Describing the Algal Community Dynamics of Six Minnesota Lakes by Examining Cyanobacterial Dominance and Toxicity

The Land of Blue Green Waters? Describing the Algal Community Dynamics of Six Minnesota Lakes by Examining Cyanobacterial Dominance and Toxicity

A Thesis SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY

Matthew Bambach

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

Dr. Andrew Bramburger

February 2020

Acknowledgements

This project was made possible by the funding, guidance, and love of too many people to recount in a reasonable way. The following is a distilled version of a list that provided me with one of the most challenging, stressful, and beautiful times of my life. Thank you to Minnesota Sea Grant for providing the financial assistance to conduct this research. Thank you Andy, for this tremendous opportunity and for your wisdom, guidance, patience, and friendship. Thank you to the rest of my examination committee (Euan, Rich, Ted, and Chris) for your expertise, honesty, and encouragement. Thank you UMD/WRS/NRRI staff and colleagues for inspiring me to be a better scientist, and for giving me so many different opportunities to broaden my horizons both professionally and personally. Finally, thank you to my project partners, happy-hour buddies, roof providers, distraction accomplices, and loving supporters… this thesis simply wouldn’t have been completed without you. To end, I’d like to acknowledge that even though the world keeps changing in many different (often troubling) ways, the best method I’ve found to find peace and purpose is to find what matters to you, focus on the task at hand, breathe deeply, and do the best job you can.

i Abstract

Cyanobacteria are a diverse and ancient group of phytoplankton that are a normal component of aquatic primary producer communities. They can become a problem when they reach high cell densities and form blooms capable of producing toxins that can threaten human and wildlife populations. These occurrences are referred to as cyanobacterial harmful algal blooms (cHABs). Increasing global cHAB frequency and potency have been attributed to warming temperatures and nutrient over-enrichment, but drivers of local and regional occurrence remain poorly understood. In this thesis, I examined six inland Minnesota lakes with different physical, chemical, and biological attributes to highlight patterns in cyanobacterial dominance of the phytoplankton community and understand which lake attributes were closely related to cHAB activity, with a focus on the cyanotoxin Microcystin, from June to September in 2016 and 2017. Cyanobacteria were found to dominate most study lakes, and visible blooms were observed at southern, central, and northern latitude lakes. Microcystin-producing taxa were observed in all study lakes. July and August were the months most likely to experience cHABs, and 2017 showed increased cHAB activity associated with elevated algal biovolume across systems. Specific drivers of cHABs differed among study lakes, but aggregated data for all lakes suggests that increased cyanobacterial dominance of the phytoplankton, total kjeldahl nitrogen, and chlorophyll-a pigment were the attributes most closely associated with harmful conditions. Finally, different harmfulness metrics to safeguard the public from cHABs are compared to discuss their respective protectiveness.

ii Table of Contents

List of Tables ------(iv)

List of Figures ------(v)

List of Abbreviations ------(vi)

Introduction------(1)

Methods ------(9)

Results ------(18)

Discussion------(30)

References------(45)

Appendix------(62)

iii List of Tables

1. Study Lake Location, Group, and Physical Attributes ------(10)

2. Observed Cyanobacterial Taxa, Sub-Groups, and Microcystin-Production------(20)

3. Measured Physical, Chemical, and Biological Data for Study Lakes------(26)

4. Significant Simple Linear Regression Modelling Results for Study Lakes ------(28)

5. Simple Linear Regression Modelling Results for Aggregated Study Lake Data - - - (29)

6. Total Microcystin Concentrations and Relevant Harmfulness Metrics------(40-1)

iv List of Figures

1. Map of Minnesota with Sentinel Lakes, Study Lakes, and Eco Regions------(9)

2. Maps of Study Lakes and Sampling Sites ------(11)

3. Study Lake Phytoplankton Community Composition ------(19)

4. Study Lake Cyanobacterial Community Composition------(21)

5. NMDS Ordination Plots Displaying Phytoplankton Community Differences- - - (22-4)

v List of Abbreviations

ANOVA- Analysis of Variance ANOSIM- Analysis of Similarity cHAB- Cyanobacterial Harmful Algal Bloom BC- Bloomer-Coccoid (Cyanobacterial Sub-Group) CD- Cyanobacterial Dominance Chla- Chlorophyll-a Pigment Cond- Specific Conductance DO- Dissolved Oxygen FNS- Filamentous-No Specialized Cells (Cyanobacterial Sub-Group) FS- Filamentous-Specialized Cells (Cyanobacterial Sub-Group) GLNPO- Great Lakes National Program Office MC- Microcystin MCP- Microcystin-Producer MCPD- Microcystin-Producer Dominance MDH- Minnesota Department of Health MN DNR- Minnesota Department of Natural Resources MPCA- Minnesota Pollution Control Agency N- Nitrogen NLA- National Lakes Assessment P- Phosphorus PNC- Pico/Nano-Coccoid (Cyanobacterial Sub-Group) TBV- Total Phytoplankton Biovolume TMC- Total Microcystin Concentration TS- Thermocline Strength SD- Secchi Disk Depth SIMPER- Similarity Percentages Test US EPA- United States Environmental Protection Agency WHO- World Health Organization

vi Introduction

“A lake is a landscape's most beautiful and expressive feature. It is Earth's eye; looking into which the beholder measures the depth of his own nature.” – H.D. Thoreau

The Cyanobacteria As primary producers, algae constitute the base of the food chain for marine and freshwater environments (Andersen 1992, Lamberti & Steinman 1997). Having first evolved over ~3.5 billion years ago (Schopf 2000), the cyanobacteria, or blue-green algae, are a diverse group of phytoplankton that are largely responsible for the development of Earth’s oxygen-enriched atmosphere (Knoll 2003). Though commonly found in freshwater lake phytoplankton communities (e.g. Smith 1983, Downing et al. 2001, Ptacnik et al. 2008), a number of cyanobacterial taxa can become an issue when they reach high densities and form visible blooms (e.g. Chorus & Bartram 1999, Wilson & Carpenter 1999, Codd et al. 2005). Algal blooms may be classified as harmful algal blooms (HABs) when they spur detrimental ecological, public health, and/or socioeconomic impacts (Watson et al. 2015). In freshwaters, most HABs are comprised of cyanobacteria and are often referred to as cyanobacterial harmful algal blooms (cHABs). Despite having been reported in the scientific literature for more than 130 years (Francis, 1878), the frequency and intensity of cHABs, as well as their associated costs, have increased in recent decades (Chorus & Bartram 1999, Carmichael 2001, 2008, Heisler et al. 2008, Paerl 2008, Paul 2008, Paerl & Huisman 2008, Hudnell 2010, Ho et al. 2019). Many of the impacts concomitant with HABs spur from cyanobacterial toxin (cyanotoxin) production, which poses a threat to the use of surface freshwater for drinking, irrigation, fishing, and other recreational uses (Carmichael 2001, Chorus 2001). The three primary classes of cyanotoxins, which are produced both by individual taxa and synergistically with others as a mixed phytoplankton assemblage (Buryskova et al. 2006, Dietrich et al. 2008), are hepatotoxins, neurotoxins, and dermatotoxins (Carmichael 1994), although some cyanobacteria can release numerous other bioactive compounds, such as protease and chitinase inhibitors, bleaching agents, and antibiotics that do not 1 harm humans but are toxic to other organisms (Suikkanen et al. 2004, Rohrlack et al. 2008, Leao et al. 2012). Aside from toxin production, other cHAB impacts include taste and odor impairments to drinking water (Watson et al. 2008), beach closures (Graham et al. 2009), wildlife and domestic animal poisonings (Stewart et al. 2008), and, in some cases, human illness or death (Paerl 2014). These impacts often lead to the loss of tourism revenues and increased costs associated with water treatment and water quality monitoring (Kouzminov et al. 2007, Steffensen 2008, Hamilton et al. 2014). It has been estimated that freshwater cHABs cost over $4 billion annually in the United States alone, though their impacts are felt globally across fresh, brackish, and saltwater systems (Kudela et al. 2015). cHAB Distribution and Drivers Globally, cyanobacteria dominate a wide variety of ecosystems (either by cell counts or overall biomass), from oligotrophic oceans to eutrophic lakes, and from the tropics to the poles (Pickney et al. 1998, Potts & Whitton 2000, Stomp et al. 2007). By investigating 143 lakes across a climate gradient from South America to northern Europe, Kosten et al. (2012) found that cyanobacterial dominance rises steeply with temperature. It is generally accepted that cyanobacterial growth is positively related to elevated water temperature (e.g. Robarts & Zohary 1987, Paerl & Huisman 2008). It follows that cHABs and their impacts have long been documented in the warm tropical regions around the globe (Carmichael et al. 2001, Mehnert et al. 2010). There has been consistent comment on the lack of general baseline information on cHAB frequency and distribution (e.g. Carmichael 2008, Winter et al. 2011, Elliott 2012, Deng et al. 2014, Kudela et al. 2015). There have been recent efforts to address this issue, however. By examining the sediment records of a suite of study lakes, Taranu et al. (2015) determined that cyanobacterial dominance has increased steeply since 1945 in north temperate subarctic lakes. Further, Ho et al. (2019) used over 30 years of satellite imagery to assess distribution and intensity of HABs in 71 large lakes around the world. The authors found an increase in bloom intensity in 68% of lakes, whereas only 8% of lakes showed significant declines in bloom intensity. Although some waterbodies are 2 understood to be more susceptible to cHABs than others due to varying morphometry, hydrology, geography, and the relative size and influence of watersheds (Fee 1978, Wetzel 2001, Huisman et al. 2005, Paerl & Otten 2013), the rise in severity and increasing geographic distribution of these events has occurred across large and small lakes (Kling et al. 2011, Paerl & Paul 2012), as well as more remote (e.g. Winter et al. 2011) and less productive lakes (e.g. Carey et al. 2008, 2012, Callieri et al. 2014). In the continental U.S., Doubek et al. (2015) found that 38% of lakes had detectable cyanotoxin concentrations during the 2007 National Lakes Assessment (NLA), and further identified cyanobacteria capable of producing toxins in all 48 states sampled. Altogether, cyanobacteria were present in 98% and dominant (more than 50% of phytoplankton biovolume) in 76% of samples (Doubek et al. 2015). An additional investigation of the 2007 NLA dataset showed that MC concentrations > 1 ppb (World Health Organization recommended threshold for safe drinking water) were primarily located in the upper Midwest region of the U.S. (Beaver et al. 2014). In more recent work on the 2012 NLA, Marion et al. (2017) also found that the Plains ecoregions had the lowest water quality in the nation, with detectable MCs in 76% of Northern Plains lakes, 79% of Southern Plains lakes, and 66% of Temperate Plains lakes. Despite increases in cHAB frequency and intensity, as well as the likelihood of human contact with cyanotoxins like MC, we still know very little about the environmental or biological stressors that regulate their development, proliferation, and senescence (Downing et al. 2001, Davis et al. 2009, Paerl 2014, Taranu et al. 2015). Diverse human activities on land can influence the hydrology, qualitative and quantitative nutrient loads, sediments, and other pollutants of freshwater systems, which lead to differential responses by cyanobacteria vs. eukaryotic algae (Smith 1983, 1990, Paerl & Otten 2013). In particular, the over-enrichment of surface waters with nitrogen (N) and phosphorus (P) from different urban, agricultural, and industrial land use have led to accelerated rates of primary production, or eutrophication, which favors cyanobacterial growth and dominance (Paerl & Huisman 2009). For example, fertilizer- intensive agriculture (Sharpley et al. 1994, 2001, Vanni et al. 2011, Schindler 2012) and increased impervious ground cover (Luo et al. 2017, Weber et al. 2019) can increase N and P inputs into lakes and help stimulate cyanobacterial proliferation (Hall et al. 1999, 3 Jeppesen et al. 2005, Schindler 2006). In fact, the relative importance of these nutrients (N and P) as drivers of cHABs (via increased phytoplankton primary production, cyanobacterial dominance, or elevated toxicity) has been hotly contested for many decades (e.g. Smith 1983, Lewis & Wurtsbaugh 2008, Schindler et al. 2008, Conley et al. 2009, Smith et al. 2016). NLA-based determinations of low water quality in areas with high levels of agricultural land use (such as the Upper Midwest) echo the general understanding that nutrient over-enrichment promotes cHAB proliferation (Schindler 1977, 1978, Anderson et al. 2002, Heisler et al. 2008, Conly et al. 2009). A shift in the phytoplankton community towards cyanobacterial dominance has been observed in freshwater bodies enriched with nutrients, particularly P (Smith 1986, Paerl & Huisman 2009). However, cHAB impacts continue to increase among lakes with a wide range of P-inputs (Paerl et al. 2001, Downing et al. 2001), including oligotrophic lakes (Salmaso 2000, Callieri et al. 2014). In addition to P, elevated N in freshwater systems has been implicated in the spread of cHABs and their impacts (Paerl & Scott 2010). Specifically, high concentrations of N have been associated with high concentrations of microcystin (a commonly detected cyanotoxin; Taranu et al. 2017). There is further debate over whether nutrient stoichiometry (i.e. N:P) or absolute nutrient concentrations are more potent drivers of cHAB formation and toxicity (Downing et al. 2001, Scott & McCarthy 2010, Paerl et al. 2016). Several studies have shown that the total pool of N and P correlates better with cyanobacterial biomass or toxin production (Downing et al. 2001, Paerl et al. 2001, Elser et al. 2007). Others have found that particular thresholds in N:P dictate phytoplankton communities dominated by either eukaryotes (N:P > 20) or cyanobacteria (N:P < 15) (Smith et al. 1999, Havens et al. 2003, Harris et al. 2014, Gobler et al. 2016). While much attention has been paid to whether N:P or total inorganic nutrient concentrations promote these events, recent research has demonstrated that organic N and P may also be important nutrient sources for cyanobacteria (O’Neil et al. 2012), which can utilize various forms of dissolved and particulate organic N and P (Paerl 1988, Glibert & O’Neil 1999, Davis et al. 2010). Therefore, it is accepted that both N and P can influence the severity, composition, and duration of cHABs (Paerl & Huisman 2009). 4 The bulk of research on cyanobacteria suggests that they will continue to thrive under conditions predicted for global climate change (Paul 2008, Paerl & Huisman 2009). Many models have forecast changes in precipitation and runoff patterns, as well as increased air temperature and the associated lengthening of open water periods in aquatic systems (Mortsch & Quinn 1996, Lofgren et al. 2002), all of which are predicted to enhance cyanobacterial dominance of lake phytoplankton communities (Paerl & Huisman 2008). Warmer temperatures tend to favor cyanobacteria over eukaryotic algae (Weyhenmeyer 2001, Elliot 2010). Cyanobacteria generally exhibit optimal growth rates in waters warmer than 25˚C, where they tend to reproduce more quickly than diatoms, green algae, cryptophytes and dinoflagellates (Coles & Jones 2000, Jöhnk et al. 2008). In addition, higher temperatures help to intensify vertical stratification of the water column in lakes, changing the manner in which elements crucial to phytoplankton growth (light and nutrients) are distributed (Reavie et al. 2014, Bramburger & Reavie 2016). Many bloom-forming cyanobacterial taxa are able to exploit stratified conditions (Paerl & Huisman 2009). These particular taxa can accumulate in dense surface scums, particularly in calm conditions, where their photoprotective accessory pigments protect from high levels of irradiance (Huisman et al. 2004, Jöhnk et al. 2008). Regional warming and changes in seasonality can also extend the period of stratification, as many temperate lakes now stratify earlier in spring and mix later in autumn (Peeters et al. 2007). Further, stratification-driven anoxia can trigger the release of ‘legacy-P’ from the sediments and constitutes a new potential nutrient source into lakes (Welch & Cooke 1995, Scavia et al. 2014). Finally, the decrease in water density associated with increased temperature drives some of cyanobacteria’s heavier, non-motile competitors (e.g. diatoms) to sink down below the photic zone (Wagner & Adrian 2009). A lakes’ physical attributes are important to consider as well. Basin morphology can influence the interactions between climate change impacts and watershed land use on cHAB development and toxicity (Donkulil & Teubner 2000, Burford et al. 2007). Mean depth, surface area, and volume strongly affect lake stratification (Butcher et al. 2015, Kraemer et al. 2015), which impacts the growth of prominent bloom-forming cyanobacteria (Paerl & Paul 2012). Further, morphometry alters the physical processes 5 of wind mixing, water circulation, and heat storage which strongly impact the distribution of favorable growing conditions in a lake (Adrian et al. 2009). Moreover, lake origin has also been shown to influence cyanobacterial ecology, as lakes and reservoirs have many known differences, such as hydrological variability and habitat connectivity (Read et al. 2015). Beaulieu et al. (2013) suggested that these differences play an important role in determining cyanobaterial dominance and cyanotoxin concentrations in U.S. surface waters.

