The Role of Bacterioplankton in Lake Erie Ecosystem Processes: Phosphorus Dynamics and Bacterial Bioenergetics

Home , Microbial ecology, Microbial food web, Microbial loop, Trophic state index

THE ROLE OF BACTERIOPLANKTON IN LAKE ERIE ECOSYSTEM PROCESSES: PHOSPHORUS DYNAMICS AND BACTERIAL BIOENERGETICS

A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Tracey Trzebuckowski Meilander

December 2006

Dissertation written by

Tracey Trzebuckowski Meilander

B.S., The Ohio State University, 1994

M.Ed., The Ohio State University, 1997

Ph.D., Kent State University, 2006

Approved by

__Robert T. Heath______, Chair, Doctoral Dissertation Committee

__Mark W, Kershner______, Members, Doctoral Dissertation Committee

__Laura G. Leff______

__Alison J. Smith______

__Frederick Walz______

Accepted by

__James L. Blank______, Chair, Department of Biological Sciences

__John R.D. Stalvey______, Dean, College of Arts and Sciences

TABLE OF CONTENTS

Page

LIST OF FIGURES ………………………….……………………………………….….xi

LIST OF TABLES ……………………………………………………………………...xvi

DEDICATION …………………………………………………………………………..xx

ACKNOWLEDGEMENTS ………………………………………………………….…xxi

CHAPTER I. Introduction ….….………………………………………………………....1

The role of bacteria in aquatic ecosystems ……………………………………………….1

Introduction ……………………………………………………………………….1

The microbial food web …………………………………………………………..2

Bacterial bioenergetics ……………………………………………………………6

Bacterial productivity ……………………………………………………..6

Bacterial respiration ……………………………………………………..10

Bacterial growth efficiency ………...……………………………………11

Phosphorus in aquatic ecosystems ………………………………………………………12

Phosphorus limitation of lake phytoplankton .…………………………………12

The role of bacteria in phosphorus dynamics ………………………………….16

The lability of dissolved organic carbon in aquatic ecosystems ……………………….19

The microbial shunt hypothesis of phosphorus apportionment ………………………..24

Rationale ……………………………………………………………………………….26

iii Introduction to the study site: Lake Erie ………………………………………26

Environmental problems in Lake Erie …………………………………………28

Cultural eutrophication ………………………………………………...28

Non-indigenous species ……………………………………………….31

Hypoxia ………………………………………………………………..33

Causes and consequences ….…………………………………..33

History of hypoxia in Lake Erie: past and present ……………35

Statement of the purpose of this research ……………………………………………...38

Dissertation objectives …………………………………………………………………39

Summary of dissertation findings ……………………………………………………...41

CHAPTER II. Distribution and apportionment of phosphate between bacterioplankton and phytoplankton in Lake Erie………………………………… ………………………44

Abstract ………………………………………………………………………………….44

Introduction ……………………………………………………………………………...45

Methods ………………………………………………………………………………….48

Site characterization ……………………………………………………………..48

Bacterial structure ……………………………………………………………….48

Bacterial productivity ……………………………………………………………49

Plankton phosphorus dynamics ………………………………………………….50

Labile dissolved organic carbon (LDOC) ……………………………………….50

Phosphate uptake ………………………………………………………………..51

Trophic state index (TSI) ….…………………………………………………….52

Results …………………………………………………………………………………52

Trophic state index …………….……………………………………………….52

Bacterial structure and productivity ……………………………………………53

Phosphorus dynamics …………………………………………………………..54

Labile dissolved organic carbon ………………………………………………..55

Phosphate apportionment ……………………………………………………….56

Discussion ………………………………………………………………………………56

Acknowledgements ……………………………………………………………………..61

CHAPTER III. Thehe role of LDOC to phosphorus dynamics in Lake Erie bacterioplankton assemblages …………………………………………………………..80

Abstract ………………………………………………………………………………….80

Introduction ……………………………………………………………………..……….82

Methods ………………………………………………………………………..………...86

Sample collection ………………………………………………..………………86

Phosphate uptake and turnover time ……………………………………..……...86

Phosphate apportionment to bacteria and algae …………………………..……..87

Particulate phosphorus and P-quota ………………………………………..……88

Phosphorus distribution to bacteria and algae ………………………………..…89

Biologically available phosphate (BAP) and Michaelis-Menten kinetics …..…..89

Labile dissolved organic carbon (LDOC) …………………………………..…...90

Total dissolved organic carbon (TDOC) ……………………………………..….91

Testing the microbial shunt hypothesis – Low-molecular weight carbon amendments …………………………………………………...………………………...91

Results …………………………………………………………………..………………91

Limnological variables ……………………………………………………..…...91

LDOC distribution and abundance ………………………………………..…….91

Biologically available phosphate (BAP), phosphate uptake,

and turnover time ….………………………………………...…………..………93

Phosphate apportionment to bacteria ………………………………………..…..95

Particulate phosphorus ………………………………………………..…………96

Available phosphorus distribution among bacterioplankton

and phytoplankton ………………………………………………………...……..97

Bacterial P-quota ………………………………………………………..……….97

Biologically available phosphate (BAP) and Michaelis-Menten kinetics ……....98

Relationship between LDOC, TSI, and phosphorus dynamics ……………….....99

Relationship between LDOC and bacterial phosphorus dynamics …………….100

Testing the microbial shunt hypothesis – Bacterial response to low

molecular weight carbon amendments …………………………………………101

Discussion ……………………………………………………………………………...103

Summary ……………………………………………………………………………….109

Acknowledgements …………………………………………………………………….110

CHAPTER III. Availability of dissolved organic carbon to lake bacterioplankton ……………………………………………………………………..…182

Abstract ………………………………………………………………………………...182

Introduction ……………………………...……………………………………………..183

Methods ….……………………………………………………………………………..185

Sample collection ……………………………………………………………...185

Labile dissolved organic carbon (LDOC) …………………………………….186

Total dissolved organic carbon (TDOC) ……………………………………...187

LDOC swap experiments ……………………………………………………..188

Results ………………………………………………………………………………...188

Site characterization …………………………………………………………..188

LDOC distribution………………..…………………………………………...189

Fraction of TDOC that is LDOC ……………………………………………..190

Correlation of LDOC with other variables ...……………...... 191

Variability of LDOC utilization by bacterioplankton ………………………...192

Discussion …………………………………………………………………………….194

Summary ……………………………………………………………………………...198

Acknowledgements ……….…………………………………………………………..199

CHAPTER V. Factors influencing the bioenergetics of Lake Erie bacterioplankton ………………………………………………………………………..241

Abstract ………...………………………………………………………………………241

Introduction ………...…………………………………………………………………..242

Methods ……...…………………………………………………………………………244

vii

Sample collection ……..……………………………………………………….244

Bacterial productivity ..………………………………………………………...245

Bacterial respiration …...………………………………………………………245

Bacterial growth efficiency ……………………………………………………246

Bacterial abundance …………………………………………………………...246

Labile dissolved organic carbon (LDOC) ….………………………………….247

Total dissolved organic carbon (TDOC) …….………………………………...247

Particulate phosphorus and P-quota .…………………………………………..248

Chlorophyll a concentration ..………………………………………………….248

Trophic state index (TSI) ..……………………………………………………..249

Results ………………………………………………………………………………...249

Limnological variables ………………………………………………………..249

Bacterial abundance and cellular biovolume …………………………………250

Bacterial productivity (BP) …………………………………………………...251

Bacterial respiration (BR) …………………………………………………….252

Bacterial growth efficiency (BGE) …………………………………………...253

Comparison of vertical profiles …….…………………………………………254

Factors influencing bacterial bioenergetics .…………………………………..256

Discussion …………………………………………………………………………….259

Summary ……………………………………………………………………………...266

Acknowledgements …………….……………………………………………………..267

CHAPTER VI. Life in the dead zone ………………………………………………….307

viii

Abstract ………………………………………………………………………………307

Introduction …………………………………………………………………………..309

Methods ………………………………………………………………………...………311

Site characterization ……………………………………………………………311

Bacterial abundance, cellular biovolume, and total biovolume ………………..312

Bacterial productivity …………………………………………………………..312

Bacterial respiration ……………………………………………………………313

Bacterial growth efficiency …………………………………………………….314

Bacterial particulate phosphorus and total phosphorus ………………………..314

Phosphate uptake and total turnover time ……………………………………..315

Microbial loop …………………………………………………………………315

Potential primary productivity …………………………………………………316

Trophic state index (TSI) ………………………………………………………316

Results …………………………………………………………………………………317

Site characterization …………………………………………………………...317

Bacterial assemblage …………………………………………………………..318

Microbial loop …………………………………………………………………318

Bacterial activity ………………………………………………………………319

Phytoplankton activity ………………………………………………………...320

Bacterial phosphorus dynamics ……………………………………………….321

Discussion ……………………………………………………………………………..322

Summary ………………………………………………………………………………322

Acknowledgements ……………………………………………………………………347

SUMMARY OF FINDINGS ………….……………………………………………….348

LITERATURE CITED …………………………….…………………………………..357

APPENDIX……………………………………………………………………………..398

x LIST OF FIGURES

Page

CHAPTER II. Distribution and apportionment of phosphate between bacterioplankton

and phytoplankton in Lake Erie

Figure 1. Map of Lake Erie with sites labeled …………………………….…………74-75

Figure 2. The relationship between bacterial phosphate uptake and labile dissolved

organic carbon…………….……………………………………………………….…76-77

Figure 3. The relationship between P distribution to bacteria (%) and labile dissolved

organic carbon (LDOC) concentration (μM) …………………………………….…..78-79

CHAPTER III. The role of LDOC to phosphorus dynamics in Lake Erie

bacterioplankton assemblages

Figure 1. Map of Lake Erie with 2005 stations labeled ……………..……………136-137

Figure 2. Distribution of LDOC by basin during 2004...... ……………………..138-139

Figure 3. Distribution of LDOC by basin during 2005……………………….…..140-141

xi Figure 4. Biologically available phosphate by basin ………………..……………142-143

Figure 5. Bacterial uptake constant by basin …………………………………..…144-145

Figure 6. Phosphate uptake velocity by basin ……………………………………146-147

Figure 7. Phosphate uptake velocity per cell by basin ...…………………………148-149

Figure 8. Total turnover time by basin …………………………...………………150-151

Figure 9. Phosphate apportionment to bacteria and algae .……………………….152-153

Figure 10. Particulate phosphorus concentrations by basin ….…...…………….154-155

Figure 11. Particulate phosphorus distribution to algae and bacteria by basin .….156-157

Figure 12. Bacterial P-Quota by basin ……………………………….…………..158-159

Figure 13. Relationship between % vuptake and % phosphate uptake ..…………...160-161

Figure 14. (a) Phosphate apportionment and (b) particulate phosphorus distribution to bacteria and algae based on TSI ….……………………………………………….162-163

Figure 15. (a) Phosphate apportionment and (b) particulate phosphorus distribution to bacteria and algae based on LDOC ……………………………………………….164-165

Figure 16. Relationship between LDOC and bacterial P-Quota …………...…….166-167

Figure 17. Relationship between LDOC and phosphate uptake velocity …..…...168-169

xii

Figure 18. Relationship between LDOC and cellular phosphate uptake

velocity …...... …………………………………………………………………..170-171

Figure 19. (a) Bacterial productivity and (b) Bacterial phosphate uptake constant

versus time following addition of glucose at stations ER 15 and 942 …………….172-173

Figure 20. (a) Bacterial productivity and (b) Bacterial phosphate uptake constant

versus time following addition of glucose at stations ER 15 and 449 ….…………174-175

Figure 21. (a) Bacterial productivity and (b) Bacterial phosphate uptake constant

versus time following addition of glucose at stations ER 73 and 958 …..………..176-177

Figure 22. (a) Bacterial productivity and (b) Bacterial phosphate uptake constant versus time following addition of glucose, glucosamine, and lysine

at station 958 ………………………………………...…………………………….178-179

Figure 23. (a) Bacterial productivity and (b) Bacterial phosphate uptake constant versus time following addition of glucose, glucosamine, and lysine

at station ER 73 …………….……………………………………………………...180-181

CHAPTER IV. Availability of dissolved organic carbon to lake bacterioplankton

Figure 1. Map of Lake Erie with (a) 2004 stations and (b) 2005 stations …….….221-222

Figure 2. Diagram of LDOC swap experiment …………………………………..223-224

Figure 3. Distribution of LDOC by basin 2004………………………….………..225-226

xiii

Figure 4. Distribution of LDOC by basin 2005 …………………………….…….227-228

Figure 5. Distribution and abundance of TDOC by basin ……………………….229-230

Figure 6. May 2004 swap experiment ……………………………………………231-232

Figure 7. June 2004 swap experiments …………………………………………..233-234

Figure 8. July 2004 swap experiments …………………………………………...235-236

Figure 9. August 2004 swap experiments ………………………………………..237-238

Figure 10. Diagram of swap experiments from stations 880 and 879 ……………239-240

CHAPTER V. Factors influencing the bioenergetics of Lake Erie bacterioplankton

Figure 1. Map of Lake Erie with stations labeled .………………………………..293-294

Figure 2. Comparison of bacterial productivity by month ……………………….295-296

Figure 3. Comparison of bacterial productivity cell-1 …………………………….297-298

Figure 4. Bioenergetics by basin (a) bacterial productivity (b) bacterial productivity cell-1 and (c) bacterial growth efficiency by basin ………...……………………………299-300

Figure 5. Comparison of bacterial respiration by month ……..………………….301-302

Figure 6. Comparison of bacterial respiration cell-1 by month …..………………303-304

xiv

Figure 7. Comparison of bacterial growth efficiency by month …….…………...305-306

CHAPTER VI. Life in the dead zone

Figure 1. Map of Lake Erie with central basin station 880 and eastern basin

879 labeled ………………………………………………………………………..340-341

Figure 2. Hypolimnetic microbial loop structure ………………………………...342-343

Figure 3. Hypolimnetic bacterial activity ………………………………………...344-345

Figure 4. Size-fractionated (>20 μm, 2-20 μm, and < 20 μm) algal potential

productivity ….…………………………………………………………………….346-347

SUMMARY OF FINDINGS

Figure 1. Proposed bacterial metabolic states in the Microbial Shunt

Hypothesis …..…………………………………………………………………….355-356

xv LIST OF TABLES

Page

CHAPTER II. Distribution and apportionment of phosphate between bacterioplankton and phytoplankton in Lake Erie

Table 1. Limnological variables for Lake Erie in August 2003 and June, July, and

August of 2004 …………………………………..…………………………………..62-64

Table 2. Bacterial structure and productivity for Lake Erie in August 2003 and June,

July, and August of 2004 …………………………………………………………….65-67

Table 3. Bacterioplankton phosphorus dynamics for Lake Erie in August 2003 and June,

July, and August of 2004 …………………………………………………………….68-70

Table 4. Labile dissolved organic carbon (LDOC) concentrations for Lake Erie in

August 2003 and June, July, and August of 2004……...…………………………….71-73

CHAPTER III. The role of LDOC to phosphorus dynamics in

Lake Erie bacterioplankton assemblages

Table 1. Limnological variables in Lake Erie during summer 2005 ….………….111-115

Table 2. LDOC concentration by station during 2004 and 2005 …………………116-120

xvi Table 3. LDOC concentration by basin during 2004 and 2005………...…………121-122

Table 4. Bacterial phosphorus dynamics by basin in Lake Erie during

summer 2005 …...……………...... ………………………………………………123-124

Table 5. Bacterial phosphorus dynamics by site in Lake Erie during

summer 2005 ……….……………………………………………………………..125-130

Table 6. Phosphate apportionment to bacteria in Lake Erie during

summer 2005 ……………….…………………………………………………………..131

Table 7. Phosphorus distribution in Lake Erie during summer 2005 ….…………132-133

Table 8. Phosphate uptake parameters for algae and bacteria in Lake Erie

during summer 2005 ………………………………………………………………134-135

CHAPTER IV. Availability of dissolved organic carbon to lake bacterioplankton

Table 1. Limnological variables for summer 2004 ……………………………….200-201

Table 2. Limnological variables for summer 2005 ……………………………….202-207

Table 3. LDOC concentration by station during 2004 and 2005 …………………208-215

Table 4. LDOC concentration by basin during 2004 and 2005 …...……………...216-217

Table 5. TDOC concentration by basin during August and September

of 2005 …………………………………………………………………………….…...218

xvii

Table 6. Results of LDOC swap experiment during 2004 ……………………….219-220

CHAPTER V. Factors Influencing the Bioenergetics of Lake Erie bacterioplankton

Table 1. Lake Erie limnological variables during 2005 …………...……………..268-273

Table 2. Lake Erie bacterial structure by basin. ………………………………………274

Table 3. Lake Erie bacterial activity by basin ……………………………………275-276

Table 4. Vertical profiles of bacterial structure by site ……..…………………...277-278

Table 5. Vertical profiles of bacterial activity by site ………..………………….279-280

Table 6. Factors influencing bacterial bioenergetics in Lake Erie …...…………..281-286

Table 7. Summary of factors influencing bacterial bioenergetics in Lake Erie ………………...…………………………………………………………..287

Table 8. Dissolved organic carbon distribution in Lake Erie …...………………..288-293

CHAPTER VI. Life in the dead zone

Table 1. Limnological variables for epi-, meta-, and hypolimnion of central and eastern basin sites during June, July, and August of 2004 ……..…………….330-331

xviii

Table 2. Microbial loop structure for epi-, meta-, and hypolimnion of central and eastern basin sites during June, July, and August of 2004 …..……………….332-333

Table 3. Bacterial activity for epi-, meta-, and hypolimnion of central and eastern basin sites during June, July, and August of 2004 ………….…………….334-335

Table 4. Potential primary productivity for epi-, meta-, and hypolimnion of central and eastern basin sites during June, July, and August of 2004 ………..….336-337

Table 5. Bacterial phosphorus dynamics for epi-, meta-, and hypolimnion of central and eastern basin sites during June, July, and August of 2004 …...……….338-339

SUMMARY OF FINDINGS

Table 1. Proposed bacterial metabolic states in the microbial shunt hypothesis …………………………….………………………………………………..354

xix DEDICATION

To Tim, Ethan, and Maya

xx Acknowledgements

While the authorship of this dissertation bears only one name, the completion of this project was truly a group effort of many individuals. First and foremost, I thank Tim

Meilander, my husband, whose encouragement helped me complete this project. Many times, too numerous to count, I wanted to quit. Tim always encouraged me to find the confidence I needed to take one more step towards completion. Tim’s emotional support has been absolutely crucial to the fulfillment of this project. I thank him for driving me to Canada on numerous occasions, assisting me in the lab and field, and for following me on trips to make presentations of my research. Leaving a secure teaching career to earn my doctorate was a financial sacrifice for our new family. I thank him for setting his personal goals aside while I achieved my own goals. My hope is that we will have a few more moments of time to spend together in the future now that this project is complete.

I thank Ethan Meilander, my son, who was born during this adventure in academia. He was such a good baby and toddler and now is a well-behaved child. His even temperament enabled me to work the long hours needed to complete this project. I thank him for traveling with me on my research trips and conferences so that I would not miss him so much. While I have missed some things in his young life, I feel that I did my best to balance a scientific career and motherhood. I know that his life will benefit greatly in the long term due to the sacrifices made since his birth.

xxi I thank Maya Meilander, my daughter, who is only one week old as I finish this

dissertation. Thankfully, she slept most of the day while I completed the formatting of

this document. My hope is that she dreams big and achieves all of her goals in life.

I thank the many family members who watched Ethan while I attended class and

research trips. My father, Robert Trzebuckowski, and mother-in-law, Lois Meilander,

watched Ethan starting at four weeks old so that I could complete my course work. My

father picked-up and dropped-off frequently at the daycare center so that I could

complete my teaching obligations and experiments. My father and mother, Gayle

Trzebuckowski, and mother-in-law also looked after Ethan during my boat trips to Lake

Erie when Tim was out-of-town. I also thank my mother-in-law for volunteering to watch Ethan so that I could start writing my dissertation. My sister-in-law, Tina

Trzebuckowski, also watched Ethan frequently during my academic commitments. I thank Tara and Jared Lilyblad, my sister and brother-in-law, Troy Trzebuckowski, my brother, and Tara Elkins, my sister-in-law who also helped out with babysitting.

I thank my advisor and mentor, Dr. Robert Heath, for taking a chance on me, a teacher with a desire to become a scientist. His high standards enabled me to reach my full potential. I appreciate his assistance with networking within the Great Lakes scientific community. We worked together to submit the International Field Year of

Lake Erie (IFYLE) grant that greatly improved the quality of this dissertation. I thank him for giving me the independence to pursue and coordinate this project. As a result, I

am confident in my abilities to coordinate my own projects in the future. I thank him for

providing funding throughout this program and for supporting the extensive travel

xxii

associated with this project. Most of all, I thank him for teaching me to think like a scientist.

I thank my committee members, Dr. Laura Leff, Dr. Mark Kershner, and Dr.

Alison Smith for providing encouragement throughout the dissertation process. I have taken your advice about balancing a scientific career and family to heart. All are excellent role models in this regard. In addition, I thank Dr. Christopher Woolverton for emotional support and general guidance.

I thank Dr. Mohi Munawar from the Canadian Department of Fisheries and

Oceans for the 2003 and 2004 cruise opportunities; Mark Fitzpatrick, Heather Niblock, and Jocelyn Gerlofsma (Canadian DFO) for technical assistance and Canadian hospitality; the captain and crew of the CCGS Limnos; Murray Charlton from

Environment Canada for a May 2004 cruise opportunity; Bob Hess and technical

operations staff (Environmental Canada) for technical assistance; the captain and crew of

the R/V Lake Guardian; Margaret Lansing, Stuart Ludsin, and Tom Johengen from the

NOAA Great Lakes Environmental Research Laboratory (GLERL) for assistance with

the IFYLE project; Dr. Michael Twiss from Clarkson University and Dr. Steven Wilhelm

from University of Tennessee for career advice and encouragement; and NOAA GLERL

and Ohio Sea Grant for the funding of this project.

Last but not least, I thank my colleagues and fellow graduate students in the

Heath lab, Xueqing Gao and Curtis Clevinger. Completion of this project would not be

possible without the assistance of Kent State University undergraduate students,

especially Dana McDermott, Patrick Hurley, and Megan Castagnaro. REU students

xxiii

Dennis Hanson from St. Cloud State University and Kathy Wilson from Sweetbriar

College also assisted with field work. I thank Bob Christy, the university photographer,

who assisted with field work while on assignment. Lastly, I thank Dr. Judy Santmire, Dr.

Mitali Das, Dr. Lenka Fedorkova, Shannon Helfinstine, Melissa Rubin, Steve Fiester, and

Dr. Margarita Gomez-Escalada for friendship and assistance over the past five years.

xxiv CHAPTER I

Introduction

The Role of Bacteria in Aquatic Ecosystems

Introduction

Planktonic bacteria have been traditionally viewed solely as organic matter

decomposers and nutrient re-mineralizers in aquatic ecosystems. As early as 1942,

Lindeman postulated that different groups of organisms form trophic levels with each

level transferring nutrients and energy to the next level when consumed. He was the first to include the “ooze” (the unknown bacteria and detritus) as the decomposed material.

The origin of the microbes in the food web began with the inclusion of saprophages, or organisms that consumed dead and decaying matter (the bacteria and fungi), as trophic links by Wiegert and Owens (1971). In their food web structure, saprophages consumed the detritus and the decomposers of the detritus. In the 1980s, Azam et al. (1983) suggested that bacteria are grazed by heterotrophic flagellates (which are grazed by zooplankton) and can move carbon through the food chain to higher trophic levels.

Bacteria in the “microbial loop” are now viewed as an essential part of the marine and freshwater food webs as consumers of 10-50% of photosynethetically-fixed carbon and

1 2

movers of carbon to higher trophic levels (Azam 1983). Sherr and Sherr (1988) proposed

that bacteria in the microbial loop are “the ultimate food resource for metazooplankton” and should be integrated completely into the microbial food web. The structure of the microbial food web shows similarities between diverse freshwater environments (e.g. The

Great Lakes, Fahnenstiel et al. 1998) and similarities between marine and freshwater

environments (Sanders et al. 1992). These similarities between microbial food web

structures suggest similar functions of the MFW in these various habitats.

Bacteria are abundant and active throughout the water column in lakes and

oceans. In Lake Zurich, bacteria accounted for >50% of microbial wet biomass in the

water column and 90% of the small organisms <0.66 μm3 volume (Button et al. 1996). In

the coastal ocean water column, larger bacteria were more active bacteria (estimated by

CTC dye that stains respiring bacteria) and were more likely to have higher rates of

production and higher grazing rates (Gasol et al. 1995). The community structure of

bacterial communities in the North Sea is similar amongst sites, exhibits patchiness, and

is independent of phytoplankton parameters (abundance, chlorophyll a) (Zubkov et al.

2002). Even in Swedish lake sediments, high bacterial activity (estimated by Baclight

Live-Dead stain) was observed despite low bacterial abundance and production (Haglund

et al. 2003).

The Microbial Food Web

Bacteria serve as a food source to many organisms in the microbial food web

including nanoflagellates, ciliates, rotifers (Arndt 1993), zooplankton (particularly

cladocera) and invertebrates. Heterotrophic nanoflagellates grazed 87% of bacterial

production in Australian coastal embayments (Safi et al. 2002). Sanders et al. (1992)

observed that bottom-up control (resource availability) regulates bacterial abundance in

oligotrophic environments while top-down control (grazing by heterotrophic

nanoflagellates) regulates bacterial abundance in eutrophic environments. Top-down

effects in a food chain or web occur when predation (usually by fish or invertebrates)

controls the structure or abundance of lower food web members (eg. zooplankton and

phytoplankton). Bottom-up effects regulate limited resources (eg. nutrients, light, and

food) to lower members (often phytoplankton) in the food chain or web (McQueen et al.

1989). Bottom-up and top-down effects were observed by Psenner and Sommaruga

(1992) in an Austrian lake; however, nutrients controlled the bacterial cell volume and

top-down grazers (flagellates and Dinobryon) controlled bacterial production. Flagellates show a grazing preference for larger bacterial cells and ingest larger bacteria at a greater frequency (Šimek and Chrzanowski 1992; Pernthaler et al. 1996; González 1996). Even filamentous forms of bacteria accounting for 45-86% of bacterial biovolume that avoid grazing by nanoflagellates (Sommaruga and Psenner 1995) These filaments and aggregates tend to form in nutrient-rich environments, and to resist grazing pressures

(Jurgens and Montserrat Sala 2000). Flagellate grazing in resource-limited conditions is capable of influencing bacterial community composition in mesocosms (Šimek 2003).

According to Sherr and Sherr (1987), grazing by ciliates accounts for 100% of protozoan bacterivory. Ciliates are even able to control production of potentially harmful bacteria from sewage overflow by grazing preferentially on pathogens (Ayo et al. 2001).

Invertebrate organisms, for example, zebra mussels, graze preferentially on larger

bacteria and, in oligotrophic environments can increase bacterial abundance (Cotner et al

1995). In mesocosms, Daphnia can simultaneously consume large amounts of protozoa

and bacteria (Jurgens et al 1997). Despite the importance of the microbial food web in

many aquatic environments, it does not dominate in certain conditions. Massana et al.

(1996) observed inefficient carbon transfer between bacteria and rotifers and zooplankton

in Lake Cisó, Spain.

Bacteria were once viewed primarily as re-mineralizers and decomposers of

organic matter in aquatic ecosystems; however, many food web ecologists now view

bacteria differently, as consumable particles with abundant energy and nutrients, used by

higher trophic levels in the microbial food web (Azam et al. 1983; Vadstein 2000; Heath

and Munawar 2003). Protists, rotifers, and crustacean zooplankton contribute to

bacterivory in lake ecosystems (Sherr and Sherr 1988) especially in oligotrophic, offshore

environments. Protistan bacterivory was greater offshore (>100% of bacterial production) than near-shore (<50% of bacterial production) in Lake Erie (Hwang and

Heath 1997). Higher up the food web in Lake Erie, micro-zooplankton (rotifers)

consumed 71% of bacteria at offshore sites and 56% of bacteria at nearshore sites

(Hwang and Heath 1999). Macrozooplankton (cladocera) can consume up to 95% of the

bacterial pool in certain cases. Wylie and Currie (1991), using radiolabeled carbon,

determined that cladoceran-dominated zooplankton communities can obtain 16-21% of

their carbon input from the bacterial population. DNA may be the source of phosphorus

released by grazing nanoflagellates (Turk et al. 1992). In oligotrophic Lake

Kjelsåsputten, Norway, the majority of grazable phosphorus was bound in bacterial cells

and bacteria were the most important source of phosphorus to bacterivorous zooplankton

(Hessen and Andersen 1990; Heath and Munawar 2003).

Nutrients and carbon can influence bacterial production and abundance in aquatic ecosystems. Using a mathematical model, Thingstad et al. (1997), proposed that organic matter that is normally degradable by bacteria may accumulate in aquatic systems due to increased grazing by protozoa and competition for nutrients with algae. In mesocosms, bacteria were carbon limited; but when released from carbon limitation, they outcompeted algae and virus particles for mineral nutrients (Jacquet et al. 2002).

Viruses can also play a major role in the recycling of nutrients in the microbial food web by impacting bacterial production and abundance. Viruses or viral-like

particles (VLPs) are found throughout the water column and sediments and are often

correlated with bacterial abundance (Drake et al.1998). However, in Lake Erie, Leff et

al. (1999) observed no correlation between VLPs and chlorophyll a concentration.

Viruses can cause lysis in bacterioplankton and phytoplankton cells. The lytic products

are used rapidly by bacterial cells in natural communities (Noble and Fuhrman 1999) for

productivity. Weinbauer and Höfle (1998) found that viral lysis is size-specific

(preferring smaller cells) suggesting that viral lysis may control the size of bacterial cells

in aquatic systems.

In addition to moving carbon through the aquatic food web to higher trophic

levels, bacteria make major contributions to other aquatic ecosystem processes including

secondary production, respiration, and the shunting of nutrients. Bacteria can influence

carbon export from aquatic systems. In oligotrophic, low nutrient systems, heterotrophic

bacterial respiration is apparently greater than primary production resulting in minimal

carbon export to sediments; however, in eutrophic, high nutrient communities, bacterial

production exceeds respiration resulting in carbon accumulation (Cotner and Biddanda

2002). In addition to shunting carbon to higher trophic levels, bacteria can also move

phosphorus to higher trophic levels, depending upon nutrient status (Heath et al. 2003).

In nearshore, eutrophic systems, more carbon and phosphorus are fixed into

phytoplankton and the microbial food web is relatively less important; however, in

offshore, oligotrophic systems, more carbon and phosphorus are fixed to heterotrophic

bacteria making the microbial food web much more important for carbon and phosphorus

fixation and transport to higher trophic levels.

Bacterial Bioenergetics

Bacterial Productivity

Bacterial Productivity (BP) is the rate of growth of a bacterial culture or natural

assemblage. The growth is usually measured as carbon production by the incorporation

of radioactive compounds into biological macromolecules within the bacterial cells. 3H- thymidine and 3H-adenine are incorporated primarily into DNA while 3H-leucine or 3H- valine is incorporated primarily into protein (Jørgensen, 1992a, b). Several studies show similar production rates for 3H-thymidine and 3H-leucine methods (Kisand 1994;

Riemann et al.). Bastviken and Tranvik (2001) showed no difference between bacterial

production (measured with 3H-leucine) during oxic and anoxic conditions.

In the 3H-leucine method (Jørgensen, 1992a,b), the protein fraction is precipitated

onto filters using cold tricholoracetic acid, while the nucleic acid fraction remains

dissolved within the filtrate. According to Chin-Leo (1990), the 3H-leucine method is

more sensitive than the 3H-thymidine method because more leucine is needed for

incorporation into protein than thymidine is needed for incorporation into DNA.

However, if cellular protein turnover is rapid, the 3H-leucine method may overestimate

bacterial productivity. Also, some cyanobacterial species (Nodularia spp.) can

incorporate leucine contributing to an overestimation of bacterial production (Hietanen et

al. 2002).

Controls of bacterial production and productivity appear to vary according to

environmental conditions. Bacterial growth can be expressed as biomass or standing

stock at a given time (bacterial production, possible units = biomass volume-1 or biomass

area-1 such as cells L-1 or μg L-1 or μg m-3) or a rate of bacterial growth (bacterial

productivity, units = μg ml-1 hr-1 or μg L-1 day-1). Several biotic and abiotic factors can

influence bacterial production and/or productivity, including inorganic nutrient and

organic carbon availability, transparency, protistan grazing, and viral lysis. Bacterial production usually increases with increasing trophic state, possibly due to higher bacterial numbers in more nutrient-rich environments (Letarte and Pinel-Alloul, 1991) with large cells (1-3 μm) producing more biomass than small cells (< 1 μm). Bacterial abundance is often higher in eutrophic, nutrient-rich environements. Here, bacteria are less likely to be nutrient limited and are capable of growing larger. With a larger size and biomass, large cells comprise a larger portion of bacterial production. In a study of eight Canadian lakes

and eight Danish lakes, Letarte and Pinel-Alloul (1991) found that primary production

positively influenced bacterial productivity in only large cells; in small cells, bacterial production increased with increasing cell abundance and chlorophyll concentration.

Abiotic factors such as temperature, total organic carbon concentration, and dissolved

organic carbon concentrations did not impact bacterial productivity. Laanbroek and

Verplanke (1986) also found a relationship between bacterial productivity, the rate of

bacterial growth, abundance of attached (but not free-living) bacteria in the Oosterschelde

basin, an estuarine channel in the Netherlands. Scavia et al. (1986) reported a correlation

between bacterial production (biomass) and bacterial abundance in Lake Michigan. More

cells produced more biomass. Even within the same ecosystem, variation in bacterial

production controls can be seen. In the Mediterranean Sea, a one-dimensional coupled

ecosystem model used to stimulate algal and bacterial productivity shows that bacterial

production in the eastern basin is nutrient limited while the production in the western basin is grazer limited (Allen et al. 2002).

Lemée et al. (2002) reported that depth can impact bacterial productivity. Very low production (mean = 0.01 μmolC l-1 d-1) was observed in the Northwest

Mediterranean Sea at depths of 90-130 m. Irradiance of photosynthetically active

radiation (PAR) increases bacterial productivity in the Mediterranean Sea (Morán et al.,

2001). Increased light penetration may stimulate algal carbon production, which in turn

stimulates bacterial production. This relationship was also observed by Scavia et al.

(1986) in Lake Michigan. Their data suggested that bacterial carbon demand could be

met almost entirely by algal exudates. However, in the coastal Northeasten Atlantic

Ocean, Morán et al. (2002) showed that algal release of DOC did not provide sufficient

carbon to supply the observed level of bacterial productivity. Here, bacteria were more

dependent upon allochthonous carbon sources. In Lake Superior, bacterial productivity

represented a greater fraction of primary productivity (0.61-3.26%) when primary

productivity was the largest (Heath and Munawar 2004). While higher light levels can

increase algal and bacterial productivity, ultraviolet radiation inhibited bacterial

productivity in the top five meters of Lake Erie (Wilhem and Smith 2000).

Grazing by protozoa, rotifers, and/or zooplankton and viral lysis can significantly

decrease bacterial productivity in both oceanic and freshwater ecosystems. In offshore

Lake Erie, protists grazed almost the entire bacterial production while nearshore protists

grazed under half of the bacterial production (Hwang and Heath, 1997). Variation in

protist diversity impacted the size distribution of bacteria in a continuous-flow system

(Posch et al., 1999); presence of smaller ciliates results in preferential grazing of smaller

bacteria, thus increasing bacterial cell volume (and possibly production). Using

fluorescently-labeled bacteria (FLB), del Giorgio et al. (1996) showed that grazing rates

on metabolically active bacteria can be four or more times greater than that of inactive

bacteria in the Mediterranean. In water from the Northwest Mediterranean, bacterial

grazing rates increased after exponential growth and that protozoan grazers preferred high-DNA (more metabolically active) bacteria (Vaqué et al. 2001). In Lake Erie, viral lysis can account for 12-23% of bacterial mortality, thus impacting bacterial production

(biomass) (Wilhelm and Smith 2000). In Lake Plußsee (Germany), the mechanism for control of bacterial production (cells/day) shifted with depth – grazing controlled

production in the epilimnion while viral lysis controlled production in the hypolimnion

(Weinbauer and Höfle 1998).

