In Vitro Trial of Lake Guard Copper-Based Algaecide Efficacy In

IN VITRO TRIAL OF LAKE GUARD COPPER-BASED ALGAECIDE EFFICACY IN

MANAGING ALGAL BLOOMS USING FIELD SAMPLED ORGANISMS

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science

David S. Lowry

May 2021 IN VITRO TRIAL OF LAKE GUARD COPPER-BASED ALGAECIDE EFFICACY IN

MANAGING ALGAL BLOOMS USING FIELD SAMPLED ORGANISMS

David S. Lowry

Thesis

Approved: Accepted:

______Co-Advisor Department Chair Dr. Teresa J. Cutright Dr. Stephen Weeks

______Co-Advisor Interim Dean of the College Dr. John M. Senko Dr. Joe Urgo

______Committee Member Interim Director, Graduate School Dr. Donald W. Ott Dr. Marnie Saunders

______Date

ABSTRACT

Harmful algal blooms, most notably toxic cyanobacterial blooms, pose serious threats to water management and the environment. Copper algaecides, having a long history of use as a biocide in controlling algal blooms, is also toxic to other aquatic organisms and requires responsible application. With the majority of algal blooms occurring at a waterbody’s surface, new copper formulations and application methods are continually developed to increase targeted action. Lake Guard Blue (BlueGreen Water

Technologies Ltd.) was formulated as a floating copper product to increase upper water strata exposure. This product is conveyed as slow releasing and capable of movement in tandem with surface blooming algae through wind and water motion.

Copper-based algaecide Lake Guard Blue was used in bench-scale experiments to determine efficacy in reducing cyanobacterial bloom densities. The product does not currently have EPA approval for use within the U.S. but is used elsewhere on the international market. Bench-scale experiments using environmentally sampled water with indigenous organisms were treated in triplicate with two dosages of Lake Guard Blue in parallel with EPA approved Cutrine Ultra (Arch Chemicals Inc.). Cyanobacteria populations were collected in the field at >10,000 cells/ml to be adjusted in the laboratory to 10,000 ±1000 cells/ml in accordance with the minimal action level required by the EPA for algaecide application. Lake Guard manufacture’s recommended maximum dose of 17.8 lbs/acre was determined to contain more elemental copper than is allowable under current

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EPA regulations. A new full dose was calculated to 8.45 lbs/acre Lake Guard Blue for experimentation, translating to 2.49 mg/L Cu. Simultaneously, a copper equivalent dose of

0.94 mg/L Cu was established for Lake Guard Blue to match that of the recommended dose for comparative algaecide Cutrine Ultra. In total seven experiments were performed with five lakes sourced for water, two of which were sampled twice. These lakes, all located in northeast Ohio, included three within the Cleveland Metro Parks (Camp Forbes, Ledge

Lake & Isaac Lake), Hudson Springs Lake and a drinking water reservoir, Lake Rockwell.

Although the new Lake Guard full dose did reduce cyanobacteria density over time, it was observed to be far too impactful to both cyanobacteria and to all other non-target algae in all experiments. Collectively, mixed cyanobacteria populations in reactors that included genera such as Microcystis, Anabaena and Oscillatoria, showed dramatic reductions in average cell density from 76.45 - 96.84% by day 2 following full dose Lake

Guard application and declined further through day 14. Application of the lower Lake

Guard dose was similarly impactful on cyanobacteria density however varied widely from

59.16 - 95.28% compared to 33.26 – 92.22% seen in the copper equivalent Cutrine Ultra by day 2. Reductions also continued for the lower Lake Guard dose as in the full dose but again with the same wide variability.

Similar extreme reductions in chl-a concentration for full dose Lake Guard were observed by day 2 but with a broader range of 35.89 – 81.74%. This was not much better for the lower Lake Guard dose with an average decrease ranging 40.82 – 86.54% by day

2. In both cases, further declines continued out to day 14. Non-target members of diverse algal communities in both Lake Guard groups that were easily located at the start of experimentation were not detected in microscopy samples by day 14. Genera of

Chlorophyceae including Elakatothrix and Ankistrodesmus were less often detected by day

14 compared to those species of Pediastrum, Scenedesmus and Radiococcus. In contrast,

Cutrine Ultra had considerable variation in its impact on chl-a concentration by day 2 ranging from an average increase of 18.20% to an average decrease of 77.82%. Further, while rebounds in chl-a concentration occurred in none of the Lake Guard experiments,

Cutrine Ultra showed signs of non-target algae rebounding by day 14 with some experiments yielding average increases in chl-a concentration from that of day 2 levels ranging 113.3 – 2876.49% by the end of the 14 day experiments.

Based on results of these experiments, it is not recommended to use Like Guard

Blue at its current manufacture’s recommended dose and a lower dose should be evaluated.

Lake Guard Blue would benefit from further laboratory studies. Monoculture and controlled mixed culture studies are among the highest recommended of those outlined by the results of this study to determine species specific impacts. Additional insights should be drawn about the product’s rate of copper release and ability to degrade in the environment as potentially hazardous debris remained at the end of 14 day experimentation.

ACKNOWLEDGEMENTS

I would like to thank my co-advisors, Dr. Teresa J. Cutright and Dr. John M. Senko, and committee member Dr. Donald W. Ott for their guidance through my graduate work.

I would also like to thank may other professionals and fellow graduate students I have worked with along with way, contributing to my knowledge and teaching me laboratory skills that only come from true experience. Particularly, Dr. Robert B. Miller, Dr. Matthew

E. Jennings, Angela Alicea-Serrano M.Sc., Dr. Elizabeth A. Crafton, Robert Holmes and

Mr. Thomas J. Quick.

I also extend my deepest thanks to my family for their endless love and support during my time at The University of Akron.

The following work was made possible through funding by the Cleveland

Foundation and water sourced from the Cleveland Metro Parks, Hudson Springs Park and

Lake Rockwell. This project was made possible through their steadfast cooperation.

TABLE OF CONTENTS

PAGE

LIST OF TABLES ...... XII

LIST OF FIGURES ...... XIII

CHAPTER I ...... 1

INTRODUCTION AND OBJECTIVES ...... 1

1.1 Causes and impacts of harmful algal blooms (HABs) ...... 1

1.2 Algae mitigation and the use of copper as an algaecide ...... 3

1.3 Research Objectives ...... 6

CHAPTER II ...... 8

LITERATURE REVIEW...... 8

2.1 Harmful Algal Blooms (HABs) ...... 8

2.2 Cyanobacteria Harmful Algal Blooms (cHABs) and Cyanotoxins ...... 8

2.2.2 Eutrophication and Climate Change ...... 10

2.3 CyanoHAB management and the use of algaecides ...... 12

2.3.1 Copper Algaecides ...... 12

CHAPTER III...... 15

MATERIALS & METHODS ...... 15

3.1 Site Collection ...... 15

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3.2 Collection Site Descriptions...... 16

3.3 HAB simulated reactors ...... 19

3.4 Experimental design ...... 20

3.5 Microscopy and identification methods ...... 22

3.6 Statistical Analysis ...... 23

CHAPTER IV ...... 24

RESULTS & DISCUSSION ...... 24

4.1 Lake Rockwell (7.23.2019 & 8.12.2019) treatments vs. control ...... 24

4.1.1 Incorporation of Lower Dose of Lake Guard ...... 24

4.1.2 Lake Rockwell visual observations and cyanobacteria response to algaecide

treatment ...... 25

4.1.2.1 Visual Observations – high dose experiments (July 23-Aug 7, 2019) . 25

4.1.2.2 Cyanobacteria response – high dose experiments (July 23-Aug 7, 2019) ...... 26

4.1.2.3 Visual Observations – low dose experiments (8.12.2019 – 8.27.2019) 29

4.1.2.4 Cyanobacteria response - low dose experiments (8.12.2019 – 8.27.2019) ...... 30

4.1.3 Lake Rockwell chlorophyll-a response to algaecide treatment ...... 33

4.1.3.1 Chlorophyll-a response - high dose experiments (July 23-Aug 7, 2019) ...... 33

4.1.3.2 Chlorophyll-a response– low dose experiments (8.12.2019 – 8.27.2019) ...... 35

4.1.4 Lake Rockwell microorganism composition response to algaecide treatment 38

4.1.4.1 Microorganism composition response– high dose experiments (July 23 - Aug 7, 2019) ...... 38

4.1.4.2 Microorganism composition response – low dose experiments (8.12.2019 – 8.27.2019) ...... 39

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4.1.5 Lake Rockwell microorganism composition response to algaecide treatment 40

4.1.5.1 Changes in pH and conductivity – high dose experiments (July 23 - Aug 7, 2019) ...... 40

4.1.5.2 Changes in pH and conductivity – low dose experiments (8.12.2019 – 8.27.2019) ...... 40

4.2 Camp Forbes treatment groups vs. control (8.14.2019 – 8.29.2019) ...... 41

4.2.1 Camp Forbes Visual Observations ...... 41

4.2.2 Camp Forbes cyanobacteria response to algaecide treatment ...... 42

4.2.3 Camp Forbes chlorophyll-a response to algaecide treatment ...... 45

4.2.4 Camp Forbes response in microorganism composition to algaecide treatment ...... 48

4.2.5 Camp Forbes responses in pH and conductivity ...... 48

4.3 Hudson Springs treatment groups vs. control (8.20.2019 – 9.4.2019) ...... 49

4.3.1 Hudson Springs visual observations ...... 49

4.3.2 Hudson Springs cyanobacteria response to algaecide treatments ...... 50

4.3.3 Hudson Springs chlorophyll-a response to algaecide treatments ...... 53

4.3.4 Hudson Springs response in microorganism composition to algaecide treatment ...... 55

4.3.5 Hudson Springs responses in pH and conductivity ...... 56

4.4 Ledge Lake treatment groups vs. control (9.5.2019 - 9.20.2019) ...... 57

4.4.1 Ledge Lake visual Observations ...... 57

4.4.2 Ledge Lake cyanobacteria response to algaecide treatment ...... 58

4.4.3 Ledge Lake chlorophyll-a response to algaecide treatment ...... 61

4.4.4 Ledge Lake response in microorganism composition to algaecide treatment ...... 63

4.4.5 Ledge Lake response in pH and conductivity ...... 64

4.5 Lake Isaac treatment groups vs. control – (9.9.2019 – 9.24.2019) ...... 65

4.5.1 Lake Isaac visual observations ...... 65

4.5.2 Lake Isaac cyanobacteria response to algaecide treatments ...... 66

4.5.3 Lake Isaac chlorophyll-a response to algaecide treatments ...... 68

4.5.4 Lake Isaac response in microorganism composition to algaecide treatment ...... 71

4.5.5 Lake Isaac responses in pH and conductivity ...... 71

CHAPTER V ...... 73

CONCLUSIONS AND RECOMMENDATIONS ...... 73

5.1 General Overview ...... 73

5.2 Conclusions ...... 73

5.3 Recommendations ...... 75

REFERENCES ...... 78

APPENDIX A ...... 82

Lake Rockwell (7.23.2019 – 8.7.2019) ...... 82

APPENDIX B ...... 88

Lake Rockwell (8.12.2019 – 8.27.2019) ...... 88

APPENDIX C ...... 94

Camp Forbes (8.14.2019 – 8.8.29.2019)...... 94

APPENDIX D ...... 101

Hudson Lake (8.20.2019 – 9.4.2019)...... 101

APPENDIX E...... 108

Ledge Lake (9.5.2019 - 9.20.2019) ...... 108

APPENDIX F ...... 117

Isaac Lake (9.9.2019 – 9.24.2019) ...... 117

APPENDIX G ...... 126

Hudson Lake (7.29.2019 – 8.13.2019)...... 126

LIST OF TABLES

Table 4.1.2.2: Lake Rockwell (7.23.2019) statistical comparison of cyanobacteria density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 28

Table 4.1.2.4: Lake Rockwell (8.12.2019) statistical comparison of cyanobcateria density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 32

Table 4.1.3.1: Lake Rockwell (7.23.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 35

Table 4.1.3.2: Lake Rockwell (8.12.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 37

Table 4.2.2: Camp Forbes statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 44

Table 4.2.3: Camp Forbes statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 47

Table 4.3.2: Hudson (8.20.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 52

Table 4.3.3: Hudson (8.20.2019) statistical comparison of chl-a (µg/L) between Time 0 - 2 and Time 2-14 for all conditions following algaecide application...... 55

Table 4.4.2: Ledge Lake statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 60

Table 4.4.3: Ledge Lake statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 63

Table 4.5.2: Lake Isaac statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 68

Table 4.5.3: Lake Isaac statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 70

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LIST OF FIGURES

Figure 4.1.2.1: Day 2 Lake Rockwell Reactors after algaecide application. (Left Image) From left to right; Control, Cutrine Ultra, Lake Guard 17.6 mg. (Right Image) Lake Guard material remaining afloat in 17.6 mg Lake Guard reactor...... 26

Figure 4.1.2.2: Lake Rockwell (7.23.2019) cyanobacterial density (cells/ml) over 14 days ...... 27

Figure 4.1.2.3: Day 14 Lake Rockwell Reactors after algaecide application. From left to right; (Left Image) Stir bar in control reactor. (Right Image) Faded and gelatinous Lake Guard material remaining afloat in 6.65 mg Lake Guard reactor...... 30

Figure 4.1.2.4: Lake Rockwell (8.12.2019) cyanobacterial density (cells/ml) over 14 days ...... 31

Figure 4.1.3.1: Lake Rockwell (7.23.2019) chl-a (µg/L) response to algaecide treatments over 14 days ...... 33

Figure 4.1.3.2: Lake Rockwell (8.12.2019) chl-a (µg/L) response to algaecide treatments over 14 days ...... 36

Figure 4.2.2: Camp Forbes Cyanobacterial density (cells/ml) over 14 days ...... 43

Figure 4.2.3: Camp Forbes chl-a (µg/L) response to algaecide treatments over 14 days . 46

Figure 4.3.2: Hudson (8.20.19) Cyanobacterial density (cells/ml) over 14 days ...... 51

Figure 4.3.2: Hudson (8.20.2019) chl-a (µg/L) response to algaecide treatments over 14 days ...... 54

Figure 4.4.1: Day 14 Ledge Lake Reactors after algaecide application. (Left Image) From left to right; Control, Cutrine Ultra, Lake Guard 17.6 mg, Lake Guard 6.65 mg. (Right Image) Swollen Lake Guard Material remaining afloat in 17.6 mg Lake Guard reactor...... 58

Figure 4.4.2: Ledge Lake Cyanobacterial density (cells/ml) over 14 days ...... 59

Figure 4.4.3: Ledge Lake chl-a (µg/L) response to algaecide treatments over 14 days ... 61

Figure 4.5.2: Lake Isaac cyanobacterial density (cells/ml) over 14 days ...... 66

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Figure 4.5.3: Lake Isaac chl-a (µg/L) response to algaecide treatments over 14 days ..... 69

Figure A1: Lake Rockwell (7.23.2019 – 8.7.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 82

Figure A2: Lake Rockwell (7.23.2019 – 8.7.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 82

Figure A3: Lake Rockwell (7.23.2019 – 8.7.2019) changes in phycocyanin RFU over 14 days ...... 83

Figure A4: Lake Rockwell (7.23.2019 – 8.7.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 83

Figure A5: Lake Rockwell (7.23.2019 – 8.7.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 84

Figure A6: Lake Rockwell (7.23.2019 – 8.7.2019) changes in chl-a RFU over 14 days . 84

Figure A7: Lake Rockwell (7.23.2019 – 8.7.2019) changes in temperature over 14 days85

Figure A8: Lake Rockwell (7.23.2019 – 8.7.2019) changes in pH over 14 days ...... 85

Figure A9: Lake Rockwell (7.23.2019 – 8.7.2019) changes in conductivity over 14 days ...... 86

Figure A10: Microorganism presence in Lake Rockwell (7.23.2019 – 8.7.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group...... 87

Figure B1: Lake Rockwell (8.12.2019 – 8.27.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 88

Figure B2: Lake Rockwell (8.12.2019 – 8.27.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 88

Figure B3: Lake Rockwell (8.12.2019 – 8.27.2019) changes in phycocyanin RFU over 14 days ...... 89

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Figure B4: Lake Rockwell (8.12.2019 – 8.27.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 89

Figure B5: Lake Rockwell (8.12.2019 – 8.27.2019) Tukey’s Pairwise comparison of chl- a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 90

Figure B6: Lake Rockwell (8.12.2019 – 8.27.2019) changes in chl-a RFU over 14 days 90

Figure B8: Lake Rockwell (8.12.2019 – 8.27.2019) changes in pH over 14 days ...... 91

Figure B9: Lake Rockwell (8.12.2019 – 8.27.2019) changes in conductivity over 14 days ...... 92

Figure B10: Microorganism presence in Lake Rockwell (8.12.2019 – 8.27.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 6.65 mg Lake Guard Group ...... 93

Figure C1: Camp Forbes (8.14.2019 – 8.8.29.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 94

Figure C2: Camp Forbes (8.14.2019 – 8.8.29.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 95

Figure C3: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in phycocyanin RFU over 14 days ...... 95

Figure C4: Camp Forbes (8.14.2019 – 8.8.29.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 96

Figure C5: Camp Forbes (8.14.2019 – 8.8.29.2019) Tukey’s Pairwise comparison of chl- a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 96

Figure C6: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in chl-a RFU over 14 days 97

Figure C7: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in temperature over 14 days ...... 97

Figure C8: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in pH over 14 days ...... 98

Figure C9: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in conductivity over 14 days ...... 98

Figure C10: Microorganism presence in Camp Forbes (8.14.2019 – 8.8.29.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group...... 100

Figure D1: Hudson (8.20.2019 – 9.4.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 101

Figure D2: Hudson (8.20.2019 – 9.4.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 102

Figure D3: Hudson (8.20.2019 – 9.4.2019) changes in phycocyanin RFU over 14 days ...... 102

Figure D4: Hudson (8.20.2019 – 9.4.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application. ... 102

Figure D5: Hudson (8.20.2019 – 9.4.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 103

Figure D6: Hudson (8.20.2019 – 9.4.2019) changes in chl-a RFU over 14 days ...... 103

Figure D7: Hudson (8.20.2019 – 9.4.2019) changes in temperature over 14 days ...... 104

Figure D8: Hudson (8.20.2019 – 9.4.2019) changes in ph over 14 days ...... 104

Figure D9: Hudson (8.20.2019 – 9.4.2019) changes in conductivity over 14 days...... 105

Figure D10: Microorganism presence in Hudson (8.20.2019 – 9.4.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group...... 107

Figure E1: Ledge Lake (9.5.2019 - 9.20.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 108

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Figure E2: Ledge Lake (9.5.2019 - 9.20.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 109

Figure E3: Ledge Lake (9.5.2019 - 9.20.2019) changes in phycocyanin RFU over 14 days ...... 109

Figure E4: Ledge Lake (9.5.2019 - 9.20.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 110

Figure E5: Ledge Lake (9.5.2019 - 9.20.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 110

Figure E6: Ledge Lake (9.5.2019 - 9.20.2019) changes in chl-a RFU over 14 days ..... 111

Figure E7: Ledge Lake (9.5.2019 - 9.20.2019) changes in temperature over 14 days ... 111

Figure E8: Ledge Lake (9.5.2019 - 9.20.2019) changes in pH over 14 days ...... 112

Figure E9: Ledge Lake (9.5.2019 - 9.20.2019) changes in conductivity over 14 days .. 112

Figure E10: Microorganism presence in Ledge Lake (9.5.2019 - 9.20.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group...... 116

Figure F1: Lake Isaac (9.9.2019 – 9.24.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 117

Figure F2: Lake Isaac (9.9.2019 – 9.24.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 118

Figure F3: Lake Isaac (9.9.2019 – 9.24.2019) changes in phycocyanin RFU over 14 days ...... 118

Figure F4: Lake Isaac (9.9.2019 – 9.24.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 119

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Figure F5: Lake Isaac (9.9.2019 – 9.24.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 119

Figure F6: Lake Isaac (9.9.2019 – 9.24.2019) changes in chl-a RFU over 14 days ...... 120

Figure F7: Lake Isaac (9.9.2019 – 9.24.2019) changes in temperature over 14 days .... 120

Figure F8: Lake Isaac (9.9.2019 – 9.24.2019) changes in pH over 14 days ...... 121

Figure F9: Lake Isaac (9.9.2019 – 9.24.2019) changes in conductivity over 14 days ... 121

Figure F10: Microorganism presence in Lake Isaac (9.9.2019 – 9.24.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group...... 125

Figure G1: Hudson (7.29.19) Cyanobacterial density (cells/ml) over 14 days ...... 126

Figure G2: Hudson (7.29.2019 – 8.13.2019) changes in phycocyanin RFU over 14 days ...... 127

Figure G3: Hudson (7.29.19) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 127

Figure G4: Hudson (7.29.2019 – 8.13.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 128

Figure G5: Hudson (7.29.2019 – 8.13.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 128

Figure G6: Hudson (7.29.19 – 8.13.2019) changes in chlorophyll-a concentration (ug/ml) over 14 days ...... 129

Figure G7: Hudson (7.29.19 – 8.13.2019) changes in chl-a RFU over 14 days ...... 129

Figure G8: Hudson (7.29.19 - 8.13.2019) statistical comparison of phycocyanin density (cells/ml) within groups from Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 130

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Figure G9: Hudson (7.29.2019 – 8.13.2019) statistical comparison of chlorophyll-a density (ug/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application...... 130

Figure G10: Hudson (7.29.2019 – 8.13.2019) Tukey’s Pairwise comparison of chlorophyll-a density (ug/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application ...... 130

Figure G12: Hudson (7.29.2019 – 8.13.2019) changes in ph over 14 days...... 131

Figure G11: Hudson (7.29.2019 – 8.13.2019) changes in conductivity over 14 days.... 131

Figure G12: Microorganism presence in Hudson (7.29.2019 – 8.13.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group...... 133

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CHAPTER I

INTRODUCTION AND OBJECTIVES

1.1 Causes and impacts of harmful algal blooms (HABs)

Over the past century, the world has seen a significant rise in the severity and geographical range of harmful algal blooms (HABs), resulting in substantial economic damages and threat to public health (Merel et al., 2013; Wehr et al., 2015; Chaffin et al.,

2019). In general, algae are vital organisms in aquatic systems, shaping a healthy ecosystem as primary producers and food source for higher organisms. Although algal blooms can be an indication of ecosystem disruption, they are also a natural phenomenon in nutrient rich waterbodies. They can become harmful when they cause adverse or dangerous effects that impact public health (i.e. drinking or recreational waters), fishing activities, aquaculture and tourism (Paerl et al., 2011; Merel et al., 2013; Wehr et al., 2015).

