (+ S.D.) prey carbon was 1071 + 503 µg C L-1. Total prey carbon peaked to ~2200 µg C L-1 between 26 and 28oC.

87 Total prey carbon during summer was related to salinity and this relationship was not significantly different between years (Fig.3. 3b). The peak (~2200 µg C L-1) occurred in lower (10 to 20 psu) salinity waters. The percent of total prey carbon composed of phytoplankton was positively related to temperature and this relationship was not significantly different between years (Fig.3. 3c). Above 15oC more than 50% of total prey carbon was composed of phytoplankton. The percent of total prey carbon composed of phytoplankton was related to salinity and this relationship was not significantly different between years (Fig.3. 3d). During summer total prey carbon was primarily (>50%) composed of phytoplankton below 32 psu. During winter total prey carbon was primarily (>50%) composed of phytoplankton above 10 psu.

Microzooplankton abundance The ciliate community was primarily (average + S.D. was 70 + 16%) composed of oligotrich ciliates <20 µm in size (S.O. Choreotrich) (Table 3.1). Average (+ S.D.) oligotrich ciliate abundance was 10.5 + 8.4 cells mL-1 during winter and 23 + 11 mL-1 during summer. Total oligotrich ciliate abundance was not related to salinity during winter or summer. We estimated dinoflagellate (>20 µm) and heterotrophic flagellate (5 to 20 µm) abundances periodically (Table 3.1). Mean abundances were higher in summer than during winter. Mean heterotrophic flagellate (5 to 20 µm) abundances were 4 x 106 cells L-1 and 5 x 105 cells L-1 during summer and winter, respectively. Mean dinoflagellate (>20 µm) abundances were 12 x 103 cells L-1 and 8 x 103 cells L-1 during summer and winter, respectively. Abundance of dinoflagellates >20 µm in size was not significantly related to herbivory. In contrast, abundances of 5 to 20 µm heterotrophic flagellates and total ciliates were significantly related to herbivory (Fig.3. 4). Abundances were transformed to a logarithmic scale so that variances were homoscedastic.

88 Microzooplankton grazing The dilution method assumes that prey growth is exponential and constant across the dilution gradient. We added nitrogen to prevent nitrogen limitation in the incubation bottles. None of the dilution plots assessing herbivory (Table 3.1) or bacterivory had positive slopes. This suggests that phytoplankton and bacterioplankton were not nutrient limited within our dilution assays. Non-linear plots of phytoplankton AGR versus ADF only occurred during summer and were frequent (74% of summer assays had non-linear plots) (Table 3.1). The -1 -1 Cc ranged from 225 to 1691 µg C L . The average (+ S.D.) Cc was 664 (+ 370) µg C L . The coefficient of determination for linear regressions ranged from 0.03 to 0.99 and on average (+ S.D.) was 0.86 (+ 0.21). Non-linear plots of bacterial AGR versus ADF occurred primarily during summer and were infrequent (22% of assays had non-linear plots). The coefficient of determination for linear regressions ranged from 0.25 to 0.98 and on average (+ S.D.) was 0.67 (+ 0.20). In both years the relationships between total carbon ingested and temperature, total carbon ingested and salinity, percent of diet composed of phytoplankton and temperature and percent of diet composed of phytoplankton and salinity, were similar (Fig.3. 5, Fig.3. 6). For example, in both years total prey carbon peaked during summer (~27oC) and in lower (8 to 18 psu) salinity waters (Fig.3. 5a, 5b, Fig.3. 6a, 6b). Percent of diet composed of phytoplankton was positively related to temperature and >50% above 20oC (Fig.3. 5c, Fig.3. 6c). During winter the percent of diet composed of phytoplankton was 30 to 60% above 10 psu (Fig.3. 5d, Fig.3. 6d). During summer percent of diet composed of phytoplankton was 60 to 100% at salinities below 26 psu (Fig.3. 5d, Fig.3. 6d). However, the ingestion rates were lower during 2004. For example, peak ingestion rates during summer 2003 were ~2700 µg C L-1 d-1 as opposed to ~1300 µg C L-1 d-1 during summer 2004 (Fig.3. 5, Fig.3. 6). Microzooplankton production was estimated with total ingestion rate and assuming a gross growth efficiency (GGE) of 33% (Striale 1997). As a result, microzooplankton production had the same relationships with temperature and salinity as microzooplankton total ingestion rate. During both years the relationships between microzooplankton production and temperature and microzooplankton production and

89 salinity were similar (Fig.3. 7). For example, during both years microzooplankton production peaked during summer (Fig.3. 7a, 7c) and in lower salinity waters (Fig.3. 7b, 7d). However, compared to 2003, microzooplankton production was lower during 2004. Peak microzooplankton production was about 900 µg C L-1 d-1 during summer 2003 as opposed to about 400 µg C L-1 d-1 during summer 2004. Because total microzooplankton carbon is ~48 µg C L-1 throughout the Bay during summer and winter (Chapter 2), microzooplankton growth was related to temperature and salinity in the same way as microzooplankton production. During summer 2003, microzooplankton growth rates ranged from 0.5 to 18.6 d-1 and, on average (+ S.D.), were 8.5 + 5.1 d-1. In contrast, average microzooplankton growth rates during summer 2004 ranged from 1.3 to 8.5 d-1 and, on average (+ S.D.) were 3.8 + 2.5 d-1. During both years microzooplankton herbivory peaked at about 26 oC (Fig.3. 8a, 8c) and was not related to salinity (Fig.3. 8b, 8d). However, microzooplankton herbivory was lower in 2004 than in 2003. For example, average (+ S.D.) microzooplankton herbivory was 1.3 + 0.4 d-1 during summer 2003 and 0.5 + 0.2 d-1 during summer 2004. Growth and herbivory were correlated and there were no significant differences between years or seasons for the relation (Fig.3. 9). The slope was not significantly different from one. The %Pp was related to temperature and the relationship was not significantly different between years (Fig.3. 10a). Below 26 oC, microzooplankton o ingested, on average, 75% Pp. Above 26 C, the amount of productivity ingested by microzooplankton was positively related to temperature and microzooplankton ingested

>75% Pp. The %Pp was not related to salinity during winter or summer in 2003 or 2004 (Fig.3. 10b).