Bacterial Respiration

Bacterial Respiration (BR) is the consumption of carbon by bacteria, under either oxic or anoxic conditions, to produce carbon dioxide. Much less is known about respiration in aquatic ecosystems because more attention has been focused on processes of production (Williams and del Giorgio, 2005). BR is often estimated from a change in oxygen concentration over a period of time or a rate of oxygen depletion (volume/time).

During this time, bacteria utilize the available carbon and oxygen and convert this to carbon dioxide. The change in oxygen concentration can be measured via Winkler titration (APHA, 1985). A conversion factor, the respiratory quotient (RQ), is used to convert oxygen to carbon dioxide. The RQ value is a calculation based on the oxidative respiration of potential organic substances:

-1 RQ = CO2 production (O2 consumption)

RQ values in aquatic ecosystems range from 0.5 for methane to1.33 for glycolic acid

depending upon the substrate respired, with most values ranging between 0.8 and 1.2

(Williams and del Giorgio, 2005). Selecting an appropriate RQ value is important because it can alter the calculation of the respiration rate. The value used in many aquatic systems is 0.82, derived from Søndergaard et al. (1995).

In large areas in the open ocean, respiration rates can exceed production (del

Giorgio et al. 1997), with bacteria contributing most greatly to the high respiration rates,

especially in nutrient limited waters (Williams 1981). However, scientists cannot yet

determine whether the oceans are overall net heterotrophic or net autotrophic (del Giorgio

and Duarte, 2002). The question of net oceanic production vs. respiration remains an

active research topic. Arístegui and Harrison (2002) observed that production rates can

shift in short time periods in the North Atlantic while respiration rates remain more

stable, making it difficult to determine regional metabolic rates.

Many lakes appear to be net heterotrophic, so respiration often exceeds primary

production (Pace and Prairie, 2005) and lakes with larger surface areas contribute more to cumulative global lake respiration. Pace and Prairie also showed that planktonic respiration correlated positively with chlorophyll concentration (r2 = 0.71), total

phosphorus concentration (r2 = 0.81), and DOC concentration (r2 = 0.49).

Bacterial Growth Efficiency

Bacterial Growth Efficiency (BGE) is the ratio of production (BP) to assimilation

(BP + BR), so

BGE = BP (BP+BR)-1

as described by del Giorgio and Cole (1998). Growth efficiencies range from less than

5% to greater than 60% in aquatic ecosystems, with lower values generally observed in

more oligotrophic environments (Sargasso Sea) and higher values observed in more

eutrophic environments (coastal esturaries) (del Giorgio and Cole 1998). Two

generalities in BGE have been observed in aquatic environments: (1) BGE is usually less

than 0.4 and (2) BGE increases with increasing BP. BGE can be calculated from

concurrent measurements of BP and BR (as described above) (del Giorgio and Cole

2000). A high variance is observed between measurements of BGE possibly due to

artifacts of methodology or metabolism (del Giorgio and Cole, 1998).

BGE can be influenced by abiotic and biotic factors, similar to those impacting bacterial production. Eiler et al. (2003) observed that increasing DOC concentrations

increased BP and BGE and shifted community composition in batch cultures of lake

bacteria. Viral lysis can increase the available P and C to Vibrio sp.; but, presence of

viruses decreased the BGE possibly due to increased metabolic demands of breaking down viral lysates (Middelboe et al. 1996). How much variation in BGE is due to

differences in community composition remains unknown. The factors controlling BGE

need to be studied further in more diverse aquatic ecosystems.

Phosphorus in Aquatic Ecosystems

Phosphorus Limitation of Lake Phytoplankton

Phosphorus is often the nutrient that limits algal growth in freshwater lake

ecosystems (Schindler 1974). Schindler and many others determined, after much

controversy, that phosphorus was the nutrient responsible for cultural eutrophication of

The Great Lakes. Eutrophication is the increase in algal biomass due to a change in the

nutrient status of a lake resulting from an increase in nitrogen or phosphorus inputs.

Oligotrophic lakes have low algal biomass and/or production and are often associated

with lower phosphorus and nitrogen levels. Eutrophic lakes have a high algal biomass

and/or production and are often associated with high phosphorus and nitrogen levels.

Cultural eutrophication results when anthropogenic inputs (e.g. from agricultural run-off)

rather than natural inputs (atmospheric precipitation, groundwater inputs, rainfall) alters the trophic state of the lake.

Several experiments and observations showed that phosphorus was the limiting nutrient in freshwater ecosystems. In Canadian experimental lakes, the portion of a lake fertilized with additional phosphorus, carbon, and nitrogen increased algal growth versus the portion of the lake fertilized with only carbon and nitrogen (Schindler 1974). Also, a

strong linear relationship between average summer chlorophyll concentration and spring

total phosphorus concentration in Canadian lakes suggested a relationship between the

two variables (Dillon and Rigler 1974). A similar relationship was observed between chlorophyll, algal production, and total phosphorus in Japanese lakes (Sakamoto 1966).

A trophic state index (TSI), to describe the nutrient and algal status of a lake, was

developed by Carlson (1977) that linked the nutrient status of the lake (oligotrophic vs.

eutrophic) with total phosphorus concentration, chlorophyll concentration, and Secchi

transparency (all surrogates for algal biomass). TSI values range from 0 to 100, with

oligotrophic values from 0-40, mesotrophic values from 40-50, and eutrophic values

above 50.

While phosphorus is the nutrient that limits phytoplankton growth and

phytoplankton growth correlates with total phosphorus concentrations, mechanistically, it

is the presence of readily available phosphorus, as orthophosphate, that actually limits

this growth. Overall, lake phosphate concentrations are extremely low (nanomolar

concentrations in oligotrophic lakes) and traditional chemical methods (e.g. molybdenum

blue method, Murphy and Riley 1962) tend to over-estimate soluble reactive phosphorus

(SRP), or readily available phosphate concentration by up to two orders of magnitude

(Hudson et al. 2000; Rigler 1966; Gao 2002). The Rigler bioassay uses the estimates of phosphate uptake by algae and/or bacteria at different amended phosphate concentrations

to estimate biologically available phosphate concentrations (Rigler 1966). Phosphorus limited communities are characterized by low bioavailability of phosphate. While the

Rigler bioassay may overestimate phosphate concentrations, it is more sensitive (able to

measure lower orthophosphate concentrations) than the molybdenum blue method

(Murphy and Riley 1962) which is complicated by the hydrolysis of dissolved

phosphorus compounds (Rigler 1968).

Phosphorus limitation has been observed in many aquatic environments, including

the Sargasso Sea (Cotner et al. 1997) the oligotrophic eastern Mediterranean Sea

(Thingstad et al. 2005), and Lake Erie. Phosphorus limitation has been identified in lake

plankton populations via rapid phosphate uptake rates (e.g. Currie and Kalff, 1984;

Currie 1990, Gao and Heath 2005), alkaline phosphatase activity (Cotner and Wetzel

1991), C:N:P ratios (Redfield 1958, Elser 1995, Sterner et al. 1998), enzyme-labeled

fluorescence (ELF) (González-Gil 1998), and phosphorus amendment experiments (Gao,

2002).

The most readily available source of phosphorus to phytoplankton is

orthophosphate (Wetzel 2001); however, phosphate can also be cleaved from dissolved

organic phosphorus compounds in the aquatic environment. Phosphate is used to

synthesize energy molecules (eg. ATP), nucleotides, phospholipids, and other phosphate

containing compounds. In addition to phosphate, low molecular weight organic compounds (e.g. phosphomonoesters such as glucose-6-phosphate and phosphoanhydrides such as ATP) and high molecular weight organic compounds (e.g. phosphodiesters such as DNA, RNA, etc.) provide additional sources of phosphorus, especially to algae (Bentzen et al. 1992). However, liberation of the phosphate moiety from these compounds requires an exoenzyme (eg. alkaline phosphatase, or 5’- nucleotidase). Depending upon the lake chemistry, either alkaline phosphatase (APA) in clear lakes, or UV radiation in humic lakes, phosphorus compounds can release orthophosphate (Franko and Heath 1979). However, ultraviolet radiation can also inhibit algal phosphate uptake in surface waters of Lake Erie (Allen and Smith 2002).

In eutrophic lakes, nutrient pulses (natural or anthropogenic) can accelerate algal growth, producing prolific algal blooms often rich with undesirable cyanobacterial species. Herbivorous zooplankton may not be able to assimilate nutrients from these filamentous forms (Kerfoot et al. 1988). Also, some of the undesirable algal species

(Microcystis sp. and Planktothrix sp.) release harmful toxins (Rinto-Kanto et al. 2005).

Toxins can decrease water quality by causing taste and odor problems and prevent grazing of harmful algae by zooplankton and zebra mussels (Kerfoot et al. 1988;

Vanderploeg et al. 2001). Internal cycling of phosphorus can provide additional sources of phosphate including “sloppy feeding” or excretion by zooplankton (Dodds et al. 1991), bacterial lysis via bacteriophage (Wilhelm and Smith 2000), and release of phosphorus from the sediments during anoxic conditions (Mortimer 1942).

The Role of Bacteria in Phosphorus Dynamics

In oligotrophic lakes exhibiting phosphorus limitation, bacteria often out-compete

algae for available phosphate. In oliotrophic Lake Nesjøvatn, Norway, bacteria can take

up 80% of available phosphorus, much more than algae, because the P-transporters of the

algae never achieve P saturation (Vadstein 1993). Lower food web heterotrophs

(protozoa, rotifers, zooplankton) rely on phosphorus rich bacteria, either directly or indirectly (eg. via recycling), for nutrients. Bacteria can take up, retain, use phosphorus

more efficiently, and have higher P requirements than algae (Vadstein 2000; Vadstein et

al. 1988). He suggested that bacteria could use alkaline phosphatase to hydrolyze organic

P sources and could store polyphosphates in granules. Because of these capabilities,

bacteria, especially in oligotrophic waters, may control algal biomass. In this way,

bacteria may indirectly affect a lake’s trophic status.

Earlier studies focused only on zooplankton as a source of P release, via excretion

and “sloppy feeding”. Later research emphasized the role of zooplankton as a trophic

link for phosphorus because zooplankton consume abundant P-rich bacteria. Dodds et al.

(1991) tested whether excretion by zooplankton influenced algal community dynamics.

Adding Daphnia to water samples decreased algal P-uptake but increased uptake by

organisms <1 μm (probably bacteria). Elemental composition of zooplankton species

were determined by in five Dutch lakes (Hessen and Lyche 1991). Values for particulate

carbon, nitrogen, and phosphorus concentrations were 0.3-12 mg L-1, 30-2367 μg L-1, and

3.4-255 μg L-1, respectively. C:P ratios ranged from 14-83, suggesting that P-rich

bacteria may be part of the cladoceran diet. Cladocerans, especially juveniles, exhibited

the greatest P content. In radiolabeled phosphate uptake experiments, over 75% of P-

uptake by certain zooplankton (especially Daphnia) comes from bacterial ingestion with

smaller individuals and species having greater nutrient requirements for growth and,

therefore, take up more P (Hessen and Anderson 1990). Variability in P uptake by

zooplankton can impact other trophic levels. Using evidence from size-fractionated

zooplankton P-uptake experiments in Lake Ontario, larger zooplankton such as Daphnia

may serve as a P sink, decreasing nutrient availability to phytoplankton (Taylor 1984).

A model by Currie (1990) proposed that phosphorus influences both algal and

bacterial abundance, and that bacteria and algae influence each other as well. Rhee

(1972) first noticed competition between algal and bacterial cultures when phosphorus

was depleted. Phosphate uptake by plankton in aquatic environments usually follows

Michaelis-Menten kinetics – initial slow uptake velocity at low substrate concentration

followed by enzyme saturation and maximum uptake velocity (Vmax) at higher substrate concentrations. Some individual algal taxa (eg. Selenastrum capricornutum) do not

follow this pattern (Brown et al. 1978). In eutrophic environments, algae can take up

phosphate more rapidly than bacteria; however, in oligotrophic environments, bacteria

are more successful at phosphate uptake than algae (Currie et al. 1986; Vadstein and

Olsen 1989; Gao and Heath 2006). Cotner and Wetzel (1991) have shown that bacteria

are more successful in obtaining available phosphate due to differences in the parameters

of enzyme kinetics. Bacteria exhibit low Vmax and low Km, so in low phosphate

environments, bacteria reach Vmax more rapidly than algae, allowing for more rapid

uptake of available phosphate. Algae, however, exhibit high Vmax and high Km, so during

opportunistic high phosphate pulses, algae take up phosphate more rapidly and long after bacteria have reached Vmax.

Bacterial physiology can change radically under conditions of phosphorus

limitation. Presence of exoenzymes and cellular P-transport systems can enhance P-

uptake by bacteria in low P environments. Exoenzymes such as alkaline phosphatase

(APA) release phosphate groups from organic compounds (Cotner and Wetzel 1991).

APA is only produced when extracellular phosphate concentration is low. Pit (phosphate

transport system) in E. coli transports phosphate from the environment into the cell via

protonmotive-force. Under low phosphate concentrations, the Pho B protein is produced

in the cytoplasm. Pho B causes induction of the pho regulon (a genetic control of P

metabolism) in P-limited E.coli results in the production of APA (from Pho A gene) and

other enzymes and proteins that enhance cellular uptake of phosphate. In their phosphate

uptake experiments with algal and bacterial cultures, Currie and Kalff (1984) showed no

difference in APA activity between algae and bacteria, suggesting that bacteria are less

likely to be phosphorus limited than algae. Heath (2003) suggested that research is

needed to determine the presence of P-transport systems in natural bacterial assemblages.

Also, more experiments are necessary to investigate the “dual nature of dissolved organic

phosphorus (DOP)”. According to the “dual nature of DOP” concept, DOP can serve as

a source of phosphorus or a source of carbon to planktonic organisms (Heath 2003).

Because cells cannot take up DOP, they depend upon the cleavage of DOP to utilize

either the P or the C moieties. In P-limited environments, DOP can be hydrolyzed by

enzymes (eg. alkaline phosphatase) and phosphate is taken up by the cell. Conversely, in

C-limited environments, DOP can be hydrolyzed by alternative enzymes (eg. 5’-

nucleotidase) and the carbonyl moiety is taken up by the cell.

The Lability of Dissolved Organic Carbon in Aquatic Ecosystems

Organic matter (OM) in aquatic systems is divided into particulate and dissolved

fractions. Different definitions exist for what is considered to be “particulate” and what

is considered to be “dissolved”. In many cases, particulate forms of OM are larger than

0.2 μm such as pollen, bacteria, phytoplankton, and zooplankton. Dissolved fractions of organic matter can include particles less than 0.2 μm, such as virus particles, macromolecules (high molecular weight (HMW)), and small molecules (low molecular weight (LMW)). Particulate and dissolved, both HMW and LMW, forms are derived from autochthonous sources, such as macrophytes, algae and algal exudates, bacteria and

bacterial components, viruses and lytic products, predation, and abiotic and biotic

transformation (Bertilsson and Jones 2003) or allochthonous/terrestrial sources.

The majority of OM in the surface and deep seawater belongs to the dissolved fraction, 60-75% to 75-80%, respectively, and are considered to be low molecular weight

(LMW) forms (Benner 2002). The exact composition of organic matter remains unknown because the resolution and sensitivity of chromatographic analysis is currently limited (Hedges 2002). Recent NMR analysis suggests that carbohydrates and branched alkyl chains may comprise a large portion of ocean water and algal exudates (Hedges

2002). The oceans contain the largest reservoir of organic carbon on the Earth, with

>97% in dissolved forms (Benner 2002).

Bacteria, algae, and viruses are important links in the transfer of carbon to higher

trophic levels. Biologic carbon particulates (bacteria, protists, and zooplankton), rapidly

cycle and move carbon to higher trophic levels in the microbial loop (Azam 1983).

Higher bacterial production and oxygen consumption in Florida lakes and estuaries was observed when phytoplankton production and algal exudates were highest (Coffin et al

1993). Up to 50% of bacterial production in Mirror Lake, NH was supported by photosynthetically produced DOC (PDOC) (Cole et al. 1982). Thingstad et al. (1997) suggested that DOC may accumulate in surface waters due to phytoplankton-bacterial

competition and grazing of bacteria resulting in chemical transformation and vertical

transport of DOC. Movement of carbon from algae and bacteria to zooplankton is not

always an efficient process, especially in highly eutrophic lakes (Havens et al. 2000).

Abundant viruses under culture conditions stimulated recycling of organic matter and

decreased bacterial production and growth efficiency (Middelboe and Lyck 2002).

Hanson et al. (2003) analyzed production-to-respiration ratios of 25 lakes in

Wisconsin and Michigan to determine that most lakes exhibited a negative ecosystem production, suggesting that DOC sources were predominantly of allochthonous origin.

Biddanda and Cotner (2002) observed that bacterial and planktonic respiration rates in

Lake Michigan were a carbon sink and terriginous inputs accounted for 10-20% of lake production. Using mesocosm experiments, Wehr et al (1998) showed that phytoplankton,

cyanobacteria, flagellates, rotifers, and zooplankton production was stimulated by

addition of macrophyte-derived carbon. In a Swedish lake, Jansson et al. (1999)

observed that the total summer bacterial production depended upon input of allochthonous organic carbon sources.

HMW DOM is the source of humic acids in lakes and is probably of allochthonous origin. HWM DOM can serve as a chelating agent that bind Fe and P. making these elements more or less available to organisms (Maranger and Pullin 2003).

These processes can be accelerated with ultraviolet radiation. Phosphate was released by

DOC-P compounds in clear lakes via alkaline phosphatase activity; however, in brown- water lakes, phosphate was liberated from DOC-P compounds via UV radiation (Franko and Heath 1979). Phosphate can be released from DOC-Fe-P complexes with UV exposure, making it more available to organisms in lakes (Franko and Heath 1982). The addition of DOC and Fe to a eutrophic lake increased phosphate uptake and alkaline phosphatase activity of phytoplankton; however, addition of DOC and Fe to a oligotrophic lake decreased phosphate uptake to phytoplankton (Franko 1986), suggesting that the trophic status of the lake may influence DOC processing by organisms. Photoreduction of DOC-Fe-P complexes by UV radiation reduced Fe and released small amounts of P to organisms (Cotner and Heath 1990).

All DOC is not labile, available or useful to all organisms. Labile dissolved organic carbon (LDOC) includes dissolved forms of LMW organic matter that are easily utilized by particular groups of organisms, especially bacteria. In Lake Michigan, Laird and Scavia (1990) observed that LDOC comprised a larger portion of the DOC pool in near-bottom waters (40.2%) during stratification versus 13.8% in near-surface waters.

Their measure of LDOC was calculated from bacterial production and growth efficiency

(assumed 20%). Laird and Scavia also observed greater bacterial numbers in areas with higher LDOC concentrations. They suggested that higher bacterial production in the summer was supplemented by allochthonous inputs of carbon in addition to phytoplankton production.

Potential LMW labile carbon compounds include monosaccharides, sugars, amino acids, amino sugars, and oligonucleotides. The assimilation of dissolved free amino acids (DFAA), dissolved combined amino acids (DCAA), and DNA accounted for 42-

60% of bacteria production in cultures from diverse marine environments (Jørgensen et al. 1993). In the Gulf of Mexico, glucose supported 5-10% of bacterial production at the surface but high molecular weight (HMW) carbon compounds inhibited glucose uptake by bacteria (Skoog et al 1999). In Lake Constance, the concentration of dissolved free monosaccharides (DFCHO) was determined using HPLC. Glucose, galactose, and mannose composed 55-70% of the DFCHO pool and glucose was turned over most rapidly by bacteria (Bunte and Simon 1999). Also in Lake Constance, bacteria mostly consumed the amino acids serine and glutamate and the monosaccharide arabinose

(Rosenstock and Simon 2003). Lability can be seasonal. In Lake Constance, amino acids were preferentially respired by bacteria during spring and glucose was preferentially respired in the summer (Weiss and Simon 1999). As little as 1-3% of the total DOC pool is enough to support bacterial growth in Florida lakes and estuaries

(Coffin et al.1993). Free amino acids comprised 50% of the dry weight of planktonic organisms and account for 1-1000% of bacterial production in diverse ocean and lake environments (Kirchman 2003).

DOC composition may be linked to bacterial community structure. The uptake of

LDOC may be dependent upon bacterial structure since α-proteobacteria exhibited higher amino acid assimilation while Cytophaga-Flavobacteria exhibited higher protein assimilation (Cottrell and Kirchman 2000). Variability in DOC utilization between marine and lake systems may be due to variability in species composition (Arnosti 2003) because oceans tend to be abundant in α-, β- and ε-proteobacteria, and Cytophaga-

Flavobacterium-Bacteroides while lakes tend to be abundant in β- and γ-proteobacteria.

This may be because DOC composition and quality differentially impact bacterial growth rate, respiration rate, rate of enzymatic degradation, and demand for inorganic nutrients

(Findlay 2003). Also, the DOC in marine and lake systems shows a higher degree of lability (22-26%) than river and estuarine systems (9-10%) (del Giorgio and Davis 2003).

Understanding the role of carbon compounds in aquatic systems is both locally and globally important. Large aquatic ecosystems, especially the oceans, may serve as a potential carbon sink for increasing CO2 production due to global warming (Hedges

2002). Downward movement of DOC in the ocean supports respiration, even in the deep waters (Arístegui et al 2002). The key to understanding and regulating global carbon processes will require knowledge of DOC sources and factors regulating the production of DOC (Benner 2002). Improved knowledge of bacterial community structure, as the primary consumers of DOC, and their functions will increase understanding of how carbon is cycled and processed in aquatic ecosystems (Sinsabaugh and Foreman 2003).

The Microbial Shunt Hypothesis of Phosphorus Apportionment

DOC compounds can also influence nitrogen and phosphorus cycles in aquatic ecosystems. Gardner et al. (1996) showed that HMW DOM compounds stimulate bacterial uptake of ammonium or DFAA in the Mississippi River. Gao and Heath (2005) observed higher bacterial P quotas (ie. bacterial P content per cell) and phosphate uptake velocities in oligotrophic environments having low LDOC concentrations, and, conversely, lower P quotas and phosphate uptake velocities in eutrophic environments having high LDOC concentrations. Similar patterns were also observed under laboratory culture conditions. Pseudomonas fluorescens, was grown in the low phosphate conditions with the varying glutamate concentrations as the sole carbon source. Cells grew slowest and exhibited the highest P-quotas in culture environments with the lowest glutamate concentrations (Gao 2002; Gao and Heath 2005). These findings suggest that bacteria in oligtrophic environments may have lower nutrient use efficiency (NUE) and tend to conserve their cellular P (ie. have a high P quota) when severely C-limited. NUE describes the ratio of carbon production or assimilation to the concentration of nutrients utilized (Vitousek 1982). Cells in more oligotrophic environments tend to exhibit a lower

NUE with less carbon production for the amount of nutrients utilized. Cells in more eutrophic environments tend to exhibit a higher NUE with greater carbon production for the amount of nutrients utilized.

The Microbial Shunt Hypothesis of Phosphorus Apportionment (Heath et al.

2003; Gao and Heath 2005) proposes two metabolic states for bacterioplankton: under low LDOC conditions (eg. <50 μM), bacteria are in a Storage State and exhibit slow

growth, high P quotas, and rapid phosphate uptake; under high LDOC conditions (eg. >

50 μM), bacteria are in a Growth State and exhibit high growth rates, lower P quotas, and modest phosphate uptake. According to the MSH, bacterioplankton in oligotrophic environments (ie. environments having lower LDOC) are in a Storage State and bacterioplankton in eutrophic environments (ie. environments having higher LDOC) are in a Growth State. Bacteria in the Storage State are more likely to be successful in competition with algae for limited phosphate resources due to having a lower Km of Pi-

transport.

Gao and Heath (2005) observed shifts in enzyme parameters with changes in

LDOC concentration. As an extension of the MSH, these shifts can influence the

apportionment of phosphate to bacteria and algae. The available phosphate pool can be

differentially apportioned to algal (> 1.0 μm) and bacterial fractions (< 1.0 μm, > 0.2 μm)

based on changes in enzyme parameters that are influenced by trophic state and/or LDOC

concentrations. In oligotrophic and/or lower LDOC conditions, the bacterial Km decreases allowing for more rapid uptake of phosphate (Gao and Heath 2005). Here, a greater portion of the available phosphate pool will be apportioned to the bacterial fraction. In higher LDOC conditions, the bacterial Km increases resulting in a slower

phosphate uptake rate. Vmax increased under higher LDOC conditions, although not

significantly. Here, a greater portion of the available phosphate pool will be apportioned

to the algal fraction. The experiments and observations of Gao and Heath (2005) have

shown that labile carbon does influence enzyme parameters in bacterioplankton. Now, it

becomes important to understand the mechanism(s) for the shifts in enzyme parameters.

The current limitation to discerning the metabolic mechanism(s) of the MSH is the lack of knowledge as to which DOC compounds are actually labile to bacteria. The

Pit transport system and/or Pho regulon, the metabolic and genetic systems regulating phosphate uptake into bacterial cells, can regulate the two different metabolic states of bacteria (as proposed by the MSH). Also, these systems may be coupled to carbon uptake systems (eg. Catabilite Responsive Proteins (CRP)) and linked due to the dual nature of DOP compounds, whereby use of both the phosphoryl and carbonyl moieties may occur (Heath 2003). In more oligotrophic environments, activation of the Pit transport system and/or Pho regulon may occur in natural bacterial assemblages.

Possibly, the carbonyl moiety from the hydrolyzed dissolved organic phosphorus (DOP) compounds will be utilized by the bacterial cell in addition to the released phosphate.

This action may explain the low carbon-high P quota and high P uptake phenomenon hypothesized by the MSH. In addition, this action may also explain the hypothesized carbon and phosphorus co-limitation in some oligotrophic environments (eg. offshore

Lake Erie). In eutrophic environments with abundant phosphorus, activation of the Pit transport system and/or Pho regulon may be inhibited.

Rationale

Introduction to the Study Site: Lake Erie

Lake Erie is most productive Laurentian Great Lake because it is the warmest, shallowest, and southernmost. The lake supports the largest freshwater commercial and recreational fisheries in the world (State of the Great Lakes 2003). It is the smallest (by

volume) and geologically oldest of the Laurentian Great Lakes. Due to the high

productivity of Lake Erie, it supports the largest human population in The Great Lakes.

Four US states (Michigan, Ohio, Pennsylvania, New York) and the province of Ontario,

Canada border Lake Erie. The following major cities impact the Lake Erie watershed –

Toledo, Cleveland, Erie, and Buffalo, in the United States, and, Windsor and London, in

Canada. The lake receives the majority of water flow from Lake St. Clair via the Detroit

River (5300 m3/s) (Government of Canada and USEPA 1995) and empties into Lake

Ontario via Niagra Falls. Other tributaries include the St. Clair, Clinton, Detroit, Rouge,

Maumee, Black, Cuyahoga, Ashtabula, and Buffalo Rivers plus the River Raisin and

Presque Isle Bay. Water retention time is short (2.6 years) relative to the other Great

Lakes (6-191 years) (State of the Great Lakes 2003).

Lake Erie is divided into three distinct basins – western, central, and eastern. The

mean depths of the individual basins are 7.4 m (western), 18.5 m (central), and 24.4 m

(eastern) (Bolsegna and Herdendorf 1993). The western basin is very shallow and does

not thermally stratify in the summer. The central basin is relatively shallow and remains

thermally stratified throughout the summer with a shallow, warm hypolimnion. The

eastern basin is thermally stratified throughout the summer; but, it has a deep, cold

hypolimnion with the maximum depth of 64 m. The western basin is nutrient rich

resulting in algal proliferation, especially of undesirable and potentially toxic species

such as Microcystis, Oscillatoria, Planktothrix, and others (Rinto-Kanto et al. 2005;

Ouellette et al. 2005). Due to its shallow, warm hypolimnion, the central basin is prone

to late summer hypoxia and/or anoxia resulting from high oxygen depletion rates. The

offshore waters of the eastern basin are oligotrophic and lack the harmful algal blooms

found in the western basin.

Environmental Problems in Lake Erie

Due to its highly urbanized population and low water volume, Lake Erie is more

vulnerable to environmental changes than the other Great Lakes. Major ecological and

economical environmental problems impacting Lake Erie include cultural eutrophication,

invasion of non-indiginous species, and recurrence of central basin hypoxia.

Eutrophication of Lake Erie was the change in the lake’s trophic status caused by

excessive anthropogenic loading of nutrients, especially growth-limiting nutrients. The

definition of eutrophication also implies above average primary production (Wetzel

2001). Recent introduction of non-indigenous species primarily enter Lake Erie and the other Great Lakes via ballast water exchange from transoceanic vessels. Examples of problematic non-indigenous species include zebra and quagga mussels (Dreissena

polymorpha and Dreissena bugensis), the round goby (Neogobius melanostomus), and the spiny water flea (Bythotrephes cederstroemi). The large region of the central basin exhibiting hypolimnetic hypoxia late in the summer and is called the “dead zone”.

Cultural Eutrophication

Despite its economic and ecological importance, Lake Erie was and continues to

be threatened by cultural eutrophication. Anthropogenic nutrient loading into the lake

from industrial, agricultural, detergent, and sewage origins resulted in prolific growth of

benthic and pelagic algae in the late 1960s and early 1970s. The algal species best

adapted to grow under high nutrient conditions are the undesirable filamentous

cyanobacteria (Anabaena, Oscillatoria, Aphanizomenon, and Microystis).

Cyanobacterial species are grazed less frequently by zooplankton than non-filamentous, eukaryotic, green algal species (Kerfoot et al. 1988) resulting in inefficient nutrient transfer to higher trophic levels. In addition, decaying algal blooms caused water quality problems including odor and taste issues. For these reasons, the traditional view of

eutrophication as a “phytoplankton problem” shifted to a “food web problem” and Great

Lakes research efforts centered on the control of algae as a means of controlling food

web efficiency.

In the 1960s and 1970s, a controversy emerged over algal growth in lakes. Was

algal growth limited by carbon, nitrogen, or phosphorus? Evidence for organic carbon,

inorganic carbon, nitrogen, or phosphorus limitation was observed in specific lake environments (Wetzel, 2001). However, in 1974, Schindler, using large-scale fertilization experiments in the Canadian Experimental Lakes region, was able to show that phosphorus was most often the limiting nutrient to algae in lakes. Increasing algal

productivity was only observed in the phosphorus-enriched lake and not in carbon- and/or

nitrogen- enriched lakes. These results prompted United States and Canadian

management agencies to enact restrictions to phosphorus loading to Lake Erie to control

primary productivity. Nutrient amendment assays showed The Great Lakes to be P-

limited (Schelske 1978).

The bi-national Great Lakes Water Quality Agreement of 1978 (amended 1987)

set guidelines, regulations, and targets for phosphorus loading into the Great Lakes

Ecosystem (IJC 1987). Previous to GLWQA, phosphorus loads were estimated as greater

than 20,000 metric tonnes per year and GLWQA set Lake Erie target loadings for Lake

Erie at 11,000 metric tones per year (Dolan 1993) and target concentrations for total

phosphorus of 15 μg L-1 in the western basin and 10 μg L-1 in the central and eastern

basins (State of the Great Lakes 2003). These loading targets have been met for most years since GLWQA was implemented with yearly variation due to weather patterns: years with increased precipitation reflected increases in phosphorus loading (Dolan

1993). Despite the acceptable decreases in phosphorus loading, Lake Erie is the only

Great Lake that has failed to consistently achieve target total phosphorus concentrations.

In most large lakes of the world, phytoplankton biomass is low, reflecting more oligotrophic conditions (Reynolds et al 2000); the phytoplankton in shallower lakes are more susceptible to changes in nutrient additions, for example, eutrophication in Lake

Erie. Improvements in water quality in Lake Erie have been identified since the enactment of GLWQA. Phosphorus loadings and concentrations have decreased, despite inability to reach target total phosphorus concentrations (Dolan 1993; State of the Great

Lakes 2003). Algal biomass decreased by 50 to 90% in all three basins of Lake Erie from

1970 to the mid-1980s (Makarewicz 1993). Nicholls and Hopkins (1993) showed that

this decrease in phytoplankton density correlated best with total phosphorus reduction in

the western basin. The correlation was less strong in the central and eastern basins. In

mesocosm studies, Holz and Hoagland (1996) observed that shifts in phytoplankton

composition due to reduction in phosphorus concentrations are reversible and predictable.

Non-Indigenous Species

Many non-indigenous or exotic species have established populations within the

Lake Erie ecosystem. Examples of exotic invaders include zebra and quagga mussels

(Dreissena polymorpha and Dreissena bugensis), the round goby (Neogobius

melanostomus), and the spiny water flea (Bythotrephes cederstroemi). Exotic species have harmful effects on the ecosystem they enter. Zebra and quagga mussels, the round goby, and spiny water flea all impact the Lake Erie food web. Round gobies impact small mouth bass populations be feeding on their eggs. In addition, they compete with game fish for food resources and habitat, resulting in decreased fish yields. The spiny

water flea competes with indigenous zooplankton for phytoplankton food sources and is, in turn, inedible to small fish, due to the long, spiny tail. The zebra and quagga mussels

are the most destructive to the ecosystem, resulting in large economic and environmental

impacts. Mussels cause biofouling by clogging water intake and sewage pipes. They

choke out native mussel species by attaching to their shells, preventing filter feeding. In

addition, mussels are efficient filter feeders and clear the water column of large volumes

of phytoplankton, making algae unavailable to zooplankton and small fish.

Zebra mussels originated in Eurasia. Mussel larvae were most likely transported

to the Great Lakes via ballast water in cargo ships in 1987 or 1988 (McMahon 1996).

The mussels were well-established and abundant in Lake Erie by 1990. The mussels

impact phytoplankton, zooplankton, macrophyte, and bacterioplankton communities.

Following the introduction of zebra mussels, populations of phytoplankton in Lake Erie

decreased by up to 90% (Nicholls and Hopkins 1993), Secchi depth increased by 100%

(Holland 1993) and chlorophyll-a concentration decreased by up to 70% in Saginaw Bay

(Fahnenstiel et al. 1993). Vanderploeg et al. (2001) observed preferential feeding by

zebra mussels and rejection of blue-green algae (Microcystis sp.) as pseudofeces. Toxic

Microcystis is more likely to be expelled as pseudofeces than non-toxic Microcystis strains (Dionisio Pires and Van Donk 2002). MacIsaac et al. (1995) observed a 50-70% decrease in zooplankton abundance in Lake Erie following zebra mussel introduction.

Increased water clarity resulted in proliferation of macrophytes including Cladophora

(Skubinna et al. 1995). In addition to phytoplankton, larger bacteria are quickly filtered by zebra mussels and used as a food source (Cotner et al. 1995). With increasing light penetration, zebra mussels may be “benthifying” Lake Erie by shifting production from the pelagic zone to the benthic zone.

The greatest impact of zebra mussels can be their ability to alter local nutrient status. High zebra mussel densities increased soluble reactive phosphorus (SRP) and decreased phosphate uptake to bacteria and phytoplankton within mesocosm enclosures in Saginaw Bay (Heath et al. 1995). Their major ecosystem influence may be as

“keystone mineralizers” of important nutrients such as nitrogen and phosphorus, especially in localized, short-term environments. By altering local water chemistry, zebra mussels can enhance the growth of unfavorable cyanobacteria (Arnott and Vanni 1996).

Hypoxia

Causes and Consequences

Oxygen is necessary for fish and many invertebrates to live in aquatic ecosystems.

Hypoxia is the condition of low oxygen that can occur when the rate of oxygen

consumption exceeds the rate of oxygen production. Hypoxia in lakes often results from

a combination of physical, chemical, and biological characteristics. Warm, shallow,

thermally stratified lakes with a shallow hypolimnion are more susceptible to hypoxia.

As the surface waters of the lake heats in the summer, thermal stratification of the water

column occurs. Higher water temperatures are at the surface while cooler water

temperatures are in the bottom waters. This unequal warming of the water column

creates a density difference between epilimnetic and hypolimnetic waters and prevents

mixing. Algal blooms can occur at the surface, especially when nutrients are readily

available. Algae fall through the water column, die, and serve as a source of organic

matter to decomposers in the bottom waters. Organisms, primarily bacteria, respire some

or all of the available oxygen in the hypolimnion (Wetzel 2001). The oxygen cannot be

replenished from upper waters via mixing and hypoxia or anoxia results. Shallow

hypolimnia are more likely to become hypoxic due to a smaller water volume with less

initial available oxygen.

In lake sediments, phosphate becomes bound to iron oxides in the sediments.

