THE INFLUENCE OF LIGHT AND NUTRIENTS ON BENTHIC FILAMENTOUS ALGAL GROWTH: A CASE STUDY OF SAGINAW BAY, LAKE HURON By Kimberly Ann Peters A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Fisheries and Wildlife 2010 ABSTRACT THE INFLUENCE OF LIGHT AND NUTRIENTS ON BENTHIC FILAMENTOUS ALGAL GROWTH: A CASE STUDY OF SAGINAW BAY, LAKE HURON By Kimberly Ann Peters In order to develop effective management strategies to alleviate shoreline-fouling events caused by nuisance-level filamentous benthic algal growth throughout the Great Lakes, we need to understand what is controlling benthic algal growth. By focusing on the benthic filamentous algae linked to shoreline-fouling in Saginaw Bay, Lake Huron, I examined how nutrient and light limit benthic algae biomass, and how this limitation varies across gradients of light and nutrient availability. Using active fluorometry and benthic algal internal nutrient content, the benthic algal community was found to be both light and nutrient limited over a large spatial range along the southwest region of Saginaw Bay in the summer of 2009. In addition, active fluorometry indicated that algal health decreased as site distance from the Saginaw River increased. Further, photosynthetic efficiency decreased as depth decreased, suggesting shallower depths are less conducive to growth than more protected, deeper depths. Analysis of light saturation indicated that light availability close to the river is sporadic and relatively constant at further distances. Furthermore, internal phosphorus significantly decreased as distance from the river increased along a 3.0 m depth contour, supporting the existence of a phosphorus gradient. My research sheds light on the degree to which key factors limit benthic algae growth in Saginaw Bay, and how their role varies along gradients common in systems with nuisance beach algae. Dedicated to my mother and father, who saw me through the ups and downs, the tears, the late nights, the many requests for home-cooked meals and fresh laundry, and somehow knew that I would make it to the end. I could not have done it without you. iii ACKNOWLEDGMENTS I would like to thank a number of individuals who made this thesis work possible. First, thank you to the funding agency, the National Oceanic and Atmospheric Administration (NOAA) Center for Sponsored Coastal Ocean Research (CSCOR). My advisor, Dr. Scott Peacor, guided me throughout this process, pushing me to be a stronger scholar, scientist, and person. My committee members, Dr. Juli Dyble Bressie, Dr. Kendra Cheruvelil, and Dr. Ace Sarnelle, who provided great insight on how to bring my research to the next level and always offered themselves as a “sounding board” for any obstacle along the way. I am deeply indebted to Dr. Steve Francoeur, who acted as a mentor and colleague, and gave me the motivation to begin this work in the first place. To Tom Johengen, Donna Kashian, and Jan Stevenson, I thank you for your many insights on methodology and the execution of the project, as well as much additional guidance along the way. I would like to thank my many peers who put in physical labor to make this research possible, including Dianna Miller (without you, this project never would have happened), Chris Henry, Cory von Achen, Rachel Teets, Mary Bammer, Ryan MacWilliams, Isaac Standish, and Bill Oeming. To Larry (“Harris”) Taylor, thank you for your extensive volunteer hours and direction with our diving efforts. I would also like to show my gratitude to Ashley Burtner, Audrey Johnson, Dave Fanslow, and Nancy Moorehead who ran countless water quality and benthic algal samples with me. I am very grateful to Lois Wolfson, who kindly loaned her pontoon for all of the field work that produced this thesis. A big thanks to the men and women of the Linwood Marina – you were such a joy and immense help throughout our field season obstacles. Thank you to Katya Ananyeva and Dr. Allison Roy for helping with iv statistics. It is an honor for me to thank the many individuals of the Limnology Lab and Department of Fisheries and Wildlife whose support was unwavering. Also, a heartfelt thank you to Stacie Auvenshine, Emily Johnston, Cory Brant, and Lissy Goralnik for always making me smile. Finally, I owe my deepest gratitude to my family and to Christopher Winslow whose support, love, and wisdom continue to amaze me. This work is part of a multi-agency project to address multiple-stressor issues in Saginaw Bay. Benthic algae is recognized as a large concern by managers, with fisheries issues and harmful algal blooms also key areas of research. Institutions involved with this project include the Michigan Department of Natural Resources and Environment, NOAA Great Lakes Environmental Research Laboratory, CILER, seven universities (including Michigan State University), and Limno-Tech, Inc. v TABLE OF CONTENTS LIST OF TABLES……... ...................................................................................................... vii LIST OF FIGURES… ........................................................................................................... viii GENERAL INTRODUCTION .............................................................................................. CHAPTER I. Nutrient and. Light Limitation of Benthic Algae along Predicted Light and Nutrient Gradients Saginaw Bay, Lake Huron .............................................. Introduction .................................................................................................... Materials and Methods ................................................................................... Overview ............................................................................................ Study Site ........................................................................................... Field Methodology ............................................................................. Benthic Light Calculations ................................................................ Statistical Analysis ............................................................................. Results ............................................................................................................ Benthic Algae Tissue Nutrient Analysis ............................................ The Effect of Distance, Depth, and Substrate on Benthic Algae Photosynthesis Parameters to Determine Light Limitation across Gradients ............................................................................................ Water Quality Analysis to Support Light and Nutrient Gradients ..... Discussion ..................................................................................................... Verification of Light and Nutrient Gradients..................................... Light as a Limiting Factor.................................................................. Phosphorus as a Limiting Factor........................................................ Light vs. Phosphorus Limitation across Gradients of Light and Nutrient Availability .......................................................................... Conclusion .................................................................................................... 1 7 10 10 10 12 18 20 23 23 24 26 28 28 29 30 31 34 APPENDIX A – Figures and Tables for Chapter I ................................................................ 36 APPENDIX B – Coordinates of Saginaw Bay Sampling Sites and Transects ...................... 57 WORKS CITED………………………………………….. .................................................. 58 vi LIST OF TABLES Table 1 Table of best-supported models for all benthic algal dependent variables .......... 47 Table 2 Table of best-supported models for all benthic algal dependent variables along a 3.0 m depth contour….. ....................................................................................... 48 Table 3 Summary table of all water quality variables measured ...................................... 49 Table 4 Summary table of all water quality variables of models including water column depth and/or distance from the Saginaw River .................................................... 50 Table 5 Table of all possible models in the best-model selection for internal phosphorus and maximum photosynthetic efficiency variables.............................................. 51 Table 6 Table of all possible models in the best-model selection for alpha and light saturation coefficient variables ............................................................................ 53 Table 7 Table of all possible models in the best-model selection for internal phosphorus and maximum photosynthetic efficiency variables along a 3.0 meter depth contour in Saginaw Bay, Lake Huron .................................................................. 55 Table 8 Table of all possible models in the best-model selection for alpha and light saturation coefficient variables along a 3.0 meter depth contour in Saginaw Bay, Lake Huron .......................................................................................................... 56 vii LIST OF FIGURES Fig. 1 Map of Saginaw Bay Sampling Sites and Transects ........................................... Fig. 2 % Tissue phosphorus of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of phosphorus limiting tissue concentrations……… ................................................................................ 37 Fig. 3 Tissue Carbon:Phosphorus of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of phosphoruslimiting tissue concentrations…….. .......................................................………. 38 Tissue Nitrogen:Phosphorus of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of phosphoruslimiting tissue concentrations .............................................................................. 39 Tissue Carbon:Nitrogen of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of nitrogen-limiting tissue concentrations ............................................................................................ 40 Internal phosphorus of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River along a 3.0 m depth contour ............................................................................................................ 41 Maximum photosynthetic efficiency of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River along a 3.0 m depth contour........................................................................................... 42 Fig. 4 Fig. 5 Fig. 6 Fig. 7 36 Fig. 8 Alpha of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River for the best supported model including distance only ........................................................................................................ 43 Fig. 9 Light saturation index (EK) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over depth of the water column for the best supported model including depth of the water column, distance from the Saginaw River, and the substrate type Chara................................................................................ 44 Fig. 10 Light saturation index (EK) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River for the best viii supported model including depth of the water column, distance from the Saginaw River, and the substrate type Chara ...................................................... 45 Fig. 11 Midday averaged benthic light (MBL) minus the light saturation index (EK) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over the midday averaged benthic light ....................................................................... 