importance of micronutrients for freshwater cyanobacterial

i

Importance of micronutrients for freshwater

cyanobacterial growth and bloom formation

Jordan A. Facey

A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy

2021

Freshwater and Estuarine Research Group

School of Life Sciences

University of Technology Sydney

ii

Certificate of original authorship

I, Jordan Facey, declare that this thesis is submitted in fulfilment of the requirements for the

award of Doctor of Philosophy, in the Faculty of Science at the University of Technology

Sydney. This thesis is wholly my own work unless otherwise referenced or acknowledged. In

addition, I certify that all information sources and literature used are indicated in the thesis. This

document has not been submitted for qualifications at any other academic institution. This

research is supported by an Australian Government Research Training Program.

Signature: Date: 9th July, 2021 Production Note:Signature removedprior to publication.

iii

Acknowledgements

Completing this research has been an incredible experience. It would never have been possible

without the help and support of friends, family and colleagues.

I am deeply grateful for the guidance of Simon Mitrovic who has been a great mentor and mate.

Thank you for growing my appreciation of fieldwork and boating (from the passenger seat) and

I’ve valued our long chats in the car or over a beer. You have helped me develop my research

and scientific skills and I’m looking forward to more exciting projects ahead.

A big thank you to my co-supervisor Simon Apte for his invaluable expertise and contributions

to this work. Thank you for being so encouraging, patient and approachable. I really appreciate

your generosity with your time and the effort you put into this project. You always provided a

fresh perspective and improved my understanding of the topic immensely.

Many thanks to Josh King for your guidance in the lab and to Anne Colville for showing me the

ropes early in my candidature. To James Hitchcock for always being available for advice and to

talk over an idea. A special mention to the UTS tech staff past and present – particularly Gemma,

Maggie, Shannon, Helen and Sue, for their support throughout this work.

A big thank you to the FERGies. In particular to Matt – I’ve really valued our chats over lunch

and teas and beers. Our time in the field together will always be remembered. Your friendship

made both the good times and the tough times in this degree a whole lot better. And to Laura -

thank you for always being there when I needed to whinge and moan – or a spare bed to sleep in.

Our trip to Utah was the absolute highlight of this whole experience. And to Ellery for

brightening everyone’s day with an impromptu visit and his genuine care for the wellbeing of

everyone in the group.

I would like to acknowledge the financial support from Hunter Water Corporation and Snowy

Valleys Council. I would also like to acknowledge funding received from the Society of

Freshwater Science Fellows Fund award.

Most importantly I’d like to thank my wonderful family for their love and support over the years.

Particularly Mum for always being my #1 fan in life.

iv

Preface

This thesis consists of six chapters. Chapters 1 to 5 have been written as separate articles that

have either been published, are in review, or are in preparation for submission to peer reviewed

scientific journals. These papers are included as their published form, and as such, some

repetition occurs. To prevent unnecessary duplication, a single reference list has been provided at

the end of this thesis.

This thesis is a compilation of my own work, carried out with guidance from my supervisors and

others. I conceptualized this research, conducted all data collection and analysis and wrote the

manuscript. Publication details and contributions of co-authors are detailed below.

Chapter 1: Facey, J. A., Apte, S.C., & Mitrovic, S.M. 2019. A review of the effect of trace metals

on freshwater cyanobacterial growth and toxin production. Toxins. 11(11), 643,

doi:10.3390/toxins11110643

S.C. Apte provided conceptual advice and reviewed the manuscript.

S.M. Mitrovic provided conceptual advice and reviewed the manuscript.

Chapter 2: Facey, J.A., Rogers, T.A., Apte, S.C., & Mitrovic, S.M. 2021. Micronutrients as

growth limiting factors in cyanobacterial blooms; a survey of freshwaters in South East

Australia. Aquatic Sciences. 83(2), doi:10.1007/s00027-021-00783-x

T.A. Rogers provided assistance in field and sample analysis

S.C. Apte provided conceptual advice and guidance.

S.M. Mitrovic provided conceptual advice, guidance and field assistance.

v

Chapter 3: Facey, J.A., Michie, L., Balzer, M., Hitchcock, J., Apte, S.C., & Mitrovic, S.M. The

role of nutrients, trace metals and thermal stratification in promoting cyanobacterial blooms: A

case study of Mannus Lake.

L. Michie provided assistance with data analysis and fieldwork

M. Balzer provided assistance with fieldwork.

J. Hitchcock provided guidance and assistance with fieldwork.


S.M. Mitrovic provided conceptual advice, guidance and field assistance

Chapter 4: Facey, J.A., King, J.J., Violi, J.P., Sarowar, C., Apte, S.C., & Mitrovic, S.M. The

effect of trace metals on Microcystis aeruginosa growth and toxin production.

J.J. King conducted ICP-MS and ICP-AES analysis.

J.P. Violi provided conceptual advice and guidance relating to LCMS analysis

C. Sarowar conducted LCMS analysis


S.M. Mitrovic provided conceptual advice and guidance.

Chapter 5: Facey, J.A., King, J.J., Apte, S.C., & Mitrovic, S.M. Assessing the importance of

cobalt as a micronutrient for freshwater cyanobacteria. Submitted to Journal of Phycology.

J.J. King performed ICP-MS and ICP-AES analysis


S.M. Mitrovic provided conceptual advice, guidance and field assistance

vi

Other papers published during my candidature but not forming part of this thesis:

Facey, J. A., Steele, J.R., Violi, J.P., Mitrovic, S.M., Cranfield, C. 2019. ‘An examination of

microcystin-LR accumulation and toxicity using tethered bilayer lipid membranes (tBLMs)’,

Toxicon, 158, pp. 51–56. doi: 10.1016/j.toxicon.2018.11.432.

Violi, J. P., Facey, J.A., Mitrovic, S.M., Colville, A., Rodgers, K.J. 2019. ‘Production of β-

methylamino-L-alanine (BMAA) and Its Isomers by Freshwater Diatoms’, Toxins, 11(9), 512,

doi:10.3390/toxins11090512.

Michie, L.E., Thiem, J.D., Facey, J.A., Boys, C.A., Crook, D.A., Mitrovic, S.M. 2020. ‘Effects

of suboptimal temperatures on larval and juvenile development and otolith morphology in three

freshwater fishes: implications for cold water pollution in rivers’, Environ Biol Fish

doi:10.1007/s10641-020-01041-z.

vii

Contents

Certificate of original authorship.......................................................................................................... ii

Acknowledgements ............................................................................................................................ iii

Preface ............................................................................................................................................... iv

List of Figures .................................................................................................................................... xi

List of Tables ................................................................................................................................... xiv

Abstract ............................................................................................................................................. xv

Chapter 1: A review of the effect of trace metals on freshwater cyanobacterial growth and toxin production ............................................................................................................................................... 1

1.1 Abstract ......................................................................................................................................... 1

1.2 Introduction to cyanobacteria in freshwater systems ....................................................................... 1

1.3 Nutrient limitation ......................................................................................................................... 3

1.4 Sources of nutrients ....................................................................................................................... 4

1.5 Colimitation and optimal nutrient ratios ......................................................................................... 5

1.6 The importance of trace metals ...................................................................................................... 6

1.7 Iron ................................................................................................................................................ 7

1.8 Zinc ............................................................................................................................................... 9

1.9 Copper ........................................................................................................................................... 9

1.10 Molybdenum ............................................................................................................................. 10

1.11 Cobalt ........................................................................................................................................ 11