Once hypoxia occurs, at around -200 mV or concentration of DO < 0.2 mg L-1, Fe (III) is

reduced in the sediments where it reacts with sulfide. Precipitation of FeS occurs resulting in the release of free phosphate into the water column (Mortimer 1942). This

can further stimulate the growth of algae in surface waters if it is mixed into upper waters

-1 -1 by storm events. Hypoxic ([O2] < 4 mg L ) and anoxic ([O2] < 0.2 mg L ) conditions

can make it difficult for organisms to survive. As a result, organisms such as fish or zooplankton, may move from oxygen depleted waters (Ludsin et al. 2006; Liebig et al.

2006). Burrowing invertebrates such as mayflies cannot move and may die when localized oxygen concentrations fall below 1.2 mg L-1 (Bridgeman and Schloesser 2003;

Winter et al. 1996).

Problems with hypoxia are abundant throughout the world and impact the following ecosystems: Lake Erie, Chesapeake Bay, Gulf of Mexico, Long Island Sound,

Black Sea, Caspian Sea, Japanese harbors, and many Swedish fjords (Diaz 2001). Even anoxic conditions during the Permian period are thought to be the cause of mass extinctions in the oceans (Wignall and Twichett 1996). In most cases, hypoxia is associated with population, urbanization, and eutrophication and/or areas of runoff, upwelling, and climate change (Grantham et al. 2004). Dodds (2006) suggests that controlling nitrogen and phosphorus inputs from the Mississippi River would decrease hypoxia in the The Gulf of Mexico. Excess nutrient inputs of nitrogen and phosphorus led to increases in primary production. Abundant algae initially led to increases in fish production. Once organic matter built up, either from decaying algae or allochthonous sources, it was decomposed rapidly by bacteria in the water column. Thermal or haline stratification prevented mixing of water layers due to density differences. As a result, respiration by heterotrophic bacteria and other organisms in the bottom waters depleted oxygen faster than it was being supplied, and hypoxia resulted. Cole et al. (1993)

observed greater bacterial abundance and cell size in anoxic waters relative to oxic waters

in 20 stratified lakes. Fish, zooplankton (Dawidowicz et al. 2002) and ciliates (Bark

1985) will relocate to upper waters during anoxic conditions. Zebra mussels, however,

can withstand low oxygen levels to about 1-2 mg L-1, although their growth is delayed

(Yu and Culver 1999). In upwelling areas, the movement of oxygen depleted waters can

result in massive fish kills (Grantham et al. 2004).

History of Hypoxia in Lake Erie – Past and Present

Hypoxic conditions in Lake Erie have been observed since the 1930s. The area of

Central Basin hypoxia increased in the 1970s, often covering nearly the entire Central

Basin. Because many organisms cannot survive in hypoxic waters, they either relocate to

well-oxygenated waters (eg. fish, zooplankton) or perish in the oxygen-depleted

sediments (eg. mayflies or other invertebrates), the Central Basin was nick-named “the

dead zone”. Increasing urbanization and eutrophication contributed to formation of

anoxic conditions. Lake Erie’s anoxic factor (AF), or the approximate duration of

anoxia, is 20 days per summer season with an areal hypolimnetic oxygen deficit of 380.0

mg m-2 day-1 (Nürnberg 1995). AF was strongly correlated with total phosphorus

concentrations.

Changes in agricultural practices, improvements in sewage treatment, regulation of point source nutrient discharge, and prohibition of phosphorus detergents improved

water quality in Lake Erie. The area of hypoxia decreased during the 1980s. In the

1990s, despite decreases in phosphorus loading, Lake Erie failed to reach its target total

phosphorus levels (15 μg L-1 in the western basin and 10 μg L-1 in the central and eastern

basins (State of the Great Lakes 2003)). In addition, decreasing oxygen depletion rates

leveled off and failed to reach their targets. In the Central Basin, an expanding area of

hypoxia was observed. Charlton et al. (1993) proposed reasons for the continuation of

hypoxia including internal phosphorus cycling stimulating microbial growth, delayed

response to phosphorus reductions, or basin morphometry. Lake Erie may have delayed

resilience to the phosphorus loadings of the past. Given reduction in phosphorus loading

it is reasonable to expect evidence of resilience including increased Secchi transparency,

reduced offshore algal populations, and lower phosphorus concentrations (Charlton et al.

1993). Boyce et al. (1987) state “As a result of scientific study of Lake Erie, we are now

engaged in one of the most important large-scale ecological experiments ever undertaken

– to determine if and when eutrophic conditions can be reversed in a large lake through control of loadings.”

Despite previous studies of lakewide hypoxia in Lake Erie including Project Hypo in the 1970s (Charlton, 1999) and the International Field Year of Lake Erie (IFYLE) in

2005, many questions remain. Does hypoxia cause significant shifts in the Lake Erie food web? What are the nutrient conditions in the Central Basin? What is the nutrient status and activity of Central Basin algae? Does hypoxia shift bacterial community structure resulting in increased activity of certain taxa or taxonomic groups? Many

environmental problems still plague Lake Erie, despite improvements in nutrient loadings

and water quality. According to the State of Lake Erie report (2003), “the state of the

Lake Erie ecosystem is mixed-deteriorating”. Zebra mussels and other non-indiginous

species continue to have wide-scale ecosystem impacts. Despite decreases in phosphorus

loading, Lake Erie is the only Great Lake that has failed to reach target total phosphorus

concentrations (15 μg L-1 in the western basin and 10 μg L-1 in the central and eastern basins) (State of the Great Lakes 2003).

Scientists and managers are now concerned that new organic matter sources

(from robust production by zebra mussels and/or allochthonous sources) and high total

phosphorus levels may promote conditions (eg. algal blooms, high energy, and high

nutrient levels) that contribute to a re-emergence of oxygen depletion in the central basin.

During summer of 2005, a team of research scientists conducted the largest investigation

of nutrients, pollutants, food web structure, harmful algal blooms, and oxygen depletion

on Lake Erie. 2005 was the International Field Year of Lake Erie (IFYLE). The

investigation was funded by the National Oceanic and Atmospheric Administration

(NOAA), Great Lakes Environmental Research Laboratory (GLERL), US Environmental

Protection Agency (EPA), Environment Canada, and national and state Sea Grant

programs. As part of this effort, we were funded to investigate the role of

bacterioplankton communities in the phosphorus and carbon dynamics of Lake Erie. The

goal of our research was to identify differential responses of bacterial assemblages, in

terms of bacterial bioenergetics (BP, BR, and BGE) and community structure, to available phosphorus and LDOC in diverse environments of Lake Erie (eg. varying trophic state, depth, nearshore vs. offshore). Extensions of this study investigated the significance of pelagic heterotrophic bacteria to central basin oxygen depletion.

Statement of the Purpose of this Research

Currently, little is known about the composition and role of DOC in lake ecosystems, especially the Laurentian Great Lakes and Lake Erie, and even less is known regarding the response of heterotrophic bacteria to labile carbon compounds. Some questions include: 1) Do different bacterial communities utilize similar DOC compounds, or is lability equal among different communities?; 2) Which DOC compounds most stimulate bacterial productivity and impact bacterial number and size?; 3) Is LDOC seasonally or vertically constant or variable in lake ecosystems?; 4) Is DOC lability dependent upon trophic status?; and 5) Does LDOC influence phosphorus dynamics in lake bacterioplankton? Answering these questions may help to elucidate the mechanism of the MSH observed in lake bacterioplankton.

The purpose of this dissertation was to examine the role of heterotrophic bacterioplankton in large scale ecosystem processes in Lake Erie including carbon dynamics, phosphorus dynamics, and the formation of hypoxia. Questions that will be addressed include: 1) Does the microbial shunt hypothesis of phosphorus apportionment, proposed by Heath et al. (2003) adequately explain the behavior of Lake Erie bacterioplankton?; 2) Is the lability of dissolved organic carbon constant with variations in bacterioplankton communities?; 3) What abiotic and biotic factors can control bioenergetic processes (productivity, respiration, and growth efficiency) in lake bacterioplankton?; and 4) Do pelagic bacterioplankton contribute significantly to Lake

Erie central basin hypoxia?

Dissertation Objectives:

1. To use field observations and controlled experimental manipulations of Lake

Erie bacterial communities to examine the assertions of the microbial shunt

hypothesis of phosphorus apportionment, proposed by Heath et al. (2003).

The results of objective #1 will be presented in chapters 2 and 3:

a. Apportionment of Phosphate between Bacterioplankton and

Phytoplankton in Lake Erie. Meilander, T. T. and R. T. Heath. In

review as a chapter for The Pulse of Lake Erie.

b. The Role of LDOC to Phosphate Uptake in the Lake Erie

Bacterioplankton Assemblages. Meilander, T. T. and R. T. Heath. To

be submitted to the Canadian Journal of Fisheries and Aquatic

Sciences

2. To examine the degree of lability of DOC compounds to lake

bacterioplankton via manipulations of Lake Erie communities.

The results of objective #2 will be presented in chapter 4:

The Availability of Dissolved Organic Carbon to Lake Bacterioplankton.

Meilander, T. T., Clevinger, C. C., and R. T. Heath. To be submitted for

review by the Canadian Journal of Fisheries and Aquatic Sciences

3. To investigate the biotic and abiotic factors controlling the bioenergetics

(productivity, respiration, and growth efficiency) of Lake Erie heterotrophic

bacterioplankton. Investigation of objective #3 was included in the

International Field Year of Lake Erie (2005).

The results of objective #3 will be presented in chapter 5:

Factors Influencing Bacterioplankton Bioenergetics in Lake Erie. Meilander,

T. T., Hurley, J. P., and R. T. Heath. To be submitted to the Canadian Journal

of Fisheries and Aquatic Sciences.

4. To observe the activity of heterotrophic bacterioplankton in the central basin

of Lake Erie during oxic and hypoxic conditions; and, using these

observations, to determine the role of bacterioplankton in the causes and

consequences of central basin hypoxia. Investigation of objective #4 was

included in the International Field Year of Lake Erie (2005).

The results of objective #4 will be presented in chapter 6:

a. Life in the Dead Zone. Meilander, T. T., Fitzpatrick, M., Munawar,

M., and R. T. Heath. To be submitted as a chapter for The Pulse of

Lake Erie.

Summary of Dissertation Findings

● Support for the microbial shunt hypothesis of phosphorus apportionment was variable.

From the data from the 2003 and 2004 field seasons, strong support for the MSH will be

presented. During these field seasons, phosphate uptake and bacterial P quota were

inversely correlated with LDOC. The data from the 2005 field season shows weaker

relationships between LDOC and bacterial P quota and phosphate uptake.

● Addition of a new metabolic state, an “Inactive State”, is proposed as an extension of

the MSH. Bacterial cells in low LDOC conditions may exhibit lower growth rates, variable P quota, and lower phosphate uptake rates. These cells, in the Inactive State,

may enter dormant conditions following the rapid uptake observed in the Storage State,

or, may enter an Inactive State due shifting community composition.

● Glucose is a likely component of the LDOC pool. Addition of glucose to carbon depleted bacterial assemblages most often increased bacterial productivity and decreased the bacterial uptake rate constant. Addition of glucosamine and lysine exhibited limited effects. These results are consistent with the MSH.

● Different bacterial assemblages (from different locations, depths, and communities of different nutrient status) utilized LDOC differently. These differences in LDOC utilization may depend upon bacterial community structure.

● Bacterial productivity (BP) and bacterial growth efficiency (BGE) correlated most strongly with estimates of phytoplankton community condition (chlorophyll a concentration and trophic state index). BP also correlated with bacterial abundance. In limited situations, BR correlated with LDOC. Bacterial bioenergetic processes in Lake

Erie are most likely controlled by the condition of the phytoplankton community and/or algal-bacterial coupling.

● Throughout most of summer 2005, BP and BGE were high in the western basin of

Lake Erie and lower in the central and eastern basins; however, late in the season, the BP per cell and BGE in the central basin increased and resembled those of the western basin.

This shift in bacterial bioenergetics may be associated with transport of nutrients,

bacteria, or phytoplankton from the western basin to the central basin late in the season.

● The “dead zone”, an area of central basin hypolimnetic hypoxia was not “dead” for

bacteria and algae. Even under reduced oxygen conditions, members of the microbial

community were abundant and active in the hypoxic hypolimnion.

● Aquatic heterotrophic bacteria played a major role in phosphorus dynamics, bacterial

bioenergetics, and potentially contributed to the formation of hypoxia in Lake Erie.

CHAPTER II

Distribution and Apportionment of Phosphate Between Bacterioplankton and

Phytoplankton in Lake Erie

Abstract

The effects of labile dissolved organic carbon (LDOC) on phosphorus dynamics of plankton were studied at diverse stations during synoptic surveys of Lake Erie aboard the CCGS Limnos in August of 2003 and June, July, and August of 2004. The sites represented diverse LDOC regimes, trophic states, and basin locations. LDOC values ranged from 13.7 to 126.5 μM. At higher LDOC concentrations, a greater portion of the

phosphorus pool was distributed to algae, and at lower LDOC concentrations, a greater

portion of the phosphorus pool is distributed to bacteria. Bacterial P-quota was greatest

at stations with the lowest LDOC concentrations. Phosphate uptake by bacteria was greater at lower LDOC sites and lower at higher LDOC sites. LDOC concentration was not related to trophic state index (TSI), calculated based on chlorophyll a concentrations.

These observations support the microbial shunt hypothesis (MSH). Phosphate apportionment to bacteria was consistent and independent of LDOC concentrations. This

observation varies from the MSH.

44 45

Introduction

The availability of phosphorus to phytoplankton is generally regarded as the major factor that controls phytoplankton production in the North American Great Lakes

(Schelske 1979) and many other freshwater ecosystems around the world (Dillon and

Rigler 1974, Sakamoto 1966). Because in large lake ecosystems phytoplankton are virtually the sole source of bioenergy necessary to support growth of organisms at higher trophic levels, the control of available P to large lake ecosystems has become the goal of watershed management plans such as the Great Lakes Water Quality Agreement of 1978 as amended in 1987 (IJC 1987). Especially in Lake Erie, the control of available P is the basis of controlling the eutrophication process that leads to noxious phytoplankton blooms, which damage water quality and its usefulness for human consumption.

Although phosphorus can be released from various complex dissolved organic and inorganic compounds (Francko and Heath 1979), the form of phosphorus that is most readily and directly available to phytoplankton is orthophosphate (Wetzel 2001). Low concentrations of orthophosphate can limit algal growth and primary productivity in freshwater ecosystems (Elser et al. 1990). Although certain P-containing compounds besides orthophosphate can and do serve as sources of P to lake water organisms, these compounds apparently satisfy only a fraction of the P-demand of these organisms at the base of the food web (Heath 1986), the majority of P-demand generally is satisfied by the available orthophosphate (Pi). Identification of those factors that influence Pi availability to phytoplankton is critical both for scientific understanding of the processes involved in eutrophication and for rational design of watershed management plans.

Traditionally, the availability of Pi has been seen as a geochemical process dependent on physical events in the watershed. Orthophosphate becomes available when it is loaded into the lake either through natural events (e.g. snowmelt runoff or rain storms) or by direct human input from industrial, municipal or agricultural activities. The availability of Pi to osmotrophs, organisms, such as bacteria and phytoplankton that take up soluble materials through membranes, in turn, is regulated by the oxidation-reduction potential of the water at the surface of sediments (Stumm and Morgan 1996) and by the affinity of organisms for the Pi present in the water (Cotner and Wetzel 1992). That is, the likelihood that any given organism may receive Pi is determined both by geochemical conditions and by competition for the chemically available forms of P. The apportionment of available Pi between bacterial and algal species is ultimately a function of the differences in the parameters of their enzyme-like membrane transporters.

Our recent investigations indicated that the apportionment of available Pi and distribution of particulate phosphorus between bacterioplankton and phytoplankton depended on the availability of labile dissolved organic compounds (LDOC) to bacterial assemblages (Gao and Heath 2005). We reported that when LDOC was lower than 50

μM bacterial growth was slow, cell biovolume was small, and cell P-quota (i.e. P-content of the cells) was relatively high and the phosphate uptake rate was rapid. As LDOC concentration increased above 50 μM, bacterioplankton growth rate and biovolume increased, but cell P-quota and phosphate uptake velocity decreased due to a decrease in

Vmax and an increase in the Km of bacterial phosphate transporters of the species in the bacterial assemblage. Based on observations both of laboratory cultures of Pseudomonas

fluorescens and observations of natural assemblages in a variety of small lakes and

limited stations in Lake Erie, we proposed that LDOC controls the metabolic status of

bacterioplankton: above about 50 μM LDOC cells are in a Growth State with modest P-

quotas and relatively low phosphate uptake rates; below 50 μM LDOC cells are in a

Storage State with very high P-quotas and rapid phosphate uptake rates. We also

suggested that alterations in enzyme parameters of bacterial phosphate uptake led to a

greater bacterial competition for available Pi, which in turn affected the relative

apportionment of Pi to phytoplankton. We call this hypothesis the Microbial Shunt

Hypothesis (MSH) of phosphate apportionment to plankton. However, that study was

confined to a very small number of stations in Lake Erie (Sandusky Bay and one offshore

station in the Sandusky subbasin), and all of the field studies were done in late June or

early July.

The purpose of this investigation was to observe whether Pi apportionment and

phosphorus distribution to plankton in Lake Erie was consistent with the MSH at many

locations over a wide range of trophic states, LDOC concentrations, and habitats in Lake

Erie, many revisited throughout the summer. We aimed in this way to assess the general

usefulness of MSH, spatially, temporally, and in relationship to overall bioenergy

availability (ie. trophic state). This study was based on field observations during August of 2003 and June, July, and August of 2004 as part of synoptic cruises aboard the CCGS

Limnos. Our site selection included stations from western, central, and eastern basins and stations from near shore and offshore locations.

Methods

Site Characterization

Five stations were investigated during August of 2003 and seven to ten stations

were investigated during June, July and August of 2004; stations were located in the

western, central and eastern basins of Lake Erie. Sampling and initial field work was

performed aboard the CCGS Limnos. Integrated water samples (from surface to one

meter above the thermocline when present) were obtained from the sites identified on the

map of Lake Erie in Figure 1. Dates, locations, sampling depths and limnological

characteristics of each of station are provided in Table 1. Bacterial structure and activity

(bacterial abundance (BA), cellular bacterial biovolume (CBV), total bacterial biovolume

(TBV), bacterial productivity (BP)), phosphorus dynamics (phosphate uptake,

apportionment, and distribution to algae and bacterioplankton), labile dissolved organic

carbon (LDOC) concentration, and trophic state index were determined for each station

investigated. Variables were compared as groups with varying LDOC concentrations –

very low (0-29 μM), low (30-49 μM), moderate (50-69 μM), high (70-89 μM), and very

high (90-130 μM) to investigate the assertions of the Microbial Shunt Hypothesis.

Regression analysis and ANOVA post hoc LSD (p < 0.05) compared the significance of

relationships between the LDOC groups.

Bacterial Structure

Bacterial samples were collected in triplicate, fixed in 10% formalin (final

concentration) and stored at 5oC. Cells were stained with 4’, 6’-diamidino-2-

phenylindole (DAPI, Sigma) and enumerated (bacterial abundance ml-1, BA) using

epifluorescence microscopy as described by Porter and Feig (1980). At least 200

bacterial cells were photographed using an RT-Spot camera (Diagnostic Instruments,

Inc.) and sized using Metamorph® Imaging Analysis software (Universal Imaging Corp.)

with halo correction to determine cellular biovolume (CBV). Size was calibrated by

using fluorescent polystyrene spheres (PolySciences, Inc.) ranging in size from 0.1 – 5.0

μm in diameter. Total bacterial biovolume (TBV ml-1) was calculated as the product of

BA and TBV.

Bacterial Productivity

Bacterial productivity (BP) was estimated by 3H-leucine incorporation into

bacterial protein (Jørgensen 1992). 3H-leucine (Perkin Elmer) with a specific activity of

50 mmol per Ci) was added to samples in 100 μL portions of a preparation containing

500 μCi mL-1. Samples were incubated at ambient temperature for exactly 60 minutes.

Protein fractions were isolated by precipitation onto 0.2 μm cellulosic filters (Osmonics)

with 5% trichloroacetic acid (Sigma), final concentration. Leucine incorporation into

protein was estimated as 3H-dpm using a calibrated Beckman 6500 liquid scintillation

counter. Samples were run in triplicate plus one formalin-fixed control; the control was

amended with formalin at least 20 min before addition of 3H-leucine.

Plankton Phosphorus Dynamics

To determine algal and bacterial P-quotas, triplicate 20 mL aliquots of each sample were filtered onto 1.0 μm PCTE phosphorus-free filters (Osmonics) to determine algal particulate phosphorus (APP). This filtrate in turn was filtered through 0.2 μm

PCTE phosphorus-free filters (Whatman) to determine bacterial particulate phosphorus

(BPP). The filters were frozen on shipboard and returned frozen to the laboratory for further processing. Total phosphorus content retained on the filters was determined colorimetrically following persulfate digestion (APHA 1995). Following development of the molybdenum blue color, absorbance was read in 1 cm quartz cuvettes at 885nm using a Spectronic® Genesys 5 spectrophotometer. Bacterial phosphorus quota was calculated from the bacterial particulate phosphorus (BPP) divided by bacterial abundance BA.

Labile Dissolved Organic Carbon (LDOC)

Labile dissolved organic carbon (LDOC) content was estimated using the method of Søndergaard et al. (1995). Whole water (950 mL) filtered through 0.2 μm PCTE filter cartridge (Whatman) was inoculated with bacteria in 50 mL of water passed through 1.0

μm filter cartridge (Whatman) to remove algae and bacterivores (greater than 1.0 μm in size). This inoculum contained about 95 percent of the bacteria found in whole water samples. Samples were amended with NH4Cl and Na2HPO4 to give final concentrations of 5.6 μM and 1.4 μM respectively, to ensure that bacterial cells were not nutrient limited by N or P. Oxygen concentrations were determined initially and after 30 days in order to estimate the amount of oxygen respired. Oxygen concentrations were determined using

the Winkler method with the alkaline azide modification as described in APHA (1995).

Oxygen consumed was converted to moles of carbon dioxide released by bacteria,

assuming a respiratory quotient of 0.82 (Søndergaard et al. 1995). Bacterial growth was

estimated by measuring the increase in TBV over the 30 day interval. LDOC was

determined as the sum of the carbon respired plus the increase in total bacterial carbon

over the 30 day time interval. We assumed that all LDOC available had been consumed

by bacteria during the 30 day incubation interval.

Phosphate Uptake

Phosphate uptake velocities were determined radiometrically with 33P-

orthophosphate (Perkin Elmer) using the procedure described in Heath (1986). 10 μCi

33P were added to triplicate water samples and a control fixed in 10% formalin. Aliquots

of 1 ml were taken at timed intervals after addition of the radiolabel and filtering through

0.2 μm PCTE filters (“total” uptake) or 1.0 μm PCTE filters (“algal” uptake). Filters

were pre-rinsed with a 1 mM solution of potassium phosphate to prevent non-specific

binding of labeled phosphate to the filters. Following filtration, the filters were rinsed

with three 10 mL portions of de-ionized water, dried, and counted by liquid scintillation

using a Beckman LS6500 liquid scintillation counter. Bacterial P-uptake was calculated

as the difference between “total” and “algal” uptake.

Trophic State Index (TSI)

Trophic state index (TSI) was calculated from chlorophyll a concentrations

according to Carlson (1977). Chlorophyll a concentration was determined from triplicate samples of whole water filtered onto GFF (Whatman) filters using the EPA 445.0 method

(Arar and Collins 1997). The TSI was calculated from chlorophyll a concentration, determined from algal particles > 0.2 μm, using the formula TSI (chl a) = 10 (6-((2.04-

0.68 LN (chl a))/LN (2)).

Results

Trophic State Index (TSI)

The stations in this investigation were chosen to represent a wide range of conditions – different basins (western, central, and eastern), various distances from shore

(near shore and offshore), and diverse LDOC concentrations and trophic states. In

August 2003, the stations in the lake are reported in an east to west order. In summer

2004, trophic state index (TSI) was calculated from the chlorophyll a concentrations at

each station (Table 1, Figure 1). TSIs indicated that the stations selected represented a

wide range of trophic conditions from oligotrophic (0-40) to mesotrophhic (40-50) to

eutrophic (50-100) (Carlson 1977). There was a general decline in TSI along a west-to-

east transect. TSI ranged from 21.3 during June at station 952 (central basin) to 69.6

during August at station 1163 (Sandusky Bay). Station 1163 (Sandusky Bay) supports

dense cyanobacterial populations composed largely of Oscillatoria spp.

Bacterial Structure and Productivity

Bacterial abundance and total bacterial biomass were greatest in the most

eutrophic environment (Sandusky Bay) and generally decreased with decreasing trophic

state and decreasing chlorophyll a concentration (Table 2). During both seasons, BA at

Sandusky Bay was approximately 5-7 times larger than the total summer average of 2 x

106 cells ml-1. Bacterial abundance, BA (BA vs. TSI: r2 = 0.42, BA vs. chl a: r2 = 0.52),

total bacterial biovolume, TBV (TBV vs. TSI: r2 = 0.60, TBV vs. chl a: r2 = 0.79), and

bacterial productivity, BP (BP vs. TSI: r2 = 0.57, BP vs. chl a: r2 = 0.78) increased

linearly with increasing TSI. These relationships may be due to the influence of the algal community on the bacterial community, because chlorophyll a concentration is used to calculate the TSI values. Cellular bacterial biovolume (CBV) showed no trend and remained constant throughout 2004 (CBV vs. TSI; r2 = 0.10, CBV vs. chl a: r2 = 0.04).

The reason for variability in cell size at each station is unclear and may be due to

taxonomic differences among the assemblages at different sites, variation in nutritional requirements, or preferential bacterivorous grazing on larger cells (Hahn and Hofle

1999). BP was greatest in the most eutrophic community (Sandusky Bay, 1163) during both seasons. BA (BA vs. LDOC: r2 = 0.17) exhibited a weak positive linear relationship

with LDOC concentration. CBV (CBV vs. LDOC: r2 = 0.09), TBV (TBV vs. LDOC: r2

= 0.08), and BP (BP vs. LDOC: r2 = 0.00) remained constant with increasing LDOC. No

differences were observed among the groups of LDOC (very low (0-29 μM), low (30-49

μM), moderate (50-69 μM), high (70-89 μM), and very high (90-130 μM)) and BA, TBV,

and BP. CBV was greatest in the group with the very lowest LDOC (0-29 μM).

Phosphorus Dynamics

During 2003, average bacterial P-quota varied among assemblages at the different stations, generally increasing west to east (Table 3) during August 2003. Cells

with the greatest P-quota occurred at the central basin station 954. P quota was lowest in

bacteria in Sandusky Bay and the western basin of the lake. No relationship was

observed between bacterial P-quota and bacterial productivity (P-quota vs. BP; r2 = 0.02) or phosphate uptake velocity and bacterial productivity (PUV vs. BP; r2 = 0.01). Neither

P-quota nor phosphate uptake velocity correlated with TSI. The distribution of

phosphorus to particulate algal and bacterial fractions varied along a west to east transect.

During 2003, in Sandusky Bay the greatest portion of plankton particulate phosphorus was in the size fraction greater than 1.0 μm (“algal”), only about 10 percent of plankton P

distributed in the size fraction that was greater than 0.2 μm but less than 1.0 μm, the

“bacterial” fraction. At western basin station 357 and central basin station 962, the

largest fraction of particulate phosphorus was observed in the algal fraction, representing

>50% of total apportionment. The remainder of particulate phosphorus was apportioned

to bacteria. The central basin station 954 and the eastern basin station 449 exhibited

approximately equal particulate phosphorus distribution among algae and bacteria.

Bacterial P-quota and phosphate uptake velocity exhibited mild relationships with

LDOC (P-quota vs. LDOC; r2 = 0.25, PUV vs. LDOC; r2 = 0.35). Seasonally, phosphate

uptake velocity (PUV) showed a strong, inverse relationship to LDOC concentration

(Figure 2; r2 = 0.79) in August of 2003, with all LDOC concentrations in the low range (<

50 μM). In June, July, and August of 2004, this relationship varied (PUV vs. LDOC: r2 =

0.49, 0.00, and 0.21, respectively). Figure 2 shows the relationship between LDOC and

phosphate uptake for each month.

Labile Dissolved Organic Carbon (LDOC)

Labile dissolved organic carbon (LDOC), the fraction of the total dissolved

organic material pool that can be respired or converted into bacterial biomass, ranged from 13.69 (at station 962 in the central basin) to 126.51 μM (at station 950 in the central

basin) during both seasons (Table 4). During August 2003, LDOC was greatest at

Sandusky Bay station (1163), and lower amounts were detected at stations in the central and eastern basins of Lake Erie. The amount of LDOC that was used for bacterial growth

(as incorporation into bacterial biomass) ranged from less than one percent (assemblages at Sandusky Bay and stations 950, 931, 948, and 882) to 8% (station 496 in the western basin). During summer of 2004, the highest LDOC concentrations were observed at stations 950 (central basin) and 958 (central basin) and the lowest LDOC concentrations were observed at stations 880 (central basin) and 318 (central basin). No relationship was observed between LDOC and TSI or between LDOC and bacterial productivity. P distribution to bacteria showed an inverse relationship with TSI (r2 = 0.43) with both algal particulate phosphorus (APP) and bacterial particulate phosphorus (BPP) increasing with increasing TSI (APP vs. TSI; r2 = 0.57, BPP vs. TSI; r2 = 0.28). Particulate

phosphorus distribution to bacteria decreased linearly with increasing LDOC

concentration (P dist vs. LDOC: r2 = 0.24) (Figure 3). The groups with the highest

LDOC concentrations, high (70-89) and very high (90-130), exhibited the lowest P distribution to bacteria (approximately < 50%).

Phosphate Apportionment

Phosphate can be apportioned, either equally or unequally, to different members

of the plankton community, primarily bacteria and algae. Phosphate apportionment describes the percentage of phosphate taken up by bacterioplankton or phytoplankton from the total available phosphate pool. In August 2003, apportionment of available

phosphate taken up by the bacterial fraction increased along a west to east gradient (Table

3). Bacteria took up the majority of phosphate at all stations examined. Algal uptake

was greatest in the most eutrophic community (Sandusky Bay), and least in the central

basin station 954. No relationship was observed between phosphate apportionment to

bacteria and LDOC concentration (P app vs. LDOC: r2 = 0.01). Also, no differences in

phosphate apportionment to bacteria were observed among the groups of varying LDOC concentrations.

Discussion

Traditionally P-availability to phytoplankton has been viewed as an issue of external loading from the watershed or internal loading of orthophosphate from the sediments (Vollenweider 1975). Recent evidence suggests that Pi availability to phytoplankton may be more of a biogeochemical process, rather then simply a physical and geochemical process. It has been recognized for many years that bacterioplankton and phytoplankton compete for Pi and that the bacteria are competitively advantaged to

the extent that they can deprive phytoplankton of much of the Pi that would be available

were it not for the presence of bacteria (Rhee 1974). This suggests that the problem of P-

availability to phytoplankton may substantially be an issue regarding apportionment of P

to phytoplankton in the presence of bacterioplankton. We have reported that Pi is not

uniformly apportioned between phytoplankton and bacteria in The Great Lakes (Heath et

al. 2003).

Our earlier observations, largely under controlled conditions, showed a strong relationship between bacterial P-quota, P-uptake and LDOC (Gao and Heath 2005). That work showed that in low phosphorus and low carbon environments (often more oligotrophic, offshore locations), phosphorus uptake by bacteria was greater and bacteria exhibited relatively high P-quotas. In contrast, in high phosphorus and high carbon environments (often more eutrophic, nearshore locations), phosphorus uptake by bacteria was slower and bacteria exhibited lower P-quotas. We designated these two extremes as examples of two metabolic states of bacterioplankton cells. Under very low LDOC

conditions bacteria grow slowly but have high P-storage capacities, taking up Pi rapidly

into cells having high P-quotas; we designated this as the “storage state” of bacteria.

Conversely, the “growth state” of bacterial occurred when LDOC was greater than about

50 μM, where bacteria grew rapidly and utilized P efficiently, exhibiting low P-quotas

and relatively slow P-uptake rates. This we called the Microbial Shunt Hypothesis

(MSH) of P-apportionment to plankton (Heath et al. 2003, Gao and Heath 2005).

Algal particulate phosphorus (APP) and bacterial particulate phosphorus (BPP)

were used to determine the portion of the phosphorus pool distributed to phytoplankton

and bacterioplankton in Lake Erie. Greater distribution of the available phosphorus pool

to bacteria was observed at stations with the lowest LDOC (< 70 μM) while less

phosphorus was distributed to algae at these locations. Conversely, at stations with the

highest LDOC concentrations (> 70 μM), more phosphorus was distributed to algae while

less was distributed to bacteria. This finding is consistent with the findings of others that

bacteria are P-rich, relative to phytoplankton (Vadstein 1993). This disparity in

phosphorus distribution in environments with varying LDOC may explain the

relationship between bacterial P-quota (low in high LDOC conditions and high in low

LDOC conditions) observed in this investigation and by others (Gao 2002, Gao and

Heath 2005). These observations satisfy the MSH: in high LDOC conditions, the

bacterial community appears to be in the Growth State with low P-quotas while in low

LDOC conditions, the bacterial community appers to be in the Storage State.

Using a radiometric procedure to trace the uptake of phosphate into bacteria and

phytoplankton separately, we found that LDOC did not influence the apportionment of

phosphate to plankton communities. Apportionment of phosphate to bacterial and algae

showed no differences in environments with varying LDOC. Because, in this case, it

appeared that LDOC did not influence apportionment, other factors, such as the taxonomic composition of the bacterial assemblages or the physiological status of the

bacteria in response to nutrient conditions may exert stronger influence over phosphate

apportionment. LDOC did not correlate with TSI. Trophic state should be investigated

further to determine the influence of the phytoplankton community on phosphate

apportionment to bacterioplankton.

Our purpose here was to provide a preliminary examination of whether

observations of communities over a wide range of trophic conditions in Lake Erie would

be consistent with the MSH. Our findings here are partially consistent with the MSH.

P-quotas, phosphorus distribution to bacteria, and bacterial phosphate uptake were

highest in those communities with the lowest LDOC, and lowest in that community

having the greatest amount of LDOC (Figure 3). This inverse relationship between

productivity vs. P-uptake velocity was similar to observations reported previously (Gao

2002, Heath et al. 2003, Gao and Heath 2005). However, our observation of consistent

phosphate apportionment, independent of LDOC concentration, was different than

observations reported previously (Gao 2002, Heath et al. 2003, Gao and Heath 2005).

Despite the wide range of values observed for LDOC concentration, LDOC did not

correlate with TSI (r2 = 0.01). The relationship between LDOC and bacterial phosphate

uptake may be more due to the composition of the LDOC pool rather than the trophic

state at a particular station.

Previously, Gao and Heath (2005) reported that LDOC decreased with decreasing

trophic status; however, no relationship was observed between LDOC and trophic state

(r2 = 0.02). No consistent pattern in the fraction of LDOC used for bacterial growth (as incorporation into bacterial biomass) and no evident relationship between LDOC and bacterial productivity was observed. Bacterial growth was slow at most central basin sites and LDOC was low at many of the stations examined. We did not observe a strong relationship between LDOC and trophic state (TSI) or LDOC and bacterial productivity.

In 2003, we observed only low amounts of LDOC, so it may be that all bacterial

assemblages were severely growth-limited by C-availability. However, a wide range of

LDOC concentrations were observed in 2004, so the availability of the LDOC may be dependent upon the bacterial assemblage and not the nutrient condition. The amount of observed LDOC was strongly related to phytoplankton abundance as indicated by the particulate P in algal cells. Linear regression algal particulate P vs. LDOC shows an r2 =

0.71, consistent with a largely autochthonous origin of LDOC. It may be that differences between our earlier observations and this study with regard to amount of LDOC and its effect on bacterial productivity may be due to seasonal differences or variations in bacterial assemblage between the studies. Also, the relationship between LDOC and bacterial phosphate uptake may be more due to the composition of the LDOC pool rather than the trophic state at a particular station.