Discussion

Estuarine planktonic food web structure Stable isotope studies have shown that estuarine consumers are supported by terrestrial derived detritus and detritus from marsh plants (Haines 1977, Peterson et al. 1986), and organic matter from vascular plants (Deegan & Garritt 1997), phytoplankton

90 (Peterson et al. 1986, Sullivan & Moncreiff 1990, Deegan & Garritt 1997, Chanton & Lewis 2002), benthic algae (Sullivan & Moncreiff 1990, Deegan & Garritt 1997), and epiphytic algae (Moncreiff & Sullivan 2001). Consumers are ultimately supported by sources of organic matter within the location where they reside (Deegan & Garritt 1997). Stable isotope analyses indicate that secondary producers in Apalachicola Bay are mainly supported by estuarine primary productivity (Chanton & Lewis 2002). The main prey items of microzooplankton are bacterioplankton and phytoplankton (Sherr & Sherr 1994); however, microzooplankton are primarily supported by phytoplankton in marine waters (Sherr & Sherr 2002, Calbet & Landry 2004). In this study, microzooplankton herbivory and bacterivory were simultaneously determined. During summer, phytoplankton carbon was the main component of carbon ingested by microzooplankton (Fig.3. 5c, 5d, Fig.3. 6c, 6d). During winter phytoplankton carbon was the main component of carbon ingested above 20 psu, while bacterial carbon was the main component of carbon ingested below 20 psu (Fig.3. 5d, Fig.3. 6d). The differences in diet are a result of seasonal changes in the relative sizes of bacterial and phytoplankton carbon stocks. Bacterial carbon was a larger fraction of carbon available during winter, particularly in lower salinity water, than during summer (Fig.3. 3c, 3d). Maximum fish production occurs in productive waters (Iverson 1990) where there is high trophic transfer efficiency and a low number of trophic levels (Ryther 1969). The classical food chain (diatoms-copepods-fish) is typically used to describe the transfer of phytoplankton productivity to higher trophic levels in productive marine waters (Riegman et al. 1993, Legendre & Rassoulzadegan 1995, Sommer et al. 2002). This planktonic food web model describes conditions in productive waters such as upwelling on continental margins (Smith 1982) and spring blooms in temperate waters (Urban et al. 1992) and may be appropriate for estuaries where riverine input of new nutrients can favor the growth of large phytoplankton (Ning et al. 2000). However, in many estuaries (for example, the Urdaibai, Chesapeake, York, Delaware, and Pensacola) phytoplankton <20 µm in size, not large diatoms, are the dominant component of the phytoplankton community (Chapter 1). Although copepods such as Acartia tonsa can ingest phytoplankton <20 µm in size (Berggreen et al. 1988), relative to microzooplankton copepods ingest a small fraction of phytoplankton productivity (Calbet 2001, Calbet &

91 Landry 2004) and may experience low growth rates on a diet composed primarily of diatoms (Kleppel 1993, Ianora et al. 1999, Miralto et al. 1999). The classical diatom-copepod food chain is not an accurate depiction of energy flow through the planktonic food web in Apalachicola Bay. First, during summer when primary productivity peaks (Mortazavi et al. 2000c) the dominant component of the phytoplankton community is <5 µm in size (Chapter 1) and therefore relatively inaccessible to adult Acartia tonsa (Berggreen et al. 1988), the main numerical constituent of the mesozooplankton community in the Bay (Edmiston 1979, Marcus 1991). Second, microzooplankton, on average, ingested ten times more phytoplankton productivity (Fig.3. 9, 10a) than A. tonsa (Chapter 2). Third, the seasonal cycle of phytoplankton biomass (Chapter 1) followed the seasonal cycle of the percent of phytoplankton productivity ingested by microzooplankton, indicating that microzooplankton are a major loss process for phytoplankton in the Bay (Fig.3. 10a). Although grazing did not balance phytoplankton growth during winter (Fig.3. 10a), lower phytoplankton growth rates (Chapter 1) and export (Mortazavi et al. 2000c) probably led to the relatively low winter stocks of phytoplankton. Phytoplankton biomass peaked during early summer (26 to 27oC) probably because phytoplankton growth peaked (Chapter 1) and was not balanced by microzooplankton grazing (Fig.3. 10a). At temperatures above 27oC, phytoplankton biomass declined (Chapter 1) probably because microzooplankton grazing exceeded phytoplankton growth (Fig.3. 10a). Microzooplankton have fast growth rates (Hansen & Bjornsen 1997, Strom & Morello 1998, Sherr & Sherr 2002, Tillman 2004), an ability to ingest a wide size range of phytoplankton (Strom & Strom 1996, Sherr & Sherr 2002, Tillman 2004), and are relatively abundant (Gifford & Caron 2000). These facts may explain why microzooplankton were the main herbivores in Apalachicola Bay. Microzooplankton growth rates averaged 8.5 d-1 during summer 2003 and 3.8 d-1 during summer 2004 and were 6 to 8 times greater than phytoplankton growth rates. Although these microzooplankton growth rates were calculated, they are not unrealistic. At 20oC, maximum growth rates of some nanoflagellates and ciliates are 6 d-1 and 3 d-1, respectively (Sherr & Sherr 1994, Hansen & Bjornsen 1997). Average (+ S.D.) abundances of oligotrich ciliates (10.5 + 8.4 cells mL-1 during winter and 25 + 14 mL-1