1.12 Manganese ................................................................................................................................. 12

1.13 Cyanotoxin production ............................................................................................................... 13

1.14 Trace metals and cyanotoxins..................................................................................................... 14

1.15 Knowledge gaps......................................................................................................................... 17

1.16 Scope and need for this study ..................................................................................................... 19

1.17 Layout of Chapters ..................................................................................................................... 20

Chapter 2: Micronutrients as growth limiting factors in cyanobacterial blooms; a survey of freshwaters in South East Australia .............................................................................................................................. 23

2.1 Abstract ....................................................................................................................................... 23

2.2 Introduction ................................................................................................................................. 24

2.3 Materials and Methods ................................................................................................................. 25

2.3.1 Study sites ............................................................................................................................. 25

2.3.2 Microcosm enrichment assays ............................................................................................... 26

2.3.4 Nutrient sampling and analysis .............................................................................................. 27

2.3.5 Trace metal micronutrient analysis ........................................................................................ 28

viii

2.3.6 Phytoplankton identification and enumeration ....................................................................... 28

2.3.7 Chlorophyll a analysis ........................................................................................................... 29

2.3.8 Statistical analysis ................................................................................................................. 29

2.4 Results ......................................................................................................................................... 29

2.5 Discussion ................................................................................................................................... 33

2.5.1 Phosphorus-driven changes in community structure............................................................... 36

2.5.2 Implications for management and research ............................................................................ 36

2.5.3 Conclusion ............................................................................................................................ 37

Chapter 3: The role of nutrients, micronutrients and thermal stratification in promoting cyanobacterial blooms: A case study of Mannus Lake, New South Wales ..................................................................... 39

3.1 Abstract ....................................................................................................................................... 39

3.2 Introduction ................................................................................................................................. 40


3.3.1 Study sites ............................................................................................................................. 42

3.3.2 Sample collection .................................................................................................................. 44

3.3.3 Nutrient sampling and analysis .............................................................................................. 44

3.3.4 Micronutrient analysis ........................................................................................................... 45

3.3.5 Phytoplankton identification and enumeration ....................................................................... 45

3.3.6 Chlorophyll a extraction and analysis .................................................................................... 45

3.3.7 Statistical analysis ................................................................................................................. 46

3.4 Results ......................................................................................................................................... 46

3.5 Discussion ................................................................................................................................... 59

3.5.1 Nutrient and micronutrient dynamics in upstream creeks ....................................................... 60

3.5.2 Nutrient and micronutrient dynamics within Mannus Lake .................................................... 61

3.5.3 Nutrient release from anoxic sediments at Mannus Lake ........................................................ 63

3.5.4 Thermal stratification as a driver of change in phytoplankton community structure ................ 65

3.5.5 Management implications...................................................................................................... 66

Chapter 4: The influence of micronutrients on Microcystis aeruginosa growth and toxin production ...... 69

4.1 Abstract ....................................................................................................................................... 69

4.2 Introduction ................................................................................................................................. 69


4.3.1 Microcystis culturing conditions ............................................................................................ 71

4.3.2 Culture media........................................................................................................................ 72

4.3.3 Sampling ............................................................................................................................... 73

4.3.4 Solution nutrient determination ............................................................................................. 74

ix

4.3.5 Intracellular iron sample preparation and analysis .................................................................. 74

4.3.6 Microcystin-LR method ........................................................................................................ 74

4.3.7 Cell volume........................................................................................................................... 76

4.3.8 Growth rate ........................................................................................................................... 76

4.3.9 Data analysis ......................................................................................................................... 77

4.4 Results ......................................................................................................................................... 77

4.5 Discussion ................................................................................................................................... 81

Chapter 5: Assessing the importance of cobalt for freshwater cyanobacteria .......................................... 87

5.1 Abstract ....................................................................................................................................... 87

5.2 Introduction ................................................................................................................................. 87


5.3.1 Microcystis culturing conditions ............................................................................................ 89

5.3.2 Culture media........................................................................................................................ 89

5.3.3 Transfers ............................................................................................................................... 90

5.3.4 Culture experiment sampling ................................................................................................. 90

5.3.5 Solution nutrient determination ............................................................................................. 91

5.3.6 Intracellular iron sample preparation and analysis .................................................................. 91

5.3.7 Field evaluation of cobalt concentrations ............................................................................... 92

5.3.8 Dissolved organic carbon ...................................................................................................... 94

5.3.9 PO4-P determination .............................................................................................................. 94

5.3.10 Data Analysis ...................................................................................................................... 94

5.4 Results ......................................................................................................................................... 95

5.4.1 Cobalt limitation experiment ................................................................................................. 95

5.4.2 Cobalamin experiment .......................................................................................................... 97

5.4.3 Field survey .......................................................................................................................... 98

5.5 Discussion ................................................................................................................................... 99

5.5.1 Cobalamin........................................................................................................................... 100

5.5.2 Cobalt requirements – linking culture experiments and natural systems ............................... 101

5.5.3 Cobalt and intracellular iron ................................................................................................ 102

5.5.4 Significance and future direction ......................................................................................... 103

5.5.5 Conclusion .............................................................................................................................. 104

Chapter 6: General discussion and conclusion ...................................................................................... 105

6.1 Effect of micronutrient inputs on cyanobacterial growth and community dominance in situ ........ 105

6.1.1 Response of cyanobacteria to micronutrient inputs .............................................................. 105

6.1.2 Changes in phytoplankton community structure driven by micronutrient inputs ................... 106

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6.2 Sources of micronutrients and their role in the formation of cyanobacterial blooms. ................... 107

6.2.1 Sources of micronutrients in Mannus Lake .............................................................................. 107

6.2.2 Causes of recurring cyanobacterial blooms in Mannus Lake .................................................... 108

6.3 Impact of low micronutrient availability on Microcystis aeruginosa in culture............................ 109

6.3.1 Quantifying the cobalt requirement of Microcystis aeruginosa and links to natural systems. 110

6.3.2 Cobalamin........................................................................................................................... 111

6.3.3 Iron/cobalt interactions ........................................................................................................ 112

6.4 Influence of micronutrients on cyanotoxin production ................................................................ 112

6.5 Further research ......................................................................................................................... 113

6.5.1 Greater spatial and temporal variation of monitoring ........................................................... 113

6.5.2 Nutrient release from sediments .......................................................................................... 114

6.5.3 Micronutrient speciation and bioavailability ........................................................................ 114

6.5.4 Expansion of batch culture experiments ............................................................................... 114

6.5.5 Influence of cobalt on iron transport .................................................................................... 115

6.6 Management recommendations .................................................................................................. 115

6.7 Conclusions ............................................................................................................................... 116

References........................................................................................................................................... 119

Appendix A ..................................................................................................................................... 146

Appendix B ..................................................................................................................................... 153

Appendix C ..................................................................................................................................... 156

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List of Figures

Figure 1.1 Simplified diagram illustrating how Fe and macronutrient dynamics may interact to alter phytoplankton community dynamics in lakes, reproduced from Molot et al., (2014). John Wiley & Sons Ltd. ......................................................................................................................8

Figure 2.1: Total phytoplankton and cyanobacterial biovolume in Mannus Lake and Burrendong Dam microcosms. Asterisk represents significant difference compared to the control (One-Way ANOVA, p-value < 0.05). The nutrient concentrations added for each treatment are listen in Table 2.2. Error bars are standard error of the mean, n=3. ......................................................... 32