The MSH adds to the increasing awareness of the role of heterotrophic bacteria in large lake ecosystem and community food web processes. Formerly, bacteria were seen solely as decomposers of organic matter and nutrient recyclers. Currently, bacteria are seen as an essential food source to higher trophic levels, especially in low nutrient, oligotrophic, offshore environments. In the microbial food web, bacteria can transfer carbon and energy to higher trophic levels via protistan, rotiferan, and zooplankton bacterivory (Sherr and Sherr 1988; Hwang and Heath 1997, 1999). Due to high phosphorus content (Vadstein 1993) and high phosphate uptake velocity (Currie & Kalff

1984; Gao and Heath 2005), bacteria may also transfer the nutrient phosphorus to higher trophic levels. The MSH suggests that the abundance of low molecular weight carbon compounds or labile dissolved organic carbon (LDOC), such as simple sugars (e.g.

glucose), amino acids, and other organic acids, may influence the distribution and uptake

of Pi to bacteria and algae in environments with different LDOC regimes, possibly due to

changing C:P stoichiometric ratios (Heath et al. 2003; Gao and Heath 2005).

Acknowledgements

We thank Dr. Mohiuddin Munawar and the Department of Fisheries and Oceans

for the opportunity to conduct this investigation, the captain and crew of the CCGS

Limnos for their assistance with the sample collection, and Mark Fitzpatrick, Heather

Niblock, Jocelyn Gerlofsma, and Dana McDermott for technical assistance. This manuscript was improved by the comments and suggestions of two anonymous reviewers. This research was funded by Ohio Sea Grant R/ER-60.

Table 1. Limnological variables for Lake Erie in August 2003 and June, July, and August of 2004 – latitude and longitude, basin, sounding depth (m), sample depth (m), temperature (ºC), dissolved oxygen concentration (mg L-1), chlorophyll a concentration (μg L-1), and trophic state index (TSI). Data for August 2003 is sorted along a east to west gradient and data for

2004 is sorted by increasing TSI.

Sounding Sample Chlorophyll a Depth Depth Temp DO (μg L-1)

TSI Year Date Station Basin Latitude Longitude (m) (m) (°C) (mg L-1) Avg

ND 2003 August 449 Eastern 42° 17' 42" 79° 42' 48" 13.0 1-11.5 24.2 9.34 ND

ND 2003 August 954 Central 42° 01' 30" 81° 26' 30" 23.5 0-11 24 9.89 ND

ND 2003 August 962 Central 41° 43' 00" 82° 11' 00" 19.5 0-10 23.1 8.74 ND

ND 2003 August 357 Western 41° 49' 30" 82° 58' 30" 8.5 0-8 24.3 9.35 ND

ND 2003 August 1163 Sand Bay 41° 28.295 82° 45.008 7.0 0-4 25.4 8.84 ND

21.3 2004 June 952 Central 42° 21’ 30” 81° 26’ 30” 22 0-16 17.70 10.04 0.39

23.9 2004 August 958 Central 41° 31’ 30” 81° 42’ 30” 12.2 0-10 22.16 9.73 0.51

24.5 2004 June 880 Central 41° 56’ 09” 81° 39’ 16” 24.4 3 18.40 10.49 0.54

29.0 2004 August 879 Eastern 42° 30’ 25” 79° 53’ 59” 62.1 1 21.10 ND 0.85

29.3 2004 August 950 Central 42° 35’ 18” 81° 26’ 30” 10.5 0-8.5 18.22 7.55 0.88

29.5 2004 June 950 Central 42° 35’ 18” 81° 26’ 30” 10 0-5 17.53 9.48 0.89 62

31.7 2004 June 879 Eastern 42° 30’ 25” 79° 53’ 59” 62.2 2 16.82 10.33 1.12

32.4 2004 July 950 Central 42° 35’ 18” 81° 26’ 30” 8.7 0-3 18.38 7.98 1.20

32.7 2004 August 934 Eastern 42° 42’ 30” 79° 30’ 30” 23.5 ND ND ND 1.24

33.6 2004 August 933 Eastern 42° 49’ 30” 79° 34’ 00” 12.9 0-10 20.95 ND 1.36

33.7 2004 July 936 Eastern 42° 28’ 30” 79° 24’ 30” 7.6 5.5 22.39 8.49 1.37

34.1 2004 July 879 Eastern 42° 30’ 25” 79° 53’ 59” 62.2 15 18.52 8.41 1.44

35.3 2004 June 934 Eastern 42° 42’ 30” 79° 30’ 30” 28 0-11 17.60 10.17 1.61

35.3 2004 July 954 Central 42° 01’ 30” 81° 26’ 30” 23.9 0-16 23.05 9.65 1.61

35.6 2004 August 880 Central 41° 56’ 09” 81° 39’ 16” 24.3 1 21.74 10.12 1.68

36.9 2004 July 931 Eastern 42° 51’ 00” 78° 56’ 30” 10 0-8 21.16 8.60 1.91

40.9 2004 July 970 Western 41° 49’ 30” 82° 58’ 30” 10.6 0-8.5 ND ND 2.86

41.3 2004 July 880 Central 41° 56’ 09” 81° 39’ 16” 24.4 2.5 22.56 9.86 2.99

43.7 2004 August 970 Western 41° 49’ 30” 82° 58’ 30” 10.5 0-8.5 11.17 0.00 3.80

45.2 2004 June 970 Western 41° 49’ 30” 82° 58’ 30” 10.6 0-8 20.92 10.11 4.43

46.4 2004 July 948 Central 41° 57’ 24” 80° 38’ 30” 10.5 0-8.5 23.32 10.48 5.01

49.3 2004 June 882 Western 41° 45’ 57” 83° 18’ 34” 6.7 0-6 21.83 10.80 6.75

52.3 2004 August 311 Central 41° 35’ 00” 82° 28’ 00” 14.3 0-12 21.95 9.09 9.21

55.5 2004 July 496 Western 41° 34’ 06” 82° 43’ 12” 9.2 0-7 24.62 13.25 12.65 63

69.0 2004 July 1163 Sand Bay 41° 28' 16" 82° 43' 05" 8.4 0-6 ND ND 50.19

69.6 2004 August 1163 Sand Bay 41° 28' 16" 82° 43' 05" 8.3 1 21.73 9.02 53.22

Table 2. Bacterial structure and productivity for Lake Erie in August 2003 and June, July, and August of 2004 – bacterial number (cells/ml), bacterial cellular biovolume (μm3 cell-1), bacterial total biovolume (μm3 ml-1), and bacterial productivity (μg

C ml-1 hr-1). Data for August 2003 is sorted along an east to west gradient and data for 2004 is sorted by increasing TSI.

Means ± SE.

BA x 106 CBV TBV x 105 BP x 10-4

(cells ml-1) (μm3 cell-1) (μm3 ml-1) (μg C ml-1 hr-1)

TSI Year Month Site Avg ± SE Avg ± SE Avg ± SE Avg ± SE

ND 2003 August 449 1.74 ± 0.01 0.108 ± 0.009 1.89 ± 0.01 5.38 ± 0.14

ND 2003 August 954 1.66 ± 0.06 0.134 ± 0.012 2.22 ± 0.08 9.04 ± 0.30

ND 2003 August 962 2.04 ± 0.06 0.113 ± 0.009 2.31 ± 0.07 17.20 ± 0.60

ND 2003 August 357 3.86 ± 0.03 0.079 ± 0.009 3.04 ± 0.08 7.71 ± 0.12

ND 2003 August 1163 15.90 ± 0.38 0.084 ± 0.011 13.3 ± 0.32 29.20 ± 1.37

21.3 2004 June 952 1.63 ± 0.07 0.057 ± 0.005 0.93 ± 0.04 1.15 ± 0.12

23.9 2004 August 958 1.66 ± 0.08 0.058 ± 0.006 0.96 ± 0.04 10.14 ± 0.91

24.5 2004 June 880 1.25 ± 0.01 0.049 ± 0.004 0.61 ± 0.01 0.26 ± 0.01

29.0 2004 August 879 1.44 ± 0.07 0.057 ± 0.005 0.82 ± 0.04 1.69 ± 0.06

29.3 2004 August 950 1.49 ± 0.03 0.042 ± 0.004 0.63 ± 0.01 1.84 ± 0.23 65

29.5 2004 June 950 4.51 ± 0.16 0.048 ± 0.003 2.16 ± 0.08 1.67 ± 0.14

30.6 2004 July 318 0.90 ± 0.08 0.096 ± 0.008 0.86 ± 0.08 2.78 ± 0.05

31.7 2004 June 879 1.50 ± 0.06 0.072 ± 0.007 1.08 ± 0.04 0.72 ± 0.18

32.4 2004 July 950 0.61 ± 0.02 0.073 ± 0.007 0.44 ± 0.02 4.36 ± 0.19

32.7 2004 August 934 1.38 ± 0.04 0.041 ± 0.003 0.57 ± 0.02 1.95 ± 0.04

33.6 2004 August 933 1.50 ± 0.02 0.056 ± 0.007 0.85 ± 0.01 1.14 ± 0.06

33.7 2004 July 936 0.53 ± 0.04 0.082 ± 0.007 0.44 ± 0.03 4.25 ± 0.15

34.1 2004 July 879 0.66 ± 0.02 0.095 ± 0.008 0.63 ± 0.02 2.69 ± 0.04

35.3 2004 June 934 1.55 ± 0.03 0.082 ± 0.008 1.27 ± 0.03 3.03 ± 0.19

35.3 2004 July 954 1.02 ± 0.06 0.082 ± 0.006 0.8 ± 0.05 0.67 ± 0.03

35.6 2004 August 880 1.59 ± 0.05 0.065 ± 0.005 1.04 ± 0.03 0.73 ± 0.05

36.9 2004 July 931 0.61 ± 0.02 0.094 ± 0.008 0.58 ± 0.02 0.97 ± 0.03

40.9 2004 July 970 1.37 ± 0.04 0.071 ± 0.008 0.96 ± 0.03 12.13 ± 0.24

41.3 2004 July 880 1.31 ± 0.05 0.117 ± 0.013 1.53 ± 0.06 1.54 ± 0.14

43.7 2004 August 970 1.76 ± 0.04 0.053 ± 0.005 0.94 ± 0.02 2.15 ± 0.17

45.2 2004 June 970 2.52 ± 0.80 0.054 ± 0.006 1.35 ± 0.43 1.40 ± 0.03

46.4 2004 July 948 1.48 ± 0.16 0.120 ± 0.011 1.77 ± 0.19 8.23 ± 0.36 66

49.3 2004 June 882 12.35 ± 0.38 0.047 ± 0.004 5.75 ± 0.17 5.49 ± 0.45

52.3 2004 August 311 1.76 ± 0.12 0.057 ± 0.005 1.00 ± 0.07 1.98 ± 0.05

55.5 2004 July 496 1.43 ± 0.07 0.115 ± 0.020 1.65 ± 0.08 13.39 ± 0.63

69.0 2004 July 1163 9.45 ± 0.50 0.094 ± 0.008 8.85 ± 0.47 25.86 ± 1.03

69.6 2004 August 1163 10.26 ± 1.26 0.069 ± 0.006 7.10 ± 0.87 23.59 ± 3.48

Table 3. Bacterioplankton phosphorus dynamics for Lake Erie in August 2003 and June, July, and August of 2004 – algal particulate phosphorus (APP), bacterial particulate phosphorus (BPP), phosphorus distribution to bacteria (%), phosphate apportionment to bacteria (%), bacterial phosphorus quota (nM cell-1), and bacterial phosphorus uptake velocity (nmol cell-1 min-1). Data for August 2003 is sorted along a east to west gradient and data for 2004 is sorted by increasing TSI. Means ± SE.

Pi Dist to Pi Apport to APP BPP Bact Bact P Quota x 10-8 Phosphate Uptake (nmol cell-1 min-1) (nM) (nM) (%) (%) (nmol cell-1) x 10-10

TSI Year Month Site Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg

ND 2003 August 449 106 ± 6 114 ± 10 51.9 82.9 22.6 ± 0.6 5.74

ND 2003 August 954 219 ± 7 222 ± 6 50.3 95.5 35.8 ± 0.2 6.01

ND 2003 August 962 228 ± 49 206 ± 12 47.4 75.0 27.4 ± 0.6 4.91

ND 2003 August 357 233 ± 38 114 ± 20 32.8 92.1 8.4 ± 0.1 2.59

ND 2003 August 1163 1872 ± 17 250 ± 17 11.8 57.5 3.7 ± 0.1 0.63

21.3 2004 June 952 126 ± 22 124 ± 24 66.4 ± 18.5 84.3 ± 1.6 7.78 ± 1.42 4.4

23.9 2004 August 958 199 ± 9 146 ± 22 41.9 ± 3.7 4.8 ± 4.8 10.65 ± 1.11 1.2

24.5 2004 June 880 126 ± 29 92 ± 11 43.0 ± 3.0 47.7 ± 17.9 7.31 ± 0.86 1.8

29.0 2004 August 879 204 ± 28 135 ± 5 40.3 ± 3.4 20.4 ± 4.3 14.23 ± 2.58 10.8

29.3 2004 August 950 154 ± 1 151 ± 32 48.5 ± 4.7 33.8 ± 22.0 9.98 ± 0.83 3.6 68

29.5 2004 June 950 288 ± 18 128 ± 7 30.8 ± 1.3 65.5 ± 5.0 2.81 ± 0.27 1.1

30.6 2004 July 318 183 ± 13 200 ± 41 51.2 ± 3.6 69.8 ± 10.0 21.72 ± 2.49 45.5

31.7 2004 June 879 135 ± 14 125 ± 31 47.0 ± 7.4 86.1 ± 9.2 8.23 ± 2.41 23.8

32.4 2004 July 950 146 ± 15 61 ± 5 29.8 ± 3.3 76.3 ± 4.0 10.15 ± 1.20 22.4

32.7 2004 August 934 92 ± 6 97 ± 7 51.4 ± 3.5 70.0 ± 3.5 7.06 ± 0.46 13.4

33.6 2004 August 933 132 ±9 99 ± 5 42.9 ± 2.8 75.4 ± 11.1 8.08 ± 0.91 7.1

33.7 2004 July 936 119 ± 11 106 ± 6 47.1 ± 2.9 55.4 ± 2.9 20.11 ± 2.25 4.6

34.1 2004 July 879 210 ± 51 118 ± 7 37.9 ± 7.0 52.0 ± 7.1 17.82 ± 0.59 3.39

35.3 2004 June 934 207 ± 17 94 ± 6 31.6 ± 3.3 46.2 ± 23.3 6.11 ± 0.54 0.7

35.3 2004 July 954 89 ± 5 82 ± 7 47.9 ± 2.1 85.5 ± 0.5 8.00 ± 0.31 22.7

35.6 2004 August 880 125 ± 11 94 ± 2 43.2 ± 2.7 34.4 ± 17.5 7.67 ± 0.83 4.9

36.9 2004 July 931 132 ± 3 90 ± 5 40.6 ± 1.5 45.0 ± 10.9 14.79 ± 1.25 1.6

40.9 2004 July 970 158 ± 25 100 ± 8 39.2 ± 3.7 75.9 ± 0.8 7.36 ± 0.81 ND

41.3 2004 July 880 114 ± 5 68 ± 7 37.2 ± 1.8 45.7 ± 6.0 5.21 ± 0.55 0.9

43.7 2004 August 970 353 ± 7 194 ± 10 35.5 ± 0.08 29.8 ± 7.4 16.42 ± 2.42 6.5

45.2 2004 June 970 414 ± 51 139 ± 34 17.7 ± 9.1 25.8 ± 15.1 6.82 ± 3.64 0.2

46.4 2004 July 948 126 ± 12 82 ± 8 39.3 ± 2.2 75.4 ± 0.9 5.57 ± 0.23 17.7

49.3 2004 June 882 347 ± 8 83 ± 7 19.3 ± 1.10 35.5 ± 8.6 0.67 ± 0.05 0.0 69

52.3 2004 August 311 209 ± 2 115 ± 5 56.5 ± 21.8 12.7 ± 4.7 8.07 ± 1.87 17.7

55.5 2004 July 496 286 ± 10 264 ± 35 47.6 ± 2.7 ND 18.48 ± 2.48 ND

69.0 2004 July 1163 1078 ± 68 242 ± 21 18.3 ± 0.6 27.6 ± 6.0 2.56 ± 0.20 ND

69.6 2004 August 1163 1994 ± 27 242 ± 12 10.8 ± 0.4 14.4 ± 9.3 22.12 ± 1.30 6.4

Table 4. Labile dissolved organic carbon (LDOC) concentrations for Lake Erie in August 2003 and June, July, and August of

2004 – moles of LDOC respired by bacteria (μM), moles of LDOC incorporated into bacteria (μM), total LDOC concentration

(μM), and portion of LDOC incorporated into bacterial biomass (%). Data for August 2003 is sorted along a east to west gradient and data for 2004 is sorted by increasing TSI. Means ± SE.

% LDOC LDOC LDOC LDOC Incorp into Respired Incorporated Total Biomass

(μM) (μM) (μM) (%)

TSI Year Month Site Avg ± SE Avg ± SE Avg ± SE Avg ± SE

ND 2003 August 449 26.8 ± 8.4 0.5 ± 0.0 26.4 ± 8.4 2.2 ± 1.0

ND 2003 August 954 21.5 ± 6.0 1.3 ± 0.1 20.2 ± 6.0 6.5 ± 1.6

ND 2003 August 962 14.0 ± 3.7 0.3 ± 0.0 13.7 ± 3.7 2.7 ± 1.0

ND 2003 August 357 28.5 ± 3.4 0.6 ± 0.1 27.9 ± 3.3 2.1 ± 0.2

ND 2003 August 1163 43.6 ± 3.6 0.2 ± 0.0 43.3 ± 3.6 0.5 ± 0.0

21.3 2004 June 952 57.9 ± 2.0 1.2 ± 0.2 58.9 ± 1.8 1.8 ± 0.4

23.9 2004 August 958 69.8 ± 3.7 1.1 ± 0.0 70.8 ± 3.7 1.5 ± 0.1

24.5 2004 June 880 52.4 ± 2.0 0.9 ± 0.2 53.3 ± 2.2 1.6 ± 0.6

29.0 2004 August 879 45.2 ± 0.7 0.8 ± 0.0 46.1 ± 0.7 1.8 ± 0.0 71

29.3 2004 August 950 49.8 ± 2.8 0.8 ± 0.0 50.6 ± 2.7 1.5 ± 0.1

29.5 2004 June 950 125.8 ± 0.9 0.7 ± 0.1 126.5 ± 0.8 0.6 ± 0.1

30.6 2004 July 318 37.6 ± 5.5 1.4 ± 0.1 39.0 ± 5.4 3.7 ± 0.8

31.7 2004 June 879 52.0 ± 2.9 1.4 ± 0.1 53.5 ± 2.3 2.8 ± 0.3

32.4 2004 July 950 52.1 ± 7.5 1.5 ± 0.3 53.6 ± 7.6 2.8 ± 0.6

32.7 2004 August 934 43.4 ± 0.6 0.6 ± 0.3 44.0 ± 0.6 1.4 ± 0.7

33.6 2004 August 933 75.5 ± 12.8 1.0 ± 0.0 76.5 ± 12.8 1.3 ± 0.2

33.7 2004 July 936 55.3 ± 12.6 0.8 ± 0.1 56.1 ± 12.7 1.5 ± 0.2

34.1 2004 July 879 77.6 ± 8.2 1.3 ± 0.1 78.8 ± 8.3 1.6 ± 0.2

35.3 2004 June 934 97.7 ± 4.5 1.2 ± 0.2 98.8 ± 4.7 1.2 ± 0.3

35.3 2004 July 954 50.2 ± 1.5 0.8 ± 0.1 51.0 ± 1.5 1.6 ± 0.0

35.6 2004 August 880 21.4 ± 6.1 0.8 ± 0.1 22.1 ± 6.0 4.1 ± 1.1

36.9 2004 July 931 87.8 ± 17.0 0.6 ± 0.0 88.4 ± 17.0 0.8 ± 0.2

41.3 2004 July 880 22.3 ± 6.0 1.0 ± 0.0 23.3 ± 6.0 4.8 ± 1.0

43.7 2004 August 970 47.8 ± 3.6 0.9 ± 0.0 48.7 ± 3.6 2.0 ± 0.1

45.2 2004 June 970 90.6 ± 4.8 1.1 ± 0.1 91.7 ± 4.8 1.2 ± 0.2

46.4 2004 July 948 54.0 ± 8.3 0.4 ± 0.2 54.3 ± 8.3 0.7 ± 0.4 72

49.3 2004 June 882 113.4 ± 0.8 0.0 ± 0.0 113.4 ± 0.8 0.0 ± 0.0

52.3 2004 August 311 31.8 ± 3.7 1.1 ± 0.1 32.9 ± 3.8 3.3 ± 0.1

55.5 2004 July 496 22.2 ± 3.7 1.9 ± 0.7 24.1 ± 3.8 8.0 ± 2.6

69.6 2004 August 1163 56.5 ± 10.2 0.6 ± 0.1 57.1 ± 10.1 1.1 ± 0.4 73

Figure 1. Map of Lake Erie with sites labeled.

▪ 931 449 ▪ 934

▪ 950 ▪ ▪ 879 ▪ 936

▪ 952

▪ 960 ▪ 954

▪ 880 ▪ 882 ▪ 357 ▪ 962 ▪ 318 ▪ 496 ▪ 311 ▪ 958 ▪ 1163

Figure 2. The relationship between bacterial phosphate uptake and labile dissolved organic carbon in Lake Erie bacterioplankton during Augst 2003 and June , July and

August 2004. The strength of this inverse exponential relationship varies seasonally:

August 2003 (y = 22.9e-0.0761x, r2 = 0.79), June 2004 (y = 60.5e-0.0478x, r2 = 0.49), July

2004 (y = 6.5e-0.0002x, r2 = 0.00), and August 2004 (y = 18.5e-0.0216x, r2 = 0.21).

50.0 45.0 40.0 -10 35.0

) x 10 Aug-03 -1 30.0 Jun-04

min 25.0 -1 Jul-04 20.0 Aug-04 15.0

(nmol cell 10.0 Phosphate Uptake Velocity Velocity Uptake Phosphate 5.0 0.0 0.0 50.0 100.0 150.0 LDOC (µM)

Figure 3. The relationship between P distribution to bacteria (%) and labile dissolved organic carbon (LDOC) concentration (μM). A greater portion of the phosphorus pool is distributed to bacteria (and less to algae) at stations with lower concentrations of LDOC and a smaller portion of the phosphorus pool is distributed to bacteria (and more to algae) at stations with higher LDOC, y = -0.22x + 53.8 (r2 = 0.24).

(%) 30

P Distribution to Bact Distribution P 20

0 0 20 40 60 80 100 120 140 LDOC (µM)

CHAPTER III

The Role of LDOC to Phosphorus Dynamics in Lake Erie

Bacterioplankton Assemblages

Abstract

Phosphorus dynamics including phosphate uptake and apportionment to bacterioplankton and phytoplankton, P-quota, phosphorus distribution to bacterioplankton and phytoplankton, and labile dissolved organic carbon (LDOC) were measured at stations of varied trophic status in Lake Erie during summer 2005 to evaluate the assertions of the Microbial Shunt Hypothesis (MSH) under field conditions. Low- molecular weight carbon compounds including glucose, glucosamine, and lysine were added to carbon depleted natural bacterial assemblages to evaluate the assertions of the

MSH under experimental conditions. This investigation was conducted aboard the R/V

Lake Guardian as part of the Interntaional Field Year of Lake Erie (IFYLE) project. Our results are consistent with the MSH since the highest bacterial phosphate uptake velocities (PUV), PUVs per cell, and P-quotas were observed at stations with low LDOC conditions (MSH Storage State). Conversely, the lowest PUVs, PUVs per cell, and P- quotas were observed at stations with high LDOC conditions (MSH Growth State). In addition, low PUVs, PUVs per cell, and P-quotas were observed at many stations with

80 81

the lowest LDOC conditions. A new state, the Inactive State, was proposed as an

addition to the MSH to describe the metabolism of these bacteria. Bacterioplankton at

the highest TSIs (55+) took up phosphate significantly less than bacterioplankton at

lower TSIs (< 55). Bacterioplankton at the lowest TSIs (20-29) received a significantly

greater portion of the particulate phosphorus pool than bacterioplankton with higher TSIs

(30+). These findings are also consistent with the MSH. Addition of glucose to carbon

depleted bacteria increased bacterial productivity and decreased the phosphate uptake rate

constant for most cases. Addition of glucosamine and lysine resulted in the increase of bacterial productivity at one station. These results are consistent with the MSH and

suggest glucose as a potential component of the LDOC pool.

Introduction

Phosphorus is often a limiting nutrient to algal communities in aquatic ecosystems. Schindler (1974) and many others determined, after much controversy, that phosphorus was the nutrient responsible for cultural eutrophication of The Great Lakes including Lake Erie. Despite its economic and ecological importance, Lake Erie was and continues to be threatened by cultural eutrophication. Anthropogenic nutrient loading into the lake from industrial, agricultural, detergent, and sewage origins resulted in prolific growth of benthic and pelagic algae in the late 1960s and early 1970s. The bi- national Great Lakes Water Quality Agreement of 1978 (amended 1987) set guidelines, regulations, and targets for phosphorus loading into the Great Lakes Ecosystem (IJC

1987). Previous to GLWQA, phosphorus loads were estimated as greater than 20,000 metric tonnes per year. Despite achieving acceptable decreases in phosphorus loading

(<11,000 metric tonnes per year) (Dolan 2003), Lake Erie is the only Great Lake that has failed to consistently achieve target total phosphorus concentrations (15 μg L-1 in the

western basin and 10 μg L-1 in the central and eastern basins (State of the Great Lakes

2003)).

Lake Erie continues to be plagued by the problems of cultural eutrophication

despite reductions in phosphorus loading. Nutrient pulses (whether natural or

anthropogenic), especially in the western basin, accelerate algal growth, producing

prolific algal blooms often rich with undesirable cyanobacterial species. Some of the

cyanobacterial species (Microcystis sp. and Planktothrix sp.) release harmful toxins

(Rinto-Kanto et al. 2005). Toxins can decrease water quality by causing taste and odor

problems. In addition, cyanobacterial species are not easily grazed by zooplankton and

zebra mussels (Kerfoot et al. 1988; Vanderploeg et al. 2001). In this situation, extensive

algal proliferation becomes a food web problem. Algal blooms also provide an organic

matter source for bacterial decomposition. When the decomposition occurs in a region

with a shallow hypolimnion (eg. the central basin of Lake Erie), oxygen depletion can occur. A large region of central basin hypoxia was observed in the 1970s (Bolsegna and

Herdendorf 1993). Today, Lake Erie fails to reach target oxygen depletion rates in the central basin (EPA 1997).

While phosphorus is the nutrient that most frequently limits phytoplankton growth and phytoplankton growth correlates with total phosphorus concentrations, mechanistically, it is the presence of readily available phosphorus, as orthophosphate, that actually limits this growth. Overall, lake phosphate concentrations are extremely low

(nanomolar concentrations in oligotrophic lakes) and traditional chemical methods (eg. molybdenum blue method, Murphy and Riley 1962) tend to over-estimate soluble reactive phosphorus (SRP), or readily available phosphate concentration by up to two

orders of magnitude (Hudson et al. 2000; Rigler 1966; Gao 2002). Phosphorus limited

communities are characterized by low bioavailability of phosphate.

In oligotrophic aquatic environments with low bioavailability of phosphate, bacteria often out-compete algae for available phosphate. Phosphorus limitation has been identified in lake plankton populations via rapid phosphate uptake rates (Currie and Kalff

1984; Currie 1990; Gao and Heath 2005; and others), alkaline phosphatase activity

(Cotner and Wetzel 1991), C:N:P ratios (Redfield 1958; Elser, 1995; Sterner et al. 1998),

enzyme-labeled fluorescence (ELF) (González-Gil 1998), and phosphorus amendment

experiments (Gao 2002). In oligotrophic conditions, bacteria take up as much as 80% of

available phosphorus, much more than algae, because the P-transporters of the algae never achieve P saturation (Vadstein 1993). Lower food web heterotrophs (protozoa,

rotifers, zooplankton) rely on phosphorus rich bacteria, either directly or indirectly (eg.

via recycling), for nutrients. Bacteria can take up, retain, use phosphorus more

efficiently, and have higher P requirements than algae (Vadstein et al. 1988; Vadstein

2000). He suggested that bacteria could use alkaline phosphatase to hydrolyze organic P

sources and could store polyphosphates in granules. Because of these capabilities,

bacteria, especially in oligotrophic waters, may control algal biomass. In this way,

bacteria may indirectly affect a lake’s trophic status.

While orthophosphate concentrations have been shown to control the phosphorus

dynamics of lake bacterioplankton, other factors may also regulate phosphorus

distribution and apportionment to bacteria and algae. Dissolved organic carbon

compounds have been shown to influence nitrogen and phosphorus cycles in aquatic

ecosystems. High-molecular weight dissolved organic matter compounds stimulate

bacterial uptake of ammonium in the Mississippi River (Gardner et al. 1996). Gao and

Heath (2005) observed higher bacterial P quotas (ie. bacterial P content per cell) and

phosphate uptake velocities in oligotrophic environments having low labile dissolved

organic carbon (LDOC) concentrations, and, conversely, lower P quotas and phosphate

uptake velocities in eutrophic environments having high LDOC concentrations. These

findings suggest that bacteria in oligtrophic environments may have lower nutrient use

efficiency (NUE) and tend to conserve their cellular P (ie. have a high P quota) when severely C-limited.

The Microbial Shunt Hypothesis of Phosphorus Apportionment (Heath et al.

2003; Gao and Heath 2005) proposes two metabolic states for bacterioplankton: under

low LDOC conditions (eg. <50 μM), bacteria are in a Storage State and exhibit slow

growth, high P quotas, and rapid phosphate uptake; under high LDOC conditions (eg. >

50 μM), bacteria are in a Growth State and exhibit high growth rates, lower P quotas, and modest phosphate uptake. According to the MSH, bacterioplankton in oligotrophic environments (i.e. environments having lower LDOC) are in a Storage State and bacterioplankton in eutrophic environments (i.e. environments having higher LDOC) are in a Growth State. Bacteria in the Storage State are more likely to be successful in competition with algae for limited phosphate resources due to having a lower Km of Pi-

transport. According to the MSH, more of the available phosphate pool is apportioned to

bacteria in low LDOC conditions and more of the available phosphate pool is

apportioned to algae in high LDOC conditions.

The purpose of this investigation was to evaluate the microbial shunt hypothesis

of phosphorus apportionment (Heath et al. 2003) in diverse trophic conditions. Our

research was conducted aboard the R/V Lake Guardian in summer 2005 (May, June, July,

August, and September) during five synoptic cruises of Lake Erie. Phosphorus dynamics

(abundance and distribution of phosphorus fractions, distribution of the particulate

phosphorus pool, and phosphate apportionment) in the algal and bacterial populations

were characterized. Relationships between carbon distribution and abundance and

phosphorus dynamics were analyzed. Phosphate uptake enzyme parameters were determined in order to assess the nutrient status of phytoplankton and bacterioplankton communities. The impacts of low molecular weight (LMW) carbon compounds on bacterial productivity and phosphate uptake were investigated. We address the implications of carbon and phosphorus dynamics on phosphorus distribution and apportionment under the diverse trophic conditions in Lake Erie.

Methods

Sample Collection

Water samples were collected aboard the R/V Lake Guardian during May, June,

July, August, and September of 2005 as part of the International Field Year of Lake Erie

(IFYLE) collaborative research effort. Samples were obtained from discrete depths from the epilimnion at each station investigated using an onboard rosette sampler. Additional samples were obtained from the metalimnion and hypolimnion at select stations. Water samples were collected from the rosette sampler using Nalgene carboys and immediately transferred to the ship-board laboratory for processing.

Phosphate Uptake and Turnover Time

Phosphate uptake was estimated from bacteria and algal fractions. 0.5 μCi (about

10 μL) of a 50 μCi/ml 33P (Perkin Elmer NEZ 080) was added to triplicate portions of whole water and a formalin fixed control. At exactly 2, 4, and 6 minute intervals, 1 ml portions of the whole water were filtered onto 0.1 M KH2PO4 pre-soaked 1.0 μm and 0.2

μm polycarbonate filters. The 1.0 μm filters captured the algal fraction and the 0.2 μm filters captured the total fraction. The bacterial fraction was the difference between total and algal fractions. Filters were rinsed with three 10 ml portions of de-ionized water and stored in polypropylene scintillation vials and dried under a hood. After drying, vials were capped and stored at room temperature until returning to the laboratory. Non- aqueous scintillation cocktail was added to the vials. Radioactivity was determined

(dpm) in a Beckman LS6500 scintillation counter. Net dpm = dpm sample – dpm of formalin-fixed control. The proportional uptake rate coefficient (k) by bacterial particles was calculated according to Heath (1986) with units of min-1. Phosphate turnover time

(min) was calculated as the inverse of proportional uptake rate coefficient of phosphate taken up by all particles > 0.2 μm.

Phosphate Apportionment to Bacteria and Algae

Phosphate apportionment to bacteria, or the portion of the available phosphate pool taken up by bacteria (%), was calculated as bacterial slope for phosphate uptake (0.2

μm < particles < 1.0 μm) divided by the total slope for phosphate uptake (> 0.2 μm)

*100. Phosphate apportionment to algae, or the portion of the available phosphate pool taken up by algae (%) was calculated as the algal slope for phosphate uptake (>1.0 μm) divided by the total slope for phosphate uptake (> 0.2 μm) * 100.

Particulate Phosphorus and P Quota

Triplicate portions of lake water were filtered through 1.0 μm polycarbonate

filters to capture algal particles (algal particulate phosphorus or APP). Triplicate

portions of the remaining filtrate were then filtered through 0.2 μm polycarbonate filters

to trap bacterial particles (bacterial particulate phosphorus or BPP). The remaining

filtrate represented the dissolved fraction or total soluble phosphorus (TSP). Filters and

filtrate were frozen in the shipboard freezer, transported via cooler, and stored in the

laboratory freezer until analysis. Upon return to the laboratory, the phosphorus content

of the samples was determined using the method of Murphy and Riley (1962) with

persulfate digestion. The absorbances of the samples at 885 nm were read in a Spectro

Genesys 5 spectrophotometer. Triplicate standards and triplicate reagent blanks were run

in parallel throughout the analysis. Bacterial phosphorus quota was calculated from the

bacterial particulate phosphorus (BPP) / bacterial abundance, BA (cells ml-1). Total

phosphorus (TP) was calculated as the sum: APP + BPP + TSP. Data for bacteria

abundance (BA) is presented in Chapter 3 – Factors Controlling the Bioenergetics of

Lake Erie Bacterioplankton.

Bacterial phosphorus quota, or the phosphorus content per cell, was calculated

from the bacterial particulate phosphorus (BPP) divide by bacterial abundance (cells ml-

1).

Phosphorus Distribution to Bacteria and Algae

Phosphorus distribution to bacteria, or the portion of the particulate phosphorus

pool taken up by bacteria (%), was calculated as bacterial particulate phosphorus, BPP,

(0.2 μm < particles < 1.0 μm) divided by the algal particulate phosphorus, APP +

bacterial particulate phosphorus, BPP,(> 0.2 μm) *100. Phosphorus distribution to algae,

or the portion of the particulate phosphorus pool taken up by algae (%) was calculated as the algal particulate phosphorus, APP, (>1.0 μm) divided by algal particulate phosphorus,

APP + bacterial particulate phosphorus, BPP,(> 0.2 μm) *100.

Biologically Available Phosphate (BAP) and Michaelis-Menten Kinetics

The concentration of phosphate available to the plankton community was

determined using the Rigler bioassay (Rigler 1966) modified by Bentzen and Taylor

(1991). This technique assumes Michaelis-Menten kinetics of phosphate uptake by the

plankton community. The bioassay was performed in the epilimnion of stations ER 15

(eastern), ER 43, ER 73, 412, 958 (all central basin), and 496 (western basin) and in the

hypolimnion of station 412 during September of 2005. The hypolimnion of station 412

was chosen to observe the behavior of bacterial phosphorus dynamics during hypoxic

conditions (DO = 0.30 mg L-1). Biologically available phosphate (BAP) was estimated

from bacterial growth rate, bacterial P-quota, and the bacterial phosphate uptake constant

at each station according to the method of Gao (2002).

Zero, 50, 100, and 150 μL of a 10,000 nM KH2PO4 were added to four 10 ml

disposable test tubes of whole water. 33Orthophosphate was added and phosphate uptake

rates were determined for each sample (as described in the phosphate uptake procedure).

According to Michaelis-Menten kinetics, v = k * S (v = velocity, k = proportional uptake constant, S = ambient phosphate concentration). S was estimated from the following equation (Gao 2002):

k = (G * Q) v-1

where k = proportional uptake rate constant, G = growth rate, Q = P-quota, and v = velocity.

