THE SPATIAL AND TEMPORAL DISTRIBUTION AND POTENTIAL ENVIRONMENTAL DRIVERS OF SAXITOXIN IN NORTHWEST OHIO

Callie A. Nauman

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

May 2020

Committee:

Timothy Davis, Advisor

George Bullerjahn

Justin Chaffin

© 2020

Callie A. Nauman

All Rights Reserved iii ABSTRACT

Timothy Davis, Advisor

Cyanobacterial harmful algal blooms threaten freshwater quality and human health around the world. One specific threat is the ability of some cyanobacteria to produce multiple types of toxins, including a range of neurotoxins called saxitoxins. While it is not completely understood, the general consensus is environmental factors like phosphorus, nitrogen, and light availability, may be driving forces in saxitoxin production. Recent surveys have determined saxitoxin and potential saxitoxin producing cyanobacterial species in both lakes and rivers across the United States and Ohio. Research evaluating benthic cyanobacterial blooms determined benthic cyanobacteria as a source for saxitoxin production in systems, specifically rivers.

Currently, little is known about when, where, why, or who is producing saxitoxin in Ohio, and

even less is known about the role benthic cyanobacterial blooms play in Ohio waterways. With

increased detections of saxitoxin, the saxitoxin biosynthesis gene sxtA, and saxitoxin producing

species in both the Western Basin of Lake Erie and the lake’s major tributary the Maumee River,

seasonal sampling was conducted to monitor saxitoxin in both systems. The sampling took place

from late spring to early autumn of 2018 and 2019. Monitoring including bi-/weekly water

column sampling in the Maumee River and Lake Erie and Nutrient Diffusing Substrate (NDS)

Experiments, were completed to evaluate saxitoxin, sxtA, potential environmental drivers, and

benthic production. Overall, saxitoxin and sxtA was found throughout the entirety of the Ohio’s

portion of the Maumee River and east of the Lake Erie Islands during both years. Detections

included sxtA peaks in July and saxitoxin detections as early as May and as late as October for

planktonic samples. However, benthic experiments suggested higher saxitoxin production in iv September and October. In general, low correlations were found between qPCR detections, nutrients, and toxin detections, however; ELISA and qPCR results in the river possibly suggests that benthic cyanobacteria are a potential source for saxitoxin in the Maumee River. Planktonic trends suggest nitrogen and dissolved reactive phosphorus may influence saxitoxin production, while benthic results highly correlated low light availability with saxitoxin production. v

To my family and friends for their love and support throughout my academic career and life.

I could not have done this without you. vi ACKNOWLEDGMENTS

Thank you to Dr. Timothy Davis, Dr. George Bullerjahn, and Dr. Justin Chaffin for the support through my graduate career and my thesis project. I greatly appreciate all the expertise, opportunities, and overall passion for the Great Lakes.

Dr. Doug Kane, Keara Stanislawczyk, Halli Bair, Audrey Laiveling, and Crista Keiley for the field sampling and proccessing, this project could not have happened without you.

Heather Raymond for the advice and help with field sampling difficulties.

The Ohio Department Of Higher Education for funding support.

The BGSU Lab: Michelle Neudeck, Kaitlyn McKindles, Laura Reitz, Emily Beers,

Matthew Kennedy, Seth Buchholz, Daniel Peck, Jay DeMarco, Dr. Paul Matson, Melanie

Edwards, Jacob Yager, and Valerie Montgomery. Thank for all the help, insight, motivation, and laughs.

vii

TABLE OF CONTENTS

Page

CHAPTER 1: INTRODUCTION ...... 1

1.1: Algal Blooms and Eutrophication...... 1

1.2: Cyanobacteria Harmful Algal Blooms ...... 2

1.3: Cyanotoxin Background ...... 3

1.4: qPCR and sxtA Monitoring ...... 5

1.5: Benthic Cyanobacterial Blooms ...... 6

1.6: Cyanotoxin Monitoring in Ohio ...... 6

1.7: Chemical- and Molecular-based Saxitoxin Monitoring in Ohio ...... 7

1.8: Cyanotoxin Production Drivers ...... 7

1.9: Project Objectives ...... 8

CHAPTER 2: METHODS ...... 10

2.1: Field Sampling- Site Background ...... 10

2.2: Planktonic Field Sampling- Collection Methods ...... 11

2.3: Planktonic- Laboratory Processing and Analysis ...... 11

2.4: Nutrient Diffusing Substrate- Site Background ...... 13

2.5: Nutrient Diffusing Substrate- Experimental Design ...... 14

2.6: Nutrient Diffusing Substrate- Field Sampling ...... 16

2.7: Statistical Analysis ...... 16

CHAPTER 3: RESULTS ...... 17

3.1: Nutrients- Planktonic ...... 17

3.2: Fluorometric Parameters- Planktonic ...... 18 viii 3.3: Molecular Parameters- Planktonic ...... 19

3.4: Nutrient Diffusing Substrate Experiments ...... 22

CHAPTER 4: DISCUSSION ...... 23

4.1: Planktonic Monitoring and Saxitoxin Detections ...... 23

4.2: Nutrient Diffusing Substrate Experiments ...... 27

4.3: Conclusion ...... 29

4.4: Future Work ...... 30

LITERATURE CITED ...... 32

APPENDIX A. FIGURES ...... 47

APPENDIX B. TABLES ...... 66 1

CHAPTER 1: INTRODUCTION

1.1: Algal Blooms and Eutrophication

Aquatic systems and their organisms play an important role in the interactions and dynamics of Earth’s physical, ecological, and economic structure (Steffensen, 2008; Dodds et al,

2009). At the lower trophic level, microscopic photosynthetic autotrophs, or phytoplankton, supply the world with half of its photosynthetic biomass (Houghton & Woodwell, 1989).

Phytoplankton not only supply the world with oxygen, but play an active role in the planet’s carbon, nitrogen, and phosphorus cycles (Falkowski, 1994). While phytoplankton are a vital food source for the aquatic food web, certain types of phytoplankton can experience uncontrollable growth, also known as harmful algal blooms (Hallegraeff, 2004).

While some algal blooms are naturally occurring and crucial to maintaining a healthy

food web, overwhelming algal concentrations and species shifts have led to destructive blooms

creating with hypoxic zones and high toxin production, thus damaging the system and those who

use it (Anderson et al., 2002). For example, both the Baltic Sea and the Benguela Current off the

southern coast of Africa are both seeing temporal and community shifts of their natural diatom

dominated blooms to dinoflagellate-dominated blooms, impacting their fisheries and altering the

nutrient cycling of the system (van der Lingen et al., 2016; Spilling et al., 2018). Some of the

dominating factors in the formation of freshwater algal blooms include availability of nitrogen

and phosphorus (Carpenter et al., 2001; Schindler et al. 2008; Gobler et al. 2016). High

enrichment of nutrients and subsequent aquatic plant and algae growth is otherwise known as

eutrophication and is thought to be increased with human activity (Smith et al., 2006). Eutrophic

water supplies plants and phytoplankton with warm and nutrient rich water allowing for optimal

growth (Schindler, 2006; Smith et al, 2006). At the turn of the century, an estimated 40% of 2 available freshwater in America, Europe, and Asia had the eutrophic conditions to support algal blooms (Chorus & Bartram, 1999). A 2007 National Lake Assessment data showed chlorophyll concentrations sustaining bloom conditions in 44% of 1161 lakes across the United States

(Loftin et al., 2016). Modeling also suggests that the combination of increased temperature, internal nutrient cycling, and external nutrient loading from agricultural practices and legacy nutrients in soil, will be a driving force behind future harmful algal blooms across the United

States (Ho and Michalak, 2019). As previously stated, the bioavailability of nitrogen and phosphorus plays a role in harmful algal bloom production. However, it may not only be the availability of the nutrients, but the type of nutrients that help drive and determine the growth, community structure, gene regulation, and toxin production of harmful algal blooms (Gobler et al., 2016; Newell et al., 2019; Harke et al., 2016b). Therefore, a dual nutrient management strategy is often recommended when mitigating algal blooms (Paerl et al. 2011). Furthermore, higher nutrient runoff from stronger rainfall events, combined with warmer water, is expected to increase algal bloom frequency, duration, intensity, and global range in the years to come (Paerl et al., 2016).

1.2: Cyanobacterial Harmful Algal Blooms

Some of the most prolific freshwater harmful algal blooms are associated with cyanobacteria, commonly known as blue-green algae, which create what are known as cyanobacterial harmful algal blooms (cHABs; O’Neil et al., 2012). As the oldest photosynthetic organisms on the planet, cyanobacteria have the adaptations, like nitrogen fixation, and the phenotypic flexibility, that allow them to adapt and survive in a range of environments (Knoll,

2008). Therefore, cyanobacteria may be equipped to outcompete and dominate over other types of phytoplankton in a range of environmental conditions and systems, like warmer temperature 3 and high and low nutrient environments (Carey et al., 2012). Furthermore, both planktonic and benthic cyanobacteria can form cHABs in marine, brackish, and freshwater systems (Chorus &

Bartram, 1999; Whitton & Potts, 2000). CHABs not only cause harm the environment in which they form they can also cause harm to humans, livestock, pets and even marine mammals when secondary metabolites they produce naturally accumulate to high concentrations. These compounds are generally referred to as cyanotoxins and are described in more detail below. By

2016, a total of 108 countries have reported cyanobacteria (Microcystis sp.) presence in their waterways, this includes reports of species capable of producing the cyanotoxin microcystin in

79 of those countries (Harke et al., 2016a). With that in mind, it is projected as the world’s waterbodies are predicted to get warmer, the range cyanobacteria species capable form toxic cHABs will continue to expand and dominate around the world (Scherer et al., 2017).

1.3: Cyanotoxin Background

There are an estimated 200 species of phytoplankton that have the ability to produce compounds that can be toxic to humans and other mammals (Landsberg, 2002). In particular, cyanobacteria are known for producing multiple types of toxins including, hepatotoxins, neurotoxins, cytotoxins, dermatoxins, and irritant toxins (Wiegand & Pflumacher, 2005). Toxins from cyanobacteria, otherwise known as cyanotoxins, are associated with human and animal illnesses and deaths (Carmichael & Falconer, 1992). In general, there is insufficient reporting and comprehensive understanding of cyanotoxin production, range, and illness cases (Svirčev et al.,

2019). Toxic blooms are hard to predict, because there are many potential and unknown factors that may influence toxin type and production by cyanobacteria (Pearson et al, 2016).

Furthermore, toxic and non-toxic strains are microscopely indistinguishable and within species there may or may not be a toxin producing gene present, which can be up or down regulated at 4 any given time (Pearson et al, 2016; Kaebernick & Neilan, 2001; Velzeboer et al., 2000). Plus, various strains of cyanobacteria are capable of producing multiple types of toxin, for instance,

Microcystis sp, Aphanizonmenon sp., and the benthic species Microseira wollei

(basionym Lyngbya wollei; Mcgregor & Sendall, 2015) have shown hepatotoxin and neurotoxin production capabilities (Park et al.,1993; Cirés & Ballot, 2016 and references within; Foss et al.

2012 and references within).

Globally, microcystin, a hepatotoxin associated with gastrointestinal, liver, and skin irritation and promoter in liver cancer, is one the most common and widespread of the cyanotoxins (Codd et al. 1997; Nishiwaki-Matsushima et al., 1992; Harke et al., 2016a).

