A Spatio-Temporal Comparison of Nutrient Deficiency Indicators in Lake Erie

A SPATIO-TEMPORAL COMPARISON OF NUTRIENT DEFICIENCY INDICATORS IN LAKE ERIE

A thesis submitted to Kent State University in partial fulfillment of the requirements for the degree of Master of Science

Leigh A. Martin

May 2013

Thesis written by Leigh A. Martin B.A., Skidmore College, 2007 M.S., Kent State University, 2013

Approved by:

Dr. Darren Bade______, Advisor

Dr. Laura Leff______, Acting Chair, Department of Biological Sciences

Dr. Raymond Craig______, Associate Dean, College of Arts and Sciences

TABLE OF CONTENTS

LIST OF FIGURES…………………………………………………………….…………v

LIST OF TABLES………………………………………………………………….…….vi

ACKNOWLEDGEMENTS……………………………………………………………...vii

OBJECTIVES OF THE STUDY……………………………………….…………………1

CHAPTER 1: Introduction Study Background …….……………………………………………………………..……2 Study Site………………………………………………………………………………….7 Conclusions………………………………………………………………………………10 References………………………………………………………………………………..11

CHAPTER 2: A Nearshore-Offshore Comparison of Phosphorus Deficiency Indicators in Lake Erie Abstract…………………………………………………………………………………..14 Introduction...……………………………………………………………………………14 Methods………………………………………………………………………………….18 Results……………………………………………………………………………...…….22 Phosphorus Debt………………………………………………………………....22 Phosphorus Turnover Time……………………………………………………....24 Alkaline Phosphatase Activity…………………………………………………...27 Linear Regression Results………………………………………………………..30 Chl a, TP, and SRP………………………………………………………………31 Discussion………………………………………………………………………………..32 Conclusions………………………………………………………………………………37 Acknowledgements……………………………………………………………………....38 References………………………………………………………………………………..38

CHAPTER 3: Seasonal Shifts of Nutrient Deficiency in Lake Erie Abstract………………………………..…………………………………………………43 Introduction ……………………………………………………………………………...43 Methods…………………………………………………………………………………..46 Results……………………………………………………………………………………52 Phosphorus Debt…………………………………………………………………52 Phosphorus Turnover Time………………………………………………………54 Alkaline Phosphatase Activity…………………………………………………...56 Ammonium Enhancement Response………………………………………….…58 iii

Nitrogen Debt ………………………………………………………………...…61 Chl a and Nutrient Concentrations………………………………………………64 Discussion………………………………………………………………………………..65 Considerations and Conclusions………………………………………………………....69 Acknowledgments……………………………………………………………………….71 References……………………………………………………………………………….72

APPENDICES…………………………………………………………………………..76

LIST OF FIGURES

1. Simplified diagram of phosphorus flow dynamics before and after the dreissenid mussel invasion, as asserted by the nearshore phosphorus shunt hypothesis…...... 6

2. Sample transect locations in Lake Erie………………………………………...….9

3. Phosphorus debt along Lake Erie transects in 2011 and 2012…………………...23

4. Phosphorus turnover times along Lake Erie transects in 2011 and 2012………..25

5. Alkaline phosphatase activity along Lake Erie transects in 2011 and 2012……..28

6. P debt values along transects in spring and fall of 2012…………………………53

7. Phosphorus turnover times along eight transects in Lake Erie in spring and fall of 2012………………………………………………………………………..……..55

8. Alkaline phosphatase concentrations along eight transects in Lake Erie in spring and fall of 2012…………………………………………………………………..57

9. Ammonium enhancement response ratios for Lake Erie transects in spring and fall of 2012…………………………………………………………………………...59

10. N debt values for Lake Erie transects in June and August 2012………………...62

LIST OF TABLES

1. Algal phosphorus limitation values and deficiency thresholds…………………………19

2. Arithmetic mean Chl a and nutrient concentrations by season and basin……….32

3. Algal nutrient limitation values and deficiency thresholds…………………………..…48

ACKNOWLEDGEMENTS

First and foremost I would like to thank my advisor, Dr. Darren Bade, whose insight and expertise on freshwater lake dynamics were invaluable when I was framing my research questions and ultimately when trying to make sense of my findings. Thanks are also owed to my committee members, Dr. Xiaozhen Mou and Dr. Ferenc de Szalay, for their feedback and encouragement.

I am incredibly grateful to Heather Kirkpatrick Smith and Curtis Clevinger for showing me the ropes in the lab and for their assistance in processing samples during our impossibly busy sampling periods. Thanks are also in order for fellow graduate students

Anna Ormiston, Margaret Gaglione, Ryan Schoeneman and Sumeda Madhuri for their help. Thanks also to Shunya Yagi and Cory Gargas, two dedicated undergraduate students whose assistance over the summer was instrumental to the success of the project.

The scope of this project was honestly greater than I ever could have anticipated and I am proud to have played even a small part in it. Enough cannot be said for the hard work of the Great Lakes Restoration Initiative and our collaborative partners from

Buffalo State College, Case Western University, the University of Toledo, and

Heidelberg University. Thanks also to Dr. Jacques Finlay from the University of

Minnesota for his assistance with refining our nitrogen debt protocol.

vii

I would also like to thank Donna Warner in the Department of Biological

Sciences office and Robin Wise and Jennifer Kipp in the stockroom, who have always been incredibly patient in answering my multitude of questions.

Last, but certainly not least, I would like to thank my husband Michael for his ongoing encouragement and understanding during this endeavor. Thank you for believing in me.

viii

OBJECTIVES OF THE STUDY

The main objective of this study was to obtain a better understanding of the nutrient dynamics affecting algal growth in Lake Erie’s photic zone. To achieve this goal, a series of physiological nutrient deficiency bioassays was conducted on water samples from Lake Erie during the growing seasons of 2011 and 2012. The study has been organized into three chapters, which are described briefly below.

CHAPTER 1: Introduction. Because Chapters 2 and 3 will be written in the format of articles to be submitted for publication, this chapter will serve to provide background information for the two nutrient deficiency studies conducted during the 2011 and 2012 growing seasons in Lake Erie, as well as present a summary of our conclusions.

CHAPTER 2: A Nearshore-Offshore Comparison of Phosphorus Deficiency Indicators in

Lake Erie. This chapter describes the data obtained from our two years of P deficiency research in Lake Erie and how they relate to the hypothesis that limitation by P should increase with depth as a result of the dreissenid mussel invasion.

CHAPTER 3: Seasonal Shifts of Nutrient Deficiency in Lake Erie. In continuance of

Chapter 2, this chapter further explores nutrient deficiency in Lake Erie with the addition of bioassays in 2012 that determined whether or not nitrogen deficiency is a contributing factor to restricting primary productivity in the lake.

CHAPTER ONE: INTRODUCTION

Study Background

When nutrients accumulate in excess in an aquatic system, phytoplankton are released from their usual growth constraints and experience an increase in primary productivity. This process of eutrophication has long been an issue of concern throughout the Laurentian Great Lakes, particularly in Lake Erie. While eutrophication occurs naturally through sedimentation over time, it can be significantly accelerated by an influx of nutrients stemming from anthropogenic sources, such as agricultural and wastewater runoff. Lake Erie, the shallowest of the Great Lakes, is largely surrounded by agricultural and urban development. The impacts of eutrophication on the Lake Erie ecosystem have been substantial, and include the loss of vascular plants and periphytic phytoplankton, fish kills resulting from anoxic conditions, and potential health risks to humans from toxins produced by harmful algae blooms (Correll 1998, de Jonge et al.

2002; Smith and Schindler 2007).

It is believed that degradation of the Lake Erie’s water quality began as early as the 1830s when the construction of the Erie Canal was completed. Population growth and the sudden increase in agricultural and industrial usage in the watershed supplied the lake with an excess of nutrients, which gradually accumulated and increased primary productivity. The issue started to receive national attention in the 1960s when algal blooms began appearing regularly in the lake and massive fish kills began to threaten the lake’s fisheries (Mortimer 1987; Lu et al. 2010). In response to public concerns, the 2

Great Lakes Water Quality Agreement between the United States and Canada established annual target loads for total phosphorus (TP) in each of the lakes beginning in 1972

(Schindler 1974; Robertson and Saad 2011; Schwab et al. 2009).

The management scheme defined in Great Lakes Water Quality Control

Agreement was based largely on data obtained from the Experimental Lakes Area (ELA) in Canada. The ELA served as the location for whole ecosystem experiments that tested hypotheses concerning eutrophication management in freshwater lakes (Schindler 1990).

The ELA’s whole ecosystem manipulations held what was arguably a distinct advantage over bottle assays in that the experiments were subject to the same nutrient cycling and natural processes as the ecosystems they were theoretically mimicking (Wang and Wang

2009).

Prior to the research conducted in the ELA there had been little consensus in limnology about which nutrients were limiting to algal growth in eutrophic lakes, although commercial interests were actively promoting the idea of carbon (C) limitation

(Sterner 2008). Data collected during the whole ecosystem experiments provided the foundation of the P paradigm – the viewpoint that P is ultimately the limiting nutrient in most temperate freshwater systems. In early experiments, fertilization by N and P in

Lake 227 yielded eutrophic conditions with phytoplanktonic biomass proportional to P additions. Further experimentation in Lake 227 and Lake 226 showed that when N was reduced to deficient concentrations, phytoplankton growth was again proportional to the amount of P added (Schindler 2008). An additional argument against reducing N concentrations as a means of reducing algal growth was that any deficiencies in N could

be neutralized with additions from the atmosphere through N2 fixation by certain species of cyanobacteria and some heterotrophic bacteria. Meanwhile, the P cycle lacks an atmospheric component and is thus more easily regulated (Schindler 1990; Wang and

Wang 2009).

The P loading restrictions in Lake Erie were initially considered to be extremely successful in reversing eutrophication. The annual phosphorus loading target of 11,000 metric tonnes has been met fairly consistently since it was first obtained in 1981; the target has only been exceeded during years with high annual precipitation (Joosse and

Baker 2011). Offshore regions in Lake Erie reached mesotrophic status in the Western

Basin and oligotrophic status in the Central and Eastern basins in the early 1990s (Hecky et al. 2004). Yet despite the apparent achievements attained from restricting P levels, some of the symptoms of eutrophication are still being felt, particularly in the form of excessive Cladophora glomerata growth in nearshore areas and annual blooms of cyanobacteria (Cha et al. 2011; Depew et al. 2006; Hecky et al. 2004).

The following two chapters will be submitted for publication in an effort to provide insight on two potential driving factors behind the persistent algal growth in Lake

Erie. Chapter two examines the effects of the nearshore phosphorus shunt hypothesis and how it relates to nearshore and offshore P deficiency indicators. According to the hypothesis, particulate phosphorus that normally would be transported offshore is instead intercepted by the filter feeding activity of invasive dreissenid mussels and deposited in the nearshore benthos (Hecky et al. 2004). In theory, the nearshore phosphorus shunt has the propensity to create two drastically different environments: a nearshore zone

characterized by improved water clarity and heightened benthic productivity, and an offshore zone suffering from extreme nutrient depletion (Figure 1). The growing dichotomy between nearshore and offshore environments could potentially impact species in higher trophic levels. The dreissenid invasion already may be responsible for the decline of several species offshore, including whitefish (Coregonus clupeaformis), bloater (C. hoyi), deepwater sculpin (Myoxocephalus thompsonii) and alewife (Alosa pseudoharengus), as well as the amphipod species Diporeia (Bunnell et al. 2009; French et al. 2009). During the course of our own research we found little evidence to support that P deficiency is highly impacted by depth; rather, seasonality was more important in determining the degree of P deficiency in a given site. Thus, it may be that while dreissenids are capable of disrupting nutrient flow dynamics to a certain degree, their effects in Lake Erie may have diminished in the two decades since their initial introduction.

Chapter three investigates the prevalence of nitrogen deficiency in Lake Erie over the course of a growing season. While P reductions appeared to alleviate eutrophic conditions in Lake Erie in the 1980s and 1990s, the current problems related to noxious blooms of cyanobacteria and green algae could be signaling a need for reductions on external N loads in addition to the P loading restrictions already in place. The results of our study showed a fairly widespread occurrence of N deficiency in late spring, which gave way to increased P deficiency in the early fall.

Pre -Dreissenid Invasion Nearshore Offshore

P Input Algae Algae

Benthos

Post -Dreissenid Invasion Nearshore Offshore

P Input Algae Algae

Benthos

Figure 1: Simplified diagram of phosphorus flow dynamics before and after the dreissenid mussel invasion, as asserted by the nearshore phosphorus shunt hypothesis. Solid and dashed arrows are respectively reflective of high and low transport of phosphorus.

Study Site

Lake Erie is located between 41° 21’ N and 42° 50 N latitude and 78° 50’ W and

83° 30’ W longitude. Of the Laurentian Great Lakes, Erie is the second smallest lake by area at 25,700 km2 and the smallest by volume at 484 km3 (Robertson and Saad 2011).

The lake is divided into three basins, which increase in depth from west to east. The

Western Basin, which is the smallest by volume, is also the shallowest of the three with an average depth of only 7.4 m. Its shallow depths, relatively small volume, and close proximity to agricultural and urban sources of nutrient inflows have historically made the

Western Basin the most productive (Lu et al. 2010, Millie et al. 2009). The Central Basin has an average depth of 18.3 m and is prone to seasonal hypoxia in the hypolimnion

(ODNR 2009; Edwards et al. 2005). The deepest waters are found in the Eastern Basin, where the average water depth is 24.4 m (ODNR 2009). Due to the dilution of nutrients in the greater volume and deeper waters, the Eastern Basin is the least productive and is currently considered the most oligotrophic of the three (Lu et al. 2010).

Agricultural areas make up a significant component of the overall Lake Erie watershed, particularly in the Western Basin, where it encompasses more than 85% of the

Sandusky and Maumee River watersheds (Richards et al. 2002). Intensive farming practices of row crops previously involved heavy applications of fertilizer and manure, as well as traditional tilling which would greatly disturb soils and remove old plant materials on an annual basis. During the 1970s, a movement towards conservation till agriculture was launched. This practice leaves part or all of the previous year’s crop remains intact as a means of reducing soil erosion and runoff of particulate nutrients

(McDowell and McGregor 1984). It also increases soils’ ability to retain moisture and staves off desertification in heavily farmed cropland (Holland 2004). The widespread usage of subsurface tile drains in the Lake Erie watershed are also intended to reduce the loss of particulate agricultural runoff (Calhoun et al. 2002).

In addition to rural land usage, six large urban areas have direct effects on the inputs to Lake Erie: Windsor, Ontario; Detroit, Michigan; Toledo, Ohio; Cleveland, OH;

Erie, PA and Buffalo, NY. Urbanization in the watershed experienced dramatic growth after World War II. While population growth has slowed, and in some areas declined, residential development has continued to expand outward from the major urban centers

(Kellogg 1997).

To assess nutrient deficiency in 2011 and 2012, water samples were taken from eight transects throughout the lake in the beginning of June and the end of August (Figure

2; Appendix A). Sampling locations along the transects were predetermined at locations where total water column depths were 2, 5, 10 and 20 m (Appendix B). Transects were chosen in areas nearby the lake’s tributaries: River Raisin and Turtle Creek in the

Western Basin; the Huron River, the Grand River, and the Ashtabula River in the Central

Basin; and Presque Isle River, Chautauqua Creek, and the Cattaraugus Creek in the

Eastern Basin. Sites less than 5 km from shore (i.e. – the 2 and 5 m sample sites) were characterized as nearshore sites in the Western Basin. In the Central and Eastern Basin,

2, 5, and 10 m sites were considered nearshore sites. A vertical tube sampler was used to collect water to twice the Secchi disk depth in order to ensure the collection of primary producers within the area of photosynthetically available radiation (PAR). All water was

Cattaraugus Cr.