The mitigation of HAB biomass and associated toxins present is an expensive and time- consuming process, with US economic losses to cyanobacteria HABs (cHABs) alone exceeding $2 billion dollars annually (Paerl et al., 2011).

Some of the most notable impacts of HABs are the production of toxins, fouling of water, hypoxia, and impacts on biodiversity (Wehr et al., 2015). HABs are typically mono- specific in nature (dominated by one algal type) with representatives from most taxonomic groups capable of blooming and producing significant biomass in short amount of time under the right conditions (Merel et al., 2013; Wehr et al., 2015). All blooms are not equal, 1 with adverse effects sometimes differing according to algal types and possible management responses. All algae do not bloom in response to the same stimuli either. The three primary environmental factors are temperature, light availability, and nutrient abundance (trophic status) (Merel et al., 2013). Most blooms are generally recognized to occur in the mid to late summer months when temperatures reach ~25°C in nutrient rich, or eutrophic, waterbodies often linked to the ratio and quantities of available nitrogen (N) and phosphorous (P) (Merel et al., 2013; Wehr et al., 2015; Chaffin et al., 2019). In many cases, excess nutrients can be traced to anthropogenic sources including urbanization, agricultural runoff and sewage waste discharge (Wehr et al., 2015; Zhang et al., 2018).

Although many types of algae have the potential to become harmful, leading concern is placed with the toxin producing species of Cyanophyceae, or cyanobacteria, that can often dominate a bloom (cHABs). The environmental triggers and physiological role of these toxic secondary metabolites, called cyanotoxins, are not well understood but they can be classified into three classes: hepatotoxins that affect liver function, neurotoxins that target neuromuscular activity, and dermatoxins that cause skin irritation (Merel et al., 2013;

Wehr et al., 2015). Release of sufficient cyanotoxins into the environment creates a significant burden for public water management and can have complex impacts on the ecosystem and the organisms living in it. It is also important to note that a bloom event does not guarantee the synthesis of cyanotoxins, nor do all cyanobacteria possess the genes to produce toxins, however during a bloom the cellular growth is exponential and along with it the potential for genetic expression and intercellular synthesis of these toxins (Wehr et al., 2015; Merel et al., 2013).

The ultimate goal for any municipal body management should be the establishment of long-term prevention practices based on a proper understanding of their unique watersheds, reservoirs, lakes, etc. from which adverse events could arise (Merel et al., 2013; Rodriguez et al., 2019). Unfortunately, monitoring environmental conditions cannot always precisely predict a bloom and critical bloom issues call for immediate mitigation for the health and safety of communities (Rodriguez et al., 2019). In these instances, algaecides are a common tool to control algae populations but do have limitations in efficacy and in some cases governmental guidelines for their potential short and long-term ecotoxicological effects after use (Merel et al., 2013; Wehr et al., 2015;

Rodriguez et al., 2019).

1.2 Algae mitigation and the use of copper as an algaecide

Algaecides, classified as either copper or non-copper based, are manufactured for both individual and commercial use. Of these, copper-based materials are considered the least expensive products and typically delivered in a solid or liquid form to target areas for dispersion through the water body (Crafton et al., 2018; Wehr et al., 2015). In most cyanobacteria bloom events, there is a single (mono-specific), or a few, target alga that have overgrown in a water body and need to be controlled. Further, the majority of HABs are concentrated in upper strata of the water column (Merel et al., 2013). In these situations of upper strata blooms, chelated formulations of copper show greater dispersion compared solid products such as copper sulfate pentahydrate, however this latter form is still widely used in mitigation practices due to easy manual application (Bishop et al., 2018; Iwinski et al., 2017).

Despite efficacy, the use of copper faces drawbacks of being simultaneously toxic to other aquatic organisms. Accumulation in sediments, especially in the case of solid form copper products, as well as within the food chain (bioaccumulation) can lead to future ecotoxicological impacts. Additionally, different aquatic organisms have varying sensitivities to copper, potentially leading to detrimental impacts on an ecosystem following events of sediment disturbance such as storms (Zhang et al., 2018; Merel et al.,

2013; Song et al., 2011). For these reasons, the EPA regulates copper algaecides to 1 mg/L

Cu as the active ingredient and restricts use to 14 days between applications (Crafton et al.,

2021).

Continuous effort has been placed on understanding the toxicology of copper and achieving lowest possible concentrations for algaecide dosage, subsequently reducing copper loading and negative impacts listed above. Specificity of abiotic water quality factors such as alkalinity have been shown to affect copper activity, sometimes requiring higher recommended dosages in hard waters or application advisories if water hardness is insufficient (Arch Chemicals, Inc. Cutrine Ultra Specimen Label; BlueGreen Water

Technologies Ltd., Lake Guard Blue U.S. EPA Pesticide Registration 2018). Type and density of algae present in a water body are biotic factors used in manufacturers’ recommended dose (Bishop et al., 2018). Guidelines can further distinguish application values based on target organism characteristics including planktonic or filamentous forms.

Additionally, studies have found problematic species of Cyanobacteria to be more sensitive to copper than other algae with variations theoretically linked to differences in species morphology, such as cell wall and mucilage (Bishop et al., 2018; Borkow and Gabbay,

2005).

Utilizing differences in susceptibility can be an advantage when combating cHABs and choosing the right algaecide application. Ideally, copper based algaecides should be used to control a bloom before it becomes dangerous, eliminate risk and allow stability to return through regeneration of algal communities (Crafton et al., 2018; Tsai 2016).

Simultaneously, it is of interest to prevent excessive damage to non-target organisms, leaving them viable for growth once competitive pressure is relieved. Previous studies have considered this while comparing copper toxicities of various target algae (typically cyanobacteria) and non-target organisms (ie. Zooplankton, green algae, fish etc.) to inform water resource managers (Bishop et al., 2018; Yan and Pan, 2002). Researchers have also compared various copper product concentrations against single cyanobacteria species (ie.

Microcystis, Anabaena, Lyngbya, etc.), often finding that chelated copper is superior for internalizing the cell, where the most toxic responses occur (Bishop et al., 2018; Crafton et al., 2021; Tsai, 2015). As an example, chelated copper product Cutrine Ultra (Arch

Chemicals, Inc.) was found to reduce cyanobacteria populations at dose values even as low as 0.125 mg/L Cu, one quarter of that recommended by the manufacturer (Crafton et al.,

2021).

Given the predicted rise in frequency and intensity of HABs into the future, appropriate use of copper-based algaecide under changing circumstances will be essential in achieving mitigation goals (Merel et al., 2013, Wehr et al., 2015, Chaffin et al., 2019).

Copper based algaecides remain relatively cheap, and it is of both economic and ecological interest to deliver these algaecides precisely and timely. As a result, understanding the effects of various copper delivery strategies has been a subject of intense study in recent decades and resulted in the development of many new and modified products that improve

5 performance (Bishop et al. 2018, Crafton et al. 2018; Tsai, 2015). Balancing the simplicity and disadvantages of solid form copper algaecides continuously brings attention to room for advancement in the performance of these solid products.

1.3 Research Objectives

Algaecides are a common tool to control algae populations. The effectiveness of any algaecide will be related to the source and amount of active ingredient used in formulation, the specific organisms present in the environment and aspects of water chemistry at the time of application.

In this study, the efficacy of the floating copper sulphate pentahydrate algaecide,

Lake Guard, was tested in bench scale experiments for its efficiency to reduce cyanobacterial populations in environmental samples at the minimum regulatory cell density (10,000 cells/ml) required before algaecide action (Ohio EPA, 2019). A secondary ethanolamine-chelated copper algaecide, Cutrine Ultra, was used alongside Lake Guard experiments for comparative results.

Therefore, three interconnected objectives were studied:

1. The evaluation of Lake Guard’s efficacy at two concentrations in parallel with

Cutrine Ultra. Hypotheses 1. Lake Guard at the adjusted allowable “full-dose” of 8.45

lb/acre (2.49 mg/L Cu) will be effective in its ability to reduce cyanobacteria

populations. Hypothesis 2: A lower than recommended dosage of Lake Guard that is

equivalent to the copper concentration of Cutrine Ultra (0.5 mg/L Cu) may be

sufficient in decreasing cyanobacteria populations.

2. Evaluate the efficacy of Lake Guard to reduce cyanobacteria in different water

sources. Hypothesis 1: Lake Guard efficacy will vary with differences in the algae

composition of water sources but will yield overall reductions in cyanobacteria bloom

densities.

3. Preliminary determination of Lake Guard impact on non-target organisms.

Hypothesis 1: Lake Guard will decrease the number of green algae present in the

source water to some degree. Hypothesis 2: A lower than recommended dose of Lake

Guard may reduce negative impacts on non-target organisms while still providing a

sufficient decrease in the higher cyanobacteria bloom density.

CHAPTER II

LITERATURE REVIEW

2.1 Harmful Algal Blooms (HABs)

Algal bloom is a general term for overgrowth of algae in a waterbody (Wehr et al.,

2015; U.S. EPA 2019a). When a bloom poses a risk to the health of humans, ecosystems, or to industry in the form of damage to filters, pumps, etc. then it is considered to be a harmful algal bloom (HAB) (Wehr et al., 2015; U.S. EPA, 2019a). HABs thus have negative downstream impacts on our economy due to mitigation costs and threaten resources such as drinking water (Paerl and Barnard, 2020). While a variety of algal species can display bloom activity in the right conditions, blooms dominated by toxin producing cyanobacteria (cHABs) are of primary concern (U.S. EPA, 2019a; Wehr et al., 2015; Merel

2013).

2.2 Cyanobacteria Harmful Algal Blooms (cHABs) and Cyanotoxins

Cyanobacteria, also known as blue-green algae, are photosynthetic bacteria that possess a variety of physiological capabilities which make them quite robust in ecosystems and even advantaged to bloom in some context. Most notable of their unique features is the ability to utilize not only the light absorbing pigment chlorophyll-a (chl-a) to produce energy but also accessory phycobilin pigments, phycocyanin and phycoerythrin (Wehr et al., 2015, Bellinger and Sigee, 2015). Conveniently, sensors and satellites can be used to

8 monitor cyanobacteria accessory pigments and population levels, by differentiating the presence against organisms that lack these pigments and only possess chlorophyll-a (ie.

Chlorophyceae, Bacteriophyceae, Euglenophyceae, etc.) (Paerl and Barnard, 2020;

Crafton et al., 2018).

Other physiological advantages by cyanobacteria include their environmentally resistant spore cells (akinetes), nitrogen fixing cells (heterocysts) and a gelatinous sheath donned by some to further protect from environmental stressors (Bellinger and Sigee,

2015). Further, gas vacuoles within planktonic species can be adjusted for buoyancy and optimize positioning in the water column where light, nutrients or temperature are favorable (Ibelings et al. 2014). While there are both motile and non-motile species of algae, the use of gas vacuoles is useful in vertical adjustments in the water column (Wehr et al., 2015; Bellinger and Sigee, 2015).

Similar to eukaryotic algal blooms that can be planktonic or benthic, cHABs have the potential to produce significant biomass at various strata in the water column, blocking light from other photosynthetic organisms as well as increase nutrient competition (Wehr et al., 2015). The post effects of a bloom may also have a negative impact for other organisms by significantly reducing dissolved oxygen in the aquatic environment, known as hypoxia, via the heterotrophic decay of bloom matter (Gobler, 2020; U.S. EPA, 2019b;

Wehr et al., 2015)

The production of toxins by cyanobacteria, cyanotoxins, are of primary concern for cHABs. Not all cyanobacteria are capable of producing cyanotoxins, nor do they produce them at all times, but these toxins can pose a serious threat beyond clogging filters and being visually unappealing. Cyanotoxins are produced intracellularly before being released

9 into the environment, with the majority of toxin existing within the cell, however amplified release is possible if the cells become broken or lysed (U.S. EPA, 2019h; Ibelings et al.,

2014; Merel et al., 2013). The environmental triggers and physiological role of these toxic secondary metabolites are not well understood but they are thought to have inhibitory effects on other organisms, or allelopathic utility (Merel et al., 2013; Leao et al., 2009).

Cyanotoxins can be classified into three groups: hepatotoxins that affect liver function, neurotoxins that target neuromuscular activity, and dermatoxins that cause skin irritation (Merel 2013; Wehr 2015; U.S. EPA, 2019h). Included under the Contaminant

Candidate List (CCL) of the Safe Drinking Water Act (SDWA), the toxicity of these compounds toward humans and animals is one of the primary reasons it is essential to monitor and mitigate blooms and their potentially associated metabolites before, during and after an event (U.S. EPA, 2019h; U.S. EPA, 2019i; Merel et al., 2013).

2.2.2 Eutrophication and Climate Change

A global rise in the severity and geographical range of HABs in recent decades has had serious impacts on public health and economies (Wehr 2015, U.S. EPA 2019b, Paerl and Barnard, 2020). Also, the magnification of these phenomena have primarily been linked to features of global warming and a wide range of human activities, including agriculture, stormwater and even residential activities that contribute to high downstream nutrient levels, or eutrophication (Gobler, 2020; U.S. EPA 2019f). In the U.S. alone, estimated annual losses attributable to nutrient pollution and HABs include almost $1 billion for the tourism industry and tens of millions of dollars for fishing industries (U.S.

EPA, 2019e; Paerl et al., 2011).

Many species of algae are capable of bloom behavior in the right conditions, with some having similar environmental triggers. Despite the differences in triggers that may exist, the three primary environmental factors are temperature, light availability, and nutrient abundance (trophic status) (Merel et al., 2013). While sunlight and temperature are ambient factors, reduction of nutrient loading by human activity is more easily under societal control. Often introduced upstream, pollutants such as excess nitrogen and phosphorus leave their point sources through various routes including stormwater runoff or wastewater overflow to become more concentrated when entering streams, rivers and lakes that contribute to the eutrophic conditions leading to a bloom (U.S. EPA 2019f, Paerl and Barnard, 2020).

Nutrient overload in North America alone during the mid-1990s led to federal actions such as the Great Lakes Water Quality Agreement (GLWQA), a cooperative action between the U.S. and Canadian governments to reduce phosphorus loading in Lake Erie

(Chaffin et al., 2019). While it is still very important to act in reducing various forms of

+ - - nitrogen loading (eg. NH4 , NO3 , NO2 ), phosphorus restrictions are a major focus in cHAB mitigation since many cyanobacteria species are capable of atmospheric nitrogen fixation, and bloom readily at high phosphorus levels alone (Paerl et al., 2019; Chaffin et al., 2019; Paerl and Barnard, 2020).

To further complicate the monitoring of HABs and attempting to predict their occurrences, the impact of global climate change adds an additional level of uncertainty into the future (Gobler, 2020). For instance, as global temperatures are forecast to rise, the frequency and intensity of HABs is projected to increase toward the poles, possibly

11 threatening aquatic systems that are not prepared for cyanotoxins or hypoxia (Gobler, 2020;

Paerl and Barnard 2020).

2.3 CyanoHAB management and the use of algaecides

Considering significant damage HABs can lead to, management of waterbodies is essential to protect valuable drinking water resources, recreational sites and ecosystems that may be at high risk. While federal and local governments attempt to mediate nutrient loading, limiting the influx of nitrogen and phosphorous from their sources is a broad and collaborative approach requiring societal cooperation from private citizens to farmers and city planners. The implementation of practices such as green infrastructure, nutrient management and catchment ditches all require monetary investment and can be complex for successful results (Merel et al., 2013; U.S. EPA, 2019c). When preventative strategies are not possible and a bloom occurs, the use of copper-based algaecides is a common mitigation practice for control of cHABs when serious risk exists toward public health, the ecosystem or industry (Paerl, 2020; Crafton et al., 2018; Merel et al., 2013).

2.3.1 Copper Algaecides

The use of copper algaecides is a common mitigation practice in the control of cHABs. Cyanobacteria are among those algae most vulnerable to Cu2+, with copper toxicity typically arising through physiological disruptions in photosynthesis, respiration,

ATP production, and enzyme function following the internalization of copper by algal cells

(Bishop et al., 2018; Borkow and Gabbay, 2005; Merel et al., 2013). Extracellular binding, or absorption, of copper to the cell membrane is also possible, but can be problematic in

12 mitigation efforts as it has been correlated with membrane instability, and potential lysis, which may lead to subsequent environmental release of internal toxins (Kinley et al., 2017;

Bishop et al., 2018; Tsai, 2015). Unfortunately, extracellular toxin levels have been shown to increase if no actions are taken at all to hinder populations from reaching bloom densities, as seen in the case of microcystin producing M. aeruginosa (Tsai, 2015).

To further complicate the use of copper algaecides, toxicity also extends to other organisms, showing effects on fish at as low as 40 µg/L and lethality to non-target algal species in varying degrees (Bishop et al., 2018; Zhang et al., 2018). In some cases however, reduced membrane permeability by green algae species such as Closterium lunula offers tolerance by these non-target algal species in the event Cu algaecide mitigation is needed

(Bishop et al., 2018). Copper can also accumulate in sediment or within food chains

(bioaccumulation), leading to a separate set of public and ecological health concerns (Merel et al., 2013). Due to these combined hesitancies regarding cyanotoxin release through lysis and ecotoxicity, delivering copper efficiently and in the appropriate amounts needed for mitigation should be recognized. This is especially important in situations where the release of excessive cyanotoxins may be a concern (Tsai, 2015).

