Nutritional stoichiometry and growth of filamentous green algae (Family Zygnemataceae) in response to variable nutrient supply

A Thesis Submitted to the Committee on Graduate Studies in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Faculty of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada

© Copyright by Colleen Middleton 2014

Environmental and Life Sciences M.Sc. Program

September 2014 ABSTRACT

Nutritional stoichiometry and growth of filamentous green algae (Family Zygnemataceae) in response to variable nutrient supply.

Colleen Middleton

In this study, I investigate the effects of nitrogen (N) and phosphorus (P) on the nutritional stoichiometry and growth of filamentous green algae of the family

Zygnemataceae in situ and ex situ . I found a mean of Carbon (C):N:P ratio of 1308:66:1 for populations growing in the Kawartha Lakes of southern Ontario during the summer of 2012. FGA stoichiometry was variable, with much of the variation in algal P related to sediment P ( p < 0.005, R 2 = 0.58). Despite large variability in their cellular nutrient

stoichiometry, laboratory analysis revealed that Mougeotia growth rates remained

relatively consistent around 0.28 day -1. In addition, Mougeotia was found to be weakly homeostatic with respect to TDN:TDP supply (1/H NP = 0.32). These results suggest that

FGA stoichiometry and growth rates are affected by sediment and water N and P.

However, they will likely continue to grow slowly throughout the summer despite variable nutrient supply.

Keywords: Filamentous green algae, Mougeotia , Spirogyra , Zygnemataceae, nutrient supply, sediment nutrients, dissolved nutrients, nutritional stoichiometry, growth rates, chlorophyll concentration, homeostatic regulation.

ii ACKNOWLDEGEMENTS

My Masters has been so much more about the journey than the final product, and I need to thank all the people who have supported me in various aspects along the way.

Starting from the beginning, I would first have to thank my grandparents and the rest of the Middleton clan for raising me to appreciate the beauty and balance of nature on the Kawartha Lakes. Because of adventures at the cottage, I knew that I wanted to preserve these lakes for future generations to enjoy as I have.

Fundamental in my pursuit of environmental stewardship was the acquisition of knowledge, which led me to begin my Masters at Trent University. I thank all of my teachers for challenging me and sharing their wisdom. I especially thank my supervisor Dr. Paul Frost, for guiding me throughout my Masters. Paul is able to break down complex ecosystem processes into their basic elements (literally!), and brought me back to the “K.I.S.S.” principle. Qualified scientific advice was also provided from start to finish by my supervisory committee: Dr. Eric Sager and Dr. Jennifer Winter.

My regular field assistants were Emily Porter-Geoff, Catharine Monaghan, Clay Prater, and Shayla Larson. Rain or shine, they joined me in my search for “elephant snot”. A number of family members and friends also joined me on the boat: Tim Racette, Trina Kaus, Andres Raun, Sean Middleton, Marianne Meyer, and Erwin Meyer.

I owe my most sincere appreciation to my long-term lab members and friends: Dr. Emily Porter-Geoff, Nicole Wagner, Clay Prater, Charlotte Narr, Dr. Keunyea Song, and Andrew Scott for helping me with project design, laboratory analysis, and editing. Thank you also Peter Lin and Ryan Franckowiak for help with statistics, Kyle Borrowman for helping me set up a laboratory experiment in the wee hours, and Andrea Hicks for her mentorship in the early stages of my Masters.

Just as important is the emotional support I received throughout the course of my thesis, especially towards the end. I am eternally grateful to my mom (Marianne Meyer), Leah Ensing, Jessica Middleton, Sean Middleton, Robert Sainsbury, Heather Leech, Trina Kaus, Eva Dwyer, Heather Hodgson, and Linda Cardwell.

Finally, thank you to members of the Kawartha Lake Stewards Association and other concerned lake-users for their questions, interest, and support. It is because of people like you that important environmental issues get recognized and addressed.

This work was supported by funds provided by the Natural Sciences and Engineering Research Council of Canada, the Kawartha Lake Stewards Association, and the David Schindler Professorship at Trent University.

iii

I dedicate my thesis to my father, Dr. Bill Middleton, who recently passed away. While his life’s work involved human health, I have no doubt that his critical thinking, strong work ethic, high expectations of me, and love of the cottage have transferred to my success in completing my M.Sc. in Environmental and Life Sciences. May his carbon, nitrogen, and phosphorus continue to travel the globe forever more.

iv TABLE OF CONTENTS

Pg

Abstract ii

Acknowledgements iii

Table of Contents v

List of Tables vii

List of Figures viii

General Introduction 1

Chapter 1: Patterns and drivers of filamentous green algae (Zygnemataceae) stoichiometry in the Kawartha Lakes of Southern Ontario 5

Abstract 6 1. Introduction 7 2. Methods 9 2.1 Study area 9 2.2 Study design 9 2.3 Sampling methods 10 2.4 Chemical analysis 11 2.5 Statistical analysis 12 3. Results 12 3.1 FGA stoichiometry 12

3.2 Algal stoichiometric variability over space and time 13 3.3 Water and sediment as sources of nutrients 14 4. Discussion 14

Chapter 2: Stoichiometric and growth responses of a freshwater filamentous green alga ( Mougeotia spp. ) to varying nutrient supplies 25

Abstract 26 1. Introduction 28 2. Methods 30 2.1 Algal collection and purification 30 2.2 Experimental procedure 31 2.3 Chemical anlysis 33 2.4 Homeostasis calculation 33 2.5 Field study 33 2.6 Statistical analysis 35

v 3. Results 35 3.1 Elemental composition of Mougeotia 35 3.2 Responses of Mougeotia to increasing media N concentrations 36 3.3 Responses of Mougeotia to increasing media P concentrations 36 3.4 Homeostatic regulation of Mougeotia elemental composition 37 3.5 Elemental composition of lake-derived Mougeotia 38 4. Discussion 38

General Conclusion 52

Literature Cited 56

vi LIST OF TABLES

Pg Table 2.1. Summary of Mougeotia’s stoichiometric and physiological traits created by varying media nutrient concentrations in the lab. Elemental ratios are molar ratios. 45

Table 2.2. Standard major axis regression statistics for the N and P experiments on Mougeotia stoichiometry 46

Table 2.3. Summary of Mougeotia’s stoichiometry and water chemistry from the Kawartha Lakes. 47

vii LIST OF FIGURES

Pg Figure 1.1. Map of Kawartha Lakes sampling sites with location map overlay. Spatial sites (n = 6 per lake) are shown with black squares and temporal sites (n = 3 per lake) are shown with white squares. Gray squares show sites used in both studies. 19

Figure 1.2. Boxplot showing alga stoichiometry for two different genera of filamentous green algae: Mougeotia (MG, n = 71) and Spirogyra (SP, n = 22) for spatial and temporal study data combined. Boxplots show the median (horizontal line), 25 th and 75 th percentiles (box limits), range (whiskers), and outliers (points). 20

Figure 1.3. Boxplot showing algal stoichiometry for six different lakes (n = 6 sites per lake, up to 3 samples per site). Boxplots show the median (horizontal line), 25 th and 75 th percentiles (box limits), range (whiskers), and outliers (points). 21

Figure 1.4. Scatter plot showing mean algal stoichiometry for Pigeon Lake (gray squares, n = 3 sites, up to 3 samples per site) and Balsam Lake (open circles, n = 2 sites, up to 3 samples per site) over time. Error bars are standard deviations. 22

Figure 1.5. Linear regression showing the effect of water stoichiometry on algal stoichiometry for the spatial study. Data is fitted with the line of best fit. 23

Figure 1.6. Linear regression showing the effect of sediment stoichiometry on algal stoichiometry for the spatial study. Data is fitted with the line of best fit. 24

Figure 2.1. Nitrogen (N) and phosphorus (P) concentrations in the growth media at the start of each experiment. Also provided are the molar N:P ratios bracketing the range of values present in each experiment. 48

Figure 2.2. The effect of media N concentration on Mougeotia ’s stoichiometric (C:N, C:P, and N:P ratios, by mol) and physiological (MSGR (day -1), Chl ( µg·L -1) and C:Chl ratio) responses at high and low P levels. Low P is shown with open circles and a dashed trend line and high P is shown with open triangles and a solid trend line. It is also noted where slopes between high and low P concentrations were common ( p > 0.05) or significantly different ( p < 0.05). 49

Figure 2.3. The effect of media P concentration on Mougeotia ’s stoichiometric (C:N, C:P, and N:P ratios, by mol) and physiological (MSGR (day -1), Chl ( µg·L -1) and C:Chl ratio) responses at high and low N levels. Low N is shown with open circles and a solid trend line and high N is shown with open triangles and a dashed trend line. It was noted where slopes between high and low N concentrations were common ( p > 0.05) or significantly different ( p < 0.05). 50

viii Figure 2.4. The effect of media N:P, N concentration, and P concentration on Mougeotia N:P ratio, Mougeotia %N, and Mougeotia %P of cultured Mougeotia . Shown also for each is the line of best fit and regression statistics. Note that the slope of the media N:P to Mougeotia N:P relationship is equal to 1/H. 51

ix GENERAL INTRODUCTION

Excessive algal growth is associated with a suite of environmental, social, and economic problems. A long-lasting monoculture bloom can decrease plant and algal diversity (Turner et al. 1995b), thereby decreasing overall biodiversity. Upon decomposition, microbial activity can result in local areas of anoxia which kill fish and result in a greater release of sediment bound phosphorus (Gao et al. 2013). In addition, excessive algal growth can clog water intake pipes, impact recreational activities, and in the case of toxin-producing algae, present a serious health concern to humans and livestock. It is therefore important to understand and address the factors that influence algal growth.

In recent years, reports and anecdotal evidence suggest that filamentous green algal growth is increasing in freshwater lakes of Ontario (Winter et al. 2011).

Filamentous green algae of the family Zygnemataceae (hereby referred to as FGA) are commonly found in shallow, calm water bodies including lakes, vernal pools and ditches

(McCourt et al . 1986). The most common genera in this family are Mougeotia ,

Spirogyra , and Zygnema , which can be easily identified microscopically by the shape of their chloroplasts. Because of their ability to form macroscopic filaments, which can grow into large metaphytic blooms that can dominate littoral zones of recreational lakes, they are considered a nuisance species.

Most studies examining Mougeotia growth pertain to its frequently observed ability to proliferate in experimentally and anthropogenically acidified lakes (eg. Klug and Fischer 2000, Vinebrooke et al . 2003). In circumneutral lakes, such as the Kawartha

1 Lakes of southern Ontario, less is understood about their growth dynamics. Specifically, little is known about how external concentrations and ratios of essential nutrients affect the cellular nutrient stoichiometry and growth rates of these algae. As macroscopic algae with diverse habitat niches, FGA may be unique in their nutrient requirements for growth compared to well-studied microscopic phytoplankton. As such, this important group of algae warrants further study.

The study of ecological stoichiometry has allowed for a greater understanding of algal growth dynamics. It applies the law of conservation of mass to show that elements in a closed system (for argument’s sake - a lake) can be rearranged and transferred from one form to another, but the overall amount of elements in a system remains constant. It is ultimately based on Liebig’s Law of the Minimum which posits that an organism or process that requires a balance of elements will be limited in its growth by the element in least supply (ie. the “limiting nutrient”). In the face of variable nutrient supply, some algae are able to vary their elemental composition considerably (i.e., non-homeostatic algae), while others maintain constant internal stoichiometry (i.e., homeostatic algae).