However, neurotoxins, like anatoxin-a and saxitoxins, and other hepatotoxins, like cylindrospermopsins, are beginning to become more prevalent in fresh waters around the world

(Fastner et al., 2003; James et al., 1997; Velzeboer et al, 2000; Grugger et al, 2005; Kleinteich et al, 2003). Specifically, saxitoxins (STXs) are commonly referred to as paralytic shellfish poisons

(PSPs) and with over 57 analogues may be produced by both marine (dinoflagellate) and freshwater (cyanobacteria) species (Weise et al., 2010). STX production has been found in multiple cyanobacterial species including Aphanizomenon sp., Anabaena sp., Lyngbya sp., and Cylindrospermopsis sp. (Weise et al., 2010). As a neurotoxic alkaloid, STX impedes neuron transmission by binding to the sodium channels in the brain’s nerve cells prompting muscle paralysis (Catteral et al., 1985). This immobilization happens specifically in the respiratory and cardiovascular system, which could potentially lead to death of the subject by respiratory paralysis (Catteral et al., 1985). Saxitoxins are known to bioaccumulate in commercially important fish and bivalves and are considered keystone metabolites with their ability to impact ecosystems (Hallegraef, 2004; Zimmer & Ferrer, 2007). There is currently no known antidote or 5

remedy for saxitoxin poisoning (Van Dolah, 2000). Furthermore, most cases are likely underreported,

around 2000 saxitoxin illness cases worldwide are reported annually and with an estimated 15%

mortality rate (Svirčev et al., 2019; Hallegraeff et al., 2014). Currently, there is no universally

accepted reason why cyanobacteria produce STX. However, nitrogen is often thought to be

factor in microcystin production due it’s rich nitrogen structure, therefore the also nitrogen rich

STX may also be driven by nitrogen (Cembella et al., 1998; Van de Waal et al, 2005)

1.4: qPCR and sxtA Monitoring

In recent years, saxitoxin and the saxitoxin producing gene cluster (sxt), specifically the

targeted saxitoxin gene sxtA, have been detected in freshwater lakes and streams systems across

the United States and around the world (Ballot et al. 2010; Smith et al., 2011; Quiblier et al.,

2013; Fetscher et al., 2015; Loftin et al., 2016). In 2008, the sxt gene cluster was identified as a

series of open reading frames (ORF) in the cyanobacterium, Cylindrospermopsis raciborskii T3,

with future PCR analysis showing sxt presence in 18 more STX positive cyanobacteria strains

(Kellman et al. 2008). The sxtA gene, the first gene of the sxt cluster, consists of four domains

(sxtA1-4) and its function is considered to be the first step required for STX biosynthesis

(Kellman et al, 2008). SxtA detection has been used to further identify STX producing cyanobacteria, including in the benthic Syctonema sp. (Al-tebrineh et al., 2010, Ballot et al.

2010; Smith et al., 2011). In order to help monitor and detect potential STX producing species,

PCR and qPCR has been developed and used as tool to detect sxtA genes in both freshwater and marine systems (Murray et al. 2011; Al-tebrineh et al., 2012; Gao et al., 2015). However, it is important to recognize that sxtA genes have been detected in cyanobacterial strains not considered to be STX producers, so it important to include other analysis techniques for STX verification (Ballot et al. 2010, Smith et al., 2011) Nonetheless, the development of multiplex 6 qPCR techniques have increased efficiency and reliability in recent years (Al-tebrineh et al.,

2012).

1.5: Benthic Cyanobacterial Blooms

In the last decade, there has been a growing awareness in the need to understand benthic cyanobacteria blooms, not just planktonic cyanobacteria (Quiblier et al., 2013). This emphasis has been motivated by increased animal deaths around the world from benthic cyanotoxins, including the first reported dog death in France in 2003 due to drinking water containing the benthic, neurotoxin-producing cyanobacterium Phormidium favosum (Gugger et al., 2005). More cyanotoxin illness events and the expanding range of toxin producing benthic species, has increased the importance to understand benthic cyanobacteria and their toxin production

(Quiblier et al., 2013). Furthermore, in relation to this study, it was discovered that the mat- forming species Microseira wollei (formally Lyngbya wollei) may produce both STX and cylindrospermopsin (Odonera et al., 1997; Seifert et al., 2007). Previously, microcystin, anatoxin-a, and cylindrospermopsin were the cyanotoxins primarily focused on in the western hemisphere (Westrick et. al., 2010). However, with saxitoxin, sxtA genes, and known saxitoxin producing cyanobacteria detected throughout the country and Ohio, it is important to understand and monitor all cyanobacterial types and their toxin production potential (Loftin et al., 2016;

Bridgeman & Penamon, 2010; Chaffin et al, 2019)

1.6: Cyanotoxin Monitoring in Ohio

Currently, there are no monitoring guidelines or exposure limits dictated by the United

States Environmental Protection Agency (U.S. EPA) for saxitoxin. However, the Ohio EPA does monitor for saxitoxin at their raw water intakes when and where past SXT or sxtA detections have been made. Common detection methods include an enzyme-linked immunosorbent assay 7

(ELISA) for toxin detection and quantitative polymerase chain reaction (qPCR) with which to detect and quantify cyanotoxin genes, including sxtA (Ohio EPA, 2019). Ohio EPA has an algal bloom monitoring season from the first week in May through the last week in October and limited “off-season” monitoring throughout the winter and early spring. This includes microcystin ELISA testing and bi-weekly qPCR, with saxitoxin testing via ELISA, or LC-

MS/MS if necessary, based on sxtA gene detections and toxin concentrations (Ohio EPA, 2019).

1.7: Chemical- and Molecular-based Saxitoxin Monitoring in Ohio

In Ohio, increased monitoring for saxitoxin and sxtA have shown that they are common and widespread throughout the state (Figure 1). The detections have included low level detections of sxtA in Lake Erie’s central basin (Chaffin et al., 2019), and 6.7% samples collected at public water systems had detectable levels of sxtA during 2016 and 2017 (Ohio EPA as in

Chaffin et al., 2019). Additionally, new and increased detections of species capable of producing

SXT have been happening in Lake Erie (Chaffin et al., 2019). Some of the STX producers found include the benthic species Microseira wollei (basionym Lyngbya wollei ) in the Maumee River and Maumee Bay region of western Lake Erie and the planktonic Dolichospermum (formerly

Anabaena; Wacklin et al., 2009) species in the central basin of Lake Erie (Bridgeman &

Penamon 2010; Chaffin et al., 2019). However, little is known about the range, variability, environmental drivers, and STX producing potential of cyanobacteria throughout Ohio.

1.8: Cyanotoxin Production Drivers

While there are many abiotic and biotic hypotheses of what might trigger cyanobacteria toxin production, there is no distinct or universally accepted explanation (Kaebernick & Nielan,

2001). Experiments have shown that high nitrogen and light availability resulted in higher toxin production in Microcystis and Planktothrix (Chaffin et al., 2018a); however there are conflicting 8

results and the correlation between light intensity and toxin production (Chaffin et al., 2018a;

Renaud et al, 2010; Semary, 2010; Gobler et al, 2016). For example, Yin et al (1997), showed

that higher STX concentrations were produced under 11 and 22 (µmol/m2 /s) light conditions

compared to light conditions higher or lower. Furthermore, some theories of toxin production are

based on nutrient availability (phosphorus and nitrogen) and nutrient form (e.g. reduced vs.

oxidized nitrogen) (see review by Gobler et al, 2016).

Nutrient dynamics of toxin production are often emphasized when considering

cyanobacterial blooms in agriculturally dominated watersheds (i.e. Lake Erie watershed) and systems that contain species capable of nitrogen fixation (i.e. Microseira wollei) (Gobler et al,

2016; Phlips et al., 1992). Additionally, seasonal and temporal timing of blooms may play an important role in bloom structure and potential toxin production (Wynne et al., 2015). With increased water temperature in July, August, and September, cyanobacteria (i.e. Microcystis sp.)

may dominate the phytoplankton community in the later summer months, therefore traditional

Lake Erie bloom season is considered to be from July 1 to October 31 (Wynne et al., 2013).

However, Dolichospermum sp., a STX producer, has been found to bloom in the central basin of

Lake Erie earlier than July (Chaffin et al., 2019). Plus, cyanotoxin poisoning and deaths of cattle

have been reported in Michigan in late October during non-typical bloom conditions (i.e. low

nutrients and cooler water; Fitzgerald and Poppega, 1993).

1.9: Project Objectives

The main objective of this project was to determine when and where saxitoxin and sxtA

genes are present in Northwest Ohio. The project included seasonal water column sampling of

the Ohio portion of Maumee River and Island region of Western Lake Erie. This involved

investigating the potential environmental drivers of sxtA gene presence within each system and 9

in-situ nutrient diffusing substrate (NDS) experiments to understand the impact of seasonal

timing, nutrient availability, and light availability on benthic cyanobacterial growth and toxin

production.

I hypothesize that with higher nitrogen levels, there will be increased saxitoxin

production and sxtA abundance in the water column samples due to saxitoxin being rich in

nitrogen. Additionally, that benthic cyanobacterial will show saxitoxin production, specifically

driven by nitrogen. However, nitrogen alone may not play the only role in saxitoxin production. I

also hypothesize that with high light intensity, increased saxitoxin concentration will be seen

within the benthic growth experiments-based results on a previous Microseira wollei STX production study.

10

CHAPTER 2: METHODS

2.1: Field Sampling- Site Background

In order to understand the environmental drivers of cyanobacterial growth and saxitoxin production, field sampling was conducted from early summer to mid-autumn of 2018 and 2019.

Planktonic water column sampling took place weekly or bi-weekly at multiple locations within

Ohio’s portion of the Maumee River (Figure 2) and the Western Basin of Lake Erie (Figure 3).

Corresponding NDS experiments were conducted at three sites between Mary Jane Thurstin and

The Bend sites of the Maumee River and off the dock of F.T. Stone Laboratory’s Peach Point

Laboratory on South Bass Island (Figure 3) to evaluate benthic cyanobacterial growth and saxitoxin production. The Maumee River watershed is located in Northwest Ohio and while covering 17,100 km2 it is considered the largest watershed within the Great Lakes region and large contributor of water and sediment to the western basin of Lake Erie (U.S. EPA, 2013).

Located within an agriculturally dominant region, the Maumee River is a large contributor of water, nutrients, and sediment into the Western Basin of Lake Erie and is considered an US EPA

Area of Concern for anthropogenic pollution and nutrient enrichment (U.S. EPA, 2013; Richards et al., 2009). While Lake Erie is the shallowest of the Great Lakes, the western basin is the shallowest basin of the three Lake Erie Basins and has a history of significant planktonic and benthic harmful algal blooms (Michalak et al., 2013; Berry et al., 2017; Bridgeman and

Penamon, 2010; respectively). Historically, Microcystis sp. and microcystin have been the main concern with western Lake Erie monitoring, however monitoring for other cyanotoxins and species is becoming a priority (Watson et al., 2016). 11

2.2: Planktonic Field Sampling- Collection Methods

Sampling was led by Dr. Justin Chaffin of Ohio State University’s F.T. Stone Laboratory

for the lake sites and by Dr. Douglas Kane of Defiance College for the Maumee River sampling.

Planktonic grab sampling took place weekly or bi-weekly from June- October 2018 and May-

October 2019 in both the Maumee River and Lake Erie. The grab samples were collected into

acid-washed polycarbonate bottles that were also rinsed with sample water at the site, including

brown polycarbonate bottles designated for chlorophyll-a analysis. Samples were either taken as a bottle dip collection from the surface (Maumee River) or from a 0-8 meter integrated sample

(Lake Erie) with an 8-m integrated tube sampler. Samples were placed in a cooler in the field

until they were processed in the lab. Water temperature and dissolved oxygen were recorded

with a Yellow Spring Instruments (YSI) sonde. Temperature and oxygen data were recorded

from a depth of 0.5 meters from the river and throughout the water column at 1-meter intervals

on the lake. Secchi disk and an Ohio sediment stick (also called “Secchi tube”) were used to measure turbidity on the lake and river, respectively. Water samples were collected for chlorophyll-a, nutrient, toxin, and algal community analysis (via fluoroprobe detection). Biomass was filtered for DNA extraction for qPCR analysis (see below).