Figure 2.2: Proportion of community made up of several key phytoplankton groups at Mannus Lake and Burrendong Dam (left). Shannon Diversity Index (middle) and nMDS plots (right) illustrating differences in phytoplankton community structure between treatments. A square root transformation was performed on the community data for nMDS. Stress < 0.2. Error bars are standard error of the mean, n=3. ................................................................................................ 33

Figure 3.1: Location of Mannus Lake and study sites. (1.) Mannus Lake outlet, (2.) Mannus Lake mid-dam, (3.) Mannus Creek, (4.) Munderoo Creek. ................................................................. 43

Figure 3.2: Composition of key phytoplankton community groups throughout the study period at Outlet (top) and mid-dam (bottom). ........................................................................................... 47

Figure 3.3: Time series data illustrating the biovolumes of total cyanobacteria and two key bloom-forming genera: Chrysosporum ovalisporum and cf. Microcystis sp. at the outlet (top) and mid-dam (bottom) sites. ............................................................................................................ 48

Figure 3.4: Temperature profiles from the outlet (left) and mid-dam (right). Thermal stratification is evident when there is a strong vertical colour gradient. Plotted from weekly average temperatures at each depth. .......................................................................................... 49

Figure 3.5: Discharge from Mannus Creek, measured at the Yarramundi gauging station upstream of Mannus Lake. ........................................................................................................ 49

Figure 3.6: Vertical cell concentrations of Chrysosporum ovalisporum at 1 m intervals during thermal stratification on 16th December 2019 at the outlet site. ................................................. 50

Figure 3.7: Dissolved oxygen concentrations in the surface and bottom waters from the outlet site. ........................................................................................................................................... 51

Figure 3.8: Filtered nutrient and micronutrient concentrations at the outlet site throughout the study period. Samples were taken from the bottom water (blue) and surface water (red). Error bars are standard error of the mean. ........................................................................................... 53

xii

Figure 3.9: Filtered nutrient and micronutrient concentrations at the Pontoon site throughout the study period. Samples were taken from the bottom water (blue) and surface water (red). Error bars are standard error of the mean. ........................................................................................... 54

Figure 3.10: Filtered nutrient and micronutrient concentrations at the upstream creeks Mannus Creek (blue line) and Munderoo Creek (red line). Error bars are standard error of the mean. ..... 55

Figure 3.11: pH of surface and bottom waters measured at the outlet site. ................................. 56

Figure 3.12: scatter plots displaying relationship between nutrients/micronutrients and biovolume of Chrysosporum ovalisporum (red circles) and cf. Microcystis sp. (blue squares)..................... 57

Figure 3.13: Correlation plots illustrating the R values of various nutrients and micronutrients in the surface water (left) and bottom water (right). Red circles indicate a positive relationship ≥0.7 and blue circles indicate a negative relationship ≤-07. ............................................................... 58

Figure 3.14: The ordination diagram for redundancy analysis (RDA) results at the outlet site. Stratification refers to the difference between surface water and bottom water temperature with a 7-day lag period. Temperature refers to the daily average surface water temperature. Species are 1. Chrysosporum sp., 2. cf. Microcystis sp., 3. Dolichospermum sp., 4. Fragillaria sp., 5. Aulacoseira sp., 6. Trachelomonas sp., 7. Peridinium sp., 8. Chroomonas sp., 9. Synedra sp., 10. Cosmarium sp., 11. Cyclotella sp., 12. Cryptomonas sp.. ........................................................... 59

Figure 4.1: Microcystis aeruginosa growth through time under variable micronutrient conditions. (A) Transfer 1 and (B) Transfer 2. Error bars are standard error of the mean. ............................ 78

Figure 4.2: Specific growth rate in treatments exposed to depletion of different micronutrients across two transfers. Asterisk denotes significant difference relative to the control of the same transfer (One-way ANOVA: p-value < 0.05). ............................................................................ 78

Figure 4.3: Scatterplot of cell volume relative to cells in the control treatment. Cell volume was measured once the treatment exhibited a growth limitation and compared to the control cell volume at the same time point. Asterisk denotes significant difference relative to the control. Error bars are ± standard error of the mean. ............................................................................... 79

Figure 4.4: Changes in intracellular microcystin-LR cell quotas throughout the experiment. Error bars are standard error of the mean. Asterisks denote significant difference to control at same time point (PERMANOVA: p-value < 0.05).............................................................................. 80

Figure 4.5: Differences in the intracellular quota of iron in treatments depleted of different micronutrients after 31 days. Samples from the FeEDTA treatment had insufficient sample mass for analysis so are excluded. Error bars are standard error of the mean. Asterisk denotes significant difference compared to the control (One-way ANOVA: p-value < 0.05). A log10 transformation was performed to satisfy the assumptions of parametric statistical analyses. ...... 81

Figure 5.1: Microcystis aeruginosa growth through time under variable trace metal conditions. Error bars are standard error of the mean. .................................................................................. 95

xiii

Figure 5.2: Relationship between cobalt concentration in the culture media and the percentage growth inhibition compared to the control. ................................................................................ 96

Figure 5.3: Differences in the intracellular quota of iron in treatments exposed to varying cobalt concentrations. Error bars are standard error of the mean. ......................................................... 96

Figure 5.4: Microcystis aeruginosa growth through time with and without the addition of vitamin B12. Error bars are standard error of the mean. ......................................................................... 98

Figure A1: Chlorophyll a results from nutrient amendment experiments at Mannus Lake (A), Burrendong Dam (B), Murray River at Euston (C), Murray River at Mildura (D), Hunter River at Morpeth (E), Windeyers Creek (F), Lake Lyell (G). Asterisk represents significant difference compared to the control (One-way ANOVA, p-value <0.05). Error bars are standard error of the mean, n=3. .............................................................................................................................. 146

Figure A2: Total phytoplankton and cyanobacterial biovolume at different sites. Asterisk represents significant difference compared to the control (One-Way ANOVA, p-value < 0.05). Error bars are standard error of the mean, n=3. ........................................................................ 147

Figure A3: Proportion of community made up of several key phytoplankton groups (left) at different sites. Shannon Diversity Index (middle) and nMDS plots (right) illustrating differences in phytoplankton community structure between treatments. A square root transformation was performed on the community data for nMDS. Stress < 0.2. Error bars are standard error of the mean, n=3. .............................................................................................................................. 148

Figure B1: Chlorophyll a concentrations at the dam sites (top) and upstream of the dam (bottom). ............................................................................................................................................... 153

Figure B2: Cyanobacterial biovolume upstream of Mannus Lake. ........................................... 154

Figure B3: High temporal resolution temperature data from the outlet site............................... 154

Figure C1: Relationship between Microcystis aeruginosa cell count and A680. ....................... 156

xiv

List of Tables

Table 1.1: Summary of published literature that assessed the effect of trace metals on the growth and toxin production of freshwater cyanobacteria. Y: Limitation was observed for this element, C: Colimitation with N and/or P was observed, N: No limitation was observed; T+ addition of element had a positive effect on cyanotoxin production; T-: limitation of nutrient increased toxin production; T±: no effect. .......................................................................................................... 16

Table 2.1: Summary of study sites, sampling dates and locations. ............................................. 26

Table 2.2: Summary of treatments and nominal concentrations of the target nutrient additions. . 27

Table 2.3: Ambient concentrations of macronutrients and micronutrients. All values are in µg/L, n=3. .......................................................................................................................................... 30

Table 2.4: Summary of results from seven nutrient amendment bioassays across South-Eastern Australia. Limiting nutrients are any nutrient treatments that had a greater chlorophyll or total biovolume than the control. ....................................................................................................... 30