92 during summer) in Apalachicola Bay (Table I) were also higher than typically reported in marine waters (Gifford & Caron 2000). Ciliates are frequently important herbivores in marine systems (Sherr & Sherr 1994). Oligotrich ciliates were relatively abundant in Apalachicola Bay (Table 3.1); however, their abundances were weakly correlated to herbivory. In contrast, flagellate abundance was strongly correlated with herbivory (Fig.3. 4). Nanoflagellates might have been important herbivores in Apalachicola Bay. However, studies in other systems show that nanoflagellates ingest a fraction of what ciliates ingest and, in turn, are ingested by ciliates (Azam et al. 1983, Sherr et al. 1991, Hall et al. 1993). Dinoflagellates greater than 20 µm in size were generally very low in abundance in the Bay (Table 3.1), possibly the result of relatively low growth rates (Strom & Morello 1998), and were unlikely important constituents of the microherbivore community. Our analysis suggests that oligotrich ciliates and heterotrophic nanoflagellates were the primary herbivores in Apalachicola Bay. There are two possible fates for carbon ingested by microzooplankton. Carbon can be remineralized by microzooplankton and it can be transferred to higher trophic levels (Azam et al. 1983, Sherr & Sherr 1988, Legendre & Rassoulzadegan 1995). Mesozooplankton (such as Acartia tonsa) actively ingest microzooplankton in estuaries (Gifford & Dagg 1988, Bollens and Penry 2003, Chapter 2). Apalachicola Bay is an important habitat to oysters and nursery ground for larval fish, crab and shrimp. Microzooplankton are food for first-feeding fish larvae (Stoecker & Capuzzo 1990). Ciliates, in particular, are important to the diet of larval fish (Lessard et al. 1996, Fukami et al. 1999, Nagano et al. 2000). Oysters can directly ingest protists (Dupuy et al. 1999, Loret et al. 2000) and crab and shrimp larvae also ingest protists (Sulkin & McKeen 1999, Nagano & Decamp 2004). Therefore, it is reasonable to assume that microzooplankton are a source of biomass for A. tonsa and larval stages of higher trophic level fauna in Apalachicola Bay. Energy and mass flow through the planktonic food web in Apalachicola Bay appears to flow mainly from phytoplankton to microzooplankton to higher trophic level organisms such as copepods, first-feeding larvae, and oysters. Therefore, a microbial food web, emphasizing the importance of microzooplankton, as opposed to copepods, as

93 primary herbivores and prey to higher trophic levels, is a more accurate depiction of energy and mass flow in Apalachicola Bay than a classical food chain. Microbial food webs are generally thought to represent the flow of mass and energy through plankton in oceanic waters. However, this study and others (Gifford 1988, McManus & Cantrell 1992, Kamiyama 1994, Dagg 1995, Gallegos et al. 1996, Strom & Strom 1996, Froneman & McQuaid 1997, Ruiz et al. 1998, Lehrter et al. 1999, Strom et al. 2001, Calbet & Landry 2004, Juhl & Murrell 2005) indicate that microzooplankton are the major herbivores in estuaries and therefore that a microbial food web represents the flow of mass and energy through plankton in estuarine waters. Knowledge of microbial food web processes has primarily been derived from studies in oceanic waters where microbial food webs support low magnitudes of higher trophic production because of low phytoplankton productivity and the relatively high number of trophic transfers between phytoplankton and higher trophic levels (Riegman et al. 1993, Legendre & Rassoulzadegan 1995, Sommer et al. 2002). Phytoplankton productivity is greater in estuarine than oceanic waters (McLusky & Elliott 2004). Furthermore, compared to an oceanic microbial food web, an estuarine microbial food web has fewer trophic transfers between phytoplankton and higher trophic levels. This is because estuaries are habitats for oysters and nurseries for larval shellfish and fish. Oysters (Dupuy et al. 1999, Loret et al. 2000) and larval shellfish (Sulkin et al. 1998, Nagano & Decamp 2004) and larval fish (Stoecker & Govoni 1984, Lessard et al. 1996, Fukami et al. 1999, Nagano et al. 2000) directly ingest microzooplankton. Therefore, an estuarine microbial food web can support more higher trophic level production than an oceanic microbial food web.

Microzooplankton-phytoplankton coupling in subtropical and tropical waters Calbet and Landry (2004) categorized regions of the ocean by temperature and showed that the percent of phytoplankton potential productivity (%Pp) ingested by microzooplankton is lower in polar than in tropical waters. They also found that %Pp varied with habitat type, %Pp being greatest in oceanic waters. However, the nature of the relationship between %Pp and temperature by habitat type has not been determined. For

94 example, it is not known whether the relationship between %Pp and temperature differs between subtropical and tropical estuarine, coastal, and oceanic waters.