46 ix INTRODUCTION Excessive benthic algal growth has impacted the Great Lakes since the 1950s, leading to offensive shoreline fouling events and the appearance of poor nearshore water quality (Bootsma et al. 2004). Research from 1960 – 1980 identified phosphorus as the key controlling factor in nuisance-level benthic algal growth and pointed to the eutrophication of the Great Lakes as the overarching cause (Gerstein 1965, Beeton 1966, Davis 1969, Wong and Clark 1976, Auer and Canale 1982, Neil and Jackson 1982, Bootsma et al. 2004). Subsequently, a multi-state phosphorus ban was established in the 1970s and 80s to reduce benthic algal growth and alleviate shoreline-fouling events (Bierman et al. 1984); the phosphorus program led to a drop in benthic biomass and was declared the solution for the shoreline fouling problem (Bootsma et al. 2004, Higgins et al. 2008a, Auer et al. 2010). However, benthic algal biomass is returning to preabatement levels, yet allochthonous phosphorus inputs have remained near or below target levels set in the 1970s (Bootsma et al. 2004, Bootsma et al. 2006, Malkin et al. 2008). This resurgence of the detrital wash-up in the past decade has researchers revisiting the ecology behind nearshore nuisance-level benthic algal growth (Malkin et al. 2010). One area where shoreline fouling is particularly problematic in the Great Lakes is Saginaw Bay, Lake Huron. Of particular concern for Saginaw Bay beach-goers and local landowners, the detritus has been impacting the housing market and economy of the area due to its offensive odor and appearance (Higgins et al. 2008b). Because the problem is strongly pronounced in summer months, beaches that were historically popular for public recreation and tourism are experiencing drops in use. Furthermore, landowners with beachfront properties must invest time and money into removing the deposits from their property fronts. 1 Few detailed historical observations exist about the composition of shoreline detritus in Saginaw Bay. A Michigan Department of Natural Resources (MDNR) memorandum issued in August 1961 described the wash-up as a “grey-black substance” composed of zooplankton (primarily ostracods and cladocerans), aquatic plant material, Fragillaria pieces, and Cladophora (Fetterolf 1961). Similar observations were made in an additional MDNR memorandum in September 1978 (Kenaga 1978). However, evidence suggests that the benthic algal detritus washing up on Saginaw Bay beaches in more recent decades is not dominated by Cladophora, a benthic algae species that attributed to cause beach fouling in many other regions in the Great Lakes. Instead, Saginaw Bay detritus appears to be composed primarily of decomposing metaphytonic chlorophytes (benthic autotrophs), including Zygnematales, Oedogonium, diatoms, and Cladophora, and vascular hydrophytes (macrophytes) (Pillsbury et al. 2002, Peters pers. obs.). According to Saginaw Bay residents, shoreline fouling events have worsened in the last few decades (Dziekan et al., unpubl.). It has been hypothesized that this change is related to the increase in water clarity caused by the invasion of the filter feeders, the zebra mussel (Dreissena polymorpha) and the quagga mussel (Dreissena rostriformis bugensis) (Auer et al. 2010). Bridgeman et al. (1995) found that phytoplankton abundance and productivity plummeted around the time of peak zebra mussel densities during the initial invasion in Saginaw Bay, which led to an increase in light penetration and potential decrease in competition for nutrients in the benthos. Lowe and Pillsbury (1995) suggested that this increased light penetration has created a shift in primary production from a planktonic- to a benthic-dominated system; increased water clarity caused by mussel filtration extended littoral zones, creating conditions favoring benthic algal growth and enhancing benthic primary productivity. This shift from a plankton-dominated 2 to a benthic-dominated system has been termed "benthification," and the effects of such a shift may be dramatic, extending throughout aquatic food webs (Lowe and Pillsbury 1995, Nalepa and Fahnenstiel 1995). Benthification reallocates nutrients and energy sources to the benthos, making them unavailable to planktonic organisms and thereby enhancing benthic productivity (Fahnenstiel et al. 1995b, Nalepa and Fahnenstiel 1995, Cecala et al. 2008). Furthermore, the additional light may be favoring a benthic community dominated by genera uncommon in the system in the past; specifically that of green algae, which require higher light environments than other algal groups, such as diatoms and cyanobacteria (Pillsbury et al. 2002). This additional benthic biomass associated with benthification may be causing the increased benthic algal detritus seen in recent shoreline fouling events. Literature describing the benthic algal community of Saginaw Bay is sparse. The first bay-wide biological survey took place in the 1990s as part of a study to monitor potential changes in periphyton community composition and nutrient limitation during the invasion of the Dreissena polymorpha (Lowe and Pillsbury 1995, Pillsbury et al. 2002). Litteral et al. (1995) completed a similar study, assessing how increased light levels from D. polymorpha filtration may have impacted the periphyton community. Likewise, Skubinna et al. (1995) quantified changes in bay-wide macrophyte community composition and distribution in response to increased water clarity, while also measuring the relative abundance of benthic filamentous algae. These few studies encompass nearly all that is known (prior to the beginning of this study) about the benthic algal community of Saginaw Bay. Nevertheless, these studies have described several important shifts in the benthic community composition. Prior to the invasion of the dreissenid, algal communities were composed primarily of diatoms (Litteral et al. 1995). Following the invasion, increases in water 3 clarity due to mussel filtration caused shifts in the benthic algal community to filamentous greens, with Cladophora, Mougeotia, Spirogyra, and Zygnema appearing as the dominant species (Litteral et al. 1995, Skubinna et al. 1995, Pillsbury et al. 2002). Litteral et al. (1995) suggested that benthic chlorophytes were light-limited prior to the dreissenid invasion, thereby explaining the drastic increase in filamentous green algae growth following increased light penetration in the bay. When water clarity decreased in 1994, the benthic algal community composition again favored diatom species and a decrease in filamentous biomass was observed (Litteral et al. 1995, Pillsbury et al. 2002); between 1993 and 1994, biomass of filamentous green algae decreased from 93% to just 29% of the total algal biomass (Litteral et al. 1995). However, anecdotal evidence and my preliminary observations in 2008 suggest that the benthos has now shifted back to primarily metaphytonic filamentous greens, specifically Cladophora, Oedogonium sp. and Spirogyra sp. (Litteral et al. 1995, Pillsbury et al. 2002, Peters pers. obs.). Additional cause for concern rests on recent research: studies have found that algal detritus harbors potentially harmful pathogens, including Escherichia coli and enterococci (Byappanahalli et al. 2003, Rose et al. 2007, Verhougstraete et al. 2010). Algal wash-up protects the micro-organisms from weather, allowing them to reproduce to levels above recommended health standards. Then, when the detritus is disturbed by strong wind or wave action, these microorganisms can be released into the water column causing potential public health issues (Verhougstraete et al. 2010). My study is designed to better understand the ecology of benthic filamentous algae in Saginaw Bay. Specifically, the motivation for this work was to assess the interaction of light and nutrients on benthic algal growth, particularly along light and nutrient gradients in the southwest region of Saginaw Bay. The overarching goals are twofold: to better understand the growth of 4 benthic algae in Saginaw bay in order to (1) elucidate factors affecting beach fouling throughout out the Great Lakes and (2) inform managers in Saginaw Bay of the conditions that lead to or control benthic algal growth. Saginaw Bay presents a particularly good study system because it offers the ability to study benthic growth across multiple light, nutrient, and substrate gradients. By better understanding this growth dynamic, especially across gradients of conditions, future research can be focused on potential control methods to aid in alleviating the nuisance-level growth, both in Saginaw Bay and in other areas of the Great Lakes. My thesis addresses the interaction of light and nutrients in limiting benthic algal growth along predicted light and nutrient gradients in the southwestern portion of Saginaw Bay. I used pulse-amplitude-modulated fluorometry to measure photosynthesis as well as the maximum fluorescence yield, which has been shown to indicate nutrient stress (Kolber et al. 1988, Falkowski and Kolber 1995). Furthermore, I measured the cell internal nutrient content and a variety of water quality parameters (i.e., water column total phosphorus and SRP concentrations, pelagic chlorophyll a) in relation to distance from the Saginaw River and water-column depth to understand basic growth parameters of the benthic algal community. My research has provided insight on how light and nutrients influence benthic algal growth. Furthermore, findings from my study supports that benthic algae adapt to varying conditions, thereby complicating the factors controlling benthic algal growth. However, this better understanding of growth allows for more focused research to aid in predicting and managing nuisance benthic algae. Further, my results will inform models that are being developed as part of the larger project in Saginaw bay to describe benthic algae growth and its transport to shoreline. My research also documents where the algae is growing, which can help managers employ physical strategies (i.e. barriers) to reduce beach fouling. These findings 5 further the understanding of a problem that has proliferated the Great Lakes. With this in mind, we hope to help guide managers on how to mediate shoreline fouling events. 6 CHAPTER I Nutrient and Light Limitation of Benthic Algae along Predicted Light and Nutrient Gradients Saginaw Bay, Lake Huron INTRODUCTION The Great Lakes have a long history with nuisance-level benthic algal growth. Associated with eutrophication, the nuisance-level growth elicited a multi-state phosphorus ban during the 1970s, which led to a substantial drop in benthic filamentous biomass (Higgins et al. 2008b). However, reports of shoreline fouling began reappearing during the 1990s even though allochthonous phosphorus inputs remained near target levels (Nicholls et al. 2001, Higgins et al. 2008b, Malkin. et al. 2010). Recent research suggests that the introduction of the invasive filter-feeders, the zebra mussel (Dreissena polymorpha) and the quagga mussel (Dreissena rostriformis bugensis), is responsible for this resurgence of benthic algal growth (Higgins et al. 2008b, Malkin. et al. 2010) by altering the habitat in favor of high productivity in the benthos. Dreissenids significantly altered the physical and chemical characteristics of the benthos by clearing the water column, engineering additional hard substrate, and shunting pelagic phosphorus to benthic primary producers (Johannsson et al. 2000, Hecky et al. 2004, Higgins et al. 2008b, Ozersky et al. 2009, Auer et al. 2010, Malkin et al. 2010). With this increase in benthic productivity, target phosphorus inputs set in the past may no longer be enough to control benthic algal growth throughout the Great Lakes (Auer et al. 2010). 