Table 4.1: The composition of unmodified MLA algal growth media. Salts in bold text indicate those examined in this experiment. ............................................................................................ 72

Table 4.2: LC-MS gradient and flow rate for microcystin-LR analysis. ..................................... 76

Table 5.1: Summary of study sites. ............................................................................................ 92

Table 5.2: Cobalt concentrations and physicochemical parameters measured at 10 freshwater sites in NSW, Australia. ............................................................................................................ 99

Table A1: Summary statistics of phytoplankton and chlorophyll a data. .................................. 149

Table A2: Output of SIMPER analysis showing the three genera contributing the most to differences between treatments ................................................................................................ 150

Table A3: The five most dominant genera on Day 0 of each experiment ................................. 152

Table B1: Summary of physicochemical data measured at the outlet site. ................................ 155

Table C1: concentrations of some macronutrients and micronutrients of interest in MLA media on day 0. ................................................................................................................................. 156

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Abstract Nutrient dynamics play a large role in structuring the phytoplankton community and regulating

the severity of cyanobacterial blooms. While the importance of the macronutrients phosphorus

and nitrogen for cyanobacterial growth is well established, little attention has been paid to the

importance of micronutrient trace metals. This thesis examines how micronutrients effect

cyanobacterial growth, bloom formation and toxin production. A combination of in-situ

microcosm experiments, lab-based culture experiments, and a long-term monitoring study were

conducted. Initially, in situ microcosms were performed at seven sites in South-Eastern Australia

to determine how increased micronutrient availability impacts cyanobacterial growth and

phytoplankton community structure, and to assess the prevalence of micronutrient growth

limitation in Australian freshwater systems. These experiments indicated that micronutrients may

be an important regulator of the severity of cyanobacterial blooms, and micronutrient limitation

of cyanobacterial growth may be more prevalent than previously anticipated.

An 18-month monitoring study on Mannus Lake assessed the role of micronutrients in the

formation of recurring high-density cyanobacterial blooms. While persistent thermal

stratification primarily drove cyanobacterial growth, some correlations between cyanobacterial

biovolume and dissolved micronutrients were observed. Within-dam processes influenced

micronutrient levels, particularly during periods of stratification and anoxia of the sediments,

whereas inflowing creeks appeared to play a minor role as a micronutrient source.

I investigated the specific micronutrient requirements of Microcystis aeruginosa under culture

conditions. Low concentrations of Fe, Co and Mn limited M. aeruginosa growth. The minimum

Co concentration required for optimal growth of M. aeruginosa was 0.06 μg/L. I compared this

value to ten freshwater reservoirs with varying levels of cyanobacteria. Interestingly, all four

sites that rarely undergo cyanobacterial blooms had cobalt concentrations below this value. This

provides evidence for the capacity of Co to limit, or colimit, cyanobacterial growth in situ. There

was some evidence that Co concentration influences microcystin-LR production in culture, but

this requires further investigation.

xvi

The individual studies forming this thesis all contribute new insights to their field. Combined,

these studies provide strong evidence that micronutrients play an important role in the

phytoplankton community and act as an important regulator of the severity of cyanobacterial

blooms in some systems. The results provide a strong case for the increased consideration of

micronutrient dynamics to aid the management of harmful algal blooms in freshwater systems.

1

Chapter 1: A review of the effect of trace metals on

freshwater cyanobacterial growth and toxin production

1.1 Abstract

Cyanobacterial blooms are becoming more common in freshwater systems, causing ecological

degradation and human health risks through the production of various cyanotoxins. The role of

phosphorus and nitrogen in cyanobacterial bloom formation is well documented and these are

regularly the focus of management plans. There is strong evidence that trace metals are also

important for a wide range of cellular processes, however their importance as a limiting factor of

cyanobacterial growth in ecological systems is unclear. Certain trace metals can directly limit

cyanobacterial growth or exhibit colimitation along with macronutrients. Further, some studies

have suggested a link between cyanotoxin production and some trace metals. This review

synthesises current knowledge on; 1. the biochemical role of trace metals (particularly iron,

cobalt, copper, manganese, molybdenum and zinc), 2. Growth limitation of cyanobacteria by

trace metals, 3. Trace metal regulation of cyanobacterial dominance of phytoplankton

communities and 4. The role trace metals in cyanotoxin production. There are numerous

examples of iron limiting or colimiting freshwater cyanobacterial growth. Understanding the

importance of trace metals in these processes may be an essential component of freshwater

management, and yet this area remains understudied.

1.2 Introduction to cyanobacteria in freshwater systems Throughout the world there is an increasing demand for freshwater utilised for irrigation,

industry, recreation and direct consumption (Cassardo and Jones 2011). Satisfying both

ecological and anthropogenic water requirements is challenging and may prove more difficult in

the context of climate change and a growing human population (Jackson et al. 2001). The

proliferation of toxin producing cyanobacteria (blue-green algae) poses a significant threat to the

integrity of freshwaters and their functions (Drobac et al. 2013). Under favourable

2

environmental conditions cyanobacteria can outcompete less harmful phytoplankton taxa and

form high cell density blooms and scums (Baptista and Vasconcelos 2006). Thick surface

blooms cause a reduction of water clarity, decreasing oxygen production in the bottom layers of

the water column and suppressing macrophyte growth, which can negatively affect invertebrate

and fish habitats (Dodds et al. 2009). Bacterial decomposition of senescent blooms can also

cause anoxic conditions, or blackwater events, often leading to fish kills (Paerl and Otten 2013;

Baptista et al. 2014).

Some bloom-forming cyanobacteria produce toxic secondary metabolites called cyanotoxins

(Quiblier et al. 2013; Paerl and Otten 2013). Cyanotoxin-containing blooms occur throughout the

world and are responsible for sporadic episodes of animal illness and death, as well as human

poisonings from municipal and recreational water supplies (Carmichael 2001; Ou et al. 2012;

Holland and Kinnear 2013). Cyanotoxins are highly variable in terms of their molecular

structure, production triggers and modes of toxicity (Baptista and Vasconcelos 2006; Holland

and Kinnear 2013; Facey et al. 2019b). Effects range from skin irritation to cancer or even

fatalities (Drobac et al. 2013; Sciuto and Moro 2015). For example, epidemiological research in

Central Serbia has established a link between cyanobacteria blooms in drinking water reservoirs

and increased incidence of liver cancer in regions consuming this water source (Svirčev et al.

2014). In Australia, Pilotto et al. (1997) observed that exposure to cyanobacteria during

recreational water-related activities was associated with gastrointestinal disturbances, flu-like

symptoms, skin rashes, mouth ulcers, fevers and eye or ear irritations up to 7 days after exposure.

Symptom occurrence was correlated with increased duration of water contact and higher

cyanobacterial cell counts.

There is an increasing frequency, severity and geographic extent of cyanobacteria blooms,

which can be attributed to the dominance of cyanobacteria in anthropogenically modified aquatic

ecosystems (Landsberg 2002a; Pearl et al. 2006; Paerl and Otten 2013). Increased temperature,

nutrient pollution and low-velocity flow regimes promotes the development of dense,

toxic, cyanobacteria blooms (Carpenter et al. 1998; Heisler et al. 2008; Mitrovic et al. 2003;

Mitrovic et al. 2011). This trend is likely to continue as cyanobacteria are expected to flourish

under the environmental conditions predicted for global climate change (Paerl et al. 2011a;

3

O’Neil et al. 2012; Rigosi et al. 2014) and toxic cyanobacterial taxa are comprising an increasing

proportion of the phytoplankton community under bloom conditions (Heisler et al. 2008; Paerl et

al. 2011a).

