Abstract Flood, Stacie Lynn

ABSTRACT

FLOOD, STACIE LYNN. Ecotoxicology of Estuarine Phytoplankton Growth and Toxicity in Response to Atrazine Exposures. (Under the direction of committee chair JoAnn M. Burkholder).

Estuarine phytoplankton play integral roles in ecosystem functioning by providing a basis for energy fluxes (through their carbon fixation capacity) and nutrient cycling, and by influencing community structure (through oxygen and food biosynthesis, and through both competitive interactions and interactions with other trophic levels). Some phytoplankton populations (strains) proliferate to such an extent that they can form nearly monospecific blooms, with devastating impacts on ecosystems. Anthropogenic inputs of chemical environmental contaminants such as toxic substances and nutrient pollution are frequently associated with developing harmful algal blooms (HABs). A critical knowledge gap in estuarine ecology concerns how phytoplankton assemblages respond to stressors in chronically disturbed habitats, and why some populations respond by producing phycotoxins. The available evidence suggests that herbicides such as atrazine can significantly affect phytoplankton assemblage composition, but little is known about how estuarine phytoplankton respond to multiple stressors in chronically disturbed habitats, and why some strains respond by producing toxins that can kill fish and other aquatic life. The goals of this research were to establish a robust protocol for testing the effects of a ubiquitous herbicide, atrazine, on estuarine phytoplankton, and then to use that protocol to compare the effects of atrazine with versus without nutrient enrichment on selected benign and harmful species. The benign species used was the cosmopolitan estuarine/marine alga, Dunaliella tertiolecta (Chlorophyta), whereas the HAB species included the ichthyotoxic haptophyte Prymnesium parvum (Haptophyta) and the toxigenic raphidophyte Chattonella subsalsa (Heterokontophyta, Raphidophyceae). The relative toxicity of atrazine to the three species in salinity 20 nutrient- replete media was D. tertiolecta > P. parvum > C. subsalsa (mean 96-hr IC50 = 139.3, 126.5, and 111.3 µg atrazine L-1, respectively). Response estimates were influenced by the strain, the geographic origin, the salinity, and the timing of the analysis endpoint. Nutrient limitation alleviated the growth-inhibiting effects of this herbicide for all three algal taxa to varying extent. Production of hemolytic substances in the HAB species increased under co-occurring nutrient stress and herbicide exposure, and the greatest toxicity was observed under combined imbalanced nitrogen/phosphorus levels and high atrazine exposures. These findings advance knowledge about how nutrient regimes and herbicides interact to control harmful estuarine phytoplankton population dynamics. The test platform that was developed here can be extended for use with other herbicides and other estuarine phytoplankton species.

Ecotoxicology of Estuarine Phytoplankton Growth and Toxicity in Response to Atrazine Exposures

by Stacie Lynn Flood

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Plant Biology

Raleigh, North Carolina

2017

APPROVED BY:

______Dr. JoAnn M. Burkholder Dr. W. Gregory Cope Committee Chair

______Dr. Stacy A. C. Nelson Dr. Parke A. Rublee

______Dr. Thomas R. Wentworth

DEDICATION This work is dedicated to my mother, Cindy Flood, who always supported my efforts to grow and encouraged my ambitions. This work is also dedicated to the memory of my grandmothers Pat Walton, who always believed in me and taught me to live with dignity and grace, and Mary Wilkinson, who kept me grounded, kept me close, and taught me to love fish, and to my great-grandmother Thelma Seals, who became a Ph.D. chemist under much more challenging circumstances than I will ever know. You are my heroes, and together you brought me to this point.

This work is also dedicated to the memory of Charlie, who was by my side throughout it all, making me laugh and keeping me focused on the beauty of the world around us. The wind has taken you from my sight, but my heart will always see you.

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BIOGRAPHY Stacie Flood was born and raised in Tulsa, Oklahoma where she attended Edison High School and developed a long-abiding interest in keeping and breeding aquarium fishes. She owned and operated a local aquarium supply shop in Tulsa and specialized in the building and care of planted aquaria and in the husbandry of Symphysodon species (Discus fish) before moving to the Greater St. Louis area to work at a tropical fish hatchery. While living in Missouri, Stacie’s primary interests shifted from the captive care of tropical fish to the ecology of North American native fishes, and she began to devote her attention to environmental sciences and the factors influencing aquatic ecosystems in United States. After earning an Associate’s Degree in General Sciences at John A. Logan College in Carbondale, Illinois, she enrolled in an undergraduate zoology program at Southern Illinois University where she was fortunate to work, in turns, on projects with or in the labs of Dr.s Matt Whiles, Frank Wilhelm and Brooks Burr studying aquatic invertebrate ecology, limnology and ichthyology, respectively. Following graduation, she applied to North Carolina State University and was accepted into the Center of Applied Aquatic Ecology under the direction of Dr. JoAnn Burkholder, who has guided her through an advanced level examination of the ecology of surface and ground waters of North Carolina, the U.S., and throughout globe. These discoveries and discussions were often challenging and always fascinating, and have provided Stacie with a greater appreciation for the scale of economic, political, cultural and civic influences on natural aquatic ecosystems - and never dulled the brilliance of the small moments of wonder found glancing through snorkel or microscope at the beauty that lies hidden under the surface of a stream.

TABLE OF CONTENTS LIST OF TABLES ...... vi

LIST OF FIGURES ...... viii

LIST OF ABBREVIATIONS ...... x

CHAPTER 1: Assessment of Atrazine Toxicity to the Benign Estuarine Phytoplankter, Dunaliella tertiolecta (Chlorophyta) Under Varying Nutrient Conditions ...... 1

1.1 INTRODUCTION ...... 1 1.2 MATERIALS AND METHODS ...... 5 1.2.i Basic Platform Design ...... 5 1.2.ii Culture Conditions ...... 7 1.2.iii Optical Density Versus Direct Cell Counts ...... 8 1.2.iv Population Growth Rate Measurements ...... 9 1.2.v Algal Bioassays ...... 9 1.2.vi Statistical Analysis ...... 12 1.3 RESULTS ...... 13 1.4 DISCUSSION ...... 15 1.5 FIGURES AND TABLES ...... 20 1.6 REFERENCES ...... 35 CHAPTER 2: Persistence Under Sub-Optimal Conditions; Imbalanced Nutrient Regimes Increase Prymnesium parvum Resilience to Herbicide Exposure...... 46 2.1 INTRODUCTION ...... 46 2.2 MATERIALS AND METHODS ...... 49 2.2.i Experimental Organism, Strains, and Culture Conditions ...... 49 2.2.i.a. Toxic Prymnesium parvum ...... 49 2.2.i.b. Strains and Culturing ...... 51 2.2.ii Optical Density Versus Direct Cell Counts ...... 52 2.2.iii Population Growth Rate Measurements ...... 53 2.2.iv Algal Bioassays ...... 53 2.2.v Hemolytic Activity Assay Protocols ...... 55 2.2.v.a Blood Collection and Erythrocyte Preparation ...... 56

2.2.v.b Preparation of Algal Extracts ...... 56 2.2.v.c Hemolytic Assays ...... 57 2.2.vi Statistical Analysis ...... 58 2.3 RESULTS ...... 59 2.3.i Atrazine x Nutrient Bioassays ...... 59 2.3.ii Erythrocyte Lysis Assays ...... 62 2.4 DISCUSSION ...... 64 2.5 FIGURES AND TABLES ...... 70 2.6 REFERENCES ...... 94 Chapter 3: Examination of Chattonella subsalsa (Raphidophyceae) Growth and Hemolytic Activity Responses to Co-Occurring Environmental Stressors ...... 106 3.1 INTRODUCTION ...... 106 3.2 MATERIALS AND METHODS ...... 108 3.2.i Experimental Organism ...... 108 3.2.ii Culture Conditions ...... 109 3.2.iii Optical Density Versus Direct Cell Counts ...... 110 3.2.iv Population Growth Rate ...... 111 3.2.v Bioassays ...... 112 3.2.vi Protocols for Assays of Hemolytic Activity ...... 115 3.2.vi.a Blood Collection and Erythrocyte Preparation ...... 115 3.2.vi.b Preparation of Algal Extracts ...... 116 3.2.vi.c Hemolytic Assays ...... 117 3.2.vii Statistical Analysis ...... 118 3.3 RESULTS ...... 119 3.3.i Growth ...... 119 3.3.ii Atrazine Effects ...... 120 3.3.iii Toxicity as Hemolytic Activity ...... 121 3.4 DISCUSSION ...... 123 3.5 FIGURES AND TABLES ...... 127 3.6 REFERENCES ...... 144 APPENDIX ...... 156

LIST OF TABLES

Assessment of Atrazine Toxicity to the Benign Estuarine Phytoplankter, Dunaliella tertiolecta (Chlorophyta) Under Varying Nutrient Conditions Table 1.1 List of species used for aquatic toxicity testing in the ECOTOX database, ranked by the number of different studies on all chemicals ...... 20 Table 1.2 List of algal groups and species used for aquatic toxicity testing in the ECOTOX database, ranked by number of different studies on atrazine . 21 Table 1.3 Marine algal species recommended for use in guideline testing protocols by international organizations ...... 22 Table 1.4 Media recipes used in D. tertiolecta culture and testing ...... 23 Table 1.5 The 96-hr point estimates based on effects on biomass estimated by manual cell counts ...... 25 Table 1.6 The 96-hr point estimates based on effects on biomass estimated by OD ...... 26 Table 1.7 The atrazine effect estimates over time based on D. tertiolecta growth rates, as estimated by OD...... 29 Table 1.8 Influence of treatment variables on D. tertiolecta growth at 96-hr for nutrient x atrazine bioassays ...... 33 Table 1.9 Influence of treatment variables on D. tertiolecta growth at day 20 ...... 34 Persistence Under Sub-Optimal Conditions; Imbalanced Nutrient Regimes Increase Prymnesium parvum (Haptophyta) Resilience to Herbicide Exposure Table 2.1 Collection locations in the U.S. for the tested Prymnesium parvum culture isolates ...... 71 Table 2.2 Media recipes used in Prymnesium testing and cultures ...... 72 Table 2.3 Erythrocyte lysis assay buffer preparation ...... 72 Table 2.4 Blood storage medium preparation ...... 73 Table 2.5 Changes in atrazine effects in nutrient-replete media over time (µg/L) and maximum growth rate kmax for all strains tested...... 75 Table 2.6 Median half-maximal activity estimates (units, µg L-1) over time for all strains in nutrient-replete media...... 76 Table 2.7 Heat map of kmax values for the growth response of P. parvum strains tested in different treatments over time ...... 77 Table 2.8 Results of 4-way ANOVA testing growth rates of P. parvum (all three strains) at 96-hr or 10-day endpoints as dependent variables. Fixed effects are atrazine concentration (Atra), nutrient regime (Nutr), geographic origin (strain) and salinity ...... 83

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Table 2.9 Results of a 3-way ANOVAs for each of the three P. parvum strains (TX, NC, SC) testing growth rates at 96-hr or 10-day endpoints as dependent variables. Fixed effects are atrazine concentration (Atra), nutrient regime (Nutr), and salinity ...... 84 Table 2.10 Results from a 3-way ANOVA testing normalized hemolytic activity as the dependent variable and atrazine concentration (Atra), nutrient regime (Nutr), and geographic origin (Strain) as the fixed effects...... 86 Table 2.11 Results from a 4-way ANOVA testing hemolytic effects at all salinities (NC and TX strains only) against atrazine concentration (Atra), nutrient regime (Nutr), geographic origin (Strain), and salinity ...... 87 Table 2.12 Results from a 3-way ANOVA testing normalized hemolytic activity in individual strains as the dependent variable and atrazine concentration (Atra), nutrient regime (Nutr), and salinity as the fixed effects ...... 88 Examination of Chattonella subsalsa (Raphidophyceae) Growth and Hemolytic Activity Responses to Co-Occurring Environmental Stressors Table 3.1 Culture media recipes used for culturing and assays of Chattonella subsalsa…………...... 127 Table 3.2 Growth inhibition and lowest-observed effect concentrations over time in 10-day bioassays with Chattonella subsalsa ...... 128 Table 3.3 Growth inhibition and lowest-observed effect concentrations over time in 28-day replete media bioassays with Chattonella subsalsa ...... 129 Table 3.4 Results of two-way ANOVAs examining atrazine concentration and nutrient regime effects on growth rates of Chattonella subsalsa in bioassays at different endpoints...... 130 Table 3.5 Results of two-way ANOVAs examining atrazine concentration and nutrient regime effects on hemolytic activity of Chattonella subsalsa in bioassays at the 10-day and 28-day endpoints...... 131

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LIST OF FIGURES

Assessment of Atrazine Toxicity to the Benign Estuarine Phytoplankter, Dunaliella tertiolecta (Chlorophyta) Under Varying Nutrient Conditions Figure 1.1 Light micrograph of Dunaliella tertiolecta, the benign test species used to design the testing protocol ...... 20 Figure 1.2 Correlation statistics between optical density and manual cell counts describing procedures for selection of most appropriate wavelength for use in monitoring test activity...... 24 Figure 1.3 Results of 96 hr atrazine exposure for Dunaliella tertiolecta ...... 27 Figure 1.4 Distribution of biomass-derived effect estimates determined by manual cell counts and OD ...... 28 Figure 1.5 Test control performance over time for the 20-day D. tertiolecta atrazine bioassays ...... 30 Figure 1.6 Growth response of Dunaliella tertiolecta to atrazine exposure under different nutrient regimes...... 31 Figure 1.7 Atrazine-induced changes in Dunaliella tertiolecta growth under different nutrient regimes over time ...... 32 Persistence Under Sub-Optimal Conditions; Imbalanced Nutrient Regimes Increase Prymnesium parvum (Haptophyta) Resilience to Herbicide Exposure Figure 2.1 Light micrograph Prymnesium parvum (Texas strain) ...... 70 Figure 2.2 Example of the relationship between optical density (OD) and Prymnesium parvum population density (PD) ...... 74 Figure 2.3 Cell densities of control cultures for the Texas strain of P. parvum at salinity 20 ...... 78 Figure 2.4 Growth rates of the NC strain of P. parvum at different salinities in all treatments at day 10 ...... 79 Figure 2.5 Atrazine inhibition curves in salinity 10 for the South Carolina strain of P. parvum over time...... 80 Figure 2.6 Atrazine inhibition curves for all P. parvum assays over time, by nutrient regime ...... 81 Figure 2.7 Atrazine inhibition curves for all P. parvum assays over time, by salinity level ...... 82 Figure 2.8 Differences in relative production of hemolytic compounds by P. parvum strains (TX, NC) in control treatments at two salinity levels (10, 20) ..... 85 Figure 2.9 Relative production of hemolytic activity plotted against atrazine concentration for all tested strains, showing line-of-fit for non-linear relationships and corresponding R2, and confidence intervals as colored zones corresponding to different nutrient treatments (half maximal

growth effect concentrations for atrazine are provided as insets with each assay display) ...... 89 Figure 2.10 Log-normal relationships between hemolytic activity and atrazine concentration, in salinity 20 f/2 media ...... 90 Figure 2.11 Log-normal relationships between hemolytic activity and atrazine concentration, in salinity 10 f/2 media ...... 91 Figure 2.12 Linear relationships between growth rates and hemolytic activity in P. parvum strains tested in salinity 10 ...... 92 Figure 2.13 Linear relationships between growth rates and hemolytic activity in P. parvum strains tested in salinity 20 ...... 93 Examination of Chattonella subsalsa (Raphidophyceae) Growth and Hemolytic Activity Responses to Co-Occurring Environmental Stressors Figure 3.1 Chattonella subsalsa cells fixed with 1% PFA + 1% glut or fixed with acidic Lugol’s solution …………...... 132 Figure 3.2 Example of the modeled relationships between optical density (OD) and C. subsalsa cell density that was used to monitor population growth for atrazine inhibition assays ...... 133 Figure 3.3 Growth of C. subsalsa in control cultures during 10-day and 28-day bioassays...... 134 Figure 3.4 Growth curves for Chattonella subsalsa in 10-day and 28-day bioassays with atrazine under nutrient-replete conditions ...... 135 Figure 3.5 Variation in atrazine half-maximal effective concentrations for Chattonella subsalsa in 28-day nutrient-replete bioassays over time ... 136 Figure 3.6 Dose-response relationships showing increasing potency of atrazine to C. subsalsa in 10-day, nutrient-replete growth bioassays …………...... 137 Figure 3.7 Inhibition concentration curves for different nutrient regimes tested in 10- day C. subsalsa atrazine exposure assay ...... 138 Figure 3.8 Inhibition concentration curves for different nutrient regimes tested in 28- day atrazine exposure assays, and measured at 10 and 28 days ...... 139 Figure 3.9 Normalized hemolytic activity in control cultures, measured at end of 10- and 28-day assays ...... 140 Figure 3.10 Growth rates (divisions day-1) and normalized hemolytic activity in all tested atrazine concentrations, nutrient treatments and controls for C. subsalsa ...... 141 Figure 3.11 Log-normal relationships between hemolytic activity (normalized to cell density and sample volume) and atrazine concentration or growth rates at 10-day endpoint ...... 142 Figure 3.12 Hemolytic activity at different test endpoints, normalized to cell density and sample volume, in various nutrient regimes under a range of atrazine concentrations ...... 143

LIST OF ABBREVIATIONS

APHA: American Public Health Association ASTM: American Society for Testing and Materials CAAE: Center for Applied Aquatic Ecology CASRN: Chemical Abstracts Service (CAS) Registration Number

ECx: Effect Concentration (x% of treatment controls) ELA: Erythrocyte Lysis Assay HA: Hemolytic Activity

ICx: Inhibition Concentration (x% of treatment controls) ISO: International Organization for Standardization IU: International Units LOEC: Lowest Observed Effect Concentration NOEC: No Observed Effect Concentration OD: Optical Density OECD: Organization for Economic Cooperation and Development ROS: Reactive Oxygen Species RPMI: Roswell Park Memorial Institute medium U.S. EPA: United States Environmental Protection Agency

CHAPTER 1: Assessment of Atrazine Toxicity to the Benign Estuarine Phytoplankter, Dunaliella tertiolecta (Chlorophyta) Under Varying Nutrient Conditions

1.1 INTRODUCTION

Located at the land-water interface, estuaries are extremely vulnerable to land-based pollution (Howarth 2008; Crossett et al. 2014). They have been evaluated as the most anthropogenically degraded habitat types on Earth (Lytle and Lytle 2001; Scott et al. 2006), and are especially threatened by nutrient pollution (cultural eutrophication - Bricker et al. 2008) and other chemical environmental contaminants (CECs) such as herbicides (DeLorenzo et al. 2001, McCarthy et al. 2007). Agricultural nonpoint source pollution is a leading source of water quality impairment and a major contributor to contamination of estuaries (Bricker et al. 2008) and according to the United States Environmental Protection Agency (U.S. EPA 2000, 2002), the leading causes of coastal impairment include pesticides and nutrients. Most pesticides entering estuaries are herbicides and, among these, atrazine (2- chloro-4-ethylamino-6-isopropylamino-s-triazine) is one of the most widely used agricultural herbicides in the U.S. (U.S. EPA 2003). This triazine herbicide is used to control annual broadleaf and grassy weeds in crops such as corn, sorghum, and sugarcane, and on golf courses (Solomon et al. 1996). Atrazine is relatively mobile and persistent (Koc 40-394, Kd 0.20–12.6; Pereira and Rostad 1990; Solomon et al. 1996; Giddings 2005; Jablonowski et al. 2011) and is the most commonly detected pesticide in U.S. surface waters (Gilliom et al. 2006). More than 20.4 million kilograms (kg) (45 million pounds) of atrazine were applied in the U.S. during 2014 (U.S. Department of Agriculture – National Agricultural Statistics Service 2017). The Albemarle-Pamlico Estuarine System was evaluated by the National Oceanic and Atmospheric Administration as historically having the highest amount of pesticide use of any estuarine watershed in the U.S. (Pate et al. 1992), but pesticide data are collected at relatively few sites, quarterly at multi-year intervals or less frequently (U.S. EPA STORage and RETrieval (STORET) database).

Atrazine can remain in some soil types for as long as 4-22 years (U.S. EPA 2007a; Jablonowski et al. 2008, 2009) and has a long aqueous half-life (335 days in light - Rohr and McCoy 2010; 742 days in darkness - Ryberg et al. 2010). Biodegradation of atrazine is minimal due to the presence of the s-triazine ring (Cunningham et al. 1984; Kemp et al. 1985; Howard 1991) leading to potential for prolonged exposures to aquatic biota (Glotfelty et al. 1984; Pinckney et al. 2002). Environmental concentrations as high as 691 µg/L have been reported in Midwestern streams (Lockert et al. 2006). Atrazine is also a common estuarine contaminant (Readman et al. 1993; Alegria et al. 2000; Pennington et al. 2001; Southwick et al. 2003; McCarthy et al. 2007). During significant runoff periods, 2-3% of the atrazine applied to crops can be transported from the point of application to estuaries (e.g. Glotfelty et al. 1984). Atrazine is frequently detected in the Chesapeake Bay region, where stormwater runoff has contained concentrations as high as 480 µg/L (Forney and Davis 1981; Eisler 1989; Lehotay et al. 1998). Like most pesticides, it is infrequently measured in many aquatic systems, so the databases for many waterbodies have concerning gaps in available information (Starr et al. 2017 and references therein).

Atrazine is a photosynthetic inhibitor that targets the QB plastoquinone-binding niche in the D1 protein of the PSII apparatus (Forney and Davis 1981; Fuerst and Norman 1991) and interrupts photosynthetic electron transport via competitive binding, resulting in oxidative stress, photooxidation of chlorophyll and, ultimately, cell necrosis (Moreland and Hill 1962). Exposure to phototrophs can also inhibit hormones, cell division, pigment synthesis, lipid synthesis, and cell metabolism (Radosevich et al. 2007). In aquatic ecosystems, atrazine has depressed primary productivity of phytoplankton assemblages (Solomon et al. 1996; Graymore et al. 2001). Toxicants affecting phytoplankton populations can potentially affect higher trophic levels and food web structure (Walsh and Merrill 1984; Hylland and Vethaak 2011). Adverse effects of atrazine on the photosynthesis of phytoplankton and other primary producers in aquatic systems have occurred at concentrations as low as 1-10 µg L-1 (Solomon et al. 1996, Giddings 2005), which commonly occur in surface waters (Kemp et al. 1985; Lakshminarayana et al. 1992).

Although phytoplankton are the foundation of pelagic food webs, toxicity data for estuarine phytoplankton and herbicides are sparse, especially in estuarine and marine waters (Starr et al. 2017). In addition, there has been no systematic investigation about how nutrient availability affects atrazine toxicity in estuarine phytoplankton. Such assessments require development of novel approaches. Historically in the regulatory decision-making process, much of the information on aquatic toxicity of chemicals was derived from tests on daphnids and fish (Mohan and Hosetti 1999). Algae repeatedly have been found to be more sensitive to many toxic substances, including many herbicides, than animals (Walsh et al. 1980; Blanck et al. 1984; Hughes et al. 1988; Lewis 1990b, 1995). Yet, there is still a paucity of toxicological data available for microalgae compared to other groups (Eisentraeger et al. 2003; e.g., Table 1.1). Toxicity determinations for chemical hazard assessments under the Toxic Substances Control Act have begun to include short-duration (< 14 days) inhibition tests with microalgae (algal toxicity bottle tests – e.g., Stephan et al. 1985; U.S. EPA 1985). Only a handful of species, typically “hardy” species from freshwaters, are commonly used in direct toxicity tests of algae (Table 1.2) (Lewis 1990a; Fairchild et al. 1998). The freshwater bias applies to indirect effects as well, which are considered as important or more important than direct effects in natural environments. More than 80% of studies about indirect effects of contaminants in aquatic systems have been conducted in freshwaters (Fleeger et al. 2003). Clearly, estuarine and marine microalgae have been too infrequently considered in developing protective criteria for chemical contaminants (Blanck et al. 1984; Lewis 1995; Lytle and Lytle 2001; Eklund and Kautsky 2003). The data generated from freshwater microalgae are inadequate to protect estuarine phytoplankton assemblages from chemical hazards because the freshwater microalgae were largely selected for being “good laboratory organisms,” (Nyholm and Källqvist 1989), meaning easy to culture, rather than for sensitivity or ecological relevance. The “hardy” freshwater species are generally more tolerant of chemical hazards than sensitive species; they are not good indicators of freshwater phytoplankton assemblage response, and they are not good surrogates for estuarine or marine microalgae (Lewis 1995; Graymore et al. 2001).