Toxicity, Harmfulness and Research Goals Several different thresholds exist to classify cHAB harmfulness in a way that water managers and policy makers can understand and use. As the focus of much of the work on NLA datasets, microcystins (MC’s) are a very common type of hepatotoxin and serve as the basis for many of the drinking and recreational water guidelines (or harmfulness metrics) across the world (Chorus & Bartram 1999). Relevant harmfulness metrics to this thesis include that of the World Health Organization (WHO 2003), the United States Environmental Protection Agency (US EPA 2015b, 2016), and the Minnesota Department of Health (MDH 2012), all of which focus on or include total MC concentrations (TMC). Understanding the factors contributing to increased cyanotoxin concentrations is quite difficult, however. Among cyanobacterial taxa known to produce toxins, both toxic and non-toxic genotypes can exist (Wood et al. 2011). In addition, the number of known toxin-producers is likely underestimated (Blaha et al. 2009, Quiblier et al. 2013) and the mechanisms behind toxin production among known species remain poorly understood (Pinheiro et al. 2013). Furthermore, the amount of toxin produced by toxic strains during a bloom has been shown to vary due to factors like temperature (Dziallas & Grossart 2011, Kleinteich et al. 2012) and nutrients (Harke & Gobler 2013, Horst et al. 2014). Large-scale studies (e.g. Doubek et al. 2015, Marion et al. 2017, Taranu et al. 2017, Beaver et al. 2018) have highlighted regional trends in phytoplankton production, cyanobacterial community composition, and cyanotoxin concentrations. However, the relative influences of climatic and water quality attributes on cyanobacterial dominance and toxicity across spatial and temporal scales remain poorly understood (Anderson et al. 6 2012, Taranu et al. 2012, Paerl & Huisman 2009, Paerl 2014). There is consensus that cHABs are complex events, typically not caused by a single environmental driver but rather multiple factors occurring simultaneously (Heisler et al. 2008). Therefore, it is now critical to highlight regional cHAB patterns across differing lake types and levels of anthropogenic stress (Brooks et al. 2016) and to develop understanding of local-scale drivers of cyanobacterial proliferation (Taranu et al. 2017). Similarly, because the composition and density of toxin-producing cyanobacterial taxa is believed to impact the MC-concentration of waterbodies (Janse et al. 2005, Gobler et al. 2007), enhanced collective knowledge of what drives the production and dominance of cyanobacterial MC-producers (MCP’s) is vital to addressing current and future water resource management needs (Hisbergues et al. 2003, Ye et al. 2009). Although it is improbable that a single mechanism controlling the development or severity of cHABs exists (e.g. specific N:P, water temperature, community structure, or other chemical, physical or biological condition), it is important to understand cyanobacterial behavior across spatial and temporal gradients in order to meet current and future management goals. Furthermore, despite historic documentation of animal deaths due to cHABs in Minnesota (e.g. Buell 1938, Olson 1949, 1960), a recent pulse of dog deaths and human health impacts has spurred amplified interest in cyanobacterial production and toxicity (Lindon & Heiskary 2009). With over 11,000 lakes spanning gradients of latitude, land use, and trophic status, the state of Minnesota (MN) presents an ideal system in which to study phytoplankton and cyanobacterial community dynamics. Most MN lakes represent four distinct eco regions (from north to south); Canadian Shield, Northern Lakes and Forests, North Central Hardwood Forests, and Western Corn Belt Plains. Descending from the Canadian border towards Iowa, the characteristics of these eco regions change; generally, percent forest cover within lake watersheds decreases, while pasture, agricultural, and developed land use increase. The Minnesota Sentinel Lakes (Fig. 1) are 24 lakes that are considered representative of the broader diversity of lake types within the state, and present an opportunity to quantify and compare water quality (and therefore the distribution, frequency, and toxicity of cyanobacterial growth) within and among each region throughout the state. For this

7 reason, focal lakes included in this study represent a subset of the Minnesota Sentinel Lakes.

Two primary research objectives were developed to address the aforementioned research needs over the course of this thesis: 1: Quantify and compare phytoplankton community composition within and among study lakes across two summer growing seasons, with a special emphasis on the density and structure of cyanobacterial taxa.

2: Understand which physical, chemical, and biological lake attributes were closely related to cHAB conditions within and among study lakes in order to compare these factors throughout the varied landscapes of MN.

In the first section of this thesis, I test the following hypotheses in order to address my first research objective:

H1: The phytoplankton communities of selected lakes will vary in composition and density both spatially (lakes, latitudes, and mixing types) and temporally (months and years).

H2: Taxa responsible for driving differences in phytoplankton community composition across spatial and temporal scales will be uncommon or occur sporadically.

H3: Total phytoplankton biovolume, cyanobacterial dominance, and MC-producing cyanobacterial dominance (MCPD) will differ between lakes, latitudes, mixing types, months, and years.

In the second section of this thesis, I test the following hypotheses in order to address my second research objective:

H1: Significant linear relationships exist between measured physical, chemical, and biological lake attributes and MCPD.

H2: Significant linear relationships exist between lake attributes and MCPD for multiple lakes with differing characteristics grouped together.

8 Methods

General Sampling Design A network of six lakes representative of the four primary MN eco regions was sampled (Fig. 1). Two lakes from northern MN, two lakes from central MN, and two from southern MN were included. Further, within these three latitudinal tiers, one ‘deep’ (generally dimictic) and one ‘shallow’ (generally polymictic) lake were chosen in an attempt to highlight any impacts basin morphometry may have on phytoplankton dynamics (Table 1).

Figure 1 Minnesota sentinel lakes and selected study lakes (boxed in red). State boundary, prominent Eco Regions (Canadian Shield, Northern Lakes and Forests, North Central Hardwood Forests, and Western Corn Belt Plains), and sentinel lakes shown. Tier One lakes are monitored intensely by project partners, while Tier Two lakes have reduced regular monitoring (MNDNR). 9 Table 1 Study lake location, grouping, and physical information. Minnesota eco region, mixing group, 3 2 latitude group, lake type, max depth (Zmax, m), mean depth (Zmean, m), volume (m ), surface area (km ), watershed area (km2), and watershed (agricultural + urban ) land use (WAU, %) shown (MNDNR).

White Iron Tait Ten Mile Hill Carrie Peltier North Northern Northern Western Minnesota Canadian Canadian Central Lakes & Lakes & Corn Belt Eco Region Shield Shield Hardwood Forests Forests Plains Forest Mixing Group Dimictic Polymictic Dimictic Polymictic Dimictic Polymictic Latitude Group North North Central Central South South Lake Type Reservoir Drained Spring Drainage Drained Reservoir

Zmax 14.3 4.5 63.4 14.6 7.9 5.5

Zmean 4.9 2.3 16.2 6.6 3.2 2.1 Volume 7.5E+07 3.4E+06 3.2E+08 2.2E+07 1.1E+06 4.6E+06 Surface Area 13.9 1.4 20.4 3.3 0.4 2.0 Watershed Area 2398.3 11.0 100.5 100.0 16.4 276.5 WAU 1.9 1.2 3.3 3.9 69.6 33.4

Multiple sampling sites were established on each study lake, but due to size differences among lakes, it was impossible to maintain constant site density. An exponential decay curve was fitted with lake surface area as a decay constant in order to calculate site densities across lakes. Sampling sites were chosen in an effort to encompass both open water and near shore water column characteristics. This resulted in 4 sampling sites on Carrie Lake (to encompass shoreline and pelagic areas), 6 on Tait Lake, 7 on Peltier Lake, 9 on Hill Lake, 18 on White Iron Lake, and 28 on Ten Mile Lake. After determining the variability in parameters of interest from initial sampling rounds (using NMDS and ANOSIM), functionally redundant sites were eliminated on White Iron (retained 9), Ten Mile (retained 14), and Hill (retained 6) Lakes for nutrients, Chla, and phytoplankton analysis (Fig. 2).

10 Figure 2 Sample sites on each Study Lake. Bathymetry contour lines not scaled to specific depth. Littoral area approximations based on aquatic plant presence/absence (data: MN DNR & MPCA, 2019). Scale bar set to 2 km (White Iron Lake), 1km (Tait Lake), 2 km (Ten Mile Lake), 1 km (Hill Lake), 0.2 km (Carrie Lake), and 0.5 km (Peltier Lake). Shapefiles courtesy of MNDNR. 11 Sample Collection Study lakes were sampled monthly by small boat, with visits in June, July, August, and September of 2016 and 2017. Secchi disk depth (SD), as well as surface temperature (Temp), dissolved oxygen (DO), specific conductance (Cond), and pH were measured in situ. Water quality parameters were measured using a HydroLab MS-5 sonde at 1m intervals extending from the surface down to a depth of 10m (or bottom). Water for phytoplankton, nutrient, and chlorophyll-a (Chla) analysis were collected simultaneously using a Van Dorn sampler. Integrated water samples were collected by taking equal volumes of water from the surface, middle, and bottom of the sample site water column and mixing them (for example, at a site 5m deep, samples taken at the surface, 2m, and 4m). Water samples were transferred into 1L cubitainers and stored in the shade until delivery to the lab, where they were stored at 4˚C until analysis.

Environmental Data

Lake depth (Zmax and Zmean), surface area, lake volume, and watershed area, in addition to watershed land use data from the 2016 National Land Cover Database, were communicated by the MNDNR and MPCA (Martin pers. comm., Engel pers. comm.). Additional parameters (total phosphorus [TP], total nitrogen [TN], nitrate + nitrite

[NO3], ammonium [NH4], and chlorophyll-a [Chla]) were measured according to methods described in detail by Ruzycki et al. (2015). Total Kjeldahl Nitrogen (TKN), or the sum of NH4 and organic-N, was calculated by subtracting NO3 from TN. The ratio of TN to TP (N:P) was presented as the molar ratio by atoms. The strength of stratification depends on the density differences between warmer surface water and colder water beneath (Paerl et al. 2016). To calculate Thermocline Strength (TS) data, temperature profile data at each sampling site was analyzed, and the greatest rate of change of temperature over 1m of descent (ΔT·Δz-1) was called the thermocline strength. In order to determine the presence of a thermocline, I used the threshold of 1˚C·m-1 as defined by Fee et al. (1996) for Canadian Shield Lakes. In study lakes where only certain sites were deep enough to stratify, the mean ΔT·Δz-1 from those

12 sites was listed as the thermocline strength for that lake during that sampling round. In cases where no site had a mean ΔT·Δz-1 > 1˚C·m-1, the mean from all sites was used.

Phytoplankton Taxonomy and Enumeration Subsamples for phytoplankton analysis were preserved with Lugol’s iodine solution (Lund et al. 1958) and returned to lab for analysis. Between 1 and 15mL of sample water were pipetted into Utermöhl (1958) counting chambers for inverted light microscope analysis at 400x. Samples were enumerated along random transects until a minimum of 250 natural units were observed. A natural unit is defined as any single cell of a taxon characterized by a unicellular life form or a single colony of a colonial taxon (Alverson et al. 2003). All main classes of phytoplankton were identified to the lowest taxonomic level possible, typically genus or species, using taxonomy keys from Komárek & Anagnostidis (1999, 2005), Komárek (2013), Komárek & Johansen (2015) and Prescott (1962). Up to five individuals of each taxon per sample were measured (length, width, est. depth, [est.] number of cells if not unicellular) in order to determine taxon-specific NU sizes and biovolume. These counting methods follow the standard Great Lakes National Program Office (GLNPO) phytoplankton enumeration techniques and QAQC procedures outlined by the United States Environmental Protection Agency (US EPA 2010). Average taxon dimensions (from up to 30 individuals per taxon) were established by-lake from recorded phytoplankton measurements and used to calculate biovolume based on formulas established by Hillebrand et al. (1999).

Cyanobacterial Sub-Groups By breaking down the cyanobacterial community based upon similarities (form and function) among taxa, I hoped to gain resolution in the detection of shifting community dynamics. The pico-/nano-coccoid (PNC) group was composed predominantly of small (typical cell diameter < 5μm), colonial cyanobacteria from the orders Chroococcales and Synechococcales. Aphanocapsa delicatissima W. & G.S. West is a good example, and was found in all study lakes. The bloom-forming coccoid (BC) sub-group is composed of Chroococcales taxa that typically contribute to cHABs, and are all larger than PNC taxa. One of the most heavily studied and commonly occurring BC taxa is Microcystis 13 aeruginosa, (Kützing) Kützing a MC-producer and primary component of the August, 2014 cHABs in Lake Erie that forced the temporary interruption of Toledo, Ohio’s domestic water supply (Steffen et al. 2017). Similar to the approach used by Filstrup et al. (2016), cyanobacteria capable of forming heterocysts to fix atmospheric-N, such as Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault, were compiled in the filamentous-specialized cells (FS) sub-group. FS taxa, also from order Chroococcales, are similarly common contributors to cHABs. The filamentous-non specialized cells group (FNS) are from the order Oscillatoriales. Limnoraphis birgei (G.M. Smith) Komárek, formerly known as Lyngbya birgei, is a good example. It is large and while sometimes occurring in surface blooms, is primarily found lower in the water column (Komárek 2013). Finally, the Others sub-group is composed of two large filamentous taxa from order Synechococcales, The first, Wolskyella spp. Claus, has been previously observed in Australia and Florida (Komárek & Johansen 2015). Jaaginema minimum (Gicklhorn) Anagnostidis & Komárek was the other taxon included.

Toxins At least one subsample for toxin (microcystin, MC) analysis was haphazardly selected from sampling sites on each study lake during each sampling round. Subsample information is located in Table A9. LC/MS/MS analysis for MC concentrations among subsamples followed EPA Method 544 (US EPA 2015c). Total MC concentration (TMC) was determined by adding together the four microcystin congener (MC-YL, -LR, -LA, -RR) concentrations determined from analysis.

Statistical Analyses Both parametric and non-parametric statistical analyses were employed to address the two primary goals of this thesis. In all cases, chlorophyll-a (Chla) data were cleaned with results below detection limit (0.5 ppb) input as 0.25 ppb to allow for potential transformation. Observational biovolume data was used for non-parametric tests. Due to the assumptions involved with parametric tests (specifically regression modelling), data were assessed for normality of distribution and potential outliers prior to analyses. Measured physical, chemical, and biological lake attribute data was visualized with 14 boxplots to identify relative normality. Any right-skewed variable distributions were log10 transformed to meet approximate normality (Table A2). Once variable distributions were all approximately normally distributed, mean and standard deviations were calculated for each variable. Because the presence of outliers degrades the effectiveness of parametric methods, (a total of 17) observations outside of three standard deviations from the mean (for each variable, for each study lake) were excluded from the dataset (Table A3). Finally, rows within each dataset containing blank cells were excluded in a list-wise manner (during analysis) in order to maintain a balanced ratio of independent variable to dependent variable observations.