1.3 Nutrient limitation The availability of key nutrients can greatly influence the phytoplankton community composition

in surface waters (Dignum et al. 2005). Low levels of the macronutrients phosphorus (P) and

nitrogen (N) are frequently the limiting factor of cyanobacterial growth in freshwater ecosystems

(Paerl et al. 2011b; Mueller and Mitrovic 2014; Rigosi et al. 2014), and therefore, N and P inputs

can stimulate cyanobacterial bloom formation (Dignum et al. 2005; Pearl and Fulton 2006).

Generally, low N and P concentrations promote a highly diverse, low biomass phytoplankton

community, often associated with good water quality (Dignum et al. 2005). Conversely, high N

and P concentrations, or eutrophication, regularly promotes the formation of dense

cyanobacterial surface blooms and subsequent deterioration of water quality (Dignum et al.

2005; Xu et al. 2010; Paerl et al. 2011b).

Since the 1970s, phosphorus reduction has been the most widely adopted solution to

eutrophication (Schindler et al. 2016). Although more recently nitrogen reduction or dual

nutrient control is being widely implemented (Conley et al. 2009; Xu et al. 2010; Paerl et al.

2011b). However, there are instances where high P and N concentrations and seemingly

favourable conditions do not produce blooms, suggesting there are unknown bloom triggers

(Bowling 1994). There is growing evidence that phytoplankton growth (including toxic bloom-

forming cyanobacteria) can also be limited by micronutrient trace metals, alone or in

combination with macronutrients (Twiss et al. Charlton 2000; North et al. 2007; Downs et al.

2008; Molot et al. 2014; Zhang et al. 2019). This may help explain the occurrence of blooms in

mesotrophic systems (Downs et al. 2008). Currently, the role of trace metal micronutrients in

cyanobacterial bloom formation is often overlooked as trace metals are rarely considered in

eutrophication management strategies. Identifying sources of trace metals and how they impact

phytoplankton communities may be important in understanding toxic cyanobacterial bloom

dynamics.

4

1.4 Sources of nutrients As with other nutrients, trace metal concentrations in aquatic systems are highly variable in

space and time (Roussiez et al. 2013). Inflows to a waterbody, such as floods and heavy rain, can

mobilise allochthonous (catchment) sources of the macronutrients nitrogen and phosphorus

(Hitchcock and Mitrovic 2015) and metals (Roussiez et al. 2013; Zhang et al. 2018). These

events can have significant effects on primary productivity and can alter phytoplankton

community structure (Jeppesen et al. 2000). Changing land use practices and anthropogenic point

sources of pollution (such as stormwater, irrigation drains or sewage effluent) can also elevate

macronutrient and trace metal concentrations in waters (Buck et al. 2005; Schindler 2006;

Withers and Sharpley 2008; Carey and Migliaccio 2009).

Despite recent advances in biological phosphorus removal, wastewater can be a significant

source of macronutrients in aquatic ecosystems (Oehmen et al. 2007; Carey and Migliaccio

2009). Wastewater treatment plants can also be ineffective at removing all trace metals and can

act as a source of these potential micronutrients (Tam and Wong 1996; Luoma 1998). The

influence of wastewater discharge on phytoplankton was examined by Luoma (1998), who

estimated that ~60% of the total input of Cd, Ni and Zn from wastewater treatment plants is

cycled through the phytoplankton community in a bay subject to regular blooms. It is likely that

bloom dynamics can be influenced by wastewater treatment discharge containing both trace

metals and macronutrients.

Sediments act as both a source and sink for nutrients including trace metals in aquatic

ecosystems and play a significant role in determining nutrient availability (Baldwin and Williams

2007; Molot et al. 2014). Thermal stratification of the water column often causes hypoxia below

the thermocline, stimulating the release of nutrients such as phosphorus, nitrogen and iron from

anoxic sediments (Baldwin and Williams 2007; Özkundakci et al. 2011; Müller et al. 2016).

Thermally stratified conditions also favour the proliferation of cyanobacteria whose buoyancy

regulation may allow vertical migration to access nutrients at the sediment/water interface

(Bormans et al. 2001; Bormans et al. 2005; Paerl et al. 2011a; Molot et al. 2014). Additionally,

when the waterbody undergoes a mixing event the nutrient rich hypolimnial water is transported

to the surface via upwelling – thereby increasing nutrient availability to cyanobacteria

5

(Özkundakci et al. 2011). For example, a cyanobacterial bloom in the Fitzroy impoundment near

Rockhampton, Australia, was at least partially attributed to upwelling of nutrient-rich,

anoxic, hypolimnetic waters into the surface layer. This large nutrient source supported a bloom

of mixed small cyanobacteria species that persisted for over three months (Bormans et al.

2005).

1.5 Colimitation and optimal nutrient ratios The traditional view of nutrient limitation is derived from Liebig’s Law of the Minimum, stating

that productivity is limited by the nutrient that is least available relative to the organism’s overall

nutritional requirement (Saito et al. 2008; Harpole et al. 2011). This implies that only a single

resource is ever limiting at one time, for example Schindler et al. (2008) suggested that reducing

phosphorus input alone is effective at reducing harmful algal blooms.

However two simultaneously added nutrients can sometimes stimulate a larger response than

their individual additions, suggesting colimitation by both nutrients and the need for dual nutrient

management (Buitenhuis and Geider 2010; Paerl et al. 2011b; Harpole et al. 2011; Mueller and

Mitrovic 2014). Harpole et al. (2011) distinguishes

between simultaneous and independent colimitation. When the addition of two nutrients or

resources in combination elicits a response, but there is no response to their individual additions,

this is classified as simultaneous colimitation. Whereas independent colimitation refers to a

greater response to resources added in combination than the response to individual additions.

Beyond total nutrient supplies, the ratio of two or more resources can also affect nutrient

limitation (Tilman et al. 1982). For example, the Redfield ratio describes the stoichiometry of

nutrients in the cytoplasm of marine phytoplankton that allows optimal growth and metabolism

(Redfield 1958). When optimal nutrient ratios are not met, for example one nutrient is

supplied at a suboptimal concentration relative to another nutrient, it will limit growth and

productivity. While the Redfield ratio was originally based on the concentration of nitrate and

phosphorus in seawater, this relationship has been extended to include some trace metals such as

cobalt (Saito et al. 2004) and zinc (Sunda and Huntsman 1992). However, these relationships

have not been thoroughly investigated in freshwaters.

6

1.6 The importance of trace metals The essentiality of trace metals to living organisms is well known. Up to a third of all microbial

proteins contain a metal cofactor (Huertas et al. 2014). Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Ni, Na

and Zn are essential to the functioning of the vast majority of organisms. Others, such as

Ba, Sr and V, are required by just some species (Baptista and Vasconcelos 2006). Cyanobacteria

have relatively high metal requirements for optimal growth compared to other bacteria largely

due to metal cofactors in the oxygenic photosynthetic electron transfer apparatus, such as

cytochromes, plastocyanin and chlorophyll rings (Shcolnick and Keren 2006). An adequate

supply of trace metals is required to maintain optimal growth, particularly as these higher metal

requirements make cyanobacteria more prone to trace nutrient limitation (Baptista and

Vasconcelos 2006; Wever et al. 2008).