These principles can be applied in the study of algal growth dynamics by examining the supply and demand of nutrients in a system, and inferring nutrient limitation of growth based on a specific alga’s known nutritional requirements and the concentration of the limiting nutrient.

Carbon (C), nitrogen (N), and phosphorus (P) are often cited as the most essential and limiting nutrients for algal growth (Sterner and Elser 2002, Cross et al .

2005, Hecky and Kilham 1988). The mean C:N:P ratio of algae has often been compared to the Redfield ratio of 106:16:1 for marine phytoplankton to infer nutrient limitation of

2 growth. Much work has found considerable departures in the C:N:P ratios from the

Redfield ratio, both within and between taxa (eg. Duarte et al. 1992). Generally, more variation in C:N:P stoichiometry has been found for freshwater algae than marine algae

(Hecky et al. 1993), and macroalgae tend to have higher C:N, C:P, and N:P ratios than microalgae (Duarte et al. 1992). It is therefore important to gain an understanding of algal C:N:P stoichiometry of an individual species in relation to its environment.

One effective approach in studying the effect of nutrients on algal growth dynamics has been to combine laboratory and field analyses (Hecky and Kilham 1988).

Field studies provide insight into the transitions of algal communities at varying nutrient supplies. The conclusions of these studies are usually properly constrained to be site- specific. Laboratory analyses can further our understanding of algal occurrence and growth rates in response to nutrient supplies, by controlling confounding variables and examining single or multiple algal strains in isolation. However, ex situ findings lack the complexities of in situ scenarios. When taken together, the relationships between cellular nutrient concentrations and growth rates, as determined in laboratory studies, and in situ nutrient supply and demand dynamics, as determined in field studies, can be the most accurate method for inferring nutrient limitation of growth in natural populations (Hecky and Kilhan 1988).

In this study, I combined laboratory and field techniques to study the effects of variable nutrient supply on FGA stoichiometry and growth rates. In the field (Chapter

1), I examined natural variability and sources of nutrients for two genera of FGA in the

Kawartha Lakes of southern Ontario. In the lab (Chapter 2), I further examined FGA stoichiometry, growth rates, and chlorophyll a concentrations under extremes of N and P

3 supply. Combining my field and laboratory findings, I inferred patterns of nutrient limitation of FGA growth on spatial and temporal scales in the Kawartha Lakes. This is the first time that FGA stoichiometry has been studied in isolation in a laboratory bioassay, and the first time the natural variability of this alga has been described in the

Kawartha Lakes. Results from this study can be used to predict the potential distribution and abundance of FGA in the Kawartha Lakes and help guide management of the sources of nutrients to these lakes.

4

CHAPTER 1

Patterns and drivers of filamentous green algal stoichiometry in the

Kawartha Lakes of southern Ontario

5 Abstract

As algal growth rates are connected to their elemental composition, understanding the causes of variable algal nutrient stoichiometry is important. I describe the elemental composition (carbon (C), nitrogen (N), phosphorus (P), and ratios of these elements) of filamentous green algae (FGA) belonging to the family Zygnemataceae in lakes of southern Ontario. I found that two common genera of FGA, Mougeotia (C. Agardh) and

Spirogyra (Link), have similar nutrient stoichiometry, with a combined mean C:N:P of

1308:66:1. These high C:P and N:P ratios suggest that FGA are P-limited in the

Kawartha Lakes. Taken together, FGA varied considerably in %C, %N, C:N, C:P, and

N:P ratios between lakes. FGA stoichiometry was less variable within lakes sampled through time. Further, I explored possible sources of FGA stoichiometric variability by examining relationships with water nutrients (dissolved C, N and P) as well as sediment nutrients (total C, N, and P). I found relatively no, or weak, relationships between dissolved nutrients in water and FGA stoichiometry. Stronger relationships occurred between FGA elemental composition and sediment nutrient content. In particular, sediment P explained large quantities of variation in %P, C:P, and N:P (R 2 = 0.58, 0.59, and 0.39, respectively p < 0.05 for all). These results suggest that FGA stoichiometry is

primarily driven by P in the Kawartha Lakes, and that most of the P that is used by FGA

is derived at the sediment open-water interface. Given long-term P-storage capabilities

of sediment and ability of these algae to greatly vary their elemental composition, it

would be difficult to manage the growth of these algae through nutrient reductions

alone.

6 1. Introduction

Filamentous green algae of the family Zygnemataceae (FGA) are often found in the littoral zones of lake ecosystems. They are known for their ability to form large, metaphytic blooms that can affect nutrient cycling, physical structure, and food web dynamics of littoral zones (Turner et al. 1995a, Gao et al. 2013, Gubelit and Berezina

2010). This ability to grow into large amorphous “blobs” has contributed to their designation as “nuisance algae” (France and Welbourn 1992). Despite this, little is known about their nutritional requirements for growth.

The stoichiometry of algae provides a link between growth demands for multiple elements in producers, and their supply in aquatic ecosystems. This link considers the balance of nutrients required for biochemical processes to be connected to growth and division. Imbalanced nutrient supplies can result in short-term modifications to the nutrient content of algal cells and longer-term effects on their growth rates (Hecky and

Kilham 1988, Frost et al. 2005). It may be that this type of physiological acclimation allows algae to persist through periods of widely varying nutrient supply (Morel 1987,

Geider et al. 1998). While relatively well studied in laboratory systems, there have been few studies of variable elemental composition of individual algal species in nature.

Consequently, it remains unclear as to what extent different algal taxa can vary in their elemental composition within and among lake ecosystems.

While intraspecific variation in elemental composition is widely acknowledged within a single species (Geider and La Roche 2002), variability between taxa can also be significant (Duarte 1992). These differences between taxa could reflect differences in

7 maximum growth rates and/or morphology. For example, faster growing species may have inherently higher N and P content than slower growing species (Duarte 1992).

Microalgae have higher nutrient uptake rates than macroalgae (Hein et al. 1995), which may result in higher nutrient content in single cells rather than in filaments or fronds.

With a greater portion of biomass allocated towards C-rich cellulostic cell walls, FGA are likely to have higher C:N and C:P ratios than microscopic algae if grown in the same nutrient concentrations (Hotchkiss et al. 1989, Naselli-Flores and Barone 2011).

However, whether there are differences among taxa within the Zygnemataceae family is yet to be determined. Given their similarities in size and structure, it seems unlikely that differences in stoichiometry would be significant among FGA genera.

Given the apparent non-homeostatic tendencies in the elemental composition of algae (Presson et al. 2010, Wang et al. 2012), the elemental composition of FGA should reflect the availability of nutrients for uptake and incorporation. Since FGA grow in the water column and are close to the sediment (Turner et al. 1995a), they could receive nutrients from one, the other, or both water and sediment (Hagerthey and Kerfoot 1998,

Wyatt et al. 2010). Connections between dissolved nutrient concentrations and the stoichiometry of planktonic algae are well established (Hecky and Kilham 1988), and similar relationships could exist for FGA. Sediment chemistry may also have an effect on algal stoichiometry, especially for these algae, which can live on or near the sediment

(Turner et al. 1995a). The relative influence of these nutrient sources on the elemental composition of FGA is yet to be assessed.

In this study, I examined the stoichiometry of FGA as it relates to the taxonomic composition of metaphyton blooms, as well as water and sediment nutrient content. I

8 collected FGA from the littoral zones in the Kawartha Lakes of southern Ontario, where

FGA are common and nutrient concentrations are known to vary within and among lakes. I predicted that C:N:P ratios of FGA would differ from that of microscopic algae, but differences between the predominant genera would be minimal. I also predicted that

FGA stoichiometry would vary as a function of both water and sediment chemistry.

2. Methods

2.1 Study area

The Kawartha Lakes, which are located in southern Ontario, form a major part of a navigation route connecting Lake Huron to Lake Ontario, known as the Trent Severn

Waterway (Figure 1.1). In the shallow bays of the Kawartha Lakes, light and pH are relatively consistent throughout, but phosphorus concentrations range from low (TP < 10

µg L -1) to high (TP ~> 27 µg L -1) over spatial and temporal scales (White 2006). As water travels southeast through the system, it passes from a forested and granite-based highland to agricultural and more developed lowlands. There is a general trend of increasing TP as water moves through the system, with the exception of Stoney Lake, which has inputs of phosphorus poor water from northern tributaries (White 2006).

2.2 Study design

I divided the study into two parts: a spatial study and a temporal study. For both studies, sites were selected in order to increase the chances of finding FGA from which to obtain samples. This limited the study sites to shallow sheltered bays. These bays

9 consisted of approximately 100 m of shoreline from a variety of shoreline uses. All were less than 2 m deep, and had varying sediment types and amounts of macrophytes. For the spatial study, I sampled from six Kawartha lakes (Balsam, Sturgeon, Pigeon,

Buckhorn (Upper), Curve (upper Chemung), and Stoney Lakes), with six sites on each lake (Figure 1.1). This study was conducted within a two-week time frame from June 18 to June 29, 2012. For the seasonal study, I chose two lakes: Pigeon (average TP 14.84

µg L -1 in 2005) and Balsam (average TP 10.11 µg L -1 in 2005; White 2006), with three sites on each lake (Figure 1.1). The seasonal study ran from the second week of June to the second week of September, for a total of eight bi-weekly visits.

2.3 Sampling methods

Up to three FGA samples were collected at each site, and data from samples collected from the same site on the same day were averaged. FGA were obtained by a diver using a small hand-held net of mesh size ~ 0.5 mm. Samples were taken from relatively clean and bright green metaphytic blooms that taken throughout the water column - sometimes mixed within plant beds, but growing unattached to any substrate.

Large debris such as detritus and plant material were picked out of the sample immediately, and the sample was rinsed using lake water. FGA samples were stored in labeled plastic bags on ice in a cooler and further processed in the lab within four hours of collection. In the lab, algae were placed in a sieve (500 µm), and thoroughly rinsed

with distilled water. Additional contamination that was missed in the initial field

cleaning was removed with tweezers. A small sub-sample was taken from each sample

to determine its taxonomic composition. Cleaned FGA samples were placed in tin boats

10 and dried in a drying oven at 60 ºC. Once dried, algae were homogenized to a fine powder in a blender and stored in acid-washed scintillation vials for later chemical analysis.

Water samples were taken from the water surface at two locations/site, stored in acid-washed containers on ice in a cooler, and processed within 10 hours. For total dissolved N and P (TDN and TDP), water was filtered through 0.2 µm micropore filters and stored in amber glass bottles in a fridge for later chemical analysis.

Sediment samples were taken for the spatial study only. Three sediment samples were taken at each site from 1 m water depth using a sediment core of 2 cm diameter.

Cores were 5 cm deep. Large debris (rocks, shells, and plants) were removed from the sediment in the field and sediment samples were transported in Ziplock bags on ice in a cooler. Upon arrival at the lab, sediment samples were dried in a drying oven at 60 ºC until dry, homogenized using a mortar and pestle, and stored in acid-washed scintillation vials for later chemical analysis.

2.4 Chemical analysis

Total dissolved phosphorus ( µg L -1) and algae phosphorus (%P) were measured on a UV-visible spectrophotometer (Varian Cary 50) according to the molybdate-blue colorimetric method (Murphy and Riley, 1962) following oxidization with K 2S2O8 solution and autoclave digestion. Sediment total phosphorus (%) was also determined by this method on diluted samples. Total dissolved nitrogen ( µg L -1) was measured on a

UV-visible spectrophotometer (Varian Cary 50) using the second-derivative

spectroscopy method (Crumpton et al. 1992) following oxidation with NaOH-K2SO 4

11 solution and autoclave digestion. Sediment and algae %N and %C were measured on a

CN analyzer (Vario EL, Elementar).