Chatauqua Cr.

Presque Isle R.

Ashtabula R. River Raisin Grand R. TurtleTurtle Cr. Cr. Huron R.

Figure 2: Sample transect locations in Lake Erie (Aerial image by NASA). Samples were taken along the transects at locations where the water depths were approximately 2 m, 5 m, 10 m, and where possible 20 m.

mixed in a bucket to achieve a homogenized state on board the sampling vessel before being transported to the laboratory for further analysis.

Conclusions

Since the 1970s, water resource managers have struggled to control the effects of eutrophication in Lake Erie. Reductions in external P loading, while initially deemed successful, have lost their desired effect of controlling algal growth in the last two decades (Hecky et al. 2004). The purpose of this study was to explore two possible explanations for the persistence of algal blooms in Lake Erie through the use of physiological nutrient deficiency indicator assays. Through the scope of the nearshore phosphorus shunt hypothesis, we had expected to find an increase in phosphorus deficient sites with greater depths. However, our results showed that seasonality rather than depth had a greater impact on P deficiency and that P deficiency overall increased from spring to fall. Our second study examined nitrogen as a potentially limiting or co-limiting nutrient to algal growth. While co-limitation by N and P appeared evident in some instances, overall we observed a seasonal shift from N deficient conditions in the spring to more P deficient conditions in the fall. A seasonal decline in soluble reactive phosphorus (SRP) was likely the driving force behind the shift, leading us to conclude that increased annual SRP loading in the lake may be prolonging symptoms of eutrophication.

References

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Calhoun, F.G., Baker, D.B., and Slater, B.K. 2002. Soils, water quality, and watershed size: Interactions in the Maumee and Sandusky River basins of northwestern Ohio. Journal of Environmental Quality 31: 47-53.

Cha, Y., Stow, C.A., Nalepa, T.F., and Reckhow, K.H. 2011. Do invasive mussels restrict offshore phosphorus transport in Lake Huron? Environmental Science & Technology 45: 7226-7231.

Correll, D.L. 1998. The role of phosphorus in the eutrophication of receiving waters: A review. Journal of Environmental Quality 27: 261-266. de Jonge, V. N., M. Elliott and Orive, E. 2002. Causes, historical development, effects and future challenges of a common environmental problem. Hydrobiologia 475/476: 1-19.

Depew, D.C., Guildford. S.J., and Smith, R.E.H. 2006. Nearshore-offshore comparison of chlorophyll a and phytoplankton production in the dreissenid-colonized eastern basin of Lake Erie. Canadian Journal of Fisheries and Aquatic Sciences 63: 1115-1129.

Edwards, W.J., Conroy, J.D. and Culver, D.A. 2005. Hypolimnetic oxygen depletion in the central basin of Lake Erie. Journal of Great Lakes Research 31(Suppl. 2): 262-271.

French III, J.R.P., Schaeffer, J.S., Roseman, E.F., Kileya, C.S., and Fouilleroux, A. 2009. Abundance and distribution of benthic macroinvertebrates in offshore soft sediments in western Lake Huron, 2001-2007. Journal of Great Lakes Research 35: 120-127.

Hecky, R.E., Smith, R.E.H, Barton, D.R., Guildford, S.J., Taylor, W.D., Charlton, M.N., and Howell, T. 2004. The nearshore phosphorus shunt: A consequence of ecosystem engineering by dreissenids in the Laurentian Great Lakes. Canadian Journal of Fisheries and Aquatic Sciences 61: 1285-1293.

Holland, J.M. 2004. The environmental consequences of adopting conservation tillage in Europe: Reviewing the evidence. Agriculture, Ecosystems and Environment 103: 1-25.

Joosse, P.J. and Baker, D.B. 2011. Context for re-evaluating agricultural source phosphorus loading to the Great Lakes. Canadian Journal of Soil Science 91: 317-327.

Kellogg, W.A. 1997. Metropolitan growth and the local role in surface water resource protection in the Lake Erie basin. Journal of Great Lakes Research 23(3): 270- 285.

Lu, Y., Meyers, P.A., Johengen, T.H., Eadie, B.J., Robbins, J.A. and Han, H. 2010. δ15N values in Lake Erie sediments as indicators of nitrogen biogeochemical dynamics during cultural eutrophication. Chemical Geology 273: 1-7.

McDowell, L.L. and K.C. McGregor. 1984. Plant nutrient losses in runoff from conservation tillage corn. Soil & Tillage Research 4: 79-91.

Millie, D.F., Fahnenstiel, G.L., Bressie, J.D., Pigg, R.J., Rediske, R.R., Klarer, D.M., Tester, P.A., Litaker, R.W. 2009. Late-summer phytoplankton in western Lake Erie (Laurentian Great Lakes): bloom distributions, toxicity, and environmental influences. Aquatic Ecology 43: 915-934.

Mortimer, C.H. 1987. Fifty years of physical investigations and related limnological studies on Lake Erie, 1928-1977. Journal of Great Lakes Research 13(4): 407- 435.

ODNR. 2009. Lake Erie Facts. Retrieved February 23, 2013 from http://www.dnr.state.oh.us/tabid/7828/default.aspx.

Richards, R.P., Calhoun, F.G., and Matisoff, G. 2002. The Lake Erie Agricultural Systems for Environmental Quality Project. Journal of Environmental Quality 31(1): 6-16.

Robertson, D.M. and Saad, D.A. 2011.Nutrient inputs and Laurentian Great Lakes by source and watershed estimated using SPARROW watershed models. Journal on the American Water Resources Association 47(5): 1011-1033.

Schindler, D.W. 1974. Eutrophication and recovery in experimental lakes: Implications for lake management. Science 184(4139): 897-899.

Schindler, D.W. 1990. Experimental perturbations of whole lakes as tests of hypotheses concerning ecosystem structure and function. Oikos 57(1): 25-41.

Schindler, D.W., Hecky, R.E, Findlay, D.L., Stainton, M.P., Parker, B.R., Paterson, M.J., Beaty, K.G., Lyng, M., and Kasian, S.E.M. 2008. Eutrophication of lakes cannot

be controlled by reducing nitrogen input: Results of a 37-year whole-ecosystem experiment. Proceedings of the National Academy of Sciences of the United States of America 105: 11254-11258.

Schwab, D.J., Beletsky, D., DePinto, J., and Dolan, D.M. 2009. A hydrodynamic approach to modeling phosphorus distribution in Lake Erie. Journal of Great Lakes Research 35: 50-60.

Smith, V.H. and Schindler, D.W. 2007. Eutrophication science: Where do we go from here? Trends in Ecology and Evolution 24(4): 201-207.

Sterner, R.W. 2008. Review paper on the phosphorus paradigm for lakes. International Review of Hydrobiology 93: 433-445.

Wang, H. and Wang, H. 2009. Mitigation of lake eutrophication: Loosen nitrogen control and focus on phosphorus abatement. Progress in Natural Science 19: 1445-1451.

CHAPTER TWO: A NEARSHORE-OFFSHORE COMPARISON OF

PHOSPHORUS DEFICENCY INDICATORS IN LAKE ERIE

Abstract

The nearshore phosphorus shunt hypothesis asserts that the invasion of filter-feeding dreissenid mussels in Lake Erie has dramatically altered the traditional pathway of phosphorus as it moves from nearshore to offshore depths. Consequently we believed that we would find evidence of intensified P deficient conditions in photic zones further offshore compared to those nearshore. Water samples were taken from eight transects along the southern coast of Lake Erie during the late spring and early fall of 2011 and

2012. We conducted a series of three proximate phosphorus deficiency indicator assays, including phosphorus turnover time, phosphorus debt, and alkaline phosphatase activity.

With few exceptions, water column depth was not found to significantly impact P deficiency, leading us to conclude that the influence of the dreissenid invasion on nutrient dynamics may not be as strong as in Lake Erie as compared to other great lakes.

Seasonality instead appeared to have a stronger effect, with measures of P deficiency generally increasing throughout the course of the growing season.

Introduction

Efforts have been made to reduce primary productivity in Lake Erie since the

1960s, when massive fish kills and extensive algal blooms in the lake began to receive

national attention (Mortimer 1987; Lu et al. 2010). The restrictions imposed on external phosphorus loading in Lake Erie by the Great Lakes Water Quality Agreement of 1972 were, for a brief period of time, considered beneficial in constraining algal growth.

During the early 1980s, no massive algal blooms were reported in the Western Basin and all three basins saw reductions in P loading and productivity. Yet despite continued restrictions and regularly meeting desired total phosphorus (TP) loading targets, harmful algal blooms (HABs) dominated largely by Microcystis aeruginosa began to appear in the mid-1990s (Lake Erie Nutrient Science Task Group 2010). HABs have since reappeared on an annual basis towards the end of the summer growing season and have most commonly been found in the shallow Western Basin and nearshore areas (Charlton et al. 2009; Depew et al. 2006; Hecky et al. 2004). The worst bloom in decades only just recently occurred in 2011, following a summer of high precipitation (Bridgeman et al.

2012).

In explanation as to why the loading restrictions would suddenly lose their desired effect, Hecky et al. (2004) proposed the nearshore phosphorus shunt hypothesis, which postulates that the filter feeding activities of invasive dreissenid mussel species has re- routed the typical transport pattern of phosphorus from nearshore to offshore depths.

Dreissena polymorpha and Dreissena bugensis were unintentionally introduced to Lake

Erie in the mid-1980s and early-1990s respectively in the ballast water of cargo ships

(Coleman and Williams 2002; Zhang et al. 2010). Though hardly the first exotic species to be released into the lakes, both D. polymorpha and D. bugensis have dramatically altered their environments. D. polymorpha are found predominantly in shallow,

nearshore areas where sufficient hard substrate is available for colonization (Nicholls et al. 1999). D. bugensis is less inhibited by location and has been found at depths of over

60 m on both hard and softer substrates (Bunnell et al. 2009; Nicholls et al. 1999). The mussels filter the water column for suspended particulate matter, thus removing fine particulate phosphorus and nitrogen as it travels from nearshore to offshore. Once ingested, particulates that are not used for the accumulation of mussel biomass may then be remineralized as soluble nutrients, providing a highly concentrated and bioavailable nutrient source for phytoplankton in the vicinity of the mussel bed (Malkin et al. 2010).

The mussels may also excrete particulates in the form of feces and pseudofeces; excreted particulates are generally larger and denser than particulates originating onshore, making them less likely to be re-suspended in the water column and transported offshore (Hecky et al. 2004; Ozersky et al. 2010).

Studies of how dreissenids have affected productivity in the Laurentian Great

Lakes have provided mixed results. Multiple studies have found increases in nearshore water clarity and declines in overall Chl a concentrations, both of which are typically associated with decreased primary productivity (Matisoff and Ciborowski 2005). In both

Lake Huron and Lake Michigan, dreissenids are credited with the absence of spring algal blooms (Fahnenstiel et al. 2010; Vanderploeg et al. 2010). Indeed, the assimilation of phosphorus by dreissenids coupled with the diversion of nearshore-to-offshore phosphorus transport is believed to have led to nutrient-poor conditions in some areas.

Cha et al. (2011) estimated an increase in the proportion of phosphorus retained in Lake

Huron’s Saginaw Bay from 46% to 70%, while citing a decrease in phosphorus levels in

the main body of the lake. In Lake Erie, external P loading is still highly regulated; yet intensified nearshore nutrient cycling in the benthos by dreissenids may be fueling recent increases in phytoplankton biomass (Conroy et al. 2005).

The re-emergence of HABs and increases in phytoplankton biomass may be due to dreissenid-mediated shifts in algal community composition. While many phytoplankton species are subject to filter feeding by the mussels, two algal species appear to benefit from their presence. Cladophora glomerata, a filamentous green algae, is able to attach and grow on the dreissenids as a form of substrate while avoiding filtration. In addition, C. glomerata benefits from the improved water transparency and higher SRP concentrations that result from the dreissenids’ filter feeding (Higgins et al.

2008; Limberg et al. 2009; Ozersky et al. 2009). The cyanobacterial species Microcystis also appears to benefit, as studies have shown that the dreissenids will actively select against filtering Lake Erie strains of the algae (Vanderploeg et al. 2001). Furthermore,

Microcystis spp. will migrate vertically through the water column when nutrient conditions are less than optimal, thus allowing them to capitalize on the excess of soluble and particulate nutrients in the benthos (Brooks and Ganf, 2001).

In order to further our understanding of how dreissenids may have altered nutrient conditions in Lake Erie, an examination of three proximate phosphorus deficiency indicator assays was conducted during the 2011 and 2012 growing seasons: Phosphorus

Turnover Time (PTT), P debt, and Alkaline Phosphatase Activity (APA). Due to P being diverted from its traditional pathway and accumulated in the nearshore benthos, we

hypothesized that we would find a higher incidence of P deficient conditions within the photic zones of offshore sites compared to those of nearshore sites.

Methods

Study Site: Lake Erie

In 2011 and 2012, water samples were taken from eight transects along the southern shore of the lake in late spring and early fall (Appendix A). Sampling locations along the transects were predetermined at locations where total water column depths reached 2, 5, 10 and 20 m (Appendix B). Transects corresponded largely with areas nearby the lake’s tributaries: River Raisin and Turtle Creek in the Western Basin; the

Huron River, the Grand River, and the Ashtabula River in the Central Basin; and Presque

Isle River, Chautauqua Creek and the Cattaraugus Creek in the Eastern Basin. During sampling periods, water was collected to twice the Secchi disk depth using a 2.5 cm diameter vertical tube sampler to ensure the collection of primary producers within the area of photosynthetically active radiation (PAR) penetration. All samples were homogenized on board the sampling vessel before being subsampled and transported to the laboratory for further analysis.

Laboratory Analysis

Chl a concentrations were determined using fluorometric analyses following EPA method 446 (USEPA 1997). Soluble reactive phosphorus and total phosphorus data were obtained using a gas segmented Traacs 800 or Auto Analyzer II system flow analyzer and

were provided by Heidelberg University’s National Center for Water Quality Research.

Three P deficiency assays were conducted and are described in detail below: phosphorus turnover time (PTT), P debt, and alkaline phosphorus activity (APA) to determine whether or not P deficiency was present (Table 1).

Table 1: Algal phosphorus limitation values and deficiency thresholds. Adapted from North et al. (2007). No Moderate Extreme Indicator limitation limitation limitation Limited P debt (µmol P µg Chl a-1) <0.075 >0.075 P Turnover Time (minutes) >60 <60 APA (µmol P µg Chl a-1 h-1) <0.003 >0.003 >0.005

Phosphorus Turnover Time Assay

PTT is the amount of time needed for algae and bacteria to take up an amount of phosphorus that corresponds with how much is bioavailable at a particular site. For this assay, the radiometric approach described by Heath (1986) was utilized. Samples from each site were treated in triplicate with 32P and incubated for intervals of 2, 4, and 6 minutes before being drawn through 0.2 µm and 1 µm filters to roughly differentiate between bacterial and algal activity, respectively. Controls for each sample site were pre- treated with formalin and allowed to sit for twenty minutes before being dosed with 32P.