7 Recent research has elucidated the seasonal patterns of benthic algal growth leading up to the algal detritus deposition events (Bootsma et al. 2004, Higgins et al. 2008a). With enough light and nutrients, facilitated by mussels, a filamentous algal community establishes on available hard substrate and around macrophyte beds throughout the benthos. Using the spring influx of phosphorus, the benthic community rapidly increases in biomass and spatial extent. Then, as the summer growing season progresses and water temperatures increase, conditions become unfavorable to growth; the filaments become highly epiphitized and are subject to the effects of self-shading (Higgins et al. 2008a). This progression inevitably leads to senescence and death of the community, which causes detritus. This detritus is then brought on shore with wind or storm events (Bootsma et al. 2004). Nevertheless, although our understanding of shoreline fouling is increasing, there is much that is not well understood concerning the role of the major factors, light and nutrients, which limit benthic algae growth. A particular case of shoreline fouling is seen in Saginaw Bay, Lake Huron. Compared to other parts of the Great Lakes, Saginaw Bay is a relatively shallow, eutrophic estuary with less wave action and a high susceptibility to local weather changes (Danek and Saylor 1977). Therefore, it is expected that different patterns of benthic algal growth exist compared to other Great Lakes bays. Furthermore, although shoreline fouling events are similar in composition and frequency to other lakes, Saginaw Bay growth is relatively heterogeneous over space, which is less common in other Great Lakes. These unique conditions of Saginaw Bay provide the opportunity to examine benthic algae across gradients of limiting factors, to pinpoint the conditions that limit benthic algae growth, and investigate if management can alleviate the washup. 8 The objective of my study was to increase understanding about basic questions concerning the ecology behind shoreline fouling in Saginaw Bay, Lake Huron. Specifically, what is the limiting factor of benthic filamentous algal growth: light, nutrients, or an interaction of the two? Furthermore, do these limitations change across predicted light and nutrient gradients? I conducted a survey of the benthic algal community at 19 sites in the southwest region of Saginaw Bay to characterize growth conditions (e.g. nutrient availability, light), community composition, bay substrate, and overall algal health. I used pulse-amplitude-modulated (PAM) fluorometry and cell nutrient measurements along predicted light and nutrient gradients extending from the mouth of the Saginaw River and across depth. PAM fluorometry was used to measure light requirements of multiple samples of benthic algae and compare these to the light availability to assess light limitation for individual samples. Measuring internal nutrients allowed me to compare individual benthic algal samples to literature derived thresholds of nutrients to understand the level of nutrient limitation throughout the sampling area. I then analyzed cell phosphorus content and parameters of fluorometry across distance from the river, depth of the water column, and substrate type to assess the potential for patterns of algal health characteristics within the zone of high benthic biomass. I compared these values to published values to determine if light and nutrients were limiting. 9 MATERIALS AND METHODS Overview The goal of this study was to determine if light or nutrients, or both, limit benthic algal growth in Saginaw Bay and examine light and nutrient limitation patterns as a function of predicted gradients. A key element of my study was examining algae over a large portion of the Inner Bay to include environmental gradients that were predicted to affect algae growth, thereby spanning a large range of benthic conditions. I examined limitation along gradients to understand how limitation can potentially change or switch throughout different portions of a habitat. I measured cell nutrients and in situ algal health and estimated photosynthetic parameters throughout the benthic algal community in the southwest region of Saginaw Bay during the summer of 2009. These measurements were made on benthic algal samples collected across predicted light and nutrient gradients extending from the mouth of the Saginaw River to measure potential light and nutrient limitation patterns within the algal community over a range of conditions. Because water column parameters and substrate availability play a key role in benthic algal growth, I also collected surface water samples and made observations of the benthos with the help of SCUBA divers. Study Site This study took place in the nearshore zone (< 10 km of shore) of the inner bay of Saginaw Bay, Lake Huron. The inner bay is a eutrophic, well-mixed system with a mean depth of 5.1 m and a dominant counterclockwise, weak circulation pattern (7 cm/s) highly influenced by local wind changes (Sloss and Saylor 1975, Nalepa et al. 2002, Nalepa et al. 2003). Bottom 10 substrates include silt/mud, cobble, and rock, and display great spatial heterogeneity (Nalepa et al. 1995). The hydraulic retention time for the inner bay is approximately 120 days and is highly influenced by flow from the Saginaw River, which makes up about 70% of the total flow into Saginaw Bay (Nalepa et al. 1995). The Saginaw Bay watershed receives extensive agricultural, industrial, and urban runoff (Millie 2006). It has been labeled an “Area of Concern” by the International Joint Commission due to excessive eutrophication, toxic substance and bacterial contamination, and the impact of other environmental stressors caused by Saginaw River eutrophication and runoff inputs (Nalepa and Fahnenstiel 1995, Nalepa et al. 2002, Millie et al. 2006). When choosing the location of sampling sites, a goal was to quantify limitation in areas of dense growth and areas with a high potential for limitation (e.g., at the boundaries of growth). Based on preliminary observations in 2008 and early 2009, a large region (approximately 50 2 km ) of benthic algae growth was identified in the southwestern quadrant of the inner bay close to the Saginaw River. Moving parallel to the shoreline, both toward and away from the river, little or no algal growth occurred beyond this large region of growth. Sampling (e.g. physical collection of algae) only took place where filamentous algal growth was present, which was limited to the 2.0 – 4.0 m depth zone. Algae was examined up to these algal growth boundaries across depth and distance from the Saginaw River in order to examine algae in areas with potentially degrees of limitation. Sites were also chosen along a 3.0 m depth contour in this region of growth to quantify light and nutrient limitation at the depth where algal growth was most commonly observed (Peters pers. observ.). Furthermore, light measurements were taken in areas beyond the extent of algal growth where light limitation was expected. In total, I chose 19 11 sites for physical collection of benthic algae within these distance and depth growth boundaries, 9 of which were at 3.0 m. A previous study by Skubinna et al. (1995) examined many sites around the entire bay and, where possible, I chose sites that coincided with this previous work. My numbering scheme for transects and sampling sites reflected that of Skubinna et al. (1995). See Figure 1 for a map of all sampling site locations and Appendix A for coordinates. Field Methodology Benthic Algae Benthic algae site selection At each sampling site, divers would collect two bags of filamentous algae from each substrate type, which included Chara, mussels, and miscellaneous. For instance, if filamentous algae was observed growing on rocks and around Chara, two samples would be collected from two separate rocks and two additional samples would be collected from two separate Chara beds. Sites did not always have algal growth on each of the three substrate types. Samples were then brought onto the boat, placed out of the sun, and processed immediately to prevent photoadaptation to conditions different from the benthic light environment. After collection, all visible detritus and non-algal material was removed to leave a homogenous, clean sample of filamentous algae. All fluorometry measurements were then taken immediately, including dark-adapted maximum photosynthetic efficiency measurements (Fv/Fm) and rapid fluorescence lightresponse curves (RLCs). Dark-adapted Fv/Fm, or the ratio of variable fluorescence to maximal fluorescence, is used to evaluate algal health in response to a variety of environmental 12 parameters and has been shown to indicate nutrient stress (Kolber et al. 1988, Falkowski and Kolber 1995, Schreiber 2004, Kruskopf and Flynn 2006). RLCs are used to estimate photosynthetic performance at different light levels, which provides insight on optimal light regimes (Schreiber 2004). Since the apically-growing Cladophora was expected to be a dominant algal species within the samples, only the apical ends of filaments were used for active fluorescence analysis, to avoid biasing photosynthesis measurements by inclusion of dying or senescent cells (Hiriart-Baer et al. 2008). See Table 1 for a summary all active fluorometric measurements taken at each sampling site. Active fluorescence measurements were made using a chronological methodology. First, using a Diving-PAM fluorometer (Heinz Walz, Effeltrich, Germany), an RLC was taken on each algal sample. RLCs were constructed by exposing algae to 9 increasing light levels (range 0 ~2250), with an exposure time of 30 s prior to measurement of photosynthetic performance (i.e. light-adapted Fv/Fm) at an individual light level. The samples were then placed in a lightexclusion box and dark-adapted for at least 15 minutes. Dark-adapted algal material was then loaded into the measuring chamber strictly by touch to prevent actinic light from effecting sample fluorescence, and dark-adapted Fv/Fm measurements were taken. At all times prior and during fluorometric measurements, algal material was held in lake water to prevent desiccation. After all active fluorometry measurements were taken, the algal samples were removed from the dark box and stored on ice in a dark cooler until further lab analysis (of cell C, N, P, and species identification). Active fluorometry measurements were only made between the hours of 9:00 and 15:00 EST to limit potential diel fluctuations in Fv/Fm (Schreiber 2004, S. Francoeur, pers. comm.). See Table 1 for a summary all active fluorometric measurements taken at each sampling site and the lab. 13 Benthic Algae Tissue Nutrient Analyses In order to capture a representative measure of internal benthic algal nutrient content, the filamentous algal samples collected by divers were blended into a homogeneous slurry by pulseblending each sample 8-10 times with a 2-speed hand blender (Hamilton Beach, Washington, NC, USA). The slurry was then filtered onto pre-combusted filters (GF/F; Whatman; AMD Manufacturing Inc., Mississauga, ON, Canada) for internal carbon and nitrogen assay and on acid washed filters (GF/F) for internal phosphorus assay and frozen until analysis. I precombusted filters for C:N analysis by drying them at 450˚C for 2 hours and rinsing with DI water. Filters for internal P were acid washed in 10% HCl for at least 2 hours and pre-rinsed with DI water. A subsample of the slurry was also taken and preserved in 2% gluteraldahyde for later algal community identification (R. J. Stevenson pers. comm.). Cell carbon and nitrogen were determined by thawing and acidifying frozen filters with 1 M HCL, and then drying the filters at room temperature for 4 hours. Measurements were made with a Perkin Elmer (model 2400) CHN elemental analyzer (PerkinElmer; Waltham, MA, USA). Tissue P measurements were determined by modifying typical total phosphorus methodology (e.g. Lind 1985) to account for the acid-washed filters. First, the sample filters were autoclaved for 30 min in a 100% potassium persulfate solution (Hiriart-Baer et al. 2008). The digestant was then filtered to remove filter particles. Finally, a 50% dilution of the filtrate was analyzed for total phosphorus following Lind (1985), with measurements made on a SEAL A2Q+ Discrete Analyzer (SEAL Analytical; Mequon, WI, USA). 