Two primary forms of copper delivery have been employed in recent practice, as copper sulfate and copper chelates (Bishop et al., 2018; Crafton et al., 2021; Tsai, 2015).

Copper sulfate supplied in a granular form can be delivered manually, despite inaccuracies in scattering. In solid form, granular copper sulfate products can potentially sink to the bottom of a water body where it may bind with organic matter or settle in sediments, accumulating over time and possibly affecting non-target benthic organisms (Zhang et al.,

2018). In comparison, copper chelates as a liquid product can disperse more readily in the

13 water column once applied. However, this method can sometimes require dilution of the product in advance of application as well as the need for spray equipment (Crafton et al.,

2021).

Importantly, both methods described above can result in significant short-term spikes of cyanotoxin release when used in excess, therefore determining suitable amounts and appropriate delivery for the control of algae under specific circumstances is again important (Tsai, 2015, Merel et al., 2013). In response, research exploring the effects of various copper delivery strategies and specific formulations has led to new and modified products that improve performance. (Bishop et al. 2018; Crafton et al., 2018; Tsai, 2015).

CHAPTER III

MATERIALS & METHODS

3.1 Site Collection

Field samples for bench-scale experiments were collected from several recreational lakes within the Cleveland Metro Parks. All selected lakes within the Cleveland Metro

Parks were chosen for their history of exhibiting some level of eutrophication and subsequent issues with algal growth. Collections were also made from a recreational lake in Hudson (Hudson Springs) and Lake Rockwell, the primary drinking water reservoir for the City of Akron, OH.

Field samples were collected after favorable cHAB bloom weather conditions (i.e., rain event followed by warm temperatures) (Merel et al., 2013). In the field, a Multi-

Parameter Water Quality Sonde (YSI 6600 V2) equipped with optical probes for phytopigments phycocyanin (PC) and chlorophyll-a (chl-a) was used to take readings at various points along the shore to determine sites suitable for collection. Phycocyanin pigmentation is solely expressed in cyanobacteria while chl-a is present in all photosynthetic organisms. Therefore, the fluorescent response of phycocyanin and chl-a can be used to measure populations densities (cells/ml) of cyanobacteria and total photosynthetic organisms, respectively (Crafton et al, 2018). Temperature and conductivity levels were also recorded with the multiparameter sonde.

Sample sites were then chosen by highest PC readings, correlating to a high cyanobacteria density. If necessary, a kayak was used to reach sample locations away from the shoreline. In the event that a waterbody exhibited an elevated PC level but was still below the Ohio EPA’s moderate action level (cell density of 10,000 – <100,000 cells/ml) required for algaecide application source water was concentrated in the field (Ohio EPA,

2019). Source water was concentrated using a Wildco plankton tow net and Wisconsin net equipped with 53 µm mesh filter to achieve cell densities of ≥10,000 cells/ml that could simulate bloom densities for in vitro experimentation. Raw source water was also collected for adjusting reactor cell densities, if necessary, during experimental set-up. Water samples were transported in 5-gallon buckets and 1 L polypropylene bottles from the water source back to The University of Akron main campus immediately after collection for reactor preparation.

3.2 Collection Site Descriptions

Recreational Lakes

Hudson Springs is a 50-acre public lake in the city of Hudson, OH located within

Hudson’s 260-acre keystone park. The lake is more precisely situated within the Hudson

Springs Park and surrounded by neighborhoods and receives daily visitation for recreational use including boating, fishing, and more. The lake has had algal blooms and detection of cyanotoxins in previous years. Samples were collected on July 29, 2019 and

August 20, 2019.

Site conditions at the time of the first sample on July 29, 2019 were roughly 88°F and partly cloudy. Samples were collected by the drainage overflow area of the lake where the density at the surface of the water was visibly high, however only occupied a very thin layer at the surface. Collection was performed using a kayak 1-4 ft from the shore using algae tow nets (City of Hudson, 2021).

Collection on August 20, 2019 was performed off the dock of the western boat launch as well as by kayak through the same area but a few feet further than was possible form the shore. The water was fairly full of debris left by geese (feathers and stool) as wind had concentrated material in this part of the lake. Water depth was roughly 4 – 6 ft deep.

Cleveland Metro Parks Sampling Locations

The pond at Camp Forbes is located on a 33-acre summer camp property in

Warrensville, OH and used primarily for recreational camp activities such as boating and fishing. It is stocked with sportfish (bluegill, largemouth bass, crappie, bullhead catfish, other sunfish species and white sucker) and receives copper based algaecides periodically to control algae. The pond itself is situated close to the road surrounded on one side by parking lot, administrative buildings and some vegetated area. Animal (waterfowl) waste is sometimes abundant. Samples were collected on August 14, 2019 when weather conditions were 78°F and partly cloudy. Source water was already a density >10,00 cells/ml and was collected by bucket adjacent to a boat launch site (City of Cleveland,

2021).

Ledge Lake is a 3-acre recreational lake within the Hinkley Reservation. The lake is directly surrounded by mostly vegetated land in different proportions of grass, shrubs or wooded edge. A recreational area exists on one side of the lake that includes parking lot, swimming pool, playground and picnic area. The lake allows fishing and is stocked with trout, largemouth bass, and bluegill. Samples were collected on September 5, 2019 with weather conditions at 70°F and partly cloudy. Bloom cell density was only moderate at the time of sampling and required concentration using algae nets from the dock to access more open water (Cleveland Metro Parks, 2021a).

Isaac Lake is a natural glacial lake designated by the Cleveland Metro Parks as a waterfowl sanctuary. Located in the Big Creek Reservation, it is mostly surrounded by wetland and woodland except for a short segment of parking lot visitation area. A hiking trail circles the lake and provides access to additional trails. Samples were collected on

September 9, 2019 with weather conditions of 70°F and partly cloudy. Sampling was performed on the opposite bank across from the parking lot kiosk from a fallen tree at the water’s edge. Water depth was shallow ~2ft and required the use of algae nets to concentrate cells to the required density (Cleveland Metro Parks, 2021b).

Drinking Water Reservoir Description

Lake Rockwell is a 33,294.80 acre man-made reservoir that serves as the primary drinking water supply for the City of Akron, OH. Constructed in 1915, the two billion gallon reservoir is situated on the Cuyahoga River and resides predominantly in Portage

County where it produces ~35 million gallons/day of drinking water (Crafton et al., 2021).

The reservoir was sampled on July 23, 2019 and August 12, 2019.

Conditions on July 23, 2019 were 79°F and few clouds. Sampling was performed by slow towing algae nets adjacent to the boat through Eckert Ditch. Conditions on August

12, 2019 were 80°F and partly cloudy. Sampling was performed by slow towing algae nets adjacent to the boat through Eckert Ditch.

3.3 HAB simulated reactors

To simulate bloom densities in the lab, concentrated water source was used to prepare 1.89 L Mason jar experimental reactors to a volume of 1.6 L and cell density of

10,000 ± 1000 cells/ml. When necessary, cell density of concentrated source water was adjusted to the desired starting density using the lower cell density raw source water that had also been collected. Tracking cell density using the YSI Water Quality Sonde, a combination of concentrated and dilute (raw) source water were added gradually with gentle mixing to individual reactors until the desired density was achieved.

Once a reactor’s volume and density were reached, it was covered with a slightly loosened piece of saran wrap as a lid to allow air and light to enter the reactor. Reactors for all experimental conditions were made in triplicate and placed within a Fisher Scientific growth Thermo Fisher Model 818 Plant Growth Chamber set to 25°C for 12 hours and

20°C for 12 hours. Two vertically oriented 48” 40W AgroBrite Fluorescent Grow Tube

Lights were used on a 12hr on/off cycle as the light source. All reactors in an experimental group were placed onto the same shelf and arranged in a grid of 3 rows by either 3 or 4 jar columns depending on the number of algaecide groups in the designated experiment. Later, each column would be one of the designated variables for triplicate condition. Reactors were left undisturbed for 24 hours to allow any natural declines in algae populations that

19 may occur due to handling or acclimation to the new environmental conditions. Initiation of the experiments are described in section 3.5.

3.4 Experimental design

Initiation of algaecide experimentation for a reactor group began 24 hours following acclimation in the growth chamber. General experimental steps included analytical readings at each time point along with removing 5 ml for visual identification of the phytoplankton present. The first of these was performed before the application of any algaecide (Time 0) to establish the baseline conditions. After Time 0, time points were established post algaecide application at 1.5 hours, 2 days, 7 days, and 14 days. At the 1.5 hour time point, only sonde and pH data were collected. Between time points, reactors were left undisturbed within the growth chamber at the previously specified conditions until the next sampling time.

At each time point, all reactors in a group were stirred for 1 minute at a slow speed using a ¾” stir bar to resuspend settled materials for a more homogenous dispersal through the reactor’s water column. After 1 minute of stirring, the YSI sonde was inserted into the reactor to measure phycocyanin and chl-a, alongside physiochemical characteristics of temperature (°C) and conductivity (µS/cm). A pH probe (Fisher Scientific pH 510 Bench

Series) was used for the pH reading. The 5 ml volume was also taken at this time using a serological pipet.

Values collected by the YSI sonde included phytopigments PC µg/L, PC RFU, chl- a µg/L, and chl-a RFU. PC values correspond to the cell density of cyanobacteria and chl-

20 a readings correspond to all photosynthetic organisms. These values were used to track algae populations in reactors over time. Tracking chl-a concentration alongside PC values, indicative of cyanobacteria, therefore communicates experimental impacts on non-target photosynthetic organisms.

Other data collected would later help support algae community behaviors seen in cell density and chl-a concentrations. Conductivity (µS/cm) is a valuable measure in the analysis of bloom conditions, as lower conductivity is favorable to growth of blue-green algae and increases with density. Readings of pH over time were collected over time using a pH probe as another indicator of bloom activity as pH shifts to become more basic as

CO2 is consumed. When algae populations rise, more individual organisms capable of photosynthesis become present, increasing the use of dissolved CO2 available in the water leading to an increase in local pH. Temperature (C°) recorded, although theoretically held constant by the growth chamber, ensures that growth temperature conditions remained in the desired range.

To prevent the cross contamination of algaecides or viable algae across reactors, via insertion of the YSI sonde, pH probe and stir bar, readings were taken within a triplicate variable reactor set. Readings were recorded starting with the control group, followed by the full dose Cutrine Ultra experimental group, then the low concentration solid Lake

Guard group and finally the high concentration Lake Guard group.

A sterile syringe and needle were used to extract water from a reactor during measurement to rinse small granules of Lake Guard or clumps of algae mass that may have been stuck to a probe back into the same reactor to conserve contents. Distilled water was then used to rinse the YSI sonde and pH probe between reactors after stuck materials were

21 cleared away. Post readings, reactors were placed back into the Fisher Scientific growth chamber at the specified conditions until the next sampling time point.

3.5 Microscopy and identification methods

The 5 ml samples collected from each reactor during time point readings were used in identifying algae present. The entire 5ml volume for each of these samples was agitated slightly then added to a glass centrifuge tube and centrifuged at a slow speed (#2 setting) for 1 minute. A small amount of pelleted material at the bottom of the test was removed using a Pasteur pipet with 2 ml bulb and was added to a microscope slide with 60 x 24 mm cover slip. Using an Olympus BX53 BF/DIC microscope, a full traverse of sample slides was performed. Images were captured of distinct organisms using an Olympus DP27 camera and Olympus Cellsens Standard Version 1.13 software to be used later to identify types and species that defined a reactor’s population at the designated time point.

Identification of algae species was performed using identification keys included in the 2015 Second Edition of Freshwater algae of North America: ecology and classification

(Wehr et al., 2015). The image based identification website PhycoKey produced by the

University of New Hampshire was also used (Baker, 2012). Additional resources included

Freshwater Algae: Identification and Use as Bioindicators (Bellinger and Sigee, 2015), and A guide to Cyanobacteria: Identification and Impact (Nienaber and Steinitz-Kannan,

2018).

3.6 Statistical Analysis

Phytopigment and physiochemical characteristic analysis

Statistical analyses were conducted using Minitab Statistical Software on the mean change of PC µg/L, PC RFU, chl-a µg/L, chl-a RFU, temperature (C°), conductivity

(µS/cm) and pH for all control and algaecide treatments over time. Each of these parameters was evaluated using ANOVA General Linear Model for within experimental groups. This was followed by Tukey pair-wise comparisons to compare triplicate treatments between each other.

CHAPTER IV

RESULTS & DISCUSSION

4.1 Lake Rockwell (7.23.2019 & 8.12.2019) treatments vs. control

4.1.1 Incorporation of Lower Dose of Lake Guard

Algaecide experiments began with a full dose of Cutrine Ultra (76 µl) and Lake

Guard (17.6 mg) according to manufacturer’s guidelines. As will be outlined below, reactors treated with a full dose of Lake Guard did not depict a rebound in chl-a or phycocyanin content between days 2 and 14. This led the research team to incorporate a lower dose of Lake Guard (6.65 mg) to match the Cu concentration of comparative algaecide Cutrine Ultra, roughly one third the original Lake Guard treatment amount. This section will present the results of the full and lower doses of Lake Guard separately for

Lake Rockwell. Results of experiments using water and phytoplankton collected from lakes within the Cleveland MetroParks as well as Hudson Springs were conducted with three algaecide treatment simultaneously and will follow this section reporting Lake

Rockwell.

4.1.2 Lake Rockwell visual observations and cyanobacteria response to algaecide treatment

4.1.2.1 Visual Observations – high dose experiments (July 23-Aug 7, 2019)

On day 0, all reactors were cloudy green throughout reactors without heavy matting on the top or bottom. After algaecide application, differences were seen between control and experimental groups over time. By day 2, control reactors had developed a ring of growth at the top of the water around an inch deep from the surface. Although similar rings of growth were also seen in reactors that had received 17.6 mg Lake Guard and Cutrine

Ultra, the growth was not as pronounced. Lake Guard reactors had also lost green coloration through the water column and became more transparent. Lake Guard material remained on the surface of the water and looked intact from time of application (Figure

4.1.2.1). By day 7, all group reactors had become more transparent with mass at the bottom.

Cutrine Ultra and 17.6 mg Lake Guard reactors also had mass at the bottom.

By day 14, the control group had become mostly clear with a tan coloration throughout the column and debris at the bottom. Cutrine Ultra reactors still maintained a green tint to the water and green mass at the bottom. Lake Guard reactors were the most different, having clear water with a hazy tan color. Large, swollen flakes of Lake Guard material were suspended in the top ¼ of the reactors similar to that depicted in Ledge Lake

(Figure 4.5.1).

4.1.2.2 Cyanobacteria response – high dose experiments (July 23-Aug 7, 2019)

Analysis of data from Lake Rockwell experimentation period 7.23.2019 to 8.7.2019 revealed changes in cyanobacterial densities both within experimental conditions as well as between conditions. A scatter plot of means with standard deviation was generated for cyanobacterial density over time showing noticeable difference between control and experimental groups (Figure 4.1.2.2). A scatter plot of means with standard deviation equivalent phycocyanin RFU data for Lake Rockwell 7.23.2019 is located in Appendix A.

Figure 4.1.2.2: Lake Rockwell (7.23.2019) cyanobacterial density (cells/ml) over 14 days

A general linear model fit to the data was able to account for 46.11% of observed variance. Statistical analysis confirmed that no significant change occurred in the control group’s cyanobacterial cell density from Day 0 to Day 2 despite an average 14.50% reduction in cell density (p > 0.05, Table 4.1.2.2). Cutrine Ultra and Lake Guard groups exhibited larger average decreases between Day 0 and Day 2 of 33.26% and 92.79%, respectively (p > 0.05, Table 4.1.2.2). Regardless of these reductions, both Cutrine Ultra and Lake Guard treatment groups were not found to be significantly different from their

Day 2 densities (p > 0.05) or significantly different from each other (p > 0.05). Comparison between the control group and the treatments found the control group to be significantly higher than Lake Guard by 94.04% (p < 0.05), but not different from the Cutrine Ultra treatment group despite a 42.40% difference (p > 0.05).

Day 14 reactors for all groups indicated reduced cyanobacterial densities from their day 2 averages. However, due to the already substantial reductions between day 0 and day

2 in the control, Cutrine Ultra and Lake Guard groups, cell density was found to be similar despite additional declines of 38.24%, 20.48% and 63.18%, respectively (p > 0.05, Table

4.1.2.2). The final densities of all groups were also not found to be statistically different for each other (p > 0.05). Average density comparison tables and supporting Tukey’s pairwise comparison for Lake Rockwell 7.23.2019 are located in Appendix A.

Table 4.1.2.2: Lake Rockwell (7.23.2019) statistical comparison of cyanobacteria density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application. Lake Rockwell (7.23.2019) change in PC cells/ml within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -14.50% 1 Control 2 - 14 -38.24% 0.878

Cutrine Ultra 0 - 2 -33.26% 0.986 Cutrine Ultra 2 - 14 -20.48% 1

17.6mg LG 0 - 2 -92.79% 0.058 17.6mg LG 2 - 14 -63.18% 1

Lack of statistical difference between algaecide groups’ day 14 cyanobacterial density in this study suggests that the lower Cu concentration of the Cutrine Ultra group was just as effective as the higher Cu content of Lake Guard 17.6 mg group (p > 0.05).

The impact of Lake Guard 17.6 mg dose in this experiment matches other results found in this study. Further, as mentioned above, the results of this lake led to the application of a reduced Lake Guard dosage in the additional experiments of this study to investigate a Cu concentration comparable to Cutrine Ultra’s.

4.1.2.3 Visual Observations – low dose experiments (8.12.2019 – 8.27.2019)

On day 0, all reactors were cloudy green throughout reactors. After algaecide application, differences were seen between control and experimental groups by day 2, with

Cutrine Ultra and Lake Guard groups becoming more transparent while the control reactors remained a cloudy green tint. All reactors had large chunks of green material at the bottom that became suspended when stirred. Lake Guard material remained on the surface of the water and looked intact from time of application similar to other experiments in this study.

By day 7, the control group reactors maintained filamentous and chunky green material at the bottom despite being more transparent that at the beginning. Lake Guard group reactors were much more transparent that the Cutrine Ultra reactors and still had some matting at the bottom that was now more yellow-green in appearance and much less dense less than the control.

On the last day of record, day 14, control reactors still had slight green tint to the water column and dense matting at the bottom (Figure 4.1.2.3). Cutrine Ultra reactors had some matting but not as pronounced while Lake Guard group was mostly tinted yellow through the water column with brown/yellow chucky material at the bottom. Lake Guard material applied on day 0 had become gelatinous and remained floating at the water’s surface discolored (Figure 4.1.2.3).

4.1.2.4 Cyanobacteria response - low dose experiments (8.12.2019 – 8.27.2019)

Analysis of algaecide exposure to water sourced from Lake Rockwell for experimentation period 8.12.2019 to 8.27.2019 revealed changes in cyanobacterial densities both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for cyanobacterial density over time and showed differences between control and experimental groups (Figure

4.1.2.4). A scatter plot of means with standard deviation equivalent phycocyanin RFU readings for Lake Rockwell 8.12.2019 is located in Appendix B.

Figure 4.1.2.4: Lake Rockwell (8.12.2019) cyanobacterial density (cells/ml) over 14 days

A general linear model fit to the data was able to account for 48.54% of observed variance. Further statistical analysis did not detect a significant change in the control group’s cyanobacterial cell density from day 0 to day 2 despite an average 23.97% reduction in cell density (p > 0.05, Table 4.1.2.4). An additional average decrease of

53.05% occurred in the control group between day 2 and day 14 but was not found to be significant (p > 0.05).

Both treatment groups experienced significant decreases in their average cyanobacterial cell densities from Day 0 to Day 2 with Cutrine Ultra and Lake Guard displaying large average decreases of 90.36% and 95.28%, respectively (p < 0.05, Table

4.1.2.4). These reductions were not found to be significantly different from the day 2 average density of the control group. Lake Guard reactors then underwent further reduction in density from day 2 to day 14 by 54.32% (p > 0.05) but was again not found to be significantly different from that of the control group.