Microalgal taxa are known to vary substantially, often by several orders of magnitude, in response to the same toxicant (Blanck et al. 1984; Lewis 1995; Rojíčková and Maršálek 1999; Faust et al. 2003; Janssen and Heijerick 2003). Moreover, bioassay toxicity estimates are chemical-, test species-, and endpoint-dependent, and also extremely sensitive to variations in physical conditions such as media, lighting, and culture volume (Blanck et al. 1984; Kasai et al. 1993; Lewis 1995; Stauber 1995; Blaise et al. 1997; Choi et al. 2012). There typically is high intra-species variation (strain differences) as well; remarkably, different populations of the same test species had EC50 (concentration causing an effect on 50% of the tested population) estimates that differed among laboratories by many orders of magnitude, even for simple compounds (Nyholm 1985; Kasai et al. 1993; Behra et al. 1999). Several researchers have recommended an alternate approach to develop protective criteria – that is, the use of standardized methods on a number of species in a “test battery”, that is, tests of several algal species from a broad taxonomic range, selected considering the community structure of ecosystems potentially affected by the toxicant (Blanck et al. 1984; Peterson et al. 1994; Rojíčková and Maršálek 1999). Guideline testing protocols have been developed to examine marine microalgae (Table 1.3), and these data are gradually being incorporated chemical safety assessments. Where “non-standard” test species have been used in toxicity testing, the approach has generally been to screen a small number of “data-rich” compounds against the responses of multiple test species; for example, Swanson et al. (1991) reported phytotoxicity of various pesticides to 68 algal species. This approach enables assessment of common test species to a given toxicant for quality assurance purposes, while also providing novel information about the scope of potential toxicant activity in non-model organisms – but taxonomic resolution generally has been poor, and restricted to a small number of freshwater groups. A survey of the ECOTOX database (Table 1.2) revealed that more than a third (36%) of the studies on atrazine listed under the grouping “Algae, Moss, Fungi” were of green algae, 18% were of diatoms, 13% were of cyanobacteria (blue-green algae), and 7% were of other groups - and only ~25% of the 719 studies on atrazine studies listed under the group “Algae” provided genus and species information.

Here, ecologically relevant endpoints were used to assess the responses of selected estuarine phytoplankton to atrazine exposures, using methods designed (i) to allow comparisons with toxicity estimates from the published literature, and (ii) to enable screening of multiple species in future studies. A robust testing platform was developed to examine ecologically relevant responses to atrazine by a robust model organism. Use of a robust, benign model organism enabled comparison of the response data with assessments of relative test performance observed in the same species by other researchers, which provided confidence in the experimental regime. This platform can then be used to screen multiple species under similar exposure conditions. By reporting standardized endpoints, the toxicity estimates developed here can be compared to values previously reported in the literature or publicly available databases. The testing platform designed here will allow for herbicide toxicity testing of as-yet-untested, ecosystem-disrupting harmful algal species (Sunda et al. 2006) to ensure that recommended exposure levels to atrazine adequately protect ecosystem function by including harmful algae in assessments. The experiments also enabled an examination of how the estuarine microalgal response to atrazine is influenced by unbalanced nutrient regimes, which now characterize most U.S. estuaries (Bricker 2008; Glibert et al. 2011; Glibert and Burkholder 2017).

1.2 MATERIALS AND METHODS

1.2.i Basic Platform Design

A hardy model organism with an established history of use in toxicity testing, Dunaliella tertiolecta Butcher (Chlorophyta) (DeLorenzo 2009) (Fig. 1.1), was selected to assess effects of atrazine, a compound with an extensive toxicity reference library, so that the testing platform can be easily applied to tests of other estuarine phytoplankton. This species has a cosmopolitan distribution in estuaries and marine coastal waters (McLachlan 1960). It is relatively easy to maintain in laboratory cultures (Byrd et al. 2016) and is a standard test organism (U.S. EPA 1974; ASTM 1990; APHA 1995; DeLorenzo 2009), allowing for inter- laboratory comparisons of results.

The responses of D. tertiolecta to atrazine were quantified using modifications to standard 96-hr algal bioassays (ASTM 1994), including benchmarks to enable comparison of sensitivities with findings from other researchers. Control performance criteria were also selected from available guideline protocol recommendations (U.S. EPA 1971; Walsh 1988; ASTM 1996; Blaise et al. 1997; Geis et al. 2000) to establish quality checks on test performance. These minimum acceptance criteria included control variability less than 20%; control biomass increased by more than 20% by test end; and growth in solvent controls did not vary from media-only cultures by more than 20%. Other environmental conditions were monitored to confirm that assay performance was compatible with standard test methods: pH did not vary by more than 1.5 units (ISO 1995); consistent lighting was maintained at ~100 µE of photosynthetically active radiation m-2 s-1 (106.3 + 26.2 µE m-2 s-1; cool white fluorescent tubes); and temperature was maintained at 24 ± 2°C (ambient air temperature). In addition, the vessel volume, cell inoculation density, and growth phase were stipulated. It was important to define these environmental factors because within-species variation in tolerance versus sensitivity to toxicants has been related to pH (Peterson et al. 1984; Fahl et al. 1995), temperature (Chalifour and Juneau 2011), light regime (Guasch et al. 1997; Guasch and Sabater 1998; Sjollema et al. 2014), growth phase (Stauber 1995), cell density (Tang et al. 1998), and container volume (Stratton and Giles 1990).

There has been debate about which endpoint parameter – growth rate or biomass – is most appropriate for use in deriving effect estimates to describe toxicity (Nyholm 1985,

1990). Therefore, both biomass (ICbx) and growth rate (ICrx) (concentrations which caused inhibition relative to control cultures) values were presented based on 96-hr IC10 and IC50, along with the no-observed effect (NOEC) and lowest-observed effect (LOEC) concentrations. Biomass-based effect estimates can show a higher degree of scatter, and are more influenced by slight variations in the specific test system used, so growth rate has previously been shown to be more robust (Nyholm 1985; Janssen and Heijerick 2003). Therefore, the bulk of this analysis focused on effect estimates derived from the population growth rate parameter. The number of assays, time intervals, and samples needed for these

and other (Chapters 2, 3) experiments could not have been completed using time-intensive cell counts via light microscopy, and required a more rapid way to quantify D. tertiolecta abundance. Optical density (OD) measurements previously had been shown to have a high correlation with cell numbers (Kasai et al. 1993; Pennington and Scott 2001), is far less labor-intensive, and has better precision than manual cell counting (Geis et al. 2000). Thus, confirming the strength of the relationship between cell counts and OD (r2 > 0.98; Fig. 1.2), we used OD techniques modified from Sorokin (1973) to quantify cell densities for population growth rates.

To address the ecologically relevant condition of altered nutrient regimes and herbicide co-exposures, a modified version of the standard 96-hr bioassay platform was assessed over an extended duration, which was required (i) to allow for complete nutritional deficits to develop when using test inoculations from the same parent culture, and (ii) to ensure that test rigor was maintained when the methodology was applied to more slowly growing harmful algal species. Evaluations of complete nutritional defects are more appropriately examined following more than one week of growth in culture (Nyholm and Lyngby 1988). Therefore, the assay was amended to assess 10-d or 20-d endpoints, which has been reported to be an appropriate time frame for examining atrazine impacts on phytoplankton (Tang et al. 1997). The atrazine concentration was analytically confirmed at both the beginning and end of tests, and did not decrease over the extended duration (data not shown).

1.2.ii Culture Conditions

A non-axenic strain of Dunaliella tertiolecta from the University of Texas Culture Collection (UTEX LB 999; Fig. 1.1) was grown in batch cultures within 250-mL Erlenmeyer flasks containing 100 mL of salinity 20-modified L1–Si medium (Guillard and Hargraves 1993). Light was measured using a 4π Biospherical QSL 101 quantum lab sensor (BSI, San Diego, California, USA) and a 16-hr : 8-hr light:dark cycle. Cultures were mixed by gentle hand agitation once per day and sterile-transferred using aseptic techniques under a laminar

flow hood every 7 to 10 days as needed to maintain log phase growth. All media (culture and test) were prepared by adjusting the salinity of ultrapure Milli-Q water (18 MΩ cm−1 at 25 °C) with Instant Ocean® artificial sea salts (Aquarium Systems, Blacksburg, VA, USA) to salinity 20. Salinity was measured using a YSI model 3200 conductivity/salinity bridge and cell (YSI, Yellow Springs, Ohio, USA). The pH was maintained at 8.1 + 0.2 using HCl or NaOH, and was measured using an Orion Versa Star Pro multi-parameter meter equipped with an Orion ROSS Sure-Flow pH electrode (Thermo Scientific, Waltham, Massachusetts, USA). Trace mineral solution was added before autoclaving. Vitamin solution was sterile- filtered (Whatman® Puradisc cellulose acetate syringe filters, nominal pore size 0.2 μm, Sigma-Aldrich, St. Louis, Missouri, USA), added to the autoclaved media aseptically, and the final media was vacuum-filtered (Corning® cellulose acetate filters, 0.22-µm nominal pore size, Sigma-Aldrich) before storage at 4°C for no more than 30 days. All equipment was initially cleaned by scrubbing in hot, soapy tap water, and then cleaned by acid stripping in 10% HCl (v/v). All equipment was sterilized by autoclaving, and all glassware and non- plastic equipment used in culturing and experiments was rinsed with pesticide-grade acetone, before use.

1.2.iii Optical Density Versus Direct Cell Counts

Samples (1 mL) were randomly drawn from each parent culture and thoroughly vortexed before absorbance was read on a Thermo Scientific Spectronic GENESYS 2 spectrophotometer (Thermo Scientific) using a freshly wiped Hellma® quartz cuvette (Sigma-Aldrich) with a 10-mm path length. All readings were performed in duplicate and adjusted using a media blank as a measurement baseline. All parent cultures were scanned individually and the three highest peaks were selected as the most appropriate wavelengths for analyzing a series of 8-point dilution curves, using progressive dilutions of parent stock culture in growth media. The curve that produced the ΔOD/ΔPD [change in optical density over change in population density (Sorokin 1973)] closest to unity (1) was used to determine the growth rates from each stock culture. Cell counts to determine ΔPD were obtained by collecting random 1-mL samples from the parent culture. Counts were made in duplicate

using a Reichert Bright-Line Phase Contrast Hemocytometer with improved Neubauer rulings (Hausser Scientific No. 1475, Horsham, Pennsylvania, USA) (Guillard 1973). Two homogenized 100-µL samples were assessed by counting 18 grids or a minimum of 400 cells (DeLorenzo and Serrano 2003, Weiner et al. 2004). Cells were preserved in 1% (v/v) acidic Lugol’s solution (Vollenweider 1974) to preserve cells for ~1 week while preparing dilution curves immediately before each experiment. The wavelength that produced the highest correlation between absorbance and cell numbers of the stock culture was used to monitor population density and calculate growth rates for the experimental duration. All modeled OD : cell number relationships used had a minimum observed 푟2 value > 0.98 (Fig. 1.2).

1.2.iv Population Growth Rate Measurements

Net cell-specific growth rates (k, day-1) were modeled using OD according to Sorokin (1973). Samples were collected daily (approximately every 24-hours post- inoculation) from hand-agitated test tubes and measured in a clean, wiped quartz cuvette as described above in duplicate, using a media blank as a baseline measurement. OD was converted to PD, data were log-transformed (log10 +1) and the linear portion of the growth curve (slope = 푎푘 ) was used to calculate growth rate:

푘 = 푎푘 ∗ 3.322

1.2.v Algal Bioassays

Toxicity experiments were based on examining the effect of atrazine, alone and in varying nutrient regimes, on algal population growth rate. Testing procedures were derivations on the standard 96-hr static algal toxicity bioassay protocols (ASTM 1996) and were conducted to assess 96-hr, 10-day, and 20-day endpoints to allow for evaluation of toxicant responses following nutrient depletion from cellular reserves. In preliminary work, cultures were most sensitive 24-48 hr after atrazine addition and generally decreased in sensitivity over time. Thus, multiple time points were required to examine toxicant effects. Tolerance/ sensitivity to toxicants has been shown to be related to previous exposure history and genetic variation (deNoyelles et al. 1982; Hamala and Kollig 1985; Behra et al. 1999).

Therefore, samples from the same parent culture were used to inoculate test cultures in test controls and treatments. This approach did not allow for immediate observation of depletion of cellular nutrient reserves in test cultures since parent cultures were nutrient-replete. Therefore, the test bioassays were extended past 96 hr to assess any responses to nutrient depletion x atrazine which occurred after additional time. The 10- and 20-day periods were selected based on preliminary observations of D. tertiolecta growth curves in replete media. Factors that can affect toxicity over extended test periods include degradation/detoxification of the substance, pH changes, adsorption of the toxicant to organic matter, and volatilization. Atrazine has low potential for volatilization and nominal test concentrations were quantified by enzyme-linked immunosorbent assays (ELISAs, below) at the end of tests to ensure that media exposure levels had not changed. The data indicated that atrazine levels did not change over the extended trial periods by more than 20% of nominal concentrations (See appendix).

Analytical grade standards of atrazine (CASRN 1912-12-9, >98.0% purity) were obtained from Chemservice (Westchester, PA, USA). Stock solutions were prepared in 100% pesticide-grade acetone (Fisher Scientific, Fair Lawn, NJ, USA), with testing doses administered to obtain a final concentration of 0.1% (v/v) acetone in each replicate, except for a media-only control series which was included for all treatments in all assays. Each control and treatment was replicated (n = 3). Assays were conducted in 50-mL round-bottom borosilicate glass tubes with Teflon-lined caps (final total volume, 25 mL). Five-point concentration curves included concentrations at 12.5, 25, 50, 100 and 200 µg atrazine L-1 as well as the solvent and media controls. Exposure levels were selected based on range-finding test estimates that were designed to expose the test organism to concentrations with lowest and highest potential effects on population growth. When stock culture cells were in log- phase growth (assessed by direct cell counts), the test culture tubes plus control cultures were inoculated under aseptic conditions to achieve the same nominal cell densities of approximately 2.5 x 104 cells mL-1 in all experimental cultures and controls. Chemical stocks were diluted in test media to achieve desired final concentrations and were added to fresh, sterile test culture media aseptically under a laminar flow hood. Time-series testing began

when algae were added to test media, and OD was measured immediately following inoculation to assess initial density (0 hr). During the test, each culture tube was measured twice at equally spaced daily intervals (0, 24, 48, 72, 96-hr) following inoculation during 96- hr bioassays, and again on days 7, 10, 15 and 20 for the 20-day bioassays by inverting the capped culture tube 2-3 times and aseptically removing 1 mL of randomly sampled test culture. All culture tubes were hand-agitated by inverting the capped tube 3-4 times per day, and were randomly repositioned (using computer-generated randomization of culture rack positioning) post-sampling to avoid persistent effects due to differential light exposures. For each sample, growth was determined using spectrophotometric determination of optical density and converted to cell density using equations derived from direct cell count : optical density relationships established using the parent culture within 1 week prior to the test initiation, as described above.

The percent inhibition (%I) of algal growth at each atrazine concentration was calculated by comparing mean growth rates for each treatment to the respective solvent controls for the treatment:

푘 %I = [1 − ( )] ∙ 100 푥̅ 푘푠표푙푣푒푛푡 푐표푛푡푟표푙 where 푘 is the growth rate of the pertinent sample, calculated as described in Section 1.2.iv. Nominal concentrations of atrazine were quantified with enzyme-linked immunosorbent assays (ELISAs) at the beginning and end of each bioassay with enzyme-labeled paramagnetic particles of atrazine (method detection limit for atrazine = 0.046 μg/L) (SDI, Delaware, MD, USA). The ELISA testing quality assurance and controls were as follows: %CV ≤10%, calibration curve correlation r > .990, reproducibility (spiked recovery of known material) and accuracy (% recovery of nominal concentrations) [ (Cobserved /

Cexpected)*100 ] between 80-120% (see appendix for data). This method has been shown to have accuracy comparable to quantification of atrazine concentrations in surface water samples by gas chromatography (Gascón et al. 1997).

Atrazine x nutrient effects were assessed by repeating the above assay procedures in a simultaneously run three-way bioassay format. Each assay included 63 50-mL flasks in identical setup, inoculation and chemical preparation approaches as described above, except that they varied in culture media nutrient additions. To assess nutrient imbalanced effects, three N:P supply ratios (selected based on deviations from the Redfield Ratio - Redfield

1958) were prepared: 1:1, N-limited (“Low N” = 2.0µM NO3, 2.0µM PO4); 160:1, P-limited

(“Low P” = 32µM NO3, 0.2µM PO4); and 16:1, non-limited (“nutrient-replete = 32µM NO3,

2.0µM PO4) media (Table 1.4). Each low-nutrient treatment series contained corresponding solvent and media controls. Salinity and pH were checked at test initiation and conclusion to insure changes were not outside acceptable benchmark limits (salinity 20 ±1, pH 8.1 ±0.2).

1.2.vi Statistical Analysis

All statistical analyses were performed using JMP software (version 12.2.0, SAS Institute, Cary, NC). Normality was checked with Shapiro-Wilk test, and variance checks were done using a Levene's test. Herbicide effects on growth were determined using a one- way analysis of variance (ANOVA) for normally distributed data and Kruskal-Wallace where normality assumptions were violated, and where differences were detected among treatments (p < 0.05), by using Dunnett’s procedure for multiple comparisons to determine which specific treatments differed significantly from the controls (Zar 1999). Atrazine x nutrient comparisons were analyzed by two-way ANOVA using nutrient level and herbicide concentration as the fixed effects, and growth rate as the dependent variable. The F statistic and associated P value for the independent variables and the interactions between them are reported. Point estimates of sublethal toxicity (IC50s) were determined based on growth rates, using the inhibition concentration linear interpolation (ICp) method described by Norberg- King (1993):

()CCJJ 1  ICp CJ [ M1 (1  p /100)  M J ] ()MMJJ 1  wherein;

CJ = tested concentration whose observed mean response is greater than M1 (1-p/100). CJ+1 = tested concentration whose observed mean response is less than M1(1-p/100). M1 = smoothed mean response for the control MJ = smoothed mean response for concentration J MJ+1 = smoothed response for concentration J+1 p = percent reduction in response relative to the control response. ICp = estimated concentration at which there is a percent reduction from the smoothed mean control response. The ICp is reported for the test together with the 95% confidence interval or standard deviation as calculated by the ICPIN.EXE program (Norberg-King 1993). Results were generated using bootstrap methods to derive point estimates and confidence intervals from no fewer than 80 resamplings (Efron 1982).

1.3 RESULTS

The first set of bioassays was performed to establish the 96-hr response of Dunaliella tertiolecta to atrazine. Biomass was quantified at test conclusion (96 hr) by manual counting -1 -1 of cells on a hemocytometer. IC50 estimates ranged from 87.7 µg L to 143.3 µg L , (푥 = 110.1 µg L-1) and the lowest NOEC was 25 µg L-1 (for 50% of tests conducted) and the highest was 50 µg L-1 (for 50% of tests) (Table 1.5, Fig. 1.3). After the test-specific effective concentrations were established, the testing system was evaluated using OD techniques to monitor biomass over time, and cell density was quantified every 24 hr for comparison.

A second set of bioassays was conducted over a 20-day interval to monitor the response of D. tertiolecta to atrazine as nutrient limitation effects became more pronounced. The 96-hr effective concentrations for this series were calculated from extrapolated cell densities to allow direct comparison with manual cell count point estimates, and the range of -1 observed 96-h IC50 values was 100.6 to 155.4 µg L (푥 = 125.9). The NOEC for all OD- derived estimates of biomass was 50 µg atrazine L-1 (Table 1.6). The 95% confidence intervals for these estimates indicated that the OD-derived point estimates were not significantly different from those calculated using manual cell counts (Fig. 1.4).

The OD estimation technique allowed for monitoring changes in growth rates over time, and point estimates of toxicity were generated for all measured time points (Table 1.7). -1 The IC50 estimates based on growth rate ranged from 11.7 µg L (at 48 hr post-inoculation) to > 200 µg L-1 (푥 =127.39 µg L-1). The lowest NOEC was < 12.5 µg L-1 (at 72-hours post- inoculation) and the highest was >200 µg L-1 (observed at multiple timepoints). The 96-hr -1 -1 growth rate-based IC50 estimates ranged from 139.3 to 179.7 µg L (푥 =159.2 µg L ). The 96-hr NOEC derived from growth rates ranged from 25 µg L-1 to 100 µg L-1.

Examination of controls for the 20-day nutrient limitation assay indicated that growth rates in carrier controls were not significantly different from those in nutrient-replete media controls, and effects of nutrient limitation were not observed until 7 days post-inoculation (Fig. 1.5). Nutrient-replete controls continued to increase in density until day 15, and did not decrease in density at any point in the testing period.

Two additional nutrient-effect bioassays were conducted over the standard 96-hr testing duration to assess Dunaliella tertiolecta growth in nutrient x herbicide assays (Fig. 1.6). Growth was increasingly stimulated over time at the lowest atrazine concentration in the nutrient-replete treatment, and at intermediate atrazine concentrations in the low phosphate treatment, in comparison to controls (Fig. 1.7). Stimulation by atrazine was also observed in the two highest herbicide concentrations x low nitrogen, but this response did not appear to increase over time. Inhibition of growth by atrazine could not be quantified in any of the nutrient-limited treatments using linear interpolation methods, as any observed effects did not appear to be concentration-dependent and growth rates in low-nutrient x atrazine treatments were generally similar to or increased over controls rather than inhibited.

For these bioassays, ANOVA models indicated that both nutrient treatment and herbicide concentration were significant factors influencing growth rates, at both the 96-hr (Table 1.8) and 20 day endpoints (Table 1.9). The concentration x nutrients interaction term was also significant in all models; however, it was much less influential at 96-hr and was

ranked higher than nutrient treatment variables in the 20-day assay (p values 0.022 and < 0.00001, respectively).

1.4 DISCUSSION

Atrazine toxicity to algal groups represents the largest body of research of a single organic pollutant on algae (Carder and Hoagland 1998), but in our survey of the ECOTOX database (U.S. EPA 2017), of the 9,067 records for aquatic toxicity studies with atrazine (pulled from database by CAS number 1912-24-9),

• 31% (2,812 records) were filed under the species group “Algae, Moss, Fungi” (2,238) or “Algae, Moss, Fungi; Standard Test Species” (574); • 19% (523) were labeled as estuarine/marine species (“SW” media type); and • only 3% of those (79, or 0.9% of total) were studies with the standard marine test organism Dunaliella tertiolecta; 14 of these records provide half-maximal effect concentrations after a minimum of 24 hr of growth; and 3 of the 14 studies which provided half-maximal concentrations used population growth rate as the measured endpoint (DeLorenzo and Serrano 2003; DeLorenzo et al. 2004; De Lorenzo and Serrano 2006). This relative lack of emphasis on algae may have occurred in part due to a general assumption in the toxicological literature that there is little concern for off-target impacts resulting from atrazine exposures (Huber 1993; Giddings 2005; Solomon et al. 2008; Van Der Kraak et al. 2014). Yet, this assumption is not supported by ecotoxicological assessments (e.g., Eisler 1989, DeLorenzo 1999, Pennington et al. 2001; DeLorenzo 2001).

The ECOTOX database 96-hr atrazine IC50s based on population changes (estimates of growth rate or biomass) range from 66.35 to 180 µg/L for Dunaliella tertiolecta (U.S. EPA 2017). For green algae as a group, the effect concentrations ranged from 1.9 µg for Chlorella vulgaris to 745 mg L-1 for Chlorella saccharophila, exemplifying the range of variability seen within the same genus (U.S. EPA 2017).

The responses of D. tertiolecta to atrazine were quantified using modifications to standard 96-hr algal bioassays (ASTM 1994) and are reported here using benchmarks which are useful in inter-laboratory comparisons of sensitivity. The results of the 96-hr atrazine bioassay tests were within the range of other reported experimental values (above). The 96-hr

ICr50 values were slighter higher than previously reported, but had less scatter (lower SD) than ICb50s. Nyholm (1985) also noted that there was less scatter among growth-based values from different laboratories than among estimates derived from biomass or from the area under growth curves (which typically do not differ significantly from each other [Nyholm and Källqvist 1989; Nyholm 1990]). Chao and Chen (2001) also found that EC50 estimates based on growth rates were greater than those based on final yields. Growth rate has been regarded as a more appropriate response variable for estimating effective concentration (Nyholm 1990; Stauber 1995), since “using relative biomass as the response variable results in numerical toxicity estimates and dose-response curve slopes which depend on the test duration, the absolute magnitude of the maximum specific growth rate µm (and thus particular system parameters), and which are also influenced by differences in inoculated cell concentrations and by the occurrence of lag phase” (Nyholm 1990). When algal growth rate is an exponential rather than a linear (biomass) function, it is less sensitive to these potentially confounding factors (Nyholm 1985). Growth rate has also been shown to be 10- fold more sensitive than ATP determinations after 4-hr exposures of Selenastrum capricornutum to metals and organic compounds (Hickey et al. 1991).