Filters were then placed in small scintillation vials with Scintiverse BD scintillation fluid, and their activity was recorded with a scintillation counter. One mL of each sample was extracted from leftover unfiltered samples and controls to act as “totals.”

The rate of 32P uptake was measured as the amount of activity counted on each filter as it pertained to each incubation period. The inverse of the uptake rate’s slope provided us with PTT values for each site. Therefore, higher phosphorus uptake would translate into a lower PTT and vice versa. Typically a low, or fast, PTT is considered any time under 60 minutes, indicating the presence of P deficiency. Higher, slower PTT values suggest that P is not limiting at a given site and that other factors could be limiting to algal growth.

Phosphorus Debt

P debt was measured using a radiometric adaptation of the techniques delineated by Healy and Hendzel (1979) in order to increase the assay’s sensitivity. P debt was measured as the amount of 32P taken up by algae and bacteria over a 24 hour time period normalized to algal biomass. Phosphorus deficiency is indicated when the amount of phosphorus taken up exceeds 0.075 µmol P/µg Chl a.

Samples from each site were treated in triplicate with 32P at a concentration of

.005 µmol P/mL and allowed to incubate in the dark for 24 hours at room temperature, along with “killed” controls treated with both 32P and formalin. After incubating, 1 mL of water was taken from each sample and control to act as totals for calculating specific activity. The remaining samples and controls were then filtered through 1 µm filters.

Both filters and totals were placed in scintillation vials with Scintiverse BD scintillation fluid and measured for activity with a scintillation counter. Filter activities were divided by specific activities and standardized to Chl a concentrations to produce P debt values.

Alkaline Phosphatase Activity Assay

Alkaline phosphatase activity (APA) measures the amount of alkaline phosphatase being produced by algae and bacteria, allowing an organism to extract phosphate from dissolved organic phosphorus sources. Higher levels of alkaline phosphatase are associated with P deficient environments. APA was measured using the

4-methyl umbelliferyl phosphate (4-MUP) fluorometric method described by Holmes et al (1999). Fluorescence measurements were taken before and after a one hour incubation period with a Turner fluorometer. Values of less than 0.003 µmoles P/µgrams Chl a/hr were indicative of no P limitation, values from 0.003-0.005 µmoles P/µgrams Chl a/hr suggested moderate P limitation, and values higher than 0.005 µmoles P/µgrams Chl a/hr indicated extreme P limitation.

Statistical Analysis

All assay results were normalized with log10 transformations. Using IBM SPSS

20 software, a series of three-way ANOVAs was conducted to analyze the effects of season, transect, and depth on each of the P deficiency indicators. Three-way ANOVAs exploring the effects of basin, season, and depth were also utilized. Post-hoc Tukey tests with a significance value of p < 0.05 and simple analyses of the main effects were computed where significant results were found. Linear regressions were also conducted for each transect to determine if increasing depths significantly correlated with increased or decreased P deficiency.

Results

Phosphorus Debt

P deficiency as measured by P debt was not apparent in the spring of 2011, increasing to a total of four incidents in the subsequent fall, or 13% of our sample sites

(Figure 3). P deficient conditions were found at the 2 m sites along the Grand River,

Presque Isle River and Chautauqua Creek transects, and at the 5 m site along the

Cattaraugus Creek site. In 2012, P deficiency was found at all sites along the River

Raisin transect in the spring. In the fall, the number of P deficient sites increased to include the 2 m sites along the River Raisin, Ashtabula River, and Chautauqua Creek transects, and at the 5 m sites along the Grand River and Ashtabula River transects. This accounted for an increase from approximately 10% of our spring samples to 16.7% of our samples in the fall. At no point during our study did our P debt assays reveal P deficient conditions at any of the 20 m sites.

When looking at the lake as a whole, we found that there was a significant interaction between the effects of season and transect on P debt values (F (7, 60) = 3.137, p = 0.007; Appendix G). Simple main effects analysis showed that P debt values in the fall were significantly higher than in the spring along the Turtle Creek (p < 0.001), Grand

River (p < 0.001), Presque Isle River (p = 0.001), Chautauqua Creek (p = 0.004), and

Cattaraugus Creek transects (p < 0.001). The largest increase occurred along the Grand

River transect, where the average P debt value rose from 0.005 µmol P/µgram Chl a in the spring to 0.075 µmol P/µgram Chl a in the fall. P debt values did not differ significantly between basins.

Figure 3. Phosphorus debt along Lake Erie transects in 2011 and 2012. P debt values above the black dashed black threshold line are indicative of P deficiency. Error bars represent one standard deviation of the mean. Spring 2012 P debt at the River Raisin transect’s 10 m site (not shown) was measured as 1.51 due to very low Chl a concentrations. Spring 2011 P debt values for the 2 and 5 m sites along the Turtle Creek transect were recorded as 6.54 x 10-5 and 7.94 x 10-5 respectively.

When looking at P debt values within a particular basin, in the Central Basin a significant season x transect interaction was again present, (F (2, 24) = 5.766, p = 0.009;

Appendix H.1). The Ashtabula River and Grand River transects both had significantly higher P debt values in the fall compared to those in the spring (p = 0.039 and p < 0.001, respectively). The Eastern Basin was affected most strongly by season, with significantly lower P debt values in the spring than in the fall, (F (1, 24) = 124.199, p < 0.001;

Appendix H.2). The average spring P debt value for the Eastern Basin was only 0.0066

µmol P/µgram Chl a compared to the fall average of 0.052 µmol P/µgram Chl a. The

Western Basin did not appear to be significantly affected by season or transect.

Additionally, depth was not shown to be a significant influence on P debt in any basin or in the lake as a whole.

Phosphorus Turnover Time

Incidents of P deficient conditions measured through PTT assays increased from spring to fall during both of our sample years (Figure 4). In spring of 2011, P deficiency was observed at the 20 m site along the Grand River transect, the 2, 5, and 10 m sites along the Presque Isle River transect, and all sites along the Cattaraugus Creek transect.

The total number of P deficient sites more than doubled in the fall of that year, increasing from 26.7% to 60% of our sample sites, and was observed in all three basins. Deficient locations included all sites along the Turtle Creek, Grand River, and Ashtabula River transects, the 5 m site along the River Raisin transect, the 2 m site along the Huron River transect, the 2, 5, and 20 m sites along the Presque Isle River transect, and the 10 and 20

Figure 4. Phosphorus turnover times along Lake Erie transects in 2011 and 2012. PTT values below the black dashed threshold line are indicative of P deficiency. In the spring of 2011, PTT values for the 2 and 5 m sites along the Grand River transect were 13,970 minutes and 21,055 minutes respectively. In the spring of 2012, the PTT for the 10 m site along the same transect was observed as 16,030 minutes. The spring 2012 PTTs for the 10 m site along the River Raisin transect and the 20 m site along the Presque Isle River transect were excluded as negative values. Error bars reflect one standard deviation of the mean.

m sites along the Chautauqua Creek transect. 2012 saw another increase in the frequency of P deficient sites from spring to fall, although fewer instances were recorded for either season than in the previous year. The numbers of P deficient sites accounted for 10% of our sites in the spring and 36.7% of our sites in the fall. In the spring, P deficiency was detected at the 2 m site along the River Raisin transect and at the 2 and 5 m sites along the Huron River transect. In the fall the number of P deficient sites increased to eleven total sites. These included the 2 m sites along the Turtle Creek and Huron River transects, the 2 and 5 m sites along the River Raisin, Grand River, and Ashtabula River transects, and the 2, 5, and 10 m sites along the Presque Isle River transect. P deficiency was not found at any of the 20 m sites in 2012.

The results of our PTT assays uncovered a significant interaction between season and transect in the lake overall (F (7, 52) = 4.562, p < 0.001; Appendix I.1). Fall turnover times were significantly faster than spring turnover times along the Ashtabula

River (p < 0.001), Grand River (p = 0.003), and River Raisin transects (p = 0.001).

Conversely, the Cattaraugus Creek transect had a significantly slower turnover time in the fall compared to the spring (p = 0.047), almost doubling from the spring average of

132 minutes to 260 minutes in the fall.

Significant season x basin interactions were observed for both the Western and

Central Basins, (F(2, 90) = 3.804, p = 0.026; Appendix I.2). PTT values in both the

Western and Central Basins were faster in the fall than in the spring (p = 0.001 and p <

0.001 respectively). The Western Basin exhibited a strong interaction between season and depth, (F (2, 10) = 4.333, p = 0.044; Appendix J.1). Turnover times at the 2 m sites

declined significantly from 720 minutes on average in the spring to 36 minutes in the fall

(p = 0.021). The times at 5 m sites also underwent a significant decline from spring to fall (p = 0.003), dropping from an average PTT of 1260 minutes to 85 minutes. In the

Central Basin, we found significant interactions between season and transect (F (2, 20) =

6.952, p = 0.005), as well as between season and depth (F (3, 20) = 3.79, p = 0.027;

Appendix J.2). Significant seasonal declines were observed at the 2 m site (p = 0.004), the 5 m site (p = 0.038), and the 10 m site (p = 0.028), with average declines ranging from 575 minutes at the 2 m sites to 1060 minutes at the 10 m sites. The Ashtabula River transect and the Grand River transect both experienced overall seasonal declines (p =

0.001 and p = 0.007). In the Eastern Basin, a strong season-transect interaction was observed, (F (2, 22) = 3.489, p = 0.048 (Appendix J.3). Turnover times along the

Cattaraugus Creek transect were again shown significantly slower in the fall compared to the spring (p = 0.028).

Alkaline Phosphatase Activity

With few exceptions, APA values during the course of our study almost always exceeded those indicative of extreme P limitation (Figure 5). In spring of 2011, only the

2 m sites along the River Raisin and Turtle Creek transects were measured as not being limited by P. In the subsequent fall, none of the Turtle Creek sites were shown to be P limited, while the 2 and 5 m sites along the River Raisin and Huron River transects were within the range of moderate limitation. In the spring of 2012, the 2 m site along the

Huron River transect was not limited, while the 5 and 10 m sites along the same transect

Figure 5. Alkaline phosphatase activity along Lake Erie transects in 2011 and 2012. APA values above the black dashed threshold line are indicative of severe P deficiency. Error bars represent one standard deviation of the mean.

were moderately limited. In the fall, the 2 and 5 m sites along the River Raisin transect and the 5 and 20 m sites along the Huron River transect were moderately limited. All

Eastern Basin sites exhibited extreme limitation during all sampling periods in our study.

APA results for the lake overall were significantly impacted by season (F (1, 60) =

8.9333, p = 0.004; Appendix K.1), with lower APA values occurring more prevalently in the fall than in the spring. On average, the lake’s spring APA was approximately 0.03

µmol P µg Chl a-1 h-1, while the fall average was calculated as 0.017 µmol P µg Chl a-1 h-

1. Significant differences were also found between different transects (F (7, 60) = 6.56, p

< 0.001). The APA results for the Huron River transect were averaged at approximately

0.008 µmol P µg Chl a-1 h-1, and were appreciably lower than those found along the

Grand River transect (p < 0.001), the Ashtabula River transect (p = 0.02), the Presque Isle

River transect (p = 0.001), the Chautauqua Creek transect (p = 0.001), and the

Cattaraugus Creek transect (p < 0.001). The Turtle Creek transect was significantly different from both the Grand River transect (p = 0.024) and the Cattaraugus Creek transect (p = 0.019). The average APA along the Turtle Creek transect was only 0.02

µmol P µg Chl a-1 h-1, compared to 0.035 µmol P µg Chl a-1 h-1 along the Grand River transect and 0.032 µmol P µg Chl a-1 h-1 along the Cattaraugus Creek transect.

APA values differed between basins (F (2, 98) = 9.132 , p < 0.001; Appendix

K.2), with both Western and Central Basin values averaging significantly lower than those recorded for the Eastern Basin sites (p < 0.001 and p = 0.006 respectively). When looking solely at the Eastern Basin, we found a significant interaction between season and transect (F (2, 42) = 7.047, p = 0.002; Appendix L.1). Simple analysis of the main

effects revealed significantly higher APA results in the spring along the Cattaraugus

Creek transect than in the fall (p < 0.001), dropping from an average of 0.04 µmol P µg

Chl a-1 h-1 to 0.02 µmol P µg Chl a-1 h-1. In the Central Basin, spring APA values along the three transects were also higher in the spring than in the fall, averaging 0.027 and

0.014 µmol P µg Chl a-1 h-1 respectively (F (1, 24) = 6.37, p = 0.019; Appendix L.2). As previously observed when looking at the lake overall, APA values along the Huron River transect were again significantly lower than along the Ashtabula River and Grand River transects (p < 0.001).

Linear Regression Results

Linear regression analyses showed only two significant instances where depth and

P debt were positively correlated. Both instances occurred along the River Raisin transect during the spring sampling periods of 2011 and 2012 (p = 0.034 and p = 0.005 respectively). One significant negative correlation occurred along the Ashtabula River transect during the fall sampling period in 2012 (p = 0.036).

There were four significant positive correlations between APA and depth. These occurred along the Huron River transect in the fall of 2011 (p = 0.041), the Grand River transect in the spring 2012 (p = 0.004), the Presque Isle transect in the spring of 2012 (p

= 0.017), and the Ashtabula River transect in the fall of 2012 (p = 0.039).

Linear regression analyses failed to show any significant negative correlations between depth and PTT, which would indicate lower PTT values with increasing depths offshore. There were, however, five significant positive correlations, all of which

occurred in the Central Basin over the course of the two sample years. These included the Huron River transect during the spring of 2011 (p = 0.003) and the fall of 2012 (p =

0.018), the Grand River transect during the fall of 2011 (p = 0.019) and the fall of 2012

(p = 0.03), and the Ashtabula River transect during the fall of 2012 (p = 0.014).

Chl a, TP and SRP

Average Chl a concentrations increased from spring to fall in all three basins

(Table 2; Appendix C). Linear regressions correlating Chl a values with increasing depths showed non-significant negative slopes for most transects. One significantly negative slope occurred along the Huron River transect during the spring of 2012 (p =

0.018). The Chl a concentration at the 2 m site was approximately 26 µgram/L, compared to only 4.7 µgrams/L at the 20 m site. A significantly positive slope occurred along the Chautauqua Creek transect in the spring of 2011 (p = 0.034), increasing from

2.995 µgrams/L at the 2 m site to 4.3 µgrams/L at the 20 m site.

TP concentrations typically declined with increased depth (Appendix D).

Significant negative slopes occurred along the River Raisin transect in the spring of 2011

(p = 0.013), the Turtle Creek transect in the spring of 2012 (p = 0.046), the Huron River transect in the fall of 2011 (p = 0.043), the Grand River transect in the spring of 2012 and the fall of 2011 (p = 0.028 and p = 0.004), the Chautauqua Creek transect in the fall of

2011 (p = 0.01), and the Cattaraugus Creek transect in the spring of 2012 and the fall of

2011 (p = 0.036 and p = 0.043). One significantly positive slope occurred along the

Cattaraugus Creek transect in the spring of 2011 (p = 0.025), which showed an increase from 0.0077 mg P/L at the 2 m site to 0.0103 mg P/L at the 20 m site.

There was no clear pattern of SRP increasing or decreasing with depth (Appendix

E). Linear regressions of SRP only revealed one significant negative slope between SRP and depth. SRP concentrations declined from nearshore to offshore by 0.0023 mg P/L (p

= 0.044) along the Presque Isle River transect in spring of 2012.