2.5 Statistical analysis

All data were log-transformed to meet assumptions of normality and heterogeneity of variance. I tested for significant differences in stoichiometry between

FGA genera using one-way T-tests. Between lakes I used one-way ANOVAs followed by TukeyHSD for multiple comparison testing where significant differences occurred.

The effect of the sampling date was tested using a two-factor ANOVA, with the lake

(Balsam and Pigeon) and date as the main effects. To determine the effect of water chemistry and sediment chemistry on FGA stoichiometry, simple linear regressions were used and R 2 values were compared. All statistical analyses were performed using R version 2.15.3.

3. Results

3.1 FGA stoichiometry

A total of 105 individual FGA samples were collected for the spatial and temporal studies combined. Seventy-one of these samples were composed of Mougeotia spp. (C. Agardh) and 23 samples were composed of Spirogyra spp. (Link). The remaining 11 were a mix of FGA genera to varying degrees, including Zygnema spp. (C.

Agardh). I excluded the mixed samples from future analysis due to their relatively small sample size.

12 I found that the elemental composition of Mougeotia and Spirogyra was nearly identical. FGA %P, %N, C:N, C:P, and N:P were not significantly different (Figure 1.2).

The only significant difference between genera was in terms of %C with Mougeotia

having significantly less than Spirogyra (40.07% versus 43.02%, respectively; p <

0.005). Given that the stoichiometry between the two genera was so similar, I pooled the data for both genera for the remainder of the analyses to increase statistical power (n =

95). The mean C:N:P ratio for FGA was 1308:66:1. FGA %C was relatively constant

(C.V. = 10%), but algal %N and %P varied by 30% and 66%, respectively.

3.2 Algal stoichiometric variability over spatial and temporal scales

The elemental composition of FGA significantly varied between lakes (Figure

1.3). For %C, I found that only Balsam Lake was different by having less algal %C than all other lakes. In terms of %N, I found Sturgeon Lake was higher in %N than Balsam and Curve Lakes. While %P was not significantly different between lakes, Sturgeon

Lake and Curve Lake were lower than all other lakes ( p = 0.08). For C:N ratios, Curve

Lake had a significantly higher algal C:N ratio than Sturgeon and Buckhorn Lakes. For

C:P ratios, Curve Lake had the highest average, which was significantly more than that of Buckhorn. Differences in N:P ratios were also found, with Sturgeon Lake having significantly higher algal N:P than Pigeon and Buckhorn Lakes ( p < 0.05 for all).

I found almost no significant differences in the C:N:P ratios of FGA over time within Balsam and Pigeon Lakes (Figure 1.4). Only algal %C showed significant differences between sampling dates, with a general increasing trend over the summer in

Balsam Lake (interaction p = 0.03, Balsam p = 0.017, Pigeon p = 0.23). As for %C, I

13 also found significant interactive effects between lake and date for %P. In this case,

Balsam Lake slightly increased over time while Pigeon Lake slightly decreased

(interaction p = 0.02, Balsam p = 0.13, Pigeon p = 0.10). Similarly, algal C:P and N:P increased in Balsam Lake (p = 0.05 and 0.03, respectively) and decreased in Pigeon

Lake (p > 0.05 for both)(interaction p < 0.01 and p < 0.05, respectively). Algal C:N did

not vary significantly over space or time ( p > 0.05 for date, lake, and interaction).

3.3 Water and sediment as sources of nutrients

I found that differences in FGA C:N:P ratios among sites and through time were related to nutrient concentrations at the sampled site. In the spatial study, algal %P was positively correlated with water column TDP ( p = 0.04, R 2 = 0.23, Figure 1.5). In contrast, algal % N was not related to water column TDN. In terms of ratios, algal C:N was not related to TDN, C:P was not related to TDP, and N:P was not related to

TDN:TDP ( p > 0.05 for all). Again, in the spatial study, sediment N did not have a

significant effect on algal N ( p > 0.05); however, sediment P had a strong positive effect

on algal P ( p < 0.005, R 2 = 0.58). Similarly, sediment N did not strongly affect algal C:N

(p > 0.05); however, sediment P was strongly related to algal C:P ( p < 0.005, R 2 = 0.59),

sediment N:P was related to algal N:P ( p < 0.005, R 2 = 0.31), and sediment N:P was also related to algal N:P ( p < 0.005, R 2 = 0.40, not shown)(Figure 1.6).

4. Discussion

14 Variable nutrient stoichiometry has important connections with the physiology of algae as algae require set proportions of N and P for the synthesis of protein, nucleic acids, and energetic nucleotides (Sterner and Elser 2002). Therefore, when light and temperature are adequate, algae can become growth-limited by the availability of inorganic N and P (Hecky and Kilham 1988). In this study, I asked: “Does the stoichiometry of FGA vary in the Kawartha Lakes and, if so, what are the primary drivers of this variability?”. I hypothesized that the genera, water nutrients, and sediment nutrients all affect FGA stoichiometry. I found that FGA %N and %P were highly variable on both spatial and temporal scales. This variation was not affected by the genera, but was related to sediment P and, to a lesser degree, water column TDP and

TDN.

Mougeotia and Spirogyra had nearly identical nutrient stoichiometry. This was not surprising given the structural similarities between these genera. While the difference in %C between genera was statistically different, a mean of 40.1%C for

Mougeotia and 43.0%C for Spirogyra is likely not biologically significant, especially given that the C:nutrient ratios are so similar.

Combining data from the spatial and temporal studies, the overall mean C:N:P of

FGA in this study was 1308:66:1. My C:P and N:P ratios are nearly double those

reported for marine macroalgae by Atkinson and Smith (1983)(C:N:P = 700:35:1) and

Duarte (1992)(C:N:P = 800:49:1), and much higher than the commonly referenced

Redfield ratio for marine phytoplankton (C:N:P = 106:16:1, Redfield 1958). The

elevated C:nutrient ratios of my algae compared to microalgae are likely due to size-

dependent nutrient uptake rates (scaled by surface area:volume (SA:V) ratios). The

15 smaller SA:V ratio in macroalgae means they are less efficient at transporting nutrients across the cell membrane than microalgae, which is true for both high and low external nutrient concentrations (Hein et al. 1995). Conversely, high C:nutrient ratios could indicate a competitive advantage for FGA over other algae. If the FGA are able to maintain a given growth rate at low C:nutrient ratios, they would ultimately out-compete other algae with a higher demand for that nutrient (Sterner and Elser 2002).

Additionally, the elevated C:nutrient ratio in my FGA could be due to an accumulation of C-rich macromolecules (for example, structural and storage components) that can accumulate under light-saturated and nutrient-limited conditions (Geider and La Roche

2002).

Few studies exist for comparisons of my C:N:P ratio to other freshwater macroscopic algae in relation to growth rates. Townsend et al. conducted a stream enrichment experiment in which they obtained a mean C:N:P ratio of 1800:87:1 for

Spirogyra fluviatilis growing in “nutrient-replete conditions” (2008). While nutrient ratios are similar, I would not suggest that FGA in the Kawartha Lakes are growing in nutrient-replete conditions. In contrast, my C:P and N:P ratios are indicative of severe P- limitation in 100% of samples, and my C:N ratios indicate severe N-limitation in 90% of my samples (Healey and Hendzel 1980). This supports the finding by Duarte et al.

(1992) that macroalgae are frequently N- and especially P-limited in nature.

Spatial patterns in FGA stoichiometry were more pronounced than temporal patterns; there were pronounced biweekly variations but no notable long-term trends.

Spatial variability could be the result of a number of factors influencing nutrient inputs into individual lakes. For example, surrounding land use, proximity to sewage treatment

16 facilities and the number of shoreline residences. As opposed to FGA %N, C:P, C:N, and N:P, FGA %P did not significantly vary between lakes. This could be due to the inherent variability in %P in FGA and other algae (Duarte et al. 1992), and can also be

exacerbated by site-specific differences in environmental factors such as nutrient

availability from sediment (Migné et al . 2011), point sources of P from streams (Bosch

et al . 2008, Higgins et al. 2012, Wing et al . 2011) and shoreline development

(Rosenberger et al . 2008), activity of grazing organisms (Hillebrand and Kahlert 2001,

Rober et al . 2011), and water temperature (Graham et al. 1996b). In particular, in this

study, sediment phosphorus was highly variable between sites within a lake (data not

shown). Alternatively, due to the small sample size of algae in some lakes, I may not

have had enough statistical power to confidently detect statistical differences in %P

between lakes (power < 0.8 when n = 5).

Dissolved nutrients were significantly related to algal stoichiometry, but

relationships were weaker than expected. Studies on phytoplankton typically show that

dissolved inorganic nutrients act as the primary source of nutrient supply to these algae

(eg. Tilman et al. 1982). Instead, I found a stronger relationship between algal P, C:P, and N:P with sediment P. This trend could be due to differences in habitat preferences exhibited by macro-versus-planktonic algae, with FGA frequently growing near the sediment. Interstitial water from sediments may act as an important supply of P to FGA, especially when oxygen levels are low enough to liberate phosphate bound in hydrous oxides and gels (Carlton and Wetzel 1988). FGA in this study were collected from pelagic as well as benthic blooms, so the strong connection to sediment P also suggests that nutrient-replete FGA might grow closer to the sediment, whereas sunlight-limited

17 FGA may rise higher in the water column (Anneville et al. 2002). Future studies should investigate the C:N:P stoichiometry of FGA in relation to their habitats and location in the water column. The greater correlation between FGA and sediment P then TDP could also be a factor of the rate of change of nutrient fluxes from sediment versus water. It is possible that short-term dissolved nutrient fluxes are taken up and used by the FGA but the correlation was not detected because water P is more variable than sedimentary release of P on a temporal scale (Hagerthey and Kerfoot 1998, Carlton and Wetzel

1988).

Algal stoichiometry links nutrient supply to algal growth rates (Hecky and

Kilham 1988). While external nutrient ratios have been used to predict patterns of algal

growth, internal concentrations have been proven to be more reliable indicators (Flynn

2010). Therefore, understanding spatial and temporal patterns in algal C:N:P

stoichiometry has important implications for the management of excessive FGA growth.

For example, FGA from Buckhorn and Pigeon Lakes obtained the lowest N:P ratios,

which could suggest that these lakes could be more prone to excessive algal growth and

should therefore be targeted in remediation efforts. In addition, it can be predicted that

FGA will reach higher biomass in areas where sediment P is high. In this study I

attempted to measure the biomass of FGA in situ , but the irregular growth form of this

algae made quantification difficult. Still, subsequent work should further examine the

relationship between FGA stoichiometry, nutrient supply stoichiometry, and the biomass

response of FGA in situ .

18

Figure 1.1. Map of Kawartha Lakes sampling sites with location map overlay. Spatial sites (n = 6 per lake) are shown with black squares and temporal sites (n = 3 per lake) are shown with white squares. Gray squares show sites that were used in both studies.

19 Algal %C Algal Algal C:Nratio Algal 30 35 40 45 50 0 2000 4000 6000

MG SP MG SP Algal %N Algal Algal C:P ratio C:P Algal 2 3 4 5 10 15 20 25 30

MG SP MG SP Algal %P Algal Algal N:P ratio N:P Algal 50 100 150 200 0.0 0.1 0.2 0.3 0.4 MG SP MG SP

Figure 1.2. Boxplot showing alga stoichiometry for two different genera of filamentous

green algae: Mougeotia (MG, n = 71) and Spirogyra (SP, n = 22) for spatial and

temporal study data combined. Boxplots show the median (horizontal line), 25 th and 75 th percentiles (box limits), range (whiskers), and outliers (points).