Several studies have examined the potential for increased sensitivity or induced tolerance in phytoplankton following short-term (< 5 day) exposures to atrazine. Proposed mechanisms include mutations in the algal analog of the psbA gene, which mutates the D1 protein binding site and increases or decreases atrazine binding affinity (deNoyelles et al. 1982; Hamala and Kollig 1985; Pennington and Scott 2001). Changes in susceptibility can also occur, resulting from changes in photoadaptation capacity or reorganization of the photosynthetic apparatus which is an adaptive acclimation similar to low-light tolerance (Hatfield et al. 1989; Millie et al. 1992). It has been suggested that the D1 protein may play a

role as a “light meter” in natural shade-adaption processes, and that its degradation and re- synthesis is a possible signaling mechanism triggering photoadaptation (Koenig 1990). Low light shunts carbon reserves into the photosynthetic apparatus, and may initiate a change in the size or in the number of photosynthetic units. When shading (or PSII-inhibition by herbicide exposure) occurs, the increase in photosynthetic units may, in turn, increase the availability of D1-binding sights, thus increasing atrazine sensitivity or resulting in acquired resistance through mutation of the algal psbA gene analog (Cheung et al. 1988).

Slight stimulatory responses (statistically different from controls) at intermediate concentrations of atrazine were observed in all cultures at some time point during the 96-hr or 20-day duration. The stimulatory response was most pronounced in low -nutrient treatments during the first 24-72 hr of testing, wherein growth was elevated in comparison to growth in nutrient-replete control cultures. In some assays, this stimulatory effect persisted longer than 96 hr, postponing observations of growth limitation attributable to nutrient depletion. Stimulation effects following atrazine exposure have been reported elsewhere (Tang et al. 1997), and may be related to mechanisms for low-light adaptation (Hatfield et al. 1989; Koenig 1990; Gustavson and Wängberg 1995). Alternatively, the increased OD in low- level atrazine exposures may have reflected the fact that inhibition by this herbicide primarily affects cell division rather than photosynthesis at these doses (Chao and Chen 2001). Other research has shown that algal assemblages from well-shaded, low-light river systems were less sensitive to atrazine exposures and had “a certain degree of protection against this herbicide” (Guasch and Sabater 1998).

The presence of atrazine may also mimic nutrient-limited conditions (Weiner et al. 2007). Increased nutrient uptake has been associated with decreased levels of toxicant uptake (Sanders 1979) and some pesticides have been shown to be more toxic to green algal cultures under phosphate-limited conditions (Van Donk et al. 1992). Pannard et al. (2009) observed that cyanobacteria with increased nutrient supply were more tolerant to atrazine, Hamala and Kollig (1985) also reported that cyanobacteria (blue-green algae, Cyanophyta) dominated atrazine-contaminated periphyton communities. Cyanobacteria have a competitive advantage

in light-limited ecosystems due to their accessory phycobilin pigments (Koenig 1990). They appear to be relatively insensitive to atrazine (Fairchild et al. 1998). Thus, as other researchers have suggested, the “increasing prevalence of cyanobacterial blooms may result from a combination of unbalanced nutrient enrichment and selective pressures from multiple toxicants” (Pannard et al. 2009). Nutrient enrichment appears to increase the toxicity of atrazine to other phytoplankton groups (deNoyelles et al. 1982; DeLorenzo et al. 2001).

Nontarget organisms in herbicide exposures, such as algae, are the main primary producers of many aquatic ecosystems, and potential water quality indicators (Blaise et al. 1997). Increasing loads of nutrients and other chemical contaminants to coastal areas are altering phytoplankton growth and succession (Cloern 1996). These chemical contaminants, although abiotic stressors, have been described as functionally similar to competition or predation in their capacity to structure aquatic communities (Rohr et al. 2006). Nontarget exposures of many toxicants, and nontarget exposures to co-ocurring contaminants such as pesticides and nutrients, is a major research need. It is important to examine which factors might play selective roles and alter community structure if toxicity assessments are to be informative about how ecosystems exposed to toxicants can be expected to respond. Atrazine alters phytoplankton assemblage structure (deNoyelles et al. 1982; Hamala and Kollig 1985; Hamilton et al. 1987) and direct effects of atrazine on algal assemblages have been found at concentrations as low as 20 µg L-1 (Graymore et al. 2001).

In this work, environmentally relevant concentrations of atrazine were assessed, together with the two key macronutrients, nitrogen and phosphorus, which limit phytoplankton growth and are increasingly found in imbalanced levels in aquatic ecosystems (Glibert et al. 2011, Burkholder and Glibert 2013, and references therein). Many other common, co-occurring chemical contaminants should be assayed, as well, to increase confidence in chemical safety. New in silico techniques such as QSAR and chemical read- across offer methodologies to quantitatively group substances based on their mode of action or functional groups (Hewitt et al. 2010; Patlewicz et al. 2013). These techniques can be combined with microscale testing procedures (Gaggi et al. 1995; Wells et al. 1997; Wood et

al. 2014) which are adapted for use with small test volumes, while still providing the control performance levels described here (based on the standardized guideline assays). Such approaches may be useful in beginning to produce studies that screen many more species within a series.

Photosystem II inhibition by atrazine can act similarly to light limitation and exert selective control over phytoplankton assemblages, favoring species which have the capacity to adapt to low light through accessory pigment composition, e.g. cyanobacteria (Koenig 1990), or which have alternative carbon fixation pathways and carbon concentrating mechanisms (Pannard et al. 2009). The assay platform developed in this research will allow the testing of atrazine effects on other estuarine microalgae, including ecologically important mixotrophic harmful algal species (Chapters 2 and 3).

1.5 FIGURES AND TABLES Table 1.1. List of species used for toxicity testing in the ECOTOX database (U.S. EPA 2017) ranked

by the number of different studies on all chemicals.

Species Count of CASN Norway Rat Rattus norvegicus 31,870 Rainbow Trout Oncorhynchus mykiss 27,932 Water Flea Daphnia magna 20,201 Fathead Minnow Pimephales promelas 16,162

House Mouse Mus musculus 14,113

Bluegill Lepomis macrochirus 10,058

Common Carp Cyprinus carpio 7,651 Zebra Danio Danio rerio 7,215 Domestic Chicken Gallus domesticus 5,929 Japanese Medaka Oryzias latipes 4,785 Goldfish Carassius auratus 4,230 Green Algae Pseudokirchneriella subcapitata 4,134

Figure 1.1. Light micrograph of Dunaliella tertiolecta, the benign test species used to design the testing protocol. Scale bar = 50 µm.

Table 1.2. List of algal species used for aquatic atrazine toxicity testing in the ECOTOX database (U.S. EPA 2017) ranked by number of different studies.

Algal Group # of Studies Species # of Studies Green Algae 997 Algae 543 Algae 719 Pseudokirchneriella subcapitata 265 Diatoms 505 Chlamydomonas reinhardtii 96 Blue-Green Algae 367 Microcystis aeruginosa 88 Cryptomonads 29 Dunaliella tertiolecta 79 Haptophytes 23 Oophila sp. 79 Flagellate Euglenoids 20 Chlorella vulgaris 74 Chrysophytes 15 Scenedesmus quadricauda 48 Yellow Green Algae 14 Chlorella pyrenoidosa 44 Green Flagellates 13 Scenedesmus acutus var. acutus 41 Marine Microalgae 13 Red Algae 9 Prasinophyte 8 Microalgae 7 Phytoplankton 7 Golden-brown Algae 5 Filamentous Green Algae 1 Golden Algae 1 Hornwort 1

Table 1.3. Marine algal species recommended for use in guideline testing protocols by indicated organizations.

Species Method Chlorophyta Dunaliella tertiolecta ASTM 1990, APHA 1995, U.S. EPA 1974 Haptophyta Isochrysis galbana OECD 1998 Heterokontophyta, Bacillariophyceae Skeletonema costatum ASTM 1990, APHA 1995, ISO 1995, OECD 1998, U.S. EPA 1985 Thalassiosira pseudonana ASTM 1990, APHA 1995, OECD 1998, U.S. EPA 1974 Phaeodactylum tricornutum ISO 1995, OECD 1998

Table 1.4. Media recipes used in D. tertiolecta culture and testing.

L1 Macro-nutrient concentrations

Volume added to Final concentration in Stock Solution final medium L1 media (µM) Replete: -1 NaNO3 2.7198 g L H2O 1 mL 32.00 -1 NaH2PO4 0.2400 g L H2O 1 mL 2.00 low N: -1 NaNO3 0.1700 g L H2O 1 mL 2.00 -1 NaH2PO4 0.2400 g L H2O 1 mL 2.00 low P: -1 NaNO3 2.7198 g L H2O 1 mL 32.00 -1 NaH2PO4 0.0240 g L H2O 1 mL 0.20

L1 Trace Element Solution

Primary stock Quantity to add to Molar concentration in

solution final medium final medium (M) -5 Na2EDTA · 2H2O --- 4.36 g 1.17 x 10 -5 FeCl3 · 6H2O --- 3.15 g 1.17 x 10 -1 -7 MnCl2 · 4H2O 178.10 g L H2O 1 mL 9.09 x 10 -1 -8 ZnSO4 · 7H2O 23.00 g L H2O 1 mL 8.00 x 10 -1 -8 CoCl2 · 6H2O 11.90 g L H2O 1 mL 5.00 x 10 -1 -8 CuSO4 · 5H2O 2.5 g L H2O 1 mL 1.00 x 10 -1 -8 Na2MoO4 · 2H2O 19.9 g L H2O 1 mL 8.22 x 10 -1 -8 H2SeO3 1.29 g L H2O 1 mL 1.00 x 10 -1 -8 NiSO4 · 6H2O 2.63 g L H2O 1 mL 1.00 x 10 -1 -8 NaVO4 1.84 g L H2O 1 mL 1.00 x 10 -1 -8 K2CrO4 1.94 g L H2O 1 mL 1.00 x 10

L1 Vitamin Solution

Primary Stock Quantity to Add Molar Concentration in

Solution to Final Medium Final Medium (M) thiamine · HCl --- 200 mg 2.96 x 10-7 (Vitamin B) -1 -9 biotin (Vitamin H) 0.1 g L H2O 10 mL 2.05 x 10 cyanocobalamin -1 -10 1.0 g L H2O 1 mL 3.69 x 10 (Vitamin B12)

Figure 1.2. Correlation statistics between optical density and manual cell counts describing procedures for selection of most appropriate wavelength for use in monitoring test activity. Where multiple wavelengths produced similarly strong correlations (as displayed in the series above) the wavelength with the highest R2 for the dilution curve was selected (in the displayed scenario, the testing wavelength was 450 nm,)

Table 1.5. The 96-hr point estimates (µg atrazine L-1) based on effects on biomass estimated by manual cell counts. Standard deviations (SDs) of point estimates were calculated by linear interpolation (Norberg-King 1993).

Test Date IC01 (SD) IC10 (SD) IC25 (SD) IC50 (SD) NOEC LOEC 4 Apr 7.1 (8.2) 29.2 (12.4) 64.8 (15.4) 143.3 (10.7) 50 100 29 May 1.2 (4.8) 11.7 (5.1) 23.4 (13.9) 114.1 (20.7) 25 50 19 Jun 1.3 (4.2) 13.6 (8.5) 60.8 (5.3) 95.3 (10.8) 50 100 4 Jul 6.8 (9.4) 30.0 (5.3) 40.9 (4.1) 87.7 (10.0) 25 50

Table 1.6. The 96-hr point estimates (µg atrazine L-1) based on effects on biomass estimated by OD.

Test Date IC01 (SD) IC10 (SD) IC25 (SD) IC50 (SD) NOEC LOEC 23-July 0.9 (3.9) 9.2 (10.3) 43.7 (8.5) 121.8 (27.3) 50 100 27-Aug 1.0 (10.3) 9.9 (18.8) 70.6 (15.6) 155.4 (25.6) 50 100 5-Mar 13.1 (5.5) 18.8 (9.6) 38.6 (16.4) 100.6 (27.0) 50 100

Figure 1.3. Results of 96 hr atrazine exposure for Dunaliella tertiolecta. Data are given as means + 1 standard error (SE); stars indicate significant differences from control).

Figure 1.4. Distribution of biomass derived effect estimates determined by manual cell counts (4 Apr, 29 May, 19 Jun, and 4 Jul) and OD (23 Jul, 27 Aug, and 5-Mar). Data are given as means + 1 SE.

Table 1.7. The atrazine effect estimates (µg L-1) over time based on D. tertiolecta growth rates (divisions day-1) as estimated by OD. Note that BD ≡ beyond detection. Data include means, ranges, and standard deviations (SDs), and the NOEC and LOEC values.

Test Hour IC50 (95% CI) SD IC10 (95% CI) SD NOEC LOEC 23 JUL 24 133.9 (23.1 - 147.8) 24.6 8.9 (3.5 - 108.2) 29.1 100 200 48 137.1 (79.0 - 189.3) 33.1 12.2 (3.1 - 103.5) 26.7 >200 --- 72 175.5 (BD) 15.8 46.6 (7.9 - 102.4) 24.3 100 200 96 179.7 (BD) 13.3 36.8 (7.3 - 69.3) 17.9 100 200 27 AUG 48 11.7 (8.7 - 31.5) 8.7 2.3 (1.7 - 3.8) 0.6 25 50 72 49.3 (35.5 - 116.4) 30.8 3.6 (2.6 - 6.5) 1.2 <12.5 12.5 96 139.3 (104.6 - 163.5) 17.0 5.3 (3.9 - 10.3) 2.9 25 50 5 MAR 24 BD --- 76.87 (BD) 27.1 >200 --- 48 BD --- BD --- >200 --- 72 BD --- 121.1 (BD) 55.2 >200 --- 96 158.5 (96.3 - 186.3) 23.2 20.0 (11.4 - 61.4) 12.4 50 100 168 161.7 (141.9 - 184.1) 12.5 19.1 (14.9 - 21.4) 1.7 50 100 240 BD --- 24.5 (19.3 - 75.2) 18.9 100 200 480 BD --- 155.9 (BD) 66.2 >200 ---

Figure 1.5. Test control performance over time for the 20-day D. tertiolecta atrazine bioassays. Note that replete ≡ nutrient-replete.

* *

* * * *

* * *

Figure 1.6. Growth response of Dunaliella tertiolecta to atrazine exposure under different nutrient regimes. Data are given as means +1 SE; stars indicate treatments that are significantly different from solvent controls).

Figure 1.7. Atrazine-induced changes in Dunaliella tertiolecta growth under different nutrient regimes over time. Data are given as means + 1 SE. Note that R ≡ nutrient-replete.

Table 1.8. Influence of treatment variables on D. tertiolecta growth at 96 hr for the three nutrient x atrazine bioassays. Model performance statistics: 23 July - summary of fit = 0.79, ANOVA F Ratio 7.9027, Prob > F < 0.0001; 27 Aug - summary of fit = 0.81, ANOVA F Ratio 9.1668, Prob > F < 0.0001; 5 March - summary of fit = 0.91, ANOVA F Ratio 22.0286, Prob > F < 0.0001). Trt ≡ treatment, Conc ≡ concentration, and DF ≡ degrees of freedom.

Source DF LogWorth F Ratio P Value 23 July Trt 6 8.268 11.3801 0.00000 Conc 2 6.793 30.9897 0.00000 Conc*Trt 12 1.651 2.3163 0.02233 27 Aug Conc 6 9.283 17.4558 0.00000 Trt 2 7.073 24.6047 0.00000 Conc*Trt 12 1.795 2.4493 0.01605 5 Mar Trt 6 19.385 6.4097 0.00000 Conc*Trt 2 6.550 154.9180 0.00000 Conc 12 4.124 7.6899 0.00008

Table 1.9. Influence of treatment variables on D. tertiolecta growth at day 20. Model performance statistics: 5 March, day 20 - summary of fit = 0.78, ANOVA F Ratio 7.7827, Prob > F < 0.0001).

Source DF LogWorth F Ratio P Value Trt 6 13.239 5.4878 0.00000 Conc*Trt 2 5.626 50.4942 0.00000 Conc 12 3.540 6.4865 0.00029

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CHAPTER 2: Persistence Under Sub-Optimal Conditions; Imbalanced Nutrient Regimes Increase Prymnesium parvum Resilience to Herbicide Exposure

2.1 INTRODUCTION

Within the past few decades there has been a global increase in the frequency, magnitude and duration of harmful algal blooms (HABs; Smayda 1990, Hallegraeff 1993, Burkholder 1998, Van Dolah 2000, Glibert et al. 2005, Anderson et al. 2008, Heisler et al. 2008, Lewitus et al. 2012). These blooms are often toxic and threaten aquatic biota, wildlife, and human health in inland and coastal waters (Burkholder 1998, 2009; Anderson et al. 2008; Lewitus et al. 2012; Brooks et al. 2016). Many if not most harmful algae are mixotrophs rather than strict phototrophs; that is, they derive nutrition from both photosynthesis and consumption of dissolved or particulate organic carbon (Burkholder et al. 2008). Thus, their behavior and physiology are markedly different from those of strict phototrophs. Observed increases in HABs mainly have been related to anthropogenic nutrient enrichment which can stimulate harmful algae directly or indirectly by stimulating growth of prey, and also to range expansion due to global climate change and increased dispersal via global transportation (Heisler et al. 2008, Pitcher 2012). Despite increasing pollutant inputs to coastal areas from rapid, ongoing watershed development and human population growth (Scott 2006; U.S. EPA 2016), the impacts of chemical contamination on coastal estuarine/marine ecosystems are poorly understood, especially the influences of interacting chemical contaminants on harmful algae (Burkholder and Shumway 2017, and references therein).

The relatively few available studies about effects of chemical pollutants on estuarine and marine phytoplankton generally have involved the assessment of direct effects of individual stressors on one or two model phototrophic species, selected for their high growth rates and “hardiness” in captivity rather than their ecological importance (Kimball and Levin 1985). In a review of toxicant impacts on marine ecosystem function, only 10% (26 of 264) of the available studies assessed the impact of herbicides, and less than 40% assessed phytoplankton responses (Johnston et al. 2015). Yet, species sensitivity comparisons

routinely have indicated that estuarine/marine microalgae are among the most sensitive organisms in response to toxicant exposures, and are especially sensitive to herbicides (Stauber 1995, Mohan and Hosetti 1999, Geis et al. 2000, Lytle and Lytle 2001). This sensitivity is not surprising, as algae would be expected to be more susceptible to the main mode of action of most herbicides (inhibition of photosynthesis) than other marine organisms (Peterson et al. 1994, Solomon et al. 1996, Giddings 2005). Microalgae are the energetic base of marine food webs, and therefore largely control the ecosystem carrying capacity and stability (Lembi and Waaland 1988). Restriction of toxicological assessments to culture studies of a few “hardy” algae (U.S. EPA 1974, Stauber 1995, ASTM 1996), with little ecological relevance, has not enabled assessment of the potential for pervasive chemical contamination to function as an important structuring mechanism in estuarine and marine communities (Johnston 2016).

Phytoplankton responses to toxicants vary widely by species (Walsh 1972, Blanck et al. 1984, Rojíčková-Padrtová et al. 1998, Rojíčková and Maršálek 1999, Ma 2005), strain, and even sub-strain (Kasai et al. 1993, Burkholder and Glibert 2006, and references therein). No single algal species can be selected as universally protective for all “algae” - a term which includes widely disparate phyla ecologically and physiologically (Graham et al. 2016). Current bioassay assessment protocols are designed to test response endpoints of predominantly phototrophic species under optimal growth conditions, and resulting “safe” levels likely do not provide an adequate framework for protecting against effects of pollutants interacting with harmful algal species. “Most sensitive” or “hardiest” species responses may not be indicative of the “most ecologically important” response. Low toxicity may not equate with low ecological risk because abundant, ubiquitous chemical contaminants commonly influence phytoplankton population dynamics through indirect mechanisms (Burkholder and Shumway 2017, and references therein). Toward the goal of developing improved management strategies to mitigate impacts from harmful algae, studies should be focused on species with the highest potential to affect ecosystem structure, and on the contaminants to which they are commonly exposed. There is an urgent need to evaluate how

high tolerance to a chemical contaminant could confer a selective advantage and lead to increased abundance or even dominance of harmful populations. Changes in algal assemblages can result in altered ecosystem function (McCormick and Cairns 1994), and increased abundance of harmful species can result in complete ecological collapse (Sunda et al. 2006 and references therein). These considerations, considered collectively, indicate that bioassay data derived from model phototrophic species cannot be used to extrapolate pollutant effects realistically to mixotrophic HAB species.

Prior to this research, interactions between nutrient levels and herbicide exposure in affecting estuarine HAB species had not been examined. Since both nutrients and herbicides commonly co-occur in stormwater runoff (Eisler 1989, Gilliom et al. 2006), a series of experiments was designed to investigate the effects of herbicide exposure under imbalanced nutrient regimes on the growth and toxicity of a widespread, common harmful phytoplankter, the haptophyte Prymnesium parvum Carter 1937. Atrazine (2-chloro-4-ethylamino-6- isopropylamino-s-triazine) was the toxicant selected for study because of its specific mechanism of action as a reversible inhibitor of photosystem II (Solomon et al. 2013), the amount of comparable information available regarding effects on marine microalgae (DeLorenzo et al. 2001), and (due to its recognized mobility) its prevalence and persistence in estuarine ecosystems in the U.S. (Eisler 1989, Giddings 2005, Gilliom et al. 2006, Jablonowski et al. 2011). This herbicide was also selected in consideration of a recent study which suggested that atrazine exposure can lead to dominance of P. parvum in phytoplankton assemblages in situ (Yates and Rogers 2011).

This research was designed to address the following questions: What is the effect of exposure to atrazine on P. parvum growth? To what extent does atrazine exposure promote production of hemolytic substances in this HAB species? Is that response affected by the nutrient or salinity regimes? How do interactions between nutrient limitation, salinity, and herbicide exposure influence growth rates and hemolytic activity in P. parvum? And, are these responses consistent for multiple strains (populations or isolates) of P. parvum?

2.2 MATERIALS AND METHODS

A series of factorial experiments was designed to expose Prymnesium parvum (Haptophyta) to a range of co-stressors including salinity, nutrients, and atrazine, using modifications of standard algal bioassay procedures (Miller and Greene 1978, ASTM 1996) and examining hemolytic activity using a modified erythrocyte lysis assay (Eschbach et al. 2001). These assays were designed to test the null hypothesis that neither herbicide activity, nutrient limitation, salinity, strain origin, nor their interactions would have a net effect on growth rates or relative production of hemolytic compounds in static batch cultures of P. parvum.

2.2.i Experimental Organism, Strains, and Culture Conditions

2.2.i.a. Toxic Prymnesium parvum

The cosmopolitan (with exception of Antarctica), toxigenic estuarine/marine phytoplankter, Prymnesium parvum (Fig. 2.1), has an extensive historical record of forming nearly monospecific, high-biomass, ecosystem-disrupting blooms (Sunda et al. 2006). This organism forms large blooms and causes major fish kills in brackish waters and aquaculture facilities in many regions (Reich and Aschner 1947, Shilo and Shilo 1953, Holdway et al. 1978, Kaartvedt et al. 1991, Moestrup 1994, Edvardsen and Paasche 1998). It has also caused kills of fish and other gill-breathing organisms in high-conductivity inland waters of the American West and other areas of the U.S. (James and De La Cruz 1989, Sager et al. 2008, Hambright et al. 2010, Southard et al. 2010, VanLandeghem et al. 2015b). This alga can grow at salinities of 0.8 to100, with optimal growth at salinity 10-20 (Edvardsen and Imai 2006), but most associated fish kills in U.S. waters have occurred at low to moderate salinities (< 10 to 20) (Baker et al. 2007, 2009; Sager et al. 2008; Roelke et al. 2011). Toxicity has been inversely related to salinity (Baker et al. 2007), and has been highest in eutrophic waters under low-nutrient (nitrogen or phosphorus) stress (Johansson and Granéli

1999, Granéli and Johansson 2003b, Baker et al. 2009, Granéli et al. 2012, Roelke et al. 2016).