14 I used two methods to determine nutrient limitation based on tissue nutrient measurements. First, I calculated percent tissue phosphorus (in mg P/g dwt) and compared to the published threshold value of 0.16% P, determined by Wong and Clark (1974) as the internal phosphorus content at which P becomes limiting. Second, I calculated the molar ratios of C:P, N:P, and C:N and compared the ratios against the literature-derived threshold 550:30:1, which is the known molar ratio for benthic marine plants and macroalgae (Atkinson and Smith 1983; Hiriart-Baer et al. 2008). Benthic Algae Photosynthesis Parameters: Pulse-Amplitude-Modulated Fluorometry I used rapid light curves to calculate photosynthetic parameters to understand the light requirements and long-term light habitats of the benthic algal samples in a variety of conditions throughout our study site. A rapid light curve consists of the irradiances emitted by the PAM fluorometer vs. the electron transport rate (ETR) at each irradiance, where the ETR is derived from light-adapted maximum photosynthetic efficiency measurements. The rapid light curve is then used to derive parameters of photosynthesis, including the initial slope of the curve (alpha), the maximum electron transport rate (ETRMAX), and the light saturation index (EK). Fv/Fm is a measure of algal health and has been shown to indicate nutrient stress (Kolber and Falkowski 1995). Fv/Fm is used to calculate the curve used to derive the photosynthetic parameters of interest. α is the initial slope of the RLC curve (or P-E curve). It is a measure of how rapidly photosynthesis will increase if light is increased from low to slightly higher levels. The alpha parameter alone can be used to indicate the light availability for an algal sample; a high alpha typically indicates a low light environment while a low alpha indicates a high light 15 environment. Alpha has been hypothesized to have a direct opposite correlation with nutrient availability, with low values indicating low availability and vice versa (Schreiber et al. 2004, Higgins et al. 2008a). The light saturation index (EK) gives the light intensity at which further increases in light no longer cause increased photosynthesis and was calculated as ETRMAX/alpha. If the EK of a sample of algae is below the average light level in which that algae grows, the algal sample is light limited. If a sample is in a light environment well above their EK, the samples have sufficient light and are limited by another factor. Both alpha and EK can be used to better understand the light environment available to benthic algal samples. ETRMAX, alpha, and EK were derived from the rapid light curves calculated via PAM fluorometry. The electron transport rate (ETR) was calculated to construct each RLC using the following equation: ETR = Fv/Fm x E x 0.5 x 0.82 where Fv/Fm is the light-adapted photosynthetic yield of the sample at a particular light intensity, E is irradiance/light intensity, 0.5 accounts for the assumption that 50% of photons are absorbed by each photosystem (Schreiber 2004), and 0.82 as the proportion of PAR absorbed by the average green leaf (Bjorkman and Demmig 1987). With the ETR calculation, photosynthetic parameters were derived by plotting the calculated ETR vs. the light intensity irradiances produced by the Diving-PAM to construct the RLC. I then parameterized the RLC by fitting the curve with a two-parameter photosynthesis model, ETR = ETRMAX * tanh((alpha*E)/ ETRMAX) 16 developed by Jassby-Platt (1976), where ETRMAX is maximum photosynthesis, alpha is the initial slope of the curve (see details below), and E is irradiance/light intensity. When photoinhibition was present in the RLC, the data points after the initial maximum were removed because the light values at which photoinhibition was observed were higher than any benthic light measurements made over the course of the summer. SAS version 9.2 statistical software was used for all PAM curve fitting. Water Chemistry A series of water quality and chemistry measurements were made at each site. A Secchi disk reading and a temperature, dissolved oxygen (DO), and pH profile of the water column were taken using a Hydrolab DS5 SONDE, calibrated daily for DO (luminescent DO probe, Hach Hydromet, Loveland, CO, USA). Light measurements were taken at the surface and 1.0 m depth using a LI-192 unidirectional (downwelling) light meter (LI-COR, Lincoln, NE, USA). A 1 L water sample was collected 0.5 m below the surface in an acid-washed polyethylene bottle prerinsed with sample water. Water samples were stored at 4˚C in the dark and processed within 24 hours. Whole water samples were measured for total phosphorus and filtered to measure water column chlorophyll a, soluble reactive phosphorus, and particulate C:N ratios. Total phosphorus was determined calorimetrically after sample digestion with 5% potassium persulfate in an autoclave for 30 min (Menzel and Corwin 1965). To measure C:N ratios of filtered water column samples, filters were frozen until analysis, then thawed, acidified with 1 M HCL, and dried at room temperature for 4 hours. C:N measurements were made with a Perkin Elmer (model 2400) CHN elemental analyzer. All nutrient concentrations were analyzed at the Great Lakes Environmental Research Laboratory, Ann Arbor, MI. 17 Chlorophyll a concentrations were determined following Welshmeyer (1994). Frozen filters were extracted with 95% cold (refrigerated) ethanol for ~12 hrs. Samples were then read on a Turner Fluorometer. Concentrations were given as μg/L chlorophyll a. All chlorophyll a concentrations were analyzed at Michigan State University, East Lansing, MI. Benthic Light Calculations The benthic light was calculated at each site for each day the site was sampled as a means of assessing the light available to the benthic algal community. The benthic light calculations were based on kPAR measurements taken at each site at the time of sampling. I also calculated midday averaged benthic light based on meteorological station data at each site for each day sampled to decrease potential variability caused by changes in cloud cover. kPAR was calculated using the formula: kPAR = log(I1 – I0)/(∆z) where I1 is irradiance at 1.0 m depth, I0 is the surface irradiance, ∆z is the change in depth between the two irradiance measurements. Using the water column light measurements, I calculated instantaneous benthic light (IBL) with the formula: Iz = I0exp(-z*kPAR) where Iz is irradiance at depth z, I0 is irradiance at the surface of the water column, and kPAR is the light extinction coefficient. Iz is reported in µmol/m2-sec. Additionally, I calculated the benthic light for sites where I predicted extreme light limitation, so much so that no benthic growth was ever observed. The benthic light calculations 18 for these sites were made off of the light measurements I took outside of the range of growth where I expected light to be the primary limiting factor of algal growth. Results are reported in 2 μmol/m -sec. 2 Midday averaged light was calculated using surface shortwave radiation (W/m -hr) measurements taken by the Linwood, Michigan, Meteorological station. The meteorological station records surface shortwave radiation measurements at the end of every hour. To calculate the midday average of these measurements, the recorded measurements taken from 9:00 to 15:00 2 EST were averaged for each sampling day. Since shortwave radiation is given in kJ/m -hr, I 2 needed to convert the measurements to μmol/m -sec. These conversions were made by (1) multiplying shortwave radiation by the constant 5.03 to convert shortwave energy to quanta (Wetzel 2001), and then (2) multiplying by 0.46 to account for the proportion of PAR in shortwave radiation, according to Kirk (1994) (Hiriart-Baer et al. 2008). Once converted to 2 μmol/m -sec, these averaged surface measurements were then set as the I0 in the equation above and kPAR values remained the same as in the calculation of IBL. This sequence of calculations produced the midday average benthic light (MBL). The level of light limitation in a sample was examined by comparing EK to MBL, where EK is an intrinsic measure of the light required by individual samples and MBL is the amount of light available to the benthic community. To determine the absolute difference in the light environment required by the organism versus the light environment experienced, I analyzed the difference between the light required by the algal sample (EK) and the average light environment available to it (MBL).Using this comparison, the farther EK was below the available benthic 19 light (MBL), the more light was limiting growth. Furthermore, in order to provide an understanding of what light levels were closest to benthic algal EK measurements, I plotted (MBL - EK) versus MBL. Also, it must be considered that benthic algae has the ability to adapt to their light environment (Hill 1996). To understand the degree of adaptation, I compared EK calculations to MBL. If no adaptation strategies were utilized, EK would remain constant regardless of the level of environmental light.). If adaptation is present, EK will fluctuate with the available light environment. Adaptation can also manifest itself in the plot of (MBL – EK) versus MBL. If no adaptation strategies were utilized, then no relationship should develop between (MBL – EK) and MBL. However, if adaptation is present, a relationship between the two parameters will be observed. Statistical Analysis All statistics were run using SAS statistical software, version 9.2 (SAS Institute Inc., Cary, NC, USA). Prior to analysis, benthic algal samples were averaged across replicate and water quality samples were averaged across site location, by date. All variables were tested for normality using the Shapiro-Wilk goodness-of-fit test and transformed when necessary. Internal phosphorus was log transformed and Fv/Fm was cube transformed. Throughout the statistical analysis, I report significance when p-values were 0.05 or less. 20 Benthic Algal Parameters Aikaike’s Information Criterion, corrected for small sample size (AICC), was used to determine the best-supported model for all benthic algal measurements (maximum photosynthetic efficiency, EK, alpha, and internal P), with distance from the Saginaw River, water column depth, and substrate type as possible independent variables. Substrate type 2 2 included three groups: Chara, mussels, and miscellaneous. R and adjusted R were also calculated for each potential model. I considered any model with a ∆AICC ≤ 2 to be equally plausible (Burnham and Anderson 2002). AIC weights were also calculated. All AIC information for each potential model and dependent variable combination can be found in Tables 5 and 6. Once the best-supported model (or models) was chosen for the benthic algal dependent variables, I ran a regression of the dependent and independent variables included, and reported the model strength (p-value). Models with p>0.05 were not used in further analysis. All best-supported 2 2 models, including p-values, parameter estimates, R , and adjusted R , can be found in Table 1. The effect of distance on internal phosphorus and Fv/Fm was also analyzed on a subset of the sampled set representing all sites at a site that was measured more intensively, 3.0 m, in order to reduce potential variability introduced by depth. The 3.0 m depth contour was chosen because it represents the depth at which growth was most commonly found. Conclusions drawn from this analysis could then be extrapolated to the larger region of growth since it represents an area of high biomass, as opposed to concentrating on the boundaries of limitation only. All AIC information for each potential model and dependent variable combination can be found in Tables 21 2 7 and 8. All best-supported models, including p-values, parameter estimates, R , and adjusted 2 R , can be found in Table 2. Analysis of both the full dataset and subdata was necessary to assess potential variability introduced by depth, which could hide underlying patterns with other parameters. If parameter significance differed between the two datasets, the results of both datasets will be presented and an explanation for future research given. If the same independent variables were found to be significant in both datasets, only the larger dataset will be discussed. Water Quality Parameters To determine the existence of light and nutrient gradients extending from the mouth of the river and across depth, I ran a simple regression with water-column depth and distance from the river as independent variables for each water quality dependent variable (kPAR, SRP, TP, and Chlorophyll a). I then ran a multiple regression with both independent variables included. Models with a significance of p>0.05 were not used in further analysis. Water quality variables are subject to a large amount of variability from daily stochastic events. Nevertheless, I expected to see the light and nutrient gradients reflected in the effect of distance and depth on each water quality independent variable. On the contrary, benthic algal variables are less stochastic than water quality variables. Because of the difference in timescales captured by water quality variables versus benthic algae variables, I did not expect the water quality variables to provide explanatory power to the benthic algal variables. Therefore, I did not measure a potential relationship between the two sets of variables. 