20 Algal %C Algal Algal C:N ratio Algal 30 35 40 45 50 14 18 22 26

Bals Buck Curve Pig Stone Stur Bals Buck Curve Pig Stone Stur Algal %N Algal Algal C:P ratio C:P Algal 2.0 2.5 3.0 3.5 500 1500 2500 3500

Bals Buck Curve Pig Stone Stur Bals Buck Curve Pig Stone Stur Algal %P Algal Algal N:P ratio N:P Algal 50 100 150 0.05 0.10 0.15 0.20

Bals Buck Curve Pig Stone Stur Bals Buck Curve Pig Stone Stur

Figure 1.3. Boxplot showing algal stoichiometry for six different lakes (n = 6 sites per lake, up to 3 samples per site). Boxplots show the median (horizontal line), 25 th and 75 th percentiles (box limits), range (whiskers), and outliers (points).

21 50 40

40 _

_ 30 30 20 20 Algal %C 10

10 Algal C:N ratio 0 0 5 3000

4 _ 2500 2000 _ 3 1500 2 1000 1

Algal %N 500

0 Algal C:P ratio 0 -1 -500 0.6 140

0.5 _ 120

_ 0.4 100 0.3 80 0.2 60

Algal Algal %P 0.1 40 0 Algal N:P ratio 20 -0.1 0 5/27 6/24 7/22 8/19 9/16 5/27 6/24 7/22 8/19 9/16 Date Date

Figure 1.4. Scatter plot showing mean algal stoichiometry for Pigeon Lake (gray squares, n = 3 sites, up to 3 samples per site) and Balsam Lake (open circles, n = 2 sites, up to 3 samples per site) from June to September of 2012. Error bars are standard deviations.

22 Algal %N Algal Algal C:Nratio Algal 2.0 2.5 3.0 3.5 14 18 22 26

200 400 600 800 1000 200 400 600 800 1000

TDN (ug/L) TDN (ug/L) Algal %P Algal Algal C:P ratio C:P Algal 0.05 0.15 500 1500 3000 2 4 6 8 10 2 4 6 8 10

TDP (ug/L) TDP (ug/L) Algal N:P ratio N:P Algal 50 100 150

100 200 300 400 500

TDN:TDP ratio

Figure 1.5. Linear regression showing the effect of water stoichiometry on algal stoichiometry for the spatial study. Data is fitted with the line of best fit.

23 Algal %N Algal Algal C:Nratio Algal 2.0 2.5 3.0 3.5 14 18 22 26

0.02 0.06 0.10 0.14 0.02 0.06 0.10 0.14

Sediment %N Sediment %N Algal %P Algal Algal C:P ratio C:P Algal 0.05 0.15 500 1500 3000 0.00 0.05 0.10 0.15 0.20 0.25 0.00 0.05 0.10 0.15 0.20 0.25

Sediment %P Sediment %P Algal N:P ratio N:P Algal 50 100 150

0 2 4 6 8 10 12 14

Sediment N:P ratio

Figure 1.6. Linear regression showing the effect of sediment stoichiometry on algal stoichiometry for the spatial study. Data is fitted with the line of best fit.

24

CHAPTER 2

Stoichiometric and growth responses of a freshwater filamentous green

alga ( Mougeotia spp. ) to varying nutrient supplies

25 Abstract

I examined how the nutrient stoichiometry of the filamentous green alga, Mougeotia sp .,

is affected by the relative availability of dissolved nitrogen (N) and phosphorus (P).

Using a bioassay experiment, I exposed Mougeotia in medium to a range of dissolved N and P concentrations and different dissolved N:P ratios (0.62 to 412 by mol). After growing Mougeotia in an environmental chamber for 17 days, I measured its carbon (C),

N, and P content (% of dry mass), molar C:N:P ratios, mass specific growth rate

(MSGR, day -1), chlorophyll a concentration (Chl, µg L -1) and carbon:chlorophyll a

(C:Chl) ratios. Due largely to variation in cellular %N and %P, Mougeotia C:N:P ratios

varied considerably at the end of the experiment, with C:N ratios ranging from 6 to 41,

C:P ratios ranging from 85 to 999 and N:P ratios ranging from 4 to 118. I found

Mougeotia to be weakly homeostatic with respect to N:P content, with non-homeostatic

tendencies found for N and P content. Despite the wide range of N and P concentrations

in the medium at the start of the experiment, Mougeotia mass-specific growth rate was relatively invariant. Nevertheless, high C:Chl ratios observed for N- and P-limited

Mougeotia largely matched those expected for algae experiencing nutrient-limited growth. I further compared the experimental results with stoichiometric data for

Mougeotia collected from the Kawartha Lakes in southern Ontario. The high C:P and

N:P ratios found in field samples matched those of laboratory Mougeotia experiencing intense and sustained P-limitation and are suggestive of severe P-limitation of

Mougeotia in Kawartha Lakes throughout the summer. The slow growth rates and

flexible stoichiometry of this alga would enable it to continue growing even during

26 periods of acute nutrient stress. Hence, future management and control of this nuisance alga may prove difficult if it is unlikely to respond strongly to reductions in external P- loading and lower water column P concentrations.

27 1. Introduction

Filamentous green algae (FGA, family Zygnemataceae) are globally distributed and found in a variety of freshwater habitats (McCourt et al. 1986, Hawes 1989). While

FGA have been found to be particularly abundant in littoral zones of experimentally

acidified lakes (Turner et al. 1995b, Fairchild and Sherman 1990, Graham et al. 1996a) these algae can also reach very high biomass in circumneutral lakes (Hillebrand 1983,

Hawes 1989). Under some conditions, FGA, such as Mougeotia sp. (C. Agardh), grow

prolifically and form large metaphytic blooms that remain on the lake bottom, drift

around in pelagic zones, and/or float to the lake surface. FGA are often considered

“nuisance species” given their ability to proliferate into large floating masses, which can

obstruct waterways and decrease the recreational, aesthetic and commercial value of

lakes (France and Welbourn 1992). Furthermore, large FGA blooms can simplify food

webs, decrease biodiversity, and alter biogeochemical cycling (Turner et al. 1995b).

Despite the socioeconomic and ecological importance of FGA, many aspects of their

physiology remain poorly understood.

Reports of filamentous green algal blooms in Ontario have increased in recent

years, which has been attributed to the effects of increased nutrient inputs and climate

change (Winter et al. 2011). The relative supply of nutrients available in the

environment can strongly affect algal stoichiometry and physiology (Frost et al. 2005).

Resource requirements of planktonic algae have been extensively studied with

laboratory chemostats, where populations are grown to steady-state equilibrium (e.g.

Droop, 1974, Tilman 1977). However, there has been less work on the nutritional

28 requirements of FGA, perhaps because as filamentous algae, they do not grow homogenously in water and are less amenable to chemostat culturing (but see Sommer

1985). While nutrient responses of FGA have been studied in situ (e.g. Hillebrand et al.

2002), these studies are potentially complicated by competitive interactions among multiple algal species and lack the specificity in nutrient-growth dynamics of individual

FGA species grown in isolation.

Laboratory and field experiments have generally shown algae can vary greatly in their elemental composition (Sterner and Hessen 1994, Sterner and Elser 2002). Further, it is often assumed that the nutrient ratios of algae closely match the nutrient ratios being supplied in their environment (Sterner and Elser 2002). In other words, algae exhibit non-homeostatic tendencies in their elemental composition (Frost et al. 2005). However, given the wide diversity of algae in terms of size, structure, function and habitat, it is unclear whether this assumption generally applies to all algal taxa. Using data from twenty freshwater algal species, Persson et al. (2010) found six species were

homeostatic, seven were weakly homeostatic, and only one ( Scenedesmus sp.) exhibited no homeostasis. These varying degrees of homeostasis exhibited by different algal taxa have important implications for understanding their growth dynamics. Elemental flexibility partly controls an alga’s ability to persist and grow during periods of nutrient deprivation, which may allow a non-homeostatic alga to out-compete a homeostatic alga

(Sterner and Elser 2002). In addition, variable elemental composition of non- homeostatic algae can provide information on the nature and strength of the nutrient limitation experienced in different aquatic ecosystems (e.g., Hecky et al. 1993).

29 In this study I investigated the nutrient stoichiometry the common FGA,

Mougeotia , both to describe its elemental responses to nutrient limitation and to provide

a means to potentially study the nature of its nutrient limitation in lake environments. In

particular, I examined the extent to which this alga varies in elemental composition and

whether this variation is proportional to changes in nutrient supply. To do so, I grew

Mougeotia with varying media concentrations of nitrogen (N) and phosphorus (P), and

measured the alga’s carbon (C), N, and P content (% dry mass). I further examined

mass-specific growth rates (MSGR, day -1), final chlorophyll a (Chl, µg L -1)

concentrations, and carbon:chlorophyll a (C:Chl) ratios of Mougeotia to verify that I

created nutrient-limited growth during the growth bioassay experiments. Data on the

elemental composition of Mougeotia were also used to determine the degree to which it

exhibits elemental homeostasis. I also measured the molar C:P, C:N, and N:P ratios of

field-collected Mougeotia to determine whether it exhibits signs of nutrient-limited

growth in the littoral zones of the circumneutral Kawartha Lakes in southern Ontario.

Altogether, this study of Mougeotia ’s growth and stoichiometry improves our

understanding of the nutritional ecology of this important FGA in freshwater

ecosystems.

2. Methods

2.1 Algal collection and purification

For the bioassay experiments, a sample of FGA was collected in October of 2011 from the northern end of Pigeon Lake (44° 34' N, 78° 28' W) in southern Ontario,

30 Canada. I collected this initial sample with a hand-held dip net (mesh diameter ~ 0.5 mm) and immediately transported the sample to the laboratory on ice in a dark cooler.

After rinsing the sample with distilled water, I used microscopy to confirm that the genus of this alga was Mougeotia . To create stock cultures, I placed three to seven

filaments of 1 to 10 cells of this rinsed sample into several 1L jars containing 500 mL of

autoclaved COMBO medium (Kilham et al. 1998). These 1L jars were then loosely

capped with an aluminium cover to reduce air-borne contamination. Algal cultures were

acclimated in an environmental chamber at 22 °C and a 16/8 hour light/dark cycle for

several months. I used broad-spectrum plant growth lamps and fluorescent lamps to

provide an irradiance of ~150 µE m -2 s -1 at the surface of the growth containers. Cultures were renewed biweekly by transplanting a few cells from the previous culture into a clean glass jar filled with fresh algal media. The algal cultures were examined under the microscope biweekly to confirm their purity. While I did not actively eliminate bacterial symbionts, there was no overt contamination of this type in my cultures. I discarded several cultures that were contaminated with non-target alga and re-established them using clean algal cells from one of the other cultures.

2.2 Experimental procedure

I placed 400 mL of autoclaved N- and P-free COMBO medium into each experimental unit, a 0.5 L glass jar. To each jar, I added 0.2 mg (wet weight) of rinsed

Mougeotia taken from a homogenized mixture of the starting cultures described above, which ensured a relatively constant elemental composition in the algae at the start of the experiment. I also added different amounts of P into the growth medium (P experiment,

31 Figure 1) at high N (560 µg L -1) or low N (56 µg L -1) concentrations. This was

complemented by a second simultaneous experiment, where I added different amounts

of N at high P (100 µg L -1) or low P (15 µg L -1) concentrations (N experiment, Figure 1).