Growth of P. parvum occurs by photosynthesis, and also by heterotrophy (organic carbon acquisition) as uptake of dissolved organics from the environment or from prey that it immobilizes with toxins (Tillmann 1998, Skovgaard and Hansen 2003), or phagotrophy (consumption of particulate matter) (Nygaard and Tobiesen 1993, Tillmann 1998, Martin- Cereceda et al. 2003, Skovgaard and Hansen 2003, Tillmann 2003, Graneli 2006, Remmel et al. 2011, Remmel and Hambright 2012). The toxins can also inhibit or kill competing phytoplankton or grazer populations, by cell lysis or by suppressing growth, feeding and/or reproduction (Fistarol et al. 2003, Granéli and Johansson 2003a, Skovgaard and Hansen 2003).

More than 15 toxins or highly bioactive substances have been identified from P. parvum cells and exudates, including proteolipids (Ulitzur and Shilo 1970), saponins (Yariv and Hestrin 1961), various fatty acids (Henrikson et al. 2010), and fatty acid amides [specifically oleamide and linoliamide (Bertin et al. 2012)], although some of this diversity may reflect differences in the extraction techniques employed (Manning and La Claire 2010). Some toxic substances from P. parvum extracts have not yet been structurally characterized, but the impacts to biological systems identified thus far have cytotoxic, hemolytic, hepatotoxic and/or neurotoxic properties (Shilo 1981, Granéli and Johansson 2003b, Manning and La Claire 2010). Substances such as hemolysins are ichthyotoxic (Shilo 1967). Most work to chemically characterize P. parvum toxins has focused on two high-molecular- weight, brevetoxin-like, cyclic polyether substances called prymnesin-1 and prymnesin-2 (Ulitzur and Shilo 1970; Shilo 1981; Igarashi et al. 1996, 1999; Manning and La Claire 2010). The available evidence suggests that each toxin has a distinct mode of action, and that the toxins interact with each other and with pH-dependent co-factors (likely divalent metals such as Ca+2 or Mg+2) in the aquatic environment which modulate toxicity (Shilo and Rosenberger 1960, Manning and La Claire 2010, Roelke et al. 2011). Various other

environmental factors (e.g., pH, total dissolved solids) influence toxicity as well (Baker et al. 2007, 2009; Valenti et al. 2010; Prosser et al. 2012; VanLandeghem et al. 2015a,b).

2.2.i.b. Strains and Culturing

Strains of Prymnesium parvum were obtained from the University of Texas culture collection (UTEX LB 2797 and LB 2827, here referred to as the Texas (TX) strain and the South Carolina (SC) strain, respectively) and from the Center for Marine Science at UNC Wilmington (laboratory of Dr. C. Tomas), referred to here as the North Carolina (NC) strain. Background information about collection of these strains is given in Table 2.1.

Non-axenic, unialgal batch cultures of all strains were maintained in 250-mL Erlenmeyer flasks with 100 mL modified f/2 (-Si) medium (Guillard and Ryther 1962) (see Table 2.2 for culture and test media recipes). The UTEX strains arrived in salinity 30 Erdschreiber’s Media, and were gradually transferred into f/2 media over two weeks and split into two subcultures. Each subculture was slowly acclimated over 2-4 weeks to either salinity 20 or 10 for experiments. The NC strain was transferred from the source lab in salinity 4 f/2- Si media, and was acclimated into salinity 10 and 20 as described above. All cultures were maintained at 24°C (ambient air temperature) with light (photosynthetically active radiation) at 106.31 (±26.16) µmol s-1 m-2 (cool white fluorescent tubes) and a 16:8 hr light:dark cycle. Light was measured using a Biospherical QSL 101 Quantum lab sensor (BSI, San Diego, CA, USA). Cultures were sterile-transferred every 7-10 days as needed to maintain log-phase growth. All media (culture and test) were prepared by adjusting the salinity of ultrapure Milli-Q water (18 MΩ cm−1 at 25°C) with Instant Ocean® artificial sea salts (Aquarium Systems, Blacksburg, VA, USA) to the desired level, also adjusting the pH as necessary to 8.1 (± 0.2 unit) using HCl or NaOH. Salinity was measured using a YSI model 3200 conductivity/salinity bridge and cell (YSI, Yellow Springs, OH, USA). The pH was measured using an Orion Versa Star Pro multi-parameter meter equipped with an Orion ROSS Sure-Flow pH electrode, Thermo Scientific, Waltham, MA, USA). Trace mineral and vitamin solutions were sterile-filtered (Whatman® Puradisc cellulose acetate syringe filter,

nominal pore size 0.2 μm, Sigma-Aldrich, St. Louis, MO, USA) and added to the autoclaved media aseptically.

The final medium was vacuum-filtered (Corning® cellulose acetate filter, 0.22 µm nominal pore size; Sigma-Aldrich) under a laminar flow hood before storage in darkness at 4°C for no more than 30 days. All equipment was initially cleaned by scrubbing in hot, soapy tap water. All glassware and non-plastic equipment used in culturing and experiments was rinsed with pesticide-grade acetone before use and all glassware and any non-metallic equipment used was cleaned by acid stripping in 10% HCl (v/v). All equipment was sterilized by autoclaving before use. All algal observation and testing methods were initially developed and optimized using the benign estuarine/marine microalga, Dunaliella tertiolecta, as described in Chapter 1 of this work.

Stock cultures were maintained at 100 µmol photons m-2 s-1 throughout the experiments, and were gently swirled once daily. Bioassays (controls and test cultures) were maintained on a culture rack under a fluorescent light bank (2.5 m long by .75 m deep). All cultures in the bioassays were gently swirled by hand and randomly repositioned daily on the culture rack following a computer-generated randomized shelf mapping, to avoid any effects from possible differential light exposure.

2.2.ii Optical Density Versus Direct Cell Counts

1-mL samples were randomly withdrawn from each parent culture and thoroughly vortexed before absorbance was read on a Thermo Scientific Spectronic GENESYS 2 spectrophotometer (Thermo Scientific, Waltham, MA, USA), using a freshly wiped Hellma® quartz cuvette (Sigma-Aldrich, St. Louis, MO, USA) with a 10-mm pathlength. All reads were performed in duplicate and adjusted using a media blank as a measurement baseline. All parent cultures were scanned individually, and the three highest peak absorbencies were selected as the most appropriate wavelengths for analysis of a series of 8-point dilution curves, using progressive dilutions of parent stock culture in growth media. The curve which

produced the ΔOD/ΔPD [change in OD divided by change in population density; Sorokin (1973)] closest to unity was used to determine the growth rates from each stock culture for each strain tested. Cell counts to determine ΔPD were obtained by collecting random 1-mL samples from the parent culture. Cell counts were made in duplicate using a Reichert Bright- Line phase contrast hemocytometer with improved Neubauer rulings (Hausser Scientific No. 1475, Horsham, PA, USA) (Guillard 1973). Two homogenized 100-µL samples were assessed by counting 18 grids or a minimum of 400 cells (DeLorenzo and Serrano 2003; Weiner et al. 2003). Cell preservation techniques (0.1% Lugol’s solution / 0.05% gluteraldhyde, v/v) were developed and used to preserve cells for about one week without appreciable cell distortion or loss while preparing dilution curves immediately before each experiment. The wavelength which produced the highest correlation between absorbance and cell number of the stock culture was used to monitor population density and calculate growth rates for the duration of the experiment. All modeled OD : cell number relationships that were used had a minimum observed 푟2 value greater than 0.98) (Fig. 2.2).

2.2.iii Population Growth Rate Measurements

Net cell-specific growth rates (day-1) were modeled using OD following Sorokin (1973). Samples were collected daily from hand-agitated test tubes and measured in duplicate in a clean, wiped quartz cuvette as described above, using a media blank as a baseline measurement. The OD was converted to PD, data was log transformed (log10 + 1) and the linear portion of the growth curve (slope = 푎푘 ) was used to calculate growth rate k (Sorokin 1973):

푘 = 푎푘 ∗ 3.322

2.2.iv Algal Bioassays

Toxicity experiments were based on examining the effect of atrazine alone and in varying nutrient regimes on the population growth rate of Prymnesium parvum. Testing procedures were derivations on the standard 96-hr static algal toxicity bioassay protocols

(ASTM 1996), and were conducted to assess both 4- and 10-day endpoints to allow for evaluation of toxicant responses following nutrient depletion from cellular reserves. Analytical grade standards of atrazine (CASRN 1912-12-9, >98.0% purity) were obtained from Chemservice (Westchester, PA, USA). Stock solutions were prepared in 100% pesticide-grade acetone, with testing doses administered to obtain a final concentration of 0.1% (v/v) acetone in each replicate, except for a medium-only control series (prepared without acetone additions) which was included in all assays. There were three replicate culture tubes for each treatment and control series incubated in 50-mL round-bottomed borosilicate glass tubes with Teflon lined caps. Each tube contained a total final volume of 25 mL. Five-point concentration curves were developed based on geometrically spaced atrazine concentrations (12.5, 25, 50, 100 and 200 µg/L) and media and solvent controls, with exposure levels selected based on range-finding test estimates designed to expose the test organism to concentrations with lowest and highest effects on population growth. When stock culture concentrations were in log phase growth (as assessed by periodic direct cell counts) the test culture tubes plus control cultures were inoculated under aseptic conditions to achieve the same initial cell densities of approximately 5.0 x 104 cells mL-1 in all experimental cultures and controls. Chemical stocks were diluted to achieve desired final concentrations and were introduced into fresh, sterile culture media aseptically under a laminar flow hood. Time-series testing began when algae were added to test media, and OD was measured immediately following inoculation to assess initial density (0 hr). During the test, each culture tube is measured twice at equally spaced daily intervals (0, 24, 48, 72, 96-h) following inoculation during 96-hr bioassays, and again on days 7 and 10 for 10-day bioassays by inverting the capped culture tube 2-3 times and aseptically removing 1mL of randomly sampled test culture. All culture tubes were hand agitated by inverting the capped tube 3-4 times and were randomly repositioned (using computational randomization of culture rack positioning) post-sampling to avoid persistent effects due to differential light exposures. For each sample, growth was determined using spectrophotometric determination of optical density and converted to cell density using equations derived from direct cell count : optical density relationships established using the parent culture within 1 week prior to the

test initiation, as described above. The percent inhibition (%I) of algal growth for each atrazine concentration was calculated by comparing mean growth rates for each treatment to the respective solvent controls for the treatment, as follows:

푘 %I = [1 − ( )] ∙ 100 푥̅ 푘푠표푙푣푒푛푡 푐표푛푡푟표푙 where 푘 is the growth rate of the pertinent sample, calculated as described above. Nominal concentrations of pesticide were quantified with enzyme-linked immunosorbent assay (ELISA) at the initiation and conclusion of each bioassay with enzyme-labeled paramagnetic particles of atrazine (Method Detection Limit for atrazine = 0.046 μg/L) (SDI, Delaware, MD, USA) (see appendix for raw ELISA data and QA/QC determination). This method has been shown to have accuracy comparable to GC quantification of atrazine concentrations in surface water samples (Gascón et al. 1997).

Nutrient x herbicide effects were assessed by repeating the above assay procedures in a simultaneously run three-way bioassay format. Each assay employed identical set-up, inoculation and chemical preparation approaches as described above, but varied in culture media nutrient additions. To assess nutrient effects, three N:P supply ratios (selected based on deviations from the Redfield Ratio, Redfield 1958) were prepared, 1:1, N-limited (“Low - -3 - -3 N” = 2.0µM NO3 , 2.0µM PO4 ), 160:1, P-limited (“Low P” = 32 µM NO3 , 0.2 µM PO4 ) - -3 and 16:1, non-limited (nutrient-replete or “Replete” = 32µM NO3 , 2.0 µM PO4 ) media - (Table 2.2). NO3 N was the only introduced nitrogen source. Each low-nutrient treatment series contained corresponding solvent and media controls.

2.2.v Hemolytic Activity Assay Protocols

Lack of commercial standards for prymnesin toxins, and the potential involvement of multiple components in Prymnesium toxicity, prevented assessment of toxicity by direct chemical analyses. Therefore, toxicological bioassays were conducted using freshly prepared

fish erythrocytes to characterize the hemolytic potential of these Prymnesium parvum strains under various experimental conditions.

2.2.v.a Blood Collection and Erythrocyte Preparation

Adult tilapia (Oreochromis niloticus) were purchased live from a local Asian fish market and blood was collected from fish anesthetized with 0.02% (w/w) solution of aminobenzoic acid ethyl ester [TricaineTM (MS-222) (Sigma-Aldrich, St. Louis, MO, USA)] in water from their respective holding tanks by gill puncture within 3 hr of purchase. Anaesthetized fish were placed on wet towels and 5 mL of blood were collected using a Pravaz No. 1 needle and 10-mL syringe prefilled with 5 mL of pH-adjusted (7.2) RPMI 1640 (without phenol red), diluted 10% (v/v) with Milli-Q water to adjust for fish serum osmolarity, and supplemented with 50 IU sodium heparin salt (Sigma-Aldrich, St. Louis, MO, USA) as an anticoagulant (Table 2.3). Blood samples were transferred to 15-mL centrifuge tubes and gently hand-agitated by inverting two or three times. Red bloods cells were separated from serum by gentle centrifugation (2,100 rpm at 4°C for 5 min) and washed by replacing supernatant with fresh media until the supernatant appeared clear following gentle mixing. Whole washed erythrocytes were diluted 1:10 with RPMI 1640 culture media containing 22.5 IU mL-1 sodium heparin anticoagulant. Erythrocytes (~107 cells mL-1) were stored at 4°C for no more than 10 days; periodic microscopic and spectrophotometric examinations showed no noticeable lytic activity over that duration. The erythrocytes were resuspended daily until use.

2.2.v.b Preparation of Algal Extracts

Following 10-day (240-hr) OD measurements for atrazine x nutrient assays, algal cells were immediately collected for hemolytic analysis by centrifugation using a Centra- CL2 centrifuge (Needham Heights, MA, USA). All hemolytic assay preparation, extraction and incubation procedures were conducted at 4°C in darkness to prevent photolytic effects on measured toxin levels, as some toxins of Prymnesium parvum are light-sensitive (P. Moeller,

personal communication). An initial volume of 14 mL of remaining test culture was transferred to a 15-mL centrifuge tube and spun at 3,600 rpm for 15 minutes. After the cell pellet had accumulated, the supernatant was removed by pipetting and the remaining volume of test culture was added and pelletized by centrifugation as described until there was virtually complete separation of test media and biomass. The resultant pellet was then gently aspirated and rinsed using cold, pH-adjusted, sterile assay buffer (150 mM NaCl, 3.2 mM

KCl, 1.25 mM MgSO4, 3.75 mM CaCl2 and 12.2 mM TRISMA base, pH adjusted to 7.4 with 1N HCl; Table 2.3) and re-consolidated. Next, any supernatant was again carefully removed, and the pellet was resuspended and rinsed in cold assay buffer for a total of three rinses, to remove any residual test medium. The cell pellet was again resuspended in 5 mL of assay buffer, and the tube contents were transferred to a sterile 20-mL glass beaker. An additional 10 mL of assay buffer were added to the centrifuge tube to rinse any remaining cells into the beaker, and the mixture was sonicated for 30 continuous seconds at amplitude 45 on a 20- kHz sonic dismembrator (Fisher Scientific Model 550, Fisher Scientific, Pittsburgh, PA, USA). The sonicated pellet was used immediately in hemolytic assays.

2.2.v.c Hemolytic Assays

Modified erythrocyte lysis assay (ELA, Eschbach et al. 2001, Schug et al. 2010) techniques were adapted to work with larger sample volumes, and hemolytic incubations were conducted in 1-mL microcentrifuge tubes instead of 96-well microtiter plates. Hemolytic components of algal cells were extracted as described above, and equal volumes of erythrocytes and algal extracts (300 µL each) were added to microcentrifuge tubes and incubated for 24 hr at 4°C in darkness. Following the test incubation period, microcentrifuge tubes were spun at 1,250 rpm for 10 minutes at 4°C in a refrigerated microcentrifuge (Micromax RF, IEC, Needham Heights, MA, USA). Erythrocytes were also incubated in an equal volume of assay buffer alone as a negative control. In addition, erythrocytes were incubated in an equal volume of 20 µg saponin solution mL-1 (Sigma-Aldrich, St. Louis, MO, USA; a known hemolytic compound prepared from the soap-bark tree, Quillaja saponaria), which caused 100% lysis of red blood cells and served as a positive control. The entire cell

contents were transferred to a quartz cuvette and absorbance was measured at 414 nm. The highest absorbance peaks in scans of initial preparations of lysed erythrocytes was measured at that wavelength, in accord with Eschbach et al. 2001). The change in absorption at 414 nm following exposure to sonicated cells was used to measure hemoglobin released by lysed erythrocytes as a result of toxic activity of Prymnesium parvum, using the following normalization equations:

[(푐푒푙푙푠⁄푚퐿) ∙ (푉푐푒푛푡푟푖푓푢푔푒푑) ∙ (푉푤푒푙푙)] = 푁

푠푎푚푝푙푒 − 푥 푛푒푔푎푡푖푣푒 푐표푛푡푟표푙 ( ) ∙ 100 = % ℎ푒푚표푙푦푠𝑖푠 푥푝표푠푖푡푖푣푒 푐표푛푡푟표푙

% ℎ푒푚표푙푦푠𝑖푠 ( ) ∙ 106 = 푁표푟푚푎푙𝑖푧푒푑 퐿푦푡𝑖푐 푉푎푙푢푒 푁 where (푉푐푒푛푡푟푖푓푢푔푒푑) was the total volume of culture centrifuged to form assay pellets, and

(푉푤푒푙푙) (volume added to each well) was always 0.300 mL.

2.2.vi Statistical Analysis

All statistical analyses were performed using JMP software (version 12.2.0, SAS Institute, Cary, NC, USA). Homogeneity of variance was tested using a Levene test. Herbicide effects on growth were determined using a one-way analysis of variance (ANOVA). Where differences were detected among treatments (p <0.05), Dunnett’s procedure for multiple comparisons was used to determine which specific treatments differed significantly from the controls (Zar 1999). Atrazine • nutrient • salinity • strain comparisons were analyzed by multifactor ANOVA procedures using nutrient levels, herbicide exposure, salinity and strain as the fixed effects, and growth rate or normalized hemolytic activity as the dependent variable. The F statistic and associated P value for the independent variables and the interactions between them were also reported. Point estimates of sublethal toxicity

(IC50s) were determined based on growth rates, using the Inhibition Concentration linear interpolation (ICp) method as described by Norberg-King (1993):

()CCJJ 1  ICp CJ [ M1 (1  p /100)  M J ] ()MMJJ 1  wherein: CJ = tested concentration whose observed mean response is greater than M1 (1-p/100);

CJ+1 = tested concentration whose observed mean response is less than M1(1-p/100); M1 = smoothed mean response for the control; MJ = smoothed mean response for concentration J; MJ+1 = smoothed response for concentration J+1; p = percent reduction in response relative to the control response; and ICp = estimated concentration at which there is a percent reduction from the smoothed mean control response. The ICp is reported for the test together with the 95% confidence interval calculated by the ICPIN.EXE program. Results were generated using bootstrap methods to derive point estimates and confidence intervals from no fewer than 80 resamplings (Efron 1982).

2.3 RESULTS

2.3.i Atrazine x Nutrient Bioassays

These assays were designed to test interactions among atrazine exposure, nutrient limitation, and salinity on growth and hemolytic potential in three geographically distinct strains of Prymnesium parvum. Atrazine concentrations were quantified at the beginning and end of assays to ensure that the test compound was not depleted over time (see appendix for measured values and enzyme-linked immunosorbent assay [ELISA] quality assurance/quality control data). Generally, effects in nutrient-replete cultures showed increasing tolerance to atrazine over time for all strains (Table 2.5). Measured atrazine half-maximal inhibition -1 concentrations (IC50 values) for all strains ranged from 6.7 µg L (at 48-hr for the TX strain at lower salinity) to 203.5 µg L-1 (for the TX strain at optimal salinity). There were multiple time points at which no concentration-related effects could be discerned at the tested -1 concentrations (Table 2.5). The median IC50 for all strains combined was 96.9 µg L .

Individually, the SC strain appeared to be the most tolerant to atrazine exposure, and no IC50 could be detected in the optimal salinity (20) treatment for that isolate. The most sensitive

strains to atrazine exposure were the TX and NC strains (in lower salinity) (median IC50s -1 were 74.5 and 73.0 µg L , respectively). Considering median IC50 estimates, in all strains the lower salinity (10) treatment had an overall more sensitive response to atrazine exposure, and sensitivity decreased over a slower time period, whereas the median response in the higher- salinity (20) treatment was tolerance that increased rapidly over a 48-hr period, and was maintained for the remaining 10-day test duration (Table 2.6).

Bioassays were initially conducted for 96-hr, and preliminary results indicated that nutrient depletion did not noticeably limit growth during that short duration. In fact, the low- nutrient treatment control cultures had some of the highest growth rates measured over the first four days of testing (Table 2.7). The highest maximum growth rate in all controls was

2.5 divisions day-1, measured 24 hr after test initiation in the salinity 20 N-limited TX culture. Thus, test durations were extended to 10-day bioassays. Atrazine concentrations did not appreciably degrade during that time, based on ELISA quantification assays. All subsequent bioassays were conducted over a 10-day testing interval, with optical density observations made every 24 hr for the first 96 hr, and thereafter on day 7 and day 10. This duration was adequate for nutrient effects to be discerned (Fig. 2.3 and 2.4), and also allowed for closer examination of the potential for induced atrazine tolerance dynamics. For example, the SC strain maintained similar growth at the highest and lowest atrazine concentrations, but was slightly stimulated by intermediate atrazine concentrations over time (Fig. 2.5). Moreover, its growth rates were higher than those of controls in atrazine concentrations of 50 µg/L by day 10 in low salinity, low nutrient (both N and P) treatments (Dunnett’s, p < 0.05) (Figs. 2.5 and 2.6). Nutrient-limited cultures grew slowly but consistently, and atrazine had reduced inhibitory effect on growth rates for all of the strains in either N- or P- limited treatment (Fig. 2.6 and 2.7). Thus, in some assays the low-nutrient treatments had decreased sensitivity to the herbicide (relative to sensitivity in replete cultures) and by day 10, the low salinity, low nutrient treatments in the SC strain had significantly higher growth rates than controls at intermediate concentrations of herbicide (25-100 µg L-1) (Figs. 2.6 and 2.7).

The results of the multifactor ANOVA using either 96-hr or 10-day growth rates as the response variable and concentration (Atra), nutrient treatment (Nutr), salinity and strain as the predictor are shown in Table 2.8. The model R2 for the 96-hr and 10-day assay endpoints was 0.87 and 0.98, respectively. The only insignificant effects in the model were interaction terms in the 96-hr duration; it should be noted, however, that at 96-hr the interaction between strain and salinity had the highest LogWorth. The interaction term of the treatment variables ranked highest at the 10-day endpoint. These results otherwise mirrored the initial observations; the effect of atrazine concentration was strongest at the 96-hr endpoint, and nutrient treatment had an increased effect at the 10-day endpoint. An analysis of variable importance using independent resamples ranked the main effects for the 96-hr test as follows: Atrazine > Strain ≈ Salinity > Nutrients. The main effects for the 10-day assay were ranked as: Nutrients >> Atrazine ≈ Strain > Salinity (see appendix for tables of variable importance).

Because strain had a relatively large interaction effect in both assay durations, the ANOVA analyses were re-run strain-by-strain as 3-way ANOVAs in an attempt to remove that effect (Table 2.9). The 96-hr model for the TX strain at high salinity had an R2 of 0.93, and all other models produced even higher correlations between actual and predicted variables (R2 > 0.98). The whole-model variable importance analysis for the TX strain at 96 hr was ranked as: Atrazine >> Nutrients >> Salinity. The analysis at 10 days was ranked as: Nutrients >> Salinity > Atrazine.

The main effects for the NC strain at 96 hr were ranked as: Atrazine >> Salinity > Nutrients; at 10 days the main effects were ranked as: Nutrients > Atrazine >> Salinity. The main effects for the SC strain at 96 hr were Salinity > Nutrients >> Atrazine; the main effects at 10 days were ranked as: Nutrients >> Atrazine ≈ Salinity. The two top ranking main effects were nutrient treatment effects in all 3-way models, and only for the NC strain at 10 days (atrazine * salinity) and for the SC strain at 96 hr (nutrients * salinity) was an interaction term ranked higher than any main variable treatment effects.