Table 2: Arithmetic mean Chl a and nutrient concentrations by season and basin. Values in parentheses represent one standard deviation of the mean. Parameter Spring Means (SD) Fall Means (SD) Chl a (µg/L) WB 14.65 (20.63) 28.55 (18.66) CB 7.49 (6.17) 13.38 (7.5) EB 3.68 (0.86) 6.42 (1.09) TP (mg/L) WB 0.052 (0.044) 0.041 (0.018) CB 0.023 (.011) 0.023 (0.012) EB 0.01 (0.004) 0.013 (0.004) SRP (mg/L) WB 0.017 (0.014) 0.007 (0.006) CB 0.009 (0.006) 0.007 (0.006) EB 0.006 (0.004) 0.006 (0.005) WB, Western Basin; CB, Central Basin; EB, Eastern Basin

Discussion

Overall, we failed to find clear evidence in support of the nearshore phosphorus shunt hypothesis when using P deficiency indicator assays during the growing seasons of

2011 and 2012. Under the assertions of the hypothesis, we had expected to find an increase in P deficiency at offshore sites compared to shallow, nearshore sites. Yet this trend did not become apparent during the course of our study.

P debt and PTT assays did not yield results that would lend support to the nearshore phosphorus shunt hypothesis. Positive identification of P deficiency as measured by P debt increased during the 2011 growing season from no incidents in spring to four incidents in fall. All occurrences of deficiency in the fall of 2011 were confined to nearshore depths and thus did not reflect the expected trend. In 2012 the number of P deficient sites as measured by P debt increased from three to five during the course of the growing season. Yet again, as in 2011, the P debt assay reflected P deficient conditions only at nearshore sites, rather than at the offshore sites that we had originally anticipated. In regards to PTT, ANOVAs revealed a significant season-depth interaction effect on turnover times in the Western and Central Basins; however, this did not equate to a higher prevalence of significantly fast turnover times offshore. PTT throughout the lake overall appeared essentially to be seasonally driven. Spring average turnover times were more than double fall average times in both years.

The results from our APA bioassays do not tend to agree with our results for the

P debt and PTT bioassays in both 2011 and 2012. Both sampling years saw a seasonal decline in average APA values from spring to fall. In addition, nearly all sites demonstrated APA levels beyond the threshold level for severe P limitation. While our values appeared extremely high, there are comparable with those obtained by North et al.

(2007) in the Eastern Basin. We also typically found our highest values in the Eastern

Basin, which has historically been the most oligotrophic of the three. APA was generally lower in the Western Basin, as would be expected from the more eutrophic conditions there.

All assays have different strengths and weaknesses, thus reinforcing the need to perform multiple experiments when determining nutrient deficiency. The assays utilized during this study were all conducted with short-term incubation periods, ranging from 6 minutes for PTT to 24 hours for P debt; thus, the results presented here are reflective of only the immediate deficiencies of the phytoplankton in our samples (Davies et al. 2010).

The factors controlling production of APA appear to differ between oligotrophic and eutrophic lakes. APA can be skewed by algal community composition, as some species produce more of the enzyme than others. Additionally, some bacteria may produce alkaline phosphatase for carbon acquisition, which could contribute to overestimates of severe P deficiency (Cao et al. 2010).

Chl a concentrations, often viewed as a proxy for algal biomass experienced an increase between June and August in all basins. These findings are normal over the course of a typical growing season in a temperate oligotrophic-mesotrophic system

(Depew et al. 2006). While generally not statistically significant, offshore Chl a concentrations were often lower than nearshore levels. Our results are in contrast to those of North et al. (2012) in eastern Lake Erie, which showed significantly lower nearshore Chl a compared to offshore concentrations; however, it should be noted that offshore depths in that study were classified as depths of equal to or greater than 20 m, with additional sample sites located at greater depths. Nevertheless, our findings may reflect a shift back to patterns observed by the EPA in the early 1970s, during which nearshore Chl a concentrations were frequently higher than offshore ones (Depew et al.

2006).

TP typically declined with depth regardless of season or transect. While data on nearshore-offshore TP dynamics before the dreissenid invasion is scarce, it is possible that this pattern was present before their introduction. Higher TP concentrations nearshore would be anticipated since P is entering along the shore from rivers.

Concentrations of TP would simply be diluted in offshore waters, regardless of dreissenid presence. In the presence of dreissenids, particulate phosphorus should be removed and deposited in the benthos as feces of pseudofeces (Hecky et al. 2004), thus decreasing offshore TP to levels lower than would be expected from dilution alone. Therefore we expect that indicators of P limitation would be more likely in offshore sites, especially in the presences of dreissenids. SRP did not demonstrate a clear pattern of increasing or decreasing with depth. The hypothesized contribution of dreissenids to the soluble fraction of P are ambiguous; the mussels may contribute to the recycling of SRP from the remineralization of particulate matter into soluble excretions, or they may allow for the increased uptake of SRP by benthic algae by providing optimal growing conditions

(Hecky et al. 2004).

A factor influencing the results of our study may be the dreissenid mussels’ filtration capacity. For example, filtration rates by D. polymorpha in the Western Basin were initially estimated to be as high as 132 m3 m-2 day-1 (MacIsaac et al. 1992).

However, studies have since shown that even in the shallow Western Basin, weak diurnal stratification is sufficient in impeding the vertical mixing necessary to continually supply dreissenids with new particulate matter (Ackerman et al. 2001; Boegman et al. 2008).

During times of stratification, a concentration barrier forms above the mussel bed,

thereby reducing grazing efficiency (Boegman et al. 2008). Thus we would expect that during times of stratification the dreissenids’ ability to induce the nearshore phosphorus shunt would be limited.

Predation on dreissenids by another invasive species may be negatively affecting their population sizes, which could in turn affect their impact on nutrient cycling. The round goby (Neogobius melanostomus) was found in Lake Erie in 1994 and has since been actively feeding on dreissenid populations (Johnson et al. 2005, North et al. 2012).

Barton et al. (2005) reported a 94% decline in D. bugensis densities along the Eastern

Basin’s northern shoreline between 2001 and 2004. In Lake Erie’s Central Basin, however, round gobies were found to consume only 0.1% of the dreissenid biomass in

1998 despite relatively high population densities, thereby having little effect on mussel nutrient cycling (Bunnell et al. 2005). While it appears that the round goby could potentially curtail dreissenid populations in some areas of the lake, more information is needed regarding dreissenid and round goby population densities and their predator-prey interactions.

Further research may also be warranted to account for shifts in community structure as D. polymorpha is being largely replaced by the more versatile D. bugensis.

The ability of D. bugensis to colonize areas further offshore may have a significant impact on nutrient dynamics in the middle depths, particularly when the nearshore zone is characterized by open areas and sandy substrates that are less conducive to dreissenid colonization. Coined the “mid-depth sink” by Vanderploeg et al. (2010), these conditions would allow higher amounts of P and C to move from nearshore depths to mid-depths,

where they would then be sequestered by D. bugensis. In areas where the nearshore zone supports viable dreissenid populations, the mid-depth sink could force nutrient levels down to even lower concentrations than with the nearshore shunt alone. The mid-depth sink was conceptualized specifically with Lake Michigan in mind, but could potentially be applied to Lake Erie in future research designs.

Overall we found that P deficiency increased throughout the lake from spring to fall. Generally, P concentrations in temperate freshwater lakes are high in late spring due to increased runoff from snowmelt and heavy precipitation, as well as mixing conditions earlier in the season which dredge P from sediments and distribute it throughout the water column (Kalff 2002). Fall fertilizer application coupled with conservation till and no-till agricultural practices in the southwestern region of the Lake Erie watershed contribute to the heightened levels of P in the spring (Richards and Baker 1993). Over the course of the summer growing season, algal and bacterial communities take up P to accumulate biomass, resulting in the drawdown of P throughout the lake and increases in Chl a (Kalff

2002). This pattern is in agreement with our findings, further indicating that changes in P over the course of seasons were more strongly linked to the occurrence of P deficiency than depth.

Conclusions

While the nearshore phosphorus shunt hypothesis has garnered a large amount of support during its existence, the three P deficiency indicator assays used in this study did not reflect a strong nearshore-offshore dichotomy. We contend that our results may

provide evidence of the weakening effects dreissenid mussels are having on nutrient transport from nearshore to offshore depths. While other studies have shown that invasive dreissenid mussels are capable of disrupting the flow of phosphorus and making soluble phosphorus more available to nearshore algal communities, it is possible that declines in TP loading and increased predation by invasive round gobies have diminished these effects. Our research has shown that seasonality, rather than spatial factors, is more of a driving force behind increased P deficiency.

Acknowledgements

Funding for this study was provided by the Great Lakes Restoration Initiative as part of the Lake Erie Nearshore and Offshore Nutrient Study (LENONS), as well as the Art and

Margaret Herrick Aquatic Ecology Research Facility Student Research Support Fund.

We would like to thank our collaborating partners at Buffalo State College, the

University of Toledo, Case Western Reserve University, and Heidelberg University.

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North, R.L., Guildford, S.J., and Smith, R.E.H. 2007. Evidence for phosphorus, nitrogen, and iron colimitation of phytoplankton communities in Lake Erie. Limnology and Oceanography 52(1): 315-328.

North, R.L., Smith, R.E.H., Hecky, R.E., Depew, D.C., León, L.F., Charlton, M.N., and Guildford, S.J. 2012. Distribution of seston and nutrient concentrations in the eastern basin of Lake Erie pre- and post-dreissenid mussel invasion. Journal of Great Lakes Research 38: 463-467.

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CHAPTER THREE: SEASONAL SHIFTS OF NUTRIENT DEFICIENCY

INDICATORS IN LAKE ERIE

Abstract

While phosphorus has traditionally been considered the dominant limiting nutrient in temperate freshwater systems, nitrogen may also play a significant role in restraining algal growth. In 2012, a study of three phosphorus deficiency indicator assays and two nitrogen deficiency indicator assays was conducted in Lake Erie. Evidence of nitrogen deficiency was more frequently found at the beginning of the growing season despite higher concentrations of bioavailable N, while incidents of phosphorus deficiency increased towards the season’s end. A seasonal decline in soluble reactive phosphorus was likely the driving force from N deficient conditions to P deficient conditions. The results of this study provide evidence that algal growth in Lake Erie is not limited solely by phosphorus throughout the growing season, and additional measures limiting nitrogen may be necessary to effectively manage primary productivity.

Introduction

Widespread eutrophication of freshwater lakes over the course of the last century has led to a point of contention among limnologists and water resource managers. While temperate freshwater lakes have conventionally been considered to be limited by phosphorus, arguments in support of nitrogen abatement programs in addition to limited

P loading have recently been gaining support (Paerl et al. 2011; Scott and McCarthy

2011). In Lake Erie, large blooms of nuisance algae became a regular occurrence during the summer in the 1960s. In response, constraints on external P loading from anthropogenic sources (primarily detergents and sewage effluent) were implemented in the early 1970s and are still in place today (Dolan and McGunagle 2005). The P abatement program was considered successful in reducing total phosphorus and algal biomass to levels consistent with those expected for long-term recovery efforts

(Makarewicz and Bertram 1991). Yet the increasingly frequent appearance of harmful algal blooms (HABs) in Lake Erie over the past two decades has raised the question of whether or not reducing only P concentrations is enough to limit algal growth (Paerl et al.

2011).

Much of the basis for P-only reductions in temperate freshwater lakes comes from research conducted in the Canadian Experimental Lakes Area. Whole lake experiments conducted in the ELA in the 1970s showed that lake basins fertilized with phosphate, nitrate, and carbon would quickly become eutrophic, while those fertilized with only nitrogen and carbon would retain their current conditions, lacking large algal blooms. In addition, rapid recovery from eutrophication was observed when phosphate fertilization ceased (Schindler 1974).

Management schemes focused on P reductions have been found to sometimes reduce and at times reverse eutrophication. Lake Washington showed signs of a quick recovery from eutrophication after sewage inputs containing high phosphorus concentrations were greatly reduced (Edmonson and Lehman 1981). In Lake Mälaren in

Sweden, a recovery from eutrophication was achieved in some of the deeper basins of the lake in only a couple of years after phosphorus loads were greatly reduced.

Eutrophication abatement in shallower basins was projected to occur within a period of twenty years to account for internal phosphorus cycling in sediments (Willén 2001).

Yet while there have certainly been a number of successful recoveries from eutrophic conditions, other lakes have not responded so strongly to reduction efforts, possibly because P is not the sole limiting nutrient to algal growth (Carvalho et al. 1995).

In response, Lewis and Wurtsbaugh (2008) have proposed an N + P paradigm, in which nitrogen and phosphorus are equally important in limiting phytoplankton growth in unpolluted lakes. In eutrophic lakes, they argued that limitation could shift from phosphorus to nitrogen and vice versa, depending heavily on the ratio of nutrient inputs

(Lewis and Wurtsbaugh 2008). Additionally, while phosphorus may dominate nutrient limitation the majority of the time, short but significant periods of co-limitation can frequently occur (Sterner 2008).

Evidence of co-limitation by N and P is actually fairly widespread, despite the exclusion of N reductions in most lake management schemes. A meta-analysis of over

100 nutrient addition assays for freshwater systems revealed higher growth responses in samples treated with both N and P in comparison to those treated by N or P alone (Elser et al. 2007). That these types of synergistic effects have been observed at such a high rate of frequency indicates that co-limitation may actually be the dominant form of nutrient limitation in many freshwater bodies (Sterner 2008).

In Lake Erie, previous studies have provided evidence of co-limitation. Nutrient enrichment assays conducted by North et al. (2007) in Lake Erie’s Eastern Basin have indicated co-limitation by N, P, and Fe in epilimnetic waters from sampling locations where lake depths met or exceeded 20 m. While N was found in non-limiting concentrations, bioavailable Fe was low enough at offshore depths as to effectively limit

- NO3 reduction and assimilation by algae, thereby increasing N limitation.

Additional research is needed to fully understand the nutrient dynamics in Lake

Erie and other freshwater systems in order to devise effective more management strategies. In a 2011 study of phosphorus deficiency indicators, we found evidence of seasonal increases of P deficiency and seasonal decreases in soluble reactive phosphorus in Lake Erie (Chp. 2). However, we failed to find a spatial pattern in P deficient sites that would explain the decline in Chl a levels at some offshore sites. It may be that limitation by N or co-limitation by N and P is causing the variable conditions in Lake Erie. With this in mind, it was hypothesized in 2012 that N limitation or co-limitation by N and P would be found at locations where strong P limitation was not readily observed.

Methods

Study Site and Sampling Methods

In 2012, water samples were taken from eight transects throughout the lake in the beginning of June and the end of August. Sampling locations along the transects were predetermined at locations where water depths were 2, 5, 10 and 20 m. Transects were chosen in areas nearby the lake’s tributaries: River Raisin and Turtle Creek in the

Western Basin; the Huron River, the Grand River, and the Ashtabula River in the Central

Basin; and Presque Isle River, Chautauqua Creek, and the Cattaraugus Creek in the

Eastern Basin. A vertical tube sampler was used to collect water to twice the Secchi disk depth in order to collect primary producers within the zone of photosynthetically active radiation. All water was mixed and homogenized on board the sampling vessel before being transported to the laboratory for further analysis.

Laboratory Analysis

Chl a concentrations were measured using EPA method 446 (USEPA 1997).