Specific Methods: Section 1 In Section 1 of the thesis, my goal was to characterize and compare the phytoplankton communities of study lakes. I accomplished this by first quantifying and then visualizing differences in phytoplankton and cyanobacterial community composition in each lake across sampling rounds in stacked bar graphs. Next, I highlighted similarities and differences in the phytoplankton communities across sampling rounds, lakes, latitude groups, and mixing groups. After this, I identified which taxa are responsible for similarities and differences across sampling rounds, lakes latitude groups, and mixing groups. Finally, I determined whether significant differences exist across years, months, lakes, latitude groups, and mixing groups for three important algal response variables: total phytoplankton biovolume (TBV), cyanobacterial dominance (CD, %TBV that is cyanobacteria), and MC-producer dominance (MCPD, %TBV that is cyanobacteria known to produce MC-congeners in temperate lakes). Dissimilarities in species composition among sampling rounds, study lakes, latitude groups, and mixing groups were determined using an Analysis of Similarities (ANOSIM) framework with Bray-Curtis similarity coefficient as the distance metric, using the program PRIMER v. 6 (Clarke & Gorley 2006). Afterwards, the Similarities Percentages test (SIMPER) was used to identify which genera made the largest contributions to the compositional dissimilarities among groups (Clarke & Warwick 2001). A non-metric multidimensional scaling (NMDS), coupled with ANOSIM, was used to visualize dissimilarities in phytoplankton assemblage between groups. 15 A one-way analysis of variance (ANOVA) was used to compare differences in key dependent variables (TBV, CD, and MCPD) among study lakes, latitude groups, and mixing groups. As mentioned in the Introduction of the thesis, MCPD was calculated for use as a key indicator of potentially harmful algal community conditions, as elevated MC-concentrations are the primary monitoring tool used to protect the public from the toxic impacts of some cHABs (Chorus & Bartram 1999). To compare dependent variables from different study lakes together across Months and Years, data was z-scored by-lake prior to ANOVA testing. Bartlett’s test was used to assess homogeneity of variance, QQ-plots for normality, and partial-ACF plots to test independence. Tukey’s HSD post-hoc test was performed to compare means among groups, with a 95% Confidence Interval. Prior to ANOVA tests, dependent variables were tested for normality and transformed if appropriate. Data expressed as proportions (CD and MCPD) were logit-transformed prior to analysis (Warton & Hui 2011), and 0.01 was added to MCPD to allow for transformation.

Specific Methods: Section 2 In Section 2 of the thesis, my goals were to understand which lake attributes were associated with potential cyanobacterial harmfulness for each study lake (and among all study lakes), as well as to assess actual toxin concentrations from study lakes. I accomplished this by first determining which measured physical, chemical, and biological parameters (independent variables) were significantly related to changes in MCPD (dependent variable) for each study lake. Next, I used standardized, aggregated study lake data to delineate what physical, chemical, and biological parameters were significantly related to changes in MCPD among all study lakes (a small representative sample for the diversity of MN lakes). Finally, I examined total microcystin concentrations (TMC) from each study lake throughout sampling rounds in the context of relevant safety thresholds for potential cHAB toxicity and/or harmfulness. Following the approach of Doubek et al. (2015), fifteen simple linear regression models were generated for each study lake between each independent variable (including TBV and CD) and MCPD to highlight important predictors of elevated cHAB risk. Prior to analysis, independent and dependent variables were tested for normality by-lake and 16 transformed if appropriate. In cases where variables were left-skewed and needed to be reflected, the value of K (reflection value) was determined by adding 1 to the maximum value for that parameter. Data expressed as proportions (CD and MCPD) were logit- transformed prior to analysis (Warton & Hui, 2011), and 0.01 was added to MCPD to allow for transformation. Correlation matrices (Pearson’s r) were constructed to assess potential collinearity between variables (Figure A1). Strength of regression models 2 2 among independent variables was compared by calculating Adjusted r (r adj), Corrected Akaike Information Criterion (AICc), Significance Value (p), and ANOVA F-Value (F). To determine broad-scale predictors of changing cyanobacterial harmfulness across MN, the same linear regression framework was used. Explanatory and algal data from each study lake were z-scored and then aggregated by sampling round. Once aggregated data from each study lake was compiled together, each variable was tested for normality and transformed if appropriate. A correlation matrix (Pearson’s r) was then built to assess potential collinearity between variables (Figure A1). Finally, fifteen simple linear regression models were generated between independent variables and MCPD.

17 Results

Section 1 Phytoplankton and Cyanobacterial Communities A total of 123 phytoplankton taxa were identified within the samples. Using all samples from both sampling years, Bacillariophyceae accounted for 22% of total phytoplankton biovolume (TBV), Cryptophyceae 3%, Chlorophyceae 2%, Chrysophyceae 8%, Dinophyceae 16%, Cyanophyceae 49%, and ~1% from Others (Haptophytes and Euglenoids). TBV fluctuated between lakes, ranging from ~2 to >100 x106 μm3·mL-1 (Fig. 3). Phytoplankton community composition varied between study lakes and throughout summer months in both 2016 and 2017. Cyanobacteria appeared to dominate from July through September, with Diatoms, Chrysophytes, and Dinoflagellates more noticeable in June. Between years, TBV increased in White Iron Lake, Hill Lake, Carrie Lake and Peltier Lake. While this increase appeared to come from cyanobacterial densities in White Iron Lake, Hill Lake, and Peltier Lake, it is likely that the clear emergence of Dinoflagellates in Carrie Lake in 2017 was responsible for this increase. A total of 43 cyanobacterial taxa were identified (Table 2). Overall, cyanobacterial community composition varied among lakes and sampling rounds (Fig. 4). Across all samples, PNC taxa accounted for 22% of TBV and 43% of cyanobacterial biovolume composition, BC 14% and 32%, FS 8% and 16%, FNS 8% and 4%, and 0% and ~1% from Others, respectfully. The month of June regularly had the greatest contribution from PNC taxa. Further, dimictic lakes (White Iron, Ten Mile, and Carrie) had greater contributions from PNC taxa than in polymictic study lakes (Tait, Hill, and Peltier). Generally, BC taxa were dominant in July, August, and September. BC taxa were also more prolific in polymictic lakes. Despite this, the contribution from BC taxa to total cyanobacterial biovolume increased from 2016 to 2017 in polymictic lakes. M. aeruginosa (BC), a taxon of concern in Minnesota and one of the most prolific producers of MC, was observed in all study lakes. FS taxa were more prominent in White Iron, Hill, and Peltier Lakes. They also were more common from July to September. FNS taxa comprised the biggest share of total cyanobacterial biovolume in Hill Lake, and

18 appeared in all sampling months. Finally, in 2017, there were noticeable spikes in total cyanobacterial BV in White Iron Lake, Hill Lake, and Peltier Lake.

Figure 3 Phytoplankton community class biovolume composition (left y-axis; % of total phytoplankton biovolume) and total biovolume (right y-axis; 106 μm3·mL-1) for study lakes throughout each sampling round. Total biovolume scale for Ten Mile Lake to 5, scale for White Iron Lake, Tait Lake, Hill Lake, and Carrie Lake to 20; scale for Peltier Lake to 120.

19 Table 2 Observed cyanobacteria taxa. Sub-groups based on taxon biology/ecology. Distinction as a common producer (bold) of MC-varieties based on observed toxin production in temperate lake systems.

Taxa Group MC-Producer? Aphanocapsa delicatissima Pico/Nano Coccoid No Aphanocapsa elachista cf. holsatica Pico/Nano Coccoid No Aphanocapsa incerta Pico/Nano Coccoid No Aphanothece minutissima Pico/Nano Coccoid No Merismopedia spp. Pico/Nano Coccoid No Synechocystis spp. Pico/Nano Coccoid No Synechococcus spp. Pico/Nano Coccoid No Rhabdogloea smithii Pico/Nano Coccoid No Rhabdoderma lineare Pico/Nano Coccoid No Chroococcus spp. Pico/Nano Coccoid No Snowella lacustris Bloom-forming Coccoid Yes Snowella litoralis Bloom-forming Coccoid No Microcystis aeruginosa Bloom-forming Coccoid Yes Microcystis wesenbergii Bloom-forming Coccoid Yes Microcystis botrys Bloom-forming Coccoid Yes Microcystis viridis Bloom-forming Coccoid Yes Microcystis flos-aquae Bloom-forming Coccoid Yes Woronichinia naegeliana Bloom-forming Coccoid Yes Gomphosphaeria natans Bloom-forming Coccoid No Cylindrospermopsis spp. Filamentous (Specialized) No Raphidiopsis mediterranea Filamentous (Specialized) No Aphanizomenon flos-aquae Filamentous (Specialized) No Aphanizomenon schindleri Filamentous (Specialized) No Dolichospermum planctonicum Filamentous (Specialized) No Dolichospermum smithii Filamentous (Specialized) No Dolichospermum flos-aquae cf. spiroides Filamentous (Specialized) Yes Dolichospermum circinale cf. sigmoidium Filamentous (Specialized) Yes Dolichospermum crissum Filamentous (Specialized) No Dolichospermum macrosporum Filamentous (Specialized) No Calothrix epiphytica Filamentous (Specialized) No Nostoc kihlmanii Filamentous (Specialized) Yes Limnoraphis birgei Filamentous (Non-Specialized) No Limnoraphis spp. Filamentous (Non-Specialized) No Pseudoanabaena limnetica Filamentous (Non-Specialized) No Pseudoanabaena voronichinii Filamentous (Non-Specialized) No Oscillatoria curviceps Filamentous (Non-Specialized) No Planktothrix agardhii Filamentous (Non-Specialized) Yes Planktothrix suspensa Filamentous (Non-Specialized) Yes Planktothrix isothrix Filamentous (Non-Specialized) No Spirulina laxa Filamentous (Non-Specialized) No Spirulina spp. Filamentous (Non-Specialized) No Wolskyella spp. Other No Jaaginema minimum Other No 20

Figure 4 Cyanobacterial community group biovolume composition (left y-axis; % of total cyanobacterial biovolume) and total cyanobacterial biovolume (right y-axis; 106 μm3·mL-1) for study lakes throughout each sampling round. Total cyanobacterial biovolume scale for Tait Lake, Ten Mile Lake, and Carrie Lake to 5; scale for White Iron Lake and Hill Lake to 15; scale for Peltier Lake to 100.

Taxa-Driven Community Comparisons ANOSIM was used to characterize dissimilarities among algal assemblages sampling rounds, lakes, latitude groups, and mixing groups. NMDS ordination plots were used to visualize dissimilarities between samples (Fig. 5 A-D). Significant dissimilarities between sampling months existed within sampling years (e.g. June 2016 vs. August 2016) and from year to year (e.g. August 2016 vs. August 2017) (Global-R = 0.130, p =

21 0.001; Table A4). Significant dissimilarities between study lakes (Global-R = 0.449, p = 0.001), latitude groups (Global-R = 0.236, p = 0.001) and mixing groups (Global-R = 0.157, p = 0.001) were also detected. No dissimilarities existed among July, August, and September 2016 (Table A2). SIMPER determined which taxa were responsible for driving most of the dissimilarities between groups (Table A3). Dissimilarities were driven by a diverse suite of taxa that included PNC cyanobacterial taxa (A. delicatissima and Aphanocapsa elachista W. & G.S. West), BC cyanobacterial taxa (M. aeruginosa and Woronichinia naegeliana (Unger) Elenkin), diatoms (Aulacoseira spp. Thwaites and Stephanodiscus spp. C.G. Ehrenberg), and dinoflagellates (Ceratium hirundinella (O.F.Müller) Dujardin). Generally, these were taxa that occurred in very high densities (A. delicatissima) or had relatively large taxa-specific biovolume (M. aeruginosa, W. naegeliana, C. hirundinella, Aulacoseira spp.).

(Figure 5 Start)

22 B)

23 D)

Figure 5 NMDS ordinations of phytoplankton assemblages in tested groups (A-D). Symbols represent sample scores. Vectors illustrate the relative direction and magnitude of species' contribution to dissimilarities among samples. Species shown were correlated to among-group differences with a Pearson correlation coefficient ≥ 0.5. Bray–Curtis dissimilarity was used as the distance metric, and distances were calculated based on species’ relative biovolumes. Phytoplankton taxa abbreviations shown stand for the following: Woronichinia naegeliana (Woro.na), Ceratium hirundinella (Cera.hi), Haptophytes (Haptos), Aphanocapsa delicatissima (Aphano.de), Merismopedia spp. (Merism.sp), and Kephyrion spp. (Keph.sp)

Algal Biovolume Comparisons Using the simple analysis of variance (ANOVA) test framework, significant differences in mean TBV were detected between months (F = 5.23, p = 0.002), years (F = 53.49, p < 0.001), sampling lakes (F = 70.21, p < 0.001), and latitude groups (F = 69.10, p < 0.001 ; Table A6). Significant differences in mean CD were detected between months (F = 37.55, p < 0.001), years (F = 4.47, p = 0.035), lakes (F = 9.13, p < 0.001), latitude groups (F = 8.27, p < 0.001), and mixing groups (F = 5.85, p = 0.016). Additionally, significant differences in mean MCPD were detected between months (F = 20.03, p < 0.001), years (F = 17.47, p < 0.001), lakes (F = 15.40, p < 0.001) and latitude groups (F = 19.68, p < 0.001). Further, Tukey’s HSD post-hoc test revealed significant differences between specific months, lakes, and latitude groups (Table A7). In particular, July and August had significantly higher TBV, CD, and MCPD than June and September. Among lakes,

24 Peltier had significantly higher TBV than all others, higher CD than all but White Iron, and higher MCPD than Carrie and Ten Mile. Comparing lakes in northern, central, and southern latitude groups, TBV, CD, and MCPD were all significantly higher in northern (White Iron and Tait) and southern (Carrie and Peltier) lakes than central lakes.

Section 2 Study Lake Conditions 368 samples were collected from the 6 study lakes over 2 years of summer sampling. Physical, chemical, and biological parameters differed among study lakes (Table 3, Table A1). Mean Temp was highest in southern latitude lakes. Carrie Lake had highest mean TN in both years. Peltier Lake had highest mean TP in both years. Mean TP and TKN increased for all lakes except for Hill Lake in 2017. Further, N:P stayed fairly consistent between years in all but Hill Lake, where it nearly doubled. Between years, mean TS stayed constant in White Iron Lake, decreased in 2017 in Tait Lake and Peltier Lake, and increased in 2017 in Ten Mile Lake, Hill Lake, and Carrie Lake. Algal data also varied between study lakes. Peltier Lake had highest mean TBV and CD. Mean TBV also increased in 2017 for all study lakes. CD increased in 2017 for White Iron Lake, Hill Lake, and Peltier Lake and decreased in Tait Lake, Ten Mile Lake, and Carrie Lake. Finally, mean MCPD increased for all but Hill Lake in 2017. Additionally, 70 samples were collected for toxin (MC) analysis (Table A9). 14 samples were taken from White Iron Lake, 8 from Tait Lake, 8 from Ten Mile Lake, 12 from Hill Lake, 14 from Carrie Lake, and 14 from Peltier Lake. Of these, 93% had detectable concentrations of some form of MC. TMC peaked in White Iron Lake at 0.10 μg·L-1, at 0.05 in Tait Lake, at 0.02 in Ten Mile Lake, at 0.38 in Hill Lake, at 0.40 in Carrie Lake, and at 22.48 in Peltier Lake.