Concentration response curves, showing the effects of nutrient limitation on growth inhibition by strain and salinity, illustrated the decreased influence of media nutritional availability over time (Fig. 2.6). Several assays showed either similar levels of growth inhibition or slightly stimulated responses to atrazine concentrations at 10 days. At that time, as well, atrazine exposure only had a strong impact on the SC strain at low salinity, and the NC strain at optimal salinity. Most of the other responses had become muted along the herbicide concentration gradient at 10 days. Concentration response curves that highlighted the comparison between atrazine effects at low and optimal salinities are shown in Fig. 2.7. Many of these relationships showed similar trends, expected for increased sensitivity to intermediate doses by the NC strain in the optimal-salinity, low-P treatment at 96 hr. Salinity-based generalizations could not be made across the three P. parvum strains.

2.3.ii Erythrocyte Lysis Assays

Modified erythrocyte lysis assays (ELAs) were conducted to examine the potential for experimental stressors to modulate toxicity in static P. parvum cultures. The relative effect of the nutrient treatments on hemolytic activity in control cultures is shown in Fig. 2.8. Multiple comparison with Dunnett’s procedures indicated significantly increased hemolytic activity in nutrient-limited treatments for all strains and salinities when compared to hemolytic activity in nutrient-replete media (p < 0.05) (Fig. 2.8). Results of 3-way ANOVAs using normalized hemolytic activity as the dependent variable and nutrient treatment, atrazine concentration and strain origin as the fixed effects are shown in Table 2.10. The main effects of the ANOVA tables are presented in order of decreasing LogWorth. Three- way ANOVA ranking of variable importance of main effects indicated a rank of: Nutrients > Strain >> Atrazine (Total Effects = 0.64, 0.50 and 0.07, respectively; Table 2.10). The ELAs were only conducted on the SC strain at low salinity (due to time constraints) so 4-way ANOVA procedures were conducted to model the 4-way interaction between nutrient treatment, atrazine concentration, strain origin and salinity by removing the SC strain from the analysis (Table 2.11). The 4-way ANOVA incorporating salinity showed that the relative variable importance was ranked as Nutrients >> Strain > Salinity > Atrazine (Total effects =

0.80, 0.36, 0.25, and 0.16, respectively; Table 2.11). Both ELA ANOVA models had high R2 values when comparing observed to predicted values, but the 3-way model without the salinity term had both a higher R2 (0.96) and a lower AIC value, indicating that it was better for predicting potential hemolytic activity. The strains were also analyzed separately to try to examine the interaction of Herbicide x Nutrients x Salinity while avoiding any confounding influences of strain variability on the model (Table 2.12, also ordered in decreasing LogWorth). Atrazine exposure had a stronger influence on predicted hemolytic activity than nutrient limitation for the SC strain (salinity was not included as a model variable). In contrast, nutrient limitation was the strongest predictor of hemolytic activity for both the TX and NC strains. Atrazine exposure had a stronger effect than salinity for the TX strain, but for the NC strain, hemolytic activity was largely a function of nutrient availability, salinity level, and their interaction with atrazine exposures. The model for the TX strain also indicated that, for that strain, herbicide exposure was a stronger predictor of hemolytic activity than salinity.

The relative production of hemolytic substances (normalized to extracted cell density) by the three strains at different salinities in a range of atrazine concentrations are shown in Fig. 2.9. The nutrient-limited treatments were all significantly more toxic than the nutrient- replete treatments, and this effect generally increased with increasing atrazine concentration. With exception of the TX strain at optimal salinity, the strains all were more hemolytic in low-N treatments. The NC and SC strains exhibited a general trend toward increasing toxicity with increasing atrazine exposure in low- nutrient treatments. The SC strain in the low-salinity, low-N, high-atrazine treatment was the most hemolytic culture tested.

There was a log-normal relationship between herbicide exposure and hemolytic activity (Figs. 2.10 [salinity 20] and 2.11 [salinity 10]). While the majority of hemolytic responses were more strongly predicted by nutrient limitation, all three strains showed a relationship between herbicide exposure and hemolytic activity. In nutrient-replete treatments, cultures had a generalized response of decreasing hemolytic activity with increasing atrazine concentration, and this relationship was moderately significant for the TX and SC strains at low salinity (r2 = 0.69 and 0.65, respectively), and for the SC strain at high

salinity as well (r2 = 0.87). Yet, in all low-nutrient treatments the SC strain showed increasing hemolytic activity with increasing atrazine concentration, and increasing hemolytic activity with increasing atrazine also was exhibited by the NC and TX strains in the low-N treatments (salinity 20 and 10, respectively). There were no significant relationships between hemolytic activity and atrazine concentration for the NC strain at low salinity (data not shown).

The relationship between hemolytic activity and growth rates (in divisions day-1) was also explored [Figs. 2.12 (salinity 10) and 2.13 (salinity 20)], and was inconstant among different strains and treatments. R2 values >0.65 were observed at salinity 10 for the TX strain in nutrient-replete and low-N treatments. In nutrient-replete treatments, growth rates were positively related to toxicity, but in low-N treatments the highest hemolytic activity corresponded to the lowest growth rates. The NC strain in low-N media and the SC strain in nutrient-replete media responded similarly. Growth rates were less well associated with hemolytic activity in optimal-salinity cultures, and only the NC strain under nutrient-replete conditions showed a significant relationship between growth rate and hemolytic activity at salinity 20 (R2 = 0.77).

2.4 DISCUSSION

This study examined interactions between co-occurring growth stressors in a stress- tolerant HAB species, and it showed the following: (1) Prymnesium parvum growth responses to atrazine exposure were influenced by salinity, nutrient status and strain, (2) sensitivity to herbicide exposure decreased over short time frames (less than 10 days) and (3) the relative production of hemolytic compounds could be enhanced in low nutrient systems by atrazine exposures. Competitive ability previously has been shown to be influenced by toxicants, and contaminant-induced impacts on R* values under resource limitation can shift a superior competitor for the limiting resource to an inferior one (Tilman 1982, Landis and Ho 1995, Interlandi 2002). Here, interactions were explored which might shift this established dynamic in the opposite direction, wherein an inferior competitor could become a

superior one in a toxicant-contaminated, nutrient-limited system. The slowly growing P. parvum can become dominant at sub-optimal growth conditions, and forms nearly monospecific blooms during the winter season in low-salinity rivers and reservoirs in Texas, with devastating impacts to fisheries and recreational economies (Southard et al. 2010, VanLandeghem et al. 2013). In September 2009, P. parvum was the causative agent of a ~48-km- (30-mile)-long bloom in an Appalachian stream in West Virginia, USA, involving sudden death of thousands of fish and salamanders (Renner 2009). The most diverse population of highly endangered freshwater mussels in the Monongahela River basin was also decimated (Renner 2009). The diverse toxins of P. parvum have been tentatively implicated (by geographic proximity) in the death of birds and other animals (Moustaka- Gouni et al. 2004), but are not thought to be toxic to humans (Manning and La Claire 2010). The ecological persistence and potential for harm is understudied in this group of compounds; indeed, some (many?) toxins from P. parvum have not yet been structurally characterized (Roelke et al. 2016). The environmental factors influencing P. parvum bloom initiation, persistence and toxicity in the field are complex, and likely involve a dynamic interplay among multiple factors including salinity (Roelke et al. 2011, Israël et al. 2014), hydrology (Roelke et al. 2010), nutrient status (Errera et al. 2008, Patiño et al. 2014), pH, water hardness, and total dissolved solids (VanLandeghem et al. 2012, VanLandeghem et al. 2015b; also see Brooks et al. 2011, Granéli et al. 2012, Roelke et al. 2016, and references therein). Confounding the ability to model and predict the distribution of P. parvum impacts at local scales is the fact that many of these responses appear to vary from basin to basin (Israël et al. 2014, VanLandeghem et al. 2015a).

The results presented here show that growth and toxicity are highly strain-dependent. The growth response to optimal and lowered salinity in the short term was strongly influenced by herbicide exposure and strain, and by the interactions between these factors and salinity (p < 0.05) (Fig. 2.7, Table 2.8). These effects were overcome, however, by the influence of nutrient limitation. By the end of the 10-day testing period, all strains of P. parvum appeared to exhibit some ‘stress-stimulation’ effect, whereby populations in low-

nutrient treatments had increased growth relative to nutrient-rich controls when exposed to intermediate levels of atrazine. Both the NC and SC strains were most stimulated in low- salinity media, whereas the Texas strain increased growth at salinity 20 (its optimal salinity for growth) in low nutrient x atrazine treatments. The Texas strain was also more resilient to atrazine stress in salinity 20 media under nutrient limitation, but it was the only strain tested that became more sensitive to growth-inhibiting effects of atrazine over time in nutrient- replete media at salinity 20.

These observations make sense if considered as a potential function of increased predatory activity by P. parvum on bacteria. Bacterial consumption was not tracked, but the studies were performed on unialgal batch cultures containing bacteria. The potential for mixotrophic activity cannot be excluded since P. parvum is known to consume bacteria (e.g., see Martin-Cereceda et al. 2003; Carvalho and Granéli 2010). If bacterivory in Prymnesium parvum is increased by reduced nutrient availability and light exposure as has been previously suggested (Carvalho and Granéli 2010, Lundgren et al. 2016), that might suggest a mechanism by which ecologically stressed, toxin-producing strains could have an advantage over strains that produce little or no toxins and do not engage in bacterivory (Glibert et al. 2009, Brutemark and Granéli 2011). The TX strain in the optimal-salinity, nutrient-replete treatment may have taken more time to become physiologically stressed from atrazine exposure, without a concomitant increase in bacterivory – which would be supported by the observation that the TX strain in salinity 20 nutrient-replete culture had negligible production of hemolytic substances (Figs. 2.8 and 2.10). Moreover, the strain and treatments which exhibited, overall, the highest tolerance to atrazine had the highest production of hemolytic substances (SC strain in low-nutrient treatments). Only the SC strain showed a strong correlation between atrazine exposure and hemolytic activity, but the direction of this relationship was reversed between replete and low nutrient treatments. The low-salinity SC strain also had the steepest dose-response curve, which can indicate strong affinity of atrazine for the mode-of-action receptor – of interest considering the large hemolytic response that was observed in that culture. A few cultures showed a strong relationship between growth

rate and toxicity, but the majority did not. Moreover, there was a positive linear correlation between growth rate and toxicity for the three strains (NC – salinity 20; TX, SC – salinity 10) in nutrient-replete treatments and a negative linear correlation for the TX and NC strains in low-nitrogen treatments (salinity 10).

While this work supports the reports that few generalizations can be made across phytoplankton strains (Burkholder and Glibert 2006, and references therein), it is worthwhile to examine the differences shown by these three P. parvum strains in response to environmental stressors. The relative ranking of hemolytic activity in nutrient-replete, low- salinity media was TX ≈ NC < SC, whereas in nutrient-replete, optimal-salinity media, NC > TX. In low-N, low-salinity cultures, hemolytic activity was ordered TX < NC < SC, whereas in low-N, optimal-salinity cultures, TX ≈ NC. In low-P, optimal-salinity cultures, the TX strain showed the highest hemolytic activity, but in low-salinity, low-P conditions, the NC and SC strains were more toxic than the TX strain. These data indicate that hemolytic responses are influenced by a complex interaction of environmental factors. Efforts to reliably predict the toxic potential of P. parvum blooms may involve development of an intricate “mandala”-like model of niche factors (e.g., see Glibert 2016).

Mixotrophy is an under-appreciated mechanism in marine phytoplankton ecological models (Tittel et al. 2003, Burkholder et al. 2008, Moore 2013, Caron 2016). Prymnesium parvum has been referred to as a “predatory phytoflagellate” (Caron 2016), a name which is appropriately descriptive depending on resource quality in the habitat. The prymnesin toxins are high molecular weight compounds rich in carbon. Production of such energetically expensive compounds would be disadvantageous unless there was an equitable return in benefits – likely in the form of enhanced access to resources. Molecular analyses of P. parvum have revealed that a gene which encodes an ABC-type phosphate transport system is one of the most highly expressed genes in the P. parvum transcriptome (La Claire II 2006), and is preferentially upregulated under P limitation (Beszteri et al. 2012). These putative phosphate transporters include enzymes which either hydrolyze extracellular organically bound phosphate or relocate intracellular phosphate, and P. parvum is capable of utilizing a

variety of organic and inorganic P sources in dissolved or particulate forms (Manning and La Claire 2010). It also has cell-surface L-amino oxidases which oxidize amino acids and primary amines, producing ammonium compounds which P. parvum can then consume (Palenik and Morel 1991). And, P. parvum is capable of assimilating dissolved organic nitrogen from sewage as well (Lindehoff et al. 2009). Considering its extracellular toxins (Blossom et al. 2014) and its assimilation of bacterial carbon stores (Lundgren et al. 2016), these features could indicate a mechanism by which P. parvum is uniquely equipped to succeed in low-nutrient environments. Toxin production facilitates prey capture by P. parvum, and toxicity increases when nutrients or light become limited (Dafni et al. 1972; Uronen et al. 2005; Carvalho and Granéli 2010, Lundgren et al. 2016; see also Graneli et al. 2012, and references within). As atrazine is a reversible inhibitor of photosystem II electron transport proteins, it also reasonable that phagotrophy would be enhanced under high levels of herbicide exposure. This research has shown that atrazine sensitivity in P. parvum is influenced by nutrient availability and salinity, but the magnitude of the effect is strain- dependent, which is indicative of a genetically-dictated response variability.

These data also indicate a potential for induced tolerance of P. parvum to atrazine over time. The IC50 estimates of growth inhibition were consistently lower at the beginning of the tests and increased by the end of the tests. A possible mechanism to explain these observations is that the populations gradually increased production of hemolytic compounds and increased reliance on phagotrophic assimilation of carbon. It is in line with considerations of dynamic energy budget theory to suggest that carbon storage products (long chain cyclic polyethers, e.g., prymnesins) would only be excreted if reliance on photosynthetic carbon assimilation was reduced. In contrast, if nutrients and light are not growth-limiting factors, these products should be increasingly retained, and reliance on autotrophic carbon storage should again be favored (Kooijman 1993, Nisbet et al. 2012). Here, P. parvum increased production of toxic substances under environmental stress, as has been reported by other researchers (Nygaard and Tobiesen 1993, Barreiro et al. 2005, Uronen et al. 2005, Errera et al. 2008). These results also support the findings of Yates and Rogers

(2011), who showed that P. parvum was less susceptible to atrazine impacts than co- occurring phytoplankton, and that P. parvum is becoming dominant in atrazine-contaminated waters of Texas.

This microalga is regarded as among the deadliest harmful algae to fish and shellfish (Moestrup 1994). While other studies have examined the linkages between eutrophication and the “global HAB epidemic” (Smayda 1990, Burkholder 2001, Glibert and Burkholder 2006, Anderson et al. 2008), less work has focused on the dynamics of highly competitive microbial communities resulting from the selective macro- or micro-nutrient depletion that is caused by rapidly growing blooms – which, themselves, are a natural consequence of nutrient over-enrichment (Heisler et al. 2008, Glibert et al. 2011, Burkholder and Glibert 2013). Other researchers have described P. parvum as “physiologically pre-adapted to invade P- limited brackish waters” (Baker et al. 2009) due to its broad salinity tolerance and its high competitive ability under P limitation. The latter characteristic results from high expression of putative phosphate transporter genes in P. parvum, and its increased consumption of relatively P-rich bacteria in P-limited waters (Carvalho and Granéli 2010, Lundgren et al. 2016; note that bacteria can sequester high levels of P relative to background concentrations in P-limited systems – Bratbak and Thingstad 1985, Andersen et al. 1986). Thus, increased expansion of P. parvum blooms in eutrophic freshwaters is expected, especially waters increasingly threatened by salinization (Williams 2001, Kaushal et al. 2005) and coastal organic nitrogen pollution leading to N:P stoichiometric imbalance (Glibert et al. 2006, Burkholder et al. 2007, Bricker et al. 2008, Burkholder and Glibert 2013, Glibert et al. 2014). The findings from this research suggest that increasing chemical contamination of inland surface waters is helping to promote ecosystem-disruptive, strongly mixotrophic algal blooms.

2.5 FIGURES AND TABLES

haptonema

Figure 2.1. Light micrograph Prymnesium parvum (Texas strain), taken using an Olympus AX70 research microscope with 60x water-immersion lens. Scale bar = 10 µm. Arrow indicates the characteristic haptonema, structurally intact following preservation techniques.

Table 2.1. Collection locations in the U.S. for the tested Prymnesium culture isolates.

Collection Date Species Collection ID Collection Site Date Acquired UTEX LB Texas Colorado River at P. parvum (TX) 12/01 04/30/2009 2797 US Hwy 183

P. parvum (SC) UTEX LB Oyster Rake Pond near 05/01 06/18/2009 2827 Charleston, South Carolina

P. parvum (NC) UNC-W Elizabeth City, NC 05/02 01/16/2013

Table 2.2. Media recipes used in Prymnesium testing and culture.

f/2 Macronutrient concentrations

Volume added to final Final concentration Stock solution medium in f/2 media (µM) Nutrient-Replete NaNO3 2.72 g L-1 H2O 1 mL 32.00 -1 NaH2PO4 0.24 g L H2O 1 mL 2.00

Low-N -1 NaNO3 0.17 g L H2O 1 mL 2.00 -1 NaH2PO4 0.24 g L H2O 1 mL 2.00

Low-P -1 NaNO3 2.72 g L H2O 1 mL 32.00 -1 NaH2PO4 0.02 g L H2O 1 mL 0.20

f/2 Trace Element Solution

Quantity added to final Molar concentration in Primary stock solution medium final medium (M) -5 Na2EDTA · 2H2O --- 4.36 g 1.17 X 10 -5 FeCl3 · 6H2O --- 3.15 g 1.17 X 10 -1 -8 ZnSO4 · 7H2O 22.00 g L H2O 1 mL 7.65 x 10 -1 -8 CoCl2 · 6H2O 10.00 g L H2O 1 mL 4.20 x 10 -1 -8 CuSO4 · 5H2O 9.80 g L H2O 1 mL 3.93 x 10 -1 -8 Na2MoO4 · 2H2O 6.30 g L H2O 1 mL 2.60 x 10 -1 -7 MnCl2 · 4H2O 180.00 g L H2O 1 mL 9.10 x 10

f/2 Vitamin Solution Primary stock Quantity added to Molar concentration in

solution final medium final medium (M) thiamine · HCL (Vitamin B) --- 200 mg 2.96 x 10-7 -1 -9 biotin (Vitamin H) 0.10 g L H2O 1 mL 2.05 X 10 -1 -10 cyanocobalamin (Vitamin B12) 1.00 g L H2O 1 mL 3.69 x 10

Table 2.3. Erythrocyte lysis assay buffer preparation. *

Mass (g L-1) Concentration (mM) NaCl 8.7700 150 KCl 0.2386 3.2 MgSO4 0.3081 1.25 CaCl2 0.4162 3.75 TRISMA base 1.4779 12.2 *pH adjusted to 7.4 at 4°C with 1N HCl

Table 2.4. Blood storage medium preparation. *

Mass (g L-1) RPMI-1640** 10.40 g L-1 NaHCO3 L-1 2.00 g L-1 Sodium Heparin 0.05 mg mL-1 *Diluted 10% (v/v) with Milli-Q

** RPMI-1640 *with L-glutamine *without phenol red *without NaHCO3

Figure 2.2. Example of the relationship between optical density (OD) and Prymnesium parvum population density (PD) Similar relationships were determined for all strains prior to use of absorbance measurement methods in toxicity endpoint estimates.

Table 2.5. Changes in atrazine effects in nutrient-replete media over time (µg/L) and maximum growth -1 rate kmax (units, divisions day ) for all strains tested. BD = beyond detection, ND = not determined.

Strain kmax Test date Hour IC50 (SD) IC10 (SD) (salinity) (controls) TX (20) 30-Jul-12 24 96.9 (30.6) 20.5 (9.8) 0.6 72 151.2 (4.4) 40.3 (4.4) 0.8 96 142.2 (4.7) 39.9 (5.5) 0.8 TX (20) 28-Jan-13 24 >200 23.4 (13.0) 1.4 48 169.2 (BD) 41.9 (17.8) 0.7 72 131.2 (20.1) 24.4 (6.8) 0.9 96 119.0 (23.9) 23.0 (14.5) 0.8 168 >200 50.1 (21.8) 0.7 240 >200 92.3 (29.6) 0.6 TX (10) 20-Aug-12 24 19.5 (4.6) 10.4 (4.7) 0.5 48 6.7 (0.2) 1.3 (0.1) 0.7 72 9.3 (0.3) 1.9 (0.1) 0.8 96 80.2 (6.3) 4.9 (1.0) 0.6 TX (10) 4-Mar-13 48 >200 24.2 (7.2) 1.3 72 78.2 (3.3) 17.4 (4.7) 0.9 96 92.3 (3.3) 14.9 (2.9) 0.9 168 177.9 (5.9) 38.7 (19.6) 0.6 240 189.6 (BD) 63.8 (14.2) 0.5 NC (20) 3-May-13 24 20.7 (2.7) 14.1 (0.4) 0.5 72 70.6 (8.7) 25.6 (13.1) 0.5 96 118.3 (8.0) 44.8 (13.6) 0.6 168 104.4 (10.2) 28.3 (14.5) 0.6 240 155.4 (5.4) 54.2 (14.2) 0.6 NC (10) 25-Mar-13 24 ND ND 2.4 48 43.4 (18.3) 9.0 (6.5) 1.4 72 88.3 (BD) 18.7 (8.2) 1.0 96 73.0 (8.7) 19.2 (7.4) 0.9 168 >200 58.9 (13.5) 0.7 240 >200 120.2(11.7) 0.5 SC (20) 7-Jun-13 72 >200 123.3 (5.2) 1.2 96 >200 99.7 (8.6) 1.1 168 >200 25.3 (8.8) 0.7 240 >200 42.0 (8.9) 0.5 SC (10) 21-Jun-13 72 67.3 (22.3) 15.1 (3.3) 0.6 96 88.2 (9.9) 22.6 (22.6) 0.6 168 148.0 (4.1) 69.0 (2.9) 0.5 240 161.0 (2.78) 81.3 (8.3) 0.4

Table 2.6. Median half-maximal activity estimates (units, µg L-1) over time for all strains in nutrient- replete media.

Salinity Strain IC50 = IC10 = Salinity Hour IC50 = 10 NC 73.0 53.7 10 24 19.5 SC 118.1 44.9 48 25.0 TX 74.5 12.9 72 72.7 20 NC 100.8 31.1 96 84.2 SC BD 69.4 168 162.9 TX 138.9 36.0 240 175.3 20 24 58.8 48 169.2 72 131.2 96 119.0 168 153.9

Table 2.7. Heat map of kmax values for the growth response of P. parvum (three strains – TX, NC, and SC) in different treatments over time (in hours) (Nutr = Nutrient treatment).

Strain Salinity Test Date Nutr 24 48 72 96 168 240 TX 20 30-Jul-12 R 0.6 0.8 0.8 Low N 1.0 0.8 0.6 Low P 0.8 0.7 0.6 28-Jan-13 R 1.4 0.7 0.9 0.8 0.7 0.6 Low N 2.5 1.5 1.0 0.7 0.4 0.3 Low P 2.4 1.5 1.1 0.8 0.5 0.4 10 20-Aug-12 R 0.5 0.7 0.8 0.6 Low N 0.7 0.9 0.8 0.6 Low P 0.8 0.9 0.8 0.6 4-Mar-13 R 1.3 0.9 0.9 0.6 0.5 Low N 1.3 0.7 0.5 0.2 0.1 Low P 1.3 1.0 0.8 0.4 0.2 NC 20 3-May-13 R 0.5 0.5 0.6 0.6 0.6 Low N 0.9 0.8 0.7 0.5 0.3 Low P 1.4 0.8 0.8 0.5 0.4 10 25-Mar-13 R 2.4 1.4 1.0 0.9 0.7 0.5 Low N 1.1 1.1 0.9 0.7 0.4 0.3 Low P 2.1 1.3 1.2 1.0 0.6 0.4 SC 20 7-Jun-13 R 1.1 1.2 1.1 0.7 0.5 Low N 0.6 0.5 0.3 0.2 Low P 0.8 0.8 0.5 0.3 10 21-Jun-13 R 0.6 0.6 0.5 0.4 Low N 0.4 0.4 0.2 0.1 Low P 0.7 0.5 0.3 0.2

Figure 2.3. Cell densities of control cultures for the Texas strain of P. parvum at salinity 20. Growth rates in low-nutrient versus nutrient-replete treatments were comparable until after 96 hr.