2.3: Planktonic- Laboratory Processing and Analysis

The samples were processed, filtered, and stored appropriately on the day of collected,

including algal community analysis with the BBE Molndaeke FluoroProbe on the day of

collection. All nutrient, chlorophyll-a, toxin, and fluoroprobe samples were processed, stored,

and analyzed at F.T. Stone Laboratory. For chlorophyll-a, the sample bottle was gently inverted several times to ensure the water was well mixed, then a subsample was measured in a graduated

cylinder and gently filtered with vacuum pump onto 0.7 µm Whatman Glass Microfiber Filters 12

(GF/F) and frozen at -20°C with silica gel. The chlorophyll-a extraction method used was modified from Golnick et al. (2016).

Total nutrients were collected into acid washed bottles and stored frozen in a -20°C freezer. Dissolved nutrients were filtered through a 0.45 µm Whatman Glass Microfiber Filters

(GFM) with an acid-washed filter tower and rinsed three times to ensure all excess nutrients were rinsed out. The dissolved nutrient samples were kept in a -20°C freezer until analyzed.

Nutrient analysis consisted of nitrate, nitrate, ammonium, urea, silicate, total phosphorus, and dissolved reactive phosphorus processed by the US EPA standard methods and conducted on a

QuAAtro SEAL Analytical flow auto-analyzer (as in Chaffin et al., 2019). Total toxin samples were stored in glass amber vials a -20°C freezer. Saxitoxin was later analyzed via Abraxis

Saxitoxins (PSP) (EC 2002/225 Compliant) 96 Test ELISA (PN: 52255B) after three freeze/thaw cycles.

DNA samples for qPCR analysis were filtered via vacuum pump onto a Versapor acrylic copolymer 1.2 micron pore size disk filters to allow for more cyanobacterial cells to be concentrated and allowing smaller particles, lysed cells, and bacteria to pass through. The filters were then frozen in sterile microcentrifuge tubes at -80°C until processed. DNA was extracted at

Bowling Green State University with the Qiagen DNeasy Blood and Tissue DNA extraction and

Qiashredder homogenizer lysate kits as per the protocols in Berry et al. (2017). The samples were eluted to 50 μL and read on a Thermo Scientific Nanodrop Lite for quantification and purity assessment, and stored at -80°C. The DNA was later used for real-time multiplex qPCR detection of target cyanotoxin genes. A Quantabio Q thermocycler was used with Phytoxigene

CyanoDTec mastermixes for total and toxin gene with the Phytoxigene CyanoNAS standards for relative quantification (https://www.phytoxigene.com/). This allowed for the amplification of the 13

16S ribosomal RNA gene of cyanobacteria and the amplification of the target genes for microcystin and nodularin (mycE/ndaF), cylindrospermopsin (cyrA) and saxitoxin (sxtA) production. The thermal cycler ran in duplicate of each sample for 40 cycles, at 95.0°C for 15 seconds with a cool down of 60°C for 30 seconds with an activation of 95°C for 2 minutes based on Phytoxigene standard protocol (Phytoxigene CyanoDTec Procedure Manual version 9 A4,

August 2019).

2.4: Nutrient Diffusing Substrate-Site Background

In order to understand and monitor benthic cyanobacterial growth and potential toxin production, NDS experiments (Modified from Tank et al, 2006 as in Capps et al., 2011) were conducted in the summer of 2018 and 2019 in Lake Erie and in the summer of 2019 in the

Maumee River. As previously mentioned, the selected sites consisted of three locations in the

Maumee River and one site in Lake Erie (Figures 2-3). The river locations were comprised of one site near the Miltonville Fishing Access north of Waterville, OH, the canoe access in downtown Grand Rapids, OH, and the fishing access near of the dam of Mary Jane Thurstin Park in Grand Rapids, OH. The three Maumee River sites were relatively similar in depth (0.25-1m), rocky substrate, and low canopy cover. The original plan consisted of NDS being placed at

0.25m and 1m for low light and high light analysis, but due to water level fluctuations only one tray was deployed at each site. All three of these river locations were selected upon preliminary benthic sampling conducted by the Ohio EPA, Bowling State University, and Cawthron Institute

(New Zealand) in August 2018. The Lake Erie NDS experiment consisted of two trays hung in tandem off a dock at 0.5m depth and 2.0m depth to represent low light and high light exposure.

The dock used is located on the southside, or bay side, of the northeast point of South Bass

Island. 14

2.5: Nutrient Diffusing Substrate- Experimental Design

The NDS experiment setup consisted of six treatments of 2% agar (20 g agar/1000 mL

H2O) filled colored plastic cups spiked with nutrient treatments (Table 1). A designated cup

color was given for each treatment and each treatment had ten cups per light level. Of each

treatment, three cups were designated for toxin analysis, three for DNA extractions and one for

algae identification. For the remaining 3 cups for the Maumee River NDS experiments were used

for chlorophyll a analysis, whereas the remaining 3 cups for the Lake Erie NDS were used for

organic matter.

The construction of the setup consisted of a plastic crate (24”x16”x5”) aligned with six

rows of angled PVC. One hole (0.25 inch) was drilled at each end of the PVC to secure it via zip

ties to the plastic crate. The six rows were broken down into two rows of eight cups and four

rows of eleven cups. Holes were also drilled along the upper angle of the PVC where the cups would be aligned, one hole on side of the cup to loop a zip tie around the cup. The cups themselves had holes (0.75 inch, 2.85 cm2) drilled into their lids and then were washed with

phosphate free soap. The agar solutions were made in 1 L batches, autoclaved, and poured into

the drilled cups. Each cup was topped with a ceramic crucible cover (EA Consumables, Porous

Crucible Cover PN: 528-042) while the agar solidified, to ensure the crucible cover was exposed

on the top. The cups were closed and secured with gorilla tape, ensuring the holes in the lids

exposed the crucible cover. The cups were then secured with zip ties and fishing line to the PVC.

Finally, each crate had a cinderblock attached each end with nylon rope, making sure the setup

would be securely placed in the correct depth of water. 15

2.6: Nutrient Diffusing Substrate- Field Sampling

The NDS setups were deployed for two weeks at a time until collected for analysis. Fresh agar and cups were prepared before each new deployment. Preliminary fluoroprobe readings of the crucible covers in the prepared trays was conducted with the BBE BenthoFluor attachment

(no longer in production, burrowed from Dr. Tom Bridgeman from the University of Toledo) before deployment to ensure blank readings. The BenthoFluoro attachment consists of a fiber optic cable that transmits the FluoroProbe emission LED lights and the algae’s fluorescence signal. Upon day of retrieval, each crucible cover was individually assessed with the Benthofluor attachment of the BBE fluoroprobe on collection day and then preserved accordingly for future analysis. Sonde data and water column samples was collected, as above, at the Maumee sites upon tray deployment and retrieval. Similar to the planktonic sampling, Lake Erie NDS samples were collected, processed, stored, and analyzed for each parameter at F.T. Stone Laboratory, with the exception of the crucibles for molecular analysis which were shipped to Bowling Green

State University (BGSU) for analysis. The samples were stored in DNA/RNAse free Falcon tubes, with 10mL of deionized water added to the samples preserved for toxin analysis. An

ELISA experiment was conducted at BGSU, which showed there was no binding of toxin to the

Falcon tube or the ceramic crucible cover during storage. The method for determining how to extract DNA off of the crucible covers is still in the research and development phase.

2.7: Statistical Analysis

SxtA, saxitoxin, and potential environmental driver data was analyzed using the Toolpak

Data Analysis function of Excel. Statistical analysis consisted of Pearson correlations, one and two-way ANOVA (α=0.05), and linear regression analysis. It is important to note that any sxtA detection above the cycle threshold (Ct) value of the lowest standard, was deemed detected, but 16 unquantifiable. Each sample above the designated Ct value was changed to 50 copies/well, which is half of the lowest standard of 100 copies/well for analysis. All ELISA STX and nutrient results below the lowest standard, but above zero concentration, were kept as is with their deduced concentrations, but any nutrient concentrations showing a negative result was subsequently changed to zero for analysis. Furthermore, to account for any community chlorophyll underestimation by fluorometry, the BBE Fluoroprobe concentrations for planktonic samples were transformed as in Chaffin et al. (2018b) before analysis for the planktonic sampling. No transformation was done on the NDS fluoroprobe concentration data due to inconsistent processing of chlorophyll a sample. In the occasion sampling between sites took place on different dates throughout a week for the Lake Erie planktonic sites, the first sampling date of that week is the one provided on the data plots. This did not occur for any of the

Maumee River sampling trips. 17

CHAPTER 3: RESULTS

3.1: Nutrients-Planktonic

Both Lake Erie and the Maumee River showed seasonal and temporal variation of

nutrients throughout the summer and early autumn of 2018 and 2019. In 2018, the Maumee

River specifically showed combined nitrate and nitrite (NOx) concentrations (Figure 4) and the

total nitrogen-to-total phosphorus (TN:TP) concentration ratio (Figure 5) decreased in July and

remained low for the rest of the season. While this decreasing trend of NOx and TN:TP was also apparent in the Lake Erie data for 2018 and 2019 (Figures 6-7), the trend was not found in the

2019 Maumee River data (Figures 4-5). In general, ammonium showed a seasonal trend of decreasing into July and then increasing sometime in August or September for both years in both systems (Figures 4, 6). The rest of the Maumee River nutrients: Dissolved Reactive Phosphorus

(DRP), Total Kjeldahl Nitrogen (TKN), and the 2018 NOx and TN:TP, showed variable concentrations throughout the season, suggesting pulses of nutrients over the sampling period

(Figures 4-5). The general trend showed nutrient concentrations decrease throughout the early summer with spike in sometime in mid to late-July and an increase of nutrients in early autumn.

The Lake Erie nutrient data showed ammonium and DRP as relatively low and stable throughout the sampling seasons, with some exception of spikes in June and October (Figure 6).

In October 2018, ammonium increased at GIW only, compared to 2019 where both sites showed spikes in June that decreased into July and remained low through October (Figure 6).

Meanwhile, TKN had a late June spike at GIW then remained relatively stable for the rest of

2018. However, TNK in 2019 showed noncorresponding trends over the course of the season between sites in 2019. GIW decreased in June then increased into September before tapering off into October. Meanwhile, WB-83 which increased steadily from June into August, before 18

decreasing in early September and quickly increasing again in September before tapering off in

October (Figure 7). It is important to note, that while not true in every case, overall 2018 data showed higher nutrient concentrations than 2019 data in both the Maumee River and Lake Erie.

In the meantime, Lake Erie concentrations were generally similar across years except for lower ammonium and a higher TN:TP maximum in 2019.

3.2: Fluorometer Parameters- Planktonic

The 2018 Maumee River data total chlorophyll data showed increases in concentration

throughout early summer and peaks in July and August, until declining into October (Figure 8).

However, for total chlorophyll the two most downstream sites started with higher concentrations

than the other four sites in June before rapidly decreasing before the July spike (Figure 9). The

cyanobacterial-chla and green algae-chla concentration data in 2018 for the Maumee, showed a

vaguely similar trends as total concentration, although with sharper peaks in concentration in

mid-July and mid-August (Figure 9). It is also important to note that the green algae-chla

photopigment concentrations were higher, sometimes double, in concentration than

cyanobacteria-chla. Both figures (9, 10) also showed that concentration, in total and community,

increased as it went from the upstream to the downstream sites. Except for a June peak at Mary

Jane Thurstin and Farnsworth, diatom-chla remained stable, although in higher concentration

than cyanobacteria-chla, but lower in concentration than green algae, through the 2018 season

(Figure 9). Diatom-chla were also the community to not have a notable increase during the month of July. The 2019 Maumee River fluorometric data showed a more variable trend than the

2018 data, including multiple large peaks throughout the season and no dominating trends between sites (Figure 8, 9). Cyanobacteria-chla was the lowest in concentration and it spiked at different sites, with no visual pattern, throughout the 2019 season (Figure 8). Meanwhile, 19

diatom-chla and total concentration showed large peaks in July, with smaller peaks in total

concentration throughout August, September and October (Figure 8, 9). The green algae-chla

concentration was a little more variable in 2019, with large spikes varying by site in early and

mid-July and mid-September.