25 Table 3 Explanatory and algal parameters by-lake, by-year. Mean ± standard deviation values shown for -1 -1 surface temperature (Temp, ˚C), total phosphorus (TP, μg·L ), total nitrogen (TN, μg·L ), secchi disk -1 depth (SD, m), chlorophyll-a pigment (Chla, μg·L ), total phytoplankton biovolume (TBV, 106 μm3·mL-1), cyanobacterial dominance (CD, % TBV), and MC-producing cyanobacterial dominance (MCPD, % TBV).

Lake - Year Temp TP TN SD Chla TBV CD MCPD

19.9 ± 18.7 ± 541.5 ± 1.5 ± 1.8 ± 5.6 ± 52.5 ± 13.9 ± White Iron 2016 4.0 3.0 26.8 0.1 2.3 3.8 26.3 25.1 20.2 ± 20.8 ± 596.5 ± 1.4 ± 2.0 ± 7.4 ± 56.6 ± 31.3 ± White Iron 2017 1.7 3.3 44.5 0.1 1.4 6.0 29.3 31.0

19.6 ± 10.2 ± 358.9 ± 2.1 ± 0.8 ± 4.2 ± 54.7 ± 12.5 ± Tait 2016 3.9 1.6 19.0 0.1 0.6 4.3 19.6 19.4

19.1 ± 12.8 ± 405.4 ± 1.9 ± 2.3 ± 5.8 ± 38.3 ± 16.6 ± Tait 2017 2.4 2.9 15.3 0.2 0.9 5.8 19.6 14.5 19.9 ± 10.2 ± 320.6 ± 4.7 ± 0.7 ± 1.7 ± 43.5 ± 1.3 ± Ten Mile 2016 4.0 2.0 37.6 0.2 0.7 0.6 15.1 5.5

19.9 ± 13.3 ± 382.9 ± 4.6 ± 1.3 ± 2.8 ± 36.8 ± 4.9 ± Ten Mile 2017 2.7 1.9 52.6 0.4 0.6 1.7 19.5 11.2 20.0 ± 40.8 ± 632.8 ± 2.3 ± 2.9 ± 4.7 ± 43.3 ± 9.5 ± Hill 2016 3.3 23.9 121.4 0.0 3.9 3.4 22.4 11.5

20.8 ± 18.4 ± 566.8 ± 3.0 ± 4.8 ± 11.4 ± 46.6 ± 7.9 ± Hill 2017 1.7 11.7 82.8 0.5 5.1 5.5 27.9 9.0

22.5 ± 17.2 ± 2128.1 ± 1.0 ± 1.1 ± 3.4 ± 46.8 ± 0.8 ± Carrie 2016 3.4 5.1 371.9 0.1 0.5 2.0 22.3 0.7

22.4 ± 18.7 ± 2308.5 ± 1.2 ± 3.8 ± 9.3 ± 26.1 ± 13.6 ± Carrie 2017 2.3 3.1 278.7 0.2 2.0 5.8 20.1 16.8 15.2 22.7 ± 178.4 ± 1523.7 ± 1.2 ± 19.3 ± 58.5 ± 31.3 ± Peltier 2016 ± 4.2 64.9 509.9 0.3 14.0 33.8 34.2 29.2 23.5 ± 211.6 ± 1702.7 ± 0.9 ± 9.8 ± 42.0 ± 64.8 ± 39.5 ± Peltier 2017 1.6 69.0 592.7 0.4 29.9 51.5 28.5 32.7

26 Modelling A total of 90 least squares regression models were developed to assess the strength of relationships between physical, chemical, and biological lake parameters and MCPD to highlight important forces linked to elevated TMC (Table A8). Of that total, 28 models were considered significant (p < 0.05; Table 3). Peltier Lake had the most significant regression models (8), while Hill Lake and Carrie Lake had the fewest (1). For Hill and Carrie, the two regression models that had the next-best fit were marked with an asterisk and included in Table 3. Overall, CD was significantly related to MCPD in 5/6 lakes. TKN was significantly related to MCPD in 4/6 lakes. TP was only significantly related to MCPD in Peltier Lake, whereas Cond was only significant in Ten Mile Lake. Temp was significantly related to MCPD in Northern lakes (White Iron and Tait). SD was important to MCPD in Southern (Carrie and Peltier) lakes, though its relationship was nonsignificant in Carrie Lake. TS was related to MCPD in Polymictic lakes (Tait, nonsignificant in Hill, and Peltier), whereas Chla was significantly related to MCPD in Dimictic lakes (White Iron, Ten Mile, and Carrie). Finally, in order to highlight important predictors of increased MCPD across all six lakes, 15 individual least squares regression models were developed (Table 4). Six of these models has significant p- values, and CD explained the most variation in MCPD, followed by TKN and Chla.

27 Table 4 Important least squares regression model results for each study lake. Model predictor variables with an asterisk have p-value’s greater than 0.05. Each model output was logit (MCPD + 0.01) (logit- transformed percent TBV that is cyanobacteria with the ability to produce MC-varieties + 0.01). Parameter abbreviations are as follows: total phytoplankton biovolume (TBV), CD (proportion of TBV that is cyanobacteria), Chla (Chlorophyll-a), Temp (Water Temperature at Surface), DO (Dissolved Oxygen), NO3 (Nitrate+Nitrite), NH4 (Ammonia+Ammonium), TN (Total Nitrogen), TKN (Total Kjeldahl Nitrogen), TS (Thermocline Strength), Cond (Specific Conductance), SD (Secchi Disk Depth), and TP (Total Phosphorus). An * indicates that the specific parameter’s model had a p-value that wasn’t < 0.05.

Lake Predictor Model df P Adj. R2 AICc White Iron logit (CD) -2.84 + 1.20x 70 <0.001 0.56 275

log10 (TBV) -33.60 + 2.02x 70 <0.001 0.43 294

log10 (Chla) -2.68 + 1.16x 70 <0.001 0.24 314 Temp -7.59 + 0.26x 66 0.007 0.09 310 DO 10.78 - 1.56x 66 0.011 0.08 310

NO3 -1.39 - 0.04x 70 0.011 0.08 328

log10 (TKN) -57.58 + 8.76x 69 0.015 0.07 324

log10 (NH4) 1.42 - 1.38x 70 0.030 0.05 330 Tait TN -10.79 + 0.02x 46 0.018 0.10 193 Temp 1.06 - 0.19x 46 0.020 0.09 193 TKN -10.36 + 0.02x 46 0.025 0.09 193 logit (CD) -2.56 + 0.53x 46 0.027 0.08 193

log10 (TS) -3.29 - 0.37x 46 0.048 0.06 194

Ten Mile log10 (216.5-Cond) -5.04 + 0.51x 109 <0.001 0.14 305

log10 (Chla) -3.84 + 0.40x 109 0.001 0.10 311 TKN -5.92 + 0.01x 108 0.005 0.07 311 TN -5.90 + 0.01x 108 0.006 0.06 311 logit (CD) -3.82 + 0.29x 109 0.007 0.05 316 Hill logit (CD) -2.64 + 0.47x 46 <0.001 0.24 144

*log10 (10.6-DO) -3.30 + 0.86x 45 0.117 0.03 153 *TS -2.34 - 0.14x 46 0.181 0.02 156

Carrie log10 (Chla) -3.68 + 0.65x 30 0.022 0.14 111 *SD -6.25 + 2.94x 22 0.114 0.07 89 *DO 1.43 - 0.53x 26 0.190 0.03 102 Peltier logit (CD) -2.37 + 1.18x 54 <0.001 0.65 212

log10 (TP) -26.11 + 4.72x 53 <0.001 0.40 237

log10 (TBV) -25.76 + 1.45x 54 <0.001 0.30 250

log10 (TS) -2.71 - 0.80x 47 <0.001 0.24 225

log10 (TKN) -31.42 + 4.08x 54 <0.001 0.24 254

log10 (TN) -31.38 + 4.07x 54 <0.001 0.23 255

log10 (SD) -1.74 - 3.06x 40 0.002 0.20 195

log10 (Chla) -1.98 + 0.49x 54 0.008 0.11 264

28 Table 5 Important least squares regression model results for general study lakes’ trends. Model response variable was MCPD (percent of total phytoplankton biovolume that is cyanobacteria with the ability to produce MC-varieties). Predictor variable abbreviations are as follows: Temp (surface temperature), DO (dissolved oxygen), pH, Cond (specific conductivity), TP (total phosphorus), TN (total nitrogen), NH4 (ammonia + ammonium), NO3 (nitrate + nitrite), TKN (total kjeldahl nitrogen), N:P (molar ratio by atoms of total nitrogen to total phosphorus), SD (secchi disk depth), Chla (chlorophyll-a), TS (thermocline strength), TBV (total phytoplankton biovolume), and CD (cyanobacterial dominance, or the percent of TBV that is cyanobacteria).

Predictor Model df P Adj. R2 AICc Temp 1.01E-3 - 0.01x 46 0.893 -0.02 93 DO -9.66E-4 - 0.18x 44 0.089 0.04 87

log10 (3 + pH) -0.05 + 0.05x 44 0.873 -0.02 90 Cond -1.16E-3 - 0.03x 44 0.758 -0.02 90

log10 (3 + TP) -0.63 + 0.59x 46 0.091 0.04 90 TN -1.39E-4 + 0.24x 46 0.035 0.07 88

NH4 2.05E-3 - 0.08x 46 0.493 -0.01 92

log10 (3 + NO3) 0.13 - 0.12x 46 0.714 -0.01 93 TKN -7.79E-4 + 0.35x 46 0.002 0.17 83

log10 (3 + N:P) 0.05 - 0.05x 46 0.896 -0.02 93 SD 0.11 - 0.02x 34 0.881 -0.03 69 Chla 9.84E-4 + 0.37x 46 0.003 0.16 84

log10 (3 + TS) 0.57 - 0.54x 44 0.044 0.07 85

log10 (3 + TBV) -0.97 + 0.90x 46 0.028 0.08 88 CD 9.84E-4 + 0.44x 46 <0.001 0.27 76

29 Discussion

Section 1 Temporal Variation The results in Section 1 indicate that the phytoplankton communities of six distinct lakes in Minnesota vary through time. Although dominant taxa and overall phytoplankton production differed between months and years, there were some clear similarities that emerged as well. Out of the four months that samples were taken (June, July, August, and September), June was the only month where cyanobacteria did not clearly dominate (Fig. 3). For over 30 years it has been accepted that during the cooler spring months, the phytoplankton communities of lakes will heavily consist of diatoms (bacillariophyceae) (Sommer et al. 1986), although understanding of what drives succession between phytoplankton group dominance is still developing (e.g. Sommer & Lengfellner 2008, Sommer et al. 2012). In general, this paradigm of diatom proliferation earlier in the year was supported, although the magnitude varied between sampling years (Fig. 3). Cyanobacteria were most prominent in July and August in all 6 lakes. This is consistent with many other authors who have commented on the dominance of cyanobacteria during the warmest months of the year (e.g. Robarts & Zohary 1987, Paerl & Huisman 2008, Paul 2008), and in this study cHAB indicator response variables (total phytoplankton biovolume [TBV], cyanobacterial dominance [CD], and microcystin- producing cyanobacterial dominance [MCPD]) were all significantly higher in July and August compared to June and September (Table A7). Several studies focusing on inland lakes within Minnesota (e.g. Lindon & Heiskary 2009, Christensen et al. 2019), Upper Midwest (e.g. Beaver et al. 2014, Coffer et al. 2020, Wang et al. 2005), and the Great Lakes Basin (e.g. Boyer 2008, Chaffin et al. 2013, Davis et al. 2014, Millie et al. 2009) have documented cyanobacterial dominance and/or elevated toxicity in these summer months. The forcing agents behind this pattern are interconnected and, though largely attributed to elevated light, temperature, and nutrient concentrations (Paerl & Huisman 2008), are thought to differ depending on regional- and local-scale watershed characteristics (Salls et al. 2007). Specific relationships between physical, chemical, and

30 biological lake attributes and cHAB risk within study lakes were explored in Section 2 of this thesis. General dominance of the phytoplankton community by cyanobacteria was observed across sampling years (2016 and 2017), with a noticeable biovolume increase in 2017. In particular, TBV and MCPD were blatantly higher in 2017, although all three response variables (TBV, CD, and MCPD) were all significantly higher between sampling years (Table A6). Two noteworthy concepts emerged out of these annual differences. First, it became clear that no lake was immune to the risk of cHAB activity. A visible, widely-distributed bloom of Gloeotrichia echinulata (J. E. Smith) P. Richter occurred in oligotrophic Ten Mile Lake in August 2017. Colonies shaped like tiny pom- poms were visible throughout the epilimnion and down deeper in the water column. A nitrogen-fixer, this meroplanktic cyanobacterial taxa forms large (1-3mm) filamentous colonies and has been observed with increasing frequency in low-nutrient systems in the northeastern United States and Canada (Carey et al. 2008, 2009, Winter et al. 2011). When temperature and/or light increases, G. echinulata’s akinates (dormant, resting cells on the water-sediment interface) germinate, assimilate P from pore water, then recruit into the water column via gas vesicles, bringing stored P with them (Carr & Whitton 1982, Pettersson et al. 1993, Tymowski & Duthie 2000). It follows, then, that some of G. echinulata’s fixed N and stored P may become available to other phytoplankton throughout the water column (like M. aeruginosa; Pitois et al. 1997, Fey et al. 2010). Its appearance in Ten Mile agreed with this paradigm, as the lake’s cyanobacterial community in August 2017 had the largest proportion of BC taxa of any sample month. These observations illustrate that even oligotrophic systems can have toxin-producing cyanobacterial taxa present, and that these taxa may bloom when conditions are favorable. Further, potentially toxic cHABs have recently been observed in other important oligotrophic lakes, like Lake Superior, which experienced an ‘unprecedented’ bloom in August 2018 (Kraker 2018). Therefore, one cannot dismiss the possibility of cHABs in historically clean and clear lakes. The second important idea to emerge out of annual production differences was that physical watershed characteristics appeared to impact phytoplankton production. Ten Mile Lake was not the only study lake to experience increased phytoplankton and 31 cyanobacterial proliferation in 2017. For instance, Carrie Lake had a large increase in BC taxa in July, August, and September of 2017. Interestingly, while TBV increased, it appears that the emergence and proliferation of dinoflagellates was likely responsible. Overall, every study lake had increased TBV in 2017. However, cyanobacterial biovolume only increased noticeably in White Iron Lake, Hill Lake, and Peltier Lake, which each have a larger drainage ratio (watershed area/lake surface area) than the other lake within their respective latitudinal groups. A larger ratio can lead to elevated nutrient loading and very long hydraulic residence times in lakes (Lindon & Heiskary 2009, Taranu et al. 2017), both of which are known to impact cHAB intensity (Roelke & Pierce 2011, Lewis et al. 2011). Clearly, further investigation into the impact that hydraulic differences have over lake phytoplankton community dynamics is needed.