22 RESULTS Benthic Algae Tissue Nutrient Analysis Comparison to Literature-Derived Threshold Values to Determine Nutrient Limitation Percent tissue phosphorus ranged from 0.032 – 0.223 mg P/g dwt, with an average of 0.096 ± 0.009 mg P/g dwt (n=27). These internal nutrient measurements were generally lower than literature-derived thresholds for nutrient limitation, indicating severe phosphorus limitation across the entire benthic community. 38 of the 43 observations fell below 0.16 mg P/g dwt, the threshold value established by Wong and Clark (1974) for Cladophora sp. at which P is limiting, thereby deeming a majority of the samples to be P-limited (Fig. 2). Additionally, 11 samples were considered severely P-limited, falling below the tissue quota of 0.06% required for growth (Auer and Canale 1982; Fig. 2). Similar results were found concerning P-limitation with the nutrient molar ratios. The mean C:P ratio was 765 ± 240, with 15 of the 19 samples measured above the 550:1 threshold value determined by Atkinson and Smith (1983) as the onset of P-limitation for benthic marine plants and macroalgae (Fig. 3.). 18 of the 19 samples were also above the threshold N:P of 50:1 (also from Atkinson and Smith, 1983), with an average of 61 ± 17 (Fig. 4). Finally, all of the samples were well below the 18.33 threshold for C:N molar ratios at which N is limiting in benthic marine plants and macroalgae (Atkinson and Smith 1983; Fig. 5). Therefore, we did not find N to be a limiting factor for any algal samples. 23 Spatial Gradient in Tissue Phosphorus Variation in distribution of tissue phosphorus was best explained by five models, including distance (p=0.10), distance + mussels (p=0.14), distance + depth (0.17), depth (p=0.39), and mussels (p=0.23) (Table 1). Assessing model strength for each of these five bestsupported models resulted in non-significant models with p-values above 0.05 (> 0.1), indicating that tissue phosphorous was independent of depth, distance from the river, and substrate type. There was a single model that best described the effect of distance from river on internal phosphorus along the 3.0 m depth contour. It indicates that internal phosphorus significantly decreased with increasing distance from the river (n=15; p= 0.015; Fig. 6). The Effect of Distance, Depth, and Substrate on Benthic Algae Photosynthesis Parameters to Determine Light Limitation across Gradients Maximum photosynthetic efficiency (Fv/Fm) ranged from 0.093 – 0.64, with an average of 0.46 ± 0.022. The three best-supported models for the maximum photosynthetic efficiency 2 parameter included depth of the water column + Chara, with an adjusted R = 0.21, depth of the 2 2 water column + mussels (adjusted R = 0.17), and depth alone (R = 0.16). In each of the three best-supported models, maximum photosynthetic efficiency significantly increased with increasing water-column depth (p≤0.02 for all models) No effect of distance was found for any of the models containing the entire dataset. In contrast, when effect of distance was analyzed on the 3.0 m depth contour subset of data, Fv/Fm significantly decreased as distance from the river 24 increased and was found to be one of the best-supported models for the benthic algal parameter at 3.0 m depth (p=0.003; Fig. 7). Alpha ranged from 0.041 – 0.23, with an average of 0.14 ± 0.0065 (n=37). The best 2 supported models for the alpha parameter included depth + distance (adjusted R = 0.24) and 2 distance alone (R = 0.25). Alpha significantly decreased (p < 0.008 for all models, Table 1) as distance from the river increased in all three of the best-supported models (Fig. 8 for distance alone). Depth alone was a marginally significant explanatory variable for alpha, where alpha 2 increased as depth increased (R = 0.095, p=0.064). None of the four best supported models along the 3.0 m depth contour were significant for α (p > 0.3 for all models, Table 2). 2 The light saturation index (EK) ranged from 216.6 – 549.6 μmol/m -sec, with an average 2 of 380 ± 12 μmol/m -sec (n=36). A single model best supported best described variation in EK 2 that included distance from the river, water-column depth, and Chara (adjusted R = 0.27). EK significantly decreased as water column depth increased (p=0.022, Figs. 9 and 10). EK was also significantly higher when found on Chara as opposed to the two other substrate types (pvalue=0.0086). When assessing EK along the 3.0 m depth contour, a total of three models were 2 2 deemed best-supported, including Chara (R = 0.20), miscellaneous substrate types (R = 0.11) 2 and distance (R = 0.05). However, only the model containing Chara elicited a significant relationship, where EK was higher on Chara than other substrates, as seen when including all depths (p = 0.047). The other models were not significant (Table 8), and therefore the subdata do not support a significant relationship with distance. 25 2 (MBL – EK) did not have a significant relationships with depth (R =0.013, p=0.40) or 2 distance from the river (R =0.032, p=0.20), but did significantly increased with increasing MBL 2 (MBL parameter estimate = 0.74, R = 0.56, p<0.0001; Fig. 11). No data points fell lower 77 2 2 μmol/m -sec, suggesting that irradiances lower than 77 μmol/m -sec do not sustain growth. 2 Furthermore, the onset of light saturation was observed beginning near 400 μmol/m -sec as samples begin to reach an irradiance above their light requirements. However, the few samples found to be in a light environment above EK were close to the threshold of MBL – EK = 0, so was not possible to determine if these samples were, in fact, light saturated. MBL was below the 75 lower light threshold recognized in the previous analysis for all areas explored in regions I predicted the absence of algae growth was due to light limitation. At 5.0 m depth, approximately 22 km from the mouth of the river, benthic light was estimated to be 2 2 between 10 μmol/m -sec in late July to 34 μmol/m -sec in mid August. In addition, at a site approximately 5 km from the river, an area assumed to be too turbid to allow growth due to 2 proximity to the river, benthic light was estimated to be 26 μmol/m -sec at 2.0 m depth and 79 2 μmol/m -sec at 3.0 m depth. Water Quality Analysis to Support Light and Nutrient Gradients The mean and range for all measured water quality variables (kPAR, SRP, TP, and chlorophyll a) are presented in Table 3. No combination of distance or depth produced a 26 significant model (p<0.05). See Table 4 for all models, the number of observations included in 2 2 each (n), R , adjusted R , and model p-values. 27 DISCUSSION Both light and phosphorus were found to limit benthic algal growth. Across a majority of the samples, benthic algal tissue P content was far below reported thresholds for P saturation, with many samples near the amount simply required for growth. All 19 sites, which were located throughout the southwestern portion of Saginaw Bay from 2 – 4 m depths, exhibited evidence of phosphorus limitation in the benthic algal community. Furthermore, the light saturation coefficient (EK) was well above typical benthic light levels experienced by algae, thereby suggesting overall light limitation. Additionally, evidence of light adaptation was noted across depth of the water column, which complicates the ability to predict the importance of each factor. Verification of Light and Nutrient Gradients My study was designed to examine processes over predicted nutrient and light gradients extending from the Saginaw River. Results of the surface water samples showed no evidence of a gradient with distance from the river. However, this is not evidence against the predicted gradients. Instead, these results indicate that the gradients are not strong enough to be seen on a daily basis. The parameters are subject to daily stochasticity, which added enough variability to hide any potential underlying patterns. My measurements of algal internal phosphorus and fluorescence parameters indicate that these gradients are present, but in order to obtain a more detailed measurement of the presence of each gradient, daily water column sampling is necessary to calculate weekly or monthly averages, which could then be compared across spatial gradients. 28 Light as a Limiting Factor When comparing Saginaw Bay to other bays and shallow regions (e.g. shorelines) in the Great Lakes with abundant filamentous algal growth, algal growth ends at a much shallower depth than elsewhere. Lake Erie and Lake Ontario have reports of growth to at least 10 m depth and upwards of 20 m in Lake Michigan (Bootsma et al. 2005, Higgins et al. 2005, Malkin et al. 2008). This shallower growth boundary is likely due to light limitation being greater within Saginaw Bay. Although this boundary depth is much different across these different Great Lakes habitats, the light conditions at the boundary of growth are similar. I found that algae growth was limited to depths less than approximately 4 to 4.5 meters. According to Lorenz et al. (1991), 2 the minimum daily light requirement for Cladophora is 27 μmol/m -sec, which he confirmed with a series of laboratory experiments and field observations in Lake Erie. Based on his 2 calculations, a Saginaw Bay surface irradiance of 807 μmol/m -sec and a mean light attenuation -1 coefficient of 0.697 m (based on my light attenuation dataset), the maximum depth of colonization for Saginaw Bay should be approximately 4.85 m, as this is where the mean 2 summer light environment is 27 μmol/m -sec. Therefore, even though the deepest sampling point was 4.0 m, it was close to the theoretical maximum depth of colonization, suggesting that the light environment at this depth is similar to other Great Lakes systems with benthic filamentous algae. Although the light environments near the boundaries of growth are similar, the extreme difference in the depth of growth in Saginaw Bay then elsewhere is consistent with my findings that light limitation may be an issue throughout the benthos of Saginaw Bay, outlined next. 29 The premise of the study was to determine the extent to which nutrients and light conditions influence benthic algal health and resulting growth. When assessing the effect of light, the light saturation index (EK) calculations suggest that a majority of the community is in a light environment below what would be saturating; therefore, they are light limited. 