The Lake Erie fluorometric data showed peaks in fluorometric algal concentrations

throughout the 2018 and 2019 season with neither site dominating in concentrations values

(Figures 10, 11) The 2018 and 2019 data showed stable increase and decrease of cyanobacterial

throughout the summer, a steady increase of total concentration, and increase of diatoms toward

the end of the summer (Figure 10, 11). Green algae concentrations followed slightly different patterns year to year, with spikes throughout 2018 and the highest concentration during early summer with a steady decline in 2019. And the individual community chlorophyll data, while lower in concentration, followed the same general trends as the total chlorophyll in all years in both years and in both systems. Furthermore, both years showed cyanobacteria dominating in late July and early August at both sites (Figure 11).

3.3: Molecular Parameters- Planktonic

STX and sxtA detections occurred throughout the Maumee River in 2018 and 2019, but

only two sxtA detections were greater than 1000 sxtA copies/mL (Figures 12,13). The highest number of sxtA gene copies was about 18,000 genes/mL measured on July 17, 2018 at

Farnsworth and the second highest was also on the same date at MJT (Figure 12). On average,

the two furthest downstream sites, Mary Jane Thurstin and Farnsworth, had the highest sxtA

detections in 2018 by one to two orders of magnitude greater than the upstream sites (Figure

12,14). In 2019, the highest sxtA detections were found in the upstream sites of the Maumee

River, but at two orders of magnitude lower than the 2018 detections (Figures 12-14). However, 20

it is important to note that the high 2018 detections only happened once during the season, the

rest of the detections were low in comparison. Additionally, 2019 had more sxtA detections than

2018. Plus, both seasons had peak sxtA detections in July, but with high standard deviation

(Figure 15). However, there was no significant difference found between sites (p=0.52) or between years (p=0.13) for sxtA detections. Across years and monthly averages there was no significant difference between months (p=0.446) or between years (p=0.30) for sxtA detections.

Only 10 samples of 155 had STX concentrations that equaled or exceeded the Ohio EPA

reporting limit of 0.020 µg/L (Figures 16, 17; Table 2). Most of the STX detection of 2018 occurred at Farnsworth (Figure 16; Table 2), including a STX detection of 0.031 µg/L which corresponded with the highest sxtA gene concentration of that year (Figure 18). However, there while there was a significance (p= 3.2x10-5) between sxtA and STX detections, it was likely

driven by one point and is not reliable. In addition, there was a the weak (R2= 0.1113) linear

relationship between sxtA and STX detections further exemplifying the need for caution (Figure

18). In 2019, the Maumee River had more STX detections above the reporting limit across sites

both upstream and downstream, with three consecutive detections at Independence (Table 2).

Additionally, while most of the higher 2019 Maumee River STX detections occurred earlier in

May and June, one reporting limit detection did occur at Farnsworth in late October (Figure 17).

No strong correlations were found between environmental drivers and sxtA detections in the

Maumee River (Table 3). However, there was a significance between sxtA concentration and

TNK, TN, and TN:TP, and cyanobacterial-chla. TN, TN:TP, and cyanobacterial-chla were

positively correlated and TKN was negatively correlated (Table 3). Although negative, TN proved to be the strongest correlated variable with sxtA in the Maumee River. A weak

relationship between sxtA and silicate was found significant (r = 0.04, p=0.14). Plus, temperature 21

was close to being significant with a p-value of 0.06 (Table 3). Although not shown in a figure

temperature for both years varied from 12-15 °C at the start and end of the season to 30°C at the

peak over the course of the summer.

There are no saxitoxin data for the GIW and WB-83 sites for either study year. However,

qPCR data did show detections below 5000 sxtA genes/mL throughout the seasons with peaks in

July of both years and in September of 2018 (Figures 18-19, 22). Two-way ANOVA results

between years and monthly averages of sxtA showed no significant difference between years (p=

0.82) or significant difference between months (p=0.06; Figures 21, 22). Furthermore, there was

no significant difference in total average sxtA detections between the two years (p=0.99. Two- way ANOVA results between years and site averages of sxtA showed no significant difference between sites (p=0.65) or between years (p=0.81; Figure 21). Additionally, the Lake Erie sxtA averages were about one magnitude lower in both 2018 and 2019 compared to the 2018 Maumee

River detections. Similar to the river, although statistically insignificant, most detections occurred in July for both years (Figures 15, 20). There was little correlation found between sxtA concentration and environmental parameters, with strongest correlation being temperature (Table

4) Temperature and ammonium, all positively correlated, were found to be significant with sxtA concentration (Table 4). Once again, temperature data was not provided in a graph, but ranged from from 12-15 °C at the start and end of the season to 25°C at the peak over the course of the summer, which is 5 °C lower peak than the river. Also, there was no significance between silicate and urea was not tested due to lack of samples. With a p<0.10, the positive weak correlation of secchi depth would be considered significant (Table 4). 22

3.4: Nutrient Diffusing Substrate Experiments

Due to low return rates, unpredictable rain events, and public disruption resulting in high

standard deviation and unsatisfactory results, the NDS Maumee River experiment results will not

be evaluated. While not explicitly true for every month or every treatment, Lake Erie NDS

experiments showed higher concentrations of cyanobacterial abundance at high light compared

to low light ( Figure 22; Table 5). However, there was no significant difference between most

+ + + nutrient treatments, except for NH4 and P+ NH4 in August 2018 and for P+ NH4 in June/July

+ 2019 (Table 5). However, based on Figure 22, the P+ NH4 treatment may only verify the impact

+ of NH4 , because the growth was rarely different than the control with just P added.

In contrast, experiments showed significantly higher STX concentrations at the low light

level compared to the high light on all monthly deployments except in August of 2020 (Figure

23; Table 6). Additionally, there was no significant difference between STX concentrations and

- + nutrient treatments. Yet, some figure suggests increased STX production with NO3 and NH4

(Figure 23). Temporally, there was a significant difference between months and saxitoxin

concentration, suggesting higher saxitoxin concentration in the September 2018 and October

2019 trays, based on individual two-way ANOVAs of treatment and months for each light

treatment and year (Table 7). Furthermore, two-way ANOVAS of treatment and year for each light treatment, found significance between years for both high light (p=0.015) and low light

(p=0.004), but not between treatments for high light (p=0.77) or low light p=0.18). 23

CHAPTER 4: DISCUSSION

4.1: Planktonic Monitoring and Saxitoxin Detections

SXT and sxtA detections occurred throughout both 2018 and 2019 in Lake Erie and the

Maumee River from early as May until late October. Although the majority sxtA concentration

detections were below 500 copies/mL, the Ohio EPA monitoring guidelines request any sxtA

genes detection in raw water to be reported (Ohio EPA, 2019). Most STX and sxtA detections

occurred in early and mid-summer, particularly in July. Furthermore, each system in both 2018

and 2019, had their highest seasonal sxtA detections in July. However, the Maumee River had

significant STX detections as early as May and June and as late as October. In contrast to the

river, the majority of the Lake sxtA detections occurred predominantly in July. The lake had low detections in May through October in 2019, but little or no detections outside of July-September

in 2018. This is consistent with a Lake Erie central basin study that showed sxtA detections

dominating in July until shifting into the microcystin gene (mycE) dominance in August and late summer (Chaffin et al., 2019).

The Ohio EPA has a detection limit of 180 copies/mL for sxtA qPCR. Based on the Ohio

2016-2019 EPA dataset, the average overall public water supply detections was about 1,300

genes/mL including all results and an average of 4,200 genes/mL including results only above

the detection limit (Ohio EPA, 2019). The highest Ohio EPA detection was 42,000 copies/mL,

but only 27% of the samples were above the 180 copies/ml detection. The results from our study indicated that the average sxtA detection for the Maumee River was 492 copies/mL (2018), including the high July detections, and 23 copies/mL (2019) and 477 copies/mL for both years in

Lake Erie. Therefore, on average the Maumee River and Lake Erie detections are about one-to

two-fold lower than what is seen on average across the state. Plus, only 31% of the Lake Erie 24

samples and 1.8% of the Maumee River samples were above the Ohio EPA detection limit. No

Lake Erie samples were above the 4,200 copies/mL average, although two samples were above

4,000 copies/mL. The Maumee River only had two detections above the 4,200 copies/mL

average; however; those two detections were around 7,500 copies/mL and 18,000 copies/mL.

Plus, only 5.6% of the Ohio EPA samples were above 7,000 copies/mL and only 1.8% were above 17,000 copies, making those two river detections higher and relatively rare compared to the average state detection. Overall, the detections found in the Maumee River and Lake Erie are low compared to the average statewide detection statewide. It is important to remember that only

27% of the detections across the state were above the detection limit, there while the average detection was higher statewide, the typically detection was likely below the detection limit.

Cyanobacteria never dominated the algal community, concentrations were often at their highest, specifically in July for each site and year, and sometimes June and September as well.

However, it is important to note that algal density and toxin concentrations do not always correlate (Gobler et al, 2016). Therefore, the typical Ohio algal bloom monitoring season (May-

October, OHIO EPA 2019) is sufficient for tracking STX and sxtA in Lake Erie. However, due to the early and late seasonal sxtA detections in the Maumee River, the start and end of the saxitoxin production in the river may not have been captured between the months of May and

October.

The highest gene detections were at the most downstream sites of the Maumee, but STX and sxtA detections throughout the span of the Maumee River and Lake Erie sites. The majority of the samples were within the range of 100-500 genes/mL, similar to the central basin study.

The July peaks were higher in the Maumee River and western basin of Lake Erie, compared to the central basin peaks. Temporally, the sxtA detections were less variable across years in Lake 25

Erie, but the Maumee River had detections two orders of magnitude higher in 2018 and detections one order of magnitude lower in 2019. This suggests that the Maumee River levels of saxitoxin and sxtA concentration may be more variable year to year compared to the Lake Erie level. However, this only exemplifies Ohio EPA’s 2016 report (Figure 1) that there is potential for STX production confirmed by the sxtA gene, spanning across a long range of locations, from

Ohio’s western portion of the Maumee River to the Central Basin of Lake Erie (Chaffin et al.,

2019).

While correlations between nutrients sxtA gene copies were weak, the river showed a significant of TKN (+), TN (-), TN:TP (-) with sxtA production, while the lake showed a relationship with ammonium (-) with sxtA production. This potentially suggests the importance of nutrient availability, specifically nitrogen, on the potential of STX production in these systems. While the drivers of SXT are not understood, research has suggested that phosphorus and nitrogen aid in cyanobacterial growth and cyanotoxin production (Schindler et. al., 2008;

Gobler et al., 2016). Furthermore, it has been suggested that concentrations, ratios, and specific form of nutrients may not only drive harmful algal blooms and their potential toxin production, but also a shift in community dominance (Harke at al., 2016b, Newell et al., 2019). While there is little research and understanding of freshwater STX production in regards to nutrient availability, STX consists of a nitrogen rich structure, like microcystin, and therefore may be driven by the same nutrients (Cembella et al., 1998; Van de Waal et al, 2005). It is important to note that the two main potential STX producers in the systems, Microseira wollei and

Dolichospermum sp., are both capable of nitrogen fixation, giving them an advantage in obtaining nitrogen in low nitrogen conditions (Mcgregor & Sendall, 2015; Li et al., 2016). Yet, even with new best management practices and regulations like Ohio Senate Bill 1 26

(https://www.legislature.ohio.gov/legislation/legislation-summary?id=GA131-SB-1), Lake Erie

is expected to remain eutrophic in the years to come (Watson et al, 2016).

However, little is understood about STX and sxtA production and nutrient availability and

studies have shown production in both low and high nutrient condition, deducing it may be other

physiological factors as well (Vargas et al, 2020; Holland & Kinnear, 2013). The results

suggested that increased temperature may play a role in sxtA production. This corresponds with

other sxt gene and STX production studies also suggesting that production happens at optimal

and typically warmer temperatures. (Yin et al, 1997; Casero et al, 2014; Cirés et al, 2017).