The inverse of k (1 k-1 = S v-1) was plotted against total phosphate concentrations

-1 (S + Sa, Sa = phosphate added). Using a Hanes-Wolff plot, -Km (x-intercept), Km Vmax

-1 (y-intercept), and 1 Vmax (slope) were determined. The measured ambient phosphate

concentration was estimated from the following equation:

-1 -1 -1 [S] v = (1 Vmax )*[S] + Km Vmax

Labile Dissolved Organic Carbon (LDOC)

Labile dissolved organic carbon (LDOC) concentration was estimated using the

method of Søndergaard et al. (1995). Whole water (950 mL) was filtered through 0.2 μm

filtration capsules (to remove bacteria, algae, and grazers) and inoculated with 50 mL

(5%) of 1.0 μm filtered (to remove algae and grazers) whole water. NH4Cl and Na2HPO4

were added to give final concentrations of 5.6 μM and 1.4 μM respectively, to ensure that

bacterial cells were not nutrient limited and to maximize carbon utilization. The mixture

was added to triplicated BOD bottles and incubated at ambient temperature in the dark

for 30 days. To estimate the amount of carbon respired, initial (time zero) and final (after

30 days) oxygen concentrations were determined using Winkler method with the alkaline

azide modification as described in APHA (1995) and converted to moles of carbon dioxide, using a respiratory quotient of 0.82 (Søndergaard et al. 1995). Bacterial incorporation of carbon was determined by measuring the increase in bacterial biovolume per mL over the 30 day interval. Over 200 bacterial cells were photographed with an RT

Spot camera attached to a Zeiss Akioskop and sized at time zero and after 30 days using

Metamorph Image analysis software. Biomass per mL was determined as the product of average bacterial biovolume and the number of bacterial cells per mL. LDOC was determined as the sum of the C respired plus the amount of C added to bacterial biomass.

Total Dissolved Organic Carbon (TDOC)

Whole water was filtered through pre-combusted GFF filters and placed into duplicate pre-combusted glass ampules. The ampules were sealed and frozen for further analysis. Upon returning to the laboratory, samples were analyzed with a Shimadzu

Total Organic Carbon Analyzer (TOC-VCPN) with parallel standards.

Testing the Microbial Shunt Hypothesis – Low-Molecular Weight Carbon

Amendments

The purpose of this experiment was to determine whether addition of potential

concentration was determined on 3 bottles (time = 30 days, final). 1 mM of glucose,

glucosamine, or leucine was added to triplicate bottles for final concentrations of 1 μM.

Bacterial abundance, bacterial cellular biovolume, bacterial productivity, and phosphate uptake were determined for each bottle at time zero, 24 hours, and 48 hours.

Results

Limnological Variables

Limnological characteristics of the sites investigated are described in Table 1. Stations investigated are labeled in Figure 1.

LDOC Distribution and Abundance

During 2004 and 2005, a wide range of LDOC concentrations utilized by bacteria were observed (range = 0.0 – 201.2 μM) (Table 2). LDOC is detected as the sum of

LDOC respired by bacteria and the LDOC incorporated into growth by bacteria. LDOC was not detectable (0.0 μM) at stations 580 (western basin) and 950 (central basin) during

May of 2005. The highest value (201.2 μM) was observed at station 496 (western basin) during June of 2005. In 2004, the lowest LDOC value (21.4 μM) was observed in the epilimnion at station 880 (central basin). The highest LDOC value (125.8 μM) was observed at station 950 (central basin).

A variety of patterns were observed between vertical profiles. At most sites during 2004, LDOC concentration was greater in the hypolimnion than the epilimnion.

However, in 2005, the LDOC concentrations from epilimnetic samples tended to be

greater than those of the hypolimnion. At most stations, the majority of LDOC was respired by bacteria with only a small portion incorporated into bacterial cells.

LDOC concentrations were similar among basins during 2004 and 2005 (Table 3).

During June and August of 2004, each basin exhibited similar LDOC concentrations

(Figure 2 a and c). In July of 2004, the eastern basin exhibited the highest LDOC concentration relative to the western and central basins (Figure 2b). During May, July and August of 2005 (Figure 3 a, c, and d) LDOC concentration remained constant among basins. During June 2005, the highest LDOC concentrations were observed in the western basin (Figure 3b) relative to the other basins. However, during September, the highest LDOC concentrations were observed in the eastern basin (Figure 3e). In 2004, the proportion of LDOC incorporated into bacterial cells remained less than 10% throughout the season (Table 2).

Biologically Available Phosphate (BAP), Phosphate Uptake, and Turnover Time

Biologically available phosphate, BAP (nM), or the amount of phosphate readily available and usable by bacteria changed seasonally in Lake Erie (Table 4, Figure 4).

During May, BAP was significantly greater in the eastern basin (383 ± 182 nM) relative to the western and central basins. In June and July, BAP was greater in the western basin

(121 ± 57 and 152 ± 39 nM, respectively), relative to the central and eastern basins.

During August and September, BAP remained constant among all basins. BAP values for individual stations are recorded in Table 5.

The bacterial phosphate uptake rate constant, k (min-1) remained fairly constant

among the western, central, and eastern basins throughout the summer (Table 4, Figure

5). Only in June was the bacterial k significantly higher in the central basin (k = 0.015 ±

0.005 min-1) relative to the western and eastern basins. The lowest bacterial k was observed in May (k < 0.001 min-1) in all basins. Bacterial k increased during July

(maximum k = 0.027) and August (maximum k = 0.039 min-1) in all basins and decreased

in September. Bacterial k values for individual stations are recorded in Table 5.

Phosphate uptake velocity, PUV (nM min-1) was significantly greater in the

western basin relative to the central and eastern basins during June, August, and

Septermber (Table 4, Figure 6). PUV remained constant in all basins during May and

July. The highest seasonal PUV values were observed in July for all basins. PUV per

cell (nM min-1 cell-1) followed a similar pattern as PUV (Table 4, Figure 7). Maximum

PUV per cell were observed during July in all basins. Also, in June, PUV per cell remained constant, but significantly lower. During May, the highest PUV per cell was observed in the central basin. During August, the western basin exhibited the highest

PUV per cell and during September, the lowest PUV per cell was observed in the eastern basin. PUV and PUV cell-1 values for individual stations are recorded in Table 5.

Turnover time represents the amount of time for the entire particulate phosphate

pool to be used up and recycled by the total plankton community (bacteria + algae). At

the beginning of the season (May and June), turnover time, TT (min) was significantly

greater in the eastern basin relative to the western and central basins (Table 4, Figure 8).

The highest TT values were observed during May in all basins – western (761 ± 186

min), central (1229 ± 350 min) and eastern (2773 ± 656 min). TT was greatest in the

western basin during July. During August and September, TT remained fairly constant

among all basins. Maximum TT was observed in the eastern basin during May (2773 ±

656). Total TT values for individual stations are recorded in Table 5.

Phosphate Apportionment to Bacteria

The portion of the available phosphate pool apportioned to bacteria fluctuated

seasonally (Table 6, Figure 9). Despite low uptakes by both groups of organisms in May, more Pi was apportioned to bacteria in the western and central basins, 80% and 62%,

respectively (Figure 9a). Less phosphate was apportioned to bacteria in the eastern basin,

34%; however, this lower than expected value may be artifact because algal and bacterial

phosphate uptake constants were equal and nearly zero in during May. In June, Pi

apportionment to bacteria ranged from lowest in the western basin (42%) to highest in the eastern basin (80%) (Figure 9b). Pi apportionment to bacteria was significantly lower in the western basin relative to the central and eastern basins. The greatest lakewide Pi apportionment to bacteria was observed in July accounting for 68% (western basin) to

89% (eastern basin) of total apportionment. The apportionment to bacteria was constant throughout the basins (Figure 9c). The majority of the available phosphate pool was apportioned to bacteria in all basins during July - 85% (western), 55% (central), and 63%

(eastern) (Figure 9d). During this time, the apportionment of phosphate to bacteria was greater in the western basin relative to the central and eastern basins. In September, Pi

apportionment to bacteria was at a seasonal low. The apportionment to bacteria was

lowest in the western basin (19%) and increased significantly in the central (43%) and

eastern (52%) basins (Figure 9e).

Particulate Phosphorus

Throughout most of the summer, total phosphorus (TP) concentration was greatest in the western basin with the algal particulate phosphorus (APP) fraction comprising the greatest majority of the total (Table 7, Figure 10). Bacterial particulate phosphate concentrations remained stable throughout the season. The highest TP was observed in May in the western basin. A trend of decreasing TP and APP from the western to eastern basin was observed. All forms of particulate and dissolved P decreased substantially in June and no differences in TP, APP, BPP, and TSP were observed between the basins. During July TP was highest in the eastern basin due to a significantly higher TSP concentration. The APP fraction decreased from the western to eastern basin while the fraction of BPP remained constant. During August, TP was 2-3 times greater in the western basin than the central and eastern basins. APP followed the same trend as TP, high in the western basin and decreasing in the central and eastern basins. BPP values remained constant lake wide. In September, moderate TP values were observed in each basin with highest TP and APP values in the western basin. BPP values remained constant in all basins and TSP was greatest in the eastern basin.

Available Phosphorus Distribution Amongst Bacterioplankton and Phytoplankton

Phosphorus distribution, or P distribution, describes the portion of the particulate phosphorus pool that is distributed preferentially to algal or bacterial particles. Early in

the season (May and June), P distribution to bacteria was constant among each of the

basins. Late in the season (July, August, and September), a trend in P distribution was

observed. P distribution to bacteria increased from the western basin to the eastern basin

(Table 7, Figure 11). During May, P distribution to bacteria accounted for 30-40% of the

particulate phosphorus pool (Figure 11a). P distribution increased in June to account for

~50-55% of the particulate P pool (Figure 11b). In the eastern basin, P distribution to

bacteria accounted for the majority of the particulate P pool. In July, the lowest P

distribution to bacteria was observed in the western basin (~30%). P distribution to

bacteria increased to ~50% in the central and eastern basins (Figure 11c). Among all

basins during August (~15-25%), P distribution to bacteria decreased; however, the trend

of increasing P distribution from west to east remained (Figure 11d). During September,

P distribution to bacteria increased from the western basin (30%) to the central and

eastern basins, ~50% and ~60%, respectively (Figure 11e). In the eastern basin, P

distribution to bacteria accounted for the majority of the particulate P pool.

Bacterial P-Quota

During most of the season, an overall trend in bacterial P-quota (nmol cell-1), or the P content per cell, was observed. Bacterial P-quota increased from the western basin to the eastern basin (Table 7, Figure 12). During May, July, and September, the highest

bacterial P-quotas were observed in the central and eastern basins relative to the western

basin (Figure 10 a, c, e). During June and August, bacterial P-quota remained constant

among all basins (Figure 10 b, d). The lowest P-quotas (< 1 nmol cell-1 x 10-7) were

observed when P-quota was constant. Overall, the highest P-quotas were observed in

July ranging from 2.5 x 10-7 nmol cell-1 in the western basin to 5 x 10-7 nmol cell-1 in the central and eastern basins.

Biologically Available Phosphate (BAP) and Michaelis-Menten Kinetics

BAP values ranged from 0.5 ± 0.1 nM at station ER 15 (eastern basin) to 28.7 ±

7.9 nM at station ER 73 (central basin) (Table 8). The hypolimnetic BAP at station 412

(26.9 ± 11.9 nM) exceeded the BAP in the epilimnion (1.5 ± 1.0). At most stations, algal

Km exceeded bacterial Km. This difference was also observed in other aquatic

environments (Cotner and Wetzel 1991) and Lake Erie (Gao 2002; Gao and Heath 2005).

At two stations (ER 15 and ER 43), the opposite phenomenon was observed. Here,

bacterial Km exceeded algal Km, allowing for more rapid phosphate uptake by algae. The

algal Km and bacterial Km in the epi- and hypolimnion of station 412 were approximately

equal. The largest algal and bacterial Vmax and Michaelis-Menten velocities (vMM) were

observed at ER 73 (central basin), the station with the highest BAP. The lowest algal and

bacterial Vmax were observed in the hypolimnion of station 412; however, the lowest algal

vMM was observed in the epilimnion of 412 and ER 15. For bacteria, the hypolimnion of

412 also exhibited the lowest vMM. At most stations, algae represented the greatest

percentage of the uptake velocity (68-100%). At two nearshore, central basin stations

(958 and 412 hypo), bacteria represented the greatest percentage of the uptake velocity

(88-89%). The percentage of vuptake correlated with the apportionment of phosphate to

2 algae and bacteria (% vuptake vs. Pi apport; r = 0.63) (Figure 13), but did not correlate

2 with LDOC concentration (LDOC vs. % vuptake; r = 0.01) or bacterial P-quota (P-quota

2 vs. % vuptake; r = 0.03).

Relationships between LDOC, TSI, and Phosphorus Dynamics

Relationships between TSI and Pi apportionment and TSI and P distribution were examined. TSI values were divided into groups (20-29, 30-34, 35-39, 40-44, 45-54, and

55+) and compared with Pi apportionment and P distribution values for stations falling

within the given TSI ranges. The group with the highest TSI values (55+) exhibited significantly lower Pi apportionment to bacteria relative to all other groups (Figure 14a)

(ANOVA post hoc LSD, p < 0.05). All other TSI groups exhibited equal Pi

apportionment to bacteria. For groups with TSI < 55, the majority of the available

phosphate pool was apportioned to bacteria (~55%+). Only for group TSI = 55+ was the

majority of the available phosphate pool apportioned to algae (64%). Using pooled data

2 for the summer, Pi apportionment did not correlate with TSI (r = 0.06).

The group with the lowest TSI values (20-29) exhibited significantly higher P

distribution to bacteria relative to all other groups (Figure 14b). Constant P distribution

to bacteria was observed for all other groups. The majority of P distribution to bacteria

(52%) was seen only in the group with the lowest TSI values (20-29). For all other groups, the majority of the available particulate phosphorus pool was distributed to algal

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particles (57%+). Using pooled data from summer epilimnetic samples, P distribution

exhibited a mild correlation with TSI (r2 = 0.18).

Relationships between LDOC and Pi apportionment and LDOC and P distribution

were also examined. LDOC values were divided into groups (0-19, 20-29, 30-39, 40-

49,50-69, 70-99, 100+) and compared with Pi apportionment and P distribution values for

stations falling within the given LDOC ranges. Both Pi apportionment and P distribution

remained constant without significant differences between groups (ANOVA post hoc

LSD p > 0.05) (Figure 15 a and b). The portion of the available phosphate pool

apportioned to bacteria ranged from 45 to 85% while the portion of the available

phosphorus pool distributed to bacterial particles ranged from 35 to 50%. Using pooled

2 data from the summer, no correlation was observed for LDOC and Pi apportionment (r =

0.00) or LDOC and P distribution (r2 = 0.00). LDOC did not correlate with TSI (r2 =

0.00). A weak correlation was observed between TDOC and TSI (r2 = 0.24).

Relationship between LDOC and Bacterial Phosphorus Dynamics

In the Microbial Shunt Hypothesis of Phosphorus Apportionment (Heath et al.

2003), bacteria exist in different metabolic states dependent upon the LDOC conditions

of the environment. In high LDOC conditions (> 50 μM), bacteria were in a Growth

State exhibiting high growth rates, lower P-quotas, and modest phosphate uptake. In low

LDOC conditions (< 50 μM), bacteria were in a Storage State exhibiting slow growth,

higher P-quotas, and rapid phosphate uptake. The relationships between LDOC

concentration and bacterial phosphorus dynamics were evaluated in Lake Erie during the

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summer of 2005. Patterns observed in Lake Erie were similar to those observed by Heath

et al. (2003) and Gao and Heath (2005). When LDOC concentration was high, bacterial

P-quota was low; and, for many stations, when LDOC concentration was low, bacterial

P-quota was high (Figure 16). However, at some stations, low bacterial P-quota was also observed under low LDOC conditions. Conversely, no examples of high P-quota under high LDOC conditions were observed. Similar patterns were observed between LDOC and phosphate uptake velocity (PUV) and PUV per cell (Figures 17 and 18, respectively).

Under the highest LDOC concentrations, PUV and PUV per cell were low and often zero.

The highest PUV and PUV per cell were observed at stations with lower LDOC concentrations (< 50 μM). Also, the PUV and PUV per cell from many stations remained low (and often zero) when LDOC concentrations were lowest. No examples of high

PUV or PUV per cell were observed under high LDOC conditions.

Testing the Microbial Shunt Hypothesis – Bacterial Response to Low-Molecular

Weight Carbon Amendments

In May, June, and July, 1 μM glucose was added to carbon depleted (via bacterial respiration) BOD bottles. Two stations were investigated for each month. During May, bacterial productivity, BP (μg C ml-1 hr-1) x 10-4 and bacterial phosphate uptake constant,

k (min-1), of station ER 15 and station 942 remained constant (ANOVA Tukey HSD p >

0.05) with the addition of glucose (Figure 19 a, b). The LDOC concentrations at both

stations were low, 7.0 ± 3.7 μM (ER 15) and 5.4 ± 4.0 μM (942). During June, addition

of glucose significantly increased BP linearly at 24 and 48 hours (Figure 20a) at stations

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ER 15 and 449. Bacterial k increased significantly after 48 hours only at station 449 and

remained constant at station ER 15 (Figure 20b). The LDOC concentrations were

moderate at each station, 73.2 ± 6.4 μM (ER 15) and 70.7 ± 22.1 μM (449). During July,

addition of glucose significantly increased BP between 24 and 48 hours (Figure 21a) at

each station, ER 73 and 958. Bacterial k decreased significantly between 0 and 24 hours and remained constant, k = 0.0000, between 24 and 48 hours (Figure 21b). This decrease was observed at each station. LDOC concentration at station ER 73 was low measuring

30.2 ± 5.8 μM while the LDOC concentration at station 958 was high measurig104.5 ±

18.5 μM.

In September, 1 μM glucose, 1μM glucosamine, or 1 μM lysine were added to the

carbon depleted BOD bottles. Two stations, 958 and ER 73, were investigated during this month. The LDOC concentrations at both stations were low, measuring 12.3 ± 3.0

μM and 31.1 ± 7.3 μM, respectively. At station 958, BP remained constant with the addition of glucose, glucosamine, or lysine (Figure 22a). Bacterial k increased significantly with the addition of glucose; however, bacterial k remained constant with

the addition of glucosamine or lysine (Figure 22b). At station ER 73, addition of glucose significantly increased BP after 24 and 48 hours (Figure 23 a). Addition of glucosamine and lysine increased BP between 24 and 48 hours only. The increase in BP observed

with the addition of glucose was greater than the increase observed with the additionof

glucosamine and lysine. Bacterial k remained low and constant with addition of each

carbon compound (Figure 23 b).

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Discussion

The relationship between TSI and phosphate apportionment and TSI and phosphorus distribution may be a result of differences in algal abundance and/or biomass

and the differences in enzyme kinetics between bacteria and algae. At the highest TSIs

(55+), significantly less phosphate was apportioned to bacteria and more phosphate was

apportioned to algae relative to lower TSI groups. At the lowest TSIs (20-29) or the most

oligorophic environments, more particulate phosphorus was distributed to bacterial

particles relative to higher TSI groups. TSI is directly proportional to chlorophyll a

concentration, a surrogate for algal biomass. Phosphorus has been shown to influence

both algal and bacterial abundance, and that bacteria and algae influence each other as

well (Currie 1990). Competition between algal and bacterial cultures was first noticed by

Rhee (1972) when phosphorus was depleted. Phosphate uptake by plankton in aquatic

environments usually follows Michaelis-Menten kinetics – initial slow uptake velocity at

low substrate concentration followed by enzyme saturation and maximum uptake velocity

(Vmax) at higher substrate concentrations. In eutrophic environments, algae can take up

phosphate more rapidly than bacteria; however, in oligotrophic environments, bacteria

are more successful at phosphate uptake than algae (Currie et al. 1986; Vadstein and

Olsen 1989; Gao and Heath 2005). These differences in phosphate uptake were observed

in Lake Erie, especially in the most eutrophic environment (TSI = 55+)

Bacteria are more successful than phytoplankton in obtaining available phosphate

due to differences in the parameters of enzyme kinetics (Cotner and Wetzel 1991).

Bacteria exhibit low Km, so in low phosphate environments, bacteria reach Vmax more

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rapidly than algae, allowing for more rapid uptake of available phosphate. Algae, however, exhibit high Vmax so during opportunistic high phosphate pulses, algae take up phosphate more rapidly and long after bacteria have reached Vmax. The observed enzyme parameters in Lake Erie mostly support expected patterns based on the research of others

(Cotner and Wetzel 1991; Gao and Heath 2005). With lower Km values at each site, bacteria can uptake phosphate more rapidly in low phosphate conditions. More phosphate was apportioned to bacteria at most stations. The bacterial community was more successful at obtaining the available phosphate in the environment. Less phosphate was apportioned to bacteria at most stations.

Phosphate apportionment and particulate phosphorus distribution to algae was constant and independent of LDOC concentration. Patchy distribution of LDOC concentration will be reported in Chapter 3. LDOC concentration may be analogous to biologically available phosphorus. When phosphate is least abundant and most limited, it is utilized most rapidly by the bacterioplankton community (Currie and Kalff 1984;

Cotner and Wetzel 1991; Gao and Heath 2005). Rapid utilization of the LDOC pool by bacterioplankton may explain the low (< 50 μM) LDOC concentrations observed at many stations. Other factors besides LDOC, including bacterial metabolic status and/or bacterial composition, may regulate phosphorus apportionment and distribution to algae.

Differential utilization of LDOC by different bacterial assemblages was noted in Chapter

3. Bacterioplankton communities may be directly responsible for the amount of DOC that is labile or usable at a particular moment. Determining the source of LDOC may

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help to understand the factors that control phosphate apportionment and phosphorus distribution to bacteria.

Neither LDOC nor TDOC correlated with TSI, suggesting that the source of

LDOC is not algal. Algal-derived DOM is usually the major autochthonous source of

DOC to aquatic ecosystems (Bertilsson and Jones 2003). The DOM can arise from extracellular release, viral lysis, or biotic/abiotic transformation. In Lake Erie, LDOC may arise from other autochthonous sources and/or allochthonous sources. Another potential autochthonous source includes viral lysis of bacteria (Middelboe and Lyck

2002). Though continual viral lysis, bacteria may serve as their own autochthonous carbon source. Allochthonous sources included carbon inputs from tributaries, groundwater, and precipitation (Aitkenhead-Peterson et al. 2003). Many tributaries contribute loads to Lake Erie including the Detroit River, Maumee River, Rouge River,

Cuyahoga River, and others. Allochthonous inputs have been shown to impact ecosystem processes in other Great Lakes. In Lake Michigan, terriginous inputs accounted for 10-20% of lake bacterial production and planktonic respiration (Biddanda and Cotner 2002).

Monosaccharides (e.g. glucose) may be an abundant component of the LDOC pool in Lake Erie at different times throughout the season. During June, July, and

August, glucose stimulated bacterial productivity at most stations investigated, irregardless of the measured LDOC concentration (low, moderate, or high). Glucose is important to bacterial production in other aquatic environments. In the Gulf of Mexico, glucose supported 5-10% of bacterial production in the surface waters (Skoog et al 1999).

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Glucose, galactose, and mannose composed 55-70% of the dissolved free

monosaccharide (DFCHO) pool in Lake Constance and glucose was turned over most

rapidly by bacteria (Bunte and Simon 1999). Other components of the LDOC pool may

include amino sugars (e.g. glucosamine) or amino acids (e.g. lysine). Glucosamine and

lysine stimulated bacterial productivity during September, but to a lesser degree than

glucose. Amino acids stimulated bacterial productivity in other aquatic environments.

The assimilation of dissolved free amino acids (DFAA), dissolved combined amino acids

(DCAA), and DNA accounted for 42-60% of bacteria production in cultures from diverse

marine environments (Jørgensen et al. 1993). Free amino acids comprised 50% of the

dry weight of planktonic organisms and account for 1-100%+ of bacterial production in

diverse ocean and lake environments (Kirchman 2003). The lability of carbon

compounds may be seasonal in Lake Erie. In Lake Constance, amino acids were

preferentially respired by bacteria during spring and glucose was preferentially respired

in the summer (Weiss and Simon 1999).

The impacts of glucose addition on bacterial phosphate uptake were variable. In

50% of the cases, bacterial phosphate uptake rate remained low constant with the addition of glucose. In 25% of the cases, bacterial k increased and in 25% of the cases, bacterial k decreased. With the addition of glucoasamine and lysine, bacterial k remained low and constant. Since the majority of bacterial k values were low initially, it is difficult to determine if the addition of the low molecular weight (LMW) carbon compounds suppressed bacterial phosphate uptake or had no effect. Repeating these experiments

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under variable conditions will be necessary to determine the true effects of LMW carbon additions.

The Microbial Shunt Hypothesis of Phosphorus Apportionment (Heath et al.

2003) proposed two metabolic states for bacteria. In the Growth State, bacteria exhibit rapid growth rates, lower bacterial P-quotas, and modest phosphate uptake. These metabolic characteristics occur in high (>50 μM) LDOC conditions. In the Storage State, bacteria exhibit slow growth rates, higher bacterial P-quotas, and rapid phosphate uptake.

These metabolic characteristics occur in low (< 50 μM) LDOC conditions. We observed bacterioplankton in the growth and storage states in Lake Erie during the summer of

2005. At the highest LDOC conditions, bacteria exhibited lower P-quotas and low phosphate uptakes indicative of high growth rates in the Growth State. Phosphate uptake velocities (PUV) were often zero. At lower LDOC concentrations, bacteria exhibited higher P-quotas and more rapid uptake velocities at some stations indicative of the

Storage State. However, at the lowest LDOC conditions, bacteria exhibited lower P- quotas and low phosphate uptake velocities.

We propose an additional metabolic state, the Inactive State, to explain the lower

P-quotas and low phosphate uptakes observed in the bacterioplankton at the lowest

LDOC conditions. In chapter 3, the patchy distribution and abundance of LDOC will be presented. Even at individual stations, LDOC concentrations were variable throughout the season. Also, at many stations, LDOC utilization was dependent upon the bacterioplankton assemblage present. As the LDOC concentration changes at a particular location, bacteria may shift from Growth, Storage, and Inactive States dependent upon

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the current LDOC conditions. Whether the shift follows a particular trajectory (ie.

Growth to Storage to Inactive) throughout the season or is random remains to be seen.

It is plausible that many bacteria in aquatic environments are inactive. Only about

30% of bacterioplankton from natural communities were considered active by Smith and del Giorgio (2003) based on analysis of 23 studies. They suggest a “nested hierarchy of physiological states” in bacterial communities with a continuum of activity that is detectable by different molecular methods. Many bacterial groups including bacilli, clostridia, and cyanobacteria are capable of forming dormant states in natural environments (Lloyd and Hayes 1995). Over 99% of the bacterial community in natural environments are viable but not culturable (VBNC) by traditional methods leading to the

“great plate count anomaly” (Staley and Konopka 1985) or the discrepancy in cell counts between traditional and molecular methods (described in detail by Amann et al. 1995).

Bacteria from natural environments may have unique nutrient requirements, low nutrient requirements, and/or may be extremely slow growing (Lloyd and Hayes 1995).

In Lake Erie and other aquatic environments, many bacteria may enter an Inactive

State when carbon and/or nutrient levels reach low levels. In the Inactive state, which may involve cystic or dormant stages, critical nutrients may be present at minimal levels

(ie. low bacterial P-quota). The metabolic rate may be reduced to maintenance levels resulting in low or no phosphate uptake by bacteria even at low nutrient levels. The use of microscopic and molecular techniques (Amann et al. 1995) and flow cytometry (Porter et al. 1993) may help to identify active versus inactive bacterial cells within Lake Erie.

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Summary

The purpose of this investigation was to evaluate the Microbial Shunt Hypothesis,

proposed by Heath et al. (2003), in a wide range of trophic states within Lake Erie. Our

results are consistent with the MSH since the highest PUVs, PUVs per cell, and P-quotas were observed at stations with low LDOC conditions (MSH Storage State). Conversely,

the lowest PUVs, PUVs per cell, and P-quotas were observed at stations with high LDOC

conditions (MSH Growth State). In addition, low PUVs, PUVs per cell, and P-quotas

were observed at many stations with the lowest LDOC conditions. As a result, a new state, the Inactive State, was added to the MSH to describe the metabolism of these bacteria. Bacterioplankton at the highest TSIs (55+) took up phosphate significantly less

than bacterioplankton at lower TSIs (< 55). In addition, TSI influenced phosphorus

distribution to bacteria. Bacterioplankton at the lowest TSIs (20-29) received a

significantly greater portion of the particulate phosphorus pool than bacterioplankton

with higher TSIs (30+). These findings are also consistent with the MSH. We found that

neither LDOC nor TDOC concentrations correlated with TSI suggesting that algae or

algal exudates may not be a major contributor to the LDOC and/or TDOC pools in Lake

Erie.

The experimental addition of glucose to carbon depleted bacteria increased bacterial productivity and decreased the phosphate uptake rate constant for most cases.

Addition of glucosamine and lysine resulted in the increase of bacterial productivity at one station. These results are consistent with the MSH and suggest glucose as a potential component of the LDOC pool.

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Acknowledgements

We thank the following individuals and organizations for assistance with this

investigation: Bob Christensen - the captain of the R/V Lake Guardian, the crew and

technicians aboard the R/V Lake Guardian; Curtis Clevinger, Dana McDermott, Megan

Castagnaro, and Bob Christy from Kent State University and Kathy Wilson from

Sweetbriar College for technical assistance; Margaret Lansing from NOAA/GLERL for

assistance with data collection; and, Michael Twiss from Clarkson University for

compiling the chlorophyll data. This research was funded as part of the International

Field Year of Lake Erie (2005) by the National Oceanic and Atmospheric Administration

(NOAA), Great Lakes Environmental Research Laboratory (GLERL), US Environmental

Protection Agency (EPA), Cooperative Institute of Limnology and Ecosystem Research

(CILER), and the Ohio Sea Grant College Program.

Table 1. Limnological variables in Lake Erie during summer 2005: Basin, station number, sample type, latitude (DDMMSS),

longitude (DDMMSS), sample depth (m), sounding depth (m), temperature (°C), dissolved oxygen concentration (mg L-1),

chlorophyll a concentration (μg L-1), and trophic state index (TSI)

Sample Sample Sounding Basin Site Type Latitude Longitude Depth Depth Temp DO chl a TSI

(DDMMSS) (DDMMSS) (m) (m) ( °C) (mg L-1) (μg L-1)

MAY

Western 496 Epi 41 33 54 82 43 29 2.0 3.0 11.9 9.61 10.56 53.7

Western 835 Epi 41 45 12 83 20 42 2.0 6.0 14.4 11.14 20.24 60.1

Western 580 Epi 41 50 56 83 06 21 2.0 10.0 14.1 9.86 1.67 35.6

Central ER 43 Epi 41 47 15 81 56 42 2.0 23.0 3.37 42.5

Central ER 73 Epi 41 58 40 81 45 25 2.0 24.0 2.24 38.5

Central 950 Epi 42 34 58 81 26 10 2.0 13.0 1.16 32.0

Central 958 Epi 41 32 59 81 42 15 2.0 13.0 4.74 45.8

Eastern ER 15 Epi 42 31 03 79 53 37 2.0 60.0 3.7 11.81 2.17 38.1

Eastern 449 Epi 42 45 40 79 59 21 2.0 13.5 6.6 11.24 0.48 23.3

Eastern 942 Epi 42 15 36 79 49 49 2.0 16.0 8.6 11.14 0.81 28.5 JUNE

Western 835 Epi 41 45 15 83 20 33 1.9 6.0 22.5 8.56 3.54 43.0

Western 580 Epi 41 50 56 83 06 17 2.0 10.0 22.6 10.10 6.68 49.2

Western ER 92 Epi 41 56 57 82 41 15 3.5 11.0 20.1 9.51 1.98 37.2 111

Western 496 Epi 41 34 04 82 43 20 3.0 10.0 20.3 8.85 2.39 39.1

Western 1163 Epi 41 28 33 82 43 20 2.5 3.0 21.5 9.01 4.57 45.5

Central 964 Epi 41 38 00 82 17 58 3.5 8.0 20.3 10.20 3.10 41.7

Central 311 Epi 41 39 59 82 29 57 4.5 13.0 20.0 10.36 1.03 30.9

Central 412 Epi 42 05 59 82 11 21 4.0 21.5 18.6 9.98 0.7 27.1

Central ER 73 Epi 41 58 41 81 45 24 5.0 24.0 18.3 10.26 1.06 31.1

Central ER 73 Hypo 41 58 41 81 45 24 14.5 24.0 8.9 13.11 1.62 35.3

Central ER 43 Epi 41 47 16 81 56 38 2.5 23.0 9.30 9.82 36.9

Central ER 37 Epi 42 06 37 81 34 28 0.6 24.0 21.5 9.56 1.98 37.3

Central ER 78 Epi 42 07 00 81 14 59 4.3 23.0 18.4 10.13 1.53 34.7

Central 950 Epi 42 33 25 81 26 30 3.6 23.0 17.7 10.52 1.20 32.4

JULY

Central 958 Epi 41 32 60 81 42 15 2.0 13.0 22.1 9.28 1.85 36.6

Eastern ER 15 Epi 42 34 35 79 53 38 5.0 60.0 14.5 13.85 3.66 43.3

Eastern ER 15 Meta 42 34 35 79 53 38 9.5 60.0 10.3 15.69 4.59 45.5

Eastern ER 15 Hypo 42 34 35 79 53 38 41.0 60.0 4.2 13.29 0.56 24.8

Eastern 449 Epi 42 45 41 79 59 20 2.5 13.5 18.0 10.57 0.98 30.3

Eastern 942 Epi 42 15 36 79 49 48 6.5 16.0 19.1 9.83 1.52 34.7

Western 835 Epi 41 45 19 83 20 27 2.0 6.0 27.0 7.91 12.77 55.6

Western 580 Epi 41 50 54 83 06 24 2.0 10.0 25.8 7.39 2.64 40.1

Western ER 92 Epi 41 56 60 82 41 11 2.0 11.0 26.1 7.85 2.42 39.3

Western 496 Epi 41 34 03 82 43 17 2.0 10.0 25.7 6.13 1.94 37.1

Western 1163 Epi 41 28 27 82 42 12 2.0 3.0 27.2 7.19 4.18 44.6 112

Central 965 Epi 41 30 04 82 30 02 2.0 13.0 2.43 39.3

Central 311 Epi 41 39 58 82 29 60 2.0 13.0 26.1 8.16 3.38 42.5

Central 412 Epi 42 05 60 82 11 24 2.0 21.5 26.0 8.12 1.11 31.6

Central ER 73 Epi 41 58 40 81 45 22 2.0 24.0 25.3 8.13 0.70 27.0

Central ER 73 Meta 41 58 40 81 45 22 13.5 24.0 16.8 10.21 1.15 31.9

Central ER 73 Hypo 41 58 40 81 45 22 22.5 24.0 9.0 6.88 1.32 33.3

Central ER 43 Epi 41 47 19 81 56 43 2.0 23.0 25.6 8.32 1.22 32.5

Central ER 37 Epi 42 06 37 81 34 27 2.0 24.0 0.50 23.7

Central ER 78 Epi 42 06 56 82 15 18 2.5 23.0 0.86 29.1

Central 950 Epi 42 33 19 81 26 36 2.0 13.0 24.5 8.10 0.73 27.5

Central 958 Epi 41 32 57 81 42 17 2.0 13.0 24.2 7.89 0.85 29.0

Eastern ER 15 Epi 42 31 02 79 53 37 2.0 60.0 25.4 8.29 0.69 26.9

Eastern ER 15 Meta 42 31 02 79 53 37 15.2 60.0 16.9 10.53 0.89 29.4

Eastern ER 15 Hypo 42 31 02 79 53 37 61.2 60.0 4.6 11.69 0.08 5.3

Eastern 449 Epi 42 45 38 79 59 05 2.5 13.5 24.8 8.26 0.83 28.7

Eastern 942 Epi 42 15 33 79 49 48 3.0 16.0 25.0 8.14 0.63 26.1

AUGUST

Western 835 Epi 41 45 17 83 20 30 2.0 6.0 26.8 6.34 10.96 54.1

Western 580 Epi 41 50 53 83 06 23 2.0 10.0 26.3 7.29 7.40 50.2

Western ER 92 Epi 41 56 59 82 41 14 2.0 11.0 26.4 6.86 7.24 50.0

Western 496 Epi 41 34 07 82 43 17 2.0 10.0 26.9 9.43 10.35 53.5

Western 496 Hypo 41 34 07 82 43 17 8.3 10.0 21.7 0.52 4.40 45.1

Western 1163 Epi 41 28 27 82 42 12 1.5 3.0 26.0 5.47 43.73 67.6 113

Western 1163 Hypo 41 28 27 82 42 12 8.3 3.0 25.0 3.07 33.12 64.9

Central 965 Epi 41 30 05 82 30 01 2.0 13.0 26.5 7.44 3.91 44.0

Central 311 Epi 41 39 59 82 29 59 2.0 13.0 26.4 7.95 6.89 49.5

Central 311 Hypo 41 39 59 82 29 59 12.8 13.0 16.7 0.94 4.11 44.4

Central 412 Epi 42 05 60 82 11 24 2.0 21.5 4.17 44.6

Central ER 73 Epi 41 58 39 81 45 26 2.0 24.0 26.3 7.71 2.30 38.7

Central ER 73 Meta 41 58 39 81 45 26 16.0 24.0 17.7 4.49 2.86 40.9

Central ER 73 Hypo 41 58 39 81 45 26 22.0 24.0 10.0 4.78 1.16 34.9

Central ER 43 Epi 41 47 19 81 56 41 2.0 23.0 26.5 7.67 1.81 36.4

Central ER 37 Epi 42 06 35 81 34 29 1.5 24.0 26.2 7.40 1.12 31.7 Central ER 78 Epi 42 06 57 81 14 58 3.0 23.0 26.1 7.66 1.83 36.5

Central 950 Epi 42 33 24 81 26 31 2.0 13.0 25.1 7.72 2.42 39.2

Central 958 Epi 41 32 57 81 42 17 2.0 13.0 26.0 7.49 2.67 40.2

Eastern ER 15 Epi 42 30 55 79 52 30 3.0 60.0 25.2 7.63 1.19 32.3

Eastern ER 15 Meta 42 30 55 79 52 30 17.0 60.0 9.6 12.41 1.72 35.9

Eastern ER 15 Hypo 42 30 55 79 52 30 61.0 60.0 4.8 10.71 0.09 6.9

Eastern 449 Epi 42 45 39 79 59 21 2.0 13.5 24.7 7.09 1.13 31.8

Eastern 942 Epi 42 15 36 79 49 49 2.0 16.0 25.9 7.64 1.93 37.0

SEPT

Western 835 Epi 41 45 09 83 20 45 2.0 6.0 23.2 6.65 7.21 49.9

Western 580 Epi 41 50 54 83 06 24 2.0 10.0 23.2 7.65 7.84 50.8

Western 496 Epi 41 34 15 82 43 23 2.0 10.0 23.9 6.26 13.69 56.2

Western 1163 Epi 41 28 27 82 41 11 2.0 3.0 23.4 5.40 18.55 59.2 114

Central 965 Epi 41 30 03 82 30 07 2.0 13.0 23.6 5.27 12.09 55.0

Central 311 Epi 41 39 59 82 29 59 2.0 13.0 23.3 6.85 8.93 52.0

Central 412 Epi 42 05 54 82 11 23 2.0 21.5 22.7 7.21 3.68 43.4

Central 412 Hypo 42 05 54 82 11 23 20.2 21.5 10.8 0.30 2.49 39.5

Central ER 73 Epi 41 58 41 81 45 24 2.0 24.0 23.0 7.94 5.94 48.1

Central ER 73 Meta 41 58 41 81 45 24 18.2 24.0 11.9 1.40 1.90 36.9

Central ER 73 Hypo 41 58 41 81 45 24 23.5 24.0 10.6 1.40 2.27 38.6 Central ER 43 Epi 41 47 20 81 56 43 2.0 23.0 22.3 7.51 4.00 44.2

Central ER 43 Hypo 41 47 20 81 56 43 21.6 23.0 11.8 1.09 2.43 39.3

Central ER 78 Epi 42 06 60 81 15 01 2.0 23.0 22.9 7.08 3.24 42.1

Central ER 78 Meta 42 06 60 81 15 01 21.0 23.0 17.3 2.42 2.05 37.6

Central ER 78 Hypo 42 06 60 81 15 01 22.5 23.0 13.7 0.12 3.54 43.0

Central 950 Epi 42 33 24 81 26 34 2.0 13.0 22.3 7.05 3.18 41.9

Central 958 Epi 41 32 59 81 42 14 2.0 13.0 22.1 6.37 3.29 42.3

Eastern ER 09 Epi 42 32 15 79 37 06 2.0 22.4 7.22 2.58 39.9

Eastern ER 09 Meta 42 32 15 79 37 06 21.0 15.9 8.61 1.94 37.1

Eastern ER 09 Hypo 42 32 15 79 37 06 49.0 5.3 9.15 0.66 26.5

Eastern ER 15 Epi 42 30 58 79 53 41 2.0 60.0 22.3 7.63 2.90 41.0

Eastern ER 15 Meta 42 30 58 79 53 41 21.7 60.0 7.2 9.16 0.60 25.5

Eastern ER 15 Hypo 42 30 58 79 53 41 62.0 60.0 5.3 9.45 0.09 7.4

Eastern 449 Epi 42 45 40 79 59 15 2.0 13.5 22.6 7.59 2.87 40.9 115

Table 2. LDOC concentration by station during 2004 and 2005: LDOC respired by bacteria (μM), LDOC incorporated into bacterial cells (μM), fraction of total LDOC incorporated into bacterial cells (%), LDOC total (μM), TDOC (μM), and percent

LDOC of TDOC (μM). Means ± 1 SE.