Each experiment had five levels of the primary (varied) nutrient and two levels of the secondary (high/low) nutrient, with two replicates for each treatment combination.

These N and P combinations created twenty different treatment combinations and fifteen unique medium N:P ratios ranging from 0.62 to ~411. I chose these N and P concentrations to ensure a wide range in the concentrations of both nutrients and a large gradient of dissolved N:P ratios. All forty experimental units were capped with parafilm and a kinked straw was inserted to permit gas exchange. I did not aerate with bubbling air lines because earlier trials had shown that Mougeotia did not grow well, nor in filamentous, form under those conditions. Capped jars were placed in an environmental chamber for 17 days at 22 °C with a 18/6 hour light/dark cycle. Light was provided in the growth incubator as described above. Due to the non-homogenous growth form of this alga, I was unable to subsample over the course of the experiment and measured algal responses only at the end of the experiment. I chose to grow Mougeotia under these conditions for 17 days due to its slow growth rates, which strongly constrained biomass accumulation and the biomass available to analyse at the end of the experiment.

On the final day of the experiment, I poured the contents of each experimental unit into a blender and lightly homogenized the solution with five short pulses. A portion of this slurry was collected onto pre-weighed and ashed glass fibre filters

(Whatman, GF/F). Filters were dried, weighed and used to determine algal C, N and P content and MSGR. MSGR was calculated using the formula MSGR = ln( mt2 /m t1 )/ time ,

32 where mt2 is the final mass and mt1 is the starting mass. The remaining algal slurry was collected on a third GF/F filter and stored frozen until processed for Chl.

2.3 Chemical analysis

The C and N content of Mougeotia was determined using a CN analyzer (Vario

EL, Elementar, Hanau, Germany). Mougeotia P content was determined using the molybdate-blue colorimetric method (APHA 1992) on persulfate-digested filters and read on a UV-Visible spectrophotometer (Cary 50, Varian, Palo Alto, USA).

Chlorophyll a was quantified using a fluorometer (Cary Eclipse, Varian, Palo Alto,

USA) after cold and dark extraction of filters in ethanol for 24 hours (Marker et al.

1980).

2.4 Homeostasis calculation

The degree of N:P homeostasis of Mougeotia was determined by quantifying the

slope of the log-log relationship between algal cellular N:P ratios (y-axis) and media

N:P ratios (x-axis) (Sterner and Elser 2002). This value is the inverse of the regulatory

coefficient (1/H), where the regulatory coefficient is denoted as “H”. 1/H can be used as

an index of the degree of homeostasis with values close to zero indicating non-

homeostasis and close to one indicating strict homeostasis (Sterner and Elser 2002).

2.5 Field study

Mougeotia samples were collected from six Kawartha Lakes in southern Ontario,

Canada (Balsam Lake, Sturgeon Lake, Pigeon Lake, Stoney Lake, Buckhorn Lake, and

33 Curve Lake), which are all part of the Trent-Severn Waterway. FGA were collected from these lakes in shallow areas (depth <2 m) of well-sheltered bays, where large masses of algae are commonly found. In these sites, FGA blooms were located by snorkelling and collected using a hand-held net of mesh diameter ~0.5 mm. Samples were collected from six sites (when available) at each of the six Kawartha Lakes between 18 June and 27 June 2012. I generally collected only "healthy” metaphyton that contained as little debris as possible. When possible, three separate samples were collected at each site and were immediately cleaned of large debris. In addition, I collected two surface water samples at each site. All samples were placed in a cooler on ice and transported to the laboratory for immediate processing.

FGA samples were further cleaned of any visible non-algal material in the laboratory using distilled water, a 500 µm mesh sieve and forceps. A small sub-sample

of algae was preserved with Lugol’s solution and stored in the dark at 4°C for taxonomic

identification. The remainder of each sample was dried in aluminium pans for 48 hours

at 60 °C, lightly homogenized with a hand-held blender and stored in scintillation vials

until C, N and P analysis. Elemental analysis was conducted as previously described.

Preserved subsamples of FGA were identified to genus using a light microscope at x400

magnification and non-Mougeotia samples were removed from subsequent C, N and P

analyses.

I filtered water samples through 0.2 µm pore size polycarbonate filters for analysis of total dissolved N (TDN) and total dissolved P (TDP). TDN was determined

- by second-derivative analysis of NO 3 following persulfate digestion (Crumpton,

Isenhart and Mitchell 1992). TDP was measured after persulfate digestion, using the

34 molybdate-blue method (APHA, 2002). Both TDN and TDP were read on a UV-Visible spectrophotometer (Cary 50, Varian, Palo Alto, USA).

2.6 Statistical analysis

I assessed the stoichiometric and growth responses of Mougeotia to medium N and P concentrations in both bioassay experiments with standard major axis (SMA) regression analysis (Falster, Warton and Wright 2006). Prior to this analysis, I transformed response and predictor variables as necessary to linearize each respective relationship. With transformed data, I used SMA to test whether the slope between the response variable and the concentration of the primary nutrient differed between the high and low concentrations of the second nutrient. I interpreted significant differences among slopes as an interactive effect between medium N and P concentrations on the response variable under study. SMA-derived regression parameters were further examined to determine the nature of any medium N and/or P effects on algal growth and elemental composition.

3. Results

3.1 Elemental composition of Mougeotia

The elemental content of Mougeotia (% of dry mass and molar ratios) exhibited a wide range of values across experiments (Table 2.1). On the whole, algal %C varied the least of the three elements, with greater variability observed in algal %N and the most variability observed for algal %P (Table 2.1). I also found different levels of

35 variability among Mougeotia elemental ratios, with C:N showing less variability than

C:P and N:P (Table 2.1). The MSGR of Mougeotia showed little variation, whereas final

Chl concentrations as well as C:Chl ratios exhibited a wider range of values (Table 2.1).

3.2 Responses of Mougeotia to increasing media N concentrations

MSGR of Mougeotia increased significantly with increasing medium N concentrations (Figure 2.2, Table 2.2). Final Chl concentration also increased with greater medium N and this response was greater in the presence of the higher P concentrations. Mougeotia C:Chl ratios decreased with greater medium N concentrations, with no effect of medium P concentration. Mougeotia C:N ratios decreased with increasing medium N, which was an effect that was not altered by medium P concentration (Figure 2.2, Table 2.2). In contrast, responses of Mougeotia

C:P and N:P ratios to increasing N concentration differed between the two medium P

concentrations (Figure 2.2, Table 2.2). Both ratios increased with increasing medium N

at low medium P concentrations, but neither changed, nor or were slightly reduced, with

increasing medium N at high media P concentrations (Figure 2.2, Table 2.2).

3.3 Responses of Mougeotia to increasing media P concentrations

MSGR increased significantly with medium P concentration (Figure 2.3, Table

2.2). While this effect appeared to hold only at the high N concentration, I did not find a difference in slopes between MSGR with P at the two N concentrations (Figure 2.3). In contrast, I found a significant difference in regression slopes between Chl and medium P

(log transformed) at the high and low N concentrations (Figure 2.3). I found a non-linear

36 decrease in C:Chl ratios in response to greater medium P concentration that varied with media N concentration (Figure 2.3, Table 2.2). This non-linearity largely resulted from the especially high C:Chl measured at the lowest medium P concentrations used in this study (Figure 2.3). Molar ratios of C:N, C:P and N:P in Mougeotia all decreased with

greater medium P concentration and showed no interactive effects with medium N

(Figure 2.3, Table 2.2).

3.4 Homeostatic regulation of Mougeotia elemental composition

Using a compilation of all N:P ratios from both experiments, I found that

Mougeotia N:P ratios increased significantly as a non-linear function of medium N:P

ratio and that the slope of this relationship did not match that of the 1:1 line (where 1/H

= 1 would indicate complete non-homeostasis) in log-log space (Figure 2.4). Mougeotia

N:P ratios were above the 1:1 line for medium N:P ratios less than approximately 20 but,

above this medium N:P ratio, Mougeotia N:P ratios were less than that in the medium.

Using these data, I calculated the inverse of the regulatory coefficient, 1/H N:P , to be 0.32, which has been previously classified as weakly homeostatic (Persson et al. 2010). While

1/H could not be calculated for Mougeotia cellular %N and %P, the responses of these variables to changing medium N and P concentrations nonetheless indicated the nature of their homeostasis in this alga. The results suggest Mougeotia was not strictly homeostatic with respect to both nutrients, as an increase in their medium supply was accompanied by increased cellular content (Figure 2.4). In particular, Mougeotia %P increased quite sharply in response to higher medium P concentrations (Figure 2.4).

37 3.5 Elemental composition of lake-derived Mougeotia

Of the 108 FGA samples collected from the study lakes, 72 were composed of

>95% Mougeotia filaments. In these samples dominated by Mougeotia , I found stoichiometric results similar to those observed in my experiments with Mougeotia .

Algal %P, C:P ratios and N:P ratios varied most, while %C, %N and C:N ratios varied

least (Table 2.3). While a range of C:P ratios and N:P ratios was observed among these

lake samples, mean C:P and N:P ratios (1272 and 65, respectively) were consistent with

elemental ratios produced by intense P-limitation in my laboratory experiment. I found a

wide range of TDN (104-1126 µg L -1) and TDP (2.34-17.61 µg L -1) in the Kawartha

Lakes. As the TDN:TDP ratios ranged from 36 to 576 with a mean of 131, there

generally appeared to be a surplus of dissolved N relative to P in these lakes.

4. Discussion

I found that Mougeotia exhibited a flexible stoichiometry in response to changing nutrient supplies under laboratory conditions. This variability in molar C:P and

N:P ratios largely resulted from changes in Mougeotia P content and less from changes in N or C content. While there was inducible variability in algal N:P stoichiometry, the magnitude of this response was relatively small compared to the change in medium N:P and indicates Mougeotia is weakly homeostatic with respect to its N:P content. The variability in Mougeotia stoichiometry observed in the laboratory was generally matched by that found in samples from the Kawartha Lakes. While these field data are suggestive of moderate to strong P-limitation, the observed variability in natural Mougeotia also

38 indicates that these algae experience variable nutrient supplies in the littoral zones of these lakes, as previously documented by Cyr et al . (2009).

In the experiment where I exposed Mougeotia to a wide range of N concentrations (N experiment), I found that its C:N ratios and C:Chl ratios decreased

with greater N availability regardless of P concentration. In contrast, the effects of

medium N on Mougeotia C:P ratios, N:P ratios and Chl differed between the high and

low medium P concentrations. More specifically, C:P and N:P ratios increased with

greater medium N concentration but only at low P concentrations. This result is

consistent with greater P limitation in Mougeotia grown under high medium N

concentration. Similar results were recently found for lake phytoplankton where greater

P-limitation was associated with elevated N concentrations resulting from widespread N

deposition (Elser et al. 2009). Based on the experiments, it seems likely that P-limitation

will be intensified, and C:P and N:P ratios elevated in Mougeotia that experience higher

N concentrations even in relatively P-poor lake ecosystems. Elevated Mougeotia C:P

and N:P ratios due to greater N supply, as seen here, could have significant effects on

the littoral zone biogeochemistry by altering the food quality and decomposition rates of

this alga (Frost et al. 2002, Cross et al. 2005).