Furthermore, other factors, like rain events may alter the temperature, nutrient concentrations,

and turbidity of the systems. In 2019 rain events were more frequent with higher discharges

across the summer, specifically in July, compared to 2018 (U.S. Geological Survey, 2020). This

could be altering temperature and driving nutrient conditions, like nitrogen availability, in the

river. While our results only showed a significance between secchi/turbidity and sxtA production at a p<0.10 in the Lake, the river is a lotic system constantly changing and the lake sites are in relatively open water with typically low turbidity. However, with light and turbidity potentially influence toxin production, specifically with benthic sources, this is something to be investigated further, potentially with a light meter (Yin, et al 1997, Quiblier et al., 2013).

Molecular results showed that there was weak and insignificant correlation between sxtA

and STX. However, the 2018 ELISA Farnsworth STX detection corresponded with the highest

sxtA detection. The weak correlation is most likely be due to the general low detection levels and

low DNA quantity and quality collected from the samples, although 16S served as an internal

control for cyanobacterial detection (Al-Tebrineh et al, 2012). For example, while the 2018

Farnsworth sxtA and STX detections aligned, and had good DNA extraction quantity and quality. 27

Meanwhile other STX detections that did not match well with gene detections, like the early

season 2019 samples, particularly the May 2019 Mary Jane Thurstin extraction, had low quantity

and quality. The Farnsworth 2018 sxtA detection had a good DNA quality and quantity. Plus, a

saxitoxin detection study showed that saxitoxin was often not detected until the gene detection

was above 7,500 copies/mL, therefore our sxtA averages may not be high enough to show a

correlation (Al-Tebrineh et al., 2010). In general, the lack of correlation exemplifies the importance of monitoring benthic and upstream sources for STX production.

The qPCR data was from planktonic water only, while the toxin analyzed was total toxin.

Thus, the ELISA showed all STX detections included intracellular and extracellular, but only intracellular DNA in the qPCR analysis. Therefore, the toxin could be produced from a source upstream or from cyanobacteria not captured in the sample. This could also explain why the toxin, but not the gene, is present in some samples. Additionally, while not saxitoxin, one cylindrospermopsin analysis study showed that because cylindrospermopsin can easily leave the cell, often times the toxin is present in the water but the gene detection is low and often times benthic sources are not accounted for properly (Gaget et al, 2017). This is further demonstrated by the Ohio EPA data (Figure 1), where 100% of the STX detected was extracellular. Therefore, the lack of genes detected by qPCR does not necessarily mean STX is not present in the water.

Further demonstrating the importance of using both qPCR and ELISA or LC-MS/MS when monitoring for STX and potential toxin producing strains (Pacheco et al, 2016).

4.2: Nutrient Diffusing Substrate Experiments

Although the benthic growth experiments from the Maumee River proved to be unsuccessful, STX production was found by benthic cyanobacteria in Lake Erie. Cyanobacteria were found in higher concentrations higher up in the water column, while STX concentrations 28 were significantly higher at lower light levels. This further suggests that high cyanobacterial density does not mean high toxin, and low cyanobacterial density does not mean low toxin. The higher cyanobacterial concentration, but lower STX production may correspond with research that suggests that if energy is spent on the growth phase for benthic production, less cell energy may go toward toxin production (Heath et al, 2016). While the role of light and benthic cyanobacteria is complicated, some research has suggested there is a tradeoff between light and growth phase that could influence toxin production, but the relationship is not well understood

(Bormans et al., 2014).

While nutrient treatments had no significant impact on STX concentration, particular trays showed influence of nitrate and ammonium on STX concentration. Furthermore, cyanobacteria growth was shown to be positively influenced by ammonium and ammonium and phosphorus availability. Additional complexities have been shown by certain species growth and toxin production under different conditions. For example, factors like the ability of a species to dominant in low light and the role of light, nutrients, nitrogenase activity, growth may also influence toxin production (Wood et al., 2012; Yin et al, 1997; Yunes et al., 2009; Heath et al.,

2016).

Temporally, significant differences in STX concentrations between months and years were found, suggesting that STX was higher later in the season and in 2018. Unlike the water column data where the majority, but insignificant amount of STX and sxtA production occurred in July, the NDS data showed the highest STX concentrations in the early autumn experiments in both years. While the NDS experiments did show STX production in early and mid-summer, the

NDS temporal results compared with the water column data and the central basin study suggest that STX production may be occurring at different times of the season by different producers 29

(Chaffin et al, 2018). Therefore, more research needs to be done to understand the role of light,

nutrients, and cyanobacteria community composition, to understand STX production in both the

Maumee River and Lake Erie.

4.3: Conclusion

In conclusion, this research shows that STX and cyanobacteria capable of producing STX

occur in both the Maumee River and in the western basin of Lake Erie. This work confirms and

detection of STX-producing and STX in freshwater systems, not only in Ohio, but across the

country (Bridgeman & Penamon, 2010; Chaffin et al, 2019; Loftin et al., 2016). In general, only

a few studies have been done on STX and STX producers in Ohio, Lake Erie, or the even across the Great Lakes region. To the best of our knowledge, little to no research has been done on STX

in the Maumee River and Lake Erie, and even less on benthic species capable of producing STX.

With SXT being detected more frequently in freshwaters around the world, from planktonic

blooms Australia to benthic blooms in St. Lawrence River and the Arctic, the results of this

project only exemplify the need to increase research and monitoring of STX in Ohio (Pereyra et

al., 2017; Langeunesse et al, 2012; Kleinteich et al., 2013). Additionally, with benthic blooms

increasingly shown as sources for saxitoxin production, including multiple instances of STX

production by Microseira wollei, benthic monitoring and research needs to be further explored to

understand its role in maintaining safe water in Ohio (Fetscher et al, 2015; Quiblier et al, 2013;

Langeunesse et al, 2012; Hudon et al, 2014; Bridgeman & Penamon, 2010). While not explicit,

the data suggests the availability of nitrogen and dissolved reactive phosphorus may play a role

in the timing of sxtA production, but the role of nutrients and STX production is complicated

(Yin et al., 1997; Yunes et al., 2009; Quiblier et al., 2013). Furthermore with light availability 30 being a potential driver in saxitoxin production, specifically with benthic cyanobacteria, the role of light on saxitoxin production should be researched further.

4.4: Future Work

The seasonal sampling results and NDS experiments in both the Maumee River and Lake

Erie, exemplifies the need for more research on STX in Ohio waterways. This includes more monitoring and sampling in both systems, including more benthic surveys. My first suggestion is to include benthic sampling in the Maumee River sampling to further pinpoint where and who are the potential producers or sources of STX in the Maumee river. Additionally, it may be beneficial to add another site downstream of Farnsworth, since a lot of STX was detected downstream in 2018. I would also suggest that STX ELISAs and qPCR should be conducted on

Lake Erie samples more frequently, or at minimum during the month of July, to determine the range of STX concentrations and STX producers throughout the western basin of Lake Erie.

Furthermore, samples should be selected for LC-MS/MS verification of STX, particularly the samples with high STX and sxtA concentrations.

To enhance the NDS experiments ran at Stone Laboratory, I would suggest measuring light throughout the course of the experiment. Also, it may be beneficial to add water column sampling for nutrients as well to have a comparison between the water column and the NDS tray, especially when looking at month to month experiments. Based on the potential relationship between growth and toxin concentration, it may be interesting to collect samples for toxin throughout of the course of deployment as well. Although, while there have been difficulties with DNA extraction, it may be interesting to figure out a way to look at RNA and transcription of the sxtA gene as well. 31

As far as the Maumee River NDS experiments go, rain events and public interference is

unpredictable. However, my first suggestion would be to potentially find sites on the west bank

close to the current site locations, or if accessible sites within that portion of the river where

public is less present. Furthermore, it may be important to include different versions of NDS

experiments, or even just benthic scraping or tile collection with nutrient amendment experiment

n lab, ensure at least some data is being collected. Finally, the last hurdle of the experiments was extracting DNA from the crucible covers. Although with some tradeoffs, one suggestion may be to use screen or filtered mesh on the cups, or at least the ones designated for DNA. Also, NDS experiments have been run with periphytonmeters attached with GF/F filters (as in Capps et al.,

2011) which may be more easily extracted compared to the ceramic crucible covers. 32

LITERATURE CITED

Al-Tebrineh, J., Mihali, T. K., Pomati, F., & Neilan, B. A. (2010). Detection of saxitoxin-

producing cyanobacteria and anabaena circinalis in environmental water blooms by

quantitative PCR. Applied and Environmental Microbiology, 76(23), 7836-7842.

doi:10.1128/AEM.00174-10

Al-Tebrineh, J., Pearson, L. A., Yasar, S. A., & Neilan, B. A. (2012). A multiplex qPCR targeting

hepato- and neurotoxigenic cyanobacteria of global

significance doi:https://doi.org/10.1016/j.hal.2011.11.001

Anderson, D. M., Glibert, P. M., & Burkholder, J. M. (2002). Harmful algal blooms and

eutrophication: Nutrient sources, composition, and consequences. Estuaries, 25(4), 704-

726. doi:10.1007/BF02804901

Ballot, A., Fastner, J., & Wiedner, C. (2010). Paralytic shellfish poisoning toxin-producing

cyanobacterium aphanizomenon gracile in northeast germany. Applied and Environmental

Microbiology, 76(4), 1173-1180. doi:10.1128/AEM.02285-09

Bajarias, F. F., T. Relox, Jr., and Y. Fukuyo. 2006. PSP in the Philippines: three decades of

monitoring a disaster. Coast. Mar. Sci. 30:104–106

Berry, M. A., Davis, T. W., Cory, R. M., Duhaime, M. B., Johengen, T. H., Kling, G. W., . . .

Denef, V. J. (2017). Cyanobacterial harmful algal blooms are a biological disturbance to

western lake erie bacterial communities. Environmental Microbiology, 19(3), 1149-1162.

doi:10.1111/1462-2920.13640 33

Bormans, M., Lengronne, M., Brient, L., & Duval, C. (2014). Cylindrospermopsin accumulation

and release by the benthic cyanobacterium oscillatoria sp. PCC 6506 under different light

conditions and growth phases. Bulletin of Environmental Contamination and

Toxicology, 92(2), 243-247. doi:10.1007/s00128-013-1144-y

Bridgeman, T. B., & Penamon, W. A. (2010). Lyngbya wollei in western lake

erie doi:https://doi.org/10.1016/j.jglr.2009.12.003

Capps, K., Booth, M., Collins, S., Davison, M., Moslemi, J., El-Sabaawi, R., . . . Flecker, A.

(2011). Nutrient diffusing substrata: A field comparison of commonly used methods to

assess nutrient limitation. Journal of the North American Benthological Society, 30, 522-

532. doi:10.1899/10-146.1

Carey, C. C., Ibelings, B. W., Hoffmann, E. P., Hamilton, D. P., & Brookes, J. D. (2012). Eco-

physiological adaptations that favour freshwater cyanobacteria in a changing

climate doi:https://doi.org/10.1016/j.watres.2011.12.016

Carmichael, W.W. and Falconer I.R. (1993). Diseases related to freshwater blue-green algal

toxins, and control measures. In I.R. Falconer (ed.). Algal Toxins in Seafood and Drinking

Water. Academic Press, London. pp. 187-209

Carpenter, S. R., Cole, J. J., Hodgson, J. R., Kitchell, J. F., Pace, M. L., Bade, D., . . . Schindler,

D. E. (2001). Trophic cascades, nutrients, and lake productivity: Whole-lake

experiments. Ecological Monographs, 71(2), 163-186. doi:10.1890/0012-

9615(2001)071[0163:TCNALP]2.0.CO;2 34

Catterall, W.A., Anderson, D.M., White, A.W., & Baden, DG. (1985) Eds.; The voltage-sensitive

sodium channel: a receptor for multiple toxins. In Toxic Dinoflagellates; Elsevier: New

York, 1985; pp. 329–342.