We compared the relationship between %Pp and temperature from Apalachicola Bay (Fig.3. 10a) to that from tropical and subtropical estuarine, coastal and oceanic waters where both %Pp and temperature were reported (Fig.3. 11). Unlike the present study there was no significant relationship between %Pp and temperature in other estuaries or coastal and oceanic waters. However, the lack of a significant relationship between %Pp and temperature is inconclusive as most studies did not conduct dilution assays across the full temperature range in each environment. Below 26 oC, average (+

S.D.) %Pp was 47 + 24% in estuarine waters (Fig.3. 11a), 40 + 26% in coastal waters (Fig.3. 11b), and 76 + 38% in oceanic waters (Fig.3. 11c). Above 26 oC, average (+

S.D.) %Pp was 71 + 30% in estuarine waters (Fig.3. 11a) and 82 + 59% in oceanic waters (Fig.3. 11c). Because of the lack of coastal data above 26 oC, we did not consider the coastal data above 26 oC in this analysis. Based upon the averages, this analysis suggests that %Pp is temperature dependant in tropical and subtropical estuarine waters. The %Pp is higher in oceanic waters than in estuarine and coastal waters, but only when o o temperature is below 26 C. Above 26 C, the %Pp in oceanic and estuarine waters are similar. Estuarine primary productivity is greatest during summer (Pennock et al. 1999, Mortazavi et al. 2000c) and higher than primary productivity in oceanic waters (Cloern

1987). Because %Pp is about the same in oceanic and estuarine waters during the summer (Fig.3. 11a, 11c), more phytoplankton productivity, per unit volume, must be transferred to microzooplankton in estuarine waters than in oceanic waters during the summer in the subtropics and tropics. Assuming a constant GGE of ~33% (Straile 1997), peak microzooplankton production can be expected to occur during summer in estuarine waters. In general, first-feeding fish larvae require small, abundant, nutritious prey (Helfman et al. 1997). Microzooplankton are nutritious (Stoecker & Capuzzo 1990, Nagano et al. 2000) and an appropriately sized prey (Stoecker & Capuzzo 1990, Fukami et al. 1999, Dupuy et al. 1999, Loret et al. 2000, Nagano et al. 2000, Nagano & Decamp 2004) for many first-feeding larvae. Microzooplankton abundance is also more than 100 times greater than that of copepods (for example, adult plus nauplii of Acartia tonsa,

95 Marcus 1991). In tropical and subtropical waters, fish may, in part, use estuaries as nurseries during summer months to ensure that first-feeding larvae harvest the peak microzooplankton production. This would agree with the match-mismatch hypothesis (Cushing 1977) that fish reproduction is synchronized with prey production to improve larval survival. Apalachicola Bay is probably one of the more important nurseries in the

Gulf of Mexico as primary productivity is relatively high (Pennock et al. 1999) and %Pp above 26 oC (Fig.3. 10a) is, on average, higher than other estuaries (Fig.3. 11a).

Management implications There were striking differences in river discharge during 2003 and 2004 (Chapter 1). Associated with the lower river discharge during 2004 were lower nutrient concentrations and phytoplankton growth rates at a particular salinity (Chapter 1). We also found that grazing (Fig.3. 8b, 8d), total ingestion (Fig.3. 5b, Fig.3. 6b) and production rates (Fig.3. 7b, 7d) of microzooplankton were lower at a particular salinity during 2004 than during 2003. It is the relationship between parameters, such as production, and salinity together with the geographical extent over which the river plume extends that determines the overall effect of a reduction in river discharge. During high river discharge periods, such as summer 2003 (Fig.3. 2a), low to mid salinity water (<20 psu) extends outward to the barrier islands (Mortazavi et al. 2001). In contrast, during low river discharge periods, such as summer 2004 (Fig.3. 2b), low to mid salinity water (<20 psu) extends from the mouth of the river to the middle of the Bay. Results from this study show that during summer total ingestion rate (Fig.3. 5b, Fig.3. 6b) and production (Fig.3. 7b, 7d) of microzooplankton were relatively low in higher (i.e. >20 psu) salinity water. Therefore, summertime reductions in river discharge can be expected to reduce rates of ingestion and production of microzooplankton at a particular salinity, as well as increase the areal extent of higher salinity water where ingestion and production of microzooplankton are relatively low. Because microzooplankton are key constituents of the estuarine food web in Apalachicola Bay, we predict that upstream water diversion that leads to lower nutrients and higher salinity (>20 psu) in the Bay will reduce higher trophic level productivity as a consequence of reduced microzooplankton production.

96 Table 3.1. Rates of phytoplankton growth (µ, d-1) and microzooplankton herbivory (g, d-1), coefficient of determination (r2), critical -1 3 -1 phytoplankton concentration (Cc, µg chl a L ), ciliate abundance (C, cells x 10 L ) (total and % oligotrichs <20 µm in size), 5 to 20 µm flagellate abundance (F, cells x 105 L-1, ) and >20 µm dinoflagellate abundance (D, cells x 105 L-1) at various surface salinities (psu) in Apalachicola Bay, Florida. PLR refers to Piecewise Linear Regression. All regressions were Model I and significant at p<0.05 except where noted as ns (not significant). Not determined (-) and not applicable (na). ______

2 Date Salinity µ g r Cc C F D Total %

<26 oC 29 Jan 03 6.70 0.52 0.39 0.98 na 10 - 10 0.05 22 Oct 03 29.80 1.11 1.53 0.99 na 20 69 - - 22 Oct 03 19.90 1.21 1.32 0.99 na 9 80 - - 22 Oct 03 9.30 0.75 0.57 0.96 na 9 60 - - 26 Nov 03 31.25 0.91 0.78 0.98 na 8 53 5 0.10 26 Nov 03 22.30 1.02 0.26 0.86 na 5 60 1 0.00 26 Nov 03 12.70 0.95 0.90 0.94 na 34 80 5 0.20 21 Jan 04 5.80 0.28 0.32 0.92 na 11 - - - 21 Jan 04 14.60 0.46 0.51 0.98 na 6 - - - 21 Jan 04 32.50 0.29 0.32 0.96 na 9 - 4 0.04 21 Jan 04 1.70 0.17 0.00 0.03, ns na 1 - - - 09 Feb 04 18.60 0.77 0.14 0.90 na 10 - - - 09 Feb 04 11.50 0.36 0.13 0.95 na 4 - - - 09 Feb 04 5.00 0.27 0.07 0.65 na 2 - - - 09 Feb 04 0.30 0.37 0.12 0.88 na 3 - - - 01 Apr 04 28.25 0.40 0.63 0.93 na 21 - - - 01 Apr 04 13.10 0.37 0.22 0.89 na 17 - - - 01 Apr 04 8.30 0.40 0.13 0.80 na 19 - - - 01 Apr 04 0.40 0.44 0.49 0.52 na 2 - - -