In the experiment where I exposed Mougeotia to a wide range of medium P

concentrations, C:P, C:N and N:P ratios of this FGA all decreased with increasing P

supply with no interactive effects observed with medium N concentration. I also found

no evidence that the responses of Mougeotia MSGR to increasing medium P

concentration were altered by changes in medium N concentrations. In most respects,

Mougeotia appears to have flexible P stoichiometry whereby the alga alters its cellular P

39 content as a singular function of medium P concentration. However, Mougeotia Chl and

C:Chl ratios were affected by a significant interaction between medium N and P in this

experiment. This interaction resulted from disproportionate reductions in Chl under low

medium N concentrations but only at the highest media P concentrations. N-limitation is

known to reduce chlorophyll production and to increase C:Chl ratios in many algal

species (Geider et al. 1998). Reducing medium N would be expected to intensify N- limitation in Mougeotia and elevate its C:Chl ratios, but this was only manifested in non-limiting P conditions.

I found that growth rates of Mougeotia were largely unresponsive to wide changes in medium N and P concentrations. This ability to continue increasing its mass, despite the creation of fairly severe nutrient limitation, may reflect the well-studied ability of algae to acclimate to low ambient nutrient concentrations (Rhee 1973, Healey and Hendzel 1980, Morel 1987). At the start of the experiment, previously acquired nutrients would have supplemented those immediately available in the medium (e.g.

Droop 1974). These nutrient stores would have supported algal growth even after dissolved supplies were eliminated but would have eventually been depleted. Additional reductions in cell quota would have allowed the alga to continue dividing and increasing mass despite an increasingly acute shortage of external nutrients. During this phase, one generally would expect increasing C:Chl ratios and C:N or C:P ratios (Geider et al.

1998), both of which were present in my experimental results. Altogether, this physiological acclimation would increase nutrient-use efficiency and may explain, at least in part, the lack of nutrient limitation of growth as evidenced in my experiments.

40 The relative insensitivity of Mougeotia growth rates to low medium nutrient

concentrations may also reflect a relatively slow growth rate of this alga. The highest

mass-specific growth rate I measured for this alga was 0.32 day -1, which is lower than that reported for unicellular algae (ranging from 0.5 to 1.0 day -1; Grover 1989). This

slow growth might be an artefact of the laboratory conditions (e.g. use of artificial

growth medium), result from limitation by light or dissolved inorganic carbon or from

an extended period of luxury P uptake. If so, my laboratory results may not accurately

reflect the ability of Mougeotia to grow in ambient lake conditions. While each explanation needs further study with Mougeotia , the growth conditions (high nutrient supply and sufficient light) I used are normally conducive to rapid growth of other algal taxa (e.g. Scenedesmus ) and the physiological data do not indicate strong or sustained carbon or light limitation.

An inherently slow growth rate, as I have documented, would reduce the rate of nutrient consumption by Mougeotia (Flynn 2009) . In that case, an inherently slow growth rate and an ability to reduce cellular P content would reduce the intensity of nutrient limitation on Mougeotia growth. For Mougeotia, the benefits of low nutrient requirements created by slow growth and its low nutrient content apparently outweigh the cost of having an inherently slow maximum growth rate. This finding is consistent with those reported by Sommer (1985), where several different algal species were exposed to pulse additions of P in laboratory chemostats. Over time, Mougeotia achieved the greatest biomass of the studied species, as it was able to maintain a consistent growth rate despite wide fluctuations in nutrient supply. Slow growth rates

41 and high nutrient storage capacities (including “luxury consumption” of nutrients) would allow Mougeotia to persist even during periods of acute nutrient limitation.

Elemental homeostasis reflects the degree to which an organism varies its elemental composition in response to changing nutrient supplies and, indirectly, its ability to acclimate to changing environmental conditions (Sterner and Elser 2002).

Non-homeostasis in the elemental content of algae has been speculated to be an adaptive trait to sustain growth in the face of inadequate nutrient supplies (e.g. Persson et al.

2010). I determined a value for 1/H N:P of 0.32 for Mougeotia , which places the alga in the “weakly homeostatic” category (Persson et al. 2010). At a proximate level, this weak homeostasis would potentially reflect constrained physiological abilities to store nutrients and/or change nutrient cell quotas; but this possibility seems inconsistent with an ability to continue growing under low nutrient supplies as discussed above. Future work should thus focus on determining the molecular and cellular mechanisms controlling the N and P content of Mougeotia and examining the ecological implications

of weak homeostasis for this FGA.

Mougeotia C:P stoichiometry was slightly more variable, and C:N and N:P ratios

less variable, in the field than in the laboratory. One likely explanation for this is that

natural P-supply varies widely and drives the greater variability in Mougeoti a P content.

The P supply to Mougeotia in the Kawartha Lakes may be more variable than indicated

by the water column samples because this alga has access to and uses P derived from

littoral sediments. Sediment P contributions to the water column are known to vary

substantially within littoral zones and with weather conditions (Cyr et al . 2009) which

could produce variable stoichiometry in FGA. In contrast, I found relatively little

42 variability in Mougeotia N content among field sites despite the wide range in TDN

concentrations and TDN:TDP ratios observed in the lake sampling. This could reflect

greater homeostatic tendencies in Mougeotia for this element, the majority of TDN

being present in organic forms and unavailable for direct algal uptake, and/or that P was

the element in least supply. Altogether, my results show that Mougeotia exhibits a

flexible stoichiometry and one that is probably driven, at least in the Kawartha Lakes, by

differences in P supply among sampling sites.

The elemental composition of algae is an important determinant of diverse

ecological processes and is known to strongly affect food web dynamics (Sterner and

Hessen 1994, Elser et al. 1998). The wide range in C:P ratios I found in Mougeotia may

translate into variable decomposition rates of its cells after senescence (Frost et al. 2002)

and altered rates of P released back into the water column. In addition, the relatively

slow but invariant growth rates found for Mougeotia in the laboratory suggest that the

alga exploits a unique ecological niche that allows it to persist in the face of faster-

growing algal competitors (Zhang et al. 2007). The ability of Mougeotia to achieve high

biomass with these inherently slow growth rates may partly result from relatively low

grazing rates on these algal filaments (e.g. Fulton 1988). This possibility, along with the

effects of metaphytic bloom age, location in the littoral zone, and the physiological

ability of this FGA growing in situ , needs more investigation to understand better the

nutritional ecology of this taxon. Regardless, a low growth rate and an ability to reduce

cellular P content would lower Mougeotia physiological demands for P and enable the

alga to continue growing - even when encountering relatively low P supplies. Given this,

future management and control of this nuisance FGA may prove difficult as it would be

43 unlikely to respond strongly to reductions in external P loading and lower water column

P concentrations. Altogether, this study of Mougeotia ’s growth and stoichiometry

provides much needed insight into the nutritional ecology of this important FGA in

freshwater ecosystems.

44 Table 2.1. Summary of Mougeotia’s stoichiometric and physiological traits created by varying media nutrient concentrations in the lab. Elemental ratios are molar ratios.

Response Variable Range Mean St. dev. C.V. (%)

%C of dry mass 36.54 – 48.83 40.41 2.43 6.00

%N of dry mass 1.19 – 7.88 3.36 1.57 43.17

%P of dry mass 0.09 – 1.20 0.49 0.32 65.35

C:P ratio 85.30 – 998.58 343.77 236.93 68.92

C:N ratio 6.40 – 41.16 15.63 7.56 47.73

N:P ratio 3.99 – 117.99 25.75 21.87 84.96

MSGR (day -1) 0.25 – 0.32 0.28 0.02 5.51

Final Chl ( µg·L -1) 9.99 – 169.07 81.56 43.90 53.82

Final C:Chl ratio 17.24 – 180.72 47.61 31.32 65.79

45 Table 2.2. Standard major axis regression statistics for the N and P experiments on

Mougeotia stoichiometry. Common slopes are provided for response variables that did not exhibit a significant response to the concentration of the second nutrient. Otherwise the slopes and their significance are provided for each respective nutrient concentration.

Experiment Response var. 1st Ind. var. 2nd Ind. var. Slope P-value

N C:N ratio Log N -0.23 < 0.001

N C:P ratio Log N Low P 110.5 < 0.05

N High P -13.6 0.48

N N:P ratio Log N Low P 17.0 0.07

N High P -0.51 0.84

N MSGR Log N 0.03 < 0.001

N Chl Log N Low P 45.2 < 0.001

N High P 57.4 < 0.05

N C:Chl ratio Log N -30.2 < 0.001

P Log C:N Log P -0.23 < 0.001

P Log C:P Log P -0.53 < 0.001

P Log N:P Log P -0.44 < 0.001

P Log MSGR Log P 0.044 < 0.001

P Chl Log P Low N 20.89 < 0.001

P High N 80.73 < 0.001

P Inv C:Chl ratio Inv P Low N -0.04 0.06

P High N -0.09 < 0.001

46 Table 2.3. Summary of Mougeotia stoichiometry and of total dissolved N and P from the Kawartha Lakes.

Variable Range Mean St. dev. C.V. (%)

%C of dry mass 26.60 – 51.81 40.08 3.85 9.60

%N of dry mass 1.39 – 5.02 2.64 0.72 27.24

%P of dry mass 0.02 – 0.28 0.11 0.05 48.06

C:P ratio 375.32 – 6294.32 1272.21 934.49 73.45

C:N ratio 10.64 – 32.29 18.74 4.48 23.89

N:P ratio 31.40 – 203.84 65.40 34.68 53.02

TDN ( µg·L -1) 103.66 – 1125.63 366.43 219.97 60.03

TDP ( µg·L -1) 2.34 – 17.61 6.83 2.57 37.60

TDN:TDP ratio 36.07 – 575.80 130.58 102.54 78.53

47 10000 N experiment )

-1 Low P High P g·L

µ 165 25 High N

1000 experiment P 412 8.23

100 Low N 41 0.82

Starting N concentration ( N concentration Starting 4.12 0.62 10 1 10 100 1000 Starting P concentration ( µg·L -1 )

Figure 2.1. Nitrogen (N) and phosphorus (P) concentrations in the growth media at the start of each experiment. Also provided are the molar N:P ratios bracketing the range of values present in each experiment.

48 0.4 40 common slopes ( p = 0.74) )

-1 0.3 30 0.2 20

common slopes ( p = 0.27) ratio C:N 0.1 10 MSGR (day MSGR 0.0 0 1.0 1.5 2.0 2.5 3.0 3.5 1.0 1.5 2.0 2.5 3.0 3.5 160 800 different slopes ( p < 0.005) )

-1 120 600 g·L

µ 80 400 C:P ratio C:P

Chl ( Chl 40 200 different slopes ( p < 0.01) 0 0 1.0 1.5 2.0 2.5 3.0 3.5 1.0 1.5 2.0 2.5 3.0 3.5 100 120 common slopes ( p = 0.36) 75 90 different slopes ( p = 0.001) 50 60 N:P ratio N:P

C:Chl ratio C:Chl 25 30

0 0 1.0 1.5 2.0 2.5 3.0 3.5 1.0 1.5 2.0 2.5 3.0 3.5 Log medium N ( µg·L -1) Log medium N ( µg·L -1)

Figure 2.2. The effect of medium N concentration (log-transformed) on Mougeotia physiological (MSGR (day -1), Chl ( µg L -1) and C:Chl ratio) and stoichiometric (C:N,

C:P and N:P ratios, by mol) responses at high and low P levels. Low P is shown with

open circles and a dashed trend line and high P is shown with open triangles and a solid

trend line. Common ( p > 0.05) or significantly different ( p < 0.05) slopes between high

and low P concentrations are noted. In cases where the slopes were not statistically

different, a single regression line based on all data together is plotted.