Cembella A.D. Ecophysiology and Metabolism of Paralytic Shellfish Toxins in Marine

Microalgae (1998). In: Anderson, DM and Cembella, AD and Hallegraeff, GM,

Physiological Ecology of Harmful Algal Blooms, Springer Verlag, Heidelberg, pp. 662.

ISBN 3-540-64117-3 (1998) [Edited Book]

Chaffin, J. D., Davis, T. W., Smith, D. J., Baer, M. M., & Dick, G. J. (2018a). Interactions

between nitrogen form, loading rate, and light intensity on microcystis and planktothrix

growth and microcystin production doi:https://doi.org/10.1016/j.hal.2018.02.001

Chaffin, J. D., Kane, D. D., Stanislawczyk, K., & Parker, E. M. (2018b). Accuracy of data buoys

for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (lake erie,

north america): Implications for estimation of cyanobacterial bloom parameters from

water quality sonde measurements doi:10.1007/s11356-018-2612-z

Chaffin, J. D., Mishra, S., Kane, D. D., Bade, D. L., Stanislawczyk, K., Slodysko, K. N., . . . Fox,

E. L. (2019). Cyanobacterial blooms in the central basin of lake erie: Potentials for

cyanotoxins and environmental drivers doi:https://doi.org/10.1016/j.jglr.2018.12.006

Chorus, I. and Bartram J. (1999) Toxic cyanobacteria in water: A guide to their public health

consequences, monitoring and management. London: WHO & E&FN Spon pp. 1–14.

http://dx.doi.org/10.4324/9780203478073 35

Cirés, S., & Ballot, A. (2016). A review of the phylogeny, ecology and toxin production of bloom-

forming aphanizomenon spp. and related species within the nostocales

(cyanobacteria) doi:https://doi.org/10.1016/j.hal.2015.09.007

Codd, G. A., Ward, C. J., & Bell, S. G. (1997). In Seiler J. P., Vilanova E.(Eds.), Cyanobacterial

toxins: Occurrence, modes of action, health effects and exposure routes. Berlin,

Heidelberg: Springer Berlin Heidelberg.

Dodds, W. K., Bouska, W. W., Eitzmann, J. L., Pilger, T. J., Pitts, K. L., Riley, A. J., . . .

Thornbrugh, D. J. (2009). Eutrophication of U.S. freshwaters: Analysis of potential

economic damages. Environmental Science & Technology, 43(1), 12-19.

doi:10.1021/es801217q [doi]

Falkowski, P. G. (1994). The role of phytoplankton photosynthesis in global biogeochemical

cycles. Photosynthesis Research, 39(3), 235-258. doi:10.1007/BF00014586

Fastner, J., Heinze, R., Humpage, A. R., Mischke, U., Eaglesham, G. K., & Chorus, I.

(2003a). Cylindrospermopsin occurrence in two german lakes and preliminary assessment

of toxicity and toxin production of cylindrospermopsis raciborskii (cyanobacteria)

isolates doi:https://doi.org/10.1016/S0041-0101(03)00150-8

Fastner, J., Heinze, R., Humpage, A. R., Mischke, U., Eaglesham, G. K., & Chorus, I.

(2003b). Cylindrospermopsin occurrence in two german lakes and preliminary assessment

of toxicity and toxin production of cylindrospermopsis raciborskii (cyanobacteria)

isolates doi:https://doi.org/10.1016/S0041-0101(03)00150-8 36

Fetscher, A. E., Howard, M. D. A., Stancheva, R., Kudela, R. M., Stein, E. D., Sutula, M. A., . . .

Sheath, R. G. (2015). Wadeable streams as widespread sources of benthic cyanotoxins in

california, USA doi:https://doi.org/10.1016/j.hal.2015.09.002

Fitzgerald S.D. and Poppenga, R.H. (1993). Toxicosis due to microcystin hepatotoxicosis in three

Holstein heifers, J. Vet. Diagn. Invest. 5 pp. 651–653.

Foss, A. J., Phlips, E. J., Yilmaz, M., & Chapman, A. (2012). Characterization of paralytic

shellfish toxins from lyngbya wollei dominated mats collected from two florida

springs doi:https://doi.org/10.1016/j.hal.2012.02.004

Gaget, V., Humpage, A. R., Huang, Q., Monis, P., & Brookes, J. D. (2017). Benthic

cyanobacteria: A source of cylindrospermopsin and microcystin in australian drinking

water reservoirs doi:https://doi.org/10.1016/j.watres.2017.07.073

Gao, Y., Yu, R., Murray, S. A., Chen, J., Kang, Z., Zhang, Q., . . . Zhou, M. (2015). High

specificity of a quantitative PCR assay targeting a saxitoxin gene for monitoring toxic

algae associated with paralytic shellfish toxins in the yellow sea. Applied and

Environmental Microbiology, 81(20), 6973-6981. doi:10.1128/AEM.00417-15

Gobler, C. J., Burkholder, J. M., Davis, T. W., Harke, M. J., Johengen, T., Stow, C. A., & Van de

Waal, Dedmer B. (2016). The dual role of nitrogen supply in controlling the growth and

toxicity of cyanobacterial blooms doi:https://doi.org/10.1016/j.hal.2016.01.010

Golnick, P. C., Chaffin, J. D., Bridgeman, T. B., Zellner, B. C., & Simons, V. E. (2016). A

comparison of water sampling and analytical methods in western lake

erie doi:https://doi.org/10.1016/j.jglr.2016.07.031 37

Gugger, M., Lenoir, S., Berger, C., Ledreux, A., Druart, J., Humbert, J., . . . Bernard, C.

(2005). First report in a river in france of the benthic cyanobacterium phormidium

favosum producing anatoxin-a associated with dog

neurotoxicosis doi:https://doi.org/10.1016/j.toxicon.2005.02.031

Hallegraef, G.M. (2004) Harmful Algae: A Global Overview. In: Hallegraeff G.M., Anderson

D.M., Cembella A.D., editors. (2014). Manual of Harmful Marine

Microalgae. International Oceanographic Commission (IOC) Manual and Guides

UNESCO; Paris, France. pp. 1–22.

Harke, M. J., Davis, T. W., Watson, S. B., & Gobler, C. J. (2016b). Nutrient-controlled niche

differentiation of western lake erie cyanobacterial populations revealed via

metatranscriptomic surveys. Environmental Science & Technology, 50(2), 604-615.

doi:10.1021/acs.est.5b03931

Harke, M. J., Steffen, M. M., Gobler, C. J., Otten, T. G., Wilhelm, S. W., Wood, S. A., & Paerl, H.

W. (2016a). A review of the global ecology, genomics, and biogeography of the toxic

cyanobacterium, microcystis spp. doi:https://doi.org/10.1016/j.hal.2015.12.007

Heath, M., Wood, S. A., Young, R. G., & Ryan, K. G. (2016). The role of nitrogen and

phosphorus in regulating phormidium sp. (cyanobacteria) growth and anatoxin

production. FEMS Microbiology Ecology, 92(3), fiw021. doi:10.1093/femsec/fiw021

Ho, J. C., & Michalak, A. M. (2019). Exploring temperature and precipitation impacts on harmful

algal blooms across continental U.S. lakes. Limnology and

Oceanography, n/a doi:10.1002/lno.11365 38

Holland, A., & Kinnear, S. (2013). Interpreting the Possible Ecological Role(s) of Cyanotoxins:

Compounds for Competitive Advantage and/or Physiological Aide? Marine Drugs, 11(7),

2239–2258. doi:10.3390/md11072239

Houghton, R. A. & Woodwell, G. M. (1989). Global Climatic Change. Scientific American

260:18.

Hudon, C., De Sève, M., & Cattaneo, A. (2014). Increasing occurrence of the benthic filamentous

cyanobacterium lyngbya wollei: A symptom of freshwater ecosystem

degradation. Freshwater Science, 33(2), 606-618. doi:10.1086/675932

James, K. J., Sherlock, I. R., & Stack, M. A. (1997). Anatoxin-a in irish freshwater and

cyanobacteria, determined using a new fluorimetric liquid chromatographic

method doi:https://doi.org/10.1016/S0041-0101(96)00201-2

Kaebernick, M., & Neilan, B. A. (2001). Ecological and molecular investigations of cyanotoxin

production. FEMS Microbiology Ecology, 35(1), 1-9. doi:10.1111/j.1574-

6941.2001.tb00782.x

Kellmann, R., Mihali, T. K., Jeon, Y. J., Pickford, R., Pomati, F., & Neilan, B. A. (2008).

Biosynthetic intermediate analysis and functional homology reveal a saxitoxin gene cluster

in cyanobacteria. Applied and Environmental Microbiology, 74(13), 4044-4053.

doi:10.1128/AEM.00353-08

Kleinteich, J., Wood, S. A., Puddick, J., Schleheck, D., Küpper, F. C., & Dietrich, D.

(2013a). Potent toxins in arctic environments – presence of saxitoxins and an unusual

microcystin variant in arctic freshwater

ecosystems doi:https://doi.org/10.1016/j.cbi.2013.04.011 39

Knoll, A.H. (2008) Cyanobacteria and earth history. In: Herrero A, Flores E (eds) The

cyanobacteria – molecular biology, genomics and evolution. Caister Academic Press,

Norfolk, pp 1–19, 484 pp

Kurmayer, R., Sivonen, K., Wilmotte, A., & Salmaso, N. (2017). Molecular tools for the detection

and quantification of toxigenic cyanobacteria. New York: Wiley.

Lajeunesse, A., Segura, P. A., Gélinas, M., Hudon, C., Thomas, K., Quilliam, M. A., & Gagnon,

C. (2012). Detection and confirmation of saxitoxin analogues in freshwater benthic

lyngbya wollei algae collected in the st. lawrence river (canada) by liquid

chromatography–tandem mass

spectrometry doi:https://doi.org/10.1016/j.chroma.2011.10.092

Landsberg, J. H. (2002). The effects of harmful algal blooms on aquatic organisms. Reviews in

Fisheries Science, 10(2), 113-390. doi:10.1080/20026491051695

Li, X., Dreher, T. W., & Li, R. (2016). An overview of diversity, occurrence, genetics and toxin

production of bloom-forming dolichospermum (anabaena)

species doi:https://doi.org/10.1016/j.hal.2015.10.015

Loftin, K. A., Graham, J. L., Hilborn, E. D., Lehmann, S. C., Meyer, M. T., Dietze, J. E., &

Griffith, C. B. (2016). Cyanotoxins in inland lakes of the united states: Occurrence and

potential recreational health risks in the EPA national lakes assessment

2007 doi:https://doi.org/10.1016/j.hal.2016.04.001

McGregor, G. B., & Sendall, B. C. (2015). Phylogeny and toxicology of lyngbya wollei

(cyanobacteria, oscillatoriales) from north-eastern australia, with a description of

microseira gen. nov. Journal of Phycology, 51(1), 109-119. doi:10.1111/jpy.12256 40

Michalak, A. M., Anderson, E. J., Beletsky, D., Boland, S., Bosch, N. S., Bridgeman, T. B., . . .

Zagorski, M. A. (2013). Record-setting algal bloom in lake erie caused by agricultural and

meteorological trends consistent with expected future conditions. Proceedings of the

National Academy of Sciences, 110(16), 6448-6452. doi:10.1073/pnas.1216006110

Murray, S. A., Wiese, M., Stüken, A., Brett, S., Kellmann, R., Hallegraeff, G., & Neilan, B. A.

(2011). sxtA-based quantitative molecular assay to identify saxitoxin-producing harmful

algal blooms in marine waters. Applied and Environmental Microbiology, 77(19), 7050-

7057. doi:10.1128/AEM.05308-11

Newell, S. E., Davis, T. W., Johengen, T. H., Gossiaux, D., Burtner, A., Palladino, D., &

McCarthy, M. J. (2019). Reduced forms of nitrogen are a driver of non-nitrogen-fixing

harmful cyanobacterial blooms and toxicity in lake

erie doi:https://doi.org/10.1016/j.hal.2018.11.003

Nishiwaki-Matsushima, R., Ohta, T., Nishiwaki, S., Suganuma, M., Kohyama, K., Ishikawa, T., . .