Nutrient concentrations, including total phosphorus (TP), soluble reactive phosphorus

- - + (SRP), total Kjeldahl nitrogen (TKN), NO3 , NO2 , and NH3 , were obtained from

Heidelberg University’s National Center for Water Quality Research. TP, SRP, TKN,

+ and NH3 were obtained using a gas segmented Traacs 800 or Auto Analyzer II system

- - flow analayzer. NO3 and NO2 were measured on a Dionex 2000 or Dionex 320 ion

- - chromatograph. Total nitrogen (TN) was calculated as the sum of TKN, NO3 and NO2 .

Three bioassays were used to measure phosphorus deficiency: phosphorus turnover time (PTT), phosphorus debt, and alkaline phosphatase activity (APA). Two bioassays were used to measure nitrogen deficiency: ammonium enhancement response

(AER) and nitrogen debt. The values indicating deficiency for each assay are summarized in Table 3.

Table 3. Algal nutrient limitation values and deficiency thresholds. Adapted from North et al. (2007). No Moderate Extreme Indicator Nutrient limitation limitation limitation Limited P debt (µmol P µg Chl a-1) P <0.075 >0.075 P Turnover Time (minutes) P >60 <60 APA (µmol P µg Chl a-1 h-1) P <0.003 >0.003 >0.005 + -1 N debt (µmol NH 4 µg Chl a ) N <0.15 >0.15 AER N <1 >1

Phosphorus Turnover Time Assay

Phosphorus turnover time (PTT) is the amount of time needed for algae to take up a given amount of phosphorus. A radiometric approach was used to determine the rate of

32P uptake by phytoplankton and bacterial communities (Heath 1986). Samples from each site were treated in triplicate with 32P and incubated for intervals of 2, 4, and 6 minutes before being run through 0.2 µm and 1 µm filters. Controls for each sample site were pre-treated with formalin and allowed to sit for twenty minutes before being treated with 32P. Filters were then placed in small scintillation vials with Scintiverse BD scintillation fluid, and their activity was recorded with a scintillation counter. One mL of each sample was extracted from leftover unfiltered samples and controls to act as totals.

The rate of 32P uptake was measured as the amount of activity counted on each filter as it pertained to each incubation period. The inverse of the uptake rate slope provided us with PTT values for each site. Typically a low, or fast, PTT is considered any time under 60 minutes, indicating the presence of phosphorus deficiency. Higher,

slower PTT values suggest that phosphorus is not limiting at a given site and that other factors could be limiting to algal growth.

Phosphorus Debt Assay

P debt measures the amount of phosphorus taken up by algae over the course of

24 hours, normalized to algal biomass. The methods used for this study was a radiometric adaptation of those described by Healy and Hendzel (1979) to ensure greater assay sensitivity. Samples from each site were treated in triplicate with 32P at a concentration of .005 µmol P/mL and allowed to incubate in the dark for 24 hours at room temperature, along with “killed” controls treated with both 32P and formalin. After incubating, 1 mL of water was taken from each sample and control to act as totals for calculating specific activity. The remaining samples and controls were then filtered through 1 µm filters. Both filters and totals were placed in scintillation vials with

Scintiverse BD scintillation fluid and measured for activity with a scintillation counter.

Filter activities were divided by specific activities and normalized to Chl a concentrations to produce P debt values.

Alkaline Phosphatase Activity Assay

Alkaline phosphatase is an enzyme produced in the cell surface layer of algae and bacteria when insufficient bioavailable phosphorus is present. Alkaline phosphatase activity (APA) was measured using the 4-methyl umbelliferyl phosphate (4-MUP) fluorometric method as described by Pettersson (1980). Fluorescence measurements

were taken before and after a one hour incubation period with a Turner fluorometer.

Values of less than 0.003 µmoles P/µgrams Chl a were indicative of no P limitation, values from 0.003-0.005 µmoles P/µgrams Chl a suggested moderate P limitation, and values higher than 0.005 µmoles P/µgrams Chl a indicated extreme P limitation.

Ammonium Enhancement Response

Ammonium enhancement response (AER) was measured by comparing the

14 average rate of dark inorganic C fixation of samples treated with NH4Cl to that of untreated samples. Phytoplankton need to fix inorganic carbon in order to assimilate nitrogen into amino acids; as a result, N deficient samples will have higher carbon fixation rates than samples with sufficient N (Davies et al. 2004). Control samples for each site were treated in triplicate with 3 µCi 14C, while enhanced samples were treated

14 in triplicate with 3 µCi C and 3.5 µM NH4-N. Both controls and enhanced samples were then incubated in the dark for 4 hours. After the incubation period, controls and enhanced samples were drawn through 1 µm Millipore filters on a vacuum pressure filter manifold. At the conclusion of the assay, all filters were placed in small scintillation vials with Scintiverse BD fluid and their activity counted on a scintillation counter. AER ratios were measured as the activity observed in treatment samples divided by the activity of the controls. A threshold ratio of 1 was utilized, with values greater than one indicating N deficiency. To determine whether the ratio could be considered significant, paired T-tests were used to determine if the enhanced samples’ activity was significantly different from the average control samples’ activity.

N Debt

Ammonium was measured with a Turner fluorometer using the fluorometric techniques described in Holmes et al (1999) and assay methodology developed by Healey and Hendzel (1980). Untreated controls were employed in triplicate in order to obtain

+ ambient NH4 concentrations. T0 and T1 samples were both spiked in triplicate with 5µM ammonium sulfate; T0 samples were preserved immediately by freezing, while T1 samples were incubated in the dark for a period of 24 hours before preservation.

Additional ambient samples were spiked with 2.5 µM ammonium sulfate in order to correct for the matrix effect. All sample sets were treated with working reagent and incubated in the dark at room temperature; rather than the 4 hour incubation period proposed by Holmes et al. (1999), we used an 18 hour incubation period suggested for

Great Lake samples (J. Finlay, personal communication). Background fluorescence was measured as the fluorescence of ambient samples immediately after receiving working reagent. Fluorescence was measured on a standard curve after subtracting background and ambient concentrations from treated samples and correcting for the matrix effect.

+ Final N debt values were then obtained by subtracting T1 NH4 concentrations from T0 concentrations and normalizing to Chl a concentrations. N deficiency was considered present in samples in which the amount of ammonium taken up during 24 hours exceeded

0.15 µmol NH4/µgrams Chl a (Healey and Hendzel 1980).

Statistical Analyses

Assay results were normalized using log10 transformations. Log10 plus one transformations were utilized in the case of our N debt results, where negative values were sometimes observed. Using IBM SPSS 20 software, three-way ANOVAs were conducted for all assays to compare the effects of basin, season, and depth. Two-way

ANOVAs were used to examine the impact of season and transect, season and depth, and transect and depth. Post-hoc Tukey tests with a significance value of p < 0.05 and simple analyses of the means were used to identify specific factors that may have had a significant influence on our results. Linear regressions were also conducted to determine if nutrient deficiency increased or decreased with depth along individual transects. All assay results were tested against each other for Pearson’s bivariate correlation comparisons.

Results

P Debt

The number of P deficient sites as measured by P debt increased from 3 in the spring to 5 in the fall, accounting for a 6.7% increase of total deficient sites (Figure 6).

Deficient locations in the spring included all three sites along the River Raisin transect, whereas fall sites included the 2 m site along the River Raisin transect, the 5 m site along the Grand River transect, the 2 and 5 m sites along the Ashtabula River transect, and the

2 m site along the Chautauqua Creek transect.

The results of our P debt assay revealed a significant season x basin interaction (F

(2, 38) = 6.732, p = 0.003; Appendix M). Both the Central and Eastern Basins

Figure 6. P debt values along transects in spring and fall of 2012. Error bars represent one standard deviation of the mean. The 10 m River Raisin transect site in June was measured as 1.5 µmol P/µmol Chl a as a result of low Chl a values. The 10 m Ashtabula River transect site in June was measured as 0.0005 µmol P/µmol Chl a.

experienced a significant seasonal increase in P debt values during the course of the growing season (p = 0.001 for both basins). The average P debt value in the Central

Basin increased from 0.0073 to 0.06. In the Eastern Basin, the average P debt value similarly increased from 0.0078 µmol P µg Chl a-1 to 0.039 µmol P µg Chl a-1.

In the Western Basin, there was a significant season x transect interaction (F (1,

8) = 7.417, p = 0.026; Appendix N.1). Along the River Raisin transect, spring P debt values were significantly lower than those in the fall (p = 0.013). The interaction between season and transect was again significant in the Central Basin (F (2, 24) = 5.856, p = 0.011; Appendix N.2). Both the Grand River and Ashtabula River transects experienced significant seasonal increases (p = 0.003 and p = 0.002 respectively). In the

Eastern Basin, only seasonality was the only significant factor impacting P debt (F(1, 18)

= 65.663, p < 0.001; Appendix N.3).

Phosphorus Turnover Time

P deficient sites increased from 10% of our sites in the spring to 36.7% of our sites in the fall. Affected spring sites included the 2 meter site along the Turtle Creek transect and the 2 and 5 m sites along the Huron River transect. In the fall, P deficient conditions were found at the 2 m sites along both the Turtle Creek and Huron River transects, the 2 and 5 m sites along the River Raisin, Grand River, and Ashtabula River transects, and the 2, 5, and 10 m sites along the Presque Isle River transect (Figure 7).

PTT in the lake overall was significantly affected by season (F (1, 33) = 4.586, p

= 0.04; Appendix O), with slower turnover times occurring more prevalently in the

Figure 7. Phosphorus turnover times along eight transects in Lake Erie in spring and fall of 2012. Values falling below the threshold line are indicative of P deficient conditions. Negative values (not shown) occurred during the spring at the 10 m site along the River Raisin transect and the 20 m site along the Presque Isle transect. Spring PTT at the 10 m site along the Grand River transect was measured as 16,030 minutes.

spring. ANOVAs revealed no significant factors in the Western Basin alone. In the

Central Basin, there was a significant interaction between season and transect (F (2, 16) =

4.512, p = 0.028; Appendix P.1). Turnover times along both the Ashtabula River and

Grand River transects were appreciably slower in the spring than in the fall (p = 0.018 and p = 0.046 respectively). The season x transect interaction was also significant in the

Eastern Basin (F (2, 16) = 4.208, p = 0.034; Appendix P.2). Turnover time along the

Presque Isle River transect were considerably slower in the spring than in the fall (p =

0.005).

Alkaline Phosphatase Activity

Values from our APA assays were generally within the range of extreme P deficiency, with few exceptions (Figure 8). In the spring, only the 2 m site along the

Huron River transect was not found to be P deficient, while the 5 and 10 m sites along the same transect were observed as moderately deficient. In the fall, all sites demonstrated some degree of deficiency, with the 2 and 5 m sites along the River Raisin transect and the 5 and 20 m sites along the Huron River transect displaying moderate deficiency.

On a whole, there were significant interactions between season and basin (F (2,

38) = 3.778, p = 0.032; Appendix Q.1). The Western Basin experienced a seasonal decline in APA values (p = 0.003). Significant transect differences were also observed (F

(7, 30) = 6.645, p < 0.001; Appendix Q.2). The average APA values along the Huron

River transect were lower than those of all other transects in our study.

Figure 8. Alkaline phosphatase concentrations along eight transects in Lake Erie in spring and fall of 2012. Values above the threshold line are indicative of severe P deficiency. Error bars are representative of one standard deviation of the mean.

When looking at basins individually, the Western Basin again showed a significant seasonal decline in APA (F (1, 8) = 8.168, p = 0.021; Appendix R.1). In the

Central Basin, we found a significant season x transect effect (F (2, 18) = 5.659, p =

0.012; Appendix R.2). APA values increased seasonally along the Ashtabula River transect (p = 0.034), while decreasing along the Grand River transect (p = 0.027).

Differences between transects also became apparent once again (F (2, 12) = 26.715, p <

0.001; Appendix R.2). APA along the Huron River transect was lower than along the

Ashtabula River transect (p = 0.003) as well as the Grand River transect (p < 0.001).

APA along the Grand River transect was also higher than that along the Ashtabula River transect (p = 0.032). In the Eastern Basin, the season x transect interaction was significantly linked to APA values (F (2, 18) = 19.596, p < 0.001; Appendix R.3). Along the Cattaraugus Creek transect, APA experienced a significant seasonal decline (p =

0.005). Meanwhile, APA values along the Presque Isle River transect increased significantly from spring to fall (p < 0.001).

Ammonium Enhancement Response

AER assays showed N deficient conditions in 13% of our sample sites in the spring and 23% of our sample sites in the fall. AER revealed one instance of N deficiency at the 5 m site along the Turtle Creek transect during the spring sampling period, followed by N deficient conditions at the 5 m site along the River Raisin transect in the fall (Figures 9). In the Central Basin, the 5 m site along the Grand River transect

Figure 9. Ammonium enhancement response ratios for Lake Erie transects in spring and fall of 2012. Ratio values greater than one are considered limited by N. Asterisks indicate statistical significance as follows: * p < 0.1; ** p < 0.05; *** p < 0.001. Error bars for ratio values represent the propagation of error. The Huron River fall 5 m site in was 2.6 (p < 0.001); Cattaraugus Creek spring 5 m site was 2.7 (p>0.05). No data was available for the Chautauqua Creek spring 5 m site.

was identified as N deficient by AER assays in the spring. In the subsequent fall, the same site was identified, along with three additional sites at the 2, 5, and 10 m depths along the Huron River transect. In the Eastern Basin, AER showed N deficient conditions at the 5 m site along the Presque Isle River transect and the 20 m site along the

Cattaraugus Creek transect. In the fall, AER results were significant for the 20 m sites along both the Presque Isle and Chautauqua Creek transects.

Depth was found to have a significant effect on AER ratio values in the lake overall (F (3, 37) = 8.531, p < 0.001; Appendix S). AER values at the 2 m sites were significantly lower than those at the 5 and 20 m sites (p = 0.002 and p = 0.04 respectively). Likewise, AER values at the 10 m sites were also significantly lower than the 5 and 20 m sites (p = 0.001 and p = 0.019 respectively). Depth was further found to have a significant impact when analyzing the Eastern Basin on its own (F (3, 15) = 9.493, p = 0.001; Appendix T.1). AER ratio values were significantly higher at the 5 m sites than at the 2 m sites (p = 0.034) and the 10 m sites (p = 0.003). AER ratio values at the

20 m site were also significantly higher than at the 10 m site (p = 0.007). In the Central

Basin, transects appeared to have a greater impact than depth on AER ratio values (F (2,

18) = 3.632, p = 0.047; Appendix T.2). Values along the Huron River transect were significantly higher than those along the Ashtabula River transect (p = 0.038). The

Western Basin was not significantly affected by depth, season, or transect.

Linear regression analyses did not indicate any significant links between depth and AER values. Furthermore, bivariate correlation comparisons did not expose any

significant correlations between the results of our AER assays and those of our other nutrient deficiency assays.

N Debt

Overall, we found a seasonal decline in N deficient conditions from 50% of our sites in the spring to approximately 6.7% of our sites in the fall. N deficient conditions were the least prevalent in the Western Basin during the course of our study. N debt assays indicated one instance of N deficiency in the Western Basin in the spring at the 2 m site along the Turtle Creek transect, followed by no instances in the fall (Figure 10). In the Central Basin, N debt assays revealed a more noticeable seasonal decline in N deficient conditions. Six sites along all three transects were identified as N deficient in the spring, compared to only one site in the fall. Additionally, the average N debt value

+ -1 + in the Central Basin dropped from 0.24 µmol NH4 µg Chl a in the spring to 0.06 µmol NH4

µg Chl a-1 in the fall. Affected spring locations included the 20 m site along the Huron

River transect, the 5, 10, and 20 m sites along the Grand River transect, and the 2 and 5 m sites along the Ashtabula River transect, while the 20 m site along the Ashtabula River transect was the singularly deficient fall site. N debt values in the Eastern Basin reacted

+ -1 similarly to that of the Central Basin, falling from an average of 0.26 µmol NH4 µg Chl a

+ -1 in the spring to 0.11 µmol NH4 µg Chl a in the fall. Eight sites along all three transects were N deficient in the spring, including all sites along the Cattaraugus Creek transect, the 2, 5, and 20 m sites along the Chautauqua Creek transect, and the 20 m site along the

Figure 10. N debt values for Lake Erie transects in June and August 2012. Values above the threshold line are indicative of N deficient conditions. Missing values were negative and were likely below detection limits.