49 0.4 48 common slopes ( p = 0.77) ) -1 0.3 _ 36 0.2 24 C:N ratio

MSGR (day MSGR 0.1 common slopes ( p = 0.09) 12 0.0 0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5

200 different slopes ( p = 0.02) 1200 )

-1 150 900

g·L common slopes ( p = 0.53) µ 100 600 C:P ratio C:P

Chl ( Chl 50 300 0 0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 300 100

240 different slopes ( p < 0.05) 75 common slopes ( p = 0.30) 180 50 120 N:P ratio N:P

C:Chl ratio C:Chl 60 25 0 0 0.0 1.0 2.0 3.0 0.0 0.5 1.0 1.5 2.0 2.5 Log medium P ( µg·L -1) Log medium P ( µg·L -1)

Figure 2.3. The effect of medium P concentration (log-transformed) on Mougeotia physiological (MSGR (day -1), Chl ( µg L -1) and C:Chl ratio) and stoichiometric (C:N,

C:P and N:P ratios, by mol) responses at high and low N levels. Low N is shown with open circles and a solid trend line and high N is shown with open triangles and a dashed trend line. Common ( p > 0.05) or significantly different ( p < 0.05) slopes between high and low P concentrations are noted. In cases where the slopes were not statistically different, a single regression line based on all data together is plotted.

50 3 1/H = 1 y = 0.32 x + 0.90 2

_ R = 0.53 2 1/H = 0.32

1 Log N:P ratio N:P Log p < 0.05 0 -1 0 1 2 3 Log medium N:P ratio 1.2 y = 0.14 x + 0.20 R2 = 0.15 0.8

Log %N Log 0.4

p < 0.05 0.0 1.0 1.5 2.0 2.5 3.0 3.5 Log medium N ( µg·L -1) 0.4

0.0 y = 0.53 x - 1.19 R2 = 0.81 -0.4 Log %P Log -0.8 p < 0.001 -1.2 0.0 0.5 1.0 1.5 2.0 2.5 Log medium P ( µg·L -1)

Figure 2.4. The effect of medium N:P, N concentration and P concentration on

Mougeotia N:P ratio, Mougeotia %N and Mougeotia %P for cultured Mougeotia . The line of best fit and regression statistics are also shown for each. Note that the slope of the log medium N:P to log Mougeotia N:P relationship is equal to 1/H.

51 GENERAL CONCLUSION

The objectives of my study were to describe the stoichiometry of FGA in terms of means and variance, determine some sources of this variability in natural lake ecosystems on spatial and temporal scales, relate FGA stoichiometry to their growth rates and describe their degree of homeostasis. I fulfilled these objectives through the use of both field and laboratory experiments.

In the field study (Chapter 1), I found that FGA stoichiometry varied in a similar fashion as other algae (Duarte et al. 1992), with %P varying the most, followed by %N, and %C remaining relatively constant. As with other macroalgae, FGA had higher C content than other groups of algae, with a mean C:N:P of 1308:19:66 for field-collected

FGA in my study (Duarte et al. 1992). The variability in %P, C:P, and N:P was related to local variations in sediment P. A significant but weaker relationship also existed between FGA stoichiometry and total dissolved P.

In the laboratory study (Chapter 2), I determined that Mougeotia maintains a slow and steady growth rate around ~ 0.28 day -1 (+/- 0.02 stdev.) in both nutrient limiting and nutrient replete conditions. In addition, Mougeotia was found to be weakly homeostatic (1/H N:P = 0.32), meaning that it was able to vary its elemental stoichiometry

in proportion to external nutrient supplies more than, for example, higher level plants or

heterotrophs (Wang et al. 2012), but less than some other species of algae, especially microalgae (eg. scenedesmus , Rhee 1973).

The results suggest that FGA are tolerant to spatially and temporally variable nutrient supplies, as well as low nutrient concentrations. This explains why other algae

52 may go through boom and bust population cycles, whereas FGA are present in lakes of a wide range of nutrient statuses from ice-off to late fall. While slow growing, this nutrient-tolerant property would allow FGA to out-compete less tolerant algae in variable and nutrient-limiting environments (Sommer 1985). In addition, these algae likely obtain some degree of P from the sediment when growing on or near the sediment

(Carlton and Wetzel 1988), whereas planktonic algae derive most of their nutrients from the water column (Tilman et al. 1982). The long-term storage of P in sediment and

unique ability of FGA to utilize this source also allows FGA to out-compete non-benthic

algae over time (within the growing season), and to persist for years even if dissolved

nutrient concentrations decrease.

FGA are also tolerant to low light levels (Graham et al. 1996b), as well as low

and variable pH levels (Graham et al. 1996a, Klug and Fischer 2000), which further

explains their ability to persist when other algae do not. FGA of the genera Mougeotia are known to grow prolifically in acidic lakes (Turner et al. 1995b, Fairchild and

Sherman 1990, Graham et al. 1996a). This is not actually due to a preference for low pH, but rather a tolerance to it, that allows FGA to out compete other types of algae, which are acid intolerant (Graham et al. 1996a). Therefore, one method of preventing excessive growth of FGA is to address sources of acidification, such as anthropogenically-caused acid rain. Another potential method to control excessive growth of FGA would be to encourage populations of organisms that graze on these algae. Due to their size, FGA are inaccessible to micrograzers, such as daphnia (Kerfoot et al. 1988); but macrograzers, such as crayfish and tadpoles, have been shown to have an effect on FGA biomass (Dickman 1968, France and Welbourn 1992).

53 In addition, many studies have pointed to climate change (ie. irregular weather patterns and warmer water temperatures) and increased phosphorus inputs into lakes as the primary culprits behind eutrophication and excessive algal growth (Winter et al.

2011, Jeppensen et al. 2010). These large-scale and long-term environmental changes will have to be addressed on a global scale if we want to keep the lakes at their current trophic status. Taken together, the environmentally tolerant properties of FGA suggest that lake users will have to learn to live with small amounts of FGA, as complete eradication would be highly unlikely.

As a point of interest, I attempted to determine the critical N:P of Mougeotia in

Chapter 2. To the best of this author’s knowledge, no study has addressed the critical

N:P ratio for maximal growth of FGA of the family Zygnemataceae in a controlled laboratory setting. To address this knowledge gap, I combined the four nutrient enrichment experiments to obtain a wide range of TDN:TDP ratios, and found that

Mougeotia’s growth rate was more dependent on overall concentrations of TDN and

TDP than on the TDN:TDP ratio. I also found that while nutrient supplies varied considerably, Mougeotia ’s mass specific growth rate was relatively invariable. For these reasons, the “optimal” N:P ratio for Mougeotia was difficult to determine with confidence. It is my belief that a single critical N:P ratio is not meaningful for these slow-growing, weakly homeostatic algae. More studies should be done to test this hypothesis.

Overall, my experiments show that FGA (specifically Mougeotia , but also

Spirogyra ) are not strongly homeostatic, nor are they weakly homeostatic, and they can maintain growth rates in a wide range of nutrient conditions at the sacrifice of

54 chlorophyll a production, when light is non-limiting. The high C:nutrient ratios in field- collected specimens of FGA suggest that they are either N/P-limited, or that they require less of these nutrients to maintain a given growth rate than other algae. Excess C may be allocated to structural components, including the cell wall and storage compartments.

These characteristics liken FGA of the family Zygnemataceae to the category “storage strategist” as described by Sommer (1985), or “stress-tolerant” as described by Grime

(1997). These algae exhibit variable biomass nutrient content, but relatively constant and low growth rates (Sterner and Elser 2002). It is possible that the underlying mechanism behind Mougeotia’ s common occurrence and persistence can be summed up as this: in a variable nutrient environment, slow and steady wins the race!

55 LITERATURE CITED

American Public Health Association. 1992. Standard methods for the examination of water and wastewater . 18th ed. Washington (DC), APHA.

Anneville, O., Ginot, V., Druart, J-C., and Angeli, N. 2002. Long-term study (1974 – 1998) of seasonal changes in phytoplankton in Lake Geneva: a multi-table approach. Journal of Plankton Research. 24 (10): 993 – 1007.

Atkinson, M. J., and Smith, S.V. 1983. C:N:P ratios of benthic marine plants. Limnology and Oceanography. 28 : 568 – 574.

Bosch, I., Makarewicz, J.C., Lewis, T.W., and Bonk, E.A. 2009. Management of agricultural practices results in declines of filamentous algae in the lake littoral. Journal of Great Lakes Research . 35 : 90 – 98.

Cross W.F., Benstead J.P., Frost P.C. and Thomas S.A. 2005. Ecological stoichiometry in freshwater benthic systems: recent progress and perspectives. Freshwater Biology. 50: 1895 – 1912.

Crumpton, W.G., Isenhart, T.M. and Mitchell, P.D. 1992. Nitrateand organic N analyses with second-derivative spectroscopy. Limnology and Oceanography. 37: 907 – 913.

Dickman, M. 1968. The effect of grazing by tadpoles on the structure of a periphyton community. Ecology . 49 (6): 1188 – 1190.

Droop, M.R. 1974. The nutrient status of algal cells in continuous culture. Journal of the Marine Biological Association of the United Kingdom. 54 : 825 – 855.

Duarte, C.M. 1992. Nutrient concentration of aquatic plants: Patterns across species. Limnology and Oceanography. 37 (4): 882 – 889.

Elser, J.J., Charzanowski, T.H., and Sterner R.W. and Mills K.H. 1998. Stoichiometric constraints on food-web dynamics: a whole-lake experiment on the Canadian shield. Ecosystems. 1: 120 – 136.

Elser J.J., Andersen T., Baron J.S., Bergström A.K., Jansson M., Kyle M., et al. 2009. Shifts in lake N:P stoichiometry and nutrient limitation driven by atmospheric nitrogen deposition. Science . 326 : 835 – 837.

Falster D.S., Warton D.I. and Wright I.J. 2006. SMATR: Standardised major axis tests and routines, ver 2.0. (online) retrieved from: http://www.bio.mq.edu.au/ecology/SMATR/, 15 August 2012.

56 Flynn, K.J. 2009. Going for the slow burn: why should possession of a low maximum growth rate be advantageous for microalgae? Plant Ecology and Diversity. 2(2): 179 – 189.

Flynn, K.J. 2010. Do external resource ratios matter? Implications for modeling eutrophication events and controlling harmful algal blooms. Journal of Marine Systems. 83: 170 – 180.

France, R.L., and Welbourn, P.M. 1992. Influence of lake pH and macrograzers on the distribution and abundance of nuisance metaphytic algae in Ontario, Canada. Canadian Journal of Fisheries and Aquatic Sciences. 49 : 185 – 195.

Frost, P.C., Cross, W.F., and Benstead, J.P. 2005. Ecological stoichiometry in freshwater benthic ecosystems: an introduction. Freshwater Biology . 50 : 1781 – 1785.

Frost, P.C., Evans-White, M.A., Finkel, Z.V., Jensen, T.C. and Matztek, V. 2005. Are you what you eat? Physiological constraints on organismal stoichiometry in an elementally imbalanced world. Oikos . 109 : 18 – 28.

Frost P.C., Stelzer R.S., Lamberti G.A. and Elser J.J. 2002. Ecological stoichiometry of trophic interactions in the benthos: understanding the role of C:N:P ratios in lentic and lotic habitats. Journal of the North American Benthological Society. 21 : 515 – 528.