Particularly, reactors treated with Cutrine Ultra experienced an 845.14% rebound of cyanobacterial density from the day 2 average (p < 0.05, Table 4.1.2.4) which was found to be significantly different from the final density of Lake Guard (p < 0.05), but not the control (p > 0.05). Average density comparison tables and supporting Tukey’s pairwise comparison for Lake Rockwell 8.12.2019 are located in Appendix B.

Table 4.1.2.4: Lake Rockwell (8.12.2019) statistical comparison of cyanobcateria density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

In this experiment, the Cutrine Ultra group showed elevated cyanobacteria density by day 14, indicting a rebound of cyanobacteria from day 2 to day 14. Cyanobacteria density did not decline significantly in the control group between day 0 and day 2 nor between day 2 and day 14, suggesting that reactor water had sufficient nutrients to support photosynthetic organisms over the course of the 14 day trial period. Additionally, at the time of application, the initially high density of cyanobacteria cells possibly offered a primary Cu absorption site and subsequent target of toxicity until the Cu in the water was mostly bound (Tsai, 2016). Therefore, this rebound could have been made possible by fast growth of cyanobacteria that remained following algaecide application, taking advantage of available nutrients and reduced competition.

4.1.3 Lake Rockwell chlorophyll-a response to algaecide treatment

4.1.3.1 Chlorophyll-a response - high dose experiments (July 23-Aug 7, 2019)

Analysis of data from Lake Rockwell experimentation period 7.23.2019 to 8.7.2019 showed changes in chl-a concentrations both within experimental conditions, as well as between conditions at various time points. Average chl-a concentrations were plotted with standard deviation over time, indicating potential differences between control and experimental groups (Figure 4.1.3.1), as well as a scatter plot of means with standard deviation reflecting equivalent chl-a RFU readings (Located in Appendix A).

Figure 4.1.3.1: Lake Rockwell (7.23.2019) chl-a (µg/L) response to algaecide treatments over 14 days

A general linear model fit to the data was able to account for 67.61% of observed variance and suggested that the control and both experimental groups had experienced decrease in chl-a concentration from day 0 to day 2, however the changes were not found to be significant within or between these groups (p > 0.05).

As seen in the scatter plot, increases in chl-a concentration were seen for control and Cutrine Ultra groups between day 2 and day 14 however considerable variation was seen in the day 14 replicates. The final 14 day average concentration of the control was not found to be significantly different from day 2 despite an average increase of 212.46% (p >

0.05, Table 4.1.3.1), but the much larger average increase of 2876.49% for the Cutrine

Ultra group was in fact found to be significantly different (p < 0.05, Table 4.1.3.1). In contrast, an additional 47.85% decrease in the Lake Guard group was detected during the same time span but was also not found to be statistically different from its average day 2 chl-a concentration (p > 0.05, Table 4.1.3.1). Further, chl-a concentration in Cutrine Ultra reactors was found to be significantly higher by day 14 from that of the control and Lake

Guard 17.6 mg groups by 395.72% and 8816.52%, respectively (p < 0.05) and was supported by a Tukey’s pair-wise analysis located in Appendix A.

In this experiment, the Cutrine Ultra group showed elevated chl-a concentration by day 14 but no change in the cyanobacteria cell density suggesting a rebound of non- cyanobacterial photosynthetic organisms. At the time of application, initially abundant cyanobacteria cell density likely offered the primary Cu absorption site and subsequent target of toxicity (Tsai 2016). This could have attributed to relief from nutrient competition or depleted cyanobacteria to a level that was difficult for rebound from as quickly as other species. Further, although no cyanotoxin analysis was performed in this study, it is known

34 that cyanotoxins can have inhibitory effects on other organisms (allelopathy) and following lethal impact on the cyanobacteria, some relief may have been provided to previously suppressed organisms (Tsai, 2016, Crafton et al., 2019, Weenink et al., 2015, Leão et al.,

2009).

Table 4.1.3.1: Lake Rockwell (7.23.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application. Lake Rockwell (7.23.2019) change in Chl-a (ug/L) within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -44.75% 1 Control 2 - 14 212.46% 0.978

Cutrine Ultra 0 - 2 -69.73% 1 Cutrine Ultra 2 - 14 2876.49% 0

17.6mg LG 0 - 2 -81.74% 0.999 17.6mg LG 2 - 14 -47.85% 1

Reactors in this experiment were not enriched with additional nutrients beyond those present when collected from their water source. Nutrient levels were not measured for water samples, but relatively insignificant changes observed in the control group’s chl- a concentration and cyanobacteria density suggest that nutrient levels were sufficient to sustain organisms over the duration of this trial. Additionally, increases in chl-a concentration, as well as cyanobacteria density, for the Cutrine group by day 14 indicates that non-cyanobacteria organisms capable of photosynthetic activity rebounded compared to the other groups.

4.1.3.2 Chlorophyll-a response– low dose experiments (8.12.2019 – 8.27.2019)

Analysis of chl-a concentration from Lake Rockwell experimentation period

8.12.2019 to 8.27.2019 revealed changes in chl-a concentrations both within experimental

35 conditions as well as between conditions. Average chl-a concentrations were plotted with standard deviation over time, reflecting these differences between control and experimental groups (Figure 4.1.3.2). A scatter plot of means with standard deviation reflecting the equivalent chl-a RFU readings corroborated these findings and is located in Appendix B.

Figure 4.1.3.2: Lake Rockwell (8.12.2019) chl-a (µg/L) response to algaecide treatments over 14 days

To further analyze relationships between experimental groups, a general linear model fit to the data was generated and accounted for 90.00% of observed variance. Results showed that a 10.84% average decline in the control group occurred by day 2 although it was not found to be a significant change from the day 0 concentration (p > 0.05, Table

4.1.3.2). Both treatment groups also experienced decreases in their average chl-a concentrations from day 0 to day 2 with Lake Guard showing an 86.54% decrease and

Cutrine Ultra declining 77.82%, however these were similarly not found to be significant

(p > 0.05, Table 4.1.3.2). Further, day 2 concentrations for all groups were not found to be 36 significantly different from each other (p > 0.05) with comparative tables and supporting

Tukey’s pairwise analysis located in Appendix B.

By day14, both control and Cutrine groups showed a rebound in average chl-a concentration. Although the control group’s increase of 38.00% during this time was found to be significantly different from day 2 (p > 0.05, Table 4.3.3), Cutrine Ultra group’s much higher 2349.21% increase was found to be significant (p < 0.05, Table 4.1.3.2).

Additionally, Lake Guard reactors continued to decline another 56.07%, but was not found to be significant (p < 0.05, Table 4.1.3.2).

Further, by day 14 Cutrine Ultra group was found to be significantly different from the others with average concentration about 100 times larger than the Lake Guard group (p

< 0.05) and about 3 times larger than the control group (p < 0.05). The average density of the Lake Guard group was also 95.58% less than the control group (p < 0.05). These findings were supported by a Tukey’s pairwise analysis located in Appendix B.

Table 4.1.3.2: Lake Rockwell (8.12.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Similar to the other experiment in this study using Lake Rockwell water source

(7.23.2019), the decline from day 0 to day 2 and following rebound by day 14 suggests that water sourced from this lake had sufficient nutrients to support photosynthetic organisms over the course of the 14 day trial period. Additionally, increases in chl-a concentration, as well as phycocyanin, expressed as cyanobacteria density, for the Cutrine Ultra group by day 14 indicates that a variety of organisms were able to rebound. This was different that the Lake Rockwell 7.23.2019 experiment where only non-cyanobacteria organisms rebounded. Again, in this case, the rebound could be attributed to temporary relief from nutrient competition and provided the opportunity for both cyanobacteria and “non-target” algae evading the effects of algaecide to grow and multiply quickly (Crafton et al., 2019).

4.1.4 Lake Rockwell microorganism composition response to algaecide treatment

4.1.4.1 Microorganism composition response– high dose experiments (July 23 - Aug 7,

2019)

Account of organisms was based on the condition of “present” or “not present” over time in all experimental groups as well as the control. The most notable results occurred in the Cutrine Ultra group. By day 14, many of the original members of Cholorphyceae, and all the Cyanophyceae members, remained present in these reactors from day 2. The higher number of different genus detected in the Cutrine Ultra group matches the higher chl-a concentration levels seen by day 14 compared to the control and Lake Guard groups in this experiment. Pediastrum and Anabaena were consistent among all groups by day 14 with the addition of Scenedesmus and Mougeotia in the control and Cutrine Ultra groups. Tables located in in Appendix A show presence of these genera over time. 38

These findings support previous results in other experiments during this study in that the lower Cu concentration of Cutrine Ultra may be sufficient in reducing cyanobacteria bloom populations while not having too much impact on non-target organisms compared to the higher Lake Guard 17.6 mg dose that seems to have more drastically impacted members of Chlorophyceae in this and other experiments.

4.1.4.2 Microorganism composition response – low dose experiments (8.12.2019 –

8.27.2019)

A slight increase in photosynthetic genera diversity occurred in the control group by day 14 in contrast to a narrowing in both algaecide treatment groups. In the control group, more Chlorophyceae and other non-cyanobacteria genera were detectable by day

14 while the cyanobacteria diversity decreased to only include Microcystis. Further,

Microcystis was the only cyanobacteria genera shared in all groups by day 14 and bloom activity in the Cutrine Ultra Group dominated by Microcystis and possibly

Cylindrospermopsis. Tables located in in Appendix B show presence of these genera over time.

The increase in detectable non-cyanobacterial organisms in the control group could be supported by the fact that there was a slight increase in chl-a concentration from day 2 to day 14. Despite that the change in chl-a concentration was not found to be significant, water sourced from this lake had sufficient nutrients to support photosynthetic organisms over the course of the 14 day trial period.

4.1.5 Lake Rockwell microorganism composition response to algaecide treatment

4.1.5.1 Changes in pH and conductivity – high dose experiments (July 23 - Aug 7, 2019)

Typically, high pH can be used as a possible indicator for favorable bloom conditions. pH is expected to increase as a bloom progresses, CO2 is consumed, and oxygen is evolved via photosynthesis (Paerl et al, 2011). In all experiments this parameter was found to begin around 7.5 pH but then observed to increase to 9 pH for the Cutrine Ultra and control groups by day 14, agreeing with higher readings in chl-a concentration recorded. A scatter plot of means with standard deviation is found in Appendix A.

Conductivity was recorded as a monitoring parameter of algal growth. Conductivity can give insight to the salinity of the environment with lower conductivity favorable for a bloom event (Haakonsson et al, 2020). A scatter plot of means with standard deviation was generated for conductivity (µS/cm) was recorded during experimentation and are found in

Appendix A.

4.1.5.2 Changes in pH and conductivity – low dose experiments (8.12.2019 – 8.27.2019)

pH was recorded during these experiments were found to begin around 8.5 pH but then observed to increase to 9 pH for the Cutrine Ultra by day 14, agreeing with higher readings in chl-a and cyanobacteria density recorded for this group. A scatter plot of means with standard deviation is found in Appendix B.

Although there was variability in reactors when they were made on day 0, conductivity for these experiments remained steady around 350 uS/cm from days 2 through

14. A scatter plot of means with standard deviation was generated for conductivity in found in Appendix B.

4.2 Camp Forbes treatment groups vs. control (8.14.2019 – 8.29.2019)

4.2.1 Camp Forbes Visual Observations

On day 0, all reactors were cloudy green throughout with a dense green mass at the water’s base. Differences were seen between control and experimental groups 2 days after algaecide application. While there was no dramatic visual change in the control group reactors between day 0 and day 2, reactors that had received Lake Guard, either 6.65 mg or 17.6 mg, were still cloudy through the water column but much less green while maintaining the denser green mass at the bottom. Cutrine Ultra had not changed as much as the Lake Guard reactors and was still a cloudy green through the water column with denser green mass at the bottom.

By day 7 control group reactors had become less green and hazy from the day 2 observations. The control group maintained the dense mat at the bottom of the reactors but had shift color to green/yellow. There was not much change in the Cutrine Ultra reactors from day 2 observations except that the dense mat at the bottom had shifted color to green/yellow. Both the control and Cutrine Ultra reactors were chucky when stirred. Both

6.65 mg and 17.6 mg Lake Guard reactors were clear/yellow in color throughout the water column with dark dense yellow matting at the bottom that was chucky when stirred.

Reactors in this experiment were not enriched with nutrients beyond those present when collected, potentially impacting available nutrients (ie. N & S) and leading to subsequent

41 chlorosis, or bleaching, in photosynthetic organisms over the 14 day experimental period

(Collier and Grossman 1992). Lake Guard material was still present at the water’s surface for both groups.

By day 14 the control group had become mostly clear throughout the column with brown mass at the bottom. Cutrine Ultra reactors were mostly clear with a green tint to the water toward the bottom and very dense green matting at the bottom. Lake Guard reactors were the most different having mostly clear water with a hazy yellow/brown color and dark yellowish/brown matting at the bottom. Large, swollen flakes of Lake Guard material were suspended in the top ¼ of the reactors.

4.2.2 Camp Forbes cyanobacteria response to algaecide treatment

Analysis of cyanobacterial cell density from Camp Forbes experimentation revealed changes in cyanobacterial densities both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for cyanobacterial density over time indicating potential differences between control and experimental groups (Figure 4.2.2), as well as a scatter plot of means with standard deviation equivalent phycocyanin RFU readings (located in

Appendix C).

Figure 4.2.2: Camp Forbes Cyanobacterial density (cells/ml) over 14 days

A general linear model fit to the data was able to account for 96.72% of observed variance. The model included both main factors (conditions, time) and interaction term.

Statistical analysis within experimental groups confirmed that a significant decline in cyanobacteria density of 39.58% occurred in the control group between Day 0 and Day 2

(p < 0.05). Similarly, reactors that received Cutrine Ultra, 17.6 mg Lake Guard, 6.65mg

Lake Guard experienced significant declines in cyanobacteria cell density from Day 0 to

Day 2 with average percent declines of 38.78% (p < 0.05), 76.45% (p < 0.05), and 59.16%

(p < 0.05), respectively (Table 4.2.2). While all groups had experienced significant declines in density by day 2, the average densities of the Cutrine group and control group were not found to be statistically different from each other (p > 0.05). The control and

Cutrine groups were found to be statistically different from both the more heavily impacted

Lake Guard groups (p < 0.05) while the two Lake Guard groups themselves were not found

43 to be statistically from each other (p < 0.05). Tables reflecting these comparisons between groups are located in Appendix C.

By day 14, the control group experienced an additional decrease of 88.49% from its day 2 cyanobacterial cell density (p < 0.05). Again, reactors that received Cutrine Ultra,

17.6 mg Lake Guard, 6.65 mg Lake Guard also experienced significant average percent declines from their day 2 densities of 84.31% (p < 0.05), 89.88% (p < 0.05), and 93.30%

(p < 0.05), respectively (Table 4.2.2). In contrast to day 2, no statistical difference was determined between the final densities of all groups by day 14 (p > 0.05). Tables, as well as a Tukey’s pairwise analysis, reflecting these comparisons between groups are located in

Appendix C.

Table 4.2.2: Camp Forbes statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application. Forbes PC (cells/ml)within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -39.58% 0 Control 2 - 14 -88.49% 0

Cutrine Ultra 0 - 2 -38.78% 0 Cutrine Ultra 2 - 14 -84.31% 0

17.6mg LG 0 - 2 -76.45% 0 17.6mg LG 2 - 14 -89.88% 0.004

6.65mg LG 0 - 2 -59.16% 0 6.65mg LG 2 - 14 -93.30% 0

Although all experimental groups underwent significant reductions in cell density throughout the experimental period, there was also significant change in the control group over time. Reactors in this experiment were not enriched with additional nutrients beyond

44 those present when collected from their water source which allows for the possibility of starvation during the 14 day experimental period if too limiting (Crafton et al. 2019). While this may have had some impact, the statistically lower densities (p < 0.05) of both Lake

Guard groups compared to the control and Cutrine groups at day 2 supports the potential potency Lake Guard product, especially at higher Cu concentration, seen in the other lake samples against cyanobacteria. As is shown in the results for each lake, a similar trend was evident in that reactors treated with Lake Guard resulted in substantial reduction of cyanobacteria as well as considerable impacts other algal types.

4.2.3 Camp Forbes chlorophyll-a response to algaecide treatment

Analysis of chl-a concentration from Camp Forbes incubations revealed changes in chl-a concentration both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for chl- a concentration over time indicating potential differences between control and experimental groups (Figure 4.2.3), as well as a scatter plot of means with standard deviation reflecting equivalent chl-a RFU readings (located in Appendix C).

Figure 4.2.3: Camp Forbes chl-a (µg/L) response to algaecide treatments over 14 days

A general linear model fit to the data was able to account for 94.63% of observed variance. Interestingly, the Cutrine group did not experience a significant decline in chl-a concentration from day 0 to day 2 while the control, Lake Guard 17.6 mg and Lake Guard

6.65 mg groups did show significant declines in density by 56.04%, 71.23%, and 51.84%, respectively (Table 4.2.3). In addition, despite the 22.76% decline (p > 0.05) in chl-a concentration of Cutrine group, it was not significantly different from the control group’s concentration at the time point (p > 0.05) but was significantly different than that of the higher Lake Guard 17.6 mg group (p < 0.05). Comparisons of these groups can be found in Appendix C.

Although the above differences were present at day 2, all groups underwent further significant reductions in chl-a concentration by greater than 80% of their day 2 concentration by day 14 (p < 0.05). Ultimately, this yielded average concentrations in all

46 groups that were not significantly different from one another (p > 0.05). Comparisons of these groups can be found in the appendix along with a Tukey’s pair-wise analysis supporting these findings.

Table 4.2.3: Camp Forbes statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application. Forbes Chl-a (ug/L) within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -56.04% 0 Control 2 - 14 -92.85% 0

Cutrine Ultra 0 - 2 -22.76% 0.189 Cutrine Ultra 2 - 14 -83.77% 0

17.6mg LG 0 - 2 -71.23% 0 17.6mg LG 2 - 14 -93.02% 0.01

6.65mg LG 0 - 2 -51.84% 0 6.65mg LG 2 - 14 -94.96% 0

Significant declines in average chl-a concentration for all groups between day 0 and day 2 occurred in this experiment, followed by further declines by day 14 that were finally not significantly different from each other (p > 0.05). The greatest difference between any of the groups over time was Cutrine at 130% greater chl-a concentration than

Lake Guard 17.6 mg on day 2 (p < 0.05). Reactors were not enriched with additional nutrients beyond those present when collected from their water source, presenting the possibility of starvation over time, and a potential factor as to why chl-a concentration responded similarly in all groups. However, the impact of Lake Guard 17.6 mg at day 2 could be informative about the potential potency of this product at higher Cu

47 concentrations. Tables reflecting these comparisons can be found in Appendix C along with a supporting Tukey’s pairwise comparison.

4.2.4 Camp Forbes response in microorganism composition to algaecide treatment

Visual observation via light microscopy was used to identify organisms as

“present” or “not present” in all groups over time. There were similar Cyanophyceae and

Chlorophyceae species profiles for the control and Cutrine groups by day 14. In contrast, although both Lake Guard 17.6 mg and Lake Guard 6.65 mg groups showed similar

Chlorophyceae groups they had lost about half the members of Chlorophyceae as the control and Cutrine groups, including Ankistrodesmus and Radiococcus, while still showing presence of the same cyanobacteria. Shared cyanobacteria present by day 14 in the control, Cutrine and Lake Guard 6.65 mg groups was Aphanocapsa and

Cylindrospermum. Lake Guard 17.6 mg group only contained Aphanocapsa. Tables in

Appendix C show presence of these genera over time.

This finding supports the conclusion that the lower Cu concentration of Cutrine and

Lake Guard 6.65 mg may be sufficient in reducing cyanobacteria bloom populations while not having too much impact on non-target organisms compared to the higher Lake Guard

17.6 mg dose that seems to have more drastically impacted members of Chlorophyceae for example. Results for other lakes used in this study reflect these findings.