Presque Isle River transect. In the fall, only the 2 m sites along the Presque Isle River and Cattaraugus Creek transects were N deficient.

A three-way ANOVA of basin, depth, and season suggested a significant interaction effect of basin, depth, and season on N debt values (F (5, 38) = 2.505, p =

0.047; Appendix U.1). Spring N debt values were significantly lower at 5 m sites in the

Western Basin than in the fall (p < 0.001). Conversely, spring N debt values at the 5 m sites in the Central Basin were significantly higher than those in the fall (p = 0.044).

Spring values in the Western Basin were found to be significantly lower than those in the Central Basin (p = 0.002) and the Eastern Basin (p < 0.001). There were also some transect differences found between the basins (F (7, 44) = 2.836, p = 0.016;

Appendix U.2). N debt values along the Cattaraugus Creek transect specifically were found to be significantly different from those along the River Raisin transect (p = 0.011) and the Turtle Creek transect (p = 0.044).

When considering the Eastern Basin alone, we found that N debt values in the spring were significantly higher than those in the fall (F (1, 18) = 8.604, p = 0.009;

Appendix V.1). Transect differences were also significant, F (2, 18) = 4.372, p = 0.028; post-hoc tests showed that values along the Cattaraugus Creek transect were higher than those along the Presque Isle River transect (p = 0.028; Appendix V.1). The Central Basin also demonstrated significantly higher N debt values in the spring than in the fall (F (1,

18) = 6.227, p = 0.023; Appendix V.2), but was not greatly impacted by transect. The

Western Basin did not appear to be affected by season or transect. Depth did not have a significant bearing on N debt values in any of the three basins.

Linear regressions linking N debt values to depth were by and large non- significant. One significantly positive slope was found in the spring along the Huron

River transect (p = 0.023). However, there were no trends apparent of positive or negative slopes during either sampling period.

Bivariate correlation comparisons failed to find significant correlations between our N debt assay results and those from our APA, PTT, and AER assays. However, a significant correlation was observed when comparing the N debt values with P debt values; the two assays were significantly negatively correlated (r = -0.413, p = 0.001, n =

60).

Chl a and Nutrient Concentrations

T-tests showed that Chl a increased in the Central and Eastern Basins from June to August (p = 0.04 and p < 0.001 respectively). In the Central Basin, Chl a concentrations increased from 8.5 µg/L to 15.4 µg/L, while Eastern Basin values climbed from 3.9 µg/L to 6.2 µg/L.

Soluble reactive phosphorus experienced a decline in all three basins. This decline was significant in the lake overall, decreasing from an average concentration of

0.005 mg/L in the spring to 0.003 mg/L in the fall (p = 0.001). When basins were examined individually, the decline was significant in both Western Basin (p = 0.003) and the Central Basin (p = 0.03). Total phosphorus concentrations did not experience a significant seasonal change, averaging 0.02 mg P/L in the spring and 0.022 mg P/L in the fall.

Seasonally, TN declined significantly in the lake overall, dropping from 0.56 mg/L to 0.36 mg/L (p < 0.001). TN fell from 0.688 mg/L to 0.35 mg/L in the Central

Basin and dropped from 0.38 mg/L to 0.26 mg/L in the Eastern Basin (p < 0.001 for both). Declines in the Western Basin were not found to be significant. NH3 concentrations decreased significantly in the Western Basin (p = 0.003) from a June average of 0.05 mg/L to an August average of 0.035 mg/L. Seasonal changes in the

Central Basin and Eastern Basins were not significant. NO3 concentrations declined significantly in all three basins (p < 0.001 for all). NO3 fell seasonally from 0.35 mg/L to

0.08 mg/L in the Western Basin, from 0.39 mg/L to 0.027 mg/L in the Central Basin, and from 0.16 mg/L to 0.02 mg/L in the Eastern Basin.

As a result of declining TN and increasing TP, mass TN:TP ratios in the lake overall dropped from 28:1 in the spring to 16:1 in the fall. In the Western Basin, TN:TP dropped from 27:1 to 19:1. In the Central Basin, TN:TP fell from 27:1 to 13:1. In the

Eastern Basin, the ratio was measured as 30:1 in the spring, decreasing to 19:1 in the fall.

Discussion

P deficiency indicators revealed a trend of increasing P deficiency overall from spring to fall. The increase in P deficiency coincided with declines of average SRP concentrations in each of the three basins. Likely, decreased SRP concentrations can be largely attributed to uptake and assimilation by algal species during the course of the growing season (Lean et al. 1983). While SRP declined, TP did not experience a significant seasonal change in any of the three basins. A 2002-2003 study of offshore

nutrient limitation in the Central Basin further supports our findings in that P limitation became increasingly more pronounced as the growing season progressed. The authors found that P limitation was relieved in late autumn when isothermal mixing occurred

(Moon and Carrick 2007).

While P deficiency generally increased from spring to fall, N deficiency was more prevalent in spring than fall, at least when measured by N debt. The Western Basin appeared the least affected by N limiting conditions, as both N assays found few instances of N deficient conditions there. This is not overly surprising as the Western

Basin has historically received the highest nutrient loads of the three basins (Barbiero and

Tuschman 2004). Conversely, the Central and Eastern Basins were more prone to N deficient conditions. AER assays were not often congruent with N debt assays. For instance there was a seasonal increase in N deficient sites in the two basins based on

AER, whereas N debt assays resulted in fewer N deficient sites in the fall.

Overall, our findings are fairly consistent with those of Rattan et al. (2012), who also found a decreased incidence of N deficiency as measured from May through

September in the Western and Central Basins. The authors indicated that temperature limitation may have induced an N deficient response early on in the growing season in spite of sufficient concentrations of bioavailable N. Phytoplankton have higher N

- requirements in lower temperatures, thus stimulating increased rates of NO3 assimilation.

A 1993-1995 study in Lake Erie also implicated low light conditions and cooler temperatures during spring mixing as important limiting factors to algal growth in the

Great Lakes (Fahnenstiel et al. 2000). Our study site temperatures in May were more in

line with Rattan et al.’s June temperatures, when the authors began to observe a decline in N deficiency; thus, N limitation in the spring was more likely due to a release from P limitation rather than the presence of temperature limitation.

Based on our observations of high TN:TP ratios in the spring and comparably lower ratios in the fall, we may have expected to find a higher frequency of N deficient sites in the fall. Our results serve to highlight the importance of differentiating between the types of information furnished by nutrient ratios and proximate nutrient deficiency assays. Total nutrient ratios are measures of which nutrient would ultimately limit phytoplankton biomass in a given ecosystem over longer timescale. Conversely, proximate nutrient deficiency assays provide information on short-term rate processes that address the immediate nutrient requirements at a specific time and place. Ultimate limiting nutrients and proximate limiting nutrients are not mutually exclusive, and uncoupling between the two has been known to occur (Davies et al. 2004; Davies et al.

2010, Chaffin et al. unpublished). In our samples, SRP experienced significant declines in two basins, and was likely the driving force behind increasing P deficiency at the end of the growing season.

In addition to the overall seasonal trends previously discussed, we also examined each sampling location’s response to deficiency indicator assays individually to determine if the changes we observed in nutrient deficiency were in fact due to shifts in dominant limiting nutrients or if co-limitation by N and P were taking place. The most dramatic seasonal responses along entire transects were observed along the Grand River and Cattaraugus Creek transects. Both transects demonstrated N deficiency in the spring

as measured by N debt, and both generally exhibited higher AER ratios in the spring compared to the fall. Concurrently, P debt and PTT assays generally produced values indicative of greater deficiency in the fall. The algal responses to four out of the five nutrient deficiency indicator assays provides evidence that nutrient limitation shifted from N to P along these two transects rather than demonstrated co-limitation by N and P.

The negative correlation between N debt values and P debt values also suggests that limiting nutrients may follow a seasonal pattern or cycle.

Most sample sites appeared to be deficient in either N or P at a given time; sites which demonstrated both N and P deficiency at the same time were believed to be co- limited. APA, which identified P deficiency at almost all of the sites during both seasons, was not taken into consideration when identifying examples of co-limitation. Due potentially to declines in both SRP and dissolved inorganic nitrogen (DIN) pools, incidents of co-limitation were more prominent in the fall than in the spring. In the spring, only the 2 m site along the River Raisin transect was observed to be co-limited by

N and P. Fall sites exhibiting co-limitation included the 2 m site along the Huron River transect, the 5 m site along the Grand River transect, and the 2 and 5 m sites along the

Presque Isle River transect. In contrast to previous studies, we did not find examples of co-limitation at any of our offshore sites (North et al. 2007).

By using multiple indicator assays we hoped to obtain a more accurate depiction of nutrient deficiency in Lake Erie. In 2012, as in 2011, the results of our APA assay appeared to often deviate from those obtained by P debt and PTT assays. Bacterial activity was not separated out from P debt or APA and may have impacted our results

(Beardall et al. 2001). Additionally, alkaline phosphatase activity can be heavily impacted by phytoplankton community composition (Cao et al 2011). PTT, while excluding bacterial activity, may overestimate P deficiency if luxury uptake of P is high

(Hameed et al. 1999). AER ratios also did not correspond overly well with the results of our N debt assays. AER has been criticized as being insensitive to levels of moderate N deficiency, and may only report incidents of severe N deficiency. N debt assays, on the other hand, may overestimate N deficiency due to luxury uptake by algae in the absence of depleted N conditions (Flynn 1990). Therefore, the extent of N deficiency found in our samples may be underestimated by AER and overestimated by N debt. Due to inconsistencies between assays, it is imperative that bioavailable nutrient concentrations are also taken into consideration.

Finally, while not addressed during the course of this study, Fe concentrations may also impact nutrient deficiency in Lake Erie, particularly when N limitation in concerned. Without a sufficient source of Fe, phytoplankton are limited in their ability to

- + produce the enzymes needed to convert NO3 into NH4 . Nutrient enrichment experiments in eastern Lake Erie have previously shown that P and Fe additions

- significantly enhanced the uptake of NO3 (North et al. 2007). Therefore a lack of Fe from terrestrial sources could have induced N limitation despite sufficient bioavailable N in our samples. However, more data is needed regarding the role of Fe throughout Lake

Erie.

Considerations and Conclusions

Since P reductions were initially enacted in the 1970s, baseline conditions in the

Lake Erie watershed have changed. Agricultural practices in the Lake Erie watershed have transformed dramatically over the past few decades. While the use of phosphorus- based fertilizers has declined, a study of the Maumee and Sandusky River watersheds from 1971 to 1995 revealed a 46% increase in nitrogen-based fertilizer sales (Richards et al. 2002a). Additionally, the utilization of conservation tillage and subsurface tile drains have been identified as potential threats to water quality. Conservation tillage has been widely adopted throughout northwestern Ohio as a means of reducing losses of particulate P through agriculture erosion; however, it may be contributing instead to increased flow-weighted mean SRP concentrations in the Maumee River and other Lake

Erie tributaries (Joosse and Baker 2011). Tile-drained soils also reduce the loss of particulate matter and suspended solids; yet, the loss of soluble nitrates and phosphates is greater in tile-drained soils than in their non-drained counterparts (Calhoun et al. 2002).

Furthermore, installing tile drainage in areas previously occupied by wetlands aerates the soil, which inhibits denitrification from taking place and further adds to the amount of nitrates present in surface water and shallow groundwater (Novotny 2011). This may be especially applicable when considering Ohio’s Great Black Swamp, which once occupied approximately 13,000 km2 along Lake Erie’s western shoreline and was largely drained during the second half of the nineteenth century for agricultural purposes (Richards et al.

2002b).

Not only have agricultural practices been dramatically transformed, but global climate change may be contributing to milder winters and longer summers. These

conditions may be advantageous to algal growth, particularly for species of cyanobacteria which thrive in warmer temperatures. In addition, periods of drought followed by intense precipitation events have been increasing in frequency, the latter of which may cause increased nutrient enrichment through erosion and runoff (Paerl et al. 2011).

Thus, in the face of recent anthropogenic and environmental changes, it is within reason that we may need to change our nutrient management strategies as well. While the reductions of external P loading in Lake Erie were at one time effective in constraining algal growth, additional measures may be needed to address the current problems associated with harmful algal blooms. The results of this study revealed what may be part of a seasonal pattern of shifting nutrient limitation in Lake Erie from N deficient conditions in the spring to P deficient conditions in the fall. Additionally, our results differed from those implied by mass TN:TP ratios, stressing the importance of using proximate nutrient indicators as a measure of current and frequently changing conditions. The use of such proximate nutrient indicators could allow for short-term modifications to current water resource management strategies, which may be more appropriate for shallow lakes with short residence times.

Acknowledgements

Funding for this study was provided by the Great Lakes Restoration Initiative as part of the Lake Erie Nearshore and Offshore Nutrient Study (LENONS), as well as the Art and

Margaret Herrick Aquatic Ecology Research Facility Student Research Support Fund.

We would like to thank our collaborating partners at Buffalo State College, the

University of Toledo, Case Western Reserve University, and Heidelberg University.