LDOC Resp LDOC Incorp LDOC Incorp LDOC Total TDOC % LDOC of TDOC

(μM) (μM) (%) (μM) (μM) (μM)

Year Month Site Depth Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg Avg ± SE

2005 May ER 15 Epi 2.8 ± 1.9 6.0 ± 0.3 75.4 ± 15.1 7.0 ± 3.7 ND ND

2005 May ER 43 Epi 36.5 ± 6.9 2.5 ± 0.4 6.9 ± 1.9 38.9 ± 6.6 ND ND

2005 May ER 73 Epi 12.4 ± 6.8 2.5 ± 0.2 41.8 ± 29.2 14.1 ± 7.5 ND ND

2005 May 449 Epi 30.9 ± 5.9 1.4 ± 0.1 4.7 ± 0.8 32.4 ± 6.0 ND ND

2005 May 496 Epi 33.4 ± 13.5 0.6 ± 0.1 2.3 ± 0.9 33.9 ± 13.4 ND ND

2005 May 580 Epi 0.0 ± 0.0 2.8 ± 0.2 100.0 ± 0.0 0.0 ± 0.0 ND ND

2005 May 835 Epi 40.4 ± 9.0 5.7 ± 0.2 13.4 ± 2.4 46.1 ± 9.1 ND ND

2005 May 942 Epi 4.7 ± 3.8 1.1 ± 0.1 48.3 ± 27.2 5.4 ± 4.0 ND ND

2005 May 950 Epi 0.0 ± 0.0 0.9 ± 0.0 100.0 ± 0.0 0.0 ± 0.0 ND ND

2005 May 958 Epi 16.4 ± 8.2 1.2 ± 0.1 36.4 ± 31.8 17.2 ± 8.6 ND ND

2005 June ER 15 Epi 71.9 ± 6.3 1.3 ± 0.1 1.7 ± 0.0 73.2 ± 6.4 ND ND

2005 June ER 15 Meta 71.1 ± 3.5 1.5 ± 0.2 2.1 ± 0.2 72.6 ± 3.7 ND ND

2005 June ER 15 Hypo 6.2 ± 3.6 0.8 ± 0.1 39.0 ± 30.5 6.8 ± 3.8 ND ND

2005 June ER 37 Epi 28.9 ± 2.7 1.5 ± 0.1 4.9 ± 0.0 30.4 ± 2.8 ND ND

2005 June ER 43 Epi 41.3 ± 2.7 1.3 ± 0.1 3.2 ± 0.4 42.7 ± 2.6 ND ND

116 2005 June ER 73 Epi 38.6 ± 6.7 1.5 ± 0.2 3.9 ± 0.8 40.1 ± 6.6 ND ND

2005 June ER 73 Hypo 2.7 ± 2.7 3.2 ± 0.2 76.7 ± 23.3 5.9 ± 3.9 ND ND

2005 June ER 78 Epi 30.1 ± 0.9 0.9 ± 0.0 3.0 ± 0.1 31.0 ± 0.9 ND ND

2005 June ER 92 Epi 76.9 ± 15.5 1.0 ± 0.0 1.4 ± 0.4 77.9 ± 15.5 ND ND

2005 June 311 Epi 31.3 ± 2.1 1.1 ± 0.0 3.3 ± 0.3 32.3 ± 2.1 ND ND

2005 June 412 Epi 37.6 ± 3.4 1.1 ± 0.1 2.8 ± 0.3 38.7 ± 3.5 ND ND

2005 June 449 Epi 69.7 ± 22.2 1.0 ± 0.1 1.8 ± 0.6 70.7 ± 22.1 ND ND

2005 June 496 Epi 201.2 ± 4.5 0.6 ± 0.0 0.3 ± 0.0 201.9 ± 4.5 ND ND

2005 June 580 Epi 33.1 ± 1.0 0.9 ± 0.1 2.7 ± 0.2 34.1 ± 1.0 ND ND

2005 June 835 Epi 39.5 ± 3.8 1.0 ± 0.0 2.6 ± 0.3 40.5 ± 3.8 ND ND

2005 June 942 Epi 55.5 ± 11.4 1.0 ± 0.0 1.9 ± 0.4 56.5 ± 11.4 ND ND

2005 June 950 Epi 26.8 ± 5.1 1.0 ± 0.0 4.0 ± 0.8 27.8 ± 5.1 ND ND

2005 June 958 Epi 42.7 ± 5.1 2.4 ± 0.5 5.4 ± 1.1 45.2 ± 5.4 ND ND

2005 June 964 Epi 46.0 ± 3.6 1.3 ± 0.1 2.8 ± 0.2 47.3 ± 3.7 ND ND

2005 June 1163 Epi 50.1 ± 9.5 1.1 ± 0.2 2.3 ± 0.3 51.2 ± 9.6 ND ND

2005 July ER 15 Epi 18.6 ± 3.5 1.2 ± 0.1 6.9 ± 1.9 19.8 ± 3.5 ND ND 2005 July ER 15 Meta 54.5 ± 3.9 1.5 ± 0.2 2.7 ± 0.4 55.9 ± 4.0 ND ND

2005 July ER 15 Hypo 16.6 ± 9.7 0.8 ± 0.1 2.1 ± 1.3 16.9 ± 8.7 ND ND

2005 July ER 37 Epi 17.8 ± 8.9 1.5 ± 0.1 3.5 ± 1.8 28.2 ± 1.5 ND ND

2005 July ER 43 Epi 43.9 ± 7.2 1.3 ± 0.1 3.1 ± 0.4 45.2 ± 7.3 ND ND

2005 July ER 73 Epi 28.7 ± 6.0 1.5 ± 0.2 5.7 ± 2.1 30.2 ± 5.8 ND ND

2005 July ER 73 Meta 25.4 ± 6.0 1.5 ± 0.2 6.2 ± 1.8 26.9 ± 5.8 ND ND

2005 July ER 73 Hypo 48.8 ± 10.9 3.2 ± 0.2 6.4 ± 0.9 52.0 ± 11.1 ND ND

2005 July ER 78 Epi 24.1 ± 8.6 0.9 ± 0.0 5.9 ± 3.1 25.0 ± 8.5 ND ND 117

2005 July ER 92 Epi 37.1 ± 18.2 1.0 ± 0.0 6.0 ± 4.0 38.0 ± 18.2 ND ND

2005 July 311 Epi 7.8 ± 1.0 1.0 ± 0.0 11.9 ± 1.1 8.9 ± 1.1 ND ND

2005 July 412 Epi 30.9 ± 6.1 1.1 ± 0.1 3.5 ± 0.6 32.0 ± 6.2 ND ND

2005 July 449 Epi 18.8 ± 9.3 1.0 ± 0.1 27.0 ± 23.3 19.8 ± 9.3 ND ND

2005 July 496 Epi 43.9 ± 16.0 1.0 ± 0.0 3.6 ± 2.0 44.9 ± 16.0 ND ND

2005 July 580 Epi 27.1 ± 12.5 0.9 ± 0.0 4.5 ± 1.6 28.0 ± 12.5 ND ND

2005 July 835 Epi 43.1 ± 5.6 1.0 ± 0.0 2.4 ± 0.3 44.1 ± 5.6 ND ND

2005 July 942 Epi 4.4 ± 4.4 1.0 ± 2.5 2.5 ± 2.5 14.2 ± 0.0 ND ND

2005 July 950 Epi 34.3 ± 4.3 1.0 ± 0.0 3.0 ± 0.5 35.4 ± 4.2 ND ND

2005 July 958 Epi 102.0 ± 18.6 2.4 ± 0.5 2.6 ± 0.9 104.5 ± 18.5 ND ND

2005 July 965 Epi 60.1 ± 2.5 1.3 ± 0.1 2.2 ± 0.3 61.5 ± 2.3 ND ND 2005 July 1163 Epi 39.1 ± 1.0 0.6 ± 0.2 1.4 ± 0.4 39.7 ± 0.8 ND ND

2005 Aug ER 15 Epi 36.8 ± 5.1 1.5 ± 0.0 4.0 ± 0.4 38.3 ± 5.2 232.5 16.5 ± 2.2

2005 Aug ER 15 Meta 40.7 ± 3.6 0.9 ± 0.0 2.3 ± 0.2 41.7 ± 3.6 289.3 14.4 ± 1.2

2005 Aug ER 15 Hypo 11.8 ± 1.7 1.2 ± 0.1 39.1 ± 30.5 14.5 ± 1.4 193.6 7.5 ± 0.7

2005 Aug ER 37 Epi 31.7 ± 11.4 1.3 ± 0.0 35.3 ± 32.4 44.3 ± 2.3 241.5 18.3 ± 1.0

2005 Aug ER 43 Epi 131.7 ± 45.5 1.1 ± 0.1 1.4 ± 0.8 132.9 ± 45.4 266.2 49.9 ± 17.1

2005 Aug ER 73 Epi 43.5 ± 3.3 0.9 ± 0.1 2.0 ± 0.2 44.4 ± 3.3 224.1 19.8 ± 1.5

2005 Aug ER 73 Meta 27.4 ± 0.4 1.4 ± 0.0 4.8 ± 0.1 28.7 ± 0.5 514.0 5.6 ± 0.1

2005 Aug ER 73 Hypo 14.1 ± 3.4 2.4 ± 0.1 15.7 ± 3.1 16.4 ± 3.5 257.4 6.4 ± 1.3

2005 Aug ER 78 Epi 29.7 ± 2.3 1.2 ± 0.0 3.8 ± 0.2 30.9 ± 2.3 240.8 12.8 ± 1.0

2005 Aug ER 92 Epi 40.2 ± 5.4 0.9 ± 0.0 2.4 ± 0.5 41.1 ± 5.3 158.3 26.0 ± 3.4

2005 Aug 311 Epi 13.5 ± 7.2 1.0 ± 0.1 36.8 ± 31.6 21.2 ± 4.2 228.0 9.3 ± 1.9 118

2005 Aug 311 Hypo 44.1 ± 1.6 1.0 ± 0.1 2.3 ± 0.3 45.2 ± 1.5 257.4 17.5 ± 0.5

2005 Aug 412 Epi 35.3 ± 2.3 1.0 ± 0.0 2.8 ± 0.2 36.3 ± 2.2 ND ND

2005 Aug 449 Epi 23.9 ± 12.0 1.0 ± 0.0 35.2 ± 32.4 36.8 ± 2.3 267.4 13.8 ± 0.9

2005 Aug 496 Epi 91.3 ± 43.1 1.7 ± 0.3 2.3 ± 0.7 92.9 ± 43.4 210.4 44.2 ± 20.6

2005 Aug 580 Epi 40.2 ± 1.7 0.8 ± 0.0 1.9 ± 0.1 41.0 ± 1.7 239.8 17.1 ± 0.7

2005 Aug 835 Epi 46.2 ± 1.9 1.1 ± 0.0 2.3 ± 0.1 47.3 ± 1.9 273.9 17.3 ± 0.7

2005 Aug 942 Epi 37.6 ± 4.9 1.4 ± 0.1 3.7 ± 0.6 39.0 ± 4.9 298.4 13.1 ± 1.6 2005 Aug 950 Epi 43.3 ± 12.2 1.0 ± 0.0 2.8 ± 0.9 44.3 ± 12.3 376.0 11.8 ± 3.3

2005 Aug 958 Epi 51.1 ± 12.4 2.0 ± 0.2 4.1 ± 1.0 53.1 ± 12.2 338.0 15.7 ± 3.6

2005 Aug 965 Epi 15.8 ± 7.3 1.3 ± 0.3 17.9 ± 12.1 17.2 ± 7.5 203.3 8.5 ± 3.7

2005 Aug 1163 Epi 45.5 ± 8.1 0.4 ± 0.0 0.9 ± 0.2 45.9 ± 8.1 437.9 10.5 ± 1.8

2005 Sept e09 Epi 72.2 ± 21.6 1.3 ± 0.1 2.0 ± 0.5 73.6 ± 12.4 222.0 33.7 ± 5.7

2005 Sept e09 Meta 65.3 ± 11.8 1.8 ± 0.5 2.6 ± 0.5 67.0 ± 7.1 251.8 28.2 ± 3.0

2005 Sept e09 Hypo 10.9 ± 4.9 1.4 ± 0.1 12.5 ± 2.5 12.3 ± 2.9 194.8 6.5 ± 1.6

2005 Sept e15 Epi 61.8 ± 8.5 1.1 ± 0.2 1.8 ± 0.5 62.9 ± 4.7 202.4 30.7 ± 2.3

2005 Sept e15 Meta 161.4 ± 12.2 1.1 ± 0.1 0.7 ± 0.1 162.5 ± 7.0 175.9 13.2 ± 6.5

2005 Sept e15 Hypo 26.1 ± 22.5 0.4 ± 0.0 9.2 ± 8.0 26.6 ± 13.0 202.8 18.3 ± 1.4

2005 Sept e43 Epi 37.4 ± 5.2 1.6 ± 0.1 4.2 ± 0.2 39.0 ± 3.0 216.9 ND

2005 Sept e43 Hypo 9.1 ± 11.8 1.4 ± 0.1 43.1 ± 28.8 10.2 ± 7.1 218.6 4.7 ± 3.3

2005 Sept e73 Epi 29.2 ± 12.7 1.9 ± 0.1 6.7 ± 1.5 31.1 ± 7.3 247.7 12.6 ± 2.9

2005 Sept e73 Meta 25.8 ± 21.4 0.9 ± 0.0 16.3 ± 13.9 26.8 ± 12.4 270.0 10.4 ± 4.8

2005 Sept e73 Hypo 3.7 ± 6.4 2.1 ± 0.1 71.6 ± 28.4 5.8 ± 3.6 202.9 2.0 ± 2.0

2005 Sept e78 Epi 28.7 ± 11.5 1.6 ± 0.0 5.8 ± 1.6 30.3 ± 6.7 256.1 12.2 ± 2.7 119

2005 Sept e78 Meta 43.2 ± 19.5 0.9 ± 0.1 2.5 ± 0.8 44.1 ± 11.2 326.9 13.4 ± 3.4

2005 Sept e78 Hypo 46.0 ± 32.5 2.0 ± 0.1 6.0 ± 2.6 48.0 ± 18.7 ND ND

2005 Sept 311 Epi 43.3 ± 11.6 1.6 ± 0.3 3.9 ± 1.0 44.9 ± 6.4 ND ND 2005 Sept 412 Epi 32.7 ± 7.7 1.2 ± 0.1 3.7 ± 0.8 33.9 ± 4.3 200.3 16.4 ± 2.1

2005 Sept 412 Hypo 7.9 ± 7.3 1.5 ± 0.1 39.9 ± 28.8 8.5 ± 4.9 208.1 4.4 ± 2.3

2005 Sept 449 Epi 20.0 ± 17.0 0.5 ± 0.0 7.0 ± 5.3 20.4 ± 9.8 165.4 12.3 ± 5.9

2005 Sept 496 Epi 31.3 ± 2.6 0.6 ± 0.0 1.7 ± 0.0 31.8 ± 1.6 321.9 10.8 ± 0.5

2005 Sept 580 Epi 53.4 ± 9.8 1.2 ± 0.0 2.2 ± 0.2 54.6 ± 5.7 ND 2.9 ± 0.3

2005 Sept 835 Epi 32.5 ± 15.3 0.9 ± 0.1 3.1 ± 0.8 33.4 ± 8.8 445.7 7.5 ± 2.0

2005 Sept 950 Epi 46.9 ± 23.7 1.6 ± 0.1 3.9 ± 0.9 48.6 ± 13.8 255.0 18.0 ± 5.1

2005 Sept 958 Epi 11.4 ± 5.2 0.9 ± 0.1 8.5 ± 3.2 12.3 ± 3.0 207.8 7.5 ± 1.8

2005 Sept 965 Epi 25.7 ± 12.7 1.3 ± 0.1 5.7 ± 1.9 26.9 ± 7.4 307.7 9.2 ± 2.5

2005 Sept 1163 Epi 33.8 ± 6.7 0.6 ± 0.0 1.7 ± 0.2 34.4 ± 3.9 264.6 13.0 ± 1.5 120

121

Table 3. LDOC concentration by basin during 2004 and 2005: LDOC respired by bacteria (μM), LDOC incorporated into bacterial cells (μM), fraction of total LDOC incorporated into bacterial cells (%) and LDOC total (μM). Means ± 1 SE.

LDOC LDOC Resp Incorp LDOC Total

(μM) (μM) (μM)

Year Month Basin Avg ± SE Avg ± SE Avg ± SE

2005 May Western 24.6 ± 7.8 3.0 ± 0.8 27.6 ± 8.0

2005 Central 16.3 ± 4.8 1.8 ± 0.2 18.1 ± 4.9

2005 Eastern 12.8 ± 5.0 2.9 ± 0.8 15.6 ± 4.7

2005 Total 17.7 ± 3.4 2.5 ± 0.3 20.2 ± 3.4

2005 June Western 80.2 ± 17.0 0.9 ± 0.1 81.1 ± 16.9

2005 Central 32.6 ± 2.4 1.5 ± 0.1 34.1 ± 2.3

2005 Eastern 54.9 ± 8.0 1.1 ± 0.1 56.0 ± 8.1

2005 Total 50.1 ± 5.4 1.3 ± 0.1 51.3 ± 5.4

2005 July Western 36.9 ± 4.3 0.9 ± 0.1 37.8 ± 4.3

2005 Central 39.3 ± 5.3 1.6 ± 0.1 40.8 ± 5.3

2005 Eastern 22.6 ± 5.2 1.1 ± 0.1 23.5 ± 5.4

2005 Total 34.6 ± 3.1 1.3 ± 0.1 35.8 ± 3.2

2005 Aug Western 52.4 ± 7.8 1.1 ± 0.1 53.5 ± 7.8

2005 Central 38.8 ± 6.7 1.2 ± 0.1 40.1 ± 6.7

2005 Eastern 29.6 ± 4.1 1.2 ± 0.1 30.8 ± 4.1

122

2005 Total 40.4 ± 4.2 1.2 ± 0.1 41.6 ± 4.2

2005 Sept Western 37.8 ± 3.7 0.8 ± 0.1 38.6 ± 3.7

2005 Central 29.3 ± 3.0 1.5 ± 0.1 29.2 ± 3.0

2005 Eastern 59.7 ± 10.9 1.1 ± 0.1 60.8 ± 11.0

2005 Total 39.4 ± 3.9 1.3 ± 0.1 39.6 ± 3.8

Table 4. Bacterial phosphorus dynamics by basin in Lake Erie during summer 2005: biologically available phosphorus, BAP

(nM), bacterial uptake constant, k (min-1), bacterial phosphate uptake velocity, PUV (nM min-1), cellular phosphate uptake velocity , PUV (nmol cell-1 min-1), and total turnover time, Total TT (min). Means ± 1 SE.

BAP Bact k PUV PUV Total TT

(nmol cell-1 min-1) (nM) (min-1) (nM min-1) x 10-11 (min)

Month Basin Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg ± SE

May Western 55 ± 14 0.0011 ± 0.0004 0.049 ± 0.019 0.4 ± 0.2 761 ± 186

June Western 121 ± 57 0.0034 ± 0.0017 0.562 ± 0.318 5.0 ± 1.7 422 ± 80

July Western 152 ± 39 0.0211 ± 0.0070 0.536 ± 0.098 25.1 ± 3.0 338 ± 109

August Western 11 ± 5 0.0390 ± 0.0074 0.199 ± 0.040 7.7 ± 1.9 75 ± 44

September Western 23 ± 19 0.0038 ± 0.0020 0.055 ± 0.005 2.5 ± 0.5 455 ± 404

May Central 150 ± 54 0.0012 ± 0.0002 0.112 ± 0.034 6.1 ± 2.0 1229 ± 350

June Central 39 ± 10 0.0150 ± 0.0052 0.074 ± 0.013 5.4 ± 1.0 545 ± 127

July Central 69 ± 22 0.0241 ± 0.0029 0.490 ± 0.073 33.4 ± 5.7 103 ± 34

August Central 5 ± 1 0.0353 ± 0.0078 0.091 ± 0.017 4.0 ± 0.6 100 ± 32

September Central 17 ± 4 0.0156 ± 0.0048 0.041 ± 0.007 2.7 ± 0.5 143 ± 25

May Eastern 383 ± 182 0.0002 ± 0.0001 0.038 ± 0.016 2.6 ± 1.1 2773 ± 656 123

June Eastern 53 ± 22 0.0016 ± 0.0005 0.047 ± 0.011 3.5 ± 0.8 1078 ± 411

July Eastern 12 ± 3 0.0270 ± 0.0072 0.388 ± 0.095 28.6 ± 0.7 33 ± 5

August Eastern 32 ± 16 0.0305 ± 0.0083 0.033 ± 0.006 2.1 ± 0.4 163 ± 78

September Eastern 9 ± 5 0.0186 ± 0.0043 0.017 ± 0.005 1.2 ± 0.3 665 ± 374

124

Table 5. Bacterial phosphorus dynamics by site in Lake Erie during summer 2005: biologically available phosphorus, BAP

BAP Bact k PUV PUV Total TT

(nmol cell-1 min-1) (nM) (min-1) (nM min-1) x 10-11 (min)

Month Site Depth Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg ± SE

May ER 15 Epi 224 ± 0 0.0001± 0.0001 0.014 ± 0.014 0.8 ± 0.8 5181 ± 0

May ER 43 Epi 174 ± 82 0.0008 ± 0.0006 0.083 ± 0.042 5.1 ± 2.8 2179 ± 1308

May ER 73 Epi 24 ± 8 0.0021 ± 0.0006 0.041 ± 0.003 2.9 ± 0.4 432 ± 80

May 449 Epi 435 ± 246 0.0004 ± 0.0002 0.100 ± 0.005 7.0 ± 0.7 1446 ± 315

May 496 Epi 63 ± 30 0.0007 ± 0.0002 0.032 ± 0.015 0.2 ± 0.1 712 ± 71

May 580 Epi 18 ± 0 0.0000 ± 0.0000 0.000 ± 0.000 0.0 ± 0.0 1313 ± 561

May 835 Epi 48 ± 5 0.0024 ± 0.0005 0.114 ± 0.023 1.1 ± 0.1 442 ± 77

May 942 Epi ND 0.0000 ± 0.0000 0.000 ± 0.000 0.0 ± 0.0 3297 ± 963

May 950 Epi 30 ± 6 0.0010 ± 0.0002 0.027 ± 0.002 0.2 ± 0.0 991 ± 219

May 958 Epi 380 ± 92 0.0009 ± 0.0002 0.297 ± 0.032 16.3 ± 2.1 1315 ± 352

125

June ER 15 Epi 9 ± 3 0.0045 ± 0.0013 0.030 ± 0.008 2.2 ± 0.6 257 ± 70

June ER 15 Meta 25 ± 10 0.0007 ± 0.0004 0.026 ± 0.010 2.2 ± 0.8 955 ± 37

June ER 15 Hypo 131 ± 0 0.0001 ± 0.0001 0.026 ± 0.000 2.7 ± 0.0 5000 ± 0

June ER 37 Epi 71 ± 39 0.0016 ± 0.0010 0.147 ± 0.082 15.6 ± 4.1 807 ± 335

June ER 43 Epi 37 ± 10 0.0016 ± 0.0006 0.049 ± 0.003 3.9 ± 0.3 676 ± 196

June ER 73 Epi 7 ± 3 0.0024 ± 0.0007 0.017 ± 0.008 1.2 ± 0.6 355 ± 78

June ER 73 Hypo 24 ± 10 0.0016 ± 0.0004 0.031 ± 0.004 2.1 ± 0.2 707 ± 204

June ER 78 Epi 168 ± 0 0.0001 ± 0.0001 0.050 ± 0.000 3.7 ± 0.0 3333 ± 0

June ER 92 Epi 14 ± 3 0.0033 ± 0.0002 0.048 ± 0.012 3.3 ± 0.8 245 ± 26

June 311 Epi 79 ± 7 0.0006 ± 0.0003 0.070 ± 0.001 4.6 ± 0.2 478 ± 63

June 412 Epi 9 ± 1 0.0031 ± 0.0003 0.027 ± 0.004 1.8 ± 0.3 324 ± 27

June 449 Epi 20 ± 8 0.0007 ± 0.0004 0.020 ± 0.006 1.5 ± 0.4 ND

June 496 Epi ND 0.0000 ± 0.0000 ND ND 512 ± 77

June 580 Epi 238 ± 132 0.0010 ± 0.0004 0.132 ± 0.016 8.2 ± 1.1 735 ± 34

June 835 Epi ND 0.0000 ± 0.0000 ND ND ND

June 942 Epi 111 ± 67 0.0021 ± 0.0011 0.102 ± 0.003 7.2 ± 0.3 755 ± 357

June 950 Epi 113 ± 28 0.0011 ± 0.0005 0.179 ± 0.028 11.5 ± 1.7 627 ± 32

June 958 Epi 1 ± 0 0.0701 ± 0.0061 0.060 ± 0.004 4.3 ± 0.4 14 ± 1 14 ± 4 June 964 Epi 2 ± 1 0.0680 ± 0.0145 0.127 ± 0.009 8.9 ± 0.9 126

June 1163 Epi 105 0.0129 ± 0.0065 1.977 ± 0.320 17.5 ± 2.7 33 ± 3

July ER 15 Epi 8 ± 1 0.0227 ± 0.0023 0.184 ± 0.016 12.1 ± 2.1 39 ± 3

July ER 15 Meta ND ND ND ND ND

July ER 15 Hypo ND 0.0000 ± 0.0000 ND ND ND

July ER 37 Epi 10 ± 2 0.0317 ± 0.0064 0.291 ± 0.011 20.6 ± 1.2 31 ± 7

July ER 43 Epi 13 ± 5 0.0358 ± 0.0052 0.427 ± 0.074 29.4 ± 5.0 25 ± 3

July ER 73 Epi 8 ± 1 0.0453 ± 0.0045 0.373 ± 0.045 26.6 ± 4.3 21 ± 2

July ER 73 Meta ND ND ND ND ND

July ER 73 Hypo 269 ± 97 0.0021 ± 0.0011 0.358 ± 0.015 23.1 ± 0.7 523 ± 164

July ER 78 Epi 9 ± 3 0.0297 ± 0.0072 0.242 ± 0.009 18.1 ± 1.1 32 ± 8

July ER 92 Epi 14 ± 3 0.0395 ± 0.0014 0.538 ± 0.081 27.8 ± 3.8 25 ± 1

July 311 Epi 69 ± 5 0.0077 ± 0.0013 0.515 ± 0.067 23.2 ± 4.8 140 ± 28

July 412 Epi 4 ± 0 0.0778 ± 0.0013 0.330 ± 0.009 21.9 ± 1.4 12 ± 0

July 449 Epi 18 ± 8 0.0540 ± 0.0168 0.714 ± 0.152 53.4 ± 11.9 24 ± 11

July 496 Epi 367 ± 62 0.0005 ± 0.0003 0.288 ± 0.010 14.0 ± 1.3 1270 ± 130

July 580 Epi 241 ± 28 0.0024 ± 0.0004 0.567 ± 0.070 30.6 ± 5.6 318 ± 48

July 835 Epi 206 ± 60 0.0063 ± 0.0009 1.189 ± 0.156 35.8 ± 7.0 125 ± 6

July 942 Epi 10 ± 3 0.0312 ± 0.0107 0.265 ± 0.056 20.3 ± 4.1 35 ± 13

July 950 Epi 17 ± 0 0.0279 ± 0.0002 0.476 ± 0.011 34.2 ± 1.8 33 ± 0 127

July 958 Epi 9 ± 1 0.0269 ± 0.0022 0.240 ± 0.010 12.8 ± 1.0 33 ± 2

July 965 Epi 216 ± 93 0.0097 ± 0.0033 1.489 ± 0.111 112.9 ± 8.6 92 ± 33

July 1163 Epi ND 0.0000 ± 0.0000 0.650 ± 0.000 5.7 ± 0.0 714 ± 0

Aug ER 15 Epi 1 ± 0 0.0675 ± 0.0072 0.034 ± 0.004 2.3 ± 0.5 13 ± 1

Aug ER 15 Meta 3 ± 2 0.0181 ± 0.0071 0.027 ± 0.002 1.3 ± 0.3 38 ± 17

Aug ER 15 Hypo ND 0.0000 ± 0.0000 ND ND ND

Aug ER 37 Epi 2 ± 0 0.0193 ± 0.0014 0.043 ± 0.002 2.5 ± 0.1 37 ± 1

Aug ER 43 Epi 1 ± 0 0.0472 ± 0.0066 0.030 ± 0.005 1.7 ± 0.4 14 ± 1

Aug ER 73 Epi 4 ± 3 0.0081 ± 0.0032 0.013 ± 0.002 0.6 ± 0.1 55 ± 9

Aug ER 73 Meta 4 ± 3 0.0165 ± 0.0078 0.029 ± 0.016 1.7 ± 1.1 41 ± 10

Aug ER 73 Hypo 18 ± 0 0.0002 ± 0.0002 0.009 ± 0.000 0.4 ± 0.0 486 ± 77

Aug ER 78 Epi 6 ± 0 0.0041 ± 0.0031 0.053 ± 0.002 3.2 ± 0.0 313 ± 132

Aug ER 92 Epi 2 ± 0 0.0609 ± 0.0055 0.105 ± 0.026 3.7 ± 0.9 15 ± 1

Aug 311 Epi 10 ± 1 0.0272 + 0.0049 0.270 ± 0.046 9.1 ± 2.4 14 ± 1

Aug 311 Hypo 4 ± 1 0.0267 ± 0.0030 0.114 ± 0.027 4.5 ± 0.7 19 ± 1

Aug 412 Epi 1 ± 0 0.1431 ± 0.0217 0.129 ± 0.006 5.7 ± 0.3 7 ± 1

Aug 449 Epi 124 ± 15 0.0004 ± 0.0002 0.048 ± 0.021 3.2 ± 1.4 588 ± 117

Aug 496 Epi 40 ± 5 0.0101 ± 0.0009 0.399 ± 0.052 17.7 ± 2.3 91 ±0

Aug 496 Hypo ND 0.0006 ± 0.0006 ND ND 588 ± 0 128

Aug 580 Epi 2 ± 1 0.0617 ± 0.0010 0.153 ± 0.075 4.5 ± 2.2 12 ± 0

Aug 835 Epi 3 ± 0 0.0619 ± 0.0010 0.206 ± 0.009 4.8 ± 0.3 12 ± 0

Aug 942 Epi 0 ± 0 0.0664 ± 0.0011 0.022 ± 0.004 1.4 ± 0.3 11 ± 0

Aug 950 Epi 3 ± 1 0.0609 ± 0.0055 0.155 ± 0.018 7.6 ± 1.4 15 ± 1

Aug 958 Epi ND 0.0000 ± 0.0000 ND ND ND

Aug 965 Epi 148 ± 0 0.0002 ± 0.0002 0.089 ± 0.000 4.2 ± 0 153 ± 60

Aug 1163 Epi 886 ± 0 0.0007 ± 0.0007 1.949 ± 0.000 15.0 ± 0 38 ± 2

Aug 1163 Hypo ND 0.0000 ± 0.0000 ND ND 464 ± 161

Sept ER 09 Epi 1 ± 1 0.0426 ± 0.0056 0.046 ± 0.019 3.1 ± 1.3 17 ± 2

Sept ER 09 Meta 1 ± 0 0.0068 ± 0.0013 0.006 ± 0.000 0.4 ± 0.0 79 ± 7

Sept ER 09 Hypo 21 ± 17 0.0010 ± 0.0008 0.016 ± 0.003 1.5 ± 0.2 2906 ± 1349

Sept ER 15 Epi 1 ± 0 0.0455 ± 0.0044 0.024 ± 0.006 1.4 ± 0.3 14 ± 1

Sept ER 15 Meta 7 ± 3 0.0035 ± 0.0031 0.008 ± 0.003 0.6 ± 0.2 397 ± 167

Sept ER 15 Hypo 92 ± 0 0.0000 ± 0.0000 0.004 ± 0.000 0.3 ± 0.0 5000 ± 0

Sept ER 43 Epi 0 ± 0 0.0486 ± 0.0073 0.012 ± 0.003 0.7 ± 0.2 13 ± 1

Sept ER 43 Hypo 29 ± 8 0.0006 ± 0.0000 0.019 ± 0.006 1.3 ± 0.4 680 ± 125

Sept ER 73 Epi 19 ± 18 0.0125 ± 0.0120 0.037 ± 0.001 1.9 ± 0.1 74 ± 59

Sept ER 73 Meta 8 ± 5 0.0013 ± 0.0001 0.009 ± 0.004 0.7 ± 0.3 221 ± 20

129 Sept ER 73 Hypo 11 ± 9 0.0056 ± 0.0041 0.049 ± 0.013 3.1 ± 0.9 97 ± 18

Sept ER 78 Epi 3 ± 2 0.0119 ± 0.0046 0.019 ± 0.000 1.1 ± 0.0 30 ± 4

Sept ER 78 Meta 4 ± 2 0.0066 ± 0.0016 0.023 ± 0.010 1.5 ± 0.6 43 ± 1

Sept ER 78 Hypo 67 ± 37 0.0019 ± 0.0006 0.092 ± 0.032 8.4 ± 2.7 285 ± 25

Sept 311 Epi 8 ± 0 0.0022 ± 0.0022 0.051 ± 0.000 2.2 ± 0.0 144 ± 46

Sept 412 Epi 2 ± 1 0.1069 ± 0.0297 0.098 ± 0.040 5.1 ± 2.2 10 ± 3

Sept 412 Hypo 23 ± 8 0.0023 ± 0.0005 0.050 ± 0.020 3.4 ± 1.4 216 ± 14

Sept 449 Epi 0 ± 0 0.0309 ± 0.0039 0.008 ± 0.002 0.6 ± 0.1 21 ± 2

Sept 496 Epi ND 0.0000 ± 0.0000 ND ND 10000 ± 0

Sept 580 Epi 4 ± 1 0.0151 ± 0.0020 0.058 ± 0.005 3.0 ± 0.4 51 ± 4

Sept 835 Epi 78 ± 0 0.0002 ± 0.0002 0.047 ± 0.000 1.2 ± 0.0 1667 ± 0

Sept 950 Epi 4 ± 1 0.0090 ± 0.0005 0.038 ± 0.009 2.0 ± 0.5 59 ± 3

Sept 958 Epi 9 ± 2 0.0081 ± 0.0036 0.053 ± 0.006 3.4 ± 0.6 170 ± 55

Sept 965 Epi ND 0.0000 ± 0.0000 ND ND 144 ± 7

Sept 1163 Epi ND 0.0000 ± 0.0000 ND ND ND 130

131

Table 6. Phosphate apportionment to bacteria in Lake Erie during summer 2005: algal

-1 -1 uptake constant, k (min ), bacterial uptake constant (min ), phosphate apportionment, Pi

Apport (%). Means ± SE.