4.2.5 Camp Forbes responses in pH and conductivity

In all experiments for Camp Forbes, this parameter was found to hold around 8 pH but never going above 9 or below 7. The lack of change in pH agrees with the absence of

48 a detectable rebound in chl-a or cyanobacteria density for any of the Camp Forbes groups during the 14 trial. A scatter plot of means with standard deviation tracking pH over time is found in the Appendix C.

Although there was variability in reactors when they were made on day 0, conductivity for these experiments remained steady around 550 uS/cm from days 2 through

14. This lack of change in conductivity also agrees with the absence of a detectable rebound in chl-a or cyanobacteria density for any of the Camp Forbes groups during the 14 trial. A scatter plot of means with standard deviation monitoring conductivity over time is found in Appendix C.

4.3 Hudson Springs treatment groups vs. control (8.20.2019 – 9.4.2019)

4.3.1 Hudson Springs visual observations

On day 0, all reactors were visibly green throughout with denser green mass at the water’s surface. After algaecide application, differences were seen between control and experimental groups by day 2. While there was no dramatic visual change in reactors between day 0 and day 2 for the control group, reactors that had received Cutrine Ultra had taken on a hazy green/yellow color to the water column. Both groups that received Lake

Guard, either 6.65 mg or 17.6 mg, were visibly the most different as pale yellow with Lake

Guard material still present near the surface of the water. Some clumping green mass was present in the Lake Guard reactors as well.

By day 7 control group reactors become a slightly paler green tint from the day 2 observations and gained a dense green ring at the water’s surface edge. The control group

49 maintained the dense green mat at the bottom of the reactors. There was not much change in the Cutrine Ultra reactors from day 2 observations but had gained a bit of green mass at the bottom. Reactors that had received 6.65 mg Lake Guard were pale yellow in color throughout the water column. Reactors that had received 17.6mg Lake Guard were a similar pale yellow. Both Lake Guard groups still had material still present at the water’s surface.

The control group had lost its green tint throughout the column by day 14 but had maintained dark green mass at the bottom, a green surface ring and some suspended green particulate. The green ring was still present at the top of the Cutrine Ultra reactors at this time along with dark green matting at the bottom. Lake Guard reactors were both pale yellow in color through the water column by day 14 with yellowish/green matting at the bottom of the reactors. Large, swollen flakes of Lake Guard material were suspended in the top ¼ of the reactors.

4.3.2 Hudson Springs cyanobacteria response to algaecide treatments

Analysis of data for Hudson Springs #2 source water revealed changes in cyanobacterial densities both within experimental conditions, as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for cyanobacterial density over time showing noticeable difference between control and experimental groups (Figure 4.3.2), as well as a scatter plot of means with standard deviation equivalent phycocyanin RFU readings (located in Appendix D).

Figure 4.3.2: Hudson (8.20.19) Cyanobacterial density (cells/ml) over 14 days

A general linear model fit to the data was able to account for 96.52% of observed variance. The model included both main factors (conditions, time) and interaction term.

Statistical analysis within experimental groups confirmed that no significant change occurred in the control group’s cyanobacterial cell density from Time 0 to Day 2 despite an average -12.82% reduction in cell density (p > 0.05). In contrast, reactors that received

Cutrine Ultra, 17.6 mg Lake Guard, 6.65mg Lake Guard experienced significant declines in cyanobacteria cell density from Day 0 to Day 2 with average percent declines of -71.84%

(p < 0.05), -83.30% (p < 0.05), and -76.35% (p < 0.05), respectively. All declines in treatment groups by Day 2 were found to be significantly different from the control group

(p < 0.05). Tables reflecting these findings are located in Appendix D.

A significant decline (-30.02%) in cyanobacteria density was observed in the control group from Day 2 to Day 14 (p < 0.05), however the average density of the control

51 group at Day 14 was still well above Cutrine Ultra, 17.6 mg Lake Guard, and 6.65mg Lake

Guard reactors by 66.28%, 84.33% and 72.21%, respectively (p < 0.05). The significantly lower average densities of treatment reactors compared to the higher density of the control group on day 14 was further supported by a Tukey’s pairwise comparison located in

Appendix D.

Additionally, very little statistical change in average cyanobacteria cell density occurred by day 14 in treatment groups that received Cutrine Ultra, 17.6 mg Lake Guard, and 6.65mg Lake Guard (p > 0.05) following the initial Day 2 reduction (p > 0.05), indicating that a recovery did not take place after algaecide treatment (Table 4.3.2).

Table 4.3.2: Hudson (8.20.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Hudson (8.20.2019) cyanobacteria (cells/ml) algaecide response within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -12.82% 0.606 Control 2 - 14 -30.02% 0.001

Cutrine Ultra 0 - 2 -71.84% 0 Cutrine Ultra 2 - 14 -14.58% 1

17.6mg LG 0 - 2 -83.30% 0 17.6mg LG 2 - 14 -30.54% 1

6.65mg LG 0 - 2 -76.35% 0 6.65mg LG 2 - 14 -13.33% 1

The overall reduction of cyanobacteria cell density, and lack of significant difference between their densities over time, in all algaecide treatment groups suggests that the lower Cu concentration of 6.65 mg Lake Guard and Cutrine Ultra were just as effective as the higher 17.6 mg Lake Guard.

The efficacy of Cutrine Ultra, as chelated copper, and Lake Guard, as microencapsulated copper sulfate pentahydrate, at the lower copper concentration (0.5 mg 52

L-1 Cu) was not totally unexpected. Previous work by Crafton et al. (2018) had demonstrated the effectiveness of Cutrine Ultra, as chelated copper, at the same concentration used in this study for its ability at reducing cyanobacteria populations.

Additionally, separate work by Tsai (2016) identified an effective EC50 of chelated copper and copper sulfate pentahydrate against Microcystis aeruginosa at 5x 106 cells/ml to be

0.101 mg L-1 and 0.112 mg L-1 Cu, respectively.

This results suggest that much less Lake Guard than is recommended on the label, or even the adjusted allowable “full-dose”, may be an effective mitigation tool when targeting toxin producing cyanobacteria species. Importantly, copper sulfate is the active ingredient in Lake Guard. Although the microencapsulation and floating technology is implemented to assist dispersal, copper delivered as copper sulphate is more susceptible settling in the environment, leading to accumulation and potential damage to ecosystems

(Wehr et al., 2015; Crafton et al., 2018).

4.3.3 Hudson Springs chlorophyll-a response to algaecide treatments

Analysis of data for Hudson Springs #2 source water revealed changes in chl-a concentration both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for the change in chl-a concentration over time in control and experimental groups (Figure 4.3.2), as well as a scatter plot of means with standard deviation reflecting equivalent chl-a RFU readings

(located in the appendix).

Figure 4.3.2: Hudson (8.20.2019) chl-a (µg/L) response to algaecide treatments over 14 days

A general linear model fit to the data was able to account for 48.72% of observed variance. The model included both main factors (conditions, time) and interaction term.

Statistical analysis within experimental groups confirmed that no significant change occurred in chl-a concentration for any groups from Time 0 to Day 2 (p > 0.05). In addition, no significant increase was seen in chl-a concentration from Day 2 to Day 14 for any of the groups (p > 0.05), indicating that a rebound in green algae did not occur (Table 4.3.2).

Table 4.3.3: Hudson (8.20.2019) statistical comparison of chl-a (µg/L) between Time 0 - 2 and Time 2-14 for all conditions following algaecide application.

Hudson (8.20.2019) Chlorophyll-a (ug/L) algaecide response within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -41.74% 0.617 Control 2 - 14 58.28% 0.883

Cutrine Ultra 0 - 2 -51.38% 0.145 Cutrine Ultra 2 - 14 -28.68% 1

17.6mg LG 0 - 2 -60.76% 0.269 17.6mg LG 2 - 14 -23.69% 1

6.65mg LG 0 - 2 -40.82% 0.647 6.65mg LG 2 - 14 -52.86% 0.935

There was no significant difference in average chl-a concentration between control and experimental groups at Day 2 following algaecide dosage. There was also no significant difference in average chl-a concentration between control and experimental groups by Day 14 except for a 72.61% difference between the control and higher 17.6 mg

LG group (p < 0.05). Comparative tables are located in Appendix D.

Again in this case, the overall reduction of chl-a concentration, and lack of significant difference between cyanobacteria densities over time, in all algaecide treatment groups suggests that the lower Cu concentration of 6.65 mg Lake Guard and Cutrine Ultra were just as effective as the higher 17.6 mg Lake Guard. This matches the trends in chl-a concentration seen in the other lake sample treatments performed in this study such as

Ledge Lake 9.5.2019 (Section 4.4) and Lake Isaac 9.9.2019 (Section 4.5).

4.3.4 Hudson Springs response in microorganism composition to algaecide treatment

Documentation of organisms by the condition of “present” or “not present” in all experimental groups over time, as well as the control, resulted similar Cyanophyceae and

Chlorophyceae species profiles by day 14. Similarities were also seen with members of the remaining Bacillariophyceae. Tables in Appendix D show presence of these genera over time.

Anabaena and Microcystis were shared cyanobacteria species in all conditions at

T0. By day 14, both organisms were still detectable for all groups except for Anabaena in the Cutrine Ultra group. Shared Chlorophyceae detected in all groups contained species

Scenedesmus, Pediastrum, Coelastrum, Staurastrum, Oocystis and Radiococcus by day 14.

The survival of more types of Cholophyceae in groups that received algaecide in comparison to the remaining Cyanophyceae after such significant reduction in biomass was surprising. This dampened impact of Cu could be credited to the much higher concentration of cyanobacteria compared to other algae types in the reactors, providing a greater reservoir of Cu sorption that protected non-target individuals that existed in lower abundance

(Bishop et al., 2018, Crafton et al., 2018, Tsai, 2016).

4.3.5 Hudson Springs responses in pH and conductivity

Readings for this parameter were clustered together in all Hudson Springs experiments typically around 8.5 pH but never going above 9 or below 8. There was no significant rebound in chl-a or cyanobacteria for these experiments, supporting the lack of change in pH. A scatter plot of means with standard deviation following pH over time is found in Appendix D.

Conductivity was very tightly clustered between 400 and 450 uS/cm when prepared on day 0 for these experiments. Over time though, a divergence was seen from day 2

56 through 14 that ultimately lead to reading averages to range from 200 to 450 uS/cm. While there was no significant change in chl-a or cyanobacteria density, the variation in these readings may be due to error. A scatter plot of means with standard deviation tracking conductivity can be found in Appendix D.

4.4 Ledge Lake treatment groups vs. control (9.5.2019 - 9.20.2019)

4.4.1 Ledge Lake visual Observations

On day 0, all reactors were cloudy green throughout reactors without heavy matting on the top or bottom. After algaecide application, differences were seen between control and experimental groups by day 2. While the control group had gained some green/yellow matting at the bottom, it also maintained its hazy green coloration by day 2. Reactors that had received Lake Guard, either 6.65 mg or 17.6 mg, had become much less green and gained a light haziness. Cutrine Ultra group had not changed as much as the Lake Guard reactors but had also lost green coloration through the water column and became more clear.

By day 7, control group reactors had become more clear with a slight yellow/green tint to the water and light green/yellow matting at the bottom. Cutrine Ultra reactors from day 2 observations had become slightly hazy with a yellow tint and with light yellow build up at the bottom. Both 6.65 mg and 17.6 mg Lake Guard reactors were very hazy yellow in color throughout the water column with yellow matting at the bottom. Lake Guard material was still present at the water’s surface for both groups.

By day 14, all reactors had lost pigmentation in the water columns and taken on new visual properties. The control group had become mostly clear throughout the column with yellow/green mass at the bottom. Cutrine Ultra reactors were mostly clear with a pale green tint to the water. Lake Guard reactors were the most different, having clear water with a very hazy yellow/green. Large, swollen flakes of Lake Guard material were suspended in the top ¼ of the reactors (Figure 4.4.1).

4.4.2 Ledge Lake cyanobacteria response to algaecide treatment

Analysis of cyanobacterial cell density (cells/ml) from Ledge Lake experimentation revealed changes in cyanobacterial densities both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for cyanobacterial density over time indicating potential differences between control and experimental groups (Figure 4.4.2), as well as a scatter plot of means with standard deviation equivalent phycocyanin RFU readings (located in

Appendix E).

Figure 4.4.2: Ledge Lake Cyanobacterial density (cells/ml) over 14 days

A general linear model fit to the data was able to account for 93.28% of observed variance. The model included both main factors (conditions, time) and interaction term. Statistical analysis within experimental groups confirmed that a significant decline in cyanobacteria cell density of 63.82% occurred in the control group between Day 0 and

Day 2 (p < 0.05). Similarly, reactors that received Cutrine Ultra, 17.6 mg Lake Guard,

6.65mg Lake Guard experienced significant declines in cyanobacteria cell density from

Day 0 to Day 2 with average percent declines of 92.22% (p < 0.05), 96.84% (p < 0.05), and 93.71% (p < 0.05), respectively (Table 4.4.2). Average density of algaecide groups were not found to be significantly different from one another by day 2 (p > 0.05), but all treatment groups were found to be statistically lower than the control group (p < 0.05), despite the significant declines in density from day 0 to day 2 by all groups. Tables reflecting these comparisons between groups are located in Appendix E.

By day 14, a slight increase in the control group was not found to be significant from its day 2 density (p > 0.05), and no significant changes were seen in the treatment groups (p > 0.05) (Table 4.4.2). The control group’s density remained statistically higher than the treatment groups by 93-96% by day 14 (p < 0.05) while all treatment groups were not found to be statistically different from each other (p > 0.05), indicating that a rebound did not occur in any treatment reactors. Tables reflecting these comparisons between groups are located in Appendix E.

Table 4.4.2: Ledge Lake statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Although all experimental groups underwent significant reductions in density throughout this trial period, there was also significant change in the control group over time. Reactors in this experiment were not enriched with additional nutrients beyond those present when collected from their water source, allowing for the possibility of starvation over the 14 day experimental period if nutrients too limiting. While this may have had some impact, the control group maintained a statistically higher cyanobacterial density

60 from day 2 to day 14 (p <0.05), suggesting that the conditions for photosynthetic organisms in the reactors was sufficient to sustain a higher density, yet experimental groups that did not match this nor rebound even with available nutrients. The lack of statistical difference between experimental groups (p > 0.05) indicates that a lower Cu concentration in these trials was sufficient in reducing cyanobacteria density and that the higher Lake Guard dose could be reduced.

4.4.3 Ledge Lake chlorophyll-a response to algaecide treatment

Analysis of chl-a concentration from Ledge Lake experimentation revealed changes in chl-a concentrations both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for chl-a concentration over time indicating potential differences between control and experimental groups (Figure 4.4.3), as well as a scatter plot of means with standard deviation reflecting equivalent chl-a RFU readings (located in Appendix E).

Figure 4.4.3: Ledge Lake chl-a (µg/L) response to algaecide treatments over 14 days

A general linear model fit to the data was able to account for 90.76% of observed variance. An average 1.59% decrease in the control group was not found to be a significant decline in chl-a concentration from day 0 to day 2 (p > 0.05) while Cutrine Ultra, Lake

Guard 17.6 mg and Lake Guard 6.65 mg groups did show significant declines in concentration of 57.01%, 75.64%, and 63.31%, respectively (Table 4.4.3). In addition, all average densities of the treatment groups on day 2 were not found to be significantly different from each other (p > 0.05), indicating that they had all experienced comparable reductions after algaecide applications.

Although an average 13.01% increase was recorded in the control group, statistical analysis did not find this to be significantly different from the day 2 concentration (p <

0.05) but was still significantly higher than all experimental groups. Measured average concentration decreases in algaecide treatment groups were also not found to be significant from day 2 densities (p < 0.05) and all experimental groups were again not found to be statically different from each other (p < 0.05). This finding again supports previous experiments indicating the lower Cu concentration used was sufficient in reducing the density of photosynthetic organisms.

Table 4.4.3: Ledge Lake statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Reactors in this experiment were not enriched with additional nutrients beyond those present when collected from their water source, allowing the possibility of starvation over time (Crafton 2018). However, the maintained chl-a concentration and cyanobacteria cell density in the control group from day 2 to day 14 suggests that the conditions for photosynthetic organisms was sufficient to sustain a higher algae population than was reflected in the algaecide reactors. In addition, not only did the algaecide reactors not reflect these maintained indicators, but they also did not rebound across the duration of the experiment.

4.4.4 Ledge Lake response in microorganism composition to algaecide treatment

Documentation of organisms by the condition of “present” or “not present” in all experimental groups over time, as well as the control, showed Cutrine Ultra group to have the most different organism profile compared to the other groups. Tables in the appendix show presence of these genera over time.

In contrast to other experiments performed in this study, Cutrine Ultra showed less members of Chlorophyceae compared to all other groups by day 14, only sharing

Coelastrum between all groups while all groups shared similar Cyanophyceae profiles by day 14 including Anabaena, Microcystis, and Woronichinia.

Where other experiments performed in this study suggested that Cutrine Ultra had less impact on members of Chlorophyceae, this particular outcome was different despite initially having organisms that had remained present out to day 14 in other experimental

Cutrine Ultra reactors including Ankistrodesmus, Golenkinia and Radiococcus. Regardless of this, the similar results of Lake Guard 6.65 mg compared to the higher Lake Guard 17.6 mg dose continues to suggest that the lower Cu concentration may be sufficient in reducing cyanobacteria bloom populations while not having too much impact on non-target organisms.

4.4.5 Ledge Lake response in pH and conductivity

In all Ledge Lake experiments, pH readings remained between 8 and 8.5 throughout the 14 day trial. There was no significant change detected in chl-a or cyanobacteria density for any of the groups from days 2 to 14, agreeing with the stable conductivity recorded. A scatter plot of means with standard deviation following changes in pH for Ledge Lake is located in Appendix E.

Conductivity was also recorded to stay fairly stable throughout the experiment.

Readings for all groups were clustered at each time point and roughly 330 uS/cm.

Similarly, this lack of change in conductivity agreed with the insignificant changes in chl- a and cyanobacteria from days 2 to 14. A scatter plot of means with standard deviation was

64 generated for conductivity was recorded during experimentation and are located in

Appendix E.

4.5 Lake Isaac treatment groups vs. control – (9.9.2019 – 9.24.2019)

4.5.1 Lake Isaac visual observations

On day 0, all reactors were visibly green throughout with denser green mass at the bottom of the reactors. As with previous experiments, differences were seen between control and experimental groups 2 days after algaecide application. While there was no dramatic visual change in reactors between day 0 and day 2 for the control group, reactors that had received Cutrine Ultra had taken on a lighter green color through the water column and denser green mass at the bottom. Both groups that received Lake Guard, either 6.65 mg or 17.6 mg, were visibly the most different as hazy green/yellow with Lake Guard material still present near the surface of the water and denser green mass at the bottom.

By day 7 control group reactors had become a slightly paler green from the day 2 observations. The control group maintained the dense mat at the bottom of the reactors but had shift color to green/yellow and was chucky when stirred. There was not much change in the Cutrine Ultra reactors from day 2 observations but was also chucky when stirred.

Both 6.65 mg and 17.6 mg Lake Guard reactors were pale green/yellow in color throughout the water column with dark green/yellow matting at the bottom that was chucky when stirred.

The control group had become mostly clear with a green tint throughout the column by the 14th day but had maintained dark green/yellow mass at the bottom. Cutrine Ultra

65 reactors were also mostly clear with a green tint and dark brown/green matting at the bottom. Lake Guard reactors were the most different having mostly clear water with a pale green/yellow color and dark yellowish/green matting at the bottom. Large, swollen flakes of Lake Guard material were suspended in the top ¼ of the reactors.

4.5.2 Lake Isaac cyanobacteria response to algaecide treatments

Analysis of data for Lake Isaac source water revealed changes in cyanobacterial densities both within experimental conditions, as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for cyanobacterial density (cells/ml) over time showing noticeable difference between control and experimental groups (Figure 4.5.2) as well as a scatter plot of means with standard deviation equivalent phycocyanin RFU readings (located in the appendix).