Gao, L., Zhang, L., Hou, J., Wei, Q., Fu, F., and Shao, H. 2013. Decomposition of macroalgal blooms influences phosphorus release from the sediments and implications for costal restoration in Swan Lake, Shandong, China. Ecological Engineering . 60 : 19 – 28.

Geider, R.J., and La Roche, J. 2002. Redfield revisited: variability of C:N:P in marine microalgae and its biochemical basis. European Journal of Phycology. 37 : 1 – 17.

Geider, R.J., MacIntyre, H.L., and Kana, T.M. 1998. A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature. Limnology and Oceanography. 43 (4): 679 – 694.

Graham, J.M., Arancibia-Avila, P., and Graham, L.E. 1996a. Effects of pH and selected metals on growth of the filamentous green alga Mougeotia under acidic conditions. Limnology and Oceanography . 41 (2): 263 – 270.

Graham, J.M., Arancibia-Avila, P., and Graham, L.E. 1996b. Physiological ecology of a species of the filamentous green alga Mougeotia under acidic conditions: light and temperature effects on photosynthesis and respiration. Limnology and Oceanography. 41 (2): 253 – 262.

57

Grime, J.P. 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. The American Naturalist . 111 : 1169 – 1194.

Grover, J.P. 1989. Phosphorus-dependent growth kenetics of 11 species of freshwater algae. Limnology and Oceanography. 34 (2): 341 – 348.

Gubelit, Y.I., and Berezina, N.A. 2010. The causes and consequences of algal blooms: the Cladophora glomerata bloom and the Neva esturary (eatern Baltic Sea). Marine Pollution Bulletin . 61 : 183 – 188.

Hagerthey, S.E., and Kerfoot, W.C. 1998. Groundwater flow influences the biomass and nutrient ratios of epibenthic algae in a north temperate seepage lake. Limnology and Oceanography. 43 (6): 1227 – 1242.

Hainz, R., Wöber, C., and Schagerl, M. 2009. The relationship between Spirogyra (Zygnemataophyceae, Streptophyta) filament type groups and environmental conditions in Central Europe. Aquatic Botany . 91 : 173 – 180.

Hawes I. 1989. Filamentous green algae in freshwater streams on Signy Island, Antarctica. Hydrobiologia , 172 : 1 – 18.

Healey, F.P., and Hendzel, L.L. 1980. Physiological indicators of nutrient deficiency in lake phytoplankton. Canadian Journal of Fisheries and Aquatic Sciences. 37 : 442 – 453.

Hecky, R.E., Campbell, P., Hendzel, L.L. 1993. The stoichiometry of carbon, nitrogen and phosphorus in particulate matter of lakes and oceans. Limnology and Oceanography. 38 : 709 – 724.

Hecky, R.E., and Kilham, P. 1988. Nutrient limitation of phytoplankton in freshwater and marine environments: a review of recent evidence on the effects of enrichment. Limnology and Oceanography. 33 (4): 796 – 822.

Hein, M., Pedersen, M.F., and Sand-Jensen, K. 1995. Size-dependent nitrogen uptake in micro-and macroalgae. Marine Ecology Progress Series. 118 : 247 – 253.

Higgins, S.N., Pennuto, C.M., Howell, E.T., Lewis. T.W., and Makarewicz, J.C. 2012. Urban influences on Cladophora blooms in Lake Ontario. Journal of Great Lakes Research . 38 : 116 – 123.

Hillebrand, H., and Sommer, U. 1999. The nutrient stoichiometry of benthic microalgal growth: Redfield proportions are optimal. Limnology and Oceanography , 44: 440 – 446.

58 Hillebrand, H. 1983. Development and dynamics of floating clusters of filamentous algae. Periphyton of Ecosystems. Edited by Wetzel. Dr. W. Junk, The Hague, The Netherlands. Pp 31 – 39.

Hillebrand, H., and Kahlert, M. 2001. Effect of grazing and nutrient supply on periphyton biomass and nutrient stoichiometry in habitats of different productivity. Limnology and Oceanography . 46 (8): 1881 – 1898.

Hillebrand H., Kahlert M., Haglund A-L., Berninger U-G., Nagel S. and Wickham, S. 2002. Control of microbenthic communities by grazing and nutrient supply. Ecology . 83 : 2205 – 2219.

Hotchkiss, A.T., Gretz, M.R., Hicks, K.B., and Brown, M.R. 1989. The composition and phylogenetic significance of the Mougeotia (Gharophyceae) cell wall. Journal of Phycology. 25 : 646 – 654.

Jeppensen, E., Moss, B., Bennion, H., Carvalho, L., De Meester, L., Feuchtmayr, H., Friberg, N., Gessner, M.O., Hefting, M., Lauridsen, T., Liboriussen, L., Malmquist, H.J., May, L., Meerhoff, M., Olafsson, J.S., Soons, M.B., Varhoeven, J.T.A. 2010. Onteraction of climate change and eutrophication. In: Kernan, M., Battarbee, R.W., and Moss, B. (eds). Climate change impacts on freshwater ecosystems. Wiley-Blackwell, Chichester, pp 119 – 151.

Kerfoot, W.C., Levitan, C., and DeMott, W.R. Daphnia-phytoplankton interactions: Density-dependent shifts in resource quality. Ecology . 69 (6) 1806 – 1825.

Kilham S.S., Kreeger D.A., Lynn S.G., Goulden C.E. and Herrera L. 1998. COMBO: a defined freshwater culture medium for algae and zooplankton. Hydrobiologia. 377 : 147-159.

Kilham S.S. 1990. Relationship of phytoplankton and nutrients to stoichiometric measures. In: Large Lakes: Ecological Structure and Function (Eds M.M. Tilzer and C. Serruya), pp. 403 - 413. Springer-Verlag, New York.

Klug, J.L., and Fischer, J.M. Factors influencing the growth of Mougeotia in experimentally acidified mesocosms. 2000. Canadian Journal of Fisheries and Aquatic Sciences. 57 : 538 – 547.

Marker A.F.H., Nusch E.A., Rai H. and Riemann B. 1980. The measurement of photosynthetic pigments in freshwaters and standardization of methods: Conclusions and recommendations. Archive für Hydrobiologie - Beiheft Ergebnisse der Limnologie. 14 : 91 – 106.

McCourt R.M., Hoshaw R.W. and Wang, J-C. 1986. Distribution, morphological diversity and evidence for polyploidy in North American Zygnemataceae (Chlorophyta). Journal of Phycology. 22 : 307 – 13.

59

Migné, A., Ouisse, V., Hubas, C., and Davoult. D. 2011. Freshwater seepages and ephemeral macroalgae proliferation in an intertidal bay: II. Effect on benthic biomass and metabolism. Estuarine, Costal and Shelf Science . 92 : 161 – 168.

Morel, F.M.M. 1987. Kinetics of nutrient uptake and growth in phytoplankton. Journal of Phycology. 23: 137 – 150.

Murphy, J., Riley, J.P. 1962. A modified single solution method for the determination of phosphate in natural waters. Analytica Chimica Acta. 27 : 31 – 36.

Naselli-Flores, L., and Barone, R. 2011. Fight on plankton! Or, phytoplankton shape and size as adaptive tools to get ahead in the struggle for life. Cryptogamie, Algologie . 32 (2): 157 – 204.

Presson, J., Fink, P., Goto, A., Hood, J.M., Jonas, J., and Kato, S. 2010. To be or not to be what you eat: regulation of stoichiometric homeostasis among autotrophs and heterotrophs. Oikos . 119 : 741 – 751.

Redfield, A.C. 1958. The biological control of the chemical factors in the environment. American Scientist. 46 : 205 – 221.

Rhee, G-Y. 1973. A continuous culture study of phosphate uptake, growth rate and polyphosphate in Scenedesmus sp. Journal of Phycology. 9: 495 – 506.

Rober, A.R., Wyatt, K.H., and Stevenson, R.J. 2011. Regulation of algal structure and function by nutrients and grazing in a boreal wetland. Journal of North American Benthological Society. 30 (3): 787 – 796.

Rosenberger, E.E., Hampton, S.E., Fradkin, S.C., and Kennedy, B.P. 2008. Effects of shoreline development on the nearshore environment in large deep oligotrophic lakes. Freshwater Biology . 53 : 1673 – 1691.

Sommer, U. 1985. Comparison between steady state and non-steady state competition: Experiments with natural phytoplankton. Limnology and Oceanography. 30 (2): 336 – 346.

Sterner, R.W. and Hessen, D.O. 1994. Algal nutrient limitation and the nutrition of aquatic herbivores. Annual Review of Ecology and Systematics. 25 : 1 – 29.

Sterner, R.W. and Elser, J.J. 2002. Ecological stoichiometry: the biology of elements from molecules to the biosphere. Princeton University Press, Princeton, NJ.

Tilman, D., Kilham, S.S., and Kilham, P. 1982. Phytoplankton community ecology: the role of limiting nutrients. Annual Review of Ecology and Systematics . 13 : 349 – 372.

60

Tilman, D. 1977. Resource competition between planktonic algae: an experimental and theoretical approach. Ecology. 58 : 338 – 348.

Townsend, S.A., Schult, J.H., Douglas, M.M., and Skinner, S. 2008. Does the Redfield ratio infer nutrient limitation in the macroalga Spirogyra fluviatilis ? Freshwater Biology. 53 : 509 – 520.

Turner, M.A., Howell, E.T., Robinson, G.G.C., Brewster, J.F., Sigurdson, L.J., and Findlay, D.L. 1995a. Growth characteristics of bloom-forming filamentous green algae in the littoral zone of an experimentally acidified lake. Canadian Journal of Fisheries and Aquatic Sciences. 52 : 2251 – 2263.

Turner, M.A., Robinson, G.G.C., Townsend, B.E., Hann B.J., and Amaral, J.A.1995b. Ecological effects of blooms of filamentous green algae in the littoral zone of an acid lake. Canadian Journal of Fisheries and Aquatic Sciences. 52 : 2264 – 2275.

Vinebrooke, R.D., Graham, M.D., Findlay, D.L., and Turner, M.A. 2003. Resilience of Epilithic algal assemblages in atmospherically and experimentally acidified boreal lakes. Ambio . 32 (3): 196 – 202.

White, M. 2006. Phosphorus and the Kawartha Lakes (Land use, Lake Morphology and Phosphorus Loading). Kawartha Lake Stewards Association. Lakefield Harold. available online at http://www.lakefieldherald.com/KLSA/MikeWhitereport.pdf

Wing, Y.T., Hadwen, W.L., and Fellows, C.S. 2011. Spatial and temporal variation in the ecological stoichiometry of aquatic organisms in an urban catchment. Journal of North American Benthological Society . 30 (2): 533 – 545.

Winter, J.G., DeSallas, A.M., Fletcher, R., Heintsch, L., Morley, A., Nakamoto, L., and Utsumi, K. 2011. Algal blooms in Ontario, Canada: increases in reports since 1994. Lake and Reservoir Management. 27 (2): 107-114.

Wyatt, K.H., Stevenson, R.J., Turetsky, M.R. 2010. The importance of nutrient co- limitation in regulating algal community composition, productivity and algal- derived DOC in an oligotrophic marsh in interior Alaska. Freshwater Biology. 55 : 1845 – 1860.

Zhang, X., Xie, P., Chen, F., Li., S., and Qin, J. 2007. Driving forces shaping phytoplankton assemblages is two subtropical plateau lakes with contrasting trophic status. Freshwater Biology . 52: 1463 – 1475.

61