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Appendix A Sample Sites and Dates

Table A.1: 2011 transects and sample dates

Transect Spring Fall River Raisin 6/22/2011 8/22/2011 Turtle Creek 6/20/2011 8/18/2011 Huron River 6/8/2011 8/17/2011 Grand River 6/9/2011 8/18/2011 Ashtabula River 6/16/2011 8/19/2011 Presque Isle River 6/17/2011 8/20/2011 Chautauqua Creek 6/15/2011 8/21/2011 Cattaraugus Creek 6/6/2011 8/26/2011

Table A.2: 2012 transects and sample dates

Transect Spring Fall River Raisin 6/12/2012 8/23/2012 Turtle Creek 5/30/2012 8/21/2012 Huron River 6/6/2012 8/13/2012 Grand River 6/7/2012 8/14/2012 Ashtabula River 6/8/2012 8/15/2012 Presque Isle River 6/9/2012 8/16/2012 Chautauqua Creek 6/11/2012 8/9/2012 Cattaraugus Creek 5/31/2012 8/8/2012

Appendix B Sample Site Locations

Table B: Lake Erie Transect Locations, 2011-2012

Basin Transect Depth (m) Latitude Longitude Western River Raisin 2 41.86449 -83.37363 5 41.85631 -83.33423 9 41.1809 -83.1856 Turtle Creek 2 41.62611 -83.3425 5 41.85528 -83.22806 8 41.75556 -83.16222 Central Huron River 2 41.466786 -82.666716 5 41.466834 -82.650257 10 41.483463 -82.633437 15 41.516938 -82.550197 Grand River 2 41.724728 -81.361209 5 41.76673 -81.216838 10 41.783364 -81.216849 20 41.800158 -81.416792 Ashtabula River 2 41.904176 -80.807958 5 41.90716 -80.80763 10 41.91036 -80.81231 20 41.966831 -80.800223 Eastern Presque Isle River 2 42.348449 -80.02821 5 42.333375 -80.01153 10 42.333476 -80.03423 20 42.366727 -80.100027 Chautauqua Creek 2 42.348449 -79.585426 5 42.333375 -79.600199 10 42.333476 -79.600154 20 42.366727 -79.600191 Cattaraugus Creek 2 42.568213 -79.142936 5 42.572258 -79.158042 10 42.574727 -79.183056 20 42.58355 -79.216912

Appendix C Average Chl a concentrations

Table C: Average Chl a concentrations at sampling locations, 2011-2012

Transect Depth (m) Chl a (µg/L) Spring 2011 Fall 2011 Spring 2012 Fall 2012 River Raisin 2 38.033 38.203 3.03 47.353 5 5.03 48.03 1.493 51.53 10 17.32 11.65 0.467 5.633 Turtle Creek 2 72.553 35.247 4.163 8.79 5 5.07 49.247 1.147 3.687 10 24.017 36.34 3.44 6.86 Huron River 2 11.334 26.006 26.024 21.024 5 11.962 27.985 19.382 22.013 10 14.801 13.597 17.527 29.847 20 9.89 10.546 4.739 24.48 Grand River 2 4.042 8.475 8.981 15.435 5 3.603 9.299 5.284 6.231 10 3.938 8.983 3.748 12.416 20 2.94 4.823 2.103 8.598 Ashtabula River 2 3.507 7.429 3.956 13.425 5 3.603 6.343 3.789 13.714 10 3.832 6.817 3.253 9.995 20 3.733 5.444 3.806 8.121 Presque Isle River 2 4.031 9.097 5.649 6.209 5 3.562 8.144 5.810 7.179 10 3.631 5.402 3.517 7.621 20 3.575 5.363 2.445 5.874 Chautauqua Creek 2 2.537 6.469 3.195 7.246 5 2.848 7.903 2.969 5.641 10 2.941 7.419 3.634 5.713 20 3.298 4.929 3.955 6.086 Cattaraugus Creek 2 2.995 7.323 4.999 6.756 5 2.867 5.65 4.498 5.8 10 4.021 6.668 3.503 4.664 20 4.328 5.378 3.429 5.633

Appendix D Average TP concentrations

Table D: Average total phosphorus concentrations at sampling locations, 2011-2012

Transect Depth (m) TP (mg/L) Spring 2011 Fall 2011 Spring 2012 Fall 2012 River Raisin 2 0.1455 0.0572 0.0179 0.043 5 0.1033 0.0657 0.0282 0.048 10 0.0274 0.0246 0.0193 0.0173 Turtle Creek 2 0.1289 0.0761 0.0327 0.0335 5 0.0523 0.0532 0.0277 0.021 10 0.0314 0.037 0.0213 0.0191 Huron River 2 0.0414 0.0454 0.0327 0.0386 5 0.027 0.0478 0.0495 0.0392 10 0.0335 0.0295 0.0422 0.0405 20 0.025 0.0172 0.0207 0.0382 Grand River 2 0.0269 0.017 0.0268 0.0197 5 0.0308 0.0142 0.0271 0.0191 10 0.0192 0.0111 0.0226 0.0187 20 0.0145 0.0053 0.0188 0.0241 Ashtabula River 2 0.0088 0.0095 0.0168 0.0204 5 0.0084 0.011 0.0158 0.0223 10 0.0077 0.0111 0.0167 0.0197 20 0.0076 0.0083 0.0138 0.0185 Presque Isle River 2 0.0090 0.0147 0.0148 0.0155 5 0.0065 0.0138 0.0175 0.0164 10 0.0065 0.0277 0.0131 0.0124 20 0.0059 0.0091 0.0112 0.0108 Chautauqua Creek 2 0.0068 0.0136 0.0091 0.0163 5 0.0068 0.0133 0.0104 0.0129 10 0.0055 0.0109 0.0117 0.0119 20 0.0060 0.0081 0.0090 0.0110 Cattaraugus Creek 2 0.0077 0.0135 0.0158 0.0163 5 0.0082 0.0108 0.0166 0.0145 10 0.0094 0.01 0.0139 0.0152 20 0.0103 0.0073 0.0101 0.0144

Appendix E Average SRP concentrations

Table E: Average total soluble reactive phosphorus concentrations at sampling locations, 2011-2012 Transect Depth (m) SRP (mg/L) Spring 2011 Fall 2011 Spring 2012 Fall 2012 River Raisin 2 0.0181 0.0027 0.0178 0.008 5 0.056 0.0009 0.0249 0.007 10 0.004 0.0249 0.0165 0.0042 Turtle Creek 2 0.0038 0.0017 0.0109 0.0053 5 0.0287 0.0006 0.0112 0.0164 10 0.0041 0.0005 0.0081 0.0082 Huron River 2 0.0062 0.0002 0.0087 0.0134 5 0.0047 0.0002 0.0128 0.0125 10 0.0045 0.002 0.0176 0.0133 20 0.003 0.0001 0.0109 0.0148 Grand River 2 0.0062 -0.0002 0.0129 0.01 5 0.0047 0.0009 0.0171 0.011 10 0.0045 0.0012 0.0168 0.0112 20 0.003 0.0005 0.0126 0.0117 Ashtabula River 2 0.0003 0.0004 0.0144 0.0122 5 0.0014 0.0004 0.0158 0.017 10 0.0014 0.0004 0.0159 0.0154 20 0.0016 0.0009 0.0109 0.0122 Presque Isle River 2 0.0025 0.0001 0.0117 0.0084 5 0.002 0.0005 0.0119 0.008 10 0.0022 0.0024 0.0104 0.0087 20 0.0011 0.0007 0.0094 0.0062 Chautauqua Creek 2 0.0014 0.0014 0.0093 0.0088 5 0.0006 0.0019 0.0091 0.0084 10 0.001 0.0014 0.009 0.0078 20 0.0003 0.001 0.01 0.0093 Cattaraugus Creek 2 0.0013 0.0207 0.011 0.0112 5 0.0026 0.0018 0.0091 0.0104 10 0.0028 0.0031 0.011 0.009 20 0.0021 0.0022 0.0079 0.0085

Appendix F Average TN, NH3, and NO3 concentrations

Table F: Average total nitrogen, ammonia, and nitrate concentrations at sampling locations, 2012 Transect Depth (m) TN (mg/L) NH3 (mg/L) NO3 (mg/L) Spring Fall Spring Fall Spring Fall River Raisin 2 0.6819 0.901 0.07 0.026 0.36 0 5 0.6598 0.999 0.05 0.025 0.33 0.03 10 0.5218 0.4849 0.03 0.027 0.22 0.22 Turtle Creek 2 0.8389 0.4552 0.04 0.031 0.5 0.12 5 0.7033 0.2668 0.06 0.015 0.37 0.01 10 0.61 0.3692 0.05 0.031 0.31 0.11 Huron River 2 0.7642 0.4552 0.03 0.028 0.32 0.01 5 0.9768 0.3744 0.03 0.008 0.365 0 10 0.7262 0.4162 0.04 0.02 0.372 0 20 0.5785 0.4161 0.02 0.016 0.332 0.04 Grand River 2 0.8999 0.3339 0.08 0.051 0.6 0.06 5 0.8736 0.3895 0.09 0.022 0.59 0.06 10 0.8008 0.3691 0.08 0.04 0.54 0.04 20 0.5943 0.2789 0.06 0.056 0.38 0.04 Ashtabula River 2 0.4902 0.3269 0.03 0.031 0.31 0.01 5 0.6033 0.3466 0.05 0.042 0.37 0.01 10 0.6416 0.3214 0.07 0.048 0.39 0.03 20 0.3173 0.2645 0.02 0.059 0.15 0.02 Presque Isle River 2 0.4342 0.2393 0.03 0.043 0.22 0.01 5 0.4114 0.2768 0.02 0.025 0.21 0.01 10 0.3558 0.246 0.02 0.03 0.16 0.01 20 0.3283 0.1932 0.04 0.033 0.11 0.01 Chautauqua Creek 2 0.4053 0.2653 0.03 0.036 0.16 0.02 5 0.3879 0.2427 0.03 0.05 0.17 0.02 10 0.3503 0.2795 0.02 0.032 0.16 0.03 20 0.2666 0.2242 0.03 0.036 0.1 0.01 Cattaraugus Creek 2 0.4972 0.2917 0.05 0.04 0.176 0.02 5 0.4077 0.2892 0.05 0.051 0.152 0.03 10 0.369 0.297 0.04 0.05 0.134 0.05 20 0.4291 0.335 0.03 0.044 0.118 0.02

Appendix G Significant ANOVA Results for 2011-2012 P Debt Assays – SPSS 20 Output

Table G: ANOVA for the effects of season, depth, and transect on overall lake P debt values

Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 35.413a 59 .600 1.566 .043 Intercept 419.859 1 419.859 1095.656 .000 Season 13.215 1 13.215 34.485 .000 Transect 6.892 7 .985 2.569 .022 Depth .164 3 .055 .143 .934 Season * Transect 8.414 7 1.202 3.137 .007 Season * Depth 1.187 3 .396 1.033 .385 Transect * Depth 3.004 19 .158 .413 .982 Season * Transect * Depth 1.233 19 .065 .169 1.000 Error 22.992 60 .383 Total 491.387 120 Corrected Total 58.405 119 a. R Squared = .606 (Adjusted R Squared = .219)

Appendix H Significant ANOVA Results for 2011-2012 P Debt Assays by Basin – SPSS 20 Output

Table H.1: ANOVA for the effects of season, depth, and transect on Central Basin P debt values

Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 10.419a 23 .453 1.618 .124 Intercept 198.314 1 198.314 708.509 .000 Depth 1.417 3 .472 1.688 .196 Transect .606 2 .303 1.082 .355 Season 3.426 1 3.426 12.239 .002 Depth * Transect .116 6 .019 .069 .998 Depth * Season 1.108 3 .369 1.320 .291 Transect * Season 3.228 2 1.614 5.766 .009 Depth * Transect * .519 6 .087 .309 .926 Season Error 6.718 24 .280 Total 215.450 48 Corrected Total 17.137 47 a. R Squared = .608 (Adjusted R Squared = .232)

Table H.2: ANOVA for the effects of season, depth, and transect on Eastern Basin P debt values

Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum df Mean F Sig. of Squares Square Corrected Model 12.039a 23 .523 6.103 .000 Intercept 156.216 1 156.216 1821.500 .000 Season 10.652 1 10.652 124.199 .000 Transect .163 2 .081 .950 .401 Depth .127 3 .042 .492 .691 Season * Transect .348 2 .174 2.032 .153 Season * Depth .315 3 .105 1.224 .322 Transect * Depth .214 6 .036 .416 .861 Season * Transect * .220 6 .037 .428 .853 Depth Error 2.058 24 .086 Total 170.313 48 Corrected Total 14.097 47 a. R Squared = .854 (Adjusted R Squared = .714)

Appendix I Significant ANOVA Results for 2011-2012 Overall Lake P Turnover Time Assays – SPSS 20 Output

Table I.1: ANOVA for the effects of season, depth, and transect on overall lake PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum of df Mean Square F Sig. Squares Corrected Model 36.282a 59 .615 2.166 .003 Intercept 468.378 1 468.378 1649.576 .000 Season 5.164 1 5.164 18.187 .000 Transect 3.824 7 .546 1.924 .084 Depth 2.346 3 .782 2.755 .052 Season * Transect 9.068 7 1.295 4.562 .000 Season * Depth 2.506 3 .835 2.942 .042 Transect * Depth 6.721 19 .354 1.246 .260 Season * Transect * Depth 5.796 19 .305 1.074 .402 Error 14.765 52 .284 Total 561.784 112 Corrected Total 51.047 111 a. R Squared = .711 (Adjusted R Squared = .383)

85 86

Table I.2: ANOVA for the effects of season, depth, and basin on overall lake PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum Df Mean F Sig. of Squares Square Corrected Model 19.087a 21 .909 2.560 .001 Intercept 445.931 1 445.931 1255.757 .000 Depth 2.162 3 .721 2.029 .115 Basin .883 2 .441 1.243 .293 Season 5.799 1 5.799 16.330 .000 Depth * Basin 2.175 5 .435 1.225 .304 Depth * Season 2.246 3 .749 2.109 .105 Basin * Season 2.702 2 1.351 3.804 .026 Depth * Basin * 2.013 5 .403 1.133 .349 Season Error 31.960 90 .355 Total 561.784 112 Corrected Total 51.047 111 a. R Squared = .374 (Adjusted R Squared = .228)

Appendix J Significant ANOVA Results for 2011-2012 P Turnover Time Assays by Basin – SPSS 20 Output

Table J.1: ANOVA for the effects of season, depth, and transect on Western Basin PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum Df Mean Square F Sig. of Squares Corrected Model 9.849a 11 .895 4.545 .012 Intercept 93.285 1 93.285 473.509 .000 Season 3.130 1 3.130 15.887 .003 Transect .685 1 .685 3.476 .092 Depth .982 2 .491 2.493 .132 Season * Transect .541 1 .541 2.747 .128 Season * Depth 1.707 2 .854 4.333 .044 Transect * Depth 1.444 2 .722 3.664 .064 Season * Transect * .164 2 .082 .417 .670 Depth Error 1.970 10 .197 Total 112.151 22 Corrected Total 11.819 21 a. R Squared = .833 (Adjusted R Squared = .650)

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Table J.2: ANOVA for the effects of season, depth, and transect on Central Basin PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Df Mean Square F Sig. Sum of Squares Corrected Model 19.187a 23 .834 3.343 .004 Intercept 209.977 1 209.977 841.515 .000 Season 5.605 1 5.605 22.465 .000 Transect .249 2 .124 .499 .615 Depth 2.156 3 .719 2.881 .061 Season * Transect 3.469 2 1.735 6.952 .005 Season * Depth 2.837 3 .946 3.790 .027 Transect * Depth 2.635 6 .439 1.760 .159 Season * Transect * Depth 3.042 6 .507 2.032 .109 Error 4.990 20 .250 Total 240.384 44 Corrected Total 24.178 43 a. R Squared = .794 (Adjusted R Squared = .556)

Table J.3: ANOVA for the effects of season, depth, and transect on Eastern Basin PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum Df Mean Square F Sig. of Squares Corrected Model 6.677a 23 .290 .818 .682 Intercept 184.345 1 184.345 519.664 .000 Season .013 1 .013 .038 .847 Transect 1.800 2 .900 2.537 .102 Depth .902 3 .301 .847 .483 Season * Transect 2.475 2 1.238 3.489 .048 Season * Depth .351 3 .117 .330 .804 Transect * Depth .630 6 .105 .296 .932 Season * Transect * .342 6 .057 .161 .985 Depth Error 7.804 22 .355 Total 209.249 46 Corrected Total 14.481 45 a. R Squared = .461 (Adjusted R Squared = -.102)

Appendix K Significant ANOVA Results for 2011-2012 Overall Lake Alkaline Phosphatase Activity Assays – SPSS 20 Output

Table K.1: ANOVA for the effects of season, depth, and transect on overall lake APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum Df Mean Square F Sig. of Squares Corrected Model 11.473a 59 .194 1.450 .077 Intercept 373.197 1 373.197 2782.143 .000 Season 1.198 1 1.198 8.933 .004 Transect 6.160 7 .880 6.560 .000 Depth .565 3 .188 1.403 .251 Season * Transect 1.122 7 .160 1.195 .320 Season * Depth .114 3 .038 .284 .837 Transect * Depth 1.304 19 .069 .511 .947 Season * Transect * .668 19 .035 .262 .999 Depth Error 8.048 60 .134 Total 399.925 120 Corrected Total 19.521 119 a. R Squared = .588 (Adjusted R Squared = .182)