97 Table 3. 1 continued. ______

2 Date Salinity µ g r Cc C F D Total %

>26 oC 07 May 03 29.95 1.64 1.32 0.95 na 34 69 40 0.20 07 May 03 14.53 1.67 1.94 PLR 3.7 23 94 200 0.10 07 May 03 7.80 1.52 1.50 PLR 4.0 10 33 20 0.00 04 Jun 03 10.10 1.41 1.13 PLR 5.7 24 54 30 0.20 04 Jun 03 17.70 1.92 1.95 PLR 4.3 24 77 20 0.01 04 Jun 03 35.00 1.44 1.02 0.98 na 32 68 20 0.20 30 Jul 03 6.50 0.81 0.95 PLR 5.9 14 53 - - 30 Jul 03 14.10 0.96 0.98 0.79 na 40 79 - - 30 Jul 03 25.20 0.94 1.20 0.84 na 15 73 - - 27 Aug 03 4.40 1.45 1.25 PLR 2.7 20 96 - - 27 Aug 03 15.50 1.31 1.18 PLR 2.4 37 84 - - 27 Aug 03 20.50 1.22 1.30 0.98 na 26 83 - - 27 Aug 03 27.10 0.36 0.59 PLR 1.9 48 47 - - 17 May 04 6.95 1.30 1.01 PLR 3.3 8 - 10 0.20 17 May 04 11.90 0.96 0.67 PLR 9.6 10 - - - 17 May 04 23.70 0.60 0.38 PLR 8.2 15 - 10 0.04 28 Jun 04 26.70 0.19 0.33 0.73 na 37 - - - 28 Jun 04 13.30 0.28 0.47 PLR 3.3 31 - - - 28 Jun 04 8.35 0.28 0.36 PLR 3.3 21 - - - 28 Jun 04 0.30 0.20 0.41 PLR 5.9 4 - - - 03 Aug 04 22.50 0.08 0.25 PLR 1.9 20 - - - 03 Aug 04 15.90 0.21 0.34 PLR 2.7 12 - - - 03 Aug 04 5.15 0.42 0.49 PLR 5.9 27 - - -

Fig. 3.2. Averaged surface salinity (psu) distribution in Apalachicola Bay during summer 2003 (A) and summer 2004 (B). Data courtesy of the Apalachicola Bay National Estuarine Research Reserve.

Fig. 3.3.A. Total prey carbon (phytoplankton plus bacterioplankton) relative to sea surface temperature during 2003 and 2004. In all figures white symbols are winter data and black symbols are summer data. Dashed line denotes 25.5oC. Regression: y = 65 + 1044e(-0.5*ln(x/28)/0.1)^2) r2=0.50, p<0.01.

B. Total prey carbon relative to sea surface salinity during 2003 and 2004. Regression for summer: y = -271 + 1705e(-0.5*((x-14)/11)^2) r2=0.64, p<01.

C. Percent of total prey carbon composed of phytoplankton carbon relative to sea surface temperature during 2003 and 2004. Regression: y = 9 + 3x , r2=0.50, p<0.01.

D. Percent of total prey carbon composed of phytoplankton carbon relative to sea surface salinity during 2003 and 2004. Regression for summer: y = 90 + 2x –0.1x2, r2=0.69, p<0.01. Regression for winter: y = 10 + 4x –0.1x2 , r2=0.75, p<0.01.

100

Total prey carbon -1 % Phytoplankton carbon (µg C L ) 1000 1500 2000 2500 3000 500 100 120 20 40 60 80 0 0 1 5 C A 01 Temperature ( 52 02 o C) 53 03 101 5 1 0 02 Salinity (psu) 03 04 D B 0 ) 2.0 -1

1.5 B

1.0

0.5

Microzooplankton herbivory (d 0.0 2345678

log Abundance (cells L-1)

Fig. 3.4. Microzooplankton herbivory relative to log heterotrophic flagellate (5 to 20 µm) and ciliate abundance. Regression for flagellates: y=-4 + 1x, r2=0.61, p<0.01. Regression for ciliates: y=-2 + 1x, r2=0.25, p<0.01. Ciliates Flagellates 5 to 20 µm

102

Fig. 3.5.A. Total carbon ingested relative to sea surface temperature during 2003. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC. Regression: y = 59 + 1697e(-0.5*((x-27)/2)^2) r2=0.58, p<0.01.

B. Total carbon ingested relative to sea surface salinity during 2003. Regression for summer: y = 143 + 1684e(-0.5*((x-14)/8)^2) r2=0.57, p<0.05.

C. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface temperature during 2003. Regression: y = -22 + 4x , r2=0.59, p<0.01.

D. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface salinity during 2003. Regression for summer: y = 75 + 3x –0.1x2 , r2=0.80, p<0.01.

103

Total carbon ingested % Diet -1 -1 composed of phytoplankton (µg C L d ) 1000 1500 2000 2500 3000 500 100 120 20 40 60 80 0 0 1 5 CD AB 01 Temperature ( 52 02 o C) 53 03 104 5 1 0 02 Salinity (psu) 03 04 0

Fig. 3.6.A. Total carbon ingested relative to sea surface temperature during 2004. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC.