Often metal limitation can occur even when total metal supply is high (Sunda 2006; Baptista and

Vasconcelos 2006). Many metals cycle between different oxidation states, which have different

solubilities and form specific complexes which may not be bioavailable (Sunda 2006). The

speciation of the metal in solution (i.e its physicochemical form) controls its bioavailability, and

therefore its status as a limiting nutrient (Sunda and Huntsman 1998). For example the highly

bioavailable ferrous iron (Fe2+) is very soluble in anoxic waters but it is rapidly oxidised to the

poorly soluble and non-bioavailable (to cyanobacteria) ferric iron (Fe3+)

in circumneutral oxygenated waters (Cavet et al. 2003; Baptista and Vasconcelos 2006; Alexova

et al. 2011; Molot et al. 2014).

A growing body of literature demonstrates the impact of trace metals (alone or in combination

with macronutrients) on phytoplankton growth (Vrede and Tranvik 2006; North et al. 2007;

Glass et al 2010; Harland et al. 2013; Fujii et al. 2016). For example, Downs et al. (2008)

observed that the addition of copper, molybdenum or cobalt during a cyanobacterial bloom in a

eutrophic lake stimulated primary productivity by up to 40%, indicating a large contribution of

micronutrients to eutrophication. Further, North et al. (2007) observed that phytoplankton

in offshore, thermally stratified regions of Lake Erie were at times colimited by iron, phosphorus

and nitrogen. Enrichment with a combination of Fe, P and N stimulated a greater increase in

7

phytoplankton biomass than the nutrients added individually, or compared to a P+N

treatment. These findings are reflected in similar experiments by Twiss et al. (2000) in Lake Erie

and Vrede and Tranvik (2006) in several oligotrophic lakes in Sweden.

Metal requirements within the phytoplankton community, and even within phyla, are highly

specific. Therefore, metal availability is a strong determinant of phytoplankton community

composition (Mitrovic et al. 2004; Downs et al. 2008; Pandey et al. 2015; Sunda

2006). Community co-limitation can occur when one segment of the phytoplankton community

is stimulated by a particular nutrient and other segments are not (Arrigo 2005). For example,

Wever et al. (2008) noted iron additions stimulated growth of cyanobacteria in Lake Tanganyiki,

East Africa, but did not stimulate diatoms or chlorophytes, suggesting cyanobacteria were more

sensitive to a decrease in Fe availability compared to other phytoplankton. While Zhang et al.

(2019) showed limitation or co-limitation of cyanobacteria by Co, Cu and Fe, and a shift in the

phytoplankton community during a nutrient amendment mesocosm at Lake Taihu, China.

1.7 Iron Of all trace metals, iron is required in the greatest quantity and most often limits algal growth

(Table 1) (Sunda 2006). Iron is particularly important to cyanobacteria due to its direct

involvement in chlorophyll-a synthesis, respiration, nitrogen fixation and photosynthesis (Li et

al. 2009; Alexova et al. 2011). It catalyses many biochemical reactions as a cofactor of enzymes,

detoxifies reactive oxygen species and has a role in electron transport (Raven et al. 1999; North

et al. 2007; Li et al. 2009). Severe iron limitation reduces the capacity of phycobilisomes to

utilise excess light energy, and leads to the formation of reactive oxygen species and

subsequently to oxidative stress (Alexova et al. 2011). Iron availability is a determinant of the

dominance of cyanobacteria over eukaryotic species due to the high iron requirements of

cyanobacteria, particularly N2-fixing species (Sterner et al. 2004; Molot et al. 2010; Molot et al.

2014). Figure 1 illustrates a simplified mechanism of how the trophic state of a lake system can

influence iron availability and subsequently phytoplankton community structure.

8

Figure 1.1 Simplified diagram illustrating how Fe and macronutrient dynamics may

interact to alter phytoplankton community dynamics in lakes, reproduced from Molot et

al., (2014). John Wiley & Sons Ltd.

The chemical form of iron strongly influences its bioavailability, toxicity, environmental fate and

transport (Saito et al. 2008b; Sevilla et al. 2008; VanBriesen and Small 2010). Despite being one

of the most abundant elements, iron deficiency is a regular source of stress in biological systems

(Baptista and Vasconcelos 2006; Sevilla et al. 2008). Ferrous iron (Fe2+) is highly bioavailable

and can be transported across cyanobacterial membranes (Lis et al. 2015a). It is very soluble in

anoxic waters but it is rapidly oxidised to the poorly soluble ferric iron (Fe3+)

in circumneutral oxygenated waters, which can cause iron limitation in freshwater systems

lacking internal Fe2+ from anoxic sediments (Cavet et al. 2003; Baptista and Vasconcelos 2006;

Alexova et al. 2011; Molot et al. 2014). However ferrous iron can also be sourced from

extracellular photoreduction of Fe3+ complexed to dissolved organic matter (DOM) (Molot et al.

2014). Some cyanobacteria can overcome the low bioavailability of particulate Fe3+ by

producing siderophores – low molecular weight metallophores which chelate and solubilise Fe3+,

but also to Zn, V, Mo, Mn, Co, Ni and Cu (Ahmed and Holmström 2014; Kraemer et al. 2015).

Siderophores can enhance the bioavailability of metals and aid in their acquisition from the

surrounding environment (Ahmed and Holmström 2014). The ability of some cyanobacterial

9

genera to produce siderophores may represent a response to a higher degree of sensitivity to low

metal availability – particularly Fe, relative to other phytoplankton groups (Wever et al. 2008).

1.8 Zinc Zinc is an essential element to cyanobacteria and plays a role in numerous physiological

processes, yet, similar to other trace metals, it is also toxic at high concentrations (Baptista and

Vasconcelos 2006; Downs et al. 2008). Zinc maintains protein structure and aids in CO2 transfer

and fixation in the enzyme carbonic anhydrase and in alkaline phosphatase, an enzyme that

acquires phosphorus from organic phosphate esters (Baptista and Vasconcelos 2006; Sunda

2006). It is also a component of zinc finger proteins, which are needed for DNA transcription

(Sunda 2006). At high concentrations, such as near sewage or industrial effluent outlets, zinc can

inhibit phytoplankton productivity and species richness by outcompeting other essential trace

metals at binding sites (Cavet et al. 2003; Downs et al. 2008; Polyak et al. 2013; Pandey et al.

2015).

Zinc availability is generally controlled by the concentration of free metal ions or dissolved

inorganic species in the environment, as organic complexes are not readily available to

phytoplankton (Sunda 2006). Due to the involvement of zinc in CO2 transfer, cellular

requirements increase under CO2 limited conditions. During blooms where CO2 is largely

consumed, cells may become colimited by zinc and CO2 (Sunda 2006). Similarly, given the

importance of zinc in phosphate acquisition, algal growth may be colimited by zinc and

phosphate in environments where both nutrients occur at low concentrations (Sunda 2006).

1.9 Copper Copper is essential to cyanobacteria as a micronutrient. It is a component of cytochrome oxidase

and plastocyanin in the electron-transport chain, converting light to chemical energy (Cavet et al.

2003; Sunda 2006). It also facilitates H2O dehydrogenation and O2 evolution in the thylakoid

lumen (Raven et al. 1999; Burnat et al. 2009). As with other metals, copper exists in many forms,

such as free ions, inorganic complexes and chelates with organic ligands such as fulvic and

10

humic acids (Sunda 2006). Free ionic copper is the most bioavailable to phytoplankton (Lehman

et al. 2004).