90 91 91

Table K.2: ANOVA for the effects of season, depth, and basin on overall lake APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 6.119a 21 .291 2.130 .007 Intercept 355.989 1 355.989 2602.987 .000 Season 1.371 1 1.371 10.025 .002 Depth .727 3 .242 1.772 .158 Basin 2.498 2 1.249 9.132 .000 Season * Depth .204 3 .068 .497 .685 Season * Basin .413 2 .206 1.509 .226 Depth * Basin .523 5 .105 .765 .577 Season * Depth * Basin .465 5 .093 .681 .639 Error 13.403 98 .137 Total 399.925 120 Corrected Total 19.521 119 a. R Squared = .313 (Adjusted R Squared = .166)

Appendix L Significant ANOVA Results for 2011-2012 Alkaline Phosphatase Activity by Basin – SPSS 20 Output

Table L.1: ANOVA for the effects of season, depth, and transect on Eastern Basin APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .655a 23 .028 .798 .704 Intercept 123.815 1 123.815 3470.667 .000 Season .067 1 .067 1.881 .183 Transect .084 2 .042 1.175 .326 Depth .046 3 .015 .432 .732 Season * Transect .342 2 .171 4.791 .018 Season * Depth .021 3 .007 .199 .896 Transect * Depth .048 6 .008 .226 .964 Season * Transect * .046 6 .008 .217 .968 Depth Error .856 24 .036 Total 125.327 48 Corrected Total 1.511 47 a. R Squared = .433 (Adjusted R Squared = -.109)

Table L.2: ANOVA for the effects of season, depth, and transect on Central Basin APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum Df Mean Square F Sig. of Squares Corrected Model 4.911a 23 .214 2.685 .010 Intercept 163.210 1 163.210 2052.503 .000 Season .507 1 .507 6.370 .019 Transect 3.548 2 1.774 22.310 .000 Depth .213 3 .071 .891 .460 Season * Transect .354 2 .177 2.223 .130 Season * Depth .008 3 .003 .034 .991 Transect * Depth .166 6 .028 .348 .904 Season * Transect * .117 6 .019 .244 .957 Depth Error 1.908 24 .080 Total 170.030 48 Corrected Total 6.820 47 a. R Squared = .720 (Adjusted R Squared = .452)

Appendix M Significant ANOVA Results for 2012 Overall Lake P Debt Assays – SPSS 20 Output

Table M: ANOVA comparison of the effects of basin, depth, and season on overall lake P debt values Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 14.031a 21 .668 2.724 .004 Intercept 153.039 1 153.039 623.980 .000 Season 1.471 1 1.471 5.997 .019 Depth .165 3 .055 .224 .879 Basin 3.705 2 1.852 7.553 .002 Season * Depth 1.007 3 .336 1.369 .267 Season * Basin 3.302 2 1.651 6.732 .003 Depth * Basin 1.643 5 .329 1.340 .269 Season * Depth * Basin .828 5 .166 .675 .645 Error 9.320 38 .245 Total 206.098 60 Corrected Total 23.351 59 a. R Squared = .601 (Adjusted R Squared = .380)

Appendix N Significant ANOVA Results for 2012 P Debt Assays by Basin – SPSS 20 Output

Table N.1: ANOVA comparison of the effects of transect and season on Western Basin P debt values

Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 3.959a 3 1.320 10.635 .004 Intercept 18.417 1 18.417 148.410 .000 Season .393 1 .393 3.165 .113 Transect 2.646 1 2.646 21.322 .002 Season * Transect .920 1 .920 7.417 .026 Error .993 8 .124 Total 23.368 12 Corrected Total 4.952 11 a. R Squared = .800 (Adjusted R Squared = .724)

Table N.2: ANOVA comparison of the effects of transect and season on Central Basin P debt values

Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum of df Mean Square F Sig. Squares Corrected Model 6.881a 5 1.376 6.606 .001 Intercept 90.701 1 90.701 435.378 .000 Season 2.968 1 2.968 14.245 .001 Transect 1.473 2 .737 3.536 .051 Season * Transect 2.440 2 1.220 5.856 .011 Error 3.750 18 .208 Total 101.332 24 Corrected Total 10.631 23 a. R Squared = .647 (Adjusted R Squared = .549)

Table N.3: ANOVA comparison of the effects of transect and season on Eastern Basin P debt values

Tests of Between-Subjects Effects Dependent Variable: LogPdebt Source Type III Sum of df Mean Square F Sig. Squares Corrected Model 2.903a 5 .581 13.586 .000 Intercept 77.726 1 77.726 1818.848 .000 Season 2.801 1 2.801 65.553 .000 Transect .008 2 .004 .098 .907 Season * Transect .093 2 .047 1.090 .357 Error .769 18 .043 Total 81.398 24 Corrected Total 3.672 23 a. R Squared = .791 (Adjusted R Squared = .732)

Appendix O ANOVA Results for 2012 Overall Lake P Turnover Time Assays – SPSS 20 Output

Table O.1: ANOVA comparison of the effects of basin, depth, and season on overall lake PTT values Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 12.637a 21 .602 1.397 .190 Intercept 234.408 1 234.408 544.092 .000 Season 1.976 1 1.976 4.586 .040 Basin .708 2 .354 .822 .448 Depth 2.378 3 .793 1.840 .159 Season * Basin .405 2 .202 .470 .629 Season * Depth 1.024 3 .341 .792 .507 Basin * Depth 3.172 5 .634 1.473 .225 Season * Basin * Depth 2.105 5 .421 .977 .446 Error 14.217 33 .431 Total 305.659 55 Corrected Total 26.854 54 a. R Squared = .471 (Adjusted R Squared = .134)

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Table O.2: ANOVA comparison of the effects of depth and season on overall lake PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum df Mean Square F Sig. of Squares Corrected 6.774a 7 .968 2.265 .045 Model Intercept 270.434 1 270.434 632.990 .000 Season 2.506 1 2.506 5.865 .019 Depth 2.828 3 .943 2.206 .100 Season * Depth .983 3 .328 .767 .518 Error 20.080 47 .427 Total 305.659 55 Corrected Total 26.854 54 a. R Squared = .252 (Adjusted R Squared = .141)

Table O.3: ANOVA comparison of the effects of transect and season on overall lake PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 12.351a 15 .823 2.214 .024 Intercept 275.592 1 275.592 741.114 .000 Season 3.870 1 3.870 10.407 .003 Transect 1.831 7 .262 .703 .669 Season * Transect 7.853 7 1.122 3.017 .012 Error 14.503 39 .372 Total 305.659 55 Corrected Total 26.854 54 a. R Squared = .460 (Adjusted R Squared = .252)

Appendix P Significant ANOVA Results for 2012 P Turnover Time Assays by Basin – SPSS 20 Output

Table P.1: ANOVA comparison of the effects of transect and season on Central Basin PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 6.251a 5 1.250 2.638 .064 Intercept 120.124 1 120.124 253.427 .000 Season 2.335 1 2.335 4.927 .041 Transect .435 2 .218 .459 .640 Season * Transect 4.278 2 2.139 4.512 .028 Error 7.584 16 .474 Total 134.136 22 Corrected Total 13.835 21 a. R Squared = .452 (Adjusted R Squared = .281)

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Table P.2: ANOVA comparison of the effects of transect and season on Eastern Basin PTT values

Tests of Between-Subjects Effects Dependent Variable: LogPTT Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 2.876a 5 .575 2.764 .055 Intercept 109.929 1 109.929 528.281 .000 Season .521 1 .521 2.502 .133 Transect .418 2 .209 1.005 .388 Season * Transect 1.751 2 .876 4.208 .034 Error 3.329 16 .208 Total 116.299 22 Corrected Total 6.205 21 a. R Squared = .463 (Adjusted R Squared = .296)

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Appendix Q ANOVA Results for 2012 Alkaline Phosphatase Activity Assays – SPSS 20 Output

Table Q.1: ANOVA comparison of the effects of basin, depth, and season on overall lake APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 2.880a 21 .137 .940 .549 Intercept 159.751 1 159.751 1094.500 .000 Season .488 1 .488 3.343 .075 Basin .861 2 .431 2.951 .064 Depth .547 3 .182 1.249 .306 Season * Basin 1.103 2 .551 3.778 .032 Season * Depth .071 3 .024 .163 .921 Basin * Depth .275 5 .055 .377 .862 Season * Basin * Depth .103 5 .021 .141 .982 Error 5.546 38 .146 Total 189.723 60 Corrected Total 8.427 59 a. R Squared = .342 (Adjusted R Squared = -.022)

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Table Q.2: ANOVA comparison of the effects of transect and season on overall lake APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 6.402a 15 .427 9.272 .000 Intercept 176.777 1 176.777 3840.694 .000 Season .281 1 .281 6.107 .017 Transect 4.116 7 .588 12.775 .000 Season * Transect 2.120 7 .303 6.580 .000 Error 2.025 44 .046 Total 189.723 60 Corrected Total 8.427 59 a. R Squared = .760 (Adjusted R Squared = .678)

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Appendix R Significant ANOVA Results for 2012 Alkaline Phosphatase Activity by Basin – SPSS 20 Output

Table R.1: ANOVA comparison of the effects of transect and season on Western Basin APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean F Sig. of Squares Square Corrected Model 1.538a 3 .513 3.492 .070 Intercept 30.842 1 30.842 210.119 .000 Transect .044 1 .044 .300 .599 Season 1.199 1 1.199 8.168 .021 Transect * .295 1 .295 2.008 .194 Season Error 1.174 8 .147 Total 33.554 12 Corrected Total 2.712 11 a. R Squared = .567 (Adjusted R Squared = .405)

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Table R.2: ANOVA comparison of the effects of transect and season on Central Basin APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean Square F Sig. of Squares Corrected Model 3.697a 5 .739 19.264 .000 Intercept 84.213 1 84.213 2193.992 .000 Season .003 1 .003 .070 .795 Transect 3.260 2 1.630 42.466 .000 Season * Transect .434 2 .217 5.659 .012 Error .691 18 .038 Total 88.601 24 Corrected Total 4.388 23 a. R Squared = .843 (Adjusted R Squared = .799)

Table R.3: ANOVA comparison of the effects of transect and season on Eastern Basin APA values

Tests of Between-Subjects Effects Dependent Variable: LogAPA Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .402a 5 .080 9.040 .000 Intercept 67.006 1 67.006 7536.589 .000 Season .006 1 .006 .710 .410 Transect .047 2 .024 2.650 .098 Season * Transect .348 2 .174 19.596 .000 Error .160 18 .009 Total 67.568 24 Corrected Total .562 23 a. R Squared = .715 (Adjusted R Squared = .636)

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Appendix S Significant ANOVA Results for 2012 Overall Lake Ammonium Enhancement Response Assays – SPSS 20 Output

Table S: ANOVA comparison of the effects of basin, depth, and season overall lake AER values

Tests of Between-Subjects Effects Dependent Variable: LogAER Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .820a 21 .039 2.045 .028 Intercept .012 1 .012 .640 .429 Season .002 1 .002 .109 .743 Depth .489 3 .163 8.531 .000 Basin .005 2 .003 .137 .872 Season * Depth .021 3 .007 .358 .784 Season * Basin .060 2 .030 1.559 .224 Depth * Basin .136 5 .027 1.419 .240 Season * Depth * .106 5 .021 1.115 .369 Basin Error .707 37 .019 Total 1.531 59 Corrected Total 1.527 58 a. R Squared = .537 (Adjusted R Squared = .275)

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Appendix T Significant ANOVA Results for 2012 Ammonium Enhancement Response Assays by Basin – SPSS 20 Output

Table T.1: ANOVA comparison of the effects of depth and season on Eastern Basin AER values

Tests of Between-Subjects Effects Dependent Variable: LogAER Source Type III Sum df Mean Square F Sig. of Squares Corrected .598a 7 .085 4.993 .004 Model Intercept .001 1 .001 .084 .775 Season .051 1 .051 2.977 .105 Depth .488 3 .163 9.493 .001 Season * Depth .108 3 .036 2.106 .142 Error .257 15 .017 Total .856 23 Corrected Total .855 22 a. R Squared = .700 (Adjusted R Squared = .560)

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Table T.2: ANOVA comparison of the effects of transect and season on Central Basin AER values

Tests of Between-Subjects Effects Dependent Variable: LogAER Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .204a 5 .041 2.718 .053 Intercept .020 1 .020 1.334 .263 Season .010 1 .010 .673 .423 Transect .109 2 .054 3.632 .047 Season * Transect .085 2 .042 2.827 .086 Error .270 18 .015 Total .494 24 Corrected Total .474 23 a. R Squared = .430 (Adjusted R Squared = .272)

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Appendix U Significant ANOVA Results for 2012 Overall Lake N Debt Assays – SPSS 20 Output

Table U.1: ANOVA comparison of the effects of basin, depth, and season on overall lake N debt values

Tests of Between-Subjects Effects

Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .260a 21 .012 2.953 .002 Intercept .067 1 .067 15.907 .000 Season .001 1 .001 .121 .730 Basin .081 2 .040 9.629 .000 Depth .016 3 .005 1.268 .299 Season * Basin .061 2 .030 7.267 .002 Season * Depth .007 3 .002 .521 .671 Basin * Depth .037 5 .007 1.774 .142 Season * Basin * Depth .052 5 .010 2.505 .047 Error .159 38 .004 Total .543 60 Corrected Total .419 59 a. R Squared = .620 (Adjusted R Squared = .410)

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Table U.2: ANOVA comparison of the effects of transect and season on overall lake N debt values

Tests of Between-Subjects Effects Dependent Variable: LogNdebt Source Type III Sum of df Mean Square F Sig. Squares Corrected Model .184a 15 .012 2.295 .017 Intercept .099 1 .099 18.578 .000 Season .004 1 .004 .803 .375 Transect .106 7 .015 2.836 .016 Season * Transect .068 7 .010 1.828 .106 Error .235 44 .005 Total .543 60 Corrected Total .419 59 a. R Squared = .439 (Adjusted R Squared = .248)

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Appendix V Significant ANOVA Results for 2012 N Debt Assays by Basin – SPSS 20 Output

Table V.1: ANOVA comparison of the effects of transect and season on Eastern Basin N debt values

Tests of Between-Subjects Effects Dependent Variable: LogNdebt Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .038a 5 .008 3.837 .015 Intercept .127 1 .127 63.927 .000 Season .017 1 .017 8.604 .009 Transect .017 2 .009 4.372 .028 Season * Transect .004 2 .002 .918 .417 Error .036 18 .002 Total .201 24 Corrected Total .074 23 a. R Squared = .516 (Adjusted R Squared = .381)

Table V.2: ANOVA comparison of the effects of transect and season on Central Basin N debt values

Tests of Between-Subjects Effects Dependent Variable: LogNdebt Source Type III Sum df Mean Square F Sig. of Squares Corrected Model .026a 5 .005 1.440 .258 Intercept .074 1 .074 20.039 .000 Season .023 1 .023 6.227 .023 Transect .002 2 .001 .293 .749 Season * Transect .001 2 .001 .194 .825 Error .066 18 .004 Total .166 24 Corrected Total .092 23 a. R Squared = .286 (Adjusted R Squared = .087)

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