Figure 4.5.2: Lake Isaac cyanobacterial density (cells/ml) over 14 days

A general linear model fit to the data was able to account for 94.63% of observed variance. Statistical analysis within experimental groups confirmed that the 8.43% decrease that occurred in the control group’s cyanobacterial cell density from Time 0 to

Day 2 was not significantly different (p > 0.05). In contrast, reactors that received Cutrine

Ultra, 17.6 mg Lake Guard, 6.65 mg Lake Guard experienced significant declines in cyanobacteria cell density from Day 0 to Day 2 with average percent declines of 65.85%

(p < 0.05), -83.21% (p < 0.05), and -86.29% (p < 0.05), respectively (Table 4.5.2). in addition, all declines in treatment groups by Day 2 were found to be significantly different from the control group (p < 0.05). Tables reflecting these findings are located in Appendix

A significant decline (-65.45%) in cyanobacteria density was observed in the control group from Day 2 to Day 14 (p < 0.05, Table 4.5.2), however the average density of the control group at Day 14 was still statistically greater than both Lake Guard treatments of 17.6 mg and 6.65 mg reactors by 82% and 85%, respectively (p < 0.05). In contrast, there was not a statistical difference between the control group and experimental Cutrine

Ultra group at only a 38.20% difference in means (p > 0.05) These comparative average densities of treatment reactors compared to the control group on day 14 was further supported by a Tukey’s pairwise comparison located in Appendix F.

Additionally, very little average change in cyanobacteria cell density occurred by day 14 in treatment groups that received Cutrine Ultra, 17.6 mg Lake Guard, and 6.65 mg

Lake Guard (p > 0.05) following the initial Day 2 reduction (p > 0.05), indicating that a recovery did not take place after algaecide treatment (Table 4.5.2).

Table 4.5.2: Lake Isaac statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application. Lake Isaac PC density (cells/ml) within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -8.43% 0.996 Control 2 - 14 -65.45% 0

Cutrine Ultra 0 - 2 -65.85% 0 Cutrine Ultra 2 - 14 -41.58% 0.727

17.6mg LG 0 - 2 -83.21% 0 17.6mg LG 2 - 14 -82.96% 0.777

6.65mg LG 0 - 2 -86.29% 0 6.65mg LG 2 - 14 -65.68% 0.991

The overall reduction of cyanobacteria cell density, and lack of significant difference between their densities over time across all of the algaecide treatment groups suggests that the lower Cu concentration of 6.65 mg Lake Guard and Cutrine Ultra were just as effective toward mitigating cyanobacteria growth as the higher 17.6 mg Lake Guard.

These results support that much less Lake Guard than is recommended on the label could be an effective mitigation tool when targeting toxin producing cyanobacteria species. This finding is similar to the results for other lakes samples used in this study.

4.5.3 Lake Isaac chlorophyll-a response to algaecide treatments

Analysis of data for Lake Isaac source water revealed changes in chl-a concentration both within experimental conditions as well as between conditions at various time points. A scatter plot of means with standard deviation was generated for the change in chl-a concentration over time in control and experimental groups (Figure 4.5.3), as well

68 as a scatter plot of means with standard deviation reflecting equivalent chl-a RFU readings

(located in Appendix F).

Figure 4.5.3: Lake Isaac chl-a (µg/L) response to algaecide treatments over 14 days

A general linear model fit to the data was able to account for 75.07% of observed variance. Statistical analysis within all groups, including the control, confirmed that no significant change occurred in chl-a concentration for any groups from Time 0 to Day 2 (p

> 0.05). Although statistical difference was not found for the changes in concentration, the chl-a concentration in reactors treated with Cutrine Ultra seemed slightly higher than on day 0 while all other groups decreased in concentration (p > 0.05). However, significant decreases did occur in chl-a from Day 2 to Day 14 for all groups (p < 0.05), indicating that a rebound in green algae did not occur (Table 4.5.2).

Table 4.5.3: Lake Isaac statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

While there was no significant difference in average chl-a content between the control and any of the experimental groups at Day 2, the chl-a concentration in Cutrine

Ultra reactors was in fact significantly different than that of Lake Guard 17.6 mg and Lake

Guard 6.65 mg by 81% and 93%, respectively (p < 0.05). Although the slight increase of

Cutrine Ultra group from day 0 to day 2 was not found to be significant (p > 0.05), this experimental group did indicate an increasing trend compared to the control over this time period. This could have led to the gap between it and the more dramatic decreases in Lake

Guard groups. Despite these differences, all groups had declined in chl-a concentration by day 14 to a level that was not significantly different from each other (p > 0.05).

Reactors in this experiment were not enriched with additional nutrients beyond those present when collected from their water source. This raises the possibility that although Lake Guard materials had an immediate effect on photosynthetic organisms, some impact of starvation may have added to declines in chl-a. Comparative tables between these groups can be found in Appendix F along with supporting Turkey’s comparisons.

4.5.4 Lake Isaac response in microorganism composition to algaecide treatment

Documentation of organisms over time, resulted in fairly similar Cyanophyceae and Chlorophyceae species profiles by day 14 for the control and Lake Guard 6.65 mg groups. Cutrine Ultra also had a similar Cholorphycae profile to these two groups but had the least members of Cyanophyceae present of all groups. In contrast to the others, Lake

Guard 17.6 mg group showed the least members of Chlorophyceae while still showing presence of the same cyanobacteria as the other samples. Tables located in Appendix F show presence of these genera over time.

This finding supports that the lower Cu concentration of Cutrine Ultra and Lake

Guard 6.65 mg may be sufficient in reducing cyanobacteria bloom populations while not having too much impact on non-target organisms compared to the higher Lake Guard 17.6 mg dose that seems to have more drastically impacted members of Chlorophyceae for example.

4.5.5 Lake Isaac responses in pH and conductivity

In all Lake Isaac experiments this parameter was found to hold around 8 pH but never going below 7. The control group indicated a pH around 9 on day two while all other groups clustered closely near 8.5 for all days. By day seven, the control group had joined the pH level of the other groups for the remainder of the readings, but the higher pH by the control group on day two is supported by the higher chl-a and cyanobacteria densities still detected on that day. A scatter plot of means with standard deviation is found in Appendix

Conductivity was recorded to stay fairly stable throughout the experiment.

Readings for all groups were clustered at each time point and roughly 850 uS/cm. No detectable rebound was seen for any of the groups in this experiment, supporting the unchanged conductivity. A scatter plot of means with standard deviation was generated for conductivity was recorded during experimentation and are found in Appendix F.

CHAPTER V

CONCLUSIONS AND RECOMMENDATIONS

5.1 General Overview

The increasing threat of HABs, and more specifically toxin producing cHABs, in the future has led researchers to advance their understandings of HAB mitigation techniques. Copper based algaecides have had a historical place as a tool in these efforts and will likely continue to play a role in various types and scales of water management.

Therefore, proper dosage and delivery methods should be well understood to safely and effectively use copper in diverse scenarios while considering ecological impacts in the short and long term future.

In this study, the efficacy of Lake Guard Blue, a floating copper delivery product, was tested against an already industrially recognized product. The development of Lake

Guard was an attempt to address major drawbacks of copper pentahydrate that included dispersion and application specificity, giving greater functionality to the material.

5.2 Conclusions

The efficacy of copper based algaecide Lake Guard was accessed at two concentrations in comparison to a chelated copper algaecide, Cutrine Ultra, utilizing field water samples. Field water samples were collected at, or concentrated to, a cHAB cell 73 density of 10,000 ± 1000 cells/ml with varying water chemistries. The performance of

Cutrine Ultra at concentration 0.05 mg/L was expected based on manufacture’s recommended dose and supported by other research groups using the Cutrine Ultra.

Overall, both concentrations of Lake Guard reduced cyanobacteria as well as chl-a over time. This indicated that a lower than recommended dosage, and subsequently lower

Cu concentration, may be sufficient in mitigation using this product. Lake Guard showed the most dramatic impacts on cyanobacteria and non-target organisms (indicated by chl-a concentration) by day 2 following application of either high or low Cu concentration used.

Further, in no cases did experimental groups treated with either Lake Guard concentration show rebounds in cyanobacteria or chl-a from 0-2 days or 2-14 days. Comparatively,

Cutrine Ultra had its greatest impact cyanobacteria density only by day 2 while not showing significant changes for chl-a concentration in some instances, indicting less impact on non- target organisms. Of interest, Lake Guard groups were more often significantly reduced in cyanobacteria and chl-a concentration compared to the control groups by day 14 while

Cutrine Ultra ranged in evaluation, with a 2876.49% rebound in chl-a even occurring from day 2 to day 14 for Lake Rockwell (7.23.2019) experimental group. Although, control groups varied in significant difference compared to treatment groups by day 14, closed- system reactors have the potential to amplify the effects of Cu exposure and nutrient limitation.

Impacts of Lake Guard treatments varied by day 2 with sometimes close to 90% reductions in cyanobacteria density and chl-a concentration while reductions by Cutrine

Ultra were less dramatic. Comparatively, in most cases the control groups did not experience significant changes in either cyanobacteria density or chl-a concentration from

74 day 0 to day 2. Of those control groups that did see significant change by day 2, Ledge

Lake showed a 63.82% decrease in only cyanobacteria while Camp Forbes indicated decreases in both cyanobacteria and chl-a concentration of 39.58% and 56.04%, respectively (p < 0.05). Even in these instances of decrease by the control group, cyanobacteria and chl-a concentrations did not match the large decreases by Cu treatments.

Reductions in cyanobacteria and chl-a for control groups were more dramatic however from day 2 to day 14 as nutrient limitation potentially played a role in starvation and can be seen in the variation of significant differences between the control group and experimental groups by day 14. Despite this, significant losses in both cyanobacteria density and chl-a concentration for control groups were only seen in Lake Isaac and Camp

Forbes samples. Hudson Springs samples also showed a significant loss from day 2 to day

14 but only in cyanobacteria density while Lake Rockwell (8.12.2019) only experienced a chl-a reduction.

Unexpectedly, Lake Guard material was still present in experimental reactors as swollen, gelatinous globules by day 14 that stayed suspended toward the top of the water column near the surface. Although water in reactors used in this study were not agitated in a natural way that would occur in a waterbody, this could have implications for water filters, sunlight penetration and other unknown effects when used in the field.

5.3 Recommendations

Based on the results, it is not recommended to use Lake Guard at the current manufacturer’s application guidance. Presently, the outlined 2.2 – 17.8 lb/acre application values are too wide and would likely lead to not only copper application in excess of EPA

75 regulations, but also more material than necessary to control algae based on the results of this study. Prior to future use, the manufacturer should modify the instructions for treatment values to provide the potential for lower dosage. To better understand the efficacy and impacts of Lake Guard, more experimentation is recommended. The first of these tests should likely be in a laboratory setting before being applied in field trials.

1. In all experiments performed, many different algae species and water chemistries

were present. Additionally, the exact numbers of organisms in comparison to one

another was not determined. In most cases Lake Guard at both high and low

concentrations made the majority of their impact on reducing cyanobacteria from

days 0 to day 2, similarly to Cutrine Ultra, indicating that a lower than

recommended concentration of Lake Guard may be sufficient in mitigation against

cyanobacteria. Varying Lake Guard treatments against monoculture bench scale

studies of cyanobacteria with more exact cell counts would give insights to minimal

dosages necessary to reduce populations over time.

2. Experiments are required to more closely determine Lake Guard’s impact on non-

target green algal species. This could also be achieved by more exact monoculture

studies or mixed cultures of limited species at known cell densities.

3. This study compared the efficacy of Lake Guard to an already industrially

employed product, Cutrine Ultra. Lake Guard’s active ingredient is copper sulfate

pentahydrate whereas Cutrine Ultra utilizes a copper chelate as a mixed copper

ethanolamine emulsified complex. It would be likely be worthwhile to compare

Lake Guard against laboratory grade copper sulphate.

4. Copper concentration over time was not monitored during this study. The rate of

copper release from Lake Guard may be worthwhile to understand as target algae

are eliminated and non-target species become the majority, increasing their chance

of exposure.

5. Degradation of the Lake Guard material over time did not seem to be sufficient and

often left globular masses at the water’s surface even until day 14. This study stored

reactors in a motionless growth chamber over the course of experimentation, so it

may be possible that the lack of water/wave action (mechanical breakdown) and

heat from direct sunlight was lacking in this process. Nonetheless, experiments

should be performed to ensure that this debris would not affect pumps and filters at

industrial sites (i.e. water processing plants) and cause costly damage or delays.

Equally important, the potential for adverse environmental impacts by these

globular remains should be determined for events such as ingestion by aquatic

organisms or its ability to block sunlight.

6. Based on the results of experiments outlined above, field trials of Lake Guard

should likely be performed first using a retainment strategy to prevent the material

from floating away from a target area. This could lead to an understanding of the

products impact range if held stationary and perhaps even its duration of impact. In

addition, a project like this could give insight into the product’s degradation

behavior over time in the field.

REFERENCES

1. Arch Chemicals, Inc. Cutrine Ultra Product Information Sheet. 2. Arch Chemicals, Inc. Cutrine Ultra Specimen Label. 3. Baker, A. L. "Phycokey--an image based key to Algae (PS Protista), Cyanobacteria, and other aquatic objects. University of New Hampshire Center for Freshwater Biology." (2012). 4. Bellinger, Edward G., and David C. Sigee. Freshwater algae: identification, enumeration and use as bioindicators. John Wiley & Sons, 2015. 5. BlueGreen Water Technologies Ltd., Lake Guard Blue U.S. EPA Pesticide Registration, 2018. 6. Bishop, W. M., Richardson, R. J., & Willis, B. E. (2018). Comparison of partitioning and efficacy between copper algaecide formulations: refining the critical burden concept. Water, Air, & Soil Pollution, 229(9), 1-17. 7. Borkow, G., & Gabbay, J. (2005). Copper as a biocidal tool. Current medicinal chemistry, 12(18), 2163-2175. 8. Briland, R. D., Stone, J. P., Manubolu, M., Lee, J., & Ludsin, S. A. (2020). Cyanobacterial blooms modify food web structure and interactions in western Lake Erie. Harmful algae, 92, 101586. 9. Calomeni, A. J., Iwinski, K. J., Kinley, C. M., McQueen, A., & Rodgers Jr, J. H. (2015). Responses of Lyngbya wollei to algaecide exposures and a risk characterization associated with their use. Ecotoxicology and environmental safety, 116, 90-98. 10. Chaffin, J. D., Mishra, S., Kane, D. D., Bade, D. L., Stanislawczyk, K., Slodysko, K. N., ... & Fox, E. L. (2019). Cyanobacterial blooms in the central basin of Lake Erie: Potentials for cyanotoxins and environmental drivers. Journal of Great Lakes Research, 45(2), 277-289. 11. City of Akron Watershed Division. (2016). Lake Rockwell and Lake Pippen. City of Akron. https://www.akronohio.gov/cms/Water/Watershed_Rockwell/index.html 12. City of Cleveland. (2021). Camp Forbes Programs for Children and Youth. http://www.city.cleveland.oh.us/CityofCleveland/Home/KidsTeens/recreation#Forbes

13. City of Hudson. (2021). Hudson Springs Park. The City of Hudson Government https://www.hudson.oh.us/Facilities/Facility/Details/4 14. Collier, J. L., & Grossman, A. R. (1992). Chlorosis induced by nutrient deprivation in Synechococcus sp. strain PCC 7942: not all bleaching is the same. Journal of bacteriology, 174(14), 4718-4726.

15. Cleveland Metroparks (2021a). Ledge Lake. https://www.clevelandmetroparks.com/parks/visit/parks/hinckley-reservation/ledge- lake

16. Cleveland Metroparks (2021b). Isaac Lake. https://www.clevelandmetroparks.com/parks/visit/parks/big-creek-reservation/lake- isaac

17. Crafton, E. A., Glowczewski, J., Ott, D. W., & Cutright, T. J. (2018). In situ field trial to evaluate the efficacy of Cutrine Ultra to manage a cyanobacteria population in a drinking water source. Environmental Science: Water Research & Technology, 4(6), 863-871. 18. Crafton, E., Glowczewski, J., Cutright, T., & Ott, D. (2021). Bench-scale assessment of three copper-based algaecide products for cyanobacteria management in source water. SN Applied Sciences, 3(3), 1-11. 19. Gallardo‐Rodríguez, Juan J., et al. "A critical review on control methods for harmful algal blooms." Reviews in Aquaculture 11.3 (2019): 661-684. 20. Gobler, C. J. (2020). Climate change and harmful algal blooms: Insights and perspective. Harmful Algae, 91, 101731. 21. Haakonsson, Signe, et al. "Predicting cyanobacterial biovolume from water temperature and conductivity using a Bayesian compound Poisson-Gamma model." Water Research (2020): 115710. 22. Iwinski, Kyla J., et al. "Influence of CuSO4 and chelated copper algaecide exposures on biodegradation of microcystin-LR." Chemosphere 174 (2017): 538-544. 23. Kinley, Ciera M., et al. "Cell density dependence of Microcystis aeruginosa responses to copper algaecide concentrations: Implications for microcystin-LR release." Ecotoxicology and environmental safety 145 (2017): 591-596. 24. Leao, Pedro N., M. Teresa SD Vasconcelos, and Vítor M. Vasconcelos. "Allelopathy in freshwater cyanobacteria." Critical reviews in microbiology 35.4 (2009): 271-282. 25. Merel, Sylvain, et al. "State of knowledge and concerns on cyanobacterial blooms and cyanotoxins." Environment international 59 (2013): 303-327.J.D. 26. Minitab Statistical Software (2021). [Computer software]. State College, PA: Minitab, Inc. (www.minitab.com)

27. Murray-Gulde, Cynthia L., et al. "Algicidal effectiveness of clearigate, cutrine-plus, and copper sulfate and margins of safety associated with their use." Archives of Environmental Contamination and Toxicology 43.1 (2002): 19-27. 28. Nienaber, Mark A., and Miriam Steinitz-Kannan. A Guide to Cyanobacteria: Identification and Impact. University Press of Kentucky, 2018. 29. Ohio EPA (2019) Public Water System Harmful Algal Bloom Response Strategy. https://www.epa.ohio.gov/Portals/28/documents/habs/2020-PWS-HAB-Strategy.pdf 30. Paerl, H. W., & Barnard, M. A. (2020). Mitigating the global expansion of harmful cyanobacterial blooms: Moving targets in a human-and climatically-altered world. Harmful algae, 96, 101845. 31. Paerl, Hans W., et al. "Controlling harmful cyanobacterial blooms in a hyper- eutrophic lake (Lake Taihu, China): the need for a dual nutrient (N & P) management strategy." water research 45.5 (2011): 1973-1983. 32. Song, L., Marsh, T. L., Voice, T. C., & Long, D. T. (2011). Loss of seasonal variability in a lake resulting from copper sulfate algaecide treatment. Physics and Chemistry of the Earth, Parts A/B/C, 36(9-11), 430-435. 33. Tsai, K. P. (2015). Effects of two copper compounds on Microcystis aeruginosa cell density, membrane integrity, and microcystin release. Ecotoxicology and environmental safety, 120, 428-435. 34. Tsai, Kuo-Pei. "Management of target algae by using copper-based algaecides: effects of algal cell density and sensitivity to copper." Water, Air, & Soil Pollution 227.7 (2016): 238. 35. United States Environmental Protection Agency (U.S. EPA) (2019a). Nutrient Pollution. “Harmful Algal Blooms.” https://www.epa.gov/nutrientpollution/harmful- algal-blooms 36. United States Environmental Protection Agency (U.S. EPA) (2019b). Nutrient Pollution. “The Issue.” https://www.epa.gov/nutrientpollution/issue 37. United States Environmental Protection Agency (U.S. EPA) (2019c). Nutrient Pollution. “Sources and Solutions.” https://www.epa.gov/nutrientpollution/sources- and-solutions 38. United States Environmental Protection Agency (U.S. EPA) (2019d). Nutrient Pollution. “The Effects: Environment.” https://www.epa.gov/nutrientpollution/effects-environment 39. United States Environmental Protection Agency (U.S. EPA) (2019e). Nutrient Pollution. “The Effects: Economy.” https://www.epa.gov/nutrientpollution/effects- economy

40. United States Environmental Protection Agency (U.S. EPA) (2019f). Nutrient Pollution. “Where Nutrient Pollution Occurs.” https://www.epa.gov/nutrientpollution/where-nutrient-pollution-occurs 41. United States Environmental Protection Agency (U.S. EPA) (2019g). Nutrient Pollution. “Where This Occurs: Lakes and Rivers.” https://www.epa.gov/nutrientpollution/where-occurs-lakes-and-rivers 42. United States Environmental Protection Agency (U.S. EPA) (2019h). Nutrient Pollution. “Learn about Cyanobacteria and Cyanotoxins.” https://www.epa.gov/cyanohabs/learn-about-cyanobacteria-and-cyanotoxins 43. United States Environmental Protection Agency (U.S. EPA) (2019i). “Drinking Water Contaminant Candidate List (CCL) and Regulatory Determination.” https://www.epa.gov/ccl 44. Weenink, Erik FJ, et al. "Combatting cyanobacteria with hydrogen peroxide: a laboratory study on the consequences for phytoplankton community and diversity." Frontiers in microbiology 6 (2015): 714. 45. Wehr, John D., Robert G. Sheath, and J. Patrick Kociolek (2015). Freshwater algae of North America: ecology and classification. Elsevier. 46. Yan, H., & Pan, G. (2002). Toxicity and bioaccumulation of copper in three green microalgal species. Chemosphere, 49(5), 471-476. 47. Zhang, Zengqiang, et al. "Physico-chemical forms of copper in water and sediments of Lake Pontchartrain basin, USA." Chemosphere 195 (2018): 448-454.Crafton, Elizabeth A., et al. "In situ field trial to evaluate the efficacy of Cutrine Ultra to manage a cyanobacteria population in a drinking water source." Environmental Science: Water Research & Technology 4.6 (2018): 863-871.