Algal k Bact k Pi Apport

(min-1) (min-1) (%)

Month Basin Avg ± SE Avg ± SE Avg ± SE

May Western 0.0005 ± 0.0002 0.0011 ± 0.0004 62.8 ± 15.2

June Western 0.0051 ± 0.0025 0.0035 ± 0.0017 42.3 ± 11.4

July Western 0.0016 ± 0.0004 0.0210 ± 0.0070 67.6 ± 9.5

Aug Western 0.0096 ± 0.0024 0.0390 ± 0.0075 84.5 ± 3.0

Sept Western 0.0012 ± 0.0006 0.0038 ± 0.0020 18.7 ± 9.8

May Central 0.0002 ± 0.0001 0.0012 ± 0.0002 80.1 ± 8.3

June Central 0.0022 ± 0.0008 0.0150 ± 0.0052 78.9 ± 5.7

July Central 0.0034 ± 0.0005 0.0240 ± 0.0029 83.2 ± 3.7

Aug Central 0.0150 ± 0.0023 0.0353 ± 0.0078 55.5 ± 5.4

Sept Central 0.0112 ± 0.0021 0.0156 ± 0.0048 42.7 ± 5.1

May Eastern 0.0002 ± 0.0001 0.0002 ± 0.0001 33.8 ± 15.1

June Eastern 0.0001 ± 0.0000 0.0016 ± 0.0005 80.0 ± 10.4

July Eastern 0.0025 ± 0.0006 0.0270 ± 0.0072 89.1 ± 2.8

Aug Eastern 0.0105 ± 0.0024 0.0305 ± 0.0083 56.6 ± 8.1

Sept Eastern 0.0100 ± 0.0023 0.0187 ± 0.0043 51.6 ± 7.0

Table 7. Phosphorus distribution in Lake Erie during summer 2005: algal particulate phosphorus, APP(nM), bacterial cellular phosphorus, BPP (nM), total soluble phosphorus, TSP (nM), total phosphorus, TP (nM), phosphorus distribution to bacteria, P

Dist (%), bacterial P-Quota (nmol cell-1). Means ± SE.

APP BPP TSP TP Pi Dist P-Quota

(nmol cell-1) (nM) (nM) (nM) (nM) (%) x 10-7

Month Basin Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg ± SE

May Western 1998 ± 299 719 ± 50 733 ± 88 3450 ± 284 29.7 ± 4.3 1.59 ± 0.51

June Western 203 ± 35 145 ± 19 166 ± 14 487 ± 39 49.9 ± 5.8 0.79 ± 0.20

July Western 1561 ± 213 605 ± 20 670 ± 55 2836 ± 253 31.7 ± 2.4 2.56 ± 0.27

Aug Western 1992 ± 346 208 ± 18 166 ± 22 2144 ± 347 15.6 ± 2.4 0.84 ± 0.17

Sept Western 1131 ± 130 465 ± 52 476 ± 48 2071 ± 150 30.4 ± 2.8 0.98 ± 0.17

May Central 1585 ± 261 667 ± 82 589 ± 59 2792 ± 302 30.8 ± 2.6 2.90 ± 0.59

June Central 173 ± 26 128 ± 14 155 ± 14 425 ± 31 48.1 ± 4.0 0.88 ± 0.09

July Central 757 ± 42 731 ± 28 763 ± 46 2251 ± 62 49.6 ± 1.8 4.87 ± 0.27

Aug Central 633 ± 65 163 ± 10 128 ± 16 885 ± 73 20.7 ± 1.4 0.83 ± 0.06

Sept Central 414 ± 31 449 ± 63 491 ± 36 1355 ± 79 49.0 ± 2.5 2.70 ± 0.40

132

May Eastern 930 ± 68 617 ± 50 609 ± 53 2155 ± 85 40.1 ± 2.9 4.03 ± 0.45

June Eastern 99 ± 14 94 ± 10 154 ± 22 303 ± 28 56.3 ± 6.0 0.76 ± 0.10

July Eastern 603 ± 57 703 ± 44 1767 ± 359 2956 ± 343 54.4 ± 3.4 5.06 ± 0.38

Aug Eastern 446 ± 54 160 ± 24 221 ± 30 768 ± 48 24.1 ± 1.7 0.85 ± 0.15

Sept Eastern 336 ± 38 591 ± 106 702 ± 42 1629 ± 120 58.8 ± 3.7 4.89 ± 1.18

133

Table 8. Phosphate uptake parameters for algae and bacteria in Lake Erie during summer 2005: biologically available phosphorus, BAP (nM), Michaelis constant, Km, maximum velocity, Vmax, Michaelis-Menten velocity, VMM, portion of uptake

-1 velocitiy, % Vuptake, bacterial P quota (nmol cell ), phosphate apportionment, Pi Apport (%), and labile dissolved organic carbon, LDOC (μM). Means ± SE.

ALGAL BAP (nM) P-quota x 10-7 LDOC

Station Avg ± SE Km Vmax vMM % vuptake Avg ± SE Pi Apport Avg ± SE

ER 15 0.5 ± 0.1 78.0 2.40 0.016 71.6 ND 37.6 62.9 ± 4.7

ER 43 12.2 ± 2.9 91.1 2.50 0.295 81.7 ND 38.4 39.0 ± 3.0

ER 73 28.7 ± 7.9 88.3 8.30 2.034 68.0 ND 81.3 31.1 ± 7.3

412 epi 1.5 ± 1.0 111.9 1.20 0.016 10.8 ND 12.8 33.9 ± 4.3

496 31.8 ± 1.6

958 8.7 ± 2.4 128.4 0.20 0.013 11.8 ND 0.0 12.3 ± 3.0

412 hypo 26.9 ± 11.9 9.5 0.10 0.074 100.0 ND 50.9 8.5 ± 4.9 P-quota x 10-7

Site Avg ± SE Km Vmax vMM % vuptake Avg ± SE Pi Apport Avg ± SE 62.9 ± 4.7 ER 15 0.5 ± 0.1 98.3 1.20 0.006 28.4 3.4 ± 0.8 62.4

ER 43 12.2 ± 2.9 135.2 0.80 0.066 18.3 1.7 ± 0.9 61.6 39.0 ± 3.0 134

ER 73 28.7 ± 7.9 25.3 1.80 0.956 32.0 1.6 ± 0.2 18.7 31.1 ± 7.3

412 epi 1.5 ± 1.0 5.4 0.60 0.130 89.2 3.7 ± 1.5 87.2 33.9 ± 4.3

496 0.7 ± 0.1 21.0 0.00 0.000 0.7 ± 0.2 31.8 ± 1.6

958 8.7 ± 2.4 9.6 0.20 0.095 88.2 2.2 ± 0.3 100.0 12.3 ± 3.0

412 hypo 26.9 ± 11.9 6.2 0.00 0.000 0.0 3.2 ± 1.2 49.1 8.5 ± 4.9

135

136

Figure 1. Map of Lake Erie with 2005 stations labeled.

137

IFYLE Sites – Summer 2005

▪ 449

▪ 950 ▪ ▪ e15 ▪ e09

▪ e37 ▪ 942 ▪ e78 ▪ 412 ▪ e73

▪ e92 ▪ e43 ▪ 835 ▪ 580 ▪ 311 496 ▪ ▪ 958 ▪ 1163▪ 965 ▪ 964

138

Figure 2. Distribution of LDOC by basin during (a) June, (b) July, and (c) August of

2004. Values for LDOC respired by bacteria, LDOC incorporated into bacterial cells, and LDOC total ± 1 SE. Different letters above bars show significant differences

(ANOVA post hoc Tukey, p <0.05) in LDOC.

139

a 120.0 a 100.0 a 80.0 LDOC Incorp 60.0 LDOC Resp 40.0 LDOC (µM) LDOC 20.0

0.0 (a) June Western Central Eastern Basin

120.0

100.0 b 80.0 LDOC Incorp 60.0 a LDOC Resp 40.0 a LDOC (µM) LDOC 20.0

0.0 (b) July Western Central Eastern Basin

120.0

100.0

80.0 a a a LDOC Incorp 60.0 LDOC Resp 40.0 LDOC (µM) LDOC 20.0

0.0 (c) August Western Central Eastern Basin

140

Figure 3. Distribution of LDOC by basin during (a) May, (b) June, (c), July, (d) August, and (e) September of 2005. Values for LDOC respired by bacteria, LDOC incorporated into bacterial cells, and LDOC total ± 1 SE. Different letters above bars show significant differences (ANOVA post hoc Tukey, p <0.05) in LDOC.

141

90.0 80.0 70.0 60.0 50.0 LDOC Incorp 40.0 a a LDOC Resp 30.0 a LDOC (µM) LDOC 20.0 10.0 0.0 (a) May Western Central Eastern Basin a 90.0 80.0 b 70.0 60.0 b 50.0 LDOC Incorp 40.0 LDOC Resp 30.0 LDOC (µM) LDOC 20.0 10.0 0.0 (b) June Western Central Eastern Basin 90.0 80.0 70.0 60.0 a a 50.0 a LDOC Incorp 40.0 LDOC Resp 30.0 LDOC (µM) LDOC 20.0 10.0 0.0 (c) July Western Central Eastern Basin 90.0 80.0 70.0 a 60.0 a 50.0 a LDOC Incorp 40.0 LDOC Resp 30.0 LDOC (µM) LDOC 20.0 10.0 (d) August 0.0 Western Central Eastern Basin

90.0 80.0 70.0 a 60.0 a 50.0 a LDOC Incorp 40.0 LDOC Resp 30.0 LDOC (µM) LDOC 20.0 10.0 0.0 (e) September Western Central Eastern Basin

142

Figure 4. Biologically available phosphate, BAP (nM) by basin during May, June, July,

August, and September of 2005. BAP (nM) ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

143

600 b

500

400 Western 300 a Central BAP (nM) abb abb Eastern 200 a 100 a,b a b aaa

0 May June July August September Month

144

Figure 5. Bacterial uptake constant, k (min-1) by basin during May, June, July, August,

and September of 2005. k (min-1) ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

145

0.0500 a a 0.0450 a 0.0400 a a a 0.0350

) 0.0300 Western

-1 aaa 0.0250 b Central

(min 0.0200 Eastern Bacterial k 0.0150 0.0100 aa 0.0050 aa a 0.0000 May June July August September Month

146

Figure 6. Phosphate uptake velocity, PUV (nM min-1) by basin during May, June, July,

August, and September of 2005. PUV ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

147

1.000 a 0.900 0.800 0.700 a

) a -1 0.600 a Western 0.500 Central PUV PUV 0.400 Eastern (nM min a 0.300 aaa b b b 0.200 b aab 0.100 0.000 May June July August September Month

148

Figure 7. Phosphate uptake velocity per cell, PUV (nmol cell-1 min-1) by basin during

May, June, July, August, and September of 2005. PUV per cell ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

149

45.0 a 40.0

) 35.0 a a -1 30.0

min Western

-1 25.0 Central

PUV PUV 20.0 Eastern 15.0 a aaa ab (nmol cell 10.0 b b a, b a a b 5.0 0.0 May June July August September Month

150

Figure 8. Total turnover time, Total TT (min) by basin during May, June, July, August, and September of 2005. TT ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

151

4000 b 3500 3000

2500 Western

2000 a b Central (min) a Total TT Total 1500 b Eastern aa 1000 a a, b aaa a 500 b b 0 May June July August September Month

152

Figure 9. Phosphate apportionment, Pi Apportionment (%) to bacteria and algae during

(a) May, (b) June, (c) July, (d) August, and (e) September of 2005. Pi Apportionment ± 1

SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p

<0.05) for the month.

153

100% 90% 80% 70% 60% Algal 50% Bacterial 40% 30% Apportionment (%) Apportionment

i 20%

P a, ba b 10% (a) May 0% Western Central Eastern Basin 100% 90% 80% 70% 60% Algal 50% Bacterial 40% 30% Apportionment (%) Apportionment i 20% abb P 10% (b) June 0% Western Central Eastern Basin

100% 90% 80% 70% 60% Algal 50% Bacterial 40% 30% aaa Apportionment (%) Apportionment

i 20% P 10% (c) July 0% Western Central Eastern Basin

100% 90% 80% 70% 60% Algal 50% Bacterial 40% 30% Apportionment (%) Apportionment

i 20% abb (d) August P 10% 0% Western Central Eastern Basin

100% 90% 80% 70% 60% Algal 50% Bacterial 40% 30% Apportionment (%) Apportionment i 20%

P (e) September 10% aba, b 0% Western Central Eastern Basin

154

Figure 10. Particulate phosphorus concentrations (nM) by basin during May, June, July,

August, and September of 2005. Particulate P ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

155

4000 3500

3000

2500 TSP 2000 BPP

1500 APP 1000

500 Particulate Phosphorus (nM) Phosphorus Particulate 0 WC EWCEWCEWC EWCE

May June July Aug Sept

Basin

156

Figure 11. Particulate phosphorus distribution to algae and bacteria, P Distribution (%) by basin during (a) May, (b) June, (c) July, (d) August, and (e) September of 2005. P

Distribution ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

157

70% 60% ) 50%

40% Algal 30% Bacterial 20% P Distribution (% P Distribution 10% aaa(a) May 0% Western Central Eastern Basin

100% 90%

) 80% 70% 60% Algal 50% Bacterial 40% 30%

P Distribution (% P Distribution 20% aaa 10% (b) June 0% Western Central Eastern Basin

100% 90%

) 80% 70% 60% Algal 50% Bacterial 40% 30%

P Distribution (% P Distribution 20% 10% abb (c) July 0% Western Central Eastern Basin

70% 60% ) 50%

40% Algal 30% Bacterial 20% P (% Distribution 10% a a, b b (d) August 0% Western Central Eastern Basin

100% 90%

) 80% 70% 60% Algal 50% Bacterial 40% 30% P Distribution (% P Distribution 20% abb(e) September 10% 0% Western Central Eastern Basin

158

Figure 12. Bacterial P-Quota (nmol cell-1) by basin during (a) May, (b) June, (c) July, (d)

August, and (e) September of 2005. P-Quota ± 1 SE. Different letters above bars are significantly different (ANOVA post hoc Tukey, p <0.05) for the month.

159

7.00 6.00 b 5.00 )

-1 a, b

-7 4.00 a x 10 3.00

(nM cell 0 2.00 (a) May

Bacterial P Quota 1.00 0.00 Western Central Eastern Basin

7.00 6.00

) 5.00 -1

-7 4.00

x 10 3.00 a aa(b) June

(nmol cell 2.00

Bacterial P Quota Bacterial 1.00 0.00 Western Central Eastern Basin

7.00 b b 6.00

) 5.00 -1

-7 4.00 a (c) July

x 10 3.00

(nmol cell (nmol 2.00

Bacterial P Quota Bacterial 1.00 0.00 Western Central Eastern Basin 7.00 6.00

) 5.00 -1

-7 4.00

x 10 3.00 (d) August aaa (nmol cell 2.00

Bacterial P Quota 1.00 0.00 Western Central Eastern Basin

7.00 b 6.00

) 5.00 -1 a, b

-7 4.00

x 10 3.00 (e) September (nmol cell 2.00 a Bacterial P Quota 1.00 0.00 Western Central Eastern Basin

160

Figure 13. Relationship between % vuptake and % phosphate uptake; y = 0.856x – 10.803, r2 = 0.63.

161

100.0

90.0 80.0 70.0 60.0 50.0 40.0 to Bacteria (%) to 30.0 20.0 uptake

v 10.0 0.0 0.0 20.0 40.0 60.0 80.0 100.0 120.0

Pi Apport to Bacteria (%)

162

Figure 14. (a) Phosphate apportionment, Pi Apportionment (%) and (b) particulate phosphorus distribution, P Distribution (%) to bacteria and algae based on TSI during summer of 2005. Different letters in bars show significant differences in apportionment and distribution (ANOVA post hoc Tukey, p <0.05).

163

100% aaaaa 80% t 60% Algal b (%) Bacterial Appor

i 40% P

20%

0% 20 30 35 40 45 55 TSI

(a)

100% 90% 80% n 70% a 60% b bbbb Algal 50%

(%) Bacterial 40% 30% P Distributio 20% 10% 0% 20 30 35 40 45 55 TSI (b)

164

Figure 15. (a) Phosphate apportionment, Pi Apportionment (%) and (b) particulate phosphorus distribution, P Distribution (%) to bacteria and algae based on LDOC during summer of 2005. Different letters in bars show significant differences in apportionment and distribution (ANOVA post hoc Tukey, p <0.05).

165

100%

80% t 60% Algal Bacterial 40% (% ) Appor i

P 20%

0% 0 2030405070100

(a) LDOC (µM)

100%

80%

60% Algal

(% ) 40% Bacterial

20% P Distribution

0% 0 2030405070100 (b) LDOC (µM)

166

Figure 16. Relationship between LDOC (μM) and bacterial P-Quota (nmol cell-1).

167

7.00 6.00

-7 5.00

) x 10 ) x 4.00 -1 3.00

P- Quota 2.00

(nmol cell 1.00 0.00

0.0 50.0 100.0 150.0 200.0 250.0

LDOC (µM)

168

Figure 17. Relationship between LDOC (μM) and phosphate uptake velocity, PUV (nM min-1).

169

2.500

2.000

) -1 1.500

PUV 1.000 (nM min 0.500

0.000

0.0 50.0 100.0 150.0 200.0 250.0

LDOC (µM)

170

Figure 18. Relationship between LDOC (μM) and cellular phosphate uptake velocity

(nmol cell-1 min-1).

171

120.0

-11 100.0

) x 10 ) 80.0

-1

60.0 min PUV PUV -1 40.0

20.0

(nmol cell 0.0 0.0 50.0 100.0 150.0 200.0 250.0

LDOC (µM)

172

Figure 19. (a) Bacterial productivity (μg C ml-1 hr-1) and (b) Bacterial phosphate uptake constant (min-1) versus time (hours) following addition of glucose at stations ER 15 and

942 during May 2005.

173

100.00 -4

80.00 ) x 10 )

-1 60.00 ER 15 hr BP

-1 40.00 942

20.00

(ug Cml 0.00 0 102030405060 Time (hours)

(a)

0.0140 0.0120 0.0100 ) ER 15 -1 0.0080 0.0060 942 (min 0.0040 Bacterial k 0.0020 0.0000 0 102030405060 Time (min)

(b)

174

Figure 20. (a) Bacterial productivity (μg C ml-1 hr-1) and (b) Bacterial phosphate uptake constant (min-1) versus time (hours) following addition of glucose at stations ER 15 and

449 during June 2005.

175

200.00 180.00 -4 160.00 140.00 ) x 10

-1 120.00 ER 15

hr 100.00 BP

-1 449 80.00 60.00 40.00

(ug C ml 20.00 0.00 0 102030405060 Time (hours)

(a)

0.0140 0.0120 0.0100 )

-1 0.0080 ER 15 0.0060 449 (min 0.0040 Bacterial k 0.0020 0.0000 0 102030405060 Time (hours)

(b)

176

Figure 21. (a) Bacterial productivity (μg C ml-1 hr-1) and (b) Bacterial phosphate uptake constant (min-1) versus time (hours) following addition of glucose at stations ER 73 and

958 during July 2005.

177

100.00

-4 80.00 ) x 10 )

-1 60.00 ER 73 hr BP

-1 958 40.00

20.00 (ug C ml

0.00 0 102030405060 Time (hours) (a)

0.0140 0.0120 0.0100 )

-1 0.0080 ER 73 0.0060 958 (min 0.0040 Bacterial kBacterial 0.0020 0.0000 0 102030405060 Time (hours)

(b)

178

Figure 22. (a) Bacterial productivity (μg C ml-1 hr-1) and (b) Bacterial phosphate uptake constant (min-1) versus time (hours) following addition of glucose, glucosamine, and lysine at station 958 during September 2005.

179

0.40 0.35 -4 0.30

) x 10 x ) 0.25 Glucose -1

hr 0.20 Glucosamine BP BP -1 0.15 Lysine 0.10

(ug C ml 0.05 0.00 0 102030405060 Time (hours)

(a)

0.3000 0.2500

) 0.2000 Glucose -1 0.1500 Glucosamine

(min 0.1000 Lysine Bacterial k 0.0500 0.0000 0 102030405060 Time (hours)

(b)

180

Figure 23. (a) Bacterial productivity (μg C ml-1 hr-1) and (b) Bacterial phosphate uptake constant (min-1) versus time (hours) following addition of glucose, glucosamine, and lysine at station ER 73 during September 2005.

181

100.00 90.00 -4 80.00 70.00 ) x 10 x ) Glucose

-1 60.00

hr 50.00 Glucosamine BP BP -1 40.00 Lysine 30.00 20.00

(ug Cml 10.00 0.00 0 102030405060 Time (hours) (a)

0.0140 0.0120 0.0100 ) Glucose

-1 0.0080 Glucosamine 0.0060

(min Lysine 0.0040 Bacterial k Bacterial 0.0020 0.0000 0 102030405060 Time (hours)

(b)

CHAPTER IV

Availability of Dissolved Organic Carbon to Lake Bacterioplankton

Abstract

The distribution and abundance of labile dissolved organic carbon (LDOC) and total dissolved organic carbon (TDOC) were studied at stations with varied trophic states within Lake Erie during the summers of 2004 and 2005. Station exchange experiments with natural bacterial assemblages tested for variation in LDOC utilization by bacterial communities. The 2004 investigation was conducted aboard the CCGS Limnos. The

2005 investigation was conducted aboard the R/V Lake Guardian as part of the

International Field Year of Lake Erie (IFYLE). LDOC concentrations varied greatly

(range 0 - ~275 μM) at specific stations within Lake Erie; however, pooled basin measurements showed fairly constant LDOC. Approximately 10-30% of TDOC was labile to the bacterial assemblages. LDOC represented a fairly consistent fraction of the

LDOC pool (~10%) in the central and eastern basin, and a larger portion (30%) in the eastern basin. LDOC was utilized differently by bacterioplankton assemblages at different stations at at different depths. These findings suggest patchy LDOC distribution that is station specific and possibly dependent upon the bacterioplankton community structure and/or sources of allochthonous and autochthonous carbon at a given location.

Utilization of LDOC appears be a function of the bacterial composition as well as the water chemistry of each station.

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Introduction

Labile dissolved organic carbon (LDOC) includes dissolved forms of low molecular weight (LMW) organic matter that are easily utilized by particular groups of organisms, especially bacteria. Particulate and dissolved forms of organic matter (OM) can be broken down biochemically (e.g. enzymatically or via ultraviolet radiation) into labile forms of carbon (LDOC). Sources of autochthonous OM include macrophytes, algae and algal exudates, bacteria and bacterial components, viruses and lytic products, predation (eg. “sloppy feeding”), and abiotic (e.g. photolysis) and biotic (e.g. enzymatic) transformation (Bertilsson and Jones 2003). Sources of OM can also be of allochthonous

(ie. terrestrial origin) (Laird and Scavia 1990; Biddanda and Cotner 2002). In oceans, the

majority of OM, belongs to the dissolved fraction, 60-75% (surface) to 75-80% (deep

waters) and is considered to be present as LMW forms (Benner 2002). However, the

exact composition of organic matter remains unknown because the resolution and

sensitivity of chromatographic analysis is currently limited (Hedges 2002).

LMW labile carbon compounds potentially usable by bacteria include

monosaccharides, sugars, amino acids, amino sugars, and oligonucleotides. The

assimilation of dissolved free amino acids (DFAA), dissolved combined amino acids

(DCAA), and DNA accounted for 42-60% of bacteria production in cultures from diverse

marine environments (Jørgensen et al. 1993). Glucose supported 5-10% of bacterial

production at the surface but high molecular weight (HMW) carbon compounds inhibited

glucose uptake by bacteria (Skoog et al. 1999) in the Gulf of Mexico. The concentration

of dissolved free monosaccharides (DFCHO) in Lake Constance was determined using

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HPLC. Glucose, galactose, and mannose composed 55-70% of the DFCHO pool and glucose was turned over most rapidly by bacteria (Bunte and Simon 1999). In this

system, bacteria mostly consumed the amino acids serine and glutamate and the

monosaccharide arabinose (Rosenstock and Simon 2003). Lability can be seasonal. In

Lake Constance, amino acids were preferentially respired by bacteria during spring and

glucose was preferentially respired in the summer (Weiss and Simon 1999). Free amino

are a likely component of the LDOC since they comprise 50% of the dry weight of

planktonic organisms and account for 1-1000% of bacterial production in diverse ocean

and lake environments (Kirchman 2003).

The distribution and abundance of LDOC was characterized in Lake Michigan by

Laird and Scavia (1990). In their study, LDOC comprised a larger portion of the DOC

pool in near-bottom waters (40.2%) during stratification versus 13.8% in near-surface

waters. LDOC was calculated from bacterial production and growth efficiency (assumed

to be 20%). Greater bacterial numbers were observed in areas with higher LDOC

concentrations. They suggested that higher bacterial production in the summer was

supplemented by allochthonous inputs of carbon in addition to phytoplankton production.

Currently, little is known about the composition and distribution of the LDOC

pool in Lake Erie, and how bacterioplankton communities respond to labile carbon

compounds in this environment. Previous studies in Lake Erie and small lakes in

Northeastern Ohio (Gao 2002; Gao and Heath 2005) have observed the influence of

LDOC on phosphorus dynamics. They found that in low LDOC conditions,

bacterioplankton exhibited low growth rates, high P-quotas, and moderate phosphate

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uptake velocities. In high LDOC conditions, bacterioplankton exhibited high growth

rates, low P-quotas and low phosphate uptake velocities. Lake Erie has been plagued by

environmental problems that could alter organic matter inputs and LDOC concentration.

Zebra mussels and excessive algal growth may contribute additional organic matter into

the carbon cycle in Lake Erie. Increasing organic matter may alter bacterial

bioenergetics, decomposition, and or phosphorus dynamics with food web implications.

The purpose of this investigation was to characterize the distribution and

abundance of LDOC and its utilization by bacterioplankton in Lake Erie and to determine the LDOC portion of the TDOC pool. We also conducted shipboard experiments to observe potential differences in LDOC utilization by natural bacterial assemblage from

varied locations within the lake (high vs. low LDOC, epilimnetic vs. hypolimnetic). Our

research was conducted aboard the CCGS Limnos in summer 2004 (June, July, and

August) and R/V Lake Guardian in summer 2005 (May, June, July, August, and

September) during synoptic cruises of Lake Erie. We address the potential influences of

LDOC on bacterial community structure and ecosystem function including microbial

food web trophic dynamics, bacterial bioenergetics, and phosphorus cycling.

Methods

Sample Collection

During May, June, July, and August of 2004, water samples were collected from

Lake Erie aboard the CCGS Limnos. Integrated samples were obtained from most

stations. Samples from discrete depths (epilimnion and the meta- and hypolimnion of

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select sites) were obtained for stations 879 and 880 using an onboard rosette sampler.

Water samples were transferred from the rosette sampler using Nalgene carboys and immediately processed shipboard

During May, June, July, August, and September of 2005, water samples were collected aboard the R/V Lake Guardian as part of the International Field Year of Lake

Erie (IFYLE) collaborative investigation. Samples were obtained from discrete depths from the epilimnion at each station investigated using an onboard rosette sampler.

Additional samples were obtained from the metalimnion and hypolimnion at select stations. Water samples were transferred from the rosette sampler using Nalgene carboys and immediately processed shipboard. Temperature (ºC) and depth (m) were recorded with a CTD attached to the rosette.

Labile Dissolved Organic Carbon (LDOC)

Labile dissolved organic carbon (LDOC) concentration was estimated using the method of Søndergaard et al. (1995). Whole water (950 mL) was filtered through 0.2 μm filtration capsule (Whatman) to remove bacteria, algae, and bacterivores. This filtrate was inoculated with 50 mL (5%) of water passed through a 1.0 μm filtration capsule

(Whatman) to remove algae and bacterivores. This inoculum contained about 95 percent of the bacteria found in whole water samples. Samples were amended with NH4Cl and

Na2HPO4 were added to give final concentrations of 5.6 μM and 1.4 μM respectively, to ensure that bacterial cells were not growth limited by N and P and to maximize carbon utilization. The mixture was added to six BOD bottles. Oxygen concentrations were

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determined initially on three BOD bottles to estimate the amount of oxygen respired.

The remaining three BOD bottles were incubated in the dark at ambient temperature for

approximately 30 days. Oxygen concentrations were determined using the Winkler

method with the alkaline azide modification as described in APHA (1995). Oxygen

consumed was converted to moles of carbon dioxide released by bacteria, assuming a respiratory quotient of 0.82 (Søndergaard et al. 1995).

Bacterial growth was estimated by measuring the increase in total bacterial biovolume [bacterial abundance (cells ml-1) * bacterial cellular biovolume (μm3 cell-1)] over the 30 day interval. To determine bacterial cellular biovolume, cells were stained with 4’,6’-diamidino-2-phenylindole (DAPI) using the method of Porter and Feig (1980), photographed with an RT Spot camera (Diagnostics, Inc.) attached to a Zeiss Akioskop, and sized (initially and at 30 days) using Metamorph Image analysis software (Universal

Imaging Corp.) with halo correction. Cell size was calibrated by using fluorescent polystyrene spheres (PolySciences, Inc.) ranging in size from 0.1 – 5.0 μm in diameter.

LDOC was determined as the sum of the carbon respired plus the increase in total bacterial carbon over 30 days. We assumed that all available LDOC had been consumed by bacteria during the 30 day incubation period.

Total Dissolved Organic Carbon (TDOC)

Whole water was filtered through pre-combusted GF/F filters and placed into duplicate pre-combusted glass ampules. The ampules were sealed immediately and frozen in the shipboard freezer (-5 °C) for further analysis. Upon return to the laboratory,

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samples were analyzed, with parallel standard, for TDOC using a Shimadzu Total

Organic Carbon Analyzer TOC-VCPN.

LDOC Swap Experiments

In the LDOC Swp Experiment, the procedure for determining LDOC concentration was followed essentially as described above. However, instead of inoculating bacteria (1.0 μm filtered water) into 0.2 μm filtered water from the same site, bacteria (1.0 μm filtered water) from one site (eg. Site 496) were inoculated into 0.2 μm filtered water from another site ( Site 954), and vice versa (Figure 3). Samples were incubated at the ambient temperature of the water, not the inoculum. Significant differences in LDOC utilization were identified using an ANOVA post hoc Tukey HSD.

Results

Site Characterization

During May, June, July, and August of 2004, water samples were collected from

Lake Erie aboard the CCGS Limnos. 2004 stations are mapped in Figure 1a.

Limnological characteristics of the 2004 stations are described in Table 1. Samples were obtained from the western, central, and eastern basins to represent the wide range of trophic conditions within the lake. The hypolimnion of station 879 became hypoxic (< 4 mg L-1 DO) during August.

During May, June, July, August, and September of 2005, water samples were collected aboard the R/V Lake Guardian as part of the International Field Year of Lake

Erie (IFYLE) collaborative investigation. 2005 stations are mapped in Figure 1b.

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Limnological characteristics of the 2005 stations are described in Table 2. Hypoxia was

observed in the central basin of Lake Erie during September of 2005. Oxygen

concentrations remained above 5 mg L-1 at all sites investigated during the summer with

the following exceptions: in August, sites 311 (Sandusky sub-basin), 496 (western basin),

and 1163 (Sandusky Bay) became hypoxic (< 4 mg L-1 DO) in the hypolimnion.

Hypoxia occurred in the lower strata of the hypolimnion at the sediment-water interface.

In September, hypoxia was observed at the central basin stations ER 43, ER 78, ER 78, and 412.

LDOC Distribution

During 2004 and 2005, a wide range of LDOC concentrations utilized by bacteria were observed (range = 0.0 – 201.2 μM) (Table 3). LDOC is detected as the sum of

LDOC respired by bacteria and the LDOC incorporated into growth by bacteria. LDOC was not detectable (0.0 μM) at stations 580 (western basin) and 950 (central basin) during

May of 2005. The highest value (201.2 μM) was observed at station 496 (western basin) during June of 2005. In 2004, the lowest LDOC value (21.4 μM) was observed in the

epilimnion at station 880 (central basin). The highest LDOC value (125.8 μM) was

observed at station 950 (central basin). A variety of patterns were observed between vertical profiles. At most sites during 2004, LDOC concentration was greater in the hypolimnion than the epilimnion. However, in 2005, the LDOC concentrations from epilimnetic samples tended to be greater than those of the hypolimnion. At most stations,

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the majority of LDOC was respired by bacteria with only a small portion incorporated into bacterial cells.

LDOC concentrations were similar among basins during 2004 and 2005 (Table 4).

During June and August of 2004, each basin exhibited similar LDOC concentrations

(Figure 3 a and c). In July of 2004, the eastern basin exhibited the highest LDOC concentration relative to the western and central basins (Figure 3b). During May, July and August of 2005 (Figure 4 a,c,d) LDOC concentration remained constant among basins. During June 2005, the highest LDOC concentrations were observed in the western basin (Figure 4b) relative to the other basins. However, during September, the highest LDOC concentrations were observed in the eastern basin (Figure 4e). In 2004, the proportion of LDOC incorporated into bacterial cells remained less than 10% throughout the season (Table 4).