Spatial Variation Section 1 results also indicate that the phytoplankton communities of six lakes in Minnesota vary across space. Cyanobacteria are commonly found in lake ecosystems (e.g. Smith 1983, Downing et al. 2001, Ptacnik et al. 2008), and were always present and often dominant in the phytoplankton communities of study lakes. In fact, cyanobacteria made up 49% of TBV in collected samples. However, different cyanobacterial taxa were responsible for shaping the respective cyanobacterial communities of study lakes. Microcystin producers (MCP’s) were identified in each lake, and although different sub groups were more prevalent in each lake, BC taxa were generally the most common. M. aeruginosa occurred in all lakes and contributed strongly to differences in phytoplankton community among lakes and other groups, along with other large-bodied and/or numerous taxa such as W. naegeliana (BC), A. flos-aquae (FS), and L. bigei (FNS). Furthermore, several rare tropical species were observed across study lakes, including the ‘invasive’ C. raciborskii (Woloszynska) Seenayya & Subba Raju (identified in samples from White Iron Lake and Peltier Lake). This species has become more common in the Midwest (Hong et al. 2006) and other temperate ecosystems around the world; a spread attributed to its diverse suite of survival adaptations (Sinha et al. 2012). With the presence of this taxa in northern and southern study lakes in mind, and considering the widespread prominence of M. aeruginosa and other bloom-forming taxa, it is apparent 32 that conditions conducive to harmful phytoplankton communities are not dictated by geography alone. Therefore, efforts to control cHAB activity must not only include mitigating lakes with existing cHABs, but also closely monitoring and having safety measures in place for lakes that don’t experience regular blooms. Many studies have focused on MCP taxa, or the actual distribution of the toxin itself (e.g. Marion et al. 2017), as their potential impacts garner much public interest. However, many now recognize that the ubiquitous distribution and production of picocyanobacteria makes them an important driver of carbon and nutrient cycling in a variety of aquatic ecosystems, as well as an underestimated contributor to overall phytoplankton biodiversity (e.g. Worden et al. 2004). In this study, these small (PNC) taxa were responsible for driving dissimilarities in phytoplankton community composition. Further, PNC taxa such as Aphanocapsa spp. C. Nägeli and Merismopedia spp. Meyen were instrumental in maintaining the cyanobacteria’s dominance of the phytoplankton community among lakes throughout this study. Specifically, this cyanobacterial sub-group was more prominent in June in White Iron Lake, Ten Mile Lake, Hill Lake, and Peltier Lake, when BC and FS taxa were not as common (Fig. 4). Further, PNC taxa were the dominant cyanobacterial sub-group in Ten Mile Lake, which agrees with the body of research that has documented high picocyanobacterial densities in oligotrophic waters (e.g. Callieri et al. 2007b, Winder 2009). This is noteworthy because when PNC taxa are included in taxonomy and biovolume calculations, cyanobacterial domination persists into oligotrophic systems like Ten Mile Lake, which is regarded as having some of the best water quality among inland Minnesota lakes (MNDNR 2019). Clearly, their role in shaping phytoplankton community composition via altered resource availability and increased competition is worthy of further study. cHAB response variables were generally highest in southern lakes, primarily driven by the production of Pelier Lake, which was the most eutrophic study lake and also showed the strongest and most common signs of cHAB conditions. Algal samples from Peltier Lake contained the most MCP taxa of any study lake, which were primarily BC and FS taxa including Microcystis spp. and Dolichospermum spp. Peltier Lake in August 2017 had the largest algal bloom observed within this study. Visible blooms on Peltier Lake were commonly observed throughout sampling, particularly at sites in the 33 southern basin of the lake. Very prominent visible scums of cyanobacteria and other phytoplankton could be seen along the rocks, trees, docks, and boats that line the southern shore of the lake. Although cyanobacterial production in August 2017 was clearly enhanced, it is likely that the combination of wind velocity, wind direction and shoreline fetch helped to accumulate algal cells into a dense surface scum at several sampling sites (Soranno 1997), which could exaggerate water column cyanobacterial biovolume estimations. However, it is still important to understand areas where this activity may occur, as they could also be locations of elevated toxicity during or following a bloom. Surprisingly, while algal conditions indicative of cHABs were most prevalent in southern lakes, northern lakes also had higher TBV, CD, and MCPD than central lakes. While this is likely due to the influence of oligotrophic Ten Mile Lake, which had the lowest mean algal response variables (Table A1), it is important to again propose that a MN lake’s latitude alone is not enough to diminish its potential to experience cHAB activity. Recent research in northern MN substantiates the hypothesis that latitudinal position, and even watershed land use, do not guarantee a lake will or will not be susceptible to blooms. Kabetogama Lake, inside MN’s Voyageurs National Park, has a watershed with <1% anthropogenic (agricultural + developed) land use, and yet still experiences recurrent late-summer cHABs (Christensen et al. 2019). Therefore, while it is clear that lakes with watersheds heavily impacted by human activity (predominantly in southern MN) are a concern when it comes to cyanobacterial dominance and toxin production, the same concern should perhaps be shared for lakes across the state, regardless of position, development, or visitation. Among lakes with different mixing regimes, continuously polymictic lakes had elevated cHAB activity in contrast to dimictic lakes. Polymictic study lakes (Tait, Hill, and Peltier) had significantly higher CD than dimictic lakes (White Iron, Ten Mile, and Carrie; Table A6). While polymictic study lake communities were also significantly dissimilar to those of dimictic study lakes, their communities were less dissimilar than when comparing months, years, lakes, or latitude groups (Table A5). While it is likely that the statistical difference between mixing groups stems from greater cyanobacterial production within Peltier Lake and lesser production in Ten Mile Lake, it is clear that 34 mixing regime had an impact on the phytoplankton communities of study lakes. Variation in lake mixing as a catalyst for cHAB activity has gained attention by scientists in recent years (e.g. Jöhnk et al. 2008, Wilhelm and Adrian 2008), and both increased and decreased lake water mixing have been shown to promote cHAB activity. In deep (generally dimictic) temperate lakes, enhanced summer air temperatures combined with low precipitation and wind events can result in strong stratification. Cyanobacteria with the ability to regulate their buoyancy, such as M. aeruginosa, have been shown to exploit these conditions and accumulate into blooms (Harke et al. 2016). Alternatively, in shallower lakes which mix throughout the summer (polymictic), increased wind speeds and storm events can help redistribute nutrients out of the sediments, particularly P, which help stimulate cHABs (Orihel et al. 2015). Consequently, it is of paramount research concern to further elucidate the specific cyanobacterial taxa, and their accompanying cyanotoxin production, that bloom in lakes with different mixing regimes, as bloom management strategies must account for the differences implicit by the lake’s morphometry.

Concluding Remarks It is important to acknowledge several limitations of this study. Although the six chosen study lakes allowed for a focused analysis of phytoplankton and cyanobacterial community dynamics across a range of lake types, more lakes need to be sampled in order to create a truly representative sample of Minnesota (and Upper Midwestern or even Temperate) lakes. With such variety detected in six lakes, it is likely that more intricacies and patterns would be detected from a larger subset of lakes. In addition, a greater number of samples would increase the power of performed statistical tests. Bartlett’s Test results were non-significant for CD (between months, years, and mixing groups) and MCPD (between years), which is not ideal and could likely be corrected for by increasing sample size. In summary, the phytoplankton communities of six distinct MN lakes were shown to be different across temporal and spatial scales. Cyanobacteria were ubiquitous and their internal community diverse. MCP’s were observed in every study lake. PNC and BC cyanobacterial taxa were critical in driving dissimilarities between phytoplankton 35 community assemblages. June was the only sampling month where cyanobacteria did not clearly dominate across study lakes, and cyanobacteria’s proliferation in July and August was particularly elevated. And finally, while southern MN lakes demonstrated the most cHAB activity, blooms of cyanobacteria and risk for elevated toxicity were present throughout the state. Using the six selected study lakes as a representative sample of the broader suite of MN lakes, it is apparent that cHAB risk persists across all MN lakes in mid- to late-summer. In other words, despite differences in individual watershed and basin characterics, MN lakes are experiencing enhanced cyanobacterial proliferation which sometimes results in cHABs. Consequently, there is a need to develop a deeper understanding behind the local-scale drivers (lake characteristics or environmental factors) of cyanobacterial proliferation and toxin production in order to guide safe use and water quality management both within MN and beyond.

Section 2 Lake-Specific Modelling Significant predictors of changing MCPD were identified for each study lake. First, N-forms (TN, NO3, NH4, & TKN) were widely related to MCPD across study lakes (Table 4). In contrast, TP was only significantly related to MCPD in Peltier Lake. The importance of N and P to the development and toxicity of cHABs has been widely accepted, and both N and P reductions are regarded as best management practices to curb eutrophication and associated cHABs (Conley et al. 2009, Paerl & Paul 2012, Paerl 2014). However, the apparent importance of N over P in shaping these conditions in study lakes is important to note, as actions like phosphate detergent bans and no till agriculture have been effective at reducing P-loading into freshwater systems, but less so for N, as it is deposited atmospherically and is more mobile throughout the environment. (Galloway & Cowling 2002, Howarth 2008). Based on these results, concentrations of N-forms are more important than TP in predicting cHAB conditions in MN. Furthermore, it is noteworthy that N:P was not identified as a significant parameter in any model, which supports the findings of Pick & Lean (1987). This was surprising, as many have commented on the apparent role that N:P plays in dictating cyanobacterial dominance of the phytoplankton (e.g. Smith et al., 1999; Havens et al., 2003; Harris et 36 al., 2014). Clearly, more research towards understanding how specific N-forms (particularly TN and TKN) may be promoting cyanobacterial dominance and toxicity in MN lakes is needed. While it is generally accepted that water temperature is a strong predictor of cHAB occurrences (Paerl & Huisman 2008), temperature was not a strong predictor of cHAB indicator metrics in this study. Overall, temperature in southern lakes was roughly 3˚C higher than central and northern lakes, and temperature also varied more in 2017 than 2016 for all lakes (Table A1). This is noteworthy because even though many have determined that increased temperature is leading to new patterns of cyanobacterial proliferation in temperate lakes (e.g. Posch et al. 2012), this data suggests that the cooler a lake is, the more vulnerable it may be to increased cHAB activity due to rising temperatures. In polymictic study lakes, TS was negatively related to MCPD. Cyanobacteria are widely thought to benefit from increasingly stratified conditions (Paerl & Huisman 2009). However, wind-driven mixing can help redistribute sediments into the water column, releasing nutrients and/or cyanobacterial resting cells, helping to promote cyanobacterial proliferation (Paerl 1988, Zhu et al. 2014). Furthermore, it could be the case that, in polymictic lakes, TS is most related to lake depth and therefore driven by cold bottom water rather than hot surface water. This could mean that other phytoplankton besides cyanobacteria, such as diatoms and/or chrysophytes, are also being distributed by wind-driven mixing and weak stratification, further confounding these modelling results. This indicates that changes to thermocline strength impact the phytoplankton communities in different ways in shallow compared to deep lakes, and that both increasing and decreasing TS can still favor cyanobacteria over other phytoplankton.

In deeper, generally dimictic study lakes, increasing Chla was a common predictor of increasing MCPD. This was not surprising, as the two parameters were significantly correlated in all but Tait and Hill Lakes (Figure A1). Although it has been noted that elevated Chla may not be the best predictor of cyanobacterial growth because it fails to differentiate eukaryotic phytoplankton from cyanobacteria (Izydorczyk et al. 2009), there is a large body of work demonstrating that cyanobacteria typically increase

37 with lake productivity (Paerl 2014), which is what appears to be the case in these study lakes. This determination was echoed by other modelling results as well. In southern lakes, SD was a significant predictor of MCPD. This relationship was positive in Carrie Lake, which was in contrast to other systems, and further demonstrates that changes to the larger phytoplankton community directly impact the potential for cHAB development and toxicity.

Furthermore, algal parameters (TBV, but especially CD) were the most common predictors of increasing MCPD. This clearly matches up with the general understanding that when plankton density increases in lakes, that increase comes largely from cyanobacterial taxa that commonly contribute to cHABs (e.g. Huisman et al. 2005). This conclusion of cyanobacterial dynamics among study lakes, despite the variety in community composition across spatial and temporal gradients, is critical in contextualizing the growing risk of increased cHABs in systems becoming more productive through the interaction of climate change and anthropogenic land use changes.

Aggregated Lakes Modelling Significant predictors of changing MCPC were detected for aggregated study lake data in order to highlight important drivers of cHAB risk across different lake types in MN. By creating standardized models across the six study lakes, many of the findings of the individual lake models were substantiated. CD, Chla, and TBV were all significant predictors of MCPD, with CD explaining the most variation in the dependent variable of any parameter tested. This suggests that lake productivity, rather than particular nutrient concentrations or ratios, is the most important factor in understanding a lake’s susceptibility to cHABs. Likewise, TN and TKN were significant predictors of increased MCPD across lakes. This finding confirms the theory that the microcystin content of blooms containing taxa like M.aeuriginosa is moderated by the N-uptake of those toxin- producing cells (Downing et al. 2005). This echoes the findings of the individual lake models, and also gives credence to the importance of organic-N in shaping the phytoplankton and cyanobacterial community. This finding makes sense, as Microcystis spp. were the dominant MCPs across study lakes, and they are adept at sequestering both

38 dissolved inorganic forms of nitrogen and organic forms, including urea, amino acids and other high molecular weight organic compounds (Blomqvist et al. 1994, Hyenstrand et al. 1998, Davis et al. 2010). Finally, TS was negatively related to increasing MCPD across study lakes, which gives more evidence to the determination that increased wind-driven mixing (i.e. decreased thermocline strength) is a key driver in Minnesota lakes. Therefore, while much time, money, and effort have been spent reducing P-inputs into lakes in an effort to curb cHABs, the important things to focus on are overall algal density increases and N-inputs. This finding is in agreement with many other studies (e.g. Paerl & Scott 2010, Posch et al. 2012, Yuan et al. 2014, Gobler et al. 2016). While local-scale conditions (growth requirements and reciprocal algal community) vary in proximal systems, regional trends in cHAB development are generalizable and echo the larger patterns demonstrated by local investigations.

Toxins and Harmfulness Lastly, TMC was displayed against relevant harmfulness metrics (Table 4). The first metric is from the WHO (YEAR) Recreation Action Levels and contains the following thresholds for estimated TMC: Low (< 10), Moderate (10 - 50), High (50 - 5,000), and Very High (> 5,000) Probability of Acute Health Effects. The second metric is from the US EPA (2016) for Water Body Status (for Recreation): Usable (< 4.0) and Unusable (> 4.0). The third metric is also from the US EPA (2015b) for Drinking Water 10-Day Health Advisory, and states who is At Risk: None (< 0.3), Children (0.3 – 1.6), and the general Public (> 1.6). Finally, the last metric used was from the MDH (YEAR) for Drinking Water Action Levels: No Action (< 1.0) or Action (> 1.0). By displaying measured TMC against four relevant cHAB harmfulness metrics, instances in which lake conditions were potentially harmful to humans were illuminated. Specifically, 6% were above the first WHO threshold, 7% above the US EPA rec threshold, 17% unsafe based on the US EPA drinking threshold, and 36% above the MDH safe drinking water action level.

39 Table 6 Total microcystin concentration (TMC) from subsampled sites. A green cell indicates TMC is below threshold (low risk/no action needed), yellow cell indicates TMC is above the first threshold (moderate/children at risk), and red cell indicates harmful conditions (high/all at risk/action needed).