. Fujiki, H. (1992). Liver tumor promotion by the cyanobacterial cyclic peptide toxin

microcystin-LR. Journal of Cancer Research and Clinical Oncology, 118(6), 420-424.

doi:10.1007/BF01629424

Ohio E.P.A, (2019). Harmful Algal Blooms (HAB) information for public water systems.

http://www.epa.ohio.gov/ddagw/hab. Accessed March 14, 2020.

O’Neil, J. M., Davis, T. W., Burford, M. A., & Gobler, C. J. (2012). The rise of harmful

cyanobacteria blooms: The potential roles of eutrophication and climate

change doi:https://doi.org/10.1016/j.hal.2011.10.027 41

Onodera, H., Satake, M., Oshima, Y., Yasumoto, T., & Carmichael, W. W. (1997). New saxitoxin

analogues from the freshwater filamentous cyanobacterium lyngbya wollei. Natural

Toxins, 5(4), 146-151. doi:10.1002/19970504NT4

Pacheco, A., Guedes, I., & Azevedo, S. (2016). Is qPCR a Reliable Indicator of Cyanotoxin Risk

in Freshwater? Toxins, 8(6), 172. doi:10.3390/toxins8060172

Paerl, H. W., Gardner, W. S., Havens, K. E., Joyner, A. R., McCarthy, M. J., Newell, S. E., . . .

Scott, J. T. (2016). Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems

impacted by climate change and anthropogenic

nutrients doi:https://doi.org/10.1016/j.hal.2015.09.009

Paerl, H. W., Xu, H., McCarthy, M. J., Zhu, G., Qin, B., Li, Y., & Gardner, W. S.

(2011). Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (lake taihu,

china): The need for a dual nutrient (N & P) management

strategy doi:https://doi.org/10.1016/j.watres.2010.09.018

Park, H., Watanabe, M. F., Harada, K., Nagai, H., Suzuki, M., Watanabe, M., & Hayashi, H.

(1993). Hepatotoxin (microcystin) and neurotoxin (anatoxin-a) contained in natural blooms

and strains of cyanobacteria from japanese freshwaters. Natural Toxins, 1(6), 353-360.

doi:10.1002/nt.2620010606

Pearson, L. A., Dittmann, E., Mazmouz, R., Ongley, S. E., D’Agostino, P. M., & Neilan, B. A.

(2016). The genetics, biosynthesis and regulation of toxic specialized metabolites of

cyanobacteria doi:https://doi.org/10.1016/j.hal.2015.11.002

Pereyra, J. P. A., D'Agostino, P. M., Mazmouz, R., Woodhouse, J. N., Pickford, R., Jameson, I., &

Neilan, B. A. (2017). Molecular and morphological survey of saxitoxin-producing 42

cyanobacterium dolichospermum circinale (anabaena circinalis) isolated from

geographically distinct regions of

australia doi:https://doi.org/10.1016/j.toxicon.2017.08.006

Phlips, E. J., Ihnat, J., & Conroy, M. (1992). Nitrogen fixation by the benthic freshwater

cyanobacterium lyngbya wollei doi:10.1007/BF00010779

Quiblier, C., Wood, S., Echenique-Subiabre, I., Heath, M., Villeneuve, A., & Humbert, J.

(2013). A review of current knowledge on toxic benthic freshwater cyanobacteria –

ecology, toxin production and risk management

doi:https://doi.org/10.1016/j.watres.2013.06.042

Richards, R. P., Baker, D. B., & Crumrine, J. P. (2009). Improved water quality in ohio tributaries

to lake erie: A consequence of conservation practices. Journal of Soil and Water

Conservation, 64(3), 200-211. doi:10.2489/jswc.64.3.200

Scherer, P. I., Millard, A. D., Miller, A., Schoen, R., Raeder, U., Geist, J., & Zwirglmaier, K.

(2017). Temporal dynamics of the microbial community composition with a focus on toxic

cyanobacteria and toxin presence during harmful algal blooms in two south german

lakes. Frontiers in Microbiology, 8, 2387. Retrieved

from https://www.frontiersin.org/article/10.3389/fmicb.2017.02387

Schindler, D. W. (2006a). Recent advances in the understanding and management of

eutrophication. Limnology and Oceanography, 51(1), 356-363.

doi:10.4319/lo.2006.51.1_part_2.0356 43

Schindler, D. W. (2006b). Recent advances in the understanding and management of

eutrophication. Limnology and Oceanography, 51(1), 356-363.

doi:10.4319/lo.2006.51.1_part_2.0356

Schindler, D. W., Hecky, R. E., Findlay, D. L., Stainton, M. P., Parker, B. R., Paterson, M. J., . . .

Kasian, S. E. M. (2008). Eutrophication of lakes cannot be controlled by reducing nitrogen

input: Results of a 37-year whole-ecosystem experiment. Proceedings of the National

Academy of Sciences, 105(32), 11254-11258. doi:10.1073/pnas.0805108105

Seifert, M., McGregor, G., Eaglesham, G., Wickramasinghe, W., & Shaw, G. (2007). First

evidence for the production of cylindrospermopsin and deoxy-cylindrospermopsin by the

freshwater benthic cyanobacterium, lyngbya wollei (farlow ex gomont) speziale and

dyck doi:https://doi.org/10.1016/j.hal.2006.07.001

Smith, F. M. J., Wood, S. A., van Ginkel, R., Broady, P. A., & Gaw, S. (2011). First report of

saxitoxin production by a species of the freshwater benthic cyanobacterium, scytonema

agardh doi:https://doi.org/10.1016/j.toxicon.2010.12.020

Smith, V. H., Joye, S. B., & Howarth, R. W. (2006). Eutrophication of freshwater and marine

ecosystems. Limnology and Oceanography, 51(1), 351-355.

doi:10.4319/lo.2006.51.1_part_2.0351

Spilling, K., Olli, K., Lehtoranta, J., Kremp, A., Tedesco, L., Tamelander, T., . . . Tamminen, T.

(2018). Shifting Diatom—Dinoflagellate dominance during spring bloom in the baltic sea

and its potential effects on biogeochemical cycling. Frontiers in Marine Science, 5, 327.

Retrieved from https://www.frontiersin.org/article/10.3389/fmars.2018.00327 44

Steffensen, D. A. (2008). Economic cost of cyanobacterial blooms. In H. K. Hudnell

(Ed.), Cyanobacterial harmful algal blooms: State of the science and research needs (pp.

855-865). New York, NY: Springer New York. doi:10.1007/978-0-387-75865-7_37

Retrieved from https://doi.org/10.1007/978-0-387-75865-7_37

Svirčev, Z., Lalić, D., Bojadžija Savić, G., Tokodi, N., Drobac Backović, D., Chen, L., . . . Codd,

G. A. (2019). Global geographical and historical overview of cyanotoxin distribution and

cyanobacterial poisonings. Archives of Toxicology, 93(9), 2429-2481. doi:10.1007/s00204-

019-02524-4

U.S. EPA (2013). Great Lakes AOCs. https://www.epa.gov/great-lakes-aocs/maumee-aoc.

Accessed April 11, 2020.

U.S. Geological Survey (2020). National Water Information System data available on the World

Wide Web (USGS Water Data for the Nation) https://waterdata.usgs.gov/nwis/. Accessed

February 20, 2020.

Van de Waal, Dedmer B., Verspagen, J. M. H., Lürling, M., Van Donk, E., Visser, P. M., &

Huisman, J. (2009). The ecological stoichiometry of toxins produced by harmful

cyanobacteria: An experimental test of the carbon-nutrient balance hypothesis. Ecology

Letters, 12(12), 1326-1335. doi:10.1111/j.1461-0248.2009.01383.x

Van der Lingen, C. D., Hutchings, L., Lamont, T., & Pitcher, G. C. (2016). Climate change,

dinoflagellate blooms and sardine in the southern benguela current large marine

ecosystem doi:https://doi.org/10.1016/j.envdev.2015.09.004 45

Van Dolah, F. M. (2000). Marine algal toxins: Origins, health effects, and their increased

occurrence. Environmental Health Perspectives, 108 Suppl 1, 133-141.

doi:10.1289/ehp.00108s1133

Vargas, S. R., dos Santos, P. V., Bottino, F., & Calijuri, M. d. C. (2020). Effect of nutrient

concentration on growth and saxitoxin production of raphidiopsis raciborskii (cyanophyta)

interacting with monoraphidium contortum (chlorophyceae). Journal of Applied

Phycology, 32(1), 421-430. doi:10.1007/s10811-019-01972-w

Velzeboer, R. M. A., Baker, P. D., Rositano, J., Heresztyn, T., Codd, G. A., & Raggett, S. L.

(2000). Geographical patterns of occurrence and composition of saxitoxins in the

cyanobacterial genus anabaena (nostocales, cyanophyta) in australia. Phycologia, 39(5),

395-407. doi:10.2216/i0031-8884-39-5-395.1

Wacklin P, Hoffmann, L, & Komárek, J. (2009). Nomenclatural validation of the genetically

revised cyanobacterial genus Dolichospermum (Ralfs ex Bornet et Flahault) comb.

novaFottea, 9 (2009), pp. 59-64

Watson, S. B., Miller, C., Arhonditsis, G., Boyer, G. L., Carmichael, W., Charlton, M. N., . . .

Wilhelm, S. W. (2016). The re-eutrophication of lake erie: Harmful algal blooms and

hypoxia doi:https://doi.org/10.1016/j.hal.2016.04.010

Westrick, J. A., Szlag, D. C., Southwell, B. J., & Sinclair, J. (2010). A review of cyanobacteria

and cyanotoxins removal/inactivation in drinking water treatment. Analytical and

Bioanalytical Chemistry, 397(5), 1705-1714. doi:10.1007/s00216-010-3709-5

Whitton, B.A., Potts, M. (Eds.), 2000. The Ecology of Cyanobacteria. Kluwer, Dordrecht, The

Netherlands. 669. 46

Wiegand, C., & Pflugmacher, S. (2005). Ecotoxicological effects of selected cyanobacterial

secondary metabolites a short review doi:https://doi.org/10.1016/j.taap.2004.11.002

Wiese, M., D’Agostino, P. M., Mihali, T. K., Moffitt, M. C., & Neilan, B. A. (2010). Neurotoxic

Alkaloids: Saxitoxin and Its Analogs. Marine Drugs, 8(7), 2185–2211.

doi:10.3390/md8072185

Wood, S. A., Kuhajek, J. M., de Winton, M., & Phillips, N. R. (2012). Species composition and

cyanotoxin production in periphyton mats from three lakes of varying trophic status. FEMS

Microbiology Ecology, 79(2), 312-326. doi:10.1111/j.1574-6941.2011.01217.x

Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., & Dyble, J. (2010). Characterizing a

cyanobacterial bloom in western lake erie using satellite imagery and meteorological

data. Limnology and Oceanography, 55(5), 2025-2036. doi:10.4319/lo.2010.55.5.2025

Yin, Q., Carmichael, W. W., & Evans, W. R. (1997). Factors influencing growth and toxin

production by cultures of the freshwater cyanobacterium lyngbya wollei farlow ex

gomont. Journal of Applied Phycology, 9(1), 55. doi:10.1023/A:1007959002191

Yunes, J.S., De La Rocha, S., Giroldo, D., Silveira, S.B.d., Comin, R., Bicho, M.d.S., Melcher,

S.S., Sant’anna, C.L. and Vieira, A.A.H. (2009), RELEASE OF CARBOHYDRATES

AND PROTEINS BY A SUBTROPICAL STRAIN OF RAPHIDIOPSIS

BROOKII (CYANOBACTERIA) ABLE TO PRODUCE SAXITOXIN AT THREE

NITRATE CONCENTRATIONS1. Journal of Phycology, 45: 585-591.

doi:10.1111/j.1529-8817.2009.00673.x

Zimmer, R. K. and Ferrer R.P. (2007). Neuroecology, chemical defense, and the keystone species

concept. Biol. Bull. 213:208–225. 47

APPENDIX A. FIGURES

Figure 1: 2016 sxtA and SXT Ohio EPA Detections.