Fraction of TDOC that is LDOC

Distribution of TDOC concentration and the fraction of LDOC comprising the

TDOC pool were measured in August and September of 2005. In August, the TDOC concentration was similar among basins (250-280 μM) (Table 5, Figure 5a). LDOC comprised between 12-22% of the total TDOC pool (Figure 5b) with no significant differences between basins. In September, TDOC decreased significantly from the western basin to the central basin (100-340 μM) (Figure 5a). LDOC comprised 9% of the

TDOC pool in the western and central basins but was 30% of the TDOC pool in the eastern basin (Figure 5b).

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Correlation of LDOC with Other Variables

During summer of 2005, LDOC concentration correlated strongly with the amount of LDOC respired by bacteria (r2 = 0.99) and the fraction of TDOC that is labile

(r2 = 0.64). However, no relationship was observed between LDOC and TDOC or LDOC and the portion of LDOC incorporated into bacterial cells. Neither TDOC nor LDOC correlated with chlorophyll a concentration. In chapter 5, Factors Influencing

Bioenergetics in Lake Erie Bacterioplankton, we will show that LDOC did not correlate with bacterial structure, bacterial activity, phosphorus dynamics, or limnological

variables. All r2 values, determined via regression analysis, were less than 0.15.

Components of bacterial structure included bacterial abundance (cells ml-1), bacterial

cellular biovolume (μm3 cell -1), and total bacterial biovolume (μm3 ml-1). Measures of

bacterial activity included bacterial productivity (μg C ml-1 hr-1), bacterial respiration (μg

C ml-1 hr-1), and bacterial growth efficiency (%). Measures of phosphorus dynamics

included bacterial particulate phosphorus (nM), algal particulate phosphorus (nM), total

soluble phosphorus (nM), total phosphorus concentration (nM), bacterial P-quota (nmol

-1 cell ), P distributed to bacteria (%), and Pi apportioned to bacteria (%). The fraction of the particulate phosphorus pool that was found in bacterial cells (1.0 μm > cell size > 0.2

μm) relative to algal cells (> 1.0 μm) represented the P distributed to bacteria. The

fraction of the available phosphate pool taken up radiometrically by bacterial cells relative to algal cells represents the Pi apportioned to bacteria. Limnological variables

included temperature (°C), depth (m), distance from the nearest shore (km), chlorophyll a concentration (μg L-1), and trophic state index (TSI). Data for bacterial structure and

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activity, phosphorus dynamics are presented in Chapter 6 – Factors Influencing the

Bioenergetics of Lake Erie Bacterioplankton.

Variability in LDOC Utilization by Bacterioplankton

Variability in LDOC utilization by bacterioplankton assemblages was analyzed by

swapping water between sites and between depths at select stations. During May 2004,

one LDOC swap experiment was performed between site 496 in the western basin and

site 954 in the central basin. The LDOC utilized by bacteria from site 496 inoculated into

site 496 water was 57.31 ± 0.75 μM and the LDOC utilization by site 954 bacteria

inoculated into site 954 was 70.97 ± 1.25 μM. However, when the bacteria from site 496

were inoculated into site 954 water, the LDOC utilization increased (ANOVA, Tukey

HSD) almost five-fold to 269.79 ± 8.38 μM (Table 6, Figure 6). This result suggests

that the bacteria in the western basin were able to utilize more of the LDOC at site 954,

even more successfully than the bacteria from that site. The reverse was not true since the central basin bacteria (site 954) inoculated into western basin water (site 496) showed a significant decrease in LDOC utilization, decreasing to 50.44 ± 2.81 μM.

During June 2004, two LDOC swap experiments were performed at station 880 in the central basin and station 879 in the eastern basin. Station 880 has a history of central basin hypoxia. At each station, epilimnetic water was swapped with hypolimnetic water to determine whether bacterial assemblages at different positions in the water column utilize DOC differently (Figure 10). At station 880, the fraction of DOC labile to

epilimnetic bacteria was more than two times greater when inoculated in hypolimnetic

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water, 118.7 ± 2.3 μM relative to epilimnetic bacteria inoculated in epilimnetic water,

53.3 ± 2.2 μM. The fraction of DOC labile to hypolimnetic bacteria was greater, 125.3 ±

2.9 μM LDOC (Table 6, Figure 7b) when inoculated into hypolimnetic water relative to the fraction of DOC labile when hypolimnetic bacteria were inoculated into epilimnetic water, 86.2 ± 3.2 μM.

The eastern basin site 879 yielded different results. The LDOC utilization by epilimnetic bacteria inoculated into epilimnetic water and hypolimnetic bacteria inoculated into hypolimnetic water at site 879 were 53.47 ± 2.86 μM and 47.74 ± 4.43

μM, respectively (Table 6). Epilimnetic bacteria inoculated into hypolimnetic water utilized 49.64 ± 4.08 μM LDOC and hypolimnetic bacteria inoculated into epilimnetic water utilized 69.51 ± 4.64 μM LDOC. No significant differences were observed during the swaps at site 879 (Figure 7a).

During July 2004, four LDOC swap experiments were performed between central basin sites 318 and 880 (epilimnetic with hypolimnetic), eastern basin site 879

(epilimnetic with hypolimnetic), and western basin site 496 with central basin site 954.

No significant differences in LDOC utilization were observed during these swap experiments (Table 6, Figure 8 a-d). In May for swap 496 with 954 and in June for swap

880 epi with 880 hypo, significant differences in LDOC utilization were observed.

In August 2004, two LDOC swaps were performed with epilimnetic and hypolimnetic waters at sites 879 (eastern basin) and 880 (central basin). During this time, site 880 exhibited hypoxia (DO < 4 mg L-1). At site 879, epilimnetic bacteria inoculated into epilimnetic water utilized 46.06 ± 0.67 μM LDOC and hypolimnetic bacteria

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inoculated into hypolimnetic water utilized 63.16 ± 3.19 μM LDOC. Both swaps resulted in increased LDOC utilization – epilimnetic bacteria in hypolimnetic water utilized

122.89 ± 21.31 μM LDOC and hypolimnetic bacteria in epilimnetic water utilized 115.71

± 4.57 μM LDOC (Table 6, Figure 9a). No differences were observed in LDOC utilization at site 880 (Figure 9b). In both swaps, the bacterial assemblages increased

LDOC utilization with the water from a different portion of the water column.

Discussion

The distribution and abundance in LDOC concentration varied greatly in Lake

Erie, from undetectable (0.0 μM) to high (> 200 μM). This patchy LDOC distribution and abundance observed in Lake Erie may be due to variability in the source of DOC, whether autochthonous or allochthonous and/or due to variability in local bacterioplankton community structure. Despite the patchy distribution of LDOC at distinct locations, LDOC concentration remained fairly constant among basins during both years of the investigation. LDOC represented a consistent fraction of the LDOC pool in the western and eastern basin (~10%); however, LDOC represented the largest fraction of TDOC in the eastern basin (30%). LDOC concentration may be analogous to biologically available phosphorus. When phosphate is least abundant and most limited, it is utilized most rapidly by the bacterioplankton community (Currie and Kalff 1984;

Cotner and Wetzel 1991; Gao and Heath 2005). Rapid utilization of the LDOC pool by bacterioplankton may explain the low (< 50 μM) LDOC concentrations observed at many stations.

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Knowledge of the distribution of LDOC and how bacterioplankton utilize LDOC

is important to understanding lake ecosystem functions. Carbon availability can impact trophic dynamics, bioenergetics, and nutrient cycling. Bacteria, in addition to algae, are important links in the transfer of carbon to higher trophic levels. Biologic carbon particulates (bacteria, protists, and zooplankton), rapidly cycle and move carbon to higher trophic levels in the microbial loop (Azam 1983; Sherr and Sherr 1988). Higher bacterial production and oxygen consumption in Florida lakes and estuaries were observed when phytoplankton production and algal exudates were highest (Coffin et al 1993). Up to

50% of bacterial production in Mirror Lake, NH was supported by photosynthetically produced DOC (PDOC) (Cole et al. 1982). Thingstad et al. (1997) suggested that DOC may accumulate in surface waters due to phytoplankton-bacterial competition and grazing of bacteria resulting in chemical transformation and vertical transport of the

DOC. Movement of carbon from algae and bacteria to zooplankton is not always an efficient process, especially in highly eutrophic lakes (Havens et al. 2000).

At the end of the season, the western basin had more available total carbon.

Higher TDOC values may suggest carbon of allochthonous origin (i.e. riverine inputs and loading) with a less lability (Bertilsson and Jones 2003). The central and eastern basins have smaller TDOC pools. Low TDOC values may suggest carbon of autochthonous origin with greater lability (Bertilsson and Jones 2003). Allochthonous carbon, whether originating from anthropogenic or non-anthropogenic sources, has been shown to impact bacterial productivity in other aquatic ecosystems. Hanson et al. (2003) analyzed production-to-respiration ratios of 25 lakes in Wisconsin and Michigan to determine that

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most lakes exhibited a negative ecosystem production, suggesting that DOC sources were

predominantly of allochthonous origin. Bacterial and planktonic respiration rates in Lake

Michigan were a carbon sink and terriginous inputs accounted for 10-20% of lake

production (Biddanda and Cotner 2002). Using mesocosm experiments, Wehr et al.

(1998) showed that phytoplankton, cyanobacteria, flagellates, rotifers, and zooplankton

production was stimulated by addition of macrophyte-derived carbon. In a Swedish lake, the total summer bacterial production depended upon input of allochthonous organic

carbon sources (Jansson et al. 1999). The quality and quantity of DOC entering Lake

Erie may be influencing bacterial bioenergetics including bacterial productivity,

respiration, and growth efficiency.

We have shown that LDOC utilization can be variable. Bacterioplankton from

30% of our stations utilized more or less LDOC when exchanged with water from a

different station. In two of these cases (May 496 vs. 954 swap and June 880 E vs. 880 H

swap), it appears that the carbon utilization by the bacterial assemblage was altered to the

LDOC conditions of the “new” or swapped water. For example, in May, station 496

bacteria increased LDOC utilization when placed into station 954 water. This increase

was the most dramatic response, representing approximately five times greater LDOC

utilization when compared with station 496 bacteria in station 496 water. Here, LDOC

utilization surpassed that of station 954 bacteria in station 954 bacteria by four-fold.

However, when station 954 bacteria was inoculated into station 496 water, LDOC

utilization decreased to a value closer to that of 496 bacteria in 496 water. Also, LDOC

utilization increased when epilimnetic bacteria were inoculated into hypolimnetic water

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of station 880 during June; however, LDOC utilization decreased when hypolimnetic

bacteria were inoculated into epilimnetic water.

Our results suggest that LDOC utilization may be dependent upon the bacterial

assemblage present at each station. Thus, the differential LDOC utilization observed in

these experiments suggests that the bacterial assemblage can alter the LDOC estimation

for each station. From the swap experiments, we have shown that epilimnetic bacteria

were more successful at utilizing the LDOC in hypolimnetic waters. Epilimnetic bacteria

may be carbon limited or in direct competition for carbon with algae in the upper waters.

This carbon limitation and/or competition may decrease in the bottom waters resulting in

an increased capacity for carbon utilization. For these cases, it appears that the LDOC in

the water influenced utilization by the assemblage. In one of these cases (August 879 E

vs. 879 H swap), it appears that the carbon utilization by the bacterial assemblage was

increased in both parts of the swap experiment, as if the swap water allowed for more

optimal LDOC utilization. For the remainder of the swap experiments, LDOC utilization

did not vary.

For these experiments, the community composition of the bacterial assemblage was unknown. Potential impacts of the bacterial assemblage on LDOC utilization in

Lake Erie are hypothetical. However, the influences of bacterial community structure on ecosystem function have been observed in other aquatic systems. Factors such as primary productivity (Horner-Devine et al. 2003), nutrient availability (Hewson et al.

2003; Olapade and Leff 2004), dissolved organic matter (Crump et al. 2003), ultraviolet radiation (Winter et al. 2001), seasonality (Yannarell et al. 2003), and grazing by protists

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(Šimek et al. 2001), ciliates and daphnids (Muylaert et al. 2002) have all shown to

influence bacterial community structure in a variety of aquatic environments. Improved

knowledge of bacterial community structure, as the primary consumers of DOC, and their functions will increase understanding of how carbon is cycled and processed in aquatic ecosystems (Sinsabaugh and Foreman 2003). Whether the bacterial assemblage can influence LDOC utilization, or whether shifts in LDOC concentration can shift bacterial community structure and further LDOC utilization in Lake Erie remains to be seen.

Further analysis of bacterial community structure of Lake Erie by modern molecular methods will be necessary to determine the bacterial influence on major ecosystem processes, like DOC utilization.

Summary

We have shown that LDOC distribution and abundance can vary greatly (range 0

- ~275 μM) at specific stations within Lake Erie. When station data is pooled into groups by basin, LDOC concentrations remain fairly constant. Approximately 10-30% of TDOC was labile to the bacterial assemblages. Late in the season, TDOC decreased from the western basin to the central basin. LDOC represented a fairly consistent fraction of the

LDOC pool (~10%) in the central and eastern basin, and a larger portion (30%) in the eastern basin. LDOC can be utilized differently by bacterioplankton assemblages at different locations. These findings suggest patchy LDOC distribution that is site specific and possibly dependent upon the bacterioplankton community structure and/or sources of allochthonous and autochthonous carbon at a given location. Utilization of LDOC

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appears be a function of the bacterial assemblages used as well as the water chemistry of

each station.

Acknowledgements

We thank the following individuals and organizations for assistance with this investigation: the captain and crew of the CCGS Limnos, the captain and crew of the R/V

Lake Guardian, Curtis Clevinger, Dana McDermott, Megan Castagnaro, and Bob Christy from Kent State University and Kathy Wilson from Sweetbriar College for technical assistance; Mohiuddin Munawar from the Canadian Department of Fisheries and Oceans for the 2004 cruise opportunity, the personnel of the Canadian DFO for technical assistance and data collection including Mark FitzpatrickJocelyn Gerlofsma; Dana

McDermott from Kent State University for technical assistance.and Margaret Lansing from NOAA/GLERL for assistance with data collection. This research was funded as

part of the International Field Year of Lake Erie (2005) by the National Oceanic and

Atmospheric Administration (NOAA), Great Lakes Environmental Research Laboratory

(GLERL), US Environmental Protection Agency (EPA), Cooperative Institute of

Limnology and Ecosystem Research (CILER), and the Ohio Sea Grant College Program.

Table 1. Limnological variables for summer 2004: year, month, station number, basin, latitude (DDMMSS), longitude

(DDMMSS), sounding depth (m), sample depth (m), temperature, temp (°C), dissolved oxygen concentration, DO (mg L-1), average chlorophyll a concentration, chl a (μg L-1), and trophic state index (TSI).

Sounding Sample Depth Depth Temp DO Chl a

Year Month Station Basin Latitude Longitude (m) (m) (°C) (mg L-1) (μg L-1) TSI

2004 June 879 Eastern 42° 30’ 25” 79° 53’ 59” 62.2 2 16.82 10.33 1.12 31.7

2004 June 880 Central 41° 56’ 09” 81° 39’ 16” 24.4 3 18.40 10.49 0.54 24.5

2004 June 882 Western 41° 45’ 57” 83° 18’ 34” 6.7 0-6 21.83 10.80 6.75 49.3

2004 June 934 Eastern 42° 42’ 30” 79° 30’ 30” 28 0-11 17.60 10.17 1.61 35.3

2004 June 950 Central 42° 35’ 18” 81° 26’ 30” 10 0-5 17.53 9.48 0.89 29.5

2004 June 952 Central 42° 21’ 30” 81° 26’ 30” 22 0-16 17.70 10.04 0.39 21.3

2004 June 970 Western 41° 49’ 30” 82° 58’ 30” 10.6 0-8 20.92 10.11 4.43 45.2

2004 July 496 Western 41° 34’ 06” 82° 43’ 12” 9.2 0-7 24.62 13.25 12.65 55.5

2004 July 879 Eastern 42° 30’ 25” 79° 53’ 59” 62.2 15 18.52 8.41 1.44 34.1

2004 July 880 Central 41° 56’ 09” 81° 39’ 16” 24.4 2.5 22.56 9.86 2.99 41.3 200

2004 July 931 Eastern 42° 51’ 00” 78° 56’ 30” 10 0-8 21.16 8.60 1.91 36.9

2004 July 948 Central 41° 57’ 24” 80° 38’ 30” 10.5 0-8.5 23.32 10.48 5.01 46.4

2004 July 950 Central 42° 35’ 18” 81° 26’ 30” 8.7 0-3 18.38 7.98 1.20 32.4

2004 July 936 Eastern 42° 28’ 30” 79° 24’ 30” 7.6 5.5 22.39 8.49 1.37 33.7

2004 July 954 Central 42° 01’ 30” 81° 26’ 30” 23.9 0-16 23.05 9.65 1.61 35.3

2004 July 970 Western 41° 49’ 30” 82° 58’ 30” 10.6 0-8.5 ND ND 2.86 40.9

2004 July 1163 Sand Bay 41° 28' 16" 82° 43' 05" 8.4 0-6 ND ND 50.19 69

2004 August 311 Central 41° 35’ 00” 82° 28’ 00” 14.3 0-12 21.95 9.09 9.21 52.3

2004 August 879 Eastern 42° 30’ 25” 79° 53’ 59” 62.1 1 21.10 ND 0.85 29

2004 August 880 Central 41° 56’ 09” 81° 39’ 16” 24.3 1 21.74 10.12 1.68 35.6

2004 August 933 Eastern 42° 49’ 30” 79° 34’ 00” 12.9 0-10 20.95 ND 1.36 33.6

2004 August 934 Eastern 42° 42’ 30” 79° 30’ 30” 23.5 ND ND ND 1.24 32.7

2004 August 950 Central 42° 35’ 18” 81° 26’ 30” 10.5 0-8.5 18.22 7.55 0.88 29.3

2004 August 958 Central 41° 31’ 30” 81° 42’ 30” 12.2 0-10 22.16 9.73 0.51 23.9

2004 August 970 Western 41° 49’ 30” 82° 58’ 30” 10.5 0-8.5 11.17 0 3.80 43.7

2004 August 1163 Sand Bay 41° 28' 16" 82° 43' 05" 8.3 1 21.73 9.02 53.22 69.6 201

Table 2. Limnological variables for summer 2005: year, month, station number, basin, latitude (DDMMSS), longitude

Sample Sample Sounding Month Basin Site Type Latitude Longitude Depth Depth Temp DO Chl a TSI

(DDMMSS) (DDMMSS) (m) (m) (°C) (mg L-1) (μg L-1)

May Eastern ER 15 Epi 42 31 03 79 53 37 2.0 60.0 3.7 11.81 2.17 38.1

May Central ER 43 Epi 41 47 15 81 56 42 2.0 23.0 3.37 42.5

May Central ER 73 Epi 41 58 40 81 45 25 2.0 24.0 2.24 38.5

May Eastern 449 Epi 42 45 40 79 59 21 2.0 13.5 6.6 11.24 0.48 23.3

May Western 496 Epi 41 33 54 82 43 29 2.0 3.0 11.9 9.61 10.56 53.7

May Western 580 Epi 41 50 56 83 06 21 2.0 10.0 14.1 9.86 1.67 35.6

May Western 835 Epi 41 45 12 83 20 42 2.0 6.0 14.4 11.14 20.24 60.1

May Eastern 942 Epi 42 15 36 79 49 49 2.0 16.0 8.6 11.14 0.81 28.5

May Central 950 Epi 42 34 58 81 26 10 2.0 13.0 1.16 32.0

May Central 958 Epi 41 32 59 81 42 15 2.0 13.0 4.74 45.8

June Eastern ER 15 Epi 42 34 35 79 53 38 5.0 60.0 14.5 13.85 3.66 43.3

202

June Eastern ER 15 Meta 42 34 35 79 53 38 9.5 60.0 10.3 15.69 4.59 45.5

June Eastern ER 15 Hypo 42 34 35 79 53 38 41.0 60.0 4.2 13.29 0.56 24.8

June Central ER 37 Epi 42 06 37 81 34 28 0.6 24.0 21.5 9.56 1.98 37.3

June Central ER 43 Epi 41 47 16 81 56 38 2.5 23.0 9.30 9.82 36.9

June Central ER 73 Epi 41 58 41 81 45 24 5.0 24.0 18.3 10.26 1.06 31.1

June Central ER 73 Hypo 41 58 41 81 45 24 14.5 24.0 8.9 13.11 1.62 35.3

June Central ER 78 Epi 42 07 00 81 14 59 4.3 23.0 18.4 10.13 1.53 34.7

June Western ER 92 Epi 41 56 57 82 41 15 3.5 11.0 20.1 9.51 1.98 37.2

June Central 311 Epi 41 39 59 82 29 57 4.5 13.0 20.0 10.36 1.03 30.9

June Central 412 Epi 42 05 59 82 11 21 4.0 21.5 18.6 9.98 0.70 27.1

June Eastern 449 Epi 42 45 41 79 59 20 2.5 13.5 18.0 10.57 0.98 30.3

June Western 496 Epi 41 34 04 82 43 20 3.0 10.0 20.3 8.85 2.39 39.1

June Western 580 Epi 41 50 56 83 06 17 2.0 10.0 22.6 10.10 6.68 49.2

June Western 835 Epi 41 45 15 83 20 33 1.9 6.0 22.5 8.56 3.54 43.0

June Central 950 Epi 42 33 25 81 26 30 3.6 23.0 17.7 10.52 1.20 32.4

June Central 958 Epi 41 32 60 81 42 15 2.0 13.0 22.1 9.28 1.85 36.6

June Eastern 942 Epi 42 15 36 79 49 48 6.5 16.0 19.1 9.83 1.52 34.7

June Central 964 Epi 41 38 00 82 17 58 3.5 8.0 20.3 10.20 3.10 41.7

June Western 1163 Epi 41 28 33 82 43 20 2.5 3.0 21.5 9.01 4.57 45.5 203

July Eastern ER 15 Epi 42 31 02 79 53 37 2.0 60.0 25.4 8.29 0.69 26.9

July Eastern ER 15 Meta 42 31 02 79 53 37 15.2 60.0 16.9 10.53 0.89 29.4

July Eastern ER 15 Hypo 42 31 02 79 53 37 61.2 60.0 4.6 11.69 0.08 5.3

July Central ER 37 Epi 42 06 37 81 34 27 2.0 24.0 0.50 23.7

July Central ER 43 Epi 41 47 19 81 56 43 2.0 23.0 25.6 8.32 1.22 32.5

July Central ER 73 Epi 41 58 40 81 45 22 2.0 24.0 25.3 8.13 0.70 27.0

July Central ER 73 Meta 41 58 40 81 45 22 13.5 24.0 16.8 10.21 1.15 31.9

July Central ER 73 Hypo 41 58 40 81 45 22 22.5 24.0 9.0 6.88 1.32 33.3

July Central ER 78 Epi 42 06 56 82 15 18 2.5 23.0 0.86 29.1

July Western ER 92 Epi 41 56 60 82 41 11 2.0 11.0 26.1 7.85 2.42 39.3

July Central 311 Epi 41 39 58 82 29 60 2.0 13.0 26.1 8.16 3.38 42.5

July Central 412 Epi 42 05 60 82 11 24 2.0 21.5 26.0 8.12 1.11 31.6

July Eastern 449 Epi 42 45 38 79 59 05 2.5 13.5 24.8 8.26 0.83 28.7

July Western 496 Epi 41 34 03 82 43 17 2.0 10.0 25.7 6.13 1.94 37.1

July Western 580 Epi 41 50 54 83 06 24 2.0 10.0 25.8 7.39 2.64 40.1

July Western 835 Epi 41 45 19 83 20 27 2.0 6.0 27.0 7.91 12.77 55.6

July Eastern 942 Epi 42 15 33 79 49 48 3.0 16.0 25.0 8.14 0.63 26.1

July Central 950 Epi 42 33 19 81 26 36 2.0 13.0 24.5 8.10 0.73 27.5

204

July Central 958 Epi 41 32 57 81 42 17 2.0 13.0 24.2 7.89 0.85 29.0 July Central 965 Epi 41 30 04 82 30 02 2.0 13.0 2.43 39.3

July Western 1163 Epi 41 28 27 82 42 12 2.0 3.0 27.2 7.19 4.18 44.6

Aug Eastern ER 15 Epi 42 30 55 79 52 30 3.0 60.0 25.2 7.63 1.19 32.3

Aug Eastern ER 15 Meta 42 30 55 79 52 30 17.0 60.0 9.6 12.41 1.72 35.9

Aug Eastern ER 15 Hypo 42 30 55 79 52 30 61.0 60.0 4.8 10.71 0.09 6.9

Aug Central ER 37 Epi 42 06 35 81 34 29 1.5 24.0 26.2 7.40 1.12 31.7

Aug Central ER 43 Epi 41 47 19 81 56 41 2.0 23.0 26.5 7.67 1.81 36.4

Aug Central ER 73 Epi 41 58 39 81 45 26 2.0 24.0 26.3 7.71 2.30 38.7

Aug Central ER 73 Meta 41 58 39 81 45 26 16.0 24.0 17.7 4.49 2.86 40.9

Aug Central ER 73 Hypo 41 58 39 81 45 26 22.0 24.0 10.0 4.78 1.16 34.9

Aug Central ER 78 Epi 42 06 57 81 14 58 3.0 23.0 26.1 7.66 1.83 36.5

Aug Western ER 92 Epi 41 56 59 82 41 14 2.0 11.0 26.4 6.86 7.24 50.0

Aug Central 311 Epi 41 39 59 82 29 59 2.0 13.0 26.4 7.95 6.89 49.5

Aug Central 311 Hypo 41 39 59 82 29 59 12.8 13.0 16.7 0.94 4.11 44.4

Aug Central 412 Epi 42 05 60 82 11 24 2.0 21.5 4.17 44.6

Aug Eastern 449 Epi 42 45 39 79 59 21 2.0 13.5 24.7 7.09 1.13 31.8

Aug Western 496 Epi 41 34 07 82 43 17 2.0 10.0 26.9 9.43 10.35 53.5

Aug Western 496 Hypo 41 34 07 82 43 17 8.3 10.0 21.7 0.52 4.40 45.1 205

Aug Western 580 Epi 41 50 53 83 06 23 2.0 10.0 26.3 7.29 7.40 50.2

Aug Western 835 Epi 41 45 17 83 20 30 2.0 6.0 26.8 6.34 10.96 54.1

Aug Eastern 942 Epi 42 15 36 79 49 49 2.0 16.0 25.9 7.64 1.93 37.0

Aug Central 950 Epi 42 33 24 81 26 31 2.0 13.0 25.1 7.72 2.42 39.2

Aug Central 958 Epi 41 32 57 81 42 17 2.0 13.0 26.0 7.49 2.67 40.2

Aug Western 1163 Epi 41 28 27 82 42 12 1.5 3.0 26.0 5.47 43.73 67.6

Aug Western 1163 Hypo 41 28 27 82 42 12 8.3 3.0 25.0 3.07 33.12 64.9

Aug Central 965 Epi 41 30 05 82 30 01 2.0 13.0 26.5 7.44 3.91 44.0

Sept Eastern ER 09 Epi 42 32 15 79 37 06 2.0 22.4 7.22 2.58 39.9

Sept Eastern ER 09 Meta 42 32 15 79 37 06 21.0 15.9 8.61 1.94 37.1

Sept Eastern ER 09 Hypo 42 32 15 79 37 06 49.0 5.3 9.15 0.66 26.5

Sept Eastern ER 15 Epi 42 30 58 79 53 41 2.0 60.0 22.3 7.63 2.90 41.0

Sept Eastern ER 15 Meta 42 30 58 79 53 41 21.7 60.0 7.2 9.16 0.60 25.5

Sept Eastern ER 15 Hypo 42 30 58 79 53 41 62.0 60.0 5.3 9.45 0.09 7.4

Sept Central ER 43 Epi 41 47 20 81 56 43 2.0 23.0 22.3 7.51 4.00 44.2

Sept Central ER 43 Hypo 41 47 20 81 56 43 21.6 23.0 11.8 1.09 2.43 39.3

Sept Central ER 73 Epi 41 58 41 81 45 24 2.0 24.0 23.0 7.94 5.94 48.1

Sept Central ER 73 Meta 41 58 41 81 45 24 18.2 24.0 11.9 1.40 1.90 36.9 206

Sept Central ER 73 Hypo 41 58 41 81 45 24 23.5 24.0 10.6 1.40 2.27 38.6

Sept Central ER 78 Epi 42 06 60 81 15 01 2.0 23.0 22.9 7.08 3.24 42.1

Sept Central ER 78 Meta 42 06 60 81 15 01 21.0 23.0 17.3 2.42 2.05 37.6

Sept Central ER 78 Hypo 42 06 60 81 15 01 22.5 23.0 13.7 0.12 3.54 43.0

Sept Central 311 Epi 41 39 59 82 29 59 2.0 13.0 23.3 6.85 8.93 52.0

Sept Central 412 Epi 42 05 54 82 11 23 2.0 21.5 22.7 7.21 3.68 43.4

Sept Central 412 Hypo 42 05 54 82 11 23 20.2 21.5 10.8 0.30 2.49 39.5

Sept Eastern 449 Epi 42 45 40 79 59 15 2.0 13.5 22.6 7.59 2.87 40.9

Sept Western 496 Epi 41 34 15 82 43 23 2.0 10.0 23.9 6.26 13.69 56.2

Sept Western 580 Epi 41 50 54 83 06 24 2.0 10.0 23.2 7.65 7.84 50.8

Sept Western 835 Epi 41 45 09 83 20 45 2.0 6.0 23.2 6.65 7.21 49.9

Sept Central 950 Epi 42 33 24 81 26 34 2.0 13.0 22.3 7.05 3.18 41.9

Sept Central 958 Epi 41 32 59 81 42 14 2.0 13.0 22.1 6.37 3.29 42.3

Sept Central 965 Epi 41 30 03 82 30 07 2.0 13.0 23.6 5.27 12.09 55.0

Sept Western 1163 Epi 41 28 27 82 41 11 2.0 3.0 23.4 5.40 18.55 59.2 207

Table 3. LDOC concentration by station during 2004 and 2005: LDOC respired by bacteria (μM), LDOC incorporated into bacterial cells (μM), fraction of total LDOC incorporated into bacterial cells (%), LDOC total (μM), TDOC (μM), and percent

LDOC of TDOC (μM). Means ± 1 SE.

LDOC LDOC LDOC % LDOC of LDOC Resp Incorp Incorp Total TDOC TDOC

(μM) (μM) (%) (μM) (μM) (μM)

Year Month Site Depth Avg ± SE Avg ± SE Avg ± SE Avg ± SE Avg Avg ± SE

2004 June 879 Epi 52.0 ± 2.9 1.4 ± 0.1 2.8 ± 0.3 53.5 ± 2.3 ND ND

2004 June 879 Meta 56.2 ± 4.7 1.5 ± 0.4 2.7 ± 1.3 57.7 ± 4.4 ND ND

2004 June 879 Hypo 100.5 ± 7.2 0.7 ± 0.1 0.7 ± 0.2 101.2 ± 7.2 ND ND

2004 June 880 Epi 52.4 ± 2.0 0.9 ± 0.2 1.6 ± 0.6 53.3 ± 2.2 ND ND

2004 June 880 Meta 64.8 ± 12.5 0.8 ± 0.1 1.3 ± 0.5 65.6 ± 12.5 ND ND

2004 June 880 Hypo 123.7 ± 2.9 1.6 ± 0.0 1.2 ± 0.1 125.3 ± 2.9 ND ND

2004 June 882 Epi 113.4 ± 0.8 0.0 ± 0.0 0.0 ± 0.0 113.4 ± 0.8 ND ND

2004 June 934 Epi 97.7 ± 4.5 1.2 ± 0.2 1.2 ± 0.3 98.8 ± 4.7 ND ND

2004 June 950 Epi 125.8 ± 0.9 0.7 ± 0.1 0.6 ± 0.1 126.5 ± 0.8 ND ND

2004 June 952 Epi 57.9 ± 2.0 1.2 ± 0.1 1.8 ± 0.4 58.9 ± 1.8 ND ND

208

2004 June 970 Epi 90.6 ± 4.8 1.1 ± 0.1 1.2 ± 0.2 91.7 ± 4.8 ND ND

2004 July 318 Epi 37.6 ± 5.5 1.3 ± 0.1 3.7 ± 0.8 39.0 ± 5.4 ND ND

2004 July 318 Hypo 70.3 ± 6.6 1.4 ± 0.0 2.0 ± 0.2 71.7 ± 6.6 ND ND

2004 July 496 Epi 22.1 ± 3.7 1.9 ± 0.7 8.0 ± 2.6 24.1 ± 3.8 ND ND

2004 July 879 Epi 77.6 ± 8.2 1.2 ± 0.1 1.6 ± 0.2 78.8 ± 8.2 ND ND

2004 July 879 Meta 96.0 ± 2.9 1.0 ± 0.0 1.1 ± 0.1 97.0 ± 2.9 ND ND

2004 July 879 Hypo 53.3 ± 6.9 0.7 ± 0.0 1.3 ± 0.1 54.0 ± 6.9 ND ND

2004 July 880 Epi 22.2 ± 6.0 1.0 ± 0.0 4.8 ± 1.0 23.2 ± 6.0 ND ND

2004 July 880 Meta 59.6 ± 5.3 1.8 ± 0.2 3.1 ± 0.6 61.5 ± 5.1 ND ND

2004 July 880 Hypo 66.3 ± 10.1 1.6 ± 0.1 2.5 ± 0.5 67.9 ± 10.0 ND ND

2004 July 931 Epi 87.8 ± 17.0 0.6 ± 0.0 0.8 ± 0.2 88.4 ± 17.0 ND ND

2004 July 936 Epi 55.3 ± 12.6 0.8 ± 0.1 1.5 ± 0.2 56.1 ± 12.7 ND ND

2004 July 948 Epi 54.0 ± 8.3 0.4 ± 0.2 0.7 ± 0.4 54.3 ± 8.3 ND ND

2004 July 950 Epi 52.1 ± 7.5 1.5 ± 0.3 2.8 ± 0.6 53.6 ± 7.6 ND ND

2004 July 954 Epi 50.2 ± 1.5 0.8 ± 0.0 1.6 ± 0.0 51.0 ± 1.5 ND ND

2004 Aug 311 Epi 31.8 ± 3.7 1.1 ± 0.1 3.3 ± 0.1 32.9 ± 3.8 ND ND

2004 Aug 879 Epi 45.2 ± 0.6 0.8 ± 0.0 1.8 ± 0.0 46.1 ± 0.7 ND ND

2004 Aug 879 Meta 31.1 ± 2.2 0.9 ± 0.0 3.0 ± 0.3 32.0 ± 2.1 ND ND

2004 Aug 879 Hypo 62.0 ± 3.2 1.1 ± 0.0 1.8 ± 0.1 63.2 ± 3.2 ND ND 209

2004 Aug 880 Epi 21.4 ± 6.1 0.8 ± 0.1 4.1 ± 1.1 22.1 ± 6.0 ND ND

2004 Aug 880 Meta 43.7 ± 4.9 0.9 ± 0.1 2.0 ± 0.2 44.6 ± 4.9 ND ND

2004 Aug 880 Hypo 26.2 ± 10.8 1.0 ± 0.0 6.9 ± 4.2 27.2 ± 10.8 ND ND

2004 Aug 933 Epi 75.5 ± 12.8 1.0 ± 0.0 1.3 ± 0.2 76.5 ± 12.8 ND ND

2004 Aug 934 Epi 43.4 ± 0.6 0.6 ± 0.3 1.4 ± 0.7 44.0 ± 0.6 ND ND

2004 Aug 950 Epi 49.8 ± 2.8 0.8 ± 0.0 1.5 ± 0.1 50.6 ± 2.7 ND ND

2004 Aug 958 Epi 69.8 ± 3.7 1.1 ± 0.0 1.5 ± 0.1 70.8 ± 3.7 ND ND

2004 Aug 960 Hypo 51.5 ± 1.1 1.2 ± 0.1 2.3 ± 0.1 52.8 ± 1.2 ND ND

2004 Aug 970 Epi 47.8 ± 3.6 0.9 ± 0.0 2.0 ± 0.1 48.7 ± 3.6 ND ND

2004 Aug 1163 Epi 56.5 ± 10.2 0.6 ± 0.1 1.1 ± 0.4 57.1 ± 10.1 ND ND