90% of the samples analyzed were found in a light level below their EK, indicating that they were in subsaturating, or limiting, light environments. The 10% of samples found in light environments 2 above their EK were within 50 μmol/m -sec of this threshold, which made it impossible to evaluate the status of limitation. Overall, nearly the entire community was found to be light limited. Phosphorus as a Limiting Factor Using a combination of measurements, it was determined that (1) filamentous algae far from the river are persistently strongly limited by P, and (2) that benthic communities are acclimated to this limitation. All of the phosphorus measurements indicate an extreme level of phosphorus limitation is present throughout the benthic community. 88% of the tissue P samples fell below the requirement needed for P-saturation, according to Wong and Clark (1976), with almost 30% of those samples falling below the level required for growth established by Auer and Canale (1982). The fact that samples had P concentrations lower than the amount required for growth suggests that these samples were not growing. The algal samples were likely depleting the stores of phosphorus beyond the amount required for growth, yet were still viable. No pattern was observed among the samples to provide a cause for why the phosphorus stores were relatively low. 30 With P-limitation present throughout the community, dark-adapted maximum photosynthetic efficiency and benthic algal tissue P measurements could have a positive linear relationship since dark-adapted maximum photosynthetic efficiency have been found to indicate nutrient stress (Parkhill et al. 2001, Higgins et al. 2008a, Hiriart-Baer et al. 2008). Dark-adapted Fv/Fm is a measure of the photosynthetic capabilities of a sample and is highly affected by any limitation on the photosynthetic machinery within the sample in question (Kruskopf and Flynn 2006). However, a relationship between Fv/Fm and internal phosphorus was not found in my study. Analysis of the results on phosphorus limitation with internal phosphorus and maximum photosynthetic efficiency, in combination, indicate that phosphorus far from the river is persistently low and that benthic communities have become acclimated to this low level. Recent research suggests that when algal communities become acclimated to nutrient-limited environments, their Fv/Fm measurements do not provide a reliable indicator of nutrient stress (Parkhill et al. 2001). In fact, it has been found that phosphorus replete algal samples and samples from algal communities acclimated to low phosphorus had the same high Fv/Fm values (Fv/Fm = ~0.65; Parkhill et al. 2001). Only when the nutrient replete samples were deprived of phosphorus did a relationship with Fv/Fm exist. Therefore, the lack of a relationship between Fv/Fm and internal P suggests that the community is likely acclimated to the P-limiting nutrient levels. Light vs. Phosphorus Limitation across Gradients of Light and Nutrient Availability Light and phosphorus limitation of the benthic filamentous algae was observed to change across expected light and nutrient gradients extending from the Saginaw River. Alpha significantly decreased as distance from the river increased (Table 1). Along the 3.0 m depth 31 contour, internal phosphorus and Fv/Fm also significantly decreased as distance from the river increased (Table 2). However, the significant relationship between alpha and distance dissolves when analyzing the 3.0 m subdata, indicating that distance may have an effect on alpha, but additional research is necessary to confirm this relationship. Nevertheless, these patterns in benthic algal growth parameters illustrate the importance of examining the heterogeneity and potential gradients within benthic habitats to achieve a full understanding of benthic light environment. The change in the benthic algal parameters also suggests that the benthic community is exposed to gradients of light over time and space. Specifically, a high alpha indicates that the uptake of light is occurring at a rapid rate, inferring that light is not consistently available over time (Schreiber 2008). A low alpha measurement indicates a slow uptake of light, suggesting a more consistent or reliable source of light availability (Schreiber 2008). Therefore, the relationship between alpha and distance from the river suggests that close to the river, light availability is sporadic, while farther from the river, light availability is more constant over time. Variations of EK provide a deeper understanding of the level of light limitation across Saginaw Bay and may even suggest that the algal community is employing adaptation strategies to the low light availability. I predicted that EK and (MBL - EK) would vary as a function of light level in the benthos, which would decrease as a function of depth and proximity to the river. I could not evaluate the relationship between (MBL – EK) and distance from the river. However, the results indicate no effect of depth on (MBL – EK), suggesting that the benthic samples were adapted to the low-light environment. To clarify, EK is an intrinsic measure of the light required by individual samples. EK significantly decreases with depth, thereby suggesting that algae at 32 deeper depths inherently require less light to survive than shallow depths; this suggests that the algae at deeper depths have acclimated to a lower light environment than algae at shallower depths. Moreover, the lack of a relationship between (MBL - EK), and depth suggests that intrinsic needs of the algae parallel the environmental light available to those individual samples. Therefore, the EK findings suggest that the community is light limited and provide more information about the level of light limitation and potential adaptation. The effect of distance on the benthic algal parameters provides additional insight into phosphorus availability and the influence of the Saginaw River outputs. The negative relationship between internal phosphorus and distance from the river suggests that near the mouth of the river, phosphorus is more readily available than sites far from the river. This relationship between phosphorus and proximity to the river indicates that the Saginaw River has a significant effect on benthic growth requirements, which may help to guide local management efforts for future research and development of effective policy. The 3.0 m depth contour data also indicates that conditions near the mouth of the river produce algae that is relatively healthier, or less stressed by its environment, then sites farther away from the river, thereby suggesting that conditions closer to the mouth of the river are more amenable to benthic filamentous algal growth (assuming enough light is available for growth). The basis for this gradient with distance and algal health is found in the effect of distance from the river on Fv/Fm. A number of conditions could cause this gradient to develop, including (but not limited to) differences in light availability, phosphorus, nitrogen, habitat type, or water movement (Hill 1996). In a larger sense, the relationship with health and proximity to the river suggests that inputs from the river have a substantial effect on algal growth, which can be helpful when designing effective management strategies to alleviate the benthic algal growth. 33 CONCLUSION This study indicates that the entire region of filamentous benthic algal growth in the southwestern portion of Saginaw Bay is both light and phosphorus limited. Furthermore, this study illustrates that important relationships exist between benthic algal parameters and expected light and nutrient gradients in Saginaw Bay and also shows that the Saginaw River as a key influence on algal health. In a larger context, this study aided in the understanding of how benthic algal growth parameters can change along gradients throughout a particular study system. With this result in mind, researchers can now work toward identifying the factors (light or nutrients, or both) that have the greatest effect on benthic growth in many areas of a heterogeneous environment. This detailed assessment of a benthic ecosystem can then be used to guide management efforts in controlling growth and subsequential shoreline fouling events. Although reducing phosphorus may seem to be the easy solution given historic successes, the Great Lakes benthos has become more complex and unpredictable, making research more vital than ever to understand how to protect and manage these crucial freshwater systems. 34 APPENDICES 35 APPENDIX A – Figures and Tables for Chapter I Figure 1 – A map of the inner bay of Saginaw Bay, Lake Huron demarcating sampling sites and transects. The numbering scheme is based off of Skubinna et al. (1995). 36 Auer and Canale (1982) found 0.06% to stop growth in Cladophora 0.0000 0.0500 Wong and Clark (1974) found 0.16% tissue P to be limiting in Cladophora 0.1000 0.1500 0.2000 0.2500 % Tissue Phosphorus (mg P/g dwt) Figure 2. % Tissue phosphorus (mg P/g dwt) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of phosphorus limiting tissue concentrations (n = 27). 37 Atkinson and Smith (1983) found the onset of Plimitation to be 550:1 0 200 400 600 800 1000 1200 1400 Tissue C:P (mol C/mol P) Figure 3. Tissue Carbon:Phosphorus (mol C/mol P) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of phosphorus-limiting tissue concentrations (n = 19). 38 Atkinson and Smith (1983) found the onset of P-limitation to be 50:1 0 20 40 60 80 100 120 Benthic Algal N:P (mol N/mol P) Figure 4. Tissue Nitrogen:Phosphorus (mol N/mol P) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of phosphorus-limiting tissue concentrations (n = 19). 39 Atkinson and Smith (1983) found the onset of P-limitation to be 18.33:1 0 5 10 15 20 Benthic Algal C:N (mol C/mol N) Figure 5. Tissue Carbon:Nitrogen (mol C/mol N) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron compared to published values of nitrogen-limiting tissue concentrations (n = 35). 40 0 10 20 30 40 0 -0.2 -0.4 Log Transformed -0.6 Internal Phosphorus -0.8 (mg P/g dwt) -1 -1.2 R² = 0.4038 -1.4 Distance from the Saginaw River along 3.0 m depth contour (km) Figure 6. Internal phosphorus (log transformed, mg P/g dwt) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River (km) along a 3.0 m depth contour (n=15). 41 0.25 0.2 Maximum 0.15 Phosynthetic Efficiency 0.1 (unitless) R² = 0.505 0.05 0 0 10 20 30 40 Distance from the Saginaw River (km) Figure 7. Maximum photosynthetic efficiency (cube transformed; unitless) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River (km) along a 3.0 m depth contour (n=19). 42 0.25 0.2 Alpha 0.15 (ETR/μmol/ m2-sec) 0.1 R² = 0.2533 0.05 0 0 10 20 30 40 Distance from the Saginaw River (km) 2 Figure 8. Alpha (ETR/μmol/ m -sec) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River (km) for the best supported model including distance only (n=34). 43 600 500 EK 400 (μmol/m2- 300 sec ) 200 R² = 0.0884 100 0 0 1 2 3 4 5 Water Column Depth (m) 2 Figure 9. Light saturation index (EK; μmol/ m -sec) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over depth of the water column (m) for the best supported model including depth of the water column (m), distance from the Saginaw River (km), and the substrate type Chara (n=34). 44 600 500 400 EK 2(μmol/m 300 sec ) 200 R² = 0.0279 100 0 0 10 20 30 40 Water Column Depth (m) 2 Figure 10. Light saturation index (EK; μmol/ m -sec) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over distance from the Saginaw River (km) for the best supported model including depth of the water column (m), distance from the Saginaw River (km), and the substrate type Chara (n=34). 