WHO EPA EPA MDH Lake Site Round TMC (μg·L-1) (rec) (rec) (drink) (drink) White Iron WI15 June 2016 0.02 White Iron WI08 July 2016 0.03 White Iron WI15 July 2016 0.04 White Iron WI02 August 2016 0.10 White Iron WI10 August 2016 0.10 White Iron WI06 September 2016 0.02 White Iron WI16 September 2016 0.07 White Iron WI07 June 2017 0.00 White Iron WI08 July 2017 0.02 White Iron WI15 July 2017 0.02 White Iron WI01 August 2017 0.04 White Iron WI15 August 2017 0.03 White Iron WI06 September 2017 0.03 White Iron WI16 September 2017 0.04 Tait T05 June 2016 0.03 Tait T06 July 2016 0.05 Tait T04 August 2016 0.05 Tait T02 September 2016 0.02 Tait T05 June 2017 0.04 Tait T06 July 2017 0.03 Tait T04 August 2017 0.05 Tait T02 September 2017 0.05 Ten Mile TM21 June 2016 0.00 Ten Mile TM10 July 2016 0.00 Ten Mile TM22 August 2016 0.01 Ten Mile TM07 September 2016 0.00 Ten Mile TM22 June 2017 0.01 Ten Mile TM22 July 2017 0.01 Ten Mile TM13 August 2017 0.02 Ten Mile TM07 September 2017 0.01 Hill H04 June 2016 0.05 Hill H03 July 2016 0.05 Hill H04 August 2016 0.16 Hill H09 August 2016 0.10 Hill H01 September 2016 0.30 Hill H03 September 2016 0.38 Hill H04 June 2017 0.02

40 Hill H03 July 2017 0.03 Hill H04 August 2017 0.05 Hill H08 August 2017 0.05 Hill H01 September 2017 0.09 Hill H03 September 2017 0.09 Carrie C04 June 2016 0.05 Carrie C02 July 2016 0.18 Carrie C04 July 2016 0.39 Carrie C02 August 2016 0.35 Carrie C04 August 2016 0.40 Carrie C01 September 2016 0.13 Carrie C04 September 2016 0.16 Carrie C05 June 2017 0.03 Carrie C02 July 2017 0.11 Carrie C04 July 2017 0.13 Carrie C01 August 2017 0.05 Carrie C04 August 2017 0.08 Carrie C01 September 2017 0.15 Carrie C04 September 2017 0.18 Peltier P06 June 2016 0.22 Peltier P04 July 2016 20.32 Peltier P06 July 2016 11.97 Peltier P02 August 2016 4.78 Peltier P07 August 2016 0.00 Peltier P01 September 2016 2.25 Peltier P06 September 2016 0.29 Peltier P04 June 2017 0.03 Peltier P04 July 2017 0.06 Peltier P06 July 2017 0.08 Peltier P04 August 2017 22.48 Peltier P07 August 2017 0.04 Peltier P01 September 2017 2.97 Peltier P04 September 2017 12.90

A few interesting patterns emerge from Table 6. More yellow and red cells were present in southern lakes. This was no surprise given the results of Section 1, which indicated more cHAB activity in southern MN. However, looking left-to-right on the table, other trends emerge. Each threshold indicated different instances where lake conditions could be harmful. While the minimum acceptable water quality standard would be expected to differ for drinking or recreational use, there was still disparity 41 between the two recreation metrics and the two drinking water metrics. The good news for Minnesotans is that the MDH metric identified the most instances of harmful conditions, thus providing increased protection against potential exposure to elevated MC concentrations. However, despite the “protectiveness” of the MDH threshold, detected TMC from study lakes still indicated ubiquitous cHAB toxicity risk for MN lakes, which means that real estate values, recreational opportunities, and drinking water quality are all at risk for Minnesotans. There was another interesting trend that emerged as well; sites from the same lake, on the same date, had drastically different TMC. For example, in Peltier Lake (Aug.17) site P04 had TMC of 22.48 while site P07 had TMC of 0.04. Thus, if toxin samples had only been taken from one location (e.g. P07), the determination of risk of MC-exposure would be much lower than P04. It is known that weather conditions (wind speed and direction; Carmichael 1994, Kanoshina et al. 2003) and lake morphometry (Rose et al. 2019) can shape differences cyanobacterial scum accumulation within the same system, and that environmental parameters appear to regulate MC indirectly, via control of cyanobacterial abundance and distribution (Millie et al. 2009). Ultimately, the embayed (to the northeast) nature of the shoreline around site P04 was the likely culprit for its elevated toxin concentrations, while the abundant aquatic plants in the lakes northern basin (sites P05 and P07) likely disrupted the formation of scums and reciprocally high toxin concentrations. This evidence further substantiates the need for water managers to incorporate within-lake processes into water quality sampling regimes (particularly toxin concentration monitoring.

Concluding Remarks This study was hampered by its limited sample size. In order to build predictive models for MCPD and even more specific metrics for potential cHAB harmfulness, a larger sample of lakes with more frequent sampling is needed to increase the power of employed statistical approaches. This was evidenced by the models constructed for Hill Lake and Carrie Lake, which had the lowest sample sizes among study lakes and only had one statistically significant driving parameter each (Table 4). Further, the frequency of lake sampling was also insufficient to provide robust inferences on cHAB predictors. 42 Cyanobacterial communities were shown to differ across months, responding to myriad changing conditions in their respective environments (Section 1). Although monthly sampling was able to capture some of this variability, the algal community changes much more rapidly than a monthly scale, and only more frequent sampling would help elucidate any hidden information left undetected by the sampling regime employed in this study. Beyond the employed sampling regime, there remain several important environmental factors shown to impact cHABs that were unaccounted for in this study. Altered patterns of precipitation and temperature, such as flood or drought events, may further enhance climate-driven impacts of cHABs (Paerl & Huisman 2008, 2009 Paerl et al. 2011). While the increased flushing from storm runoff may decrease the likelihood of a new bloom in the short term, the ‘nutrient pulse’ will eventually be exploited by the cyanobacteria (Paerl & Huisman 2008, 2009). This phenomenon could also be enhanced if a high-runoff period is followed by prolonged drought, which increases lake residence time, strength of stratification, and epilimnetic water temperatures (Paerl & Huisman 2008, 2009). In addition to specific weather patterns, the role of dissolved organic carbon (DOC), atmospheric CO2 conditions, hydraulic residence time, trace metal concentrations, and food-web interactions are all important in dictating lake phytoplankton and cyanobacterial community dynamics (Paerl & Huisman 2009, Paerl et al. 2011). Further, the viral community in lakes has been shown to interact significantly with phytoplankton dynamics and likely impacts the timing, composition and intensity of cyanobacterial proliferation and toxicity (e.g. Tucker & Pollard 2005, Parvathi et al. 2014, Staniewski et al. 2017). Ideally, these relevant factors would all be incorporated into a larger study seeking to advance understanding of cHAB characteristics and drivers. Finally, the only type of toxin producers modelled in this study were those of MC-congeners. While MC is prolific across lakes and particularly in the Upper Midwest (e.g. Marion et al. 2017), many other cyanotoxins exist that can have detrimental impacts on aquatic ecosystems and their proximal environments (Chorus & Bartram 1999). For example, two of the most common cyanobacterial genera observed from study lakes (Aphanizomenon and Dolichospermum) are known to produce anatoxins and saxitoxins in Minnesota lakes (Christensen et al. 2019). Therefore, while it is important to understand that phytoplankton and cyanobacterial densities, N-forms, and TS all impact the 43 proliferation of MCP’s in Minnesota lakes, further study is needed to understand what drives all types of cHABs and their detrimental impacts. In summary, despite the differences in shape, location, and watershed characteristics among study lakes, significant predictors of changing cHAB risk were identified for individual and aggregated lake data. Phytoplankton productivity indicator variables like Chla, TBV, and CD were important, as were TN and TKN. The timing, composition, and intensity of cyanobacterial proliferation has been shown to fluctuate greatly (Section 1). Therefore, it follows that observed MC concentrations across 6 distinct lakes in Minnesota would also vary. Despite differences in “protectiveness” of relevant cHAB monitoring thresholds, it was determined that MC toxicity risk persists across MN’s broad suite of lakes. Furthermore, toxin concentration from various locations within the same lake varied, demonstrating the need for integrated, site-specific monitoring and management strategies in order to address the growing threat cHABs pose to humans and wildlife alike. Ultimately, some Minnesota lakes will be less likely to have cHAB-related issues (e.g. Ten Mile Lake), others will have more variable bloom occurrence and potency (e.g. White Iron Lake and Hill Lake), and still others appear to bloom consistently in the warm summer months (e.g. Peltier Lake). The goal of current and future cHAB monitoring should be to safeguard the public, not to shut down all use of potentially-harmful waterbodies. Therefore, for ‘bloomy’ lakes (e.g. Peltier), integrated monitoring should focus on determining when the MDH safety threshold has been crossed, and what the associated bloom conditions were. In all other lakes (but particularly ‘variable’ lakes with occasional blooms), intensified and cooperative monitoring efforts should focus on developing a deeper understanding of the seasonal dynamics and key indicators of bloom formation and toxin production, about which we still know too little.

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61 Appendix

Table A1 Mean ± standard deviation values for measured explanatory (physical, chemical, biological) and algal parameters for each study lake (A-F) by-year. A) White Iron Lake Parameter 2016 2017 Temperature (˚C) 19.9 ± 4.0 20.2 ± 1.7 Dissolved Oxygen (mg·L-1) 8.5 ± 0.4 8.3 ± 0.5 Specific Conductance (μS·cm-1) 56.6 ± 5.3 46.9 ± 3.5 pH 7.1 ± 0.4 7.1 ± 0.3 Total Phosphorus (μg·L-1) 18.7 ± 3.0 20.8 ± 3.3 Total Nitrogen (μg·L-1) 541.5 ± 26.8 596.5 ± 44.5 Ammonium (μg·L-1) 16.7 ± 6.4 20.2 ± 9.2 Nitrate (μg·L-1) 17.3 ± 13.6 26.3 ± 13.9 Total Kjeldahl Nitrogen (μg·L-1) 524.2 ± 30.3 570.2 ± 51.7 N:P 65.4 ± 9.8 65.1 ± 10.8 Secchi Disk Depth (m) 1.5 ± 0.1 1.4 ± 0.1 Chlorophyll-a (μg·L-1) 1.8 ± 2.3 2.0 ± 1.4 Thermocline Strength (ΔT·ΔZ-1) 1.0 ± 1.0 1.0 ± 0.7 Total Biovolume (106 μm3·mL-1) 5.6 ± 3.8 7.4 ± 6.0 Cyanobacterial Dominance (% TBV) 52.5 ± 26.3 56.6 ± 29.3 Microcystin-Producing Cyanobacterial Dominance (% TBV) 13.9 ± 25.1 31.3 ± 31.0

B) Tait Lake Parameter 2016 2017 Temperature (˚C) 19.6 ± 3.9 19.1 ± 2.4 Dissolved Oxygen (mg·L-1) 8.6 ± 0.6 8.8 ± 0.3 Specific Conductance (μS·cm-1) 38.3 ± 2.4 31.2 ± 2.2 pH 7.1 ± 0.7 7.0 ± 0.3 Total Phosphorus (μg·L-1) 10.2 ± 1.6 12.8 ± 2.9 Total Nitrogen (μg·L-1) 358.9 ± 19.0 405.4 ± 15.3 Ammonium (μg·L-1) 8.0 ± 4.1 11.7 ± 4.5 Nitrate (μg·L-1) 3.8 ± 2.8 3.9 ± 3.1 Total Kjeldahl Nitrogen (μg·L-1) 355.1 ± 18.4 401.5 ± 15.4 N:P 79.2 ± 11.6 73.3 ± 17.0 Secchi Disk Depth (m) 2.1 ± 0.1 1.9 ± 0.2 Chlorophyll-a (μg·L-1) 0.8 ± 0.6 2.3 ± 0.9 Thermocline Strength (ΔT·ΔZ-1) 0.6 ± 0.8 0.2 ± 0.1 Total Biovolume (106 μm3·mL-1) 4.2 ± 4.3 5.8 ± 5.8 Cyanobacterial Dominance (% TBV) 54.7 ± 19.6 38.3 ± 19.6 Microcystin-Producing Cyanobacterial Dominance (% TBV) 12.5 ± 19.4 16.6 ± 14.5 62

C) Ten Mile Lake Parameter 2016 2017 Temperature (˚C) 19.9 ± 4.0 19.9 ± 2.7 Dissolved Oxygen (mg·L-1) 9.2 ± 0.4 9.5 ± 0.5 Specific Conductance (μS·cm-1) 208.7 ± 4.4 203.1 ± 8.2 pH 8.5 ± 0.7 8.5 ± 0.2 Total Phosphorus (μg·L-1) 10.2 ± 2.0 13.3 ± 1.9 Total Nitrogen (μg·L-1) 320.6 ± 37.6 382.9 ± 52.6 Ammonium (μg·L-1) 8.7 ± 3.4 9.8 ± 3.3 Nitrate (μg·L-1) 3.7 ± 3.4 2.9 ± 1.4 Total Kjeldahl Nitrogen (μg·L-1) 316.9 ± 37.1 379.9 ± 52.9 N:P 71.0 ± 10.9 71.3 ± 20.0 Secchi Disk Depth (m) 4.7 ± 0.2 4.6 ± 0.4 Chlorophyll-a (μg·L-1) 0.7 ± 0.7 1.3 ± 0.6 Thermocline Strength (ΔT·ΔZ-1) 1.3 ± 1.2 2.3 ± 1.3 Total Biovolume (106 μm3·mL-1) 1.7 ± 0.6 2.8 ± 1.7 Cyanobacterial Dominance (% TBV) 43.5 ± 15.1 36.8 ± 19.5 Microcystin-Producing Cyanobacterial Dominance (% TBV) 1.3 ± 5.5 4.9 ± 11.2

D) Hill Lake Parameter 2016 2017 Temperature (˚C) 20.0 ± 3.3 20.8 ± 1.7 Dissolved Oxygen (mg·L-1) 8.3 ± 1.0 8.9 ± 0.4 Specific Conductance (μS·cm-1) 312.3 ± 8.1 314.6 ± 5.1 pH 8.0 ± 0.5 8.2 ± 0.2 Total Phosphorus (μg·L-1) 40.8 ± 23.9 18.4 ± 11.7 Total Nitrogen (μg·L-1) 632.8 ± 121.4 566.8 ± 82.8 Ammonium (μg·L-1) 77.0 ± 67.3 23.1 ± 34.9 Nitrate (μg·L-1) 7.6 ± 8.6 3.3 ± 1.7 Total Kjeldahl Nitrogen (μg·L-1) 625.2 ± 117.0 563.4 ± 83.6 N:P 43.6 ± 19.5 84.9 ± 40.4 Secchi Disk Depth (m) 2.3 ± 0.0 3.0 ± 0.5 Chlorophyll-a (μg·L-1) 2.9 ± 3.9 4.8 ± 5.1 Thermocline Strength (ΔT·ΔZ-1) 2.5 ± 1.8 3.3 ± 1.2 Total Biovolume (106 μm3·mL-1) 4.7 ± 3.4 11.4 ± 5.5 Cyanobacterial Dominance (% TBV) 43.3 ± 22.4 46.6 ± 27.9 Microcystin-Producing Cyanobacterial Dominance (% TBV) 9.5 ± 11.5 7.9 ± 9.0

E) Carrie Lake Parameter 2016 2017 Temperature (˚C) 22.5 ± 3.4 22.4 ± 2.3 Dissolved Oxygen (mg·L-1) 9.2 ± 0.5 8.3 ± 0.5 Specific Conductance (μS·cm-1) 466.7 ± 9.2 491.7 ± 17.7 pH 8.2 ± 0.3 8.1 ± 0.2 Total Phosphorus (μg·L-1) 17.2 ± 5.1 18.7 ± 3.1 Total Nitrogen (μg·L-1) 2128.1 ± 371.9 2308.5 ± 278.7 Ammonium (μg·L-1) 99.6 ± 54.9 143.5 ± 50.3 Nitrate (μg·L-1) 1464.6 ± 381.4 1597.0 ± 341.1 Total Kjeldahl Nitrogen (μg·L-1) 663.5 ± 97.4 711.5 ± 104.4 N:P 300.4 ± 118.7 280.8 ± 58.8 Secchi Disk Depth (m) 1.0 ± 0.1 1.2 ± 0.2 Chlorophyll-a (μg·L-1) 1.1 ± 0.5 3.8 ± 2.0 Thermocline Strength (ΔT·ΔZ-1) 1.3 ± 1.2 2.4 ± 2.4 Total Biovolume (106 μm3·mL-1) 3.4 ± 2.0 9.3 ± 5.8 Cyanobacterial Dominance (% TBV) 46.8 ± 22.3 26.1 ± 20.1 Microcystin-Producing Cyanobacterial Dominance (% TBV) 0.8 ± 0.7 13.6 ± 16.8

F) Peltier Lake Parameter 2016 2017 Temperature (˚C) 22.7 ± 4.2 23.5 ± 1.6 Dissolved Oxygen (mg·L-1) 8.8 ± 3.2 9.4 ± 2.3 Specific Conductance (μS·cm-1) 400.5 ± 22.4 387.4 ± 18.0 pH 8.2 ± 1.0 8.5 ± 0.6 Total Phosphorus (μg·L-1) 178.4 ± 64.9 211.6 ± 69.0 Total Nitrogen (μg·L-1) 1523.7 ± 509.9 1702.7 ± 592.7 Ammonium (μg L-1) 48.1 ± 47.6 55.7 ± 66.7 Nitrate (μg·L-1) 46.2 ± 72.9 14.5 ± 20.6 Total Kjeldahl Nitrogen (μg·L-1) 1477.6 ± 519.9 1688.1 ± 587.8 N:P 19.4 ± 4.7 18.6 ± 5.4 Secchi Disk Depth (m) 1.2 ± 0.3 0.9 ± 0.4 Chlorophyll-a (μg·L-1) 15.2 ± 29.2 9.8 ± 29.9 Thermocline Strength (ΔT·ΔZ-1) 1.8 ± 2.1 0.5 ± 0.6 Total Biovolume (106 μm3·mL-1) 19.3 ± 14.0 42.0 ± 51.5 Cyanobacterial Dominance (% TBV) 58.5 ± 33.8 64.8 ± 28.5 Microcystin-Producing Cyanobacterial Dominance (% TBV) 31.3 ± 34.2 39.5 ± 32.7

Table A2 A priori variable transformations on measured physical, chemical, and biological attributes to help find potential outliers. The below transformations were applied to respective variables prior to calculating mean and standard deviation.