B. Total carbon ingested relative to sea surface salinity during 2004.

C. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface temperature during 2004.

D. Percent of total prey carbon ingested composed of phytoplankton carbon relative to sea surface salinity during 2004. Regression for summer: y = 100 - 1x – 0.04x2 , r2=0.54, p<0.05. Regression for winter: y = -2 + 3x – 0.04x2 , r2=0.90, p<0.01.

105

Total carbon ingested % Diet -1 -1 composed of phytoplankton (µg C L d ) 1000 1500 500 100 120 20 40 60 80 0 0 1 5 CD AB 01 Temperature ( 52 02 o C) 53 03 106 5 1 0 02 Salinity (psu) 03 04 0

Fig. 3.7.A. Calculated microzooplankton production relative to sea surface temperature in 2003. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC. Regression: y = 20 + 560e(-0.5*((x-27)/2)^2) r2=0.58, p<0.01.

B. Calculated microzooplankton production relative to sea surface salinity in 2003. Regression for summer: y = 47 + 556e(-0.5*((x-14)/8)^2) r2=0.57, p<0.05.

C. Calculated microzooplankton production relative to sea surface temperature in 2004.

D. Calculated microzooplankton production relative to sea surface salinity in 2004.

107

Microzooplankton production -1 (µg C L d-1) 1000 1000 200 400 900 200 400 600 800 0 0 01 02 035 30 25 20 15 10 2004 2003 C AB Temperature ( o C) 108 1 0 02 Salinity (psu) 03 2004 2003 04 D 0

Fig. 3.8.A. Rates of microzooplankton herbivory relative to sea surface temperature in 2003. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC. Regression: y = 0.5 + 0.9e(-0.5*((x-26)/4)^2) r2=0.45, p<0.01.

B. Rates of microzooplankton herbivory relative to sea surface salinity in 2003.

C. Rates of microzooplankton herbivory relative to sea surface temperature in 2004. Regression: y = 0.2 + 0.5e(-0.5*((x-25)/4)^2) r2=0.37, p<0.05.

D. Rates of microzooplankton herbivory relative to sea surface salinity in 2004.

109 2.4

2.0 A B

1.6

) 1.2 -1 0.8

0.4

0.0 2.4

2.0 CD

1.6

1.2 Microzooplankton herbivory (d (d herbivory Microzooplankton 0.8

0.4

0.0 10 15 20 25 30 35 0 10203040 Temperature (oC) Salinity (psu)

110 ) 2.0 -1

1.5

1.0

0.5

Microzooplankton herbivory (d 0.0 0.00.51.01.52.0

Phytoplankton growth (d-1)

Fig. 3.9. Coupling of microzooplankton herbivory and phytoplankton growth during 2003 and 2004. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Solid line denotes steady state or 100% coupling between growth and grazing. Regression: y=0.01+0.9x, r2=0.78, p<0.01.

111 400 A A * B 300

200

100 by microzooplankton by

% Phytoplankton productivity ingested % Phytoplankton 10 15 20 25 30 35 0 10203040

o Salinity (psu) Temperature ( C)

Fig. 3.10. A. Percent of potential phytoplankton production ingested (%Pp) by microzooplankton relative to sea surface temperature in 2003 and 2004. Black symbols are summer (above 25.5oC) and white symbols are winter (below 25.5oC). Dashed line denotes 25.5oC, 2 below which the average %Pp is 75% and above which %Pp depends on temperature (y=-346 + 16x, r =0.54, p<0.01). Asterisk denotes data point not included in regression. Dashed line indicates 67%, the global average %Pp by microzooplankton in marine waters (Calbet & Landry 2004).

B. %Pp by microzooplankton relative to sea surface salinity during 2003 and 2004.

112

Fig. 3.11. Percentage of potential phytoplankton production ingested by microzooplankton (%Pp) relative to sea surface temperature in various subtropical and tropical estuarine, coastal, and oceanic waters. Dashed line indicates 67%, the global average %Pp by microzooplankton in marine waters (Calbet & Landry 2004).

A. Estuarine: Dagg (1995) Dolan et al. (2000) Murrell et al. (2002) a Juhl & Murrell (2005)

B. Coastal: Kamiyama (1994) Strom & Strom (1996) Garcia & Lara (2001)

C. Oceanic: Landry et al. (1995) Reckermann & Veldhuis (1997) Lessard & Murrell (1998) Landry et al. (1998) Caron & Dennett (1999) Edwards et al. (1999) Stelfox et al. (2000) Quevedo & Anadon (2001)

113 300 Mean = 47% Mean = 71% 250 A Estuarine 200

150

100

0 300 Mean = 40% Mean = 97% 250 B B Coastal 200

150

100

50 by microzooplankton

0 300 Mean = 76% Mean = 82% % Phytoplankton productivity ingested C 250 Oceanic 200

150

100

0 5 101520253035 o Temperature ( C)

114 CONCLUSIONS

Predicted effect of water diversion on the lower planktonic food web Secondary production is primarily supported by phytoplankton productivity in Apalachicola Bay (Chanton & Lewis 2002). Zooplankton are the main secondary producers in the Bay (Mortazavi et al. 2000c, Chapter 3). Relationships between nutrient, phyto- and zooplankton parameters and salinity during summer were examined to predict the effect that summertime water diversion may have on higher trophic level production in Apalachicola Bay. During summer, it was found that:

(1) nutrient concentrations were highest in lower salinity waters (Chapter 1); (2) phytoplankton biomass, growth, and productivity were highest in lower salinity waters (Chapter 1); (3) Acartia tonsa egg production and efficiency were highest in lower salinity waters (Chapter 2); (4) microzooplankton ingestion rate, production, and growth were highest in lower salinity waters (Chapter 3).