45 0 100 200 100 0 MBL - EK -100 (μmol/m2- -200 sec) -300 -400 -500 200 300 400 500 R² = 0.5555 MBL (μmol/m2-sec) 2 Figure 11. Midday averaged benthic light (MBL; μmol/ m -sec) minus the light saturation index 2 (EK; μmol/ m -sec) of benthic filamentous algae in the inner bay of Saginaw Bay, Lake Huron over the midday averaged benthic light (n=59). 46 TABLE 1. Table of best-supported models for all benthic algal dependent variables Benthic Algae Parameter Coeff. Var R 0.10 -16.54 0.11 -- -0.015 (p=0.10) distance + mussels 0.14 -16.41 0.17 0.09 -0.015 (p=0.084) distance + depth 0.17 -16.59 0.15 0.07 -0.014 (p=0.12) Depth 0.39 -18.94 0.03 -- -- Mussels 0.23 -18.66 0.06 -- -- depth + chara 0.026 49.77 0.20 0.15 -- depth 0.016 50.22 0.16 -- -- depth + mussels 0.020 49.34 0.22 0.17 -- Distance 0.0024 25.18 0.25 -- distance + depth Fv/Fm Model pvalue Distance Internal Phosphorus Models 0.0053 25.00 0.29 distance + depth + chara 0.0058 17.29 0.34 2 Depth Adj. Distance Parameter Parameter 2 R Estimate Estimate Chara Parameter Estimate Mussels Parameter Estimate -- -- -- -- -- -0.086 (p=0.25) -- -- -- -- -- -- -0.099 (p=0.23) 0.055 (p=0.02) 0.058 (p=0.016) -0.02556 (p=0.2152) -- -- -- 0.05206 (p=0.029) -- 0.03125 (p=0.15) -0.0030 (p=0.0024) -- -- -- 0.24 -0.0027 (p=0.0078) 0.017 (p=0.23) -- -- 0.27 -4.05 (p=0.313) -62.91 (p=0.022) 65.50 (p=0.0086) -- Alpha EK 47 0.085 (p=0.36) 0.08922 (p=0.39) TABLE 2. Table of best-supported models for all benthic algal dependent variables along a 3.0 m depth contour Benthic Algae Variable Model Model pvalue Coeff. Var R Distance Chara Mussels Misc Parameter Parameter Parameter Parameter Estimate Estimate Estimate Estimate IP (3.0 m) distance 0.015 -12.74 0.40 -0.029 (p=0.015) -- -- -- Fv/Fm (3.0 m) distance 0.003 24.29 0.51 -0.0081 (p=0.003) -- -- -- misc 0.297 21.02 0.06 -- -- -- -0.017 (p=0.30) mussels 0.316 21.07 0.06 -- -- 0.016 (p=0.32) -- distance 0.363 22.31 0.06 -0.0019 (p=0.36) -- -- -- chara 0.986 21.68 0.00 -- -0.00026 (p=0.99) -- -- misc 0.157 18.97 0.11 -- -- -- -52.3 (p=0.16) chara 0.047 17.94 0.20 -- 62.2 (p=0.047) -- -- distance 0.366 19.99 0.05 -4.09 (p=0.37) -- -- -- 2 Alpha (3.0 m) EK (3.0 m) 48 TABLE 3. Summary table of all water quality variables measured (n=number of water column samples analyzed) Water Quality Range (after Variable Transformed? transformation) ShapiroWilk p-value Units Mean Median n 0.88 0.49 0.143 7.47 0.148 7.91 37 21 0.9995 377.15 380.83 36 0.29 0.43 0.47 19 2 Alpha Chlorophyll a EK No No 0.1 - 0.23 4.03 - 12.32 ETR/μmol/m sec μg chla/L No 216.6 - 549.57 μmol/m -sec KPAR Midday Benthic Light Fv/Fm SRP Total Phosphorus Yes - Cubed 0.07 - 0.82 Yes - Log Yes - Cubed No 1.88 - 2.64 0.0008 - 0.27 0.17 - 1.5 μmol/m -sec (unitless) μg P/L 0.2183 0.53 0.14 2.22 0.12 0.96 2.18 0.13 0.93 18 35 19 No 6.73 - 16.72 μg TP/L 0.45 10.62 11.03 17 2 m -1 2 49 TABLE 4. Summary table of all water quality variables of models including water column depth and/or distance from the Saginaw River. Water Model Distance Depth Adj. Coeff. 2 Quality Model pParameter Parameter 2 R Var R Variable value Estimate Estimate -0.87 depth 0.42 29.59 -0.02 --(p=0.42) Chlorophyll 0.033 distance 0.68 31.45 -0.05 --a (p=0.68) 0.024 -0.81 distance + depth 0.72 31.93 -0.08 35.82 (p=0.78) (p=0.50) -0.093 depth 0.44 56.17 -0.03 --(p=0.44) 0.0032 distance 0.72 57.97 -0.06 --kPAR (p=0.72) 0.0022 -0.089 distance + depth 0.73 58.95 -0.09 -43.70 (p=0.81) (p=0.49) 0.52 depth 0.71 20.33 -0.06 --(p=0.71) Total -0.11 distance 0.19 20.98 0.05 --Phosphorus (p=0.19) -0.12 -0.37 distance + depth 0.42 21.00 -0.01 31.33 (p=0.21) (p=0.81) depth Soluble Reactive Phosphorus 0.89 32.20 -0.07 -- distance 0.15 31.92 0.07 -- distance + depth 0.34 32.82 0.02 -36.60 50 -0.018 (p=0.15) 0.020 (p=0.15) -0.027 (p=0.89) -0.093 (p=0.67) TABLE 5. Table of all possible models in the best-model selection for internal phosphorus and maximum photosynthetic efficiency variables Models depth + distance + chara + misc depth + distance + chara + mussels depth + distance + misc + mussels depth + distance + chara depth + distance + misc depth + distance + mussels depth + chara + misc depth + chara + mussels depth + misc + mussels distance + chara + misc distance + chara + mussels distance + misc + mussels depth + chara depth + mussels depth + misc distance + depth distance + chara distance + mussels distance + misc chara + mussels chara + misc mussels + misc R 2 Internal P 2 Adj. R AICC wi Maximum Photosynthetic Efficiency 2 2 R Adj. R AICC wi 0.2223 0.0667 -77.393 0.008 0.2718 0.1598 -167.995 0.012 0.2223 0.0667 -77.393 0.008 0.2718 0.1598 -167.995 0.012 0.2223 0.1591 0.1786 0.2112 0.1056 0.1056 0.1056 0.1716 0.1716 0.1716 0.0554 0.0975 0.0691 0.1466 0.1245 0.165 0.1354 0.0443 0.0443 0.0443 0.0667 0.0389 0.0613 0.0985 -0.0222 -0.0222 -0.0222 0.0533 0.0533 0.0533 -0.0305 0.0155 -0.0156 0.069 0.0449 0.0891 0.0568 -0.0425 -0.0425 -0.0425 -77.393 -78.948 -79.538 -80.547 -77.408 -77.408 -77.408 -79.325 -79.325 -79.325 -79.199 -80.342 -79.565 -81.738 -81.098 -82.283 -81.414 -78.909 -78.909 -78.909 0.008 0.017 0.023 0.038 0.008 0.008 0.008 0.021 0.021 0.021 0.020 0.035 0.024 0.070 0.051 0.092 0.059 0.017 0.017 0.017 0.2718 0.2706 0.2167 0.2438 0.2624 0.2624 0.2624 0.1466 0.1466 0.1466 0.2619 0.2231 0.2001 0.2105 0.1355 0.1293 0.0705 0.1038 0.1038 0.1038 0.1598 0.1896 0.1296 0.1598 0.1804 0.1804 0.1804 0.0518 0.0518 0.0518 0.2092 0.1676 0.143 0.1541 0.0738 0.0671 0.0041 0.0398 0.0398 0.0398 -167.995 -171.044 -168.832 -169.927 -170.696 -170.696 -170.696 -166.177 -166.177 -166.177 -173.538 -171.947 -171.044 -171.451 -168.638 -168.414 -166.390 -167.520 -167.520 -167.520 0.012 0.055 0.018 0.031 0.046 0.046 0.046 0.005 0.005 0.005 0.191 0.086 0.055 0.067 0.016 0.015 0.005 0.009 0.009 0.009 51 TABLE 5 (cont’d) Models depth distance chara mussels misc R 2 0.0442 0.1126 0.0103 0.0404 0.0156 Internal P 2 Adj. R AICC wi 0.0027 0.0741 -0.0327 -0.0013 -0.0272 -81.763 -83.621 -80.892 -81.665 -81.025 0.071 0.179 0.046 0.067 0.049 52 Maximum Photosynthetic Efficiency 2 2 R Adj. R AICC wi 0.1881 0.1601 -173.232 0.164 0.0703 0.0382 -169.031 0.020 0.0962 0.065 -169.907 0.031 0.0693 0.0372 -169.000 0.020 0.0042 -0.0302 -166.903 0.007 TABLE 6. Table of all possible models in the best-model selection for alpha and light saturation coefficient variables Models depth + distance + chara + misc depth + distance + chara + mussels depth + distance + misc + mussels depth + distance + chara depth + distance + misc depth + distance + mussels depth + chara + misc depth + chara + mussels depth + misc + mussels distance + chara + misc distance + chara + mussels distance + misc + mussels depth + chara depth + mussels depth + misc distance + depth distance + chara distance + mussels distance + misc chara + mussels chara + misc mussels + misc 0.2878 Alpha 2 Adj. R AICC 0.1896 -217.012 wi 0.010 Light Saturation Coefficient 2 R Adj. R AICC wi 0.3537 0.2646 292.4782 0.091032 0.2878 0.1896 -217.012 0.010 0.3537 0.2646 292.4782 0.091032 0.2878 0.2875 0.2871 0.2878 0.1121 0.1121 0.1121 0.2572 0.2572 0.2572 0.1111 0.1021 0.1074 0.2871 0.2539 0.257 0.2548 0.0157 0.0157 0.0157 0.1896 0.2163 0.2158 0.2166 0.0234 0.0234 0.0234 0.1829 0.1829 0.1829 0.0538 0.0442 0.0498 0.2411 0.2057 0.2091 0.2067 -0.0478 -0.0478 -0.0478 -217.012 -219.965 -219.945 -219.978 -212.483 -212.483 -212.483 -218.548 -218.548 -218.548 -215.206 -214.864 -215.065 -222.707 -221.159 -221.303 -221.2 -211.741 -211.741 -211.741 0.010 0.045 0.045 0.046 0.001 0.001 0.001 0.022 0.022 0.022 0.004 0.004 0.004 0.179 0.082 0.089 0.084 0.001 0.001 0.001 0.3537 0.3372 0.2651 0.1824 0.2322 0.2322 0.2322 0.2094 0.2094 0.2094 0.2244 0.1121 0.1622 0.1627 0.2073 0.071 0.0887 0.1475 0.1475 0.1475 0.2646 0.2709 0.1916 0.1006 0.1554 0.1554 0.1554 0.1304 0.1304 0.1304 0.1744 0.0548 0.1081 0.1087 0.1562 0.0111 0.0299 0.0925 0.0925 0.0925 292.4782 290.3688 293.8767 297.5038 295.3693 295.3693 295.3693 296.3617 296.3617 296.3617 292.9467 297.5461 295.5708 295.5501 293.6877 299.0832 298.4293 296.162 296.162 296.162 2 R 53 2 0.091032 0.261371 0.04524 0.007378 0.021449 0.021449 0.021449 0.013059 0.013059 0.013059 0.072021 0.007223 0.019393 0.019595 0.049723 0.003349 0.004644 0.01443 0.01443 0.01443 TABLE 6 (cont’d) Models depth distance chara mussels misc 2 R 0.1011 0.2533 0.0149 0.0089 0.0013 Alpha 2 Adj. R AICC --217.406 --223.712 --214.291 --214.087 --213.825 54 wi 0.013 0.295 0.003 0.002 0.002 Light Saturation Coefficient 2 R Adj. R AICC wi 0.0932 0.0648 295.683 0.018335 0.0279 -0.0024 298.0447 0.005629 0.1465 0.1198 293.6218 0.051389 0.0384 0.0083 297.6777 0.006763 0.0481 0.0183 297.333 0.008035 2 TABLE 7. Table of all possible models in the best-model selection for internal phosphorus and maximum photosynthetic efficiency variables along a 3.0 meter depth contour in Saginaw Bay, Lake Huron Models distance + chara + misc distance + chara + mussels distance + misc + mussels distance + chara distance + mussels distance + misc chara + mussels chara + misc mussels + misc distance chara mussels misc R 2 0.4449 0.4449 0.4449 0.4447 0.4232 0.4126 0.0608 0.0608 0.0608 0.4038 0.0583 0.0367 0.0069 Internal P 2 Adj. R AICC wi 0.2784 0.2784 0.2784 0.3438 0.3183 0.3058 -0.1099 -0.1099 -0.1099 0.3542 -0.0202 -0.0435 -0.0758 -45.035 -45.035 -45.035 -49.696 -49.163 -48.908 -42.339 -42.339 -42.339 -52.520 -46.120 -45.802 -45.376 0.013 0.013 0.013 0.136 0.104 0.092 0.003 0.003 0.003 0.559 0.023 0.019 0.016 55 Maximum Photosynthetic Efficiency 2 2 R Adj. R AICC wi 0.5137 0.381 -90.862 0.011 0.5137 0.381 -90.862 0.011 0.5137 0.381 -90.862 0.011 0.5137 0.4326 -95.529 0.109 0.5091 0.4273 -95.388 0.102 0.5068 0.4246 -95.319 0.098 0.0378 -0.1226 -85.294 0.001 0.0378 -0.1226 -85.294 0.001 0.0378 -0.1226 -85.294 0.001 0.505 0.4669 -99.082 0.645 0.0007 -0.0762 -88.544 0.003 0.0261 -0.0488 -88.931 0.004 0.025 -0.05 -88.914 0.004 TABLE 8. Table of all possible models in the best-model selection for alpha and light saturation coefficient variables along a 3.0 meter depth contour in Saginaw Bay, Lake Huron Models distance + chara + misc distance + chara + mussels distance + misc + mussels distance + chara distance + mussels distance + misc chara + mussels chara + misc mussels + misc distance chara mussels misc 2 R 0.0923 0.0923 0.0923 0.0562 0.0834 0.0775 0.0424 0.0424 0.0424 0.0555 0 0.028 0.0296 Alpha 2 Adj. R AICC -0.1171 -105.297 -0.1171 -105.297 -0.1171 -105.297 -0.0786 -108.755 -0.0475 -109.252 -0.0543 -109.142 -0.0944 -108.508 -0.0944 -108.508 -0.0944 -108.508 -0.0075 -112.228 -0.0666 -111.259 -0.0368 -111.741 -0.0351 -111.769 56 wi 0.007 0.007 0.007 0.040 0.052 0.049 0.036 0.036 0.036 0.229 0.141 0.179 0.182 Light Saturation Coefficient 2 R Adj. R AICC wi 0.1881 0.0007 154.5527 0.010246 0.1881 0.0007 154.5527 0.010246 0.1881 0.0007 154.5527 0.010246 0.1849 0.0685 150.4975 0.077825 0.0858 -0.0448 152.4486 0.029339 0.1101 -0.017 151.99 0.0369 0.1491 0.0275 151.2289 0.053988 0.1491 0.0275 151.2289 0.053988 0.1491 0.0275 151.2289 0.053988 0.0549 -0.0081 149.5272 0.126425 0.1442 0.0872 147.8391 0.294035 0.0309 -0.0338 149.9539 0.102135 0.0667 0.0044 149.3141 0.14064 2 APPENDIX B – GPS COORDINATES OF SAMPLING SITES AND TRANSECTS Transect Depth (m) Latitude Longitude T7.75 3.00 43.54.192N 83.51.385W T8 2.25 43.53.867N 83.52.824W T8 3.00 43.53.627N 83.52.086W T8 3.75 43.53.365N 83.51.200W T9 2.75 43.52.325N 83.53.184W T9 3.00 43.52.103N 83.52.172W T9 3.25 43.52.045N 83.51.959W T10 3.00 43.50.782N 83.51.884W T10.5 3.00 43.49.643N 83.52.550W T11 2.50 43.48.634N 83.53.710W T11 3.00 43.48.628N 83.53.545W T11 3.75 43.48.633N 83.53.110W T11.5 3.00 43.49.670N 83.53.177W T12 2.75 43.47.671N 83.54.190W T12 3.00 43.47.636N 83.53.842W T12 3.50 43.47.623N 83.53.800W T12.5 3.00 43.46.204N 83.54.691W T13 2.00 43.44.580N 83.55.514W T15 4.00 43.41.383N 83.51.325W 57 WORKS CITED 58 WORKS CITED Atkinson, M. J., & Smith, S. V. (1983). C:N:P ratios of benthic marine plants. Limnology and Oceanography, 28(3), 568 – 574. Auer, M. T., & Canale, R. P. (1982). Ecological studies and mathematical modeling of Cladophora in Lake Huron: 