* * *

Figure 2.4. Growth rates of the NC strain of P. parvum at different salinities in all treatments at day 10. Growth in nutrient-replete treatments was more sensitive to atrazine effects than growth in either nutrient-limited treatment.

Figure 2.5. Atrazine inhibition curves in salinity 10 for the South Carolina strain of P. parvum over time. At timepoints > 168 (day 7) growth was slightly stimulated at intermediate atrazine concentrations (25-50 µg/L) in comparison to growth in controls with no atrazine, whereas % inhibition at the highest atrazine level (200 µg/L) did not vary significantly over time. Data are given as means + 1 SE.

Figure 2.6. Concentration inhibition curves for all atrazine assays over time. Colors represent different nutrient regime treatments. Data are given as means + 1 SE.

Figure 2.7. Concentration inhibition curves for all atrazine assays over time point. Colors represent different levels of salinity. Data are given as means + 1 SE.

Table 2.8. Results of 4-way ANOVA testing growth rates of P. parvum (all three strains) at 96-hr or 10-day endpoints as dependent variables. Fixed effects are atrazine concentration (Atra), nutrient regime (Nutr), geographic origin (strain) and salinity. DF ≡ degrees of freedom. Significance is indicated by P values < 0.0088 to < 0.001 (bold), or < 0.0126 to < 0.0292 (bold/italics).

96-Hr Growth Effects Day-10 Growth Effects Parameter DF F Ratio P Value Parameter DF F Ratio P Value Strain*Salinity 2 257.22 < 0.0001 Nutr 2 1486.45 0.0009 Atra 5 92.58 < 0.0001 Strain 2 618.06 < 0.0001 Strain 2 176.97 < 0.0001 Salinity 1 1047.05 0.0088 Nutr*Salinity 2 45.49 < 0.0001 Atra 5 108.36 < 0.0001 Nutr*Strain*Salinity 4 23.94 < 0.0001 Strain*Salinity 2 209.49 < 0.0001 Nutr 2 41.86 < 0.0001 Atra*Nutr 10 37.02 < 0.0001 Salinity 1 62.93 < 0.0001 Nutr*Strain 4 33.76 < 0.0001 Atra*Nutr 10 7.75 < 0.0001 Atra*Strain*Salinity 10 15.21 < 0.0001 Atra*Nutr*Salinity 10 4.04 < 0.0001 Atra*Nutr*Strain*Salinity 20 4.12 < 0.0001 Nutr*Strain 4 4.12 0.0029 Nutr*Strain*Salinity 4 9.33 < 0.0001 Atra*Strain 10 2.48 0.0072 Atra*Nutr*Strain 20 3.30 < 0.0001 Atra*Nutr*Strain*Salinity 20 1.89 0.0126 Atra*Salinity 5 5.58 < 0.0001 Atra*Strain*Salinity 10 1.07 0.3847 Atra*Nutr*Salinity 10 3.14 0.0292 Atra*Salinity 5 1.01 0.4108 Nutr*Salinity 2 4.84 < 0.0001 Atra*Nutr*Strain 20 0.87 0.6291 Atra*Strain 10 2.06 < 0.0001

Table 2.9. Results of a 3-way ANOVAs for each of the three P. parvum strains (TX, NC, SC) testing growth rates at 96-hr or 10-day endpoints as dependent variables. Fixed effects are atrazine concentration (Atra), nutrient regime (Nutr), and salinity. Significance is indicated by P values < 0.0067 to < 0.0001 (bold), or < 0.0117 (bold/italics).

TX NC SC End Parameter DF F Ratio P Value F Ratio P Value F Ratio P Value 96 Atra 5 72.04 < 0.0001 64.34 < 0.0001 94.55 < 0.0001 hr Nutr 2 74.59 < 0.0001 35.91 < 0.0001 74.67 < 0.0001 Salinity 1 31.91 < 0.0001 255.19 < 0.0001 2258.92 < 0.0001 Atra*Nutr 10 6.06 < 0.0001 7.53 < 0.0001 11.48 < 0.0001 Atra*Salinity 5 3.52 0.0067 0.76 0.5837 5.70 0.0002 Nutr*Salinity 2 34.97 < 0.0001 23.17 < 0.0001 377.98 < 0.0001 Atra*Nutr*Salinity 10 0.99 0.4580 7.59 < 0.0001 8.22 < 0.0001 10 Atra 5 38.87 < 0.0001 25.04 < 0.0001 64.72 < 0.0001 days Nutr 2 835.93 < 0.0001 243.10 < 0.0001 670.13 < 0.0001 Salinity 1 1121.16 < 0.0001 9.86 0.0025 630.87 < 0.0001 Atra*Nutr 10 14.43 < 0.0001 5.29 < 0.0001 36.55 < 0.0001 Atra*Salinity 5 3.19 0.0117 18.50 < 0.0001 10.41 < 0.0001 Nutr*Salinity 2 0.74 0.4811 0.97 0.3832 35.08 < 0.0001 Atra*Nutr*Salinity 10 1.18 0.3217 3.82 0.0004 7.84 < 0.0001

Figure 2.8. Differences in relative production of hemolytic compounds by P. parvum strains (TX, NC) in control treatments at both salinity levels (10, 20). Data are given as means + 1 SE.

Table 2.10. Results from a 3-way ANOVA testing normalized hemolytic activity as the dependent variable and atrazine concentration (Atra), nutrient regime (Nutr), and geographic origin (Strain) as the fixed effects. The table is ordered in decreasing LogWorth. All strains were tested at salinity 10. Significance is indicated by P values < 0.0001 (bold), or 0.0256 to 0.0281 (bold/italics).

Parameter LogWorth DF F Ratio P Value Nutr 59.393 2 625.6194 < 0.0001 Strain 53.275 2 469.569 < 0.0001 Nutr*Strain 33.644 4 94.5214 < 0.0001 Atra*Nutr 11.425 10 10.4307 < 0.0001 Atra*Nutr*Strain 6.454 20 4.3046 < 0.0001 Atra*Strain 1.592 10 2.1609 0.0256 Atra 1.551 5 2.6211 0.0281

Whole Model

R2 0.962779 AICc 1050.116

Analysis of variable importance for 3-way ANOVA, random independent resampling

Column Main Effect Main Effect Total Effect Total Effect SE SE Nutr 0.458 0.009 0.641 0.01 Strain 0.345 0.009 0.503 0.009 Atra 0.026 0.004 0.074 0.004

Table 2.11. Results from a 4-way ANOVA testing hemolytic effects at all salinities (NC and TX strains only) against atrazine concentration (Atra), nutrient regime (Nutr), geographic origin (Strain), and salinity. Significance is indicated by P values < 0.0001 to 0.0007 (bold), or 0.0435 (bold/italics). Hemolytic activity in the South Carolina strain was only tested at salinity 10, so that strain was excluded from this analysis.

Parameter LogWorth DF F Ratio P Value Nutr 69.258 2 587.5598 < 0.0001 Strain 32.29 1 246.6901 < 0.0001 Nutr*Salinity*Strain 29.161 2 110.9587 < 0.0001 Salinity*Strain 16.669 1 93.8258 < 0.0001 Nutr*Strain 14.53 2 42.5887 < 0.0001 Atra*Nutr*Salinity*Strain 8.815 10 7.4987 < 0.0001 Atra*Nutr*Salinity 8.766 10 7.4604 < 0.0001 Atra*Nutr 7.718 10 6.6541 < 0.0001 Atra 6.768 5 9.041 < 0.0001 Nutr*Salinity 6.496 2 16.626 < 0.0001 Atra*Strain 3.734 5 5.2518 0.0002 Atra*Nutr*Strain 3.334 10 3.4321 0.0005 Atra*Salinity 3.149 5 4.5419 0.0007 Salinity 1.362 1 4.1501 0.0435 Atra*Salinity*Strain 0.453 5 1.1195 0.3527

Whole Model R2 0.938837 AICc 1355.593

Analysis of variable importance for hemolytic activity - 4-way ANOVA, random independent resampling

Column Main Effect Main Effect Total Effect Total Effect SE SE Nutr 0.548 0.008 0.8 0.01 Strain 0.112 0.009 0.358 0.007 Salinity 0.036 0.006 0.245 0.006 Atra 0.047 0.006 0.158 0.005

Table 2.12. Results from a 3-way ANOVA testing normalized hemolytic activity in individual strains as the dependent variable and atrazine concentration (Atra), nutrient regime (Nutr), and salinity as the fixed effects. Significance is indicated by P values < 0.0007 (bold), or 0.0315 (bold/italics).

Source LogWorth DF F Ratio P Values TX Nutr 47.484 2 714.4232 < 0.0001 Nutr*Salinity 38.617 2 389.5878 < 0.0001 Atra 17.481 5 35.722 < 0.0001 Salinity 15.146 1 106.8492 < 0.0001 Atra*Nutr*Salinity 12.606 10 13.9804 < 0.0001 Atra*Salinity 9.82 5 15.937 < 0.0001 Atra*Nutr 6.603 10 6.7638 < 0.0001 NC Nutr 32.497 2 251.7324 < 0.0001 Salinity 7.695 1 39.8104 < 0.0001 Atra*Nutr*Salinity 6.287 10 6.4484 < 0.0001 Nutr*Salinity 4.536 2 12.117 < 0.0001 Atra*Nutr 4.512 10 4.7702 < 0.0001 Atra 1.502 5 2.6139 0.0315 Atra*Salinity 0.231 5 0.7519 0.5874 SC Atra 3.142 5 5.5128 0.0007 Atra*Nutr 11.588 10 21.5613 <.0001 Nutr 30.512 2 874.0726 <.0001

Nutrient-Replete IC50 = BD

Nutrient-Replete IC50 = 189.6 Low N IC50 = BD Low N IC50 = BD Nutrient-Replete IC50 = BD

Nutrient-Replete IC50 = BD

Replete IC50 = 155.8 Replete IC50 = 129.5 Low N IC50 = 165.7 Low N IC50 = BD Replete IC50 = 176.3 Replete IC50 = BD

SC (10 psu) Replete IC50 = 161.3 Low N IC50 = BD Replete IC50 = BD

Figure 2.9. Relative production of hemolytic activity plotted against atrazine concentration for all tested strains, showing line-of-fit for non-linear relationships and corresponding R2, and confidence intervals as colored zones corresponding to different nutrient treatments (half maximal growth effect concentrations for atrazine are provided as insets with each assay display). Data are given as means + 1 SE; BD ≡ below detection.

Figure 2.10. Log-normal relationships between hemolytic activity and atrazine concentration, in salinity 20 f/2 media: Panels (a, b) are from in nutrient-replete media; panels (c, d) are from low- nitrogen media; and panels (e-f) are from low-phosphorus media. Panels (a, c, e) are for the TX strain, and panels (b, d, f) are for the NC strain.

Figure 2.11. Log-normal relationships between hemolytic activity and atrazine concentration, in salinity 10 f/2 media. Panels (a-b) are from nutrient-replete media; panels (c-d) are from low- nitrogen media; and panels (e-f) are from low-phosphorus media. Panels (a, c, e) are for the TX strain, and panels (b, d, f) are for the SC strain.

Figure 2.12. Linear relationships between growth rates (divisions day-1) and hemolytic activity in P. parvum strains tested in salinity 10 f/2 media. Panels (a-c) were in nutrient-replete media; panels (d-f) were in low-nitrogen media, and panels (g-i) were in low-phosphorus media. Panels (a, d and g) are for TX strain; panels (d, e, h) are for the NC strain; and panels (c, f, i) are for the SC strain.

b a r 2 = 0.77 r 2 = 0.55

c r 2 = 0.00 d r 2 = 0.54

e f r 2 = 0.03 r 2 = 0.46

Figure 2.13. Linear relationships between hemolytic activity and growth rates (divisions day-1), at salinity 20 f/2 media. Panels (a, b) are from nutrient-replete media; panels (c, d) are from low-nitrogen media; and panels (e, f) are from low-phosphorus media. Panels (a, c, e) are for the TX strain, and panels (b, d, f) are for the NC strain.

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Chapter 3: Examination of Chattonella subsalsa (Raphidophyceae) Growth and Hemolytic Activity Responses to Co-Occurring Environmental Stressors

3.1 INTRODUCTION

Coastal zone estuaries and wetland habitats are now recognized as being among the most highly stressed natural systems due to rapid urbanization and development of coastlands, and concomitant pollution from land-based runoff (Scott et al. 2006, U.S. EPA 2016a). About two-thirds of the nation's coastal areas and more than one-third of the nation's estuaries showed impairment from nutrient (nitrogen, N, and phosphorus, P) pollution (U.S. EPA 2016b). As anthropogenic nutrient pollution increases, many estuarine and marine coastal waters are sustaining harmful algal blooms (HABs) which are increasing in duration and frequency (Hallegraeff 1993, Glibert 2005, Anderson et al. 2008, Heisler et al. 2008). Eutrophication is now recognized as one of the most important factors contributing to the global expansion of HAB species (Burkholder 1998, Glibert 2005; Heisler et al. 2008). High levels of phosphate loading relative to dissolved inorganic nitrogen have been related to the increased occurrence of harmful algal species such as toxigenic raphidophytes (Heterokontophyta, Raphidophyceae; Lewitus et al. 2003 a,b).

The majority of chemical contaminants reaching estuaries are from agriculture, and many of them are herbicides (Gilliom et al. 2006), which commonly co-occur in runoff with nutrients from fertilizers and animal wastes (Zimba 2000; Hapeman et al. 2002; Peters et al. 2005; Burkholder et al. 2007). Atrazine (1-chloro-3-ethylamino-5-isopropylamino-2,4,6- triazine) is the second most commonly used herbicide in the U.S., and the most common surface water contaminant (Gilliom et al. 2006, Ryberg et al. 2010). This herbicide is a photosynthetic inhibitor which works by blocking electron transport in photosystem II (DeLorenzo et al. 2001, Vencill 2002), and can elicit effects similar to nutrient dificiency in exposed algal populations (Weiner et al. 2007). It has been found at environmental concentrations as high as 1,000 µg L-1 in surface waters adjacent to treated fields (deNoyelles et al. 1982, Eisler 1989, Penninigton et al. 2001).

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Recently, blooms of the toxigenic raphidophyte Chattonella subsalsa B. Biecheler were linked to fish kills in eutrophic waters of coastal South Carolina in the southeastern U.S. (Lewitus et al. 2003b, 2008). This and other species of Chattonella are widely distributed in temperate and subtropical/tropical estuarine and marine waters, and frequently have been linked to fish kills (Imai et al. 1998, Cortés-Altamirano et al. 2006, Zhang et al. 2006, Hallegraeff et al. 1998, Lewitus et al. 2012). The species C. subsalsa appears to be especially well-adapted to thrive in shallow, eutrophic habitats (Zhang et al. 2006, Band- Schmidt et al. 2012). Several mechanisms have been suggested for Chattonella spp. ichthyotoxicity, including production of neurotoxic brevetoxins or brevetoxin-like substances (Ahmad 1995; Khan et al. 1995, 1996); a synergistic interaction between reactive oxygen species (ROSs) and free fatty acids (FFAs) (Marshall et al. 2003, 2005); and gill damage by unidentified hemolytic substances (Shimada et al. 1983) or polyunsaturated fatty acids (Marshall et al. 2002) which can have hemolytic effects (Fu et al. 2004); and physical clogging of gills (Matsusato and Kobayashi 1974). Hemolytic and hemagglutinating effects are common (Shimada et al. 1983, Ahmed et al. 1995, Kuroda et al. 2005, Pistocchi et al. 2012). The first signs of physiological disturbance in exposed fish include a decrease in the oxygen partial pressure of arterial blood (Ishimatsu et al. 1996b), and many affected fish have shown production of mucus-like substances and gill injury (Hiroishi et al. 2005, Shen et al. 2011). Suffocation appears to be the ultimate cause of death (Kim et al. 2001, Tiffany et al. 2001, Imai and Yamaguchi 2012). Bioactive substances released by Chattonella spp. have also adversely affected diatoms (Matsuyama et al. 2000) and dinoflagellates (Fernández- Herrera et al. 2016), allowing these relatively large and slowly growing flagellates to become dominant under conducive conditions.

This study examined the interactive effects of nutrients and atrazine exposure on the toxicity of Chattonella subsalsa. Little is known about herbicidal effects on harmful algal species, which can be difficult to culture and are rarely used in toxicity testing (Thursby et al. 1993; Lytle and Lytle 2001; and see Chapter 1 and references therein). Autecological and ecotoxicological studies of harmful algal species are needed to gain insights about the

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mechanisms which lead to toxic HABs and which regulate toxicity in estuarine waters that are increasingly contaminated by chemical substances from land-based runoff (Cloern 1996; Pelley 1998; Zimba 2000; Hapeman et al. 2002; Bricker et al. 2008).

3.2 MATERIALS AND METHODS

3.2.i. Experimental Organism

The strain of Chattonella subsalsa used in this study (CCMP2191) was obtained in unialgal, non-axenic culture from the National Center for Marine Algae and Microbiota on 11 February 2009. It was originally isolated from on 13 August 2001 from the Indian River Bay, Delaware, USA.

This raphidophyte species is a relatively large flagellate (length and width up to 50 µm and 25 µm, respectively; Hallegraeff and Hara 2003), with highly variable shape due to the lack of a rigid cell wall (Band-Schmidt et al. 2012, Graham et al. 2016). The fragile cells are morphologically plastic and the cell shape is frequently lost with fixation, making identification difficult if based solely on morphological characteristics of preserved samples (Band-Schmidt et al. 2012). This species has broad environmental tolerances; it grows well at temperatures ranging from 10o to 30oC, and salinities of 5-30, with optima (although strain-dependent) reported at 20-30oC and salinities of 15-25 (Zhang et al. 2006). Like many other microalgae under environmentally unfavorable conditions, C. subsalsa forms cysts (benthic stage), which can remain dormant for months to years. This ability to remain a member of the “hidden flora” (Smayda 2002) means that previous blooms can leave “seed” populations as cysts for years until conditions become favorable for germination. Thus, C. subsalsa may remain undetected and then suddenly become dominant in the plankton and potentially lethal to aquatic life (Imai et al. 1991; Imai et al. 1998; Imai and Yamaguchi 2012).

Other characteristics of C. subsalsa (based, however, on assessment of few strains) that were important considerations for this work are its slow growth, its preference of

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ammonium over nitrate as a nitrogen (N) source, its mixotrophic capabilities, and its toxin production. The maximum nutrient-saturated growth rate of this species is ~0.2 to 1.26 divisions day-1 (e.g., Zhang et al. 2006, Band-Schmidt et al. 2012, Imai and Yamaguchi 2012), and it is generally considered to be a large, slowly growing flagellate. Half-saturation 3 - constants (Ks) for C. subsalsa have been reported at 0.84 µM for PO4 , 8.98 µM for NO3 and + + 1.46 µM for NH4 , indicating that this raphidophyte can use low concentrations of NH4 - more efficiently than NO3 (Zhang et al. 2006). Moreover, it appears to attain higher biomass + (~double) when NH4 is the major inorganic N source (Zhang et al. 2006). Organic forms of N (e.g., glutamic acid) and phosphorus (P, e.g. ATP/ADP, adenosine triphosphate and adenosine diphosphate, respectively) can be used by C. subsalsa, as well as inorganic N and P forms (Zhang et al. 2006, Yamaguchi et al. 2008). In addition to photosynthetic carbon assimilation, C. subsalsa can consume coccoid unicellular cyanobacteria, and possibly other small (< 2 µm) organisms, but phagotrophy apparently is limited to the size of the mucocyst openings on the cell surface which are believed to be the sites of ingestion (Jeong et al. 2010, Jeong 2011). Regarding toxicity, Chattonella spp. can produce ROSs (e.g., hydrogen peroxide, superoxide, and hydroxyl radicals) at levels about 100-fold higher than other microalgal species (Marshall 2005a), and ROSs can greatly enhance the toxicity of hemolytic free fatty acids such as eicosapentaenoic acid, which occurs at high levels in C. subsalsa and other raphidophytes (Marshall et al. 2002).

3.2.ii Culture Conditions

Batch cultures of Chattonella subsalsa strain CCMP2191 were maintained in 500-mL Erlenmeyer flasks containing 250 mL of salinity 20-modified L1–Si medium (Guillard and Hargraves 1993) at 24°C (ambient air temperature) and 106.31 (±26.16) µmol photons of photosynthetically active radiation (PAR) m-2 s-1 (cool white fluorescent tubes) under a 16:8 hr light:dark cycle. Cultures were sterile-transferred under a laminar flow hood every 10-15 days as needed to maintain log growth phase. All media (culture and test) were prepared by adjusting the salinity of ultrapure Milli-Q water (18 MΩ cm−1 at 25 °C) with Instant Ocean® artificial sea salts (Aquarium Systems, Blacksburg, VA, USA) to the desired level, adjusting

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the pH as necessary to 8.1 (± 0.2) using HCl or NaOH, and adding trace mineral and vitamin solutions following autoclaving. Light was measured using a Biospherical QSL 101 quantum lab sensor (BSI, San Diego, CA, USA); salinity was measured using a YSI model 3200 conductivity/salinity bridge and cell (YSI [Yellow Springs Instruments], Yellow Springs, OH, USA); and pH was measured using an Orion Versa Star Pro multi-parameter meter equipped with an Orion ROSS Sure-Flow pH electrode (Thermo Scientific, Waltham, MA, USA). Trace mineral and vitamin solutions were sterile filtered (Whatman® Puradisc cellulose acetate syringe filter, nominal pore size 0.2 μm, Sigma-Aldrich, St. Louis, MO, USA), added to the autoclaved media aseptically, and the final media was vacuum filtered (Corning® cellulose acetate filter, 0.22 µm nominal pore size, Sigma-Aldrich) before storage at 4°C for no more than 30 days. All equipment was initially cleaned by scrubbing in hot, soapy tap water. All glassware and non-plastic equipment used in culturing and experiments was rinsed with pesticide-grade acetone before use, and all glassware and any non-metallic equipment used was cleaned by acid stripping in 10% HCl (v/v). All equipment was sterilized by autoclaving before use. Testing and culture transfers and media prep were conducted using aseptic techniques, and algal observation and testing methods were initially developed and optimized using Dunaliella tertiolecta as described in Chapter 1 of this work.

Stock cultures were maintained at 106.31 (±26.16) µmol photons m-2 s-1 and a 16:8-hr light:dark period throughout the experiments, and were gently swirled once daily. Bioassays (controls and test cultures) were maintained on a culture rack under a light bank (2.5 m long by .75 m deep). All cultures in the bioassays were gently swirled by hand and randomly repositioned daily on the culture rack following a computer-generated randomized shelf mapping, to avoid any effects from possible differential light exposure.

3.2.iii Optical Density Versus Direct Cell Counts

Samples (1 mL) were randomly withdrawn from each parent culture and thoroughly vortexed before absorbance was read on a Thermo Scientific Spectronic GENESYS 2 spectrophotometer (Thermo Scientific) using a freshly wiped Hellma® quartz cuvette

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(Sigma-Aldrich) with a 10-mm pathlength. All readings were performed in duplicate and adjusted using a media blank as a measurement baseline. Preceding all testing procedures, the absorbance of the parent culture of C. subsalsa was scanned, and the three highest peaks were selected as the most appropriate wavelengths for analysis of a series of 8-point dilution curves, using progressive dilutions of stock culture in growth media. The curve that produced the ΔOD/ΔPD [the change in optical density divided by the change in population density; Sorokin 1973)] closest to unity (1) was used to determine the growth rates from each stock culture for each strain tested. Cell counts to determine ΔPD were obtained by collecting random 1-mL samples from the parent culture. Cells were quantified from duplicate preserved samples using a Reichert bright-line phase contrast hemocytometer with improved Neubauer rulings (Hausser Scientific No. 1475, Horsham, PA, USA) (Guillard 1973). Two well mixed 100-µL samples were assessed by counting 18 grids or a minimum of 400 cells. Because C. subsalsa cells lyse quickly upon exposure to standard preservatives, a preservative which minimized lysis was used (1% HEPES-buffered paraformaldehyde with 1% glutaraldehyde; Katano et al. 2009) (Fig. 3.1). Preservation was adequate to preserve cells for ~1 week without appreciable cell distortion or loss while preparing dilution curves immediately before each experiment. The wavelength that produced the highest correlation between absorbance and cell numbers of the stock culture was used to monitor population density and calculate growth rates for the duration of the experiment. All modeled OD:cell number relationships used had a minimum observed 푟2 value >0.95 (e.g., Fig. 3.2).