Figure from Heather Raymond previously of Ohio EPA

Figure 2: Maumee River Sampling Sites.

Figure from Justin Chaffin. 48

Figure 3: Lake Erie Sampling Sites. Planktonic sampling sites, GIW and WB-83, in red and NDS experiment location in blue. 49

Figure 4: Maumee River Dissolved Nutrient Concentrations.

Concentrations (μmol/L) for (A-B) combined nitrate and nitrite (NOx), (C-D) ammonium + (NH4 ), and (E-F) dissolved reactive phosphorus (DRP) during 2018 and 2019. 50

Figure 5: Maumee River Total Nutrient Concentrations. Concentrations (molar) for (A-B) total Kjeldahl nitrogen (TKN) and (C-D) TN:TP (Total

Nitrogen: Total Phosphorus) during 2018 and 2019.

51

Figure 6: Lake Erie Dissolved Nutrient Concentrations.

Concentrations (μmol/L) for (A-B) combined nitrate and nitrite (NOx), (C-D) ammonium

+ (NH4 ), and (E-F) dissolved reactive phosphorus (DRP) during 2018 and 2019.

52

Figure 7: Lake Erie Total Nutrient Concentrations.

Concentrations (molar) for (A-B) Total Kjeldahl Nitrogen (TKN) and (C-D) TN:TP (Total

Nitrogen: Total Phosphorus) during 2018 and 2019.

53

Figure 8: Maumee River Fluorometric Total Algae Concentrations. Chlorophyll concentrations (μg/L) during (A) 2018 and (B) 2019. Total chlorophyll is a combination of cyanobacterial, green algae, and diatom concentrations. Missing 2019 data was due to no fluororobe readings taken on those sampling dates. 54

Figure 9: Maumee River Fluorometric Algae Community Concentrations. Chlorophyll concentrations (μg/L) for (A-B) cyanobacterial, (C-D) green algae, and (E-F)

diatoms for 2018 and 2019. Missing 2019 data was due to no fluororobe readings taken on those

sampling dates. 55

Figure 10: Lake Erie Fluorometric Total Algae Concentrations. Concentrations (μg/L) during (A) 2018 and (B) 2019. Total chlorophyll is a combination of cyanobacterial, green algae, and diatom concentrations.

56

Figure 11: Lake Erie Fluorometric Algae Community Concentrations. Concentrations (μg/L) for (A-B) cyanobacterial, (C-D) green algae, and (E-F) diatoms for 2018 and 2019.

57

1000 20000 900 18000 800 16000 700 14000 600 12000 genes/mL 500 10000 genes/mL 400 8000

sxtA 300 6000 200 4000 100 2000 sxtAbolded 0 0

Antwerp The Bend Independence Napoleon Mary Jane Thurst Farnsworth

Figure 12: 2018 Maumee River sxtA Gene Detections.

2018 Maumee River sxtA gene detection concentrations (genes/mL) across all sites. The two bolded bar graphs are for the right y-axis.

58

350 300 250 200

genes/mL 150

sxtA 100 50 0

Antwerp The Bend Independence Napolean Mary Jane Farnsworth

Figure 13: 2018 Maumee River sxtA Gene Detections.

2019 Maumee River sxtA gene detection concentrations (genes/mL) across all sites.

2000 100 1800 90 1600 80 1400 70 1200 60 genes/ml

1000 50 copies/mL 800 40 sxtA 600 30

2018 400 20 2019 sxtA 200 10 0 0 Antwerp The Bend Independence Napoleon Mary Jane Farnsworth 2018 2019 Thurstin

Figure 14: Maumee River 2018 and 2019 sxtA Site Averages. Maumee River site average sxtA detection concentrations (genes/mL) for 2018 (left axis) and

2019 (right axis). 59

2000 120 1800 1600 100 1400 80 1200 genes/mL genes/mL 1000 60 800 600 40 2019 sxtA 2018 sxtA 400 20 200 0 0 May June July August September October 2018 2019

Figure 15: Maumee River 2018 and 2019 sxtA Monthly Averages. Maumee River monthly average sxtA detection concentrations (genes/mL) for 2018 (left axis) and 2019 (right axis) of combines sites

60

0.035 0.030 0.025 0.020 0.015 0.010 Saxitoxin (µg/L) 0.005 0.000

Antwerp The Bend Independence Napoleon Mary Jane Thurtstin Farnsworth Figure 16: 2018 Maumee River ELISA Saxitoxin Detections. 2018 Maumee River saxitoxin detection concentration (µg/L). Solid red line denotes the 0.015

µg/L method detection limit for ELISA and dotted red line denotes the 0.02 µg/L Ohio EPA reporting limit for saxitoxin.

0.040 0.035 0.030 0.025 0.020 0.015 0.010

Saxitoxin (µg/L) 0.005 0.000

Antwerp The Bend Independence Figure 17: 2019 Maumee River ELISA Saxitoxin Detections. Solid red line denotes the 0.15 µg/L method detection limit for ELISA and dotted red line

denotes the 0.02 µg/L Ohio EPA reporting limit for saxitoxin. 61

Maumee River sxtA Concetration vs Saxitoxin Concentration

0.040 y = 1E-06x + 0.0111 0.035 R² = 0.1113 0.030 0.025 0.020 0.015

Saxitoxin (µg/L) 0.010 0.005 0.000 0 5000 10000 15000 20000 sxtA genes/mL

Figure 18: Maumee River sxtA Concentration vs Saxitoxin Concentration. 2018 and 2018 Maumee River sxtA gene concentration (genes/mL) vs saxitoxin concentration

(µg/L) (p=3.2x10-5). Solid red line denotes the 0.15 µg/L method detection limit for ELISA and dotted red line denotes the 0.02 µg/L Ohio EPA reporting limit for saxitoxin. 62

4500 4000 3500 3000 2500

(genes/mL) 2000 1500 sxtA 1000 500 0

GIW WB-83

Figure 19: 2018 Lake Erie sxtA Gene Detections 2018 Lake Erie sxtA gene detection concentrations (genes/L) across both sites.

4000 3500 3000 2500 2000 (genes/mL) 1500 sxtA 1000 500 0

GIW WB-83

Figure 20: 2019 Lake Erie sxtA Gene Detections. 2019 Lake Erie sxtA gene detection concentrations (genes/L) across both sites. 63

2000 1800 1600 1400 1200 1000 (genes/mL) 800

sxtA 600 400 200 0 2018 2019 GIW WB-83

Figure 21: Lake Erie 2018 and 2019 sxtA Site Averages. 2018 and 2019 Lake Erie seasonal site average sxtA gene detection concentrations (genes/mL).

2500

2000

1500

(genes/mL) 1000 sxtA 500

0 June July August September October 2018 2019

Figure 22: Lake Erie 2018 and 2019 sxtA Monthly Averages. 2018 and 2019 Lake Erie seasonal monthly average sxtA gene detection concentrations (genes/mL). 64

Figure 23: Lake Erie Nutrient Diffusion Substrate Cyanobacterial Concentrations. Lake Erie NDS average fluorometric cyanobacterial concentrations (RFUs) for (A) July, (B)

Late June-Early July, (C-D) August, (E) September, and (F) October from either 2018 or 2019 at high light (0.5m) and low light (2m).

65

Figure 24: Lake Erie Nutrient Diffusion Substrate Saxitoxin Concentrations Lake Erie NDS average saxitoxin concentrations (ng/cm2) for (A) July, (B) Late June-Early July,

(C-D) August, (E) September, and (F) October from either 2018 or 2019 at high light (0.5m) and low light (2m). 66

APPENDIX B. TABLES Table 1: Nutrient Treatments for Nutrient Substrata Experiments

Treatment Molar Nutrient Source Control - - Nitrate 0.01 NaNO₃

Ammonium 0.01 NH4Cl

Phosphorus 0.005 K3PO4

Phosphorus + Nitrate 0.005 + 0.01 K3PO4 + NaNO₃

Phosphorus + Ammonium 0.005 + 0.01 K3PO4 + NH4Cl

Table 2: Saxitoxin Concentrations of the Maumee River in 2018 and 2019. The Mary Jane Antwerp Bend Independence Napoleon Thurstin Farnsworth 25-Jul-18 - - - - - 0.031 16-May-19 0.021 - 0.020 - 0.035 0.020 6-Jun-19 - 0.024 0.020 0.024 - - 19-Jul-19 - - 0.020 - - - 23-Oct-19 - - - - - 0.22 Maumee River 2018 and 2019 saxitoxin concentrations in (μg/L) via ELISA. All detections listed above are detections that met the Ohio EPA reporting limit for saxitoxin of 0.02 μg/L.

67

Table 3: Maumee River 2018 and 2019 Correlations with sxtA Gene Quantifications.

Pearson's Correlation Variable p-value Coefficient (r) Cyanobacteria chl a Concentration 0.24 0.004 (fluoroprobe) Total chl a Concentration (fluoroprobe) 0.04 0.66 Ammonium -0.06 0.48 Nitrate + Nitrite -0.12 0.14 Urea 0.20 0.20 Dissolve Reactive Phosphorus (DRP) 0.13 0.12 Total Phosphorus (TP) -0.05 0.56 Total Kjeldahl Nitrogen (TKN) 0.36 9.3 x 10-6 Total Nitrogen (TN) -0.44 2.9 x 10-8 Total Nitrogen (TN):Total Phosphorus (TP) -0.21 0.01 Temperature 0.15 0.06 Secchi 0.22 0.08 Significant (<0.05) p-values are in bold.

Table 4: Lake Erie 2018 and 2019 Correlations with sxtA Gene Quantifications.

Pearson's Correlation Variable p-value Coefficient (r) Cyanobacteria chl a Concentration (fluoroprobe) 0.09 0.50 Total chl a Concentration (fluoroprobe) 0.16 0.20 Ammonium -0.09 0.003 Nitrate + Nitrite 0.16 0.20 Urea - - Dissolve Reactive Phosphorus (DPR) 0.06 0.61 Total Phosphorus (TP) -0.07 0.67 Total Kjeldahl Nitrogen (TKN) -0.15 0.22 Total Nitrogen (TN) 0.06 0.62 Total Nitrogen (TN):Total Phosphorus (TP) 0.10 0.44 Temperature 0.28 0.03 Secchi 0.06 0.45 Significant (<0.05) p-values are in bold.

68

Table 5: Nutrient Diffusing Substrate Experiment Effects on Cyanobacterial Concentration. Sampling Nutrient Date Light Treatment Treatment Jun-18 HL'' - + + Aug-18 HL'" NH4 , P+NH4 ' Sep-18 HL' -

+ Jun-19 HL"' P+NH4 ' Aug-19 HL" - Oct-19 HL"' - ’p<0.05, ’’p<0.01, ”’p<0.001 NDS experimental treatment results with light treatment, high light (HL), low light (LL), and nutrient interactions on STX. Only HL is shown for the light treatments, do to it being the potential driver.

Table 6: Nutrient Diffusing Substrate Experiment Effects on Saxitoxin Concentration. Sampling Nutrient Date Light Treatment Treatment Jun-18 LL'' - Aug-18 LL' - Sep-18 LL' -

Jun-19 LL'' - Aug-19 - - Oct-19 LL'' - ’p<0.05, ’’p<0.01, ”’p<0.001 NDS experimental treatment results with light treatment, high light (HL), low light (LL), and nutrient interactions on fluorometric cyanobacterial growth. Only LL is shown for the light treatments, do to it being the potential driver.

69

Table 7: Nutrient Diffusing Substrate Experiment Saxitoxin Concentrations by Month and Nutrient Treatment.

2018 2019 Treatment Month Treatment Month High Light 0.72 0.0005 0.57 0.18 Low Light 0.46 9.2x10-7 0.86 0.0013 Significant (<0.05) p-values are in bold NDS saxitoxin concentrations based on nutrient treatment and month, broken down by light treatment and year.