At high concentrations copper can be highly toxic to cyanobacteria, causing

a hyperoxidative state, chlorosis and inhibiting growth (Pinto et al. 2003). Its toxic effects have

seen copper commonly utilised as an algaecide to treat blooms in lakes and reservoirs (Bishop et

al. 2015). Elevated copper concentrations in surface waters are often linked to human activity,

due to its presence in antifouling paint, wood preservatives or from municipal waste (Cavet et al.

2003; Pinto et al. 2003). Lehman et al. (2004) found that copper additions as small as 1 µgL–

1 suppressed phytoplankton growth in the Great Lakes, indicating that in some instances ambient

concentrations may already be at the threshold for toxicity to algae and other taxa. In contrast,

Zhang et al. (2019) observed that the addition of 20 μg/L Cu had a stimulatory effect on algal

growth, including Microcystis aeruginosa, in the hypereutrophic Lake Taihu.

1.10 Molybdenum Molybdenum is required for the assimilation of inorganic nitrogen and is therefore particularly

important to heterocystous cyanobacteria (ter Steeg et al. 1986; Glass et al. 2010). It is a cofactor

in the N2 fixing enzyme nitrogenase, among others (Healey 1973; ter Steeg et al. 1986; Pearl and

Fulton 2006). The absence of molybdenum from growth media regularly causes N-limitation in

heterocystous cyanobaceria (Glass et al. 2012) and as such, molybdenum facilitates the

introduction of nitrogen into the food web and low molybdenum concentrations can

cause colimitation of phytoplankton growth alongside nitrogen (Glass et al. 2010).

Molybdenum generally occurs as the oxyanion MoO42- in natural waters, in concentrations

typically < 20 nmol/L (< ~2 μg/L) in freshwater environments (Cole et al. 1993). These low

molybdenum concentrations are often insufficient for optimal nitrogen fixation

by heterocystous cyanobacteria (ter Steeg et al. 1986; Zerkle et al. 2006). Contributing to this

deficiency, competitive inhibition of transport proteins by sulfate further limits molybdenum

availability to N2-fixing cyanobacteria (ter Steeg et al. 1986; Cole et al. 1993). The interactions

between molybdenum and sulfate may cause a switch in the nutrient requirements of

phytoplankton along a salinity gradient. Howarth and Cole (1985) outline a general trend of

11

phosphorus limitation in inland freshwater and nitrogen limitation in sulfate-rich coastal

waterways due to inhibited molybdenum assimilation. However Paerl and Fulton (2006) suggest

that some cyanobacteria possess nitrogenases that do not require molybdenum and they would

therefore have a way of circumventing low molybdenum availability.

1.11 Cobalt A number of studies have assessed the cobalt requirements of marine cyanobacteria and

concluded that Co can act as a determinant of marine cyanobacteria distribution and productivity

(Huertas et al. 2014). However, micronutrient requirements often differ between marine and

freshwater cyanobacteria (Quigg 2016). Downs et al. (2008) noted a stimulation of primary

productivity upon addition of cobalt during a bloom of the freshwater heterocystous

cyanobacteria Anabaena flos-aquae. Yet, the importance and role of cobalt in freshwater

cyanobacterial species is severely understudied.

Cobalt is often associated with vitamin B12, a diverse group of corrinoids involved in the transfer

of methyl groups and rearrangement reactions in cellular metabolism (Healey 1973; Huertas et

al. 2014; Rodriguez and Ho 2015; Helliwell et al. 2016). B12 is required by the majority of

microalgae for growth, however it can only be synthesized de novo by certain prokaryotes,

including most cyanobacteria (Helliwell et al. 2016). However, recent work by Helliwell et al.

(2016) demonstrates that pseudocobalamin, which is relatively non-bioavailable, is the dominant

form produced by cyanobacteria, suggesting a complex B12 cycle in aquatic systems. Rodriguez

and Ho (2015) conducted batch cultures of Trichodesmium with varying concentrations of Co

and vitamin B12. Low cobalt concentrations appeared to limit Trichodesmium growth. Upon

addition of vitamin B12, growth was elevated. These results support cobalt requirements for

vitamin B12 synthesis in some cyanobacteria. Interestingly, vitamin B12 deficiency appears to

promote nitrogen fixation of marine cyanobacteria, perhaps because vitamin B12 is a nitrogen-

rich molecule (Healey 1973; Rodriguez and Ho 2015).

Cobalt can substitute for other micronutrients, such as zinc and cadmium. For example, the

marine diatom Contricribra (Thalassiosira) weissflogii can utilise Co in place of Zn in the

enzyme carbonic anhydrase (Quigg 2016). When both micronutrients are available, Zn is

12

favoured (Intwala et al. 2008; Quigg 2016). However, some marine cyanobacteria (e.g.

Prochlorococcus, Trichodesmium and Synechococcus) appear to have an absolute cobalt

requirement (Sunda and Huntsman 1995; Saito et al. 2002; Rodriguez and Ho 2015). For

example, Rodriguez and Ho (2015) showed that Trichodesmium has an absolute cobalt

requirement that can’t be alleviated by the addition of zinc Saito et al. (2002) observed a similar

phenomenon in the cyanobacterium Prochlorococcus.

Ji and Sherrell (2008) observed that Microcystis sp. subjected to phosphorus limitation exhibited

an increase in both cellular Co and alkaline phosphatase (APase) activity. When cyanobacteria

are subjected to extended phosphorus deficiency, extracellular APase is excreted to catalyse the

hydrolysis of dissolved organic phosphorus when the preferred inorganic phosphorus is limited

(Pandey and Tiwari 2003; Ji and Sherrell 2008). The dominant phosphatase in Microcystis may

require cobalt, as reported for other prokaryotes, and may be accumulated upon phosphate

deficiency due to the upregulated activity of APase (Ji and Sherrell 2008).

1.12 Manganese Manganese is one of the most abundant transition metals on earth and is required by all known

organisms (Salomon and Keren 2011). Manganese exists in various chemical forms,

predominantly as the highly soluble and bioavailable Mn(II) ion (Salomon and Keren 2011) and

also as Mn(III) and Mn(IV) which are present mainly in particulate forms which are insoluble

and non-bioavailable (Sunda and Huntsman 1998). Similar to iron, manganese is essential for

photosynthesis due to its role in the thylakoids, where four manganese atoms are required by

every water-splitting oxygen-evolving complex in PSII (Raven et al. 1999; Cavet et al. 2003;

Sunda 2006). Despite the importance of manganese, it is generally not considered to limit

phytoplankton growth or primary productivity in aquatic ecosystems due to its high abundance

(Salomon and Keren 2011). However, Salomon and Keren (2011) indicated that even small

changes in the natural ambient concentrations of manganese can impose changes in

photosynthetic activity of the freshwater cyanobacterium Synechocystis sp. Kraemer et al. (2015)

suggest that siderophores may play a role in manganese biochemistry, primarily by

forming Mn(III)-siderophore complexes, thereby increasing manganese availability to

cyanobacteria.