APPENDIX A

Lake Rockwell (7.23.2019 – 8.7.2019)

Lake Rockwell (7.23.2019) Day 2 cyanobacteria cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 42.40% 0.783 17.6mg LG Control 94.04% 0.008 17.6mg LG Cutrine Ultra 866.82% 0.509

Lake Rockwell (7.23.2019) Day 14 cyanobacteria cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 25.84% 1 17.6mg LG Control 96.45% 0.293 17.6mg LG Cutrine Ultra 1988.14% 0.75 Figure A1: Lake Rockwell (7.23.2019 – 8.7.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Lake Rockwell (7.26.2019) PC cells/ml Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping control 0 3 14075.9 A control 2 3 12034.6 A 76 ul Cutrine Ultra 0 3 10385.9 A 17.6mg lake Guard 0 3 9945 A B control 14 3 7432.7 A B C 76 ul Cutrine Ultra 2 3 6931.4 A B C 76 ul Cutrine Ultra 14 3 5512.2 A B C 17.6mg lake Guard 2 3 716.9 B C 17.6mg lake Guard 14 3 264 C Means that do not share a letter are significantly different. Figure A2: Lake Rockwell (7.23.2019 – 8.7.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure A3: Lake Rockwell (7.23.2019 – 8.7.2019) changes in phycocyanin RFU over 14 days

Lake Rockwell (7.23.2019) Day 2 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 47.96% 1 17.6mg LG Control 66.69% 1 17.6mg LG Cutrine Ultra 56.24% 1

Lake Rockwell (7.23.2019) Day 14 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 395.72% 0 17.6mg LG Control 94.44% 0.798 17.6mg LG Cutrine Ultra 8816.52% 0 Figure A4: Lake Rockwell (7.23.2019 – 8.7.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Lake Rockwell (7.26.2019) Chl-a (ug/L) Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping 76 ul Cutrine Ultra 14 3 136.72 A control 14 3 27.58 B 17.6mg lake Guard 0 3 16.103 B control 0 3 15.977 B 76 ul Cutrine Ultra 0 3 15.173 B control 2 3 8.827 B 76 ul Cutrine Ultra 2 3 4.593 B 17.6mg lake Guard 2 3 2.94 B 17.6mg lake Guard 14 3 1.533 B Means that do not share a letter are significantly different. Figure A5: Lake Rockwell (7.23.2019 – 8.7.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure A6: Lake Rockwell (7.23.2019 – 8.7.2019) changes in chl-a RFU over 14 days

Figure A7: Lake Rockwell (7.23.2019 – 8.7.2019) changes in temperature over 14 days

Figure A8: Lake Rockwell (7.23.2019 – 8.7.2019) changes in pH over 14 days

Figure A9: Lake Rockwell (7.23.2019 – 8.7.2019) changes in conductivity over 14 days

A10 a

Rockwell 7.23.2019 Control Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Planktothrix Snowella Mougeotia Oocystis Pandorina Pediastrum Radiococcus Scenedesmus Scenedesmus Fragilaria Ceratium Melosira Closterium Navicula Euglena Rotifer Fragilaria Trachelomonas

A10 b

Rockwell 7.23.2019 Cutrine Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Aphanizomenon Aphanizomenon Microcystis Microcystis Oscillatoria Chlamydomonas Woronichina Coelastrum Mougeotia Coelastrum Oocystis Dictyosphaerium Pandorina Golenkinia Pediastrum Mougeotia Staurastrum Pediastrum Scenedesmus Ceratium Ciliate Eunotia? Fragilaria Fragilaria Trachelomonas Melosira

A10 c

Rockwell 7.23.2019 Lake Guard 17.6 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Aphanizomenon Aphanizomenon Microcystis Pediastrum Chlamydomonas Oocystis Fragilaria Pandorina Melosira Navicula Fragilaria Trachelomonas Melosira Trachelomonas Figure A10: Microorganism presence in Lake Rockwell (7.23.2019 – 8.7.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group.

APPENDIX B

Lake Rockwell (8.12.2019 – 8.27.2019)

Rockwell (8.12.2019) Day 2 cyanobacteria cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 88% 0.103 Cutrine Ultra 6.65mg LG 129% 1 6.65mg LG Control 95% 0.058

Rockwell (8.12.2019) Day 14 cyanobacteria cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 143.86% 0.419 Cutrine Ultra 6.65mg LG 4632.46% 0.012 6.65mg LG Control 94.85% 0.916 Figure B1: Lake Rockwell (8.12.2019 – 8.27.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Rockwell (8.12.19) PC cells/ml Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping control 0 3 7456.82 A 76 ul Cutrine Ultra 0 3 7124.66 A 76 ul Cutrine Ultra 14 3 6490.53 A B 6.65 mg lake Guard 0 3 6357.66 A B control 2 3 5669.18 A B C D control 14 3 2661.59 A B C D E 76 ul Cutrine Ultra 2 3 686.73 C D E 6.65 mg lake Guard 2 3 300.21 D E 6.65 mg lake Guard 14 3 137.15 E Means that do not share a letter are significantly different. Figure B2: Lake Rockwell (8.12.2019 – 8.27.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure B3: Lake Rockwell (8.12.2019 – 8.27.2019) changes in phycocyanin RFU over 14 days

Rockwell (8.12.19) Day 2 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 75% 0.494 Cutrine Ultra 6.65mg LG 80% 1 6.65mg LG Control 86% 0.286 Rockwell (8.12.19) Day 14 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 343.77% 0 Cutrine Ultra 6.65mg LG 9938.22% 0 6.65mg LG Control 95.58% 0.01 Figure B4: Lake Rockwell (8.12.2019 – 8.27.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure B5: Lake Rockwell (8.12.2019 – 8.27.2019) Tukey’s Pairwise comparison of chl- a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure B6: Lake Rockwell (8.12.2019 – 8.27.2019) changes in chl-a RFU over 14 days

Figure B7: Lake Rockwell (8.12.2019 – 8.27.2019) changes in temperature over 14 days

Figure B8: Lake Rockwell (8.12.2019 – 8.27.2019) changes in pH over 14 days

Figure B9: Lake Rockwell (8.12.2019 – 8.27.2019) changes in conductivity over 14 days

B10 a

Rockwell 8.12.2019 Control Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Microcystis Aphanizomenon Microcystis Closterium Coelastrum Chlamydomonas? Crucigenia Pediastrum Elakatothrix Oedogonium Cryptomonas? Pediastrum Fragilaria? Radiococcus Or Westella? Glenodinium Scenedesmus Melosira Staurastrum

Cosmarium Cyclotella Euglena Fragilaria Melosira Navicula Ulnaria?

B10 b

Rockwell 8.12.2019 Cutrine Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Cylindrospermopsis? Aphanocapsa Microcystis Microcystis Planktothrix Or Aphanizomenon? Chlamydomonas Woronichinia Chlorella Dictyosphaerium Pediastrum Nitzschia Phacotus? Planctonema? Radiococcus Scenedesmus Scenedesmus Staurastrum Navicula Vitreochlamys? Trachelomonas

Cryptomonas? Fragilaria Navicula Trachelomonas

B10 c

Rockwell 8.12.2019 Lake Guard 6.65 mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Aphanizomenon Microcystis Aphanocapsa Planktothrix Or Aphanizomenon? Planktothrix Or Aphanizomenon? Synechococcus Chlorella? Crucigenia Actinastrum Pediastrum Chlamydomonas? Radiococcus Dictyosphaerium Pandorina Or Volvulina? Trachelomonas Pediastrum Phacotus? Radiococcus Staurastrum

Acanthoceras Cryptomonas? Cyclotella Fragilaria Glenodinium Melosira? Trachelomonas

APPENDIX C

Camp Forbes (8.14.2019 – 8.8.29.2019)

Forbes Day 2 PC density (cells/ml) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 8% 1 Cutrine Ultra 6.65mg LG 48% 0.045 17.6mg LG Control 59% 0 17.6mg LG Cutrine Ultra 124% 0 6.65mg LG Control 38% 0.003 6.65mg LG 17.6mg LG 51% 0.464 Forbes Day 14 PC density (cells/ml) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 25.25% 1 Cutrine Ultra 6.65mg LG 247.22% 0.998 17.6mg LG Control 63.93% 1 17.6mg LG Cutrine Ultra 247.22% 0.998 6.65mg LG Control 63.93% 1 6.65mg LG 17.6mg LG 0.00% 1 Figure C1: Camp Forbes (8.14.2019 – 8.8.29.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Forbes PC (clls/ml) Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping 17.6mg lake Guard 0 3 11581.7 A control 0 3 11001.9 A 6.65 mg lake Guard 0 3 10096 A CU 0 3 9981.3 A control 2 3 6647.6 B CU 2 3 6110 B 6.65 mg lake Guard 2 3 4123.1 C D 17.6mg lake Guard 2 3 2728 D E CU 14 3 958.5 E F control 14 3 765.2 E F 6.65 mg lake Guard 14 3 276.1 F 17.6mg lake Guard 14 3 276.1 F Means that do not share a letter are significantly different.

Figure C2: Camp Forbes (8.14.2019 – 8.8.29.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure C3: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in phycocyanin RFU over 14 days

Forbes Day 2 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 44% 0.148 Cutrine Ultra 6.65mg LG 45% 0.136 17.6mg LG Control 37% 0.374 17.6mg LG Cutrine Ultra 130% 0 6.65mg LG Control 1% 1 6.65mg LG 17.6mg LG 59% 0.398 Forbes Day 14 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 226.70% 0.999 Cutrine Ultra 6.65mg LG 366.32% 0.996 17.6mg LG Control 38.94% 1 17.6mg LG Cutrine Ultra 435.02% 0.995 6.65mg LG Control 29.94% 1 6.65mg LG 17.6mg LG 12.84% 1 Figure C4: Camp Forbes (8.14.2019 – 8.8.29.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Forbes Lake Chl-a (ug/L) Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping control 0 3 215.713 A B 17.6mg lake Guard 0 3 206.117 A B C 6.65 mg lake Guard 0 3 195.847 A B C CU 0 3 176.72 B C D CU 2 3 136.5 D E control 2 3 94.817 E F 6.65 mg lake Guard 2 3 94.32 E F 17.6mg lake Guard 2 3 59.297 F G CU 14 3 22.15 G H control 14 3 6.783 H 6.65 mg lake Guard 14 3 4.753 H 17.6mg lake Guard 14 3 4.137 H Means that do not share a letter are significantly different. Figure C5: Camp Forbes (8.14.2019 – 8.8.29.2019) Tukey’s Pairwise comparison of chl- a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure C6: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in chl-a RFU over 14 days

Figure C7: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in temperature over 14 days

Figure C8: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in pH over 14 days

Figure C9: Camp Forbes (8.14.2019 – 8.8.29.2019) changes in conductivity over 14 days

C10 a

Camp Forbes Control Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Pseudanabaena Or Cylindrospermum Microcycstis Snowella Pseudanabaena Or Cylindrospermum

Scenedesmus Ankistrodesmus Ankistrodesmus Chlamydomonas Ankistrodesmus? Coelastrum? Chloridella Crucigenia Oocystis Oocystis Pediastrum Pediastrum Staurastrum Radiococcus Dictyosphaerium Scenedesmus Tetrallantos? Westella Or Radiococcus? Treubaria Chloridella Amphipleura? Chloridella? Asterionella Cosmarium Ciliate Cyclotella? Cosmarium Fragilaria Euglena Melosira? Navicula Navicula Nitzschia? Nitzschia? Phacus Rotifer Tetradriella Tetradriella Trachelomonas Trachelomonas C10 b

Camp Forbes Cutrine Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Pseudanabaena Or Cylindrospermum Pseudanabaena Or Cylindrospermum

Ankistrodesmus Ankistrodesmus Cosmarium Chlamydomonas Dictyosphaerium Crucigenia Oocystis Dictyosphaerium Pediastrum Dictyosphaerium? Radiococcus? Oocystis? Scenedesmus Pediastrum Staurastrum Scenedesmus Treubaria Staurastrum Westella Or Radiococcus? Chloridella Ciliate Chloridella? Euglena Ciliate Isthmochloron Fragilaria Melosira? Lakeguard Debris Nitzschia? Navicula Rotifer Nitzschia? Tetradriella Trachelomonas Trachelomonas

C10 c

Camp Forbes Lake Guard 17.6 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Pseudanabaena Or Cylindrospermum Crucigenia Ankistrodesmus Dictyosphaerium Coelastrum Oocystis Cosmarium Pediastrum Dictyosphaerium Oocystis Asterionella Pediastrum Chloridella? Phacotus Or Chlamydomonas? Cosmarium? Radiococcus? Navicula Scenedesmus Nitzschia? Staurastrum Tetradriella Treubaria Trachelomonas

Asterionella Chloridella Ciliate Euglena Navicula Nitzschia? Tetradriella Trachelomonas

C10 d

Camp Forbes Guard 6.65 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Dactylococcopsis? Pseudanabaena Or Cylindrospermum Pseudanabaena Or Cylindrospermum Chlamydomonas Ankistrodesmus Chlamydomonas? Chlamydomonas Dictyosphaerium Dictyosphaerium Pediastrum Pediastrum Scenedesmus Radiococcus Scenedesmus Asterionella Staurastrum Chloridella? Ciliate Chloridella Fragilaria Cosmarium Navicula Euglena Nitzschia? Navicula Trachelomonas Nitzschia? Phacus Tetradriella Trachelomonas Vorticella Figure C10: Microorganism presence in Camp Forbes (8.14.2019 – 8.8.29.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group.

100

APPENDIX D

Hudson Lake (8.20.2019 – 9.4.2019)

Hudson (8.20.2019) Day 2 cyanobacteria cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 72% 0 Cutrine Ultra 6.65mg LG 23% 1 17.6mg LG Control 84% 0 17.6mg LG Cutrine Ultra 75% 0.886 6.65mg LG Control 78% 0 6.65mg LG 17.6mg LG 42% 1

Hudson (8.20.2019) Day 14 cyanobacteria cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 66.28% 0 Cutrine Ultra 6.65mg LG 21.34% 1 17.6mg LG Control 84.33% 0 17.6mg LG Cutrine Ultra 115.19% 0.824 6.65mg LG Control 72.21% 0 6.65mg LG 17.6mg LG 43.61% 0.995 Figure D1: Hudson (8.20.2019 – 9.4.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Hudson 8.20.2019 Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping control 0 3 11352.2 A control 2 3 9896.7 A B CU 0 3 9709.5 A B 6.65mg lake Guard 0 3 9389.4 A B 17.6mg Lake Guard 0 3 9353.2 A B control 14 3 6925.4 C CU 2 3 2734.1 D CU 14 3 2335.5 D 6.65mg lake Guard 2 3 2220.7 D 6.65mg lake Guard 14 3 1924.8 D 17.6mg Lake Guard 2 3 1562.4 D 17.6mg Lake Guard 14 3 1085.3 D Means that do not share a letter are significantly different.

101

Figure D2: Hudson (8.20.2019 – 9.4.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure D3: Hudson (8.20.2019 – 9.4.2019) changes in phycocyanin RFU over 14 days

Hudson (8.20.2019) Day 2 Chlorophyll-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 8% 1 Cutrine Ultra 6.65mg LG 10% 1 17.6mg LG Control 43% 0.992 17.6mg LG Cutrine Ultra 62% 0.999 6.65mg LG Control 2% 1 6.65mg LG 17.6mg LG 80% 0.987

Hudson (8.20.2019) Day 14 Chlorophyll-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 58.63% 0.196 Cutrine Ultra 6.65mg LG 36.14% 1 17.6mg LG Control 72.61% 0.033 17.6mg LG Cutrine Ultra 51.04% 1 6.65mg LG Control 69.61% 0.051 6.65mg LG 17.6mg LG 9.87% 1 Figure D4: Hudson (8.20.2019 – 9.4.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

102

Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping CU 0 3 90.8567 A 6.65mg lake Guard 0 3 82.95 A B C control 0 3 82.59 A B C control 14 3 76.1567 A B C D 17.6mg Lake Guard 0 3 69.6633 A B C D E 6.65mg lake Guard 2 3 49.0933 A B C D E control 2 3 48.1167 A B C D E CU 2 3 44.1767 A B C D E CU 14 3 31.5067 C D E 17.6mg Lake Guard 2 3 27.3367 D E 6.65mg lake Guard 14 3 23.1433 D E 17.6mg Lake Guard 14 3 20.86 E Means that do not share a letter are significantly different. Figure D5: Hudson (8.20.2019 – 9.4.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure D6: Hudson (8.20.2019 – 9.4.2019) changes in chl-a RFU over 14 days

103

Figure D7: Hudson (8.20.2019 – 9.4.2019) changes in temperature over 14 days

Figure D8: Hudson (8.20.2019 – 9.4.2019) changes in ph over 14 days

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Figure D9: Hudson (8.20.2019 – 9.4.2019) changes in conductivity over 14 days

D10 a

Hudson (8.20.2019) Control Group Organism Presence over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List unknown species unknown species unknown species unknown species

anabaena anabaena anabaena microcystis microcystis microcystis Aphanocapsa? Pseudanabaena? Aphanocapsa Woronichinia microcystis microcystis? microcystis? anabaena? pediastrum oocystis Pseudanabaena galeata? Tetrallantos or Romeria chlamydomonas? pediastrum staurastrum staurastrum pediastrum scenedesmus coelastrum coelastrum staurastrum Sorastrum? radiococcus westella? oocystis pediastrum chlamydomonas scenedesmus scenedesmus coelastrum oocystis? Pandorina? coelastrum staurastrum coelastrum? Eudorina? radiococcus Ankistrodesmus scenedesmus radiococcus Ankistrodesmus Kirchneriella Crucigenia Chlamydomonas coelastrum? Elakatothrix elakatothrix Elakatothrix Pandorina? oocystis Ankistrodesmus Crucigenia elakatothrix radiococcus? westella? Dimorphococcus? radiococcus? Glenodinium cosmarium coelastrum? Crucigenia? ceratium fragilaria Characiopsis? Trachelomonas Fragilaria or Fragilariopsis? rotifer rotifer Navicula navicula ceratium Euglena Codosiga? Cyclotella? Glenodinium Trachelomonas rotifer rotifer Trachelomonas ciliate Glenodinium Ulnaria? Fragilaria isthmochloron melosia? vorticella isthmochloron

105

D10 b

Hudson (8.20.2019) Cutrine Ultra Group Organism Presence over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List unknown species unknown species unknown species unknown species anabaena anabaena microcystis Pseudanabaena? Aphanocapsa microcystis microcystis anabaaena anabaena? radiococcus coelastrum oocystis coelastrum coelastrum scenedesmus coelastrum pediastrum Chlamydomonas pediastrum pediastrum chlamydomonas oocystis oocystis Ankistrodesmus radiococcus radiococcus staurastrum scenedesmus pandorina staurastrum radiococcus? radiococcus? staurastrum pediastrum Chlamydomonas Carteria eugametos Ankistrodesmus Crucigenia crucigenia staurastrum chlamydomonas? scenedesmus oocystis? Crucigenia Eudorina ciliate scenedesmus Elakatothrix ciliate rotifer oocystis westella rotifer navicula Ankistrodesmus radiococcus? trachelomonas? fragilaria carteria ciliate? Glenodinium westella or radiococcus Fragilaria ceratium crucigenia vorticella Trachelomonas ciliate? Trachelomonas Isthmochloron vorticella rotifer ceratium euglena isthmochloron ciliate rotifer

D10 c

Hudson (8.20.2019) Lake Guard 17.6mg Group Organism Presence over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List unknown species unknown species unknown species unknown species

anabaena anabaena anabaena anabaena microcystis oscillarotia? microcystis? microcystis planktothrix microcystis microcystis oscillatoria planktothrix? planktothrix pediastrum Carteria? Woronichinia or Coelosphaerium? chlamydomonas westella or radiococcus anabaena? pediastrum coelastrum pediastrum staurastrum eudorina Chlamydomonas coelastrum oocystis staurastrum radiococcus oocystis coelastrum coelastrum? radiococcus? pediastrum radiococcus clamydomonas? scenedesmus oocystis? Crucigenia lauterbornii radiococcus staurastrum staurastrum cosmarium? elakatothrix coelastrum coelastrum? scenedesmus eudornia radiococcus? rotifer oocystis? Kirchneriella ciliate Glenodinium oocystis eudorinia? Glenodinium bipes? ciliate? crucigenia fragilaria ceratium ceratium scenedesmus melosira Vorticella Trachelomonas? euglena Trachelomonas rotifer rotifer Glenodinium Glenodinium ciliate ciliate Fragilaria Characiopsis trachelomonas? fragilaria? trachelomonas Isthmochloron fragilaria?