This study was conducted during 2 years with different summer river discharge rates. River discharge was above and below average during summer 2003 and 2004, respectively. Compared to 2003, it was found that during 2004:

(1) the areal extent of lower (<20 psu) salinity water was lower in the Bay (Chapter 1); (2) nutrient concentrations were lower at a specific salinity (Chapter 1); (3) phytoplankton growth and productivity were lower at a specific salinity (Chapter 1); (4) microzooplankton grazing, ingestion, production, and growth were lower at a specific salinity (Chapter 3).

115 Based on the present research, it is expected that withdrawal of freshwater from the Apalachicola River during summer will lead to reduced higher trophic level production in the Bay because of reduced productivity of phyto- and zooplankton at a specific salinity and reduced areal extent of lower salinity water where phyto- and zooplankton biomass and productivity are highest.

Estuarine planktonic food web structure The hypothesis that, a classical food chain characterizes the planktonic food web structure in high nutrient-low salinity waters and a microbial food web characterizes the planktonic food web structure in low nutrient-high salinity waters in Apalachicola Bay, was rejected. Energy and mass flowed primarily through a microbial food web during winter and summer throughout Apalachicola Bay (Fig. C.1). During winter, small and large cells were the main component of phytoplankton biomass in low and high salinity waters, respectively (Chapter 1) and microzooplankton (Chapter 2, 3) were the main herbivores throughout the Bay. During summer, small cells (<20 µm) were the main component of phytoplankton biomass (Chapter 1) and microzooplankton were the main herbivores (Chapter 2, 3) throughout the Bay. The predominance of a microbial food web in Apalachicola Bay is probably not unique. There are other estuaries, for example Moreton Bay (O’Donohue & Dennison 1997), Urdaibai Estuary (Ansotegui et al. 2003), Pensacola Bay (Murrell & Lores 2004), York River Estuary (Ray et al. 1989, Sin et al. 1999, Sin et al. 2000), and Chesapeake Bay (McCarthy et al. 1974), where small phytoplankton dominate phytoplankton biomass and productivity. Large phytoplankton can make a significant contribution to phytoplankton biomass and productivity in other estuaries; however, this often occurs during winter/spring (Chapter 1) when low temperatures can limit abundances and grazing activity of mesozooplankton (Peinert et al. 1989, Pomeroy et al. 1991, Riegman et al. 1993, Dagg 1995). Microzooplankton are effective grazers of small phytoplankton and can graze larger phytoplankton (Tillman 2004), even at low temperatures (Archer et al. 1996). Therefore, a microbial food web, where microzooplankton are the main herbivores, should predominate in estuarine waters. Indeed, the syntheses of Calbet & Landry (2004) indicate that microzooplankton ingest about 60% of phytoplankton

116 productivity in estuaries and mesozooplankton generally ingest <30% of phytoplankton productivity in estuarine waters (Dagg 1995, Sautour et al. 2000, Froneman 2000, Froneman 2001, Froneman 2004). Knowledge of microbial food web processes has primarily been acquired from studies in oceanic waters. In oceanic waters, microbial food webs support low magnitudes of higher trophic level production because of low phytoplankton productivity and the relatively high number of trophic transfers between phytoplankton and higher trophic levels (Riegman et al. 1993, Legendre & Rassoulzadegan 1995, Sommer et al. 2002). Phytoplankton productivity is greater in estuarine than oceanic waters (McLusky & Elliott 2004). Furthermore, compared to an oceanic microbial food web, an estuarine microbial food web has fewer trophic transfers between phytoplankton and higher trophic levels. This is because estuaries are habitats for oysters and nurseries for larval shellfish and fish. Oysters (Dupuy et al. 1999, Loret et al. 2000) and larval shellfish (Sulkin et al. 1998, Nagano & Decamp 2004) and larval fish (Stoecker & Govoni 1984, Lessard et al. 1996, Fukami et al. 1999, Nagano et al. 2000) directly ingest microzooplankton. Therefore, an estuarine microbial food web can support more higher trophic level production than an oceanic microbial food web.

Epilogue Estuaries are critical habitats and nurseries to fish, shellfish, and waterfowl and provide recreational opportunities for humans. Pollution, eutrophication, habitat loss, and reductions in river flow threaten the existence of estuarine habitats (Schlacher & Woolridge 1996). Our understanding of estuarine food web processes lags behind that of oceanic food web processes. This lack of understanding impedes the ability to manage estuaries for sustainable use (Hobbie 2000). Recent research indicates that organic matter derived from benthic algae, epiphytic algae, and phytoplankton (Sullivan & Moncreiff 1990, Deegan & Garritt 1997, Moncreiff & Sullivan 2001, Chanton & Lewis 2002) supports estuarine food webs. To date, information is lacking on the spatial, as well as temporal, patterns of estuarine phytoplankton size composition, growth, biomass, and productivity. In addition, the main pathways through which phytoplankton is transferred to higher trophic levels and the spatial and temporal patterns of zooplankton productivity

117 in estuaries are largely unknown. The present study is unique in that it simultaneously examined the temporal and spatial patterns of phytoplankton size composition, growth, biomass, and productivity, microzooplankton bacterivory and herbivory, and Acartia tonsa carnivory, herbivory, and egg production rates in Apalachicola Bay. Data acquired from this study has improved our understanding of estuarine planktonic food web structure in Apalachicola Bay and can be used to manage the Bay.