3.2.iv Population Growth Rate

Net cell-specific growth rates (day-1) were modeled using OD following Sorokin (1973). Samples were collected in duplicate daily from gently hand-agitated test tubes and measured in a clean, wiped quartz cuvette as described above. A media blank served as a baseline measurement. The OD was converted to population density (PD), data were log- transformed (log10 + 1), and the linear portion of the growth curve (slope = 푎푘 ) was used to calculate growth rate k (Sorokin 1973):

푘 = 푎푘 ∗ 3.322

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3.2.v Bioassays

Concentration-response bioassays were conducted to describe the effects of atrazine exposures and imbalanced versus balanced nutrient levels on the population growth rate of the Chattonella subsalsa strain, using the Redfield ratio (Redfield 1934, 1956; Harris 1986) as balanced. Three nutrient regimes were used including low N, low P, and N- plus P-replete media (below).

Analytical-grade atrazine standards (CASRN 1912-12-9, >98% purity) were obtained from Chemservice (Westchester, PA, USA), and stock solutions were prepared in 100% pesticide-grade acetone (Fisher Scientific, Fair Lawn, NJ, USA). Chemical stocks were diluted in pesticide-grade acetone to desired final concentrations and were introduced into fresh, sterile culture media aseptically under a laminar flow hood. Testing doses were administered to obtain a final concentration of 0.1% (v/v) acetone in each replicate, with exception of a media-only control series which was included for all treatments (n = 3) in all assays. Controls and treatments were incubated in 50-mL Erlenmeyer flasks, filled to 25 mL and capped with foil. Five concentrations of atrazine were used to develop response curves (12.5, 25, 50, 100 and 200 µg atrazine L-1, in addition to the media and solvent controls). Exposure levels were selected based on range-finding test estimates in preliminary work, and were designed to expose C. subsalsa to concentrations with 0% and 100% growth inhibition effects.

The three nutrient regimes were as follows: To assess low-nutrient effects, three inorganic N : inorganic P supply ratios (molar basis; selected based on deviations from the Redfield Ratio as explained above) were prepared, including 1:1, N-limited (“Low N” = 2.0

µM NO3, 2.0 µM PO4); 160:1, P-limited (“Low P” = 32 µM NO3, 0.2 µM PO4); and 16:1, nutrient-replete (“Replete” = 32 µM NO3, 2.0 µM PO4) media (Table 3.1). Each low-nutrient treatment series contained corresponding solvent and media controls.

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Nutrient x herbicide effects were assessed by repeating the above assay procedures in a simultaneously run, three-way bioassay format. Each assay included 63 50-mL flasks in identical set-up, inoculation and chemical preparation approaches as described above, but varied in culture media nutrient additions.

Testing procedures were derivations on the standard 96-h static algal toxicity bioassay protocols (ASTM 1996), and were extended to assess 10- and 28-day endpoints. The 10-day endpoint was used to evaluate growth responses of C. subsalsa to treatments during early exponential growth phase. In a second series of assays, for hemolytic activity (below), a 28-day endpoint was used to enable assessment of treatment effects during late exponential growth under nutrient depletion, which can take 14 days or longer to observe in Chattonella spp. (Kim et al. 2004; Yamaguchi et al. 2008). Growth of C. subsalsa was slow in comparison to bioassays conducted with the small green flagellate, Dunaliella tertiolecta -1 (Chlorophyta) (replete media Kmax = 0.95 div day versus 2.04, respectively), in work to develop the general test platform for these assays (see Chapter1). In assays with C. subsalsa, the lag phase continued for 48 to 72 hr post-inoculation, and exponential growth did not begin to occur until about 96-hr post-inoculation. Therefore, 96-hr bioassay protocols commonly used in standard toxicity testing were not considered appropriate to assess effects of atrazine x nutrient stress, and were modified to accommodate the slow growth of this test species. The 96-hr lowest-observed (LOEC) and half-maximal effective concentrations (IC50) were calculated in this work to facilitate comparisons of atrazine tolerance and sensitivity reported in the literature for other organisms, but these values were presented with the caveat that the 96-hr endpoint may not effectively capture the response of C. subsalsa to the treatments. These analyses instead emphasized responses to treatments at endpoints that were 10 days and 28 days post-exposure.

For all experimental assays, when stock culture of Chattonella subsalsa was in exponential growth phase (assessed by periodic direct cell counts), test and control cultures were inoculated into foil-capped 50 mL Erlenmeyer flasks (with each flask containing a total final volume of 25 mL) under aseptic conditions to attain initial cell densities of ~1.5 x 103

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cells mL-1 (10-day assays) or 1.0 x 103 cells mL-1 (28-day assays) in all experimental cultures and controls. Initially in experiments, the cultures were not nutrient-limited (as they had been taken from nutrient-replete stock culture), the cells were in exponential growth phase, and all cells had the same nutritional history. The inoculum was less than 5% of the total test culture volume (less than 1.25 mL) to minimize the influence from the high nutrient levels in the stock culture on controls and treatments. Time-series testing began when C. subsalsa was added to testing media, and samples were collected immediately following inoculation (0 hr) to assess the initial OD. Samples (n = 2) for OD measurements were taken during the assays at equally spaced daily intervals for the first 4 days following inoculation (0, 24, 48, 72, and 96 hr), and then every third day for the remainder of the test duration. Immediately before each sample was taken, tubes were gently swirled by hand for 30-60 s, and then 1 mL was gently, randomly siphoned using a sterile transfer pipette and aseptically removed for OD measurement.

For each sample taken from control and test replicates, growth of C. subsalsa was determined by spectrophotometric determination of OD. The OD was then converted to cell density using equations derived from direct cell count : optical density relationships that had been established using the parent culture within one week prior to the test initiation, as described above. The percent inhibition (%I) of algal growth at each atrazine concentration was calculated by comparing mean growth rates for each treatment to the respective solvent controls for the treatment, as follows:

푘 %I = [1 − ( )] ∙ 100 푥̅ 푘푠표푙푣푒푛푡 푐표푛푡푟표푙 where 푘 is the growth rate of the pertinent sample, calculated as described previously. Nominal concentrations of pesticide were quantified with enzyme-linked immunosorbent assay (ELISA) at the initiation and conclusion of each bioassay with enzyme-labeled paramagnetic particles of atrazine (method detection limit for atrazine = 0.046 μg/L; SDI, Delaware, MD, USA; see appendix for raw ELISA data and QA/QC determination). The

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accuracy of this method has been shown to be comparable to gas chromatographic quantification of atrazine concentrations in surface water samples (Gascón et al. 1997).

Salinity and pH were checked at test initiation and conclusion to ensure that changes were within acceptable limits (salinity 20 ±1, pH 8.1 ±0.2) based on the test platform developed in Chapter 1. At assay initiation, all cultures were inoculated from the same the same parent culture. Solvent controls showed similar growth as the media-only controls for all tests (Fig. 3.3), so the solvent controls were used as the baseline activity for analytical comparisons.

3.2.vi Protocols for Assays of Hemolytic Activity

The lack of chemical structure analysis for toxins from C. subsalsa prevented analysis of toxicity by toxin quantification. As a proxy for the toxic potential of the control and test cultures, erythrocyte lysis assays (ELAs, developed for 96-well microtiter plates with well volume 120 μl; Eschbach et al. 2001; Kuroda et al. 2005) were modified for use with a larger sample volume (1 mL), and then ELAs were performed using freshly prepared fish erythrocytes to characterize the hemolytic activity (HA) of C. subsalsa.

3.2.vi.a Blood Collection and Erythrocyte Preparation

Adult tilapia (Oreochromis niloticus) were purchased live from a local Asian fish market and transported to CAAE in ~20 l of water from their holding tanks. Fish were anesthetized with a pH-adjusted 0.02% (w/v) solution of aminobenzoic acid ethyl ester [TricaineTM (MS-222) (Sigma-Aldrich, St. Louis, MO, USA)] in transport water. Blood was collected by gill puncture within 3 hr of purchase. To collect blood samples, anaesthetized fish were placed on wet towels and 5 mL of blood were collected using a Pravaz No. 1 needle and a 10-mL syringe prefilled with 5 mL of pH-adjusted (7.2) RPMI 1640 media (without phenol red), diluted to 10% (v/v) with Milli-Q water (to adjust for fish serum osmolarity), and supplemented with 50 IU sodium heparin salt (Sigma-Aldrich) added as an anticoagulant. Blood samples were transferred to 15-mL centrifuge tubes and gently hand-

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agitated by inverting two or three times. Red bloods cells were then separated from serum by gentle centrifugation (2,100 rpm at 4°C for 5 min) and washed by replacing supernatant with fresh media until the supernatant appeared clear after gentle mixing. Whole, washed erythrocytes were diluted 1:10 with RPMI 1640 culture medium containing 22.5 IU sodium heparin anticoagulant mL-1. Erythrocytes (~107 cells mL-1) were stored at 4°C for no more than 10 days, based on preliminary work wherein microscopic observations and spectrophotometric measurements (λ = 415) indicated no noticeable lytic activity over that duration. The erythrocytes were resuspended daily until use.

3.2.vi.b Preparation of Algal Extracts

Because the hemolytic component of Chattonella is light-dependent (Kuroda et al. 2005), all hemolytic (ELA) preparation and procedures were conducted at 4oC (Eschbach et al. 2001) under continuous fluorescent lighting (30 µmol photons PAR m-2 s-1). Following 10-day or 28-day assay endpoint OD measurements, Chattonella subsalsa cells were immediately centrifuged (IEC Centra-CL2, Needham Heights, MA, USA) for hemolytic analysis. Because of the low cell densities in high herbicide treatments, treatment replicates were pooled prior to extraction procedures as described below. Before replicates were pooled, they were checked to ensure that the variation in cell density between individual replicates was less than the variation between treatments (F-test p <0.05). Thus, all remaining culture from two of the three replicates was transferred to a 50-mL centrifuge tube (which could fit the volume of only two replicates at a time) and spun at 2,000 rpm for 10 minutes, during which time a cell pellet accumulated. The supernatant was carefully removed by pipetting and the volume of third test replicate was added and centrifuged, thus combining all replicates of a control or a treatment into one pellet (n = 21 different pellets for assessment of treatment effects, including 5 concentrations and solvent and media controls for 3 different nutrient regimes).

Once the three replicates had been combined into a single pellet, the pellet was gently aspirated and rinsed using cold, pH-adjusted, sterile ELA buffer (150 mM NaCl, 3.2 mM

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KCl, 1.25 mM MgSO4, 3.75 mM CaCl2, and 12.2 mM Trisma base [TRIS], pH-adjusted to 7.4 with 1N HCl), and re-consolidated by centrifugation as above. The supernatant was again carefully removed, and the pellet was resuspended and rinsed in cold assay buffer (for a total of three rinses) to remove any residual test medium. The cell pellet was then resuspended in 5 mL of assay buffer, and the tube contents were transferred to a sterile, acid-stripped, 20-mL glass beaker. An additional 10 mL of assay buffer was added to the centrifuge tube to rinse any remaining cells into the beaker. The beaker contents were sonicated for 30 continuous seconds at amplitude 45 on a 20kHz Sonic Dismembrator (Fisher Scientific Model 550, Fisher Scientific, Pittsburgh, PA, USA). The sonicated pellet was used immediately in ELAs.

3.2.vi.c Hemolytic Assays

The modified ELAs used for analysis of hemolytic activity by Chattonella subsalsa were conducted in 1-mL conical microcentrifuge tubes. Algal cellular contents were extracted as described above, and equivolume amounts of erythrocytes and algal sample (300 µL) were added to a microcentrifuge tube (to enhance pellet formation; n = 3) and incubated for 24 hr at 4°C and 30 µmol photons PAR m-2 s-1. Microcentrifuge tubes were then spun at 1250 rpm for 10 minutes in a refrigerated microcentrifuge (4oC; Micromax RF, IEC, Needham Heights, MA, USA). Erythrocytes were also incubated in an equal volume of assay buffer alone as a negative control, and in an equal volume of 20 µg mL-1 saponin solution (Sigma-Aldrich preparation from the soap-bark tree Quillaja saponaria, a known hemolytic compound) which was microscopically verified to cause 100% lysis of red blood cells and served as a positive control. The microcentrifuge tube supernatant was transferred to a quartz cuvette for measurement of sample absorbance (wavelength 414 nm, based on preliminary scans of initial preparations of lysed erythrocytes which showed the highest hemoglobin absorbance peaks, following Eschbach et al. 2001). The change in absorption at 414 nm following exposure to sonicated cells was used to measure hemoglobin released by lysed erythrocytes as a result of toxic activity of C. subsalsa, using the following normalization equations:

[(푐푒푙푙푠⁄푚퐿) ∙ (푉푐푒푛푡푟푖푓푢푔푒푑) ∙ (푉푤푒푙푙)] = 푁

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푠푎푚푝푙푒 − 푥 푛푒푔푎푡푖푣푒 푐표푛푡푟표푙 ( ) ∙ 100 = % ℎ푒푚표푙푦푠𝑖푠 푥푝표푠푖푡푖푣푒 푐표푛푡푟표푙

(푉푤푒푙푙) (volume added to well) was 0.300 mL (300 µL).

3.2.vii Statistical Analysis

All statistical analyses were performed using JMP software (version 12.2.0, SAS Institute, Cary, NC). Normality of the data was checked with the Shapiro-Wilke test, and Bartlett's test for unequal variance was used to check variation. The only sample which failed normality checks was the low P series on day 4 of the 10-day assays. Analysis of variance (ANOVA) comparing nutrient and herbicide effects on growth considered the day 10 data from that assay series, and days 10 and 28 from the 28-d assay series. The 10-day and 28-day bioassays were independently analyzed.

Herbicide effects on growth were determined using a one-way analysis of variance (ANOVA). No-observed effect concentrations (NOECs) and lowest-observed effect concentrations (LOECs) were determined (p < 0.05) using Dunnett’s procedure for multiple comparisons to determine which specific treatments differed significantly from controls (Zar 1999). Atrazine x nutrient comparisons were analyzed by two-way ANOVAs using nutrient levels and herbicide concentration as the fixed effects, and growth rate or normalized hemolytic activity as the dependent variable. The F statistic and associated P value for the independent variables and the interactions between them were also reported. Point estimates of sublethal toxicity (IC50s) were based on growth rates, and were calculated using the Inhibition Concentration linear interpolation (ICp) method of Norberg-King (1993):

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()CCJJ 1  ICp CJ [ M1 (1  p /100)  M J ] ()MMJJ 1  where:

CJ ≡ tested concentration whose observed mean response is greater than M1 (1-p/100);

CJ+1 ≡ tested concentration whose observed mean response is less than M1(1-p/100);

M1 ≡ smoothed mean response for the control;

MJ ≡ smoothed mean response for concentration J;

MJ+1 ≡ smoothed response for concentration J +1; P ≡ percent reduction in response relative to the control response; and ICp ≡ estimated concentration at which there is a percent reduction from the smoothed mean control response. The ICp is reported for the test together with the 95% confidence interval calculated by the ICPIN.EXE program. Results were generated using bootstrap methods to derive point estimates and confidence intervals from no fewer than 80 resamplings (Efron 1982).

3.3 RESULTS

3.3.i Growth

Media controls in short-term (10-d) assay had cell density had more than doubled by 96 hours (1341 ±23 cells mL-1 versus 2794 ±261 cells mL-1) and had an almost 200% increase in density by day 10 (4005 ±299 cells mL-1) (Fig. 3.3). Controls in the longer- duration bioassays (28 days) began to decrease in cell density after day 23. Controls in nutrient-replete (Redfield ratio) media remained in lag phase for the first 48 to 72 hr, and then increased in cell density until day 23. Exponential growth occurred approximately during days 4 through 20 and stationary growth began thereafter. Growth in nutrient-replete cultures was similar to that in “nutrient-limited” cultures for the first seven days, reflecting the influence of the nutrient supplies added with the initial parent stock solution of Chattonella subsalsa. After that time, effects of nutrient depletion were detected.

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3.3.ii Atrazine Effects

Atrazine concentration significantly affected the growth of Chattonella subsalsa in nutrient-replete media at all time points after 96 hr, as shown by LOECs and IC50s (Tables 3.2 and 3.3). In the 28-day assays, overall sensitivity to atrazine decreased until day 15, although there was no herbicide degradation based on ELISA checks of test samples on day 28. Effective dose estimates for C. subsalsa in nutrient-replete media leveled off, and responses of C. subsalsa to atrazine concentrations were similar on days 15-28 (mean IC50 = 122.6 µg l-1). Atrazine effects on cell growth in nutrient-replete media followed typical dose- response curves after 7-10 days. In the 10-day assays, nutrient-replete C. subsalsa cultures -1 were most resistant to atrazine at 96 hr (IC50 = 186.5 µg L ), and sensitivity to atrazine -1 increased continuously until day 10 (IC50 = 46.9 µg L ; Table 3.2, Fig. 3.4). In the 28-day assays with nutrient-replete media, higher atrazine doses (100 to 200 µg L-1) had stronger growth-limiting effects on C. subsalsa over time, whereas responses in mid-range atrazine doses (25-50 µg L-1) were similar to responses of control cultures without atrazine by day 28 and the lowest dose (12.5 µg L-1) had slightly denser cell numbers than controls by day 28 (Fig. 3.4).

Effective concentration estimations based on growth rate inhibition were most precise from days 4 through 10 in the 28-day assay (Fig. 3.5) and post-day 4 in the 10-day assay (data not shown). Dose-response curves showed the increasing potency of atrazine from days 4 to 10 in the 10-day assay (Fig. 3.6), and between days 2-4 in the 28 day assay. In the ANOVA model, atrazine concentration had the strongest effect on cell growth at day-10 endpoints in both assays, but all model variables were significant at that endpoint, including the nutrient x atrazine interaction term (which did not outrank either treatment effect). At the 28-day endpoint there also were significant effects from all tested variables, but the effect of nutrient limitation and the interaction term had much stronger influence on modeled growth rates than the effect of herbicide concentration (two-way ANOVA, p ≤ 0.05, Table 3.4), resulting in greatly diminished influence of herbicide inhibition on cell growth relative to nutrient depletion by day 28.

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Nitrogen and phosphorus limitation had approximately equal effects on growth rates until day 10 in the assays, when nutrient limitation began to have observable effects on cell growth (the actual point of initial growth depression effects was between days 7 and 10, but observation was restricted to the measured endpoints). In the 10-day assay series, nutrient- limited cultures of C. subsalsa showed atrazine sensitivity in both nutrient limited treatments was highest at the 24 hour mark (43.2µg L-1 in low N and 11.5 µg L-1 in Low P), but again, following a period of wide ranging point estimates, effect estimates became more consistent and precise by day 10 (Table 3.2). There were similar growth inhibition relationships by atrazine on day 10 in the 10-day assays and 28-day assays (Figs. 3.7 and 3.8a). In the 28-day assay series, however, there was significant growth stimulation by atrazine in both the N- and

P-limited cultures (Fig. 3.8b) relative to the low-nutrient controls. Thus, IC50 values could not be determined for C. subsalsa in the low-nutrient treatments during late exponential growth.

3.3.iii Toxicity as Hemolytic Activity

Considering nutrient regimes without atrazine, nutrient depletion, especially of P, increased hemolytic activity in the Chattonella subsalsa no-atrazine controls at the 28-day timepoint (Fig. 3.9), in agreement with previous reports of increased toxicity under nutrient depletion in other harmful algal species (Johansson and Graneli 1999; Granéli and Johansson 2003b; Granéli et al. 2012). The 10-day series, by contrast, measured hemolytic activity (HA) was significantly higher in nutrient-replete cultures, and HA was reduced in the low-N and low-P treatments (Fig. 3.9). These findings are in contrast to reports that ROS production (and, by proxy, toxicity) is not related to nutrient availability in Chattonella (Liu et al. 2007) and potentially offers some context for reports that hemolytic activity is not increased by nutrient limitation in other harmful raphidophytes (de Boer et al. 2004). As nutrient depletion progressed over time, P-limited C. subsalsa exhibited significantly higher hemolytic activity than the other treatments, and C. subsalsa hemolytic activity in both N- and P-limited cultures was significantly higher than in nutrient-replete cultures in the late-exponential growth phase. In the atrazine + nutrient-replete treatments, the toxicity response of C.

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subsalsa differed in the 10-day versus 28-day assay series as well. In the 10-day assays, highest hemolytic activity occurred in nutrient replete cultures at an atrazine concentration of 25 µg L-1, and toxicity decreased at higher atrazine concentrations. Hemolytic activity was comparable to that of controls at intermediate atrazine concentrations (12.5 µg L-1 and 50 µg L-1), and hemolytic activity in controls was significantly higher than in the highest atrazine concentrations (100 µg L-1 and 200 µg L-1) (Fig. 10a). In the atrazine x low-nutrient treatments in the 10-day assays, C. subsalsa became increasingly toxic with increasing atrazine concentration except for in the 200 µg L-1 treatment (Fig. 3.10a); at that concentration, cell density and hemolytic activity were not significantly different from zero in any treatment. The low-N and low-P cultures showed similar responses to atrazine in terms of hemolytic activity (Fig. 3.10a), but were much lower in hemolytic activity than the nutrient-replete controls without atrazine. An exception occurred at 100 µg atrazine L-1; at that herbicide concentration, the low-nutrient cultures exhibited significantly higher hemolytic activity than nutrient-replete cultures.

In the 28-day assay series, there was no apparent effect of atrazine concentration on hemolytic activity in nutrient-replete cultures, which had low toxicity in comparison to nutrient-limited cultures at all atrazine concentrations. At the 28-d endpoint, the end-of-test cell density in replete culture was negligible at 200µg L-1, as noted above, however all low nutrient • atrazine treatments had higher cell density by day 28 than 200 µg atrazine L-1 replete cultures (see appendix Table A3 for 28-d nutrient limited culture cell density). With the exception of the 200 µg L-1 treatment, cultures in the low-P treatment were comparable in hemolytic activity across the atrazine concentration gradient, whereas in the low-N treatment, the hemolytic activity of C. subsalsa was significantly higher at 100 µg atrazine L-1 than at other herbicide levels (Fig. 3.10b). Both N- and P-limited C. subsalsa in late exponential growth had significantly higher hemolytic activity at atrazine concentrations above 25 µg L-1 (again, with exception of the 200 µg L-1 level). Overall, hemolytic activity in C. subsalsa was more influenced by atrazine concentration in the 10-day assay series, whereas the nutrient regime was the best predictor of hemolytic activity in the 28-day series (Table 5). Log-

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normal relationships between hemolytic activity (normalized to cell density and sample volume, and omitting the 200 µg atrazine L-1 treatment in low nutrient regimes) and atrazine concentration had high r2 values (0.82 to 0.92) across nutrient treatments in 10-day assay (Fig. 3.11) but the strength of these relationships declined over time (see appendix Fig. A4 for modeled relationships at 28-d endpoint). Hemolytic activity was inversely related to log- atrazine concentration in nutrient-replete cultures, but positively related to the log-atrazine concentration in low nutrient treatments. The r2 values were somewhat lower for hemolytic activity versus growth rate in the low-nutrient cultures (0.64 to 0.77) than in the nutrient- replete regime (0.85), but the relationships still were clear: In nutrient-replete cultures, hemolytic activity decreased with increasing atrazine concentration, but under nutrient- limited conditions hemolytic activity increased with increasing exposure to herbicide. Hemolytic activity also increased up to intermediate growth rates and then declined (parabolic response curve) in nutrient replete media, whereas in nutrient-limited cultures, hemolytic activity was inversely related to growth rate (Fig. 3.11).

3.5 DISCUSSION

This study was the first to test atrazine sensitivity in a toxigenic raphidophyte species, wherein the toxicity (as hemolytic activity) of Chattonella subsalsa was exposed to a range of photosystem II-inhibiting atrazine concentrations under different nutrient regimes. Inhibition by atrazine was the dominant predictor of growth rates for this strain in early exponential growth phase (10-day assay series), whereas the nutrient regime was a more important predictor of C. subsalsa population growth (as cell density) during late exponential growth (28-day assay series). Hemolytic activity in the absence of atrazine was highest in low-P cultures among all controls and treatments. Hemolytic activity in the presence of > 25 µg atrazine L-1 was highest in low-N cultures, and increased with increasing atrazine concentration.