106

D10 d

Hudson (8.20.2019) Lake Guard 6.65mg Group Organism Presence over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List unknown species unknown species unknown species unknown species

anabaena anabaena anabaena anabaena microcystis microcystis microcystis Aphanocapsa? planktothrix planktothrix? planktothrix? microcystis

pediastrum radiococcus eudorinia? coelastrum Crucigenia radiococcus? radiococcus? pediastrum staurastrum coelastrum pediastrum westella pandorina pediastrum coelastrum radiococcus oocystis radiococcus or westella scenedesmus scenedesmus radiococcus eudorina? staurastrum oocystis coelastrum scenedesmus crucigenia staurastrum coelastrum? staurastrum oocystis westella? scenedesmus eudorina Dictyosphaerium eudorina Ankistrodesmus oocystis? rotifer Nephrocytium? Dictyosphaerium? oocytsis trachelomonas navicula? rotifer ciliate rotifer Cyclotella? Glenodinium rotifer ciliate Trachelomonas Trachelomonas cosmarium? vorticella ceratium Figure D10: Microorganism presence in Hudson (8.20.2019 – 9.4.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group.

107

APPENDIX E

Ledge Lake (9.5.2019 - 9.20.2019)

Figure E1: Ledge Lake (9.5.2019 - 9.20.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

108

Figure E2: Ledge Lake (9.5.2019 - 9.20.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure E3: Ledge Lake (9.5.2019 - 9.20.2019) changes in phycocyanin RFU over 14 days

109

Ledge Day 14 Chl-A (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 84.31% 0 Cutrine Ultra 6.65mg LG 37.62% 1 17.6mg LG Control 87.62% 0 17.6mg LG Cutrine Ultra 26.75% 1 6.65mg LG Control 88.60% 0 6.65mg LG 17.6mg LG 8.58% 1 Figure E4: Ledge Lake (9.5.2019 - 9.20.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure E5: Ledge Lake (9.5.2019 - 9.20.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

110

Figure E6: Ledge Lake (9.5.2019 - 9.20.2019) changes in chl-a RFU over 14 days

Figure E7: Ledge Lake (9.5.2019 - 9.20.2019) changes in temperature over 14 days

111

Figure E8: Ledge Lake (9.5.2019 - 9.20.2019) changes in pH over 14 days

Figure E9: Ledge Lake (9.5.2019 - 9.20.2019) changes in conductivity over 14 days

112

E10 a

Ledge Lake Control Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Aphanizomenon Aphanizomenon Aphanocapsa? Aphanocapsa Cylindrospermum? Eucapsis Dactylococcopsis? Microcystis Eucapsis Plantothrix? Microcystis Woronichinia Plantothrix Synechococcus Ankistrodesmus Woronichinia Botryococcus Coelastrum Ankistrodesmus Golenkinia Chlamydomonas Elakatothrix Chlorella Micractinium Dictyosphaerium? Oedogonium? Elakatothrix Oonephris? Gonium Pediastrum Oocystis Radiococcus Pandorina Scenedesmus Pandorina? Staurastrum Pediastrum Radiococcus Navicula Sorastrum Chloridella? Staurastrum Ciliate Westella Cosmarium Fragilaria Ceratium Melosira Ciliate Rotifer Cosmarium Trachelomonas Euglena Glenodinium Mallomonas Rotifer Trachelomonas

113

E10 b

Ledge Cutrine Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Aphanizomenon Eucapsis Aphanocapsa Microcystis Aphanothece Woronichinia Cylindrospermum Eucapsis Chlamydomonas Microcystis Coelastrum Oscillatoria Oocystis Planktothrix Synechococcus Chloridella? Woronichinia Ciliate Navicula Ankistrodesmus Botryococcus Chlamydomonas Elakatothrix Gloeomonas Golenkinia Gonium Micractinium Oedogonium Oocystis Pandorina Radiococcus Staurastrum Tetrastrum Westella

Ceratium Cosmarium Euglena Lepocinclis Rotifer Trachelomonas

114

E10 c

Ledge Lake Guard 17.6 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Eucapsis Aphanocapsa Microcystis Eucapsis Merismopedia Microcystis Planktothrix Plantothrix? Synechococcus Snowella Woronichinia Woronichinia

Ankistrodesmus Botryococcus? Asterococcus? Coelastrum Botryococcus Golenkinia Chlamydomonas Oocystis Elakatothrix Pediastrum Golenkinia Radiococcus Gonium Scenedesmus Oedogonium Staurastrum Oocystis Westella? Pandorina Pediastrum Ciliate Staurastrum Ceratium Westella Cosmarium Navicula Ceratium Rotifer Chloridella? Trachelomonas? Cosmarium Euglena Fragilaria Rotifer Trachelomonas

115

E10 d

Ledge Guard 6.65 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Anabaena Anabaena Aphanocapsa Aphanocapsa Eucapsis Eucapsis Microcystis Plantothrix? Synechococcus Woronichinia Woronichinia Botryococcus? Ankistrodesmus Coelastrum Botryococcus Golenkinia Chlamydomonas Oocystis Coelastrum Pediastrum Elakatothrix Staurastrum Oedogonium Oocystis Ciliate Radiococcus Cosmarium Staurastrum Fragilaria Westella Trachelomonas

Navicula Ceratium Ciliate Cosmarium Euglena Rotifer Trachelomonas Vorticella Figure E10: Microorganism presence in Ledge Lake (9.5.2019 - 9.20.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group.

116

APPENDIX F

Isaac Lake (9.9.2019 – 9.24.2019)

Figure F1: Lake Isaac (9.9.2019 – 9.24.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

117

Figure F2: Lake Isaac (9.9.2019 – 9.24.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Figure F3: Lake Isaac (9.9.2019 – 9.24.2019) changes in phycocyanin RFU over 14 days

118

Figure F4: Lake Isaac (9.9.2019 – 9.24.2019) statistical comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping 76 ul Cutrine Ultra 2 3 97.46 A control 0 3 92.7 A B 6.65 mg lake Guard 0 3 88.69 A B C D control 2 3 88.14 A B C D 17.6mg lake Guard 0 3 84.05 A B C D 76 ul Cutrine Ultra 0 3 82.4533 A B C D 17.6mg lake Guard 2 3 53.8867 B C D E 6.65 mg lake Guard 2 3 50.5133 C D E F 76 ul Cutrine Ultra 14 3 49.4467 D E F G control 14 3 35.9733 E F G 17.6mg lake Guard 14 3 14.3767 F G 6.65 mg lake Guard 14 3 10.5967 G Means that do not share a letter are significantly different. Figure F5: Lake Isaac (9.9.2019 – 9.24.2019) Tukey’s Pairwise comparison of chl-a (µg/L) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

119

Figure F6: Lake Isaac (9.9.2019 – 9.24.2019) changes in chl-a RFU over 14 days

Figure F7: Lake Isaac (9.9.2019 – 9.24.2019) changes in temperature over 14 days

120

Figure F8: Lake Isaac (9.9.2019 – 9.24.2019) changes in pH over 14 days

Figure F9: Lake Isaac (9.9.2019 – 9.24.2019) changes in conductivity over 14 days

121

F10 a

Isaac Control Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Chlorogloea Gentilis? Aphanothece? Coelosphaerium? Cylindrospermum? Cuspidothrix Geitlerinema? Cuspidothrix? Snowella Oscillatoria Raphidiopsis Ankistrodesmus Snowella Coccomonas Or Phacotus? Coelastrum Ankistrodesmus Dictyosphaerium Dictyosphaerium Golenkinia? Elakatothrix Oocystis Golenkinia Pediastrum Isthmochloron Or Tetradriella? Radiococcus Lagerheimia Scenedesmus Oocystis Staurastrum Oocystsis Westella Pediastrum Planktosphaeria? Chloridella? Scenedesmus Ciliate Scenedesmus Acuminatus Cosmarium Scenedesmus Acutiformis Fragilaria Selenastrum Melosira Staurastrum Navicula Tetraedron Trachelomonas Westella

Ceratium Chloridella? Ciliate Cosmarium Cryptomonas Euglena Frustulia Melosira Melosira Granulata Tetraedriella Trachelomonas

122

F10 b

Isaac Cutrine Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Cuspidothrix Oscillatoria Oscillatoria Raphidiopsis? Ankistrodesmus Snowella Dictyosphaerium Golenkinia? Ankistrodesmus Oocystis Dimorphococcus? Pediastrum Golenkinia Radiococcus Lagerheimia Scenedesmus Oocystis? Treubaria Pediastrum Westella Scenedesmus Selenastrum Cosmarium Staurastrum Cryptomonas Westella Euglena Isthmochloron? Botryococcus? Navicula Ciliate Tetraplektron Cosmarium Cryptomonas Cyclotella? Euglena Fragilaria Melosira Navicula Rotifer Tetradriella Trachelomonas

123

F10 c

Isaac Lake Guard 17.6 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Cuspidothrix Cuspidothrix Oscillatoria Eucapsis Plantothrix Microcystis? Snowella Oscillatoria Snowella Ankistrodesmus Crucigenia Ankistrodesmus Golenkinia? Dictyosphaerium Oocystis Dimorphococcus? Pediastrum Elakatothrix Phacotus? Golenkinia Scenedesmus Lagerheimia Sorastrum Oocystis Staurastrum Pediastrum Radiococcus Chlorellidium? Scenedesmus Chloridella? Staurastrum Goniochloris? Tetraedriella Melosira Treubaria Trachelomonas Westella

Ceratium Chloridella Chloridella? Ciliate Cryptomonas Cyclotella Euglena Fragilaria Glenodinium Isthmochloron Melosira Rotifer

124

F10 d

Isaac Lake Guard 6.65 Mg Group Organism Presence Over Time T0 Genus List T14 Genus List

Aphanocapsa Aphanocapsa Cuspidothrix Oscillatoria Cylindrospermopsis Plantothrix Snowella Ankistrodesmus Botryococcus Golenkinia? Golenkinia Oocystis Oocystis Pediastrum Pediastrum Scenedesmus Radiococcus Scenedesmus Cosmarium Staurastrum Cyclotella? Treubaria Navicula Westella

Chloridella Ciliate Cryptomonas Cyclotella Euglena Melosira Navicula Tetradriella Trachelomonas Figure F10: Microorganism presence in Lake Isaac (9.9.2019 – 9.24.2019) groups sorted by time. Cyanophyceae indicated by solid outline, Chlorophyceae indicated by double outline, and Other indicated by dotted outline. (a) Control Group (b) Cutrine Ultra Group (c) 17.6 mg Lake Guard Group (d) 6.65 mg Lake Guard Group.

125

APPENDIX G

Hudson Lake (7.29.2019 – 8.13.2019)

Figure G1: Hudson (7.29.19) Cyanobacterial density (cells/ml) over 14 days

126

Figure G2: Hudson (7.29.2019 – 8.13.2019) changes in phycocyanin RFU over 14 days

Hudson Springs (7.29.2019) change in PC cells/ml within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -25.43% 0.994 Control 2 - 14 -55.77% 0.768

Cutrine Ultra 0 - 2 -87.04% 0.01 Cutrine Ultra 2 - 14 57.09% 1

17.6mg LG 0 - 2 -90.50% 0.013 17.6mg LG 2 - 14 -67.89% 1 Figure G3: Hudson (7.29.19) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

127

Hudson Springs (7.29.2019) Day 2 PC cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 82% 0.215 17.6mg LG Control 88% 0.146 17.6mg LG Cutrine Ultra 45% 1

Hudson Springs (7.29.2019) Day 14 PC cells/ml comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 36.81% 1 17.6mg LG Control 91.12% 0.972 17.6mg LG Cutrine Ultra 611.79% 1 Figure G4: Hudson (7.29.2019 – 8.13.2019) statistical comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Hudson Springs (7.29.2019) PC cells/ml Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping 76 ul Cutrine Ultra 0 3 10283.2 A B control 0 3 10047.7 A B 17.6mg lake Guard 0 3 9643.1 A B C control 2 3 7493.1 A B C D control 14 3 3313.8 B C D 76 ul Cutrine Ultra 14 3 2093.9 C D 76 ul Cutrine Ultra 2 3 1332.9 D 17.6mg lake Guard 2 3 916.2 D 17.6mg lake Guard 14 3 294.2 D Means that do not share a letter are significantly different. Figure G5: Hudson (7.29.2019 – 8.13.2019) Tukey’s Pairwise comparison of phycocyanin density (cells/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

128

Figure G6: Hudson (7.29.19 – 8.13.2019) changes in chlorophyll-a concentration (ug/ml) over 14 days

Figure G7: Hudson (7.29.19 – 8.13.2019) changes in chl-a RFU over 14 days

129

Hudson Springs (7.29.2019) change in Chl-a (ug/L) within experimental conditions Condition Time point comparison (days) % difference in Means p-value (adj) Control 0 - 2 -42.77% 0.788 Control 2 - 14 37.72% 0.999

Cutrine Ultra 0 - 2 -17.21% 1 Cutrine Ultra 2 - 14 113.30% 0.877

17.6mg LG 0 - 2 -55.83% 0.298 17.6mg LG 2 - 14 -77.73% 0.909 Figure G8: Hudson (7.29.19 - 8.13.2019) statistical comparison of phycocyanin density (cells/ml) within groups from Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Hudson Springs (7.29.2019) Day 2 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 59% 0.951 17.6mg LG Control 17% 1 17.6mg LG Cutrine Ultra 91% 0.77

Hudson Springs (7.29.2019) Day 14 Chl-a (ug/L) comparisons between conditions Condition vs % difference in Means P-Value (adj) Cutrine Ultra Control 146.52% 0.001 17.6mg LG Control 86.54% 0.15 17.6mg LG Cutrine Ultra 1731.96% 0 Figure G9: Hudson (7.29.2019 – 8.13.2019) statistical comparison of chlorophyll-a density (ug/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application.

Hudson Springs (7.29.2019) Chl-a (ug/L) Grouping Information Using the Tukey Method and 95% Confidence Condition*Time (days) N Mean Grouping 76 ul Cutrine Ultra 14 3 39.9367 A 76 ul Cutrine Ultra 0 3 22.6167 B 17.6mg lake Guard 0 3 22.1633 B control 0 3 20.5533 B 76 ul Cutrine Ultra 2 3 18.7233 B C control 14 3 16.2 B C D control 2 3 11.7633 B C D 17.6mg lake Guard 2 3 9.79 B C D 17.6mg lake Guard 14 3 2.18 D Means that do not share a letter are significantly different. Figure G10: Hudson (7.29.2019 – 8.13.2019) Tukey’s Pairwise comparison of chlorophyll-a density (ug/ml) between Time 0 -2 and Time 2-14 for all conditions following algaecide application

130

Figure G12: Hudson (7.29.2019 – 8.13.2019) changes in ph over 14 days

Figure G11: Hudson (7.29.2019 – 8.13.2019) changes in conductivity over 14 days

131

G12 a

Hudson (7.29.2019) Control Group Organism Presence over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List

Anabaena Anabaena Microcystis Aphanocapsa? Microcystis Aphanizomenon Woronichinia Borzia? Westella? Aphanocapsa Microcystis Microcystis Oocystis Microcystis Or Woronichinia Chlamydomonas Woronichinia Pediastrum Westella Or Radiococcus? Chlorella? Westella Or Gloeocystis?? Oocystis Chlamydomonas Coelastrum Westella? Coelastrum Trachelomonas Pediastrum Radiococcus Micractinium Radiococcus? Oocystis Aulacseira Or Melosira? Pediastrum Fragilaria Codosiga? Radiococcus Melosira Hemidinium Or Gymnodinium Navicula Rotifer Ceratium Trachelomonas Euglena? Glenodinium Isthmochloron Melosira Rotifer Trachelomonas

G12 b

Hudson (7.29.2019) Cutrine Ultra Group Organism Presence over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List

Anabaena Anabaena Microcystis Microcystis Aphanizomenon Microcystis Microcystis Woronichinia Coelastrum Actinastrum? Oscillatoria? Oocystis Coelastrum Woronichinia Chlamydomonas Pediastrum Coelastrum Or Westella? Coelastrum Westella Or Gloeocystis?? Kirchneriella Chlamydomonas Dictyosphaerium Westella Or Radiococcus Oocystis Eudorina Eudorina? Pediastrum Pediastrum Oocystis Schroederia Radiococcus Pediastrum Vorticella Colorless? Westella Or Radiococcus? Radiococcus Fragilaria? Staurastrum Navicula Aulacseira Or Melosira? Ceratium Crucigenia Ciliate Melosira Granulata Crucigenia? Trachelomonas Euglena Glenodinium Trachelomonas

132

G12 c

Hudson (7.29.2019) Lake Guard Group Organism Presence Over Time T0 Genus List T2 Genus List T7 Genus List T14 Genus List

Anabaena Anabaena Anabaena Anabaena Aphanizomenon Anabaenopsis? Microcystis Microcystis Microcystis Aphanocapsa Planktothrix? Oscillatoria? Microcystis Coelastrum Woronichinia Woronichinia Woronichinia Radiococcus Coelastrum Botryococcus Ceratium Pediastrum Coelastrum Coelastrum Radiococcus? Oocystis Gonium? Pediastrum Oocystis Fragilaria Radiococcus Pediastrum Melosira Staurastrum Radiococcus Navicula? Staurastrum Glenodinium Westella? Melosira Rotifer Euglena? Trachelomonas Glenodinium? Isthmochloron Melosira Trachelomonas

133