White Iron Tait Ten Mile Hill Carrie Peltier Temp

DO log10 (x)

Cond log10 (x)

pH log10 (x) log10 (x)

TP log10 (x) log10 (x) log10 (x)

TN log10 (x) log10 (x) log10 (x) log10 (x)

NH4 log10 (x) log10 (x) log10 (x)

NO3 log10 (x) log10 (x) log10 (x) log10 (x)

TKN log10 (x) log10 (x) log10 (x)

N:P log10 (x) log10 (x) log10 (x)

SD log10 (x)

Chla log10 (x) log10 (x) log10 (x) log10 (x) log10 (x)

TS log10 (x) log10 (x) log10 (x)

TBV log10 (x) log10 (x) log10 (x) log10 (x) log10 (x) log10 (x)

CD log10 (x) log log log log log log MCPD 10 10 10 10 10 10 (x+0.01) (x+0.01) (x+0.01) (x+0.01) (x+0.01) (x+0.01)

Table A3 Statistical outliers identified as falling either above (H) or below (L) three standard deviations from the mean of the respective distribution of variable data.

Lake Site Round Variable(s) H/L White Iron WI18 A pH H White Iron WI08 H TN, TKN H, H Tait T02 H NH4 H Ten Mile TM13 F TP, N:P H, L Ten Mile TM18 F TP H Ten Mile TM04 F TP H Ten Mile TM22 G TN, TKN H Ten Mile TM25 F NH4 H Ten Mile TM28 B NH4 H Ten Mile TM14 G MCPD H Hill H08 C DO L Hill H08 A Cond L Carrie C02 D TP H Carrie C01 E NH4 H Peltier P07 D TP H

65 Table A4 Analysis of similarity test (ANOSIM) results between each study lake, latitude group, mixing group, and sample month. A higher R Statistic indicates larger dissimilarity between groups. Non- significant results indicated with *.

ANOSIM Comparison R Statistic Significance Months (Global) 0.130 0.001 June.16-August.16 0.103 0.001 June.16-September.16 0.085 0.002 June.16-July.16 0.083 0.004 July.16-September.16 0.033 *0.079 July.16-August.16 0.022 *0.149 Augsut.16-September.16 -0.017 *0.888 June.17-August.17 0.254 0.001 June.17-September.17 0.154 0.001 June.17-July.17 0.117 0.002 August.17-September.17 0.114 0.001 July.17-September.17 0.063 0.009 July.17-August.17 0.042 0.024 August.16-August.17 0.252 0.001 July.16-July.17 0.116 0.001 June.16-June.17 0.090 0.001 September.16-September.17 0.031 0.044

Lakes (Global) 0.449 0.001 Peltier-Ten Mile 0.709 0.001 Hill-Ten Mile 0.698 0.001 White Iron-Ten Mile 0.525 0.001 Carrie-Ten Mile 0.524 0.001 Tait-Ten Mile 0.412 0.001 Peltier-Tait 0.363 0.001 Hill-Tait 0.348 0.001 Carrie-White Iron 0.344 0.001 Hill-Carrie 0.338 0.001 Hill-Peltier 0.315 0.001 Carrie-Peltier 0.285 0.001 Carrie-Tait 0.264 0.001 Peltier-White Iron 0.243 0.001 Hill-White Iron 0.178 0.001 White Iron-Tait 0.143 0.001

Latitude (Global) 0.236 0.001 Central-South 0.321 0.001 Central-North 0.192 0.001 South-North 0.215 0.001

Mixing (Global) 0.157 0.001

66 Table A5 Similarity percentage test (SIMPER) results between sampling months (A), study lakes (B), and latitude & mixing groups (C). Larger Av.Diss (Average Dissimilarity) values indicate groups more different from one another. % Cont. (% contribution) values indicate each taxa’s influence in driving group dissimilarities.

A) Group 1 Group 2 Av.Diss Taxa % Cont. Within Years June.16 Aug.16 76.69 Aphanocapsa delicatissima 11.47 Stephanodiscus sp. 9.76 Aulacoseira sp. 8.81 July.16 Aug.16 75.96 Aphanocapsa delicatissima 11.47 Stephanodiscus sp. 9.76 Aulacoseira sp. 8.81 June.16 Sept.16 75.00 Aphanocapsa delicatissima 10.65 Stephanodiscus sp. 10.16 Aulacoseira sp. 10.15 July.16 Sept.16 74.95 Woronichinia naegeliana 10.88 Aphanocapsa delicatissima 9.50 Aphanizomenon flos-aquae 6.06 Aug.16 Sept.16 74.25 Aphanocapsa elachista 9.05 Aphanocapsa delicatissima 8.98 Microcystis aeruginosa 7.42 June.16 July.16 73.73 Aphanocapsa delicatissima 11.77 Stephanodiscus sp. 11.10 Woronichinia naegeliana 8.84 June.17 Aug.17 84.63 Woronichinia naegeliana 7.99 Ceratium hirundinella 7.83 Tabellaria flocculosa 7.53 June.17 Sept.17 82.16 Ceratium hirundinella 8.75 Aulacoseira sp. 6.85 Aphanocapsa delicatissima 6.81 Aug.17 Sept.17 81.63 Woronichinia naegeliana 11.59 Microcystis aeruginosa 11.11 Ceratium hirundinella 8.92 June.17 July.17 80.70 Ceratium hirundinella 8.94 Tabellaria flocculosa 6.78 Woronichinia naegeliana 6.49 July.17 Aug.17 80.02 Woronichinia naegeliana 11.15 Ceratium hirundinella 9.10 Microcystis aeruginosa 8.69 July.17 Sept.17 79.75 Woronichinia naegeliana 10.58 Ceratium hirundinella 9.81 Microcystis aeruginosa 7.64 Among Years Aug.16 Aug.17 83.23 Microcystis aeruginosa 10.21

67 Woronichinia naegeliana 9.45 Aphanocapsa delicatissima 9.10 July.16 July.17 79.56 Woronichinia naegeliana 12.19 Aphanocapsa delicatissima 9.58 Ceratium hirundinella 8.36 June.16 June.17 77.62 Aphanocapsa delicatissima 12.03 Aulacoseira sp. 11.81 Stephanodiscus sp. 9.95 Sept.16 Sept.17 77.43 Woronichinia naegeliana 8.87 Microcystis aeruginosa 8.16 Aphanocapsa delicatissima 7.81

B) Group 1 Group 2 Av.Diss Taxa % Con. Peltier Tait 85.43 Microcystis aeruginosa 17.59 Aulacoseira sp. 11.38 Aphanocapsa delicatissima 9.62 Carrie White Iron 85.12 Woronichinia naegeliana 12.84 Ceratium hirundinella 11.36 Aphanocapsa elachista 8.39 Peltier Ten Mile 85.07 Microcystis aeruginosa 17.80 Aulacoseira sp. 10.83 Aphanocapsa delicatissima 9.54 Hill Peltier 84.21 Microcystis aeruginosa 17.22 Aulacoseira sp. 11.38 Ceratium hirundinella 9.82 Hill Tait 83.91 Ceratium hirundinella 10.69 Stephanodiscus sp. 8.29 Woronichinia naegeliana 8.16 Carrie Peltier 83.88 Microcystis aeruginosa 18.11 Aulacoseira sp. 11.75 Ceratium hirundinella 11.24 Hill Ten Mile 83.70 Ceratium hirundinella 10.20 Aphanocapsa delicatissima 7.71 Stephanodiscus sp. 6.63 Peltier White Iron 83.24 Microcystis aeruginosa 17.72 Woronichinia naegeliana 13.09 Aulacoseira sp. 11.60 Carrie Tait 82.66 Ceratium hirundinella 12.20 Aphanocapsa elachista 9.67 Aphanocapsa delicatissima 8.18 Hill Carrie 82.32 Ceratium hirundinella 15.49 Aphanocapsa elachista 8.26 Limnoraphis birgei 6.48 White Iron Ten Mile 81.47 Woronichinia naegeliana 13.32 Aphanocapsa delicatissima 8.59 68 Aphanizomenon flos-aquae 7.15 Hill White Iron 80.83 Woronichinia naegeliana 13.61 Ceratium hirundinella 10.62 Limnoraphis birgei 6.60 White Iron Tait 79.55 Woronichinia naegeliana 16.01 Aphanocapsa delicatissima 9.06 Aphanizomenon flos-aquae 7.32 Carrie Ten Mile 77.70 Ceratium hirundinella 12.45 Aphanocapsa elachista 10.50 Aphanocapsa delicatissima 8.69 Tait Ten Mile 76.50 Aphanocapsa delicatissima 9.68 Woronichinia naegeliana 8.38 Stephanodiscus sp. 7.12

C) Group 1 Group 2 Av.Diss Taxa % Con.

Latitude South North 84.14 Microcystis aeruginosa 12.39 Woronichinia naegeliana 10.8 Aulacoseira sp. 8.95 Central South 82.75 Microcystis aeruginosa 12.71 Aulacoseira sp. 8.52 Aphanocapsa delicatissima 8.43 Central North 80.18 Woronichinia naegeliana 11.31 Aphanocapsa delicatissima 8.39 Tabellaria flocculosa 5.96 Mixing Polymictic Dimictic 81.66 Aphanocapsa delicatissima 8.52 Woronichinia naegeliana 7.57 Microcystis aeruginosa 6.97

69 Table A6 Analysis of Variance (ANOVA) model comparisons of mean total phytoplankton biovolume (TBV), mean cyanobacterial dominance (CD, percent of TBV that is cyanobacteria), and MC-producer dominance (MCPD, percent TBV that is cyanobacteria with the potential to produce microcystin-varieties). Data for month and year comparisons was z-scored by-lake, while lake, latitude, and mixing comparisons used observational data. Model outputs marked with an * indicate a Bartlett’s test p-value greater than 0.05.

Group and Response Variable F df P Months

log10 (TBV + 3) 5.23 364 0.002 *CD 37.55 364 <0.001

log10 (MCPD + 3) 20.03 363 <0.001 Years

log10 (TBV + 3) 53.49 366 <0.001 *CD 4.47 366 0.035

*log10 (MCPD + 3) 17.47 365 <0.001 Lakes

log10 (TBV) 70.21 362 <0.001 logit (CD) 9.13 362 <0.001 logit (MCPD + 0.01) 15.40 361 <0.001 Latitude Groups

log10 (TBV) 69.10 365 <0.001 logit (CD) 8.27 365 <0.001 logit (MCPD + 0.01) 19.68 364 <0.001 Mixing Groups

log10 (TBV) 0.64 366 0.423 *logit (CD) 5.85 366 0.016 logit (MCPD + 0.01) 0.21 365 0.649

Table A7 Tukey’s HSD post-hoc test results comparing total phytoplankton biovolume, cyanobacterial dominance, and microcystin-producer dominance across months (A), lakes (B), and latitude groups (C). Group estimate (Est.) and significance value (P) shown.

A) Comparison Est. P

log10 (TBV + 3) Aug-July 0.05 0.692 Aug-June 0.07 0.339 Aug-Sept 0.16 0.001 July-June 0.02 0.939 July-Sept 0.11 0.032 June-Sept 0.09 0.133 CD Aug-July 0.36 0.028

70 Aug-June 1.31 <0.001 Aug-Sept 0.42 0.006 July-June 0.95 <0.001 July-Sept 0.06 0.962 June-Sept -0.89 <0.001

log10 (MCPD + 3) Aug-July 0.06 0.389 Aug-June 0.29 <0.001 Aug-Sept 0.10 0.051 July-June 0.23 <0.001 July-Sept 0.04 0.753 June-Sept -0.19 <0.001

B) Comparison Est. P

log10 (TBV) Carrie-Hill -0.30 0.508 Carrie-Peltier -1.37 <0.001 Carrie-Tait 0.28 0.581 Carrie-Ten Mile 0.90 <0.001 Carrie-White Iron -0.04 0.999 Hill-Peltier -1.07 <0.001 Hill-Tait 0.59 0.003 Hill-Ten Mile 1.20 <0.001 Hill-White Iron 0.26 0.460 Peltier-Tait 1.66 <0.001 Peltier-Ten Mile 2.28 <0.001 Peltier-White Iron 1.33 <0.001 Tait-Ten Mile 0.62 <0.001 Tait-White Iron -0.33 0.193 Ten Mile-White Iron -0.95 <0.001 logit (CD) Carrie-Hill -0.43 0.695 Carrie-Peltier -1.38 <0.001 Carrie-Tait -0.53 0.469 Carrie-Ten Mile -0.20 0.970 Carrie-White Iron -1.00 0.004 Hill-Peltier -0.95 0.003 Hill-Tait -0.10 0.999 Hill-Ten Mile 0.22 0.916 Hill-White Iron -0.58 0.163 Peltier-Tait 0.85 0.012 Peltier-Ten Mile 1.17 <0.001 71 Peltier-White Iron 0.37 0.584 Tait-Ten Mile 0.33 0.688 Tait-White Iron -0.47 0.364 Ten Mile-White Iron -0.80 0.001 logit (MCPD + 0.01) Carrie-Hill -0.53 0.785 Carrie-Peltier -1.74 <0.001 Carrie-Tait -0.65 0.613 Carrie-Ten Mile 0.67 0.430 Carrie-White Iron -0.83 0.255 Hill-Peltier -1.20 0.010 Hill-Tait -0.12 0.999 Hill-Ten Mile 1.21 0.002 Hill-White Iron -0.30 0.951 Peltier-Tait 1.09 0.027 Peltier-Ten Mile 2.41 <0.001 Peltier-White Iron 0.91 0.055 Tait-Ten Mile 1.32 <0.001 Tait-White Iron -0.18 0.995 Ten Mile-White Iron -1.50 <0.001