Research with other harmful algae, such as the haptophyte Prymnesium parvum and the dinoflagellate Karenia brevis, have shown increasing toxin production under nutrient

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depletion or nutrient limitation (Johansson and Graneli 1999a,b; Granéli and Johansson 2003; Hardison et al. 2013). These findings are consistent with the “chemical ecology hypothesis,” which predicts enhanced toxicity of nutrient-limited HAB species (Ianora et al. 2011). That hypothesis was derived, in turn, from a “carbon:nutrient balance hypothesis” (Bryant et al. 1983; Lambers et al. 2008), which predicted increased production of toxins and other defenses in terrestrial plants under low resource conditions wherein grazing is reduced. The latter hypothesis may not hold up well in the terrestrial systems for which it was designed (Hamilton et al. 2001), but it has been used successfully to explain increased toxicity with nutrient limitation in unicellular algae (e.g., Ianora et al. 2006; Ianora et al. 2011; Hardison et al. 2012). Production of toxic compounds by phytoplankton has frequently been associated with allelopathic interactions (Legrand 2001; Fistarol et al. 2005; Uronen et al. 2005; Granéli et al. 2008; Driscoll et al. 2013; Fernández-Herrera et al. 2016), which may help reduce grazing and competition for scarce resources and promote survival in competitive (nutrient- imbalanced) systems.

Here, increasing toxicity as hemolytic activity was also observed at low to moderate growth rates in nutrient-replete C. subsalsa cultures, and at low growth rates in nutrient- limited cultures. Others have reported that the closely related species, Chattonella marina, is most toxic during late exponential growth phase and toxicity decreases over stationary phase (measured as putative brevetoxins affecting fish; Khan et al. 1995; Marshall et al. 2003). The toxicity of C. marina has been highly correlated with growth phase, but not with cell density (Shen et al. 2010); or, lower cell densities of C. marina (below 104 cells mL-1) produced more superoxide on a per-cell basis than denser cultures (Marshall et al. 2005b). These differences may relate to the tested strain or other factors. Similarly, other studies on individual phytoplankton species responses to chronic atrazine exposures have yielded mixed results. Some have reported that algal populations with a history of exposure became more sensitive to atrazine upon repeated exposure (Pennington and Scott 2001), whereas others have reported that phytoplankton became more tolerant over time (Baxter et al. 2013). These discrepancies may be attributed to the wide range of phylogenic diversity which is often

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mistakenly “lumped” under the umbrella of “phytoplankton responses.” The ecological diversity of the huge, extremely diverse organisms referred to as algae cannot be considered as one functional group (although they frequently are so considered), or even as one trophic level (e.g., phototrophs versus predatory mixotrophs).

Phytoplankton assemblages have been investigated for response to repeated atrazine exposure – also with conflicting results, including increased sensitivity (Hamala and Kollig 1985; Guasch et al. 2007), no effect (Nyström et al. 2000; Pinckney et al. 2002), and increased tolerance (deNoyelles et al. 1982; Hamilton et al. 1987). These findings are not surprising, as the ability of a phytoplankton assemblage to recover from herbicide exposure following photosystem II inhibition depends on the species composition of the original assemblage, their physiological state, the herbicide concentration (Gustavson and Wängberg 1995; Kasai 1999), the season (Guasch et al. 1997; Lorente et al. 2015), and other environmental factors. Several consistent findings, however, are that (i) phytoplankton species and strains vary greatly in response to atrazine exposure (Blanck et al. 1984; Choi et al. 2012); (ii) intraspecific variation in responses can be very high (Kasai et al. 1993); and (iii) atrazine exposure can alter phytoplankton assemblage composition as sensitive species are eliminated and tolerant species gain a selective advantage (Hamala and Kollig 1985; Hamilton et al. 1987; Graymore et al. 2001). Thus, it is important to strengthen understanding about how harmful, potentially ecosystem-disruptive phytoplankton species (Sunda et al. 2006) respond to common environmental contaminants, despite the challenges involved in attempting to apply standard ecotoxicological methods to organisms which may be more challenging to culture than standard model organisms. Autecological and ecotoxicological studies of potentially toxic species are relevant for understanding the mechanisms that lead to the formation of harmful algal blooms and the influence of common contaminants such as atrazine on toxin production. Regarding Chattonella subsalsa, this research offers insights about the complexities of nutrient interactions with atrazine in influencing growth and toxicity, yet – as only one strain was examined – it is only a first step toward understanding C. subsalsa response.

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In the past few decades there has been a dramatic increase in the frequency, magnitude, and duration of HABs, with devastating impacts on global fisheries stocks, coastal economies and freshwater and marine ecosystems (Hallegraeff 1993, Burkholder 1998, Van Dolah 2000, Glibert et al. 2005, Heisler et al. 2008, Lewitus et al. 2012). It has also been more than two decades since Cloern (1996) discussed the potential for toxic contaminants and nutrient pollution to alter natural cycles of estuarine phytoplankton blooms, yet there still are very few studies about how these combined stressors affect HAB species. This research indicates that imbalanced nutrient regimes can act in conjunction with herbicide exposures to promote hemolytic activity in C. subsalsa, a toxigenic algal species that is expected to continue to thrive in increasingly urbanized coastal zones.

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3.6 FIGURES AND TABLES Table 3.1. Culture media recipes used for culturing and assays of Chattonella subsalsa.

L1 Macro-nutrient concentrations

Volume added to Final concentration in Stock Solution final medium L1 media (µM) Replete: -1 NaNO3 2.7198 g l H2O 1 mL 32.00 -1 NaH2PO4 0.2400 g l H2O 1 mL 2.00 low N: -1 NaNO3 0.1700 g l H2O 1 mL 2.00 -1 NaH2PO4 0.2400 g l H2O 1 mL 2.00 low P: -1 NaNO3 2.7198 g l H2O 1 mL 32.00 -1 NaH2PO4 0.0240 g l H2O 1 mL 0.20

L1 Trace Element Solution

Molar Primary stock Quantity to add to concentration in solution final medium final medium (M) Na EDTA · 2 --- 4.36 g 1.17 x 10-5 2H2O -5 FeCl3 · 6H2O --- 3.15 g 1.17 x 10 -1 -7 MnCl2 · 4H2O 178.10 g L H2O 1 mL 9.09 x 10 -1 -8 ZnSO4 · 7H2O 23.00 g L H2O 1 mL 8.00 x 10 -1 -8 CoCl2 · 6H2O 11.90 g L H2O 1 mL 5.00 x 10 -1 -8 CuSO4 · 5H2O 2.5 g L H2O 1 mL 1.00 x 10 -1 -8 Na2MoO4 · 2H2O 19.9 g L H2O 1 mL 8.22 x 10 -1 -8 H2SeO3 1.29 g L H2O 1 mL 1.00 x 10 -1 -8 NiSO4 · 6H2O 2.63 g L H2O 1 mL 1.00 x 10 -1 -8 NaVO4 1.84 g L H2O 1 mL 1.00 x 10 -1 -8 K2CrO4 1.94 g L H2O 1 mL 1.00 x 10

L1 Vitamin Solution

Primary Stock Quantity to Add Molar Concentration in

Solution to Final Medium Final Medium (M) thiamine · HCl (Vitamin B) --- 200 mg 2.96 x 10-7

-1 -9 biotin (Vitamin H) 0.1 g L H2O 10 mL 2.05 x 10

-1 -10 cyanocobalamin (Vitamin B12) 1.0 g L H2O 1 mL 3.69 x 10

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Table 3.2. Growth inhibition and lowest-observed effect concentrations (µg atrazine L-1) over time in 10-day bioassays with Chattonella subsalsa. BD ≡ beyond detection.

IC50: CI(lower) CI(upper) IC10: CI(lower) CI(upper) LOEC: Replete 1 18.8 BD 13.8 BD 200 3 88.6 BD 57.7 2.9 65.5 BD 4 186.5 166.7 208.6 62.8 5.6 65.8 BD 7 74.4 44.0 114.2 29.5 9.9 35.2 50 10 46.9 40.4 56.9 12.9 7.5 21.3 25 Low N 1 43.2 6.3 104.5 28.4 1.3 55.0 BD 3 147.8 BD 40.2 4.1 72.5 BD 4 99.5 BD 8.3 3.6 54.4 200 7 107.7 84.5 151.7 7.1 4.5 26.7 100 10 119.7 105.7 140.2 9.9 8.4 25.6 100 Low P 1 11.5 7.8 82.1 2.3 1.5 30.5 BD 3 102.5 BD 21.8 17.8 62.4 BD 4 88.2 48.2 126.4 18.0 16.5 20.9 200 7 50.0 39.3 57.1 7.3 5.2 12.6 25 10 64.7 58.5 68.0 8.5 6.0 25.7 50

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Table 3.3. Growth inhibition and lowest-observed effect concentrations (µg atrazine L-1) over time in 28-day replete media bioassays with Chattonella subsalsa. BD ≡ beyond detection; ND ≡ not determined.

Day IC50 (CI) IC10 (CI) LOEC 1 146.2 (BD) 61.5 (5.1 – 122.9) BD 2 ND ND ND 3 42.3 (32.8 - 8.7) 10.6 (3.8 – 10.4) 100 4 36.1 (27.7- 43.2) 7.5 (3.5 - 28.3) 50 7 46.5 (36.9 - 55.3) 6.3 (3.9 - 25.3) 50 10 69.1 (56.5 - 81.0) 8.8 (5.4 - 18.5) 25 15 105.3 (89.0 - 128.2) 11.69 (8.9 - 21.2) 50 20 130.8 (100.2 - 147.4) 36.7 (12.4 - 58.2) 100 23 124.3 (97.8 - 142.3) 55.6 (10.1 - 62.3) 100 28 129.7 (96.4 - 154.7) 61.9 (51.2 - 64.6) 100

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Table 3.4. Results of two-way ANOVAs examining atrazine concentration (Conc) and nutrient regime (Trt) effects on growth rates of Chattonella subsalsa in bioassays at different endpoints.

Source DF F Ratio Prob > F Day 10; 10-day assay Conc 5 150.80 <.0001 Trt 2 15.64 <.0001 Conc*Trt 10 7.19 <.0001 Day 10; 28-day assay Conc 5 84.76 <.0001 Trt 2 41.25 <.0001 Conc*Trt 10 3.03 0.0070 Day 28; 28-day assay Conc 5 4.44 0.0030 Trt 2 25.59 <.0001 Conc*Trt 10 25.33 <.0001

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Table 3.5. Results of two-way ANOVAs examining atrazine concentration (Conc) and nutrient regime (Trt) effects on hemolytic activity of Chattonella subsalsa in bioassays at the 10-day and 28- day endpoints.

Source DF F Ratio Prob > F 10-day assay Conc 5 118.90 <.0001 Trt 2 191.05 <.0001 Conc*Trt 10 55.79 <.0001 28-day assay Conc 5 70.95 <.0001 Trt 2 297.94 <.0001 Conc*Trt 10 13.69 <.0001

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a b

Figure 3.1. a) Chattonella subsalsa cells fixed with 1% PFA + 1%glut (as described in procedures). b) Chattonella subsalsa cells fixed with acidic Lugol’s solution. (Scale bar = 50 µm).

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Figure 3.2. Example of the modeled relationships between optical density (OD) and C. subsalsa cell density that was used to monitor population growth for atrazine inhibition assays.

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Figure 3.3. Growth of C. subsalsa in control cultures during 10-day (a) and 28-day (b) bioassays. Data are given as means + 1 SE.

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Figure 3.4. Growth curves for Chattonella subsalsa in (a) 10-day and (b) 28-day bioassays with atrazine under nutrient-replete conditions. Data are given as means + 1 SE.

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Figure 3.5. Variation in atrazine half-maximal effective concentrations for Chattonella subsalsa in 28-day nutrient-replete bioassays over time. Confidence intervals (bars) around the means are shown.

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Figure 3.6. Dose-response relationships showing increasing potency of atrazine in 10- day, nutrient-replete growth bioassays (+ sign, dashed line ≡ day 4; x sign, solid line ≡ day 7; * sign, solid line ≡ day 10).

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Figure 3.7. Inhibition concentration curves for different nutrient regimes tested in 10-day atrazine exposure assay. Data are given as means + 1 SE.

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10 days

28 days

Figure 3.8. Inhibition concentration curves for different nutrient regimes tested in 28-day atrazine exposure assays, and measured at 10 and 28 days (upper and lower panel, respectively). Error bars are 1 SEM.

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Figure 3.9. Normalized hemolytic activity in control cultures, measured at end of 10- and 28-day assays. Data are given as means + 1 SE.

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Figure 3.10. Growth rates (divisions day-1) and normalized hemolytic activity in all tested atrazine concentrations, nutrient treatments and controls. Data are given as means + 1 SE.

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Nutrient-replete

Low N

Low P

Figure 3.11. Log-normal relationships between hemolytic activity (normalized to cell density and sample volume) and atrazine concentration (a, c, e) or growth rates (b, d, f) at 10-day endpoint. Panels a and b ≡ nutrient-replete media; and panels c and d ≡ low N; panels e and f ≡ low P. Note that panels c-f (low-nutrient treatments) exclude 200 µg atrazine L-1 cultures due to near-complete cell loss (see text).

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Figure 3.12. Hemolytic activity at different test endpoints, normalized to cell density and sample volume, in various nutrient regimes under a range of atrazine concentrations. Data are given as means + 1 SE

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APPENDIX

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Table A1. Effect concentrations for all tested strains of Prymnesium parvum, over time. (BD =Beyond Detection) Strain Salinity Nutrient Point Estimate

TX 10 R IC01 0.5 (0.3 - 0.7) IC10 4.9 (3.3 - 7.1) IC25 24.1 (9.4 - 55.9) IC50 80.2 (67.2 - 91.2) Low N IC01 0.8 (0.5 - 1.5) IC10 6.9 (4.9 - 13.7) IC25 54.5 (12.4 - 73.1) IC50 158.9 (148.3 - 168.6) Low P IC01 0.7 (0.5 - 0.8) IC10 6.5 (5.3 - 8.2) IC25 37.1 (28.8 - 49.8) IC50 162.5 (140.1 - 175.3)

20 R IC01 7.0 (2.4 - 26.2)

IC10 40.2 (31.5 - 52.1) IC25 75.8 (66.5 - 84.6) IC50 142.2 (132.5 - 149.0) Low N IC01 32.7 (15.7 - 52.8) IC10 72.8 (55.0 - 101.5) IC25 113.3 (82.6 - 133.6) IC50 174.3 (156.5 - 192.2) Low P IC01 21.2 (14.3 - 30.1) IC10 55.1 (46.8 - 60.5) IC25 87.1 (74.6 - 102.2) IC50 150.5 (132.1 - 166.6) NC 10 R IC01 3.9 (0.7 - 14.8) IC10 19.2 (7.3 - 29.7) IC25 38.8 (31.4 - 47.5) IC50 73.0 (50.6 - 83.8) Low N IC01 29.8 (7.3 - 54.9) IC10 87.8 (49.5 - 106.6) IC25 152.0 (132.9 - 170.4) IC50 BD Low P IC01 5.1 (2.2 - 14.6) IC10 36.6 (20.1 - 54.0) IC25 79.6 (72.3 - 86.5) IC50 162.2 (154.8 - 170.6) NC 20 R IC01 14.7 (1.8 - 1.8) IC10 44.8 (22.0 - 60.1) IC25 71.8 (60.7 - 79.5)

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Table A1 (continued) IC50 118.3 (103.4 - 133.9) Low N IC01 4.9 (1.1 - 31.9) IC10 36.4 (10.7 - 59.0) IC25 64.2 (47.6 - 75.3) IC50 108.7 (94.6 - 125.3) Low P IC01 10.0 (0.6 - 13.2) IC10 14.3 (4.7 - 20.2) IC25 21.3 (11.8 - 32.7) IC50 66.0 (24.3 - 141.0) SC 10 R IC01 13.8 (13.0 - 16.7) IC10 22.6 (18.2 - 31.9) IC25 41.9 (34.4 - 53.7) IC50 88.2 (69.8 - 106.0) Low N IC01 54.1 (52.4 - 61.3) IC10 86.8 (73.3 - 118.3) IC25 137.5 (111.5 - 159.0) IC50 BD Low P IC01 24.2 (13.9 - 51.6) IC10 61.4 (53.1 - 66.9) IC25 88.2 (82.6 - 91.6) IC50 161.7 (151.0 - 175.7) SC 20 R IC01 10.8 (4.1 - 57.2) IC10 101.6 (86.1 - 115.7) IC25 151.4 (143.1 - 159.6) IC50 BD Low N IC01 3.8 (1.0 - 17.7) IC10 26.8 (10.1 - 58.0) IC25 115.7 (100.5 - 146.6) IC50 BD Low P IC01 6.2 (0.9 - 27.6) IC10 57.0 (10.4 - 122.5) IC25 BD IC50 BD

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Figure A1. Distribution of P. parvum effective concentrations Table A2. 4-way ANOVA modeling the effects of herbicide concentration, nutrient regime, salinity and strain on P. parvum growth: 96-hour variable importance; independent resamples

Column Main Effect Main Effect Total Effect Total Effect Std Err Std Err Conc 0.402 0.009 0.533 0.01 Strain 0.066 0.008 0.381 0.009 Salinity 0.088 0.009 0.372 0.009 Nutr 0.081 0.008 0.265 0.008 day 10 variable importance:

Column Main Effect Main Effect Total Effect Total Effect Std Err Std Err Nutr 0.509 0.008 0.622 0.01 Conc 0.101 0.007 0.205 0.006 Strain 0.106 0.007 0.199 0.005 Salinity 0.078 0.006 0.151 0.005

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Table A3. 3-way ANOVA modeling the effects of herbicide concentration, nutrient regime, salinity on P. parvum growth, strains are analyzed separately: TX variable importance: independent resamples

Column; Main Effect Main Effect Total Effect Total Effect Overall Std Err Std Err Nutr 0.372 0.004 0.54 0.005 Conc 0.304 0.004 0.451 0.005 Salinity 0.118 0.003 0.188 0.003 Column: Main Effect Main Effect Total Effect Total Effect 96 -hour Std Err Std Err Conc 0.557 0.007 0.781 0.01 Nutr 0.116 0.007 0.362 0.007 Salinity 0.052 0.004 0.146 0.004 Column: Main Effect Main Effect Total Effect Total Effect day-10 Std Err Std Err Nutr 0.628 0.005 0.718 0.007 Salinity 0.183 0.005 0.23 0.004 Conc 0.052 0.004 0.12 0.003

NC variable importance: independent resamples

Column; Main Effect Main Effect Total Effect Total Effect overall Std Err Std Err Conc 0.42 0.005 0.581 0.006 Nutr 0.262 0.004 0.399 0.005 Salinity 0.128 0.004 0.226 0.003 Column; Main Effect Main Effect Total Effect Total Effect 96-hour Std Err Std Err Conc 0.542 0.006 0.684 0.008 Salinity 0.225 0.007 0.365 0.006 Nutr 0.054 0.004 0.187 0.004 Column; Main Effect Main Effect Total Effect Total Effect day 10 Std Err Std Err Nutr 0.469 0.008 0.61 0.01 Conc 0.298 0.009 0.478 0.009 Salinity 0.031 0.003 0.087 0.003

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Table A3. (continued) SC variable importance: independent resamples

Column; Main Effect Main Effect Total Effect Total Effect overall Std Err Std Err Nutr 0.441 0.005 0.59 0.006 Salinity 0.259 0.005 0.37 0.005 Conc 0.111 0.005 0.195 0.004 Column; Main Effect Main Effect Total Effect Total Effect 96-hour Std Err Std Err Salinity 0.422 0.008 0.586 0.009 Nutr 0.226 0.009 0.409 0.008 Conc 0.128 0.007 0.193 0.005 Column; Main Effect Main Effect Total Effect Total Effect day 10 Std Err Std Err Nutr 0.657 0.007 0.77 0.01 Conc 0.094 0.007 0.196 0.006 Salinity 0.096 0.006 0.154 0.005

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Table A4. ELISA sample QA/QC checks to quantify measurement of nominal doses. Samples: Sample ID Accuracy: Test ID Treatment Actual conc Measured conc (% recovery) 1 PpNC20 d10 R 12.5 10.45 84 2 PpNC20 d10 R 25 24.99 100 3 PpNC20 d10 R 50 35.31 71 4 PpNC20 d10 R 100 95.51 96 5 PpNC20 d10 R 200 149.02 75 6 PpNC20 d10 -P 12.5 11.88 95 7 PpNC20 d10 -N 25 20.20 81 8 PpNC20 d10 -P 50 42.84 86 9 PpNC20 d10 -N 100 111.70 112 10 PpNC20 d10 -P 200 183.80 92 11 Csub d28 R 12.5 13.31 106 12 Csub d28 R 25 26.53 106 13 Csub d28 R 50 44.48 89 14 Csub d28 R 100 89.96 90 15 Csub d28 R 200 197.88 99 16 Csub d28 -N 12.5 13.16 105 17 Csub d28 -P 25 27.96 112 18 Csub d28 -N 50 55.85 112 19 Csub d28 -P 100 98.04 98 20 Csub d28 -N 200 166.98 83 1 PpNC20 d10 R 12.5 13.56 109 2 PpNC20 d10 R 25 20.20 81 3 PpNC20 d10 R 50 53.01 106 4 PpNC20 d10 R 100 102.53 103 5 PpNC20 d10 R 200 219.22 110 6 Csub d28 R 12.5 19.23 154 7 Csub d28 R 25 18.82 75 8 Csub d28 R 50 72.84 146 9 Csub d28 R 100 119.46 119 10 Csub d28 R 200 209.05 105 11 PpNC20 d10 -N 25 24.99 100 12 PpNC20 d10 -P 100 80.97 81 13 Csub d28 -N 100 96.58 97 14 Csub d28 -P 100 84.71 85

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Table A4. (continued) 1 PpNC10 day 10 R 100 99.76 100 2 PpNC10 day 10 R 100 103.34 103 3 PpNC10 day 10 -N 12.5 10.11 81 4 PpNC10 day 10 -N 25 20.26 81 5 PpNC10 day 10 -N 100 110.65 111 6 PpNC10 day 10 -N 200 163.79 82 7 PpNC10 day 10 -P 12.5 14.68 117 8 PpNC10 day 10 -P 25 30.91 124 9 PpNC10 day 10 -P 50 56.56 113 10 PpNC10 day 10 -P 100 120.61 121 11 PpNC10 day 10 -P 200 200.89 100 12 STANDARD 0 0.24 13 STANDARD 1 1.04 104 14 STANDARD 3 2.62 87 15 Csub6hr R 12.5 3.28 105 16 Csub6hr R 25 2.99 120 17 Csub6hr R 50 2.21 111 18 Csub6hr R 100 2.36 118 19 Csub6hr R 200 3.58 90 20 Csub6hr -N 12.5 3.46 111 21 Csub6hr -N 25 2.81 112 22 Csub6hr -N 50 2.52 126 23 Csub6hr -N 100 2.59 130 24 Csub6hr -N 200 3.91 98 25 Csub6hr -P 12.5 3.96 127 26 Csub6hr -P 50 2.49 125 27 Csub6hr -P 100 2.43 122 28 Csub6hr -P 200 4.20 105 1 PpNC10 day 10 R 100 99.56 100 2 PpNC10 day 10 R 100 103.36 103 3 PpNC10 day 10 -N 12.5 10.01 80 4 PpNC10 day 10 -N 25 20.35 81 5 PpNC10 day 10 -N 100 110.48 110 6 PpNC10 day 10 -N 200 161.16 81 7 PpNC10 day 10 -P 12.5 14.40 115 8 PpNC10 day 10 -P 25 30.56 122 9 PpNC10 day 10 -P 50 56.31 113

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Table A4. (continued) 10 PpNC10 day 10 -P 100 120.09 120 11 PpNC10 day 10 -P 200 196.81 98 12 STANDARD 0 0.23 13 STANDARD 1 1.05 105 14 STANDARD 3 2.61 87 1 PpTX10 d10 R 12.5 8.73 94 2 PpTX10 d10 R 25 22.64 107 3 PpTX10 d10 R 50 58.26 125 4 PpTX10 d10 R 100 123.72 107 5 PpTX10 d10 R 200 102.62 86 6 PpTX10 d10 -N 12.5 8.23 110 7 PpTX10 d10 -P 25 21.95 116 8 PpTX10 d10 -N 50 65.49 93 9 PpTX10 d10 -P 100 111.50 139 10 PpTX10 d10 -P 200 100.79 90 11 Dt d10 R 12.5 8.44 102 12 Dt d10 -N 25 22.57 108 13 Dt d10 -N 50 62.92 103 14 Dt d10 R 100 124.22 106 15 Dt d10 -P 200 110.28 72

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Figure A2. Growth curves for D. tertiolecta over test duration of 20-d atrazine exposure.

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Figure A3. Growth curves for C. subsalsa over test duration of 28-d atrazine exposure.

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Figure A4. Relationship between hemolytic activity and atrazine or growth in C. subsalsa at 28-days post inoculation