13

1.13 Cyanotoxin production The increasing prevalence of toxic cyanobacterial blooms has led many researchers to investigate

the causes and stimulants of toxin production (Lukac and Aegerter 1993; Utkilen and Gjolme

1995; Schatz et al. 2007; Gouvêa et al. 2008; Polyak et al. 2013; Pimentel and Giani 2014;

Mowe et al. 2016; Yeung et al. 2016). The complex structure and high energetic cost of

cyanotoxin production is only justified if they confer some benefit to the producing organism

(Lukac and Aegerter 1993). The benefits of cyanotoxins have been demonstrated in a number of

studies, for example competition experiments conducted by Briand et al. (2008) showed

microcystin-producing strains of Planktothrix agardhii were more successful than non-

microcystin-producing strains under limiting temperature, light and nitrate conditions. Whereas,

under favourable conditions the non-toxic strain was more successful, suggesting that the

energetic cost of producing microcystin outweighed the benefit. Further, a genetic study by

Zilliges et al. (2011) noted increased transcription of mcy mRNA when Microcystis was exposed

to high light, iron-limitation and other oxidative stress conditions. They suggested microcystin-

producing strains of Microcystis have an advantage over non-toxic strains under oxidative stress

conditions due to a protein-modulating role of microcystin.

However, the precise role of cyanotoxins remains highly contentious. Given the deleterious

effect of cyanotoxins on a multitude of organisms, it is perhaps logical to conclude that

cyanotoxins are produced as a grazing deterrent or to reduce competition (Holland and Kinnear

2013). As observed by Rohrlack et al. (1999), cyanotoxins can act as an anti-predator defence

mechanism as a toxin-producing strain of Microcystis was lethal to Daphnia whereas a mutant

deficient of the microcystin biosynthesis genes (mcy) did not have lethal effects. However,

defence against grazers is unlikely to be the primary function of cyanotoxins due to the early

evolution of the genes responsible for their synthesis, prior to the evolution of metazoans and the

subsequent grazing pressure (Schatz et al. 2007; Harke et al. 2016). The toxic effects

of microcystin may have aided in the retention of microcystin biosynthesis genes or may be a

more recently evolved secondary function.

14

Cyanotoxin production, particularly microcystin, has been widely studied as a function of

various physiochemical properties in an attempt to understand their possible functions. For

example, macronutrients (Orr and Jones 1998; Pimentel and Giani 2014), radiation, pH and

temperature (Wiedner et al. 2003; Neilan et al. 2013) and some trace metals (Lukac and Aegerter

1993; Utkilen and Gjolme 1995; Gouvêa et al. 2008; Polyak et al. 2013). Often toxin production

is simply correlated with cell division and growth, suggesting that there is no direct effect on the

metabolic pathway (Orr and Jones 1998; Long et al. 2001; Wiedner et al. 2003; Gouvêa et al.

2008), while in others, a relationship appears (Ross et al. 2006; Polyak et al. 2013; Pimentel and

Giani 2014). Neilan et al. (2013) reasoned that while there is a strong correlation between toxin

production and growth rate, a more complex relationship with some physiochemical conditions

exists.

1.14 Trace metals and cyanotoxins Some cyanotoxins form complexes with metal ions (Fe2+, Zn2+, Cu2+, Mg2+), and consequently

there have been suggestions that this points to their role in nature as trace metal complexing

ligands (Humble, Gadd, and Codd 1997; Saito et al. 2008). If trace metal availability influences

the rate of cyanotoxin production metals may be an important regulator of the toxicity of blooms

(Alexova et al. 2011). Birch and Bachofen (1990) state that complexing ligands produced by

microorganisms are usually part of a transformative, detoxifying process. Cyanotoxins may

therefore be produced in response to high trace metal concentrations as a means of detoxification

(Martínez-Ruiz and Martínez-Jerónimo 2016). Huang et al. (2015) observed the effects of toxic

levels of cadmium on Microcystis aeruginosa and found no evidence that microcystin can affect

metal toxicity by regulating metal accumulation or by directly assisting in the detoxification.

Alternatively, metals could be complexed by cyanotoxins as a means of acquisition or

storage. Lukac and Aegerter (1993) found that trace metal concentration influenced the

production of microcystin in Microcystis aeruginosa. Severe iron and zinc limitation

increased toxin production, indicating that microcystin may function as an

intracellular chelator aiding in trace metal accumulation. This hypothesis is supported by Yeung

et al. (2016) who also observed higher intra and extracellular microcystin quotas in iron-limited

Microcystis cultures. Further, Sevilla et al. (2008) found that iron starvation increased

15

transcription of the mcyD gene involved in microcystin synthesis and Polyak et al. (2013) noted

concentrations of 25–100 μg/L Zn2+ increased intracellular microcystin concentration. However,

a number of studies have found that trace metals have no effect on cyanotoxin production. For

example, Harland et al. (2013) studied anatoxin-a production by Phormidium autumnale and

found no relationship with iron or copper concentrations, similarly, Gouvêa et al. (2008) suggests

that toxin production paralleled specific growth rate and biomass rather than being directly

influenced by metals.

Chelators often enhance the availability of metals to phytoplankton by maintaining them in a

soluble, diffusible form and preventing precipitation or adsorption onto particle surfaces (Vrede

and Tranvik 2006). The acquisition hypothesis implies that cyanotoxins function similarly

to siderophores, molecules that are actively transported across the cell membrane to form strong

extracellular complexes with ferric iron and increase iron bioavailability via a reduction reaction

to form ferrous iron (Wilhelm et al. 1996; Alexova et al. 2011; Martínez-Ruiz and Martínez-

Jerónimo 2016; Pearl and Fulton 2006). Klein et al. (2013) showed that Fe3+ forms weaker

complexes with microcystin-LR than is typical of other siderophores, and proposed

that microcystin is more likely to regulate iron via intracellular processes or by acting as a shuttle

across the cell membrane. Another feature of siderophores which is not observed

in microcystin is the lack of active extracellular translocation (Gouvêa et al. 2008; Zilliges et al.

2011). Despite identification of a putative microcystin ABC transporter, the majority

of microcystin (>90%) is released only upon cell lysis (Dittmann and Börner 2005).

Further, Fujii et al. (2011) compared a microcystin-producing strain of Microcystis and

a mcy- mutant and found that microcystin did not facilitate iron-uptake in the microcystin-

producing strain. These observations point towards a primary intracellular role for microcystin,

perhaps by acting as transporters, increasing membrane permeability, forming complexes on the

cell surface or increasing phagocytic ability of algal cells (Saito et al. 2008; Wang et al. 2012).

16

Table 1.1: Summary of published literature that assessed the effect of trace metals on the growth

and toxin production of freshwater cyanobacteria. Y: Limitation was observed for this element,

C: Colimitation with N and/or P was observed, N: No limitation was observed; T+ addition of

element had a positive effect on cyanotoxin production; T-: limitation of nutrient increased toxin

production; T±: no effect.

Location Taxa Co Cu Fe Mn Mo Zn Mix Study

Culture Microcystis

aeruginosa

T- Alexova et al. (2011)

Culture Microcystis

aeruginosa

T+ Amé and Wunderlin (2005)

Culture Anabaena spp. C Attridge and Rowell (1997)

Canadian Shield

lakes

Pico-cyanobacteria C Auclair (1995)

Torrão reservoir Microcystis

aeruginosa

N N N N N N Baptista et al. (2014)

Culture Anacystis sp. Y Cheniae and Martin (1967)

Lake Tanganyika,

East Africa

Pico-cyanobacteria Y, C Wever et al. (2008)

Lake Waihola,

New Zealand

Anabaena flos-aquae Y

Y

N Y Y Downs et al. (2008)

Lake Mahinerangi,

New Zealand

N N N N N

Culture Microcystis

aeruginosa

Y Fujii et al. (2016)

Culture Nostoc sp. C Glass et al. (2010)

Culture Microcystis

aeruginosa

T± T± Gouvêa et al. (2008)