C) Comparison Est. P

log10 (TBV) Central-North -0.45 <0.001 Central-South -1.41 <0.001 North-South -0.96 <0.001 logit (CD) Central-North -0.54 0.002 Central-South -0.61 0.002 North-South -0.06 0.941 logit (MCPD + 0.01) Central-North -1.07 <0.001 Central-South -1.41 <0.001 North-South -0.35 0.384

72 (Figure A1 Start) A) White Iron

B) Tait

74 C) Ten Mile

75 D) Hill

76 E) Carrie

77 F) Peltier

78 G) All Lakes

Figure A1 Pearson’s r correlation matrices with respective p-values between lake parameters for each study lake (A-F) and all study lakes z-scored, aggregated by sampling round (G). Information on transformed variables (“_T”) is listed in Table A6. 79 Table A8 Linear model results for study lakes (A-F). Model dependent variable was logit (MCPD + 0.01)

A) White Iron Predictor Model F df P Adj. R2 AICc Temp -7.59 + 0.26x 7.73 66 0.007 0.09 310 DO 10.78 - 1.56x 6.93 66 0.011 0.08 310

log10 (pH) -25.44 + 11.82x 3.49 65 0.066 0.04 309 Cond 0.82 - 0.06x 1.92 66 0.171 0.01 315 TP -2.96 + 0.03x 0.08 70 0.773 -0.01 335

log10 (TN) -41.80 + 6.21x 2.59 69 0.112 0.02 328

log10 (NH4) 1.42 - 1.38x 4.93 70 0.030 0.05 330

NO3 -1.39 - 0.04x 6.80 70 0.011 0.08 328

log10 (TKN) -57.58 + 8.76x 6.27 69 0.015 0.07 324

log10 (N:P) -4.16 + 0.51x 0.07 70 0.785 -0.01 335 SD -0.42 - 1.38x 0.12 52 0.725 -0.02 249

log10 (Chla) -2.68 + 1.16x 23.00 70 <0.001 0.24 314 TS -1.88 - 0.60x 3.38 70 0.070 0.03 331

log10 (TBV) -33.60 + 2.02x 53.72 70 <0.001 0.43 294 logit (CD) -2.84 + 1.20x 90.76 70 <0.001 0.56 275

B) Tait Predictor Model F df P Adj. R2 AICc Temp 1.06 - 0.19x 5.85 46 0.020 0.09 193 DO -8.20 + 0.64x 1.57 46 0.217 0.01 197 pH 8.83 - 5.87x 2.86 46 0.098 0.04 196 Cond -0.01 - 0.07x 1.51 46 0.226 0.01 197

log10 (TP) -8.03 + 2.22x 3.77 46 0.058 0.06 195 TN -10.79 + 0.02x 6.05 46 0.018 0.10 193

NH4 -1.51 - 0.13x 3.62 45 0.063 0.05 190

log10 (NO3) -3.19 + 0.52x 2.88 46 0.097 0.04 196 TKN -10.36 + 0.02x 5.39 46 0.025 0.09 193 N:P -1.03 - 0.02x 1.40 46 0.243 0.01 197 SD -2.88 + 0.44x 0.08 34 0.775 -0.02 145 Chla -2.77 + 0.08x 0.09 46 0.763 -0.02 198

log10 (TS) -3.29 - 0.37x 4.14 46 0.048 0.06 194

log10 (TBV) -3.81 + 0.08x 0.06 46 0.811 -0.02 198 logit (CD) -2.56 + 0.53x 5.20 46 0.027 0.08 193

80 C) Ten Mile Predictor Model F df P Adj. R2 AICc

log10 (26-Temp) -3.65 - 0.19x 1.37 109 0.245 0.00 322 DO -2.18 - 0.19x 0.94 109 0.335 0.00 323 pH -6.93 + 0.35x 3.45 109 0.066 0.02 320

log10 (216.5-Cond) -5.04 + 0.51x 19.43 109 <0.001 0.14 305

log10 (TP) -3.49 - 0.22x 0.27 106 0.606 -0.01 306 TN -5.90 + 0.01x 8.02 108 0.006 0.06 311

NH4 -4.61 + 0.07x 3.36 107 0.070 0.02 315

log10 (NO3) -3.75 - 0.24x 2.86 109 0.093 0.02 321 TKN -5.92 + 0.01x 8.41 108 0.005 0.07 311 N:P -4.70 + 0.01x 2.55 108 0.113 0.01 319 SD -5.90 + 0.44x 1.81 81 0.183 0.01 249

log10 (Chla) -3.84 + 0.40x 12.62 109 0.001 0.10 311 TS -4.18 + 0.12x 2.87 109 0.093 0.02 321

log10 (TBV) -5.32 + 0.09x 0.28 109 0.600 -0.01 323 logit (CD) -3.82 + 0.29x 7.56 109 0.007 0.05 316

D) Hill Predictor Model F df P Adj. R2 AICc Temp -3.36 + 0.02x 0.19 46 0.667 -0.02 158

log10 (10.6-DO) -3.30 + 0.86x 2.55 45 0.117 0.03 153 pH -2.42 - 0.04x 0.01 46 0.925 -0.02 158 Cond 3.71 - 0.02x 0.52 45 0.473 -0.01 153

log10 (TP) -2.43 - 0.11x 0.16 46 0.692 -0.02 158

log10 (TN) -6.60 + 0.60x 0.33 46 0.568 -0.01 158

log10 (NH4) -3.10 + 0.10x 0.34 46 0.562 -0.01 158

log10 (NO3) -2.65 - 0.94x 0.23 46 0.638 -0.02 158

log10 (TKN) -6.76 + 0.63x 0.35 46 0.559 -0.01 158

log10 (N:P) -3.58 + 0.20x 0.42 46 0.522 -0.01 158 SD -1.33 - 0.52x 1.81 34 0.187 0.02 121

log10 (Chla) -2.89 + 0.17x 1.41 46 0.242 0.01 157 TS -2.34 - 0.14x 1.85 46 0.181 0.02 156

log10 (TBV) -3.33 + 0.04x 0.02 46 0.883 -0.02 158 logit (CD) -2.64 + 0.47x 15.48 46 <0.001 0.24 144

81 E) Carrie Predictor Model F df P Adj. R2 AICc

log10 (27.2-Temp) -3.84 + 0.40x 1.26 30 0.271 0.01 115 DO 1.43 - 0.53x 1.82 26 0.190 0.03 102 pH -2.58 - 0.07x 0.00 26 0.950 -0.04 104 Cond -10.74 + 0.02x 1.23 26 0.278 0.01 103 TP -3.40 + 0.01x 0.01 29 0.925 -0.03 114 TN -3.33 + (1.15E-5)x 0.00 30 0.988 -0.03 116

NH4 -4.14 + 0.01x 1.85 29 0.184 0.03 112

log10 (NO3) -2.85 - 0.01x 0.04 30 0.834 -0.03 116 TKN -4.92 + (2.36E-3)x 0.95 30 0.339 0.00 115

log10 (N:P) -3.45 + 0.03x 0.00 30 0.975 -0.03 116 SD -6.25 + 2.94x 2.72 22 0.114 0.07 89

log10 (Chla) -3.68 + 0.65x 5.87 30 0.022 0.14 111 TS -3.01 - 0.08x 0.36 26 0.552 -0.02 104

log10 (TBV) -7.30 + 0.26x 0.73 30 0.399 -0.01 116 logit (CD) -3.16 + 0.21x 1.13 30 0.297 0.00 115

F) Peltier Predictor Model F df P Adj. R2 AICc

log10 (27.5-Temp) -1.46 - 0.07x 0.02 54 0.882 -0.02 271 DO -1.83 + (8.67E-4)x 0.00 47 0.995 -0.02 240 pH -5.22 + 0.41x 0.67 47 0.417 -0.01 239 Cond -11.41 + 0.02x 1.79 47 0.187 0.02 238

log10 (TP) -26.11 + 4.72x 37.29 53 <0.001 0.40 237

log10 (TN) -31.38 + 4.07x 17.35 54 <0.001 0.23 255

log10 (NH4) 0.11 - 0.48x 1.76 54 0.191 0.01 269

log10 (NO3) -1.57 + (3.35E-3)x 0.00 54 0.988 -0.02 271

log10 (TKN) -31.42 + 4.08x 18.54 54 <0.001 0.24 254 N:P 0.03 - 0.08x 1.6 54 0.212 0.01 269

log10 (SD) -1.74 - 3.06x 11.42 40 0.002 0.20 195

log10 (Chla) -1.98 + 0.49x 1.59 54 0.008 0.11 264

log10 (TS) -2.71 - 0.80x 16.28 47 <0.001 0.24 225

log10 (TBV) -25.76 + 1.45x 24.07 54 <0.001 0.30 250 logit (CD) -2.37 + 1.18x 100.90 54 <0.001 0.65 212

82 Table A9 Subsample information for sites analyzed for total microcystin concentration (TMC) using LC/MS/MS analysis. Results below the detection limit reported as BDL. MC-congener (-YR, -LR, -LA, and –RR) concentrations reported in ng·L-1.

MC- MC- MC- MC- TMC TMC Lake Site Round YR LR LA RR (ng·L-1) (μg·L-1) White Iron WI15 June.16 BDL 6.7 14.8 BDL 21.5 0.02 White Iron WI08 July.16 BDL 20.2 14.1 BDL 34.3 0.03 White Iron WI15 July.16 BDL 31.7 10.4 BDL 42.1 0.04 White Iron WI02 Aug.16 BDL 46.1 49.6 BDL 95.7 0.10 White Iron WI10 Aug.16 BDL 91.3 7.2 BDL 98.5 0.10 White Iron WI06 Sept.16 BDL 24.6 BDL BDL 24.6 0.02 White Iron WI16 Sept.16 2.6 49.1 15.3 2.7 69.7 0.07 White Iron WI07 June.17 BDL BDL 4.3 BDL 4.3 0.00 White Iron WI08 July.17 BDL 7.7 16.4 BDL 24.1 0.02 White Iron WI15 July.17 BDL BDL 15.2 BDL 15.2 0.02 White Iron WI01 Aug.17 BDL 25.6 15.5 BDL 41.1 0.04 White Iron WI15 Aug.17 BDL 17.2 8.4 BDL 25.6 0.03 White Iron WI06 Sept.17 BDL 12.2 14.5 BDL 26.7 0.03 White Iron WI16 Sept.17 BDL 26.8 13.5 BDL 40.3 0.04 Tait T05 June.16 BDL 16.7 14.0 3.2 33.9 0.03 Tait T06 July.16 BDL 21.0 26.6 BDL 47.6 0.05 Tait T04 Aug.16 BDL 30.5 18.3 BDL 48.8 0.05 Tait T02 Sept.16 BDL 15.0 9.2 BDL 24.2 0.02 Tait T05 June.17 BDL 7.6 16.8 10.6 35.0 0.04 Tait T06 July.17 BDL 7.5 23.0 BDL 30.5 0.03 Tait T04 Aug.17 BDL 14.1 28.3 4.0 46.4 0.05 Tait T02 Sept.17 BDL 12.4 35.5 4.0 51.9 0.05 Ten Mile TM21 June.16 BDL BDL BDL BDL 0.0 0.00 Ten Mile TM10 July.16 BDL BDL BDL BDL 0.0 0.00 Ten Mile TM22 Aug.16 BDL BDL 7.9 BDL 7.9 0.01 Ten Mile TM07 Sept.16 BDL BDL BDL BDL 0.0 0.00 Ten Mile TM22 June.17 BDL BDL 7.9 BDL 7.9 0.01 Ten Mile TM22 July.17 BDL BDL 7.5 BDL 7.5 0.01 Ten Mile TM13 Aug.17 BDL BDL 19.4 BDL 19.4 0.02 Ten Mile TM07 Sept.17 BDL BDL 14.8 BDL 14.8 0.01 Hill H04 June.16 BDL BDL 50.6 BDL 50.6 0.05 Hill H03 July.16 BDL 5.2 41.2 BDL 46.4 0.05 Hill H04 Aug.16 BDL 31.6 129.3 BDL 160.9 0.16 Hill H09 Aug.16 BDL 15.7 79.4 BDL 95.1 0.10 Hill H01 Sept.16 BDL 21.4 282.8 BDL 304.2 0.30 Hill H03 Sept.16 BDL 56.1 328.6 BDL 384.7 0.38 Hill H04 June.17 BDL BDL 18.5 BDL 18.5 0.02 Hill H03 July.17 BDL BDL 29.0 BDL 29.0 0.03

83 Hill H04 Aug.17 BDL BDL 52.1 BDL 52.1 0.05 Hill H08 Aug.17 BDL 8.6 46.2 BDL 54.8 0.05 Hill H01 Sept.17 BDL 13.8 80.0 BDL 93.8 0.09 Hill H03 Sept.17 BDL 7.5 78.1 BDL 85.6 0.09 Carrie C04 June.16 BDL 9.7 34.8 3.0 47.5 0.05 Carrie C02 July.16 BDL 35.0 146.6 2.3 183.9 0.18 Carrie C04 July.16 BDL 38.8 349.8 BDL 388.6 0.39 Carrie C02 Aug.16 1.3 113.5 231.5 3.1 349.4 0.35 Carrie C04 Aug.16 BDL 92.3 307.1 BDL 399.4 0.40 Carrie C01 Sept.16 BDL 46.1 79.7 2.6 128.4 0.13 Carrie C04 Sept.16 9.9 55.3 98.5 BDL 163.7 0.16 Carrie C05 June.17 BDL 5.5 25.7 BDL 31.2 0.03 Carrie C02 July.17 BDL 15.0 98.5 BDL 113.5 0.11 Carrie C04 July.17 BDL 26.0 103.6 BDL 129.6 0.13 Carrie C01 Aug.17 BDL 8.1 45.9 BDL 54.0 0.05 Carrie C04 Aug.17 BDL 11.5 66.9 BDL 78.4 0.08 Carrie C01 Sept.17 BDL 28.3 126.0 BDL 154.3 0.15 Carrie C04 Sept.17 BDL 27.4 156.1 BDL 183.5 0.18 Peltier P06 June.16 8.3 32.0 173.5 6.8 220.6 0.22 Peltier P04 July.16 313.6 4483.0 14534.4 989.8 20320.8 20.32 Peltier P06 July.16 228.3 1977.2 8811.8 948.7 11966.0 11.97 Peltier P02 Aug.16 2152.5 1143.7 869.0 610.2 4775.4 4.78 Peltier P07 Aug.16 BDL BDL BDL BDL 0.0 0.00 Peltier P01 Sept.16 219.9 695.4 1097.8 239.8 2252.9 2.25 Peltier P06 Sept.16 36.4 118.9 99.9 30.3 285.5 0.29 Peltier P04 June.17 4.0 6.0 12.0 4.1 26.1 0.03 Peltier P04 July.17 BDL 23.6 27.0 12.5 63.1 0.06 Peltier P06 July.17 10.4 34.8 1.4 28.4 75.0 0.08 Peltier P04 Aug.17 2855.0 7770.8 568.9 11287.8 22482.5 22.48 Peltier P07 Aug.17 10.9 17.5 BDL 14.6 43.0 0.04 Peltier P01 Sept.17 276.8 968.9 1124.6 600.2 2970.5 2.97 Peltier P04 Sept.17 1047.5 4655.9 4549.8 2648.5 12901.7 12.90