3. The dependence of growth rates on internal phosphorus pool size. Journal of Great Lakes Research, 8(1), 93 – 99. Auer, M. T., Tomlinson, L. M., Higgins, S. N., Malkin, S. Y., Howell, E. T., & Bootsma, H. A. (2010). Great Lakes Cladophora in the 21st century: Same algae – different ecosystem. Journal of Great Lakes Research, 36, 248 – 255. Beeton, A. M. (1966). Indices of Great Lakes eutrophication. In Proc. 9th Conference on Great Lakes Research, Great Lakes Research Division Publication No. 15. (pp. 1 – 8). Ann Arbor: University of Michigan. Bierman, V. J., Dolan, D. M., Kasprzyk, R., & Clark, J. L. (1984). Retrospective analysis of the response of Saginaw Bay, Lake Huron, to reductions in phosphorus loadings. Environmental Science & Technology 18, 23-31. Bjorkman, O. & Demmig, B. (1987). Photon yield of O2 evolution and chlorophyll fluorescence characteristics at 77 K among vascular plants of diverse origins. Planta, 170(4), 489 – 504. Bootsma, H. A., Jensen, E. T., Young, E. B. & Berges, J. A. (2004). Introduction. In Cladophora Research and Management in the Great Lakes (Workshop Proceedings). GLWI Special Report No. 2005-01. (pp. 1 – 4). University of Wisconsin-Milwaukee: Great Lakes Water Institute. Bootsma, H. A., Young, E. B. & Berges, J. A. (2005). Temporal and spatial patterns of Cladophora biomass and nutrient stoichiometry in Lake Michigan. In Cladophora Research and Management in the Great Lakes (Workshop Proceedings). GLWI Special Report No. 2005-01. (pp. 81 – 88). University of Wisconsin-Milwaukee: Great Lakes Water Institute. Bootsma, H. A., Young, E. B., & Berges, J. A. (2006). Cladophora abundance and physical/chemical conditions in the Milwaukee Region of Lake Michigan. GLWI Technical Report No. 2005 – 02. University of Wisconsin-Milwaukee: Great Lakes Water Institute. 59 Bridgeman, T. B., Fahnenstiel, G. L., Lang, G. A., & Nalepa, T. F. (1995). Zooplankton grazing during the zebra mussel (Dreissena polymorpha) colonization of Saginaw Bay, Lake Huron. Journal of Great Lakes Research, 21, 567-573. Burnham, K. P. & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. New York: Springer. Byappanahalli, M. N., Shively, D. A., Nevers, M. B., Sadowsky, M. J., & Whitman, R. L. (2003). Growth and survival of Escherichia coli and enterococci populations in the macro-alga Cladophora (Chlorophyta). FEMS Microbiology Ecology 46, 203-211. Cecala, R. K., Mayer, C. M., Schultz, K. L., & Mills, E. L. (2008). Increased benthic algal primary production in response to the invasive zebra mussel (Dreissena polymorpha) in a productive ecosystem, Oneida Lake, New York. Journal of Integrative Plant Biology, 50, 1452-1466. Danek, L. J., & Saylor, J. H. (1977). Measurements of the summer currents in Saginaw Bay, Michigan. Journal of Great Lakes Research, 3(1-2), 65-71. Davis, C. C. (1969). Plants in Lake Erie and Ontario, and changes of their numbers and kinds. In Proc. Of the conference on changes in the biota of Lakes Erie and Ontario, April 16-17, 1968. Ed. R. A. Sweeny. Bulletin of the Buffalo Society of Natural Sciences 25(1), 18-44. Davis, C.O., & Simmons, M. S. (1979). Water chemistry and phytoplankton field and laboratory procedures. Special Report No. 70. Great Lakes Research Division, University of Michigan, Ann Arbor, MI. Fahnenstiel, G. L., Lang, G. A., Nalepa, T. F., & Johengen, T.H. (1995). Effects of zebra mussel (Dreissena polymorpha) colonization on water quality parameters in Saginaw Bay, Lake Huron. Journal of Great Lakes Research, 21, 435-448. Falkowski, P. G., & Kolber, Z. S. (1995). Variations in chlorophyll fluorescence yields in phytoplankton in the world oceans. Aust. J. Plant. Physiol., 22, 341-355. Fetterolf, C. N. (1961). Memorandum: deposits of organic material on the beaches in the Bay City-Linwood area. (pp. 1). MI Dept. of Natural Resources. Gerstein, H. H. (1965). Lake Michigan pollution and Chicago’s supply. Journal of the American Water Works Association, 57(7), 841-857. Hecky, R. E., Smith, R. E. H., Barton, D. R., Guildford, S. J., Taylor, W. D., Charlton, M. N., & Howell, T. (2004). The nearshore phosphorus shunt: A consequence of ecosystem 60 engineering by dreissenids in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci., 61, 1285 – 1293. Higgins, S. N., Hecky, R. E., & Guildford, S. J. (2005). Modeling the growth, biomass, and tissue phosphorus concentration of Cladophora glomerata in eastern Lake Erie: Model description and field testing. Journal of Great Lakes Research, 31, 439-455. Higgins, S. N., Hecky, R. E., & Guildford, S. J. (2008a). The collapse of benthic macroalgal blooms in response to self-shading. Freshwater Biology, 53, 2557 – 2572. Higgins, S. N., Malkin, S. Y., Howell, E. T., Guildford, S. J., Campbell, L., Hiriart-Baer, V., & Hecky, R. E. (2008b). An ecological review of Cladophora glomerata (Chlorophyta) in the Laurentian Great Lakes. Journal of Phycology, 44, 1- 16. Hill, W. R. (1996). Factors affecting benthic algae: Effects of light. In R. J. Stevenson, M. Bothwell, & R. Lowe (Eds.), Algal ecology: Freshwater benthic ecosystems (pp. 121 – 148). San Diego: Academic Press. Hiriart-Baer, V.P., Arciszewski, T. J., Malkin, S. Y., Guildford, S. J., & Hecky, R. E.. (2008). Use of pulse-amplitude-modulated fluorescence to assess the physiological status of Cladophora sp. along a water quality gradient. Journal of Phycology, 44, 1604-1613. Jassby, A. D., & Platt, T. (1976). Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnology and Oceanography, 21(4), 540 – 547. Johannsson, O. E., Dermott, R., Graham, D. M., Dahl, J. A., & Millard, E. S. (2000). Benthic and pelagic secondary production in Lake Erie after the invasion of Dreissena spp. With implications for fish production. Journal of Great Lakes Research, 26, 31 – 54. Kenaga, D. (1978). Memorandum: Bay City state park beach residue. (pp. 1-6). MI Dept. of Natural Resources. Kolber, Z., Zehr, J. & Falkowski, P. (1988). Effects of growth irradiance and nitrogen limitation on photosynthetic energy conversion in photosystem II. Plant Physiology, 88, 923–9. Kruskopf, M., & Flynn, K. J. (2006). Chlorophyll content and fluorescence responses cannot be used to gauge reliably phytoplankton biomass, nutrient status or growth rate. New Phytologist, 169, 525-536. Lind, O. T. (1985). Handbook of common methods in limnology. St. Louis: Mosby. 61 Litteral, R. L., Pillsbury, R. W., & Lowe, R. L. (1995). The response of the benthic algal community of Saginaw Bay, near the Charity Islands, to changes in light penetration. In Proceedings of The Fifth International Zebra Mussel and Other Aquatic Nuisance Organisms Conference, (pp. 275-290). Toronto, Canada. Lorenz, R. C., Monaco, M. E., & Herdendorf, C. E. (1991). Minimum light requirements for substrate colonization by Cladophora glomerata. Journal of Great Lakes Research, 17(4), 536 – 542. Lowe, R. L. & Pillsbury, R. W. (1995). Shifts in benthic algal community structure and function following the appearance of zebra mussels (Dreissena polymorpha) in Saginaw Bay, Lake Huron. Journal of Great Lakes Research, 21, 558-566. Malkin, S. Y., Guildford, S. J., and Hecky, R. E. (2008). Modeling the growth response of Cladophora in a Laurentian Great Lake to the exotic invader Dreissena and to lake warming. Limnology and Oceanography, 53(3), 1111-1124. Malkin, S. Y., Dove, A., Depew, D., Smith, R. E., Guildford, S. J., & Hecky, R. E. (2010). Spatiotemporal patterns of water quality in Lake Ontario and their implications for nuisance growth of Cladophora. Journal of Great Lakes Research, 36, 477 – 489. Menzel, D.W., & Corwin, N. (1965). The measurement of total phosphorus liberated in seawater based on the liberation of organically bound fractions by persulfate oxidation. Limnology Oceanography, 10, 280-281. Millie, D. F., Weckman, G. R., Pigg, R. J., Tester, P. A., Dyble, J., Litaker, R. W., Carrick, H. J., & Fahnenstiel, G.L. (2006). Modeling phytoplankton abundance in Saginaw Bay, Lake Huron: using artificial neural networks to discern functional influence of environmental variables and relevance to a Great Lakes observing system. Journal of Phycology 42, 336-349. Mills, E. L., Rosenberg, G., Spidle, A. P., Ludyanskiy, M., Pligin, Y., & May, B. (1996). A review of the biology and ecology of the quagga mussel (Driessena bugensis), a second species of freshwater dreissenid introduced to North America. Amer. Zool., 36, 271 – 286. Nalepa, T. F. & Fahnenstiel, G. L. (1995). Dreissena polymorpha in the Saginaw Bay, Lake Huron ecosystem: Overview and perspective. Journal of Great Lakes Research 21, 411416. Nalepa, T. F., D. L. Fanslow, M. B. Lansing, G. A. Lang, M. Ford, G. Gostenik, and D. J. Hartson. (2002). Abundance, biomass, and species composition of benthic 62 macroinvertebrate populations in Saginaw Bay, Lake Huron, 1987-96. (pp. 32). In U. S. D. o. Commerce, (Eds.). National Oceanic and Atmospheric Administration. Nalepa, T. F., Fanslow, D. L., Lansing, M. B., & Lang, G. A. (2003). Trends in the benthic macroinvertebrate community of Saginaw Bay, Lake Huron, 1987 to 1996: Responses to phosphorus abatement and the zebra mussel, Dreissena polymorpha. Journal of Great Lakes Research 29, 14-33. Nalepa, T. F., Wojcik, J. A., Fanslow, D. L., & Lang, G.A. (1995). Initial colonization of the zebra mussel (Dreissena polymorpha) in Saginaw Bay, Lake Huron: Population recruitment, density, and size structure. Journal of Great Lakes Research 21, 417-434. Neil, J. H., & Jackson, M. B. (1982). Monitoring Cladophora growth conditions and the effect of phosphorus additions at a shoreline site in northeastern Lake Erie. Journal of Great Lakes Research, 8(1), 30-34. Nicholls, K. H., Hopkins, G. J., Standke, S. J., & Nakamoto, L. (2001). Trends in total phosphorus in Canadian near-shore waters of the Laurentian Great Lakes: 1976 – 1999. Journal of Great Lakes Research, 27(4), 402 – 422. Ozersky, T., Malkin, S. Y., Barton, D. R., & Hecky, R. E. (2009). Dreissenid phosphorus excretion can sustain C. glomerata growth along a portion of Lake Ontario shoreline. Journal of Great Lakes Research, 35, 321 – 328. Parkhill, J. P., Maillet, G., & Cullen, J. J. (2001). Fluorescence-based maximal quantum yield for PSII as a diagnostic of nutrient stress. Journal of Phycology, 37, 517 – 529. Pillsbury, R. W., Lowe, R. L., Pan, Y. D., & Greenwood, J. L. (2002). Changes in the benthic algal community and nutrient limitation in Saginaw Bay, Lake Huron. Journal of the North American Benthological Society, 21(2), 238 – 252. Rose, J. B., Alexander, C., Bolen, T., Dyble, J., Joseph, S., Lipar, D., Strasz, J., Tenbusch, G., Wade, D., & Wade, T. (2007). Saginaw Bay Coastal Initiative: Potential public health risks associated with pathogens in detritus material ("muck") in Saginaw Bay. The Saginaw Bay Science Committee Pathogen Work Group. Schreiber, U. (2004). Pulse-amplitude-modulation (PAM) fluorometry and saturation pulse method: An overview. In G. Papageorgiou & Govindjee (Eds.), A signature of photosynthesis (pp. 279 – 319). The Netherlands: Springer. Skubinna, J. P., Coon, T. G., & Batterson, T. R. (1995). Increased abundance and depth of submersed macrophytes in response to decreased turbidity in Saginaw Bay, Lake Huron. Journal of Great Lakes Research, 21(4), 476 – 488. 63 Sloss, P.W. & Saylor, J. H. (1975). Measurements of current flow during summer in Lake Huron. NOAA Tech. Rep ERL 353 GLERL 5. Verhougstraete, M. P., Byappanahalli, M. N., Rose, J. B., & Whitman, R. L. (2010). Cladophora in the Great Lakes: Impacts on beach water quality and human health. Water Science and Technology, 62(1), 68 – 76. Welschmeyer, N. A. (1994). Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39, 1985 – 1992. Wetzel, R. G. (2001). Limnology: Lake and reservoir ecosystems. San Diego: Academic Press. Wong, S. L., & Clark, B. (1976). Field determination of the critical nutrient concentrations for Cladophora in streams. J. Fish. Res. Board Can., 33, 85 – 92. 64