118 Fish Fish larvae

Shellfish

Acartia tonsa

Microzoo- Wetlands Coastal marshes

Benthic macrophytes Phyto-

Bacteria

DOM

Nutrients

ESTUARINE PLANKTONIC FOOD WEB Fig. C.1. Proposed estuarine planktonic food web structure throughout Apalachicola Bay. Width of solid arrows denotes the relative magnitude of carbon transfer. Dashed arrows denote known pathways that have not been quantified. Microzoo- and phyto- refer to microzooplankton and phytoplankton, respectively. DOM refers to dissolved organic matter.

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137 BIOGRAPHICAL SKETCH

EDUCATION Ph.D. (Biological Oceanography), Dec. 2005, Florida State University Dissertation: Ecology of phytoplankton, Acartia tonsa, and microzooplankton in Apalachicola Bay, Florida.

M.Sc. (Marine Biology), Oct. 1998, Memorial University of Newfoundland Thesis: Microzooplankton bacterivory and herbivory in oceanic and coastal environments: comparisons of the subarctic Pacific with Newfoundland coastal waters.

B.Sc. (Biology), May 1993, University of British Columbia

AWARDS Florida State University Dissertation Research Grant (2004) $500 National Estuarine Research Reserve Graduate Research Fellowship (2002-2005) $55,000

EMPLOYMENT Sep. 2001 – Oct. 2005, Graduate Research Assistant Department of Oceanography, Florida State University, Tallahassee, FL

Jun. 2001 – Aug. 2001, Research Biologist Institute of Ocean Sciences, Department of Fisheries and Oceans, Victoria, B.C.

Apr. 1998 - May 2001, Oceanographic Research Technician Institute of Ocean Sciences, Department of Fisheries and Oceans, Victoria, B.C. Central Research Institute of the Electric Power Industry, Chiba, Japan (via D.F.O.)

Nov. 1997 - Mar. 1998, Research Associate School of Earth and Ocean Sciences, University of Victoria, Victoria, B.C.

Sep. 1994 - Oct. 1997, Graduate Research Assistant Ocean Sciences Center, Memorial University of Newfoundland, St. John’s, NF

PUBLICATIONS

Putland, J.N., F.A. Whitney, and D.W. Crawford. 2004. Survey of bottom-up controls on Emiliania huxleyi in the subarctic Northeast Pacific. Deep-Sea Research I (51): 1793-1802.

Crawford, D.W., M.S. Lipsen, D.A. Purdie, M.C. Lohan, P.J. Stratham, F.A. Whitney, J.N. Putland,

138 W.K. Johnson, N. Sutherland, T.D. Peterson, P.J. Harrison, and C.S. Wong. 2003. Influence of zinc and iron enrichments on phytoplankton growth in the northeastern subarctic Pacific. Limnology and Oceanography 48(4): 1583-1600.

Putland, J.N. 2000. Microzooplankton herbivory and bacterivory in Newfoundland coastal waters during spring, summer, and winter. Journal of Plankton Research 22 (2): 253-277.

Rivkin, R.B., J.N. Putland, M.R. Anderson, and D. Deibel. 1999. Microzooplankton bacterivory and herbivory in the Northeast subarctic Pacific. Deep-Sea Research II, 46 (11-12): 2579-2618.

Putland, J.N. and R.B. Rivkin. 1999. Influence of storage mode and duration on the enumeration of Synechococcus. Aquatic Microbial Ecology 17(2): 191-199.

PRESENTATIONS

Putland, J.N. and R.L. Iverson. 2004. Summertime grazing dynamics by micro- and mesozooplankton in Apalachicola Bay, Florida. American Society of Limnology and Oceanography Meeting. Savannah, GA.

Putland, J.N. and R.L. Iverson. 2003. Summertime herbivory and carnivory by micro- and mesozooplankton in Apalachicola Bay, Florida. Estuarine Research Federation Conference. Seattle, WA.

Putland, J.N., F.A. Whitney, and D.W. Crawford. 2002. Chemical and physical factors influencing Emiliana huxleyi abundances in the Northeast subarctic Pacific. Coccolithophores – From Molecular Processes To Global Impact Meeting, Ascona, Switzerland.

Putland, J.N., R.B. Rivkin, D. Deibel, and M.R. Anderson. 1997. Seasonal patterns of microzooplankton herbivory and bacterivory in the subarctic Pacific. CJGOFS Annual Meeting, Victoria, B.C. and American Society of Limnology and Oceanography Meeting, Sante Fe, NM.

Putland, J.N., R.B. Rivkin, D. Deibel, and M.R. Anderson. 1996. Protistan control of microbial populations in the subarctic Pacific. CJGOFS Annual Meeting, Montreal, P.Q.

PROFESSIONAL and HONARARY SOCIETY AFFILIATION American Society of Limnology and Oceanography Phi Kappa Phi

139 FIELD EXPERIENCE

Florida State University Cruise Mission: Apalachicola Bay, Gulf of Mexico (NERR) Duration: Jul. 02- Jul. 05 (1-2 d/month)

Cruise Mission: Gulf of Mexico (Unusual Mortality Event Survey) Duration: Mar. 04 (5 days)

Department of Fisheries and Oceans Cruise Missions: Northeast Pacific (LaPerouse, Line P, Eddy Monitoring); Saanich Inlet (CRIEPI Mesocosm Experiment) Duration: Oct. 97 (1 week), Aug. 98 (1 month), Feb.-Apr. 99 (1-2 d/week), Jul. 99 (1 month), Aug. 99 (1 month), Jun. 00 (2 weeks), May 01 (2 weeks), Jun. 01 (1 month)

Memorial University of Newfoundland Cruise Mission: Northeast Pacific (Canadian JGOFS) Duration: Aug. 95 (1 month), Feb. 96 (3 weeks), May 96 (1 month)

Cruise Mission: Logy Bay, North Atlantic (Canadian JGOFS) Duration: Sep. 94 – Sep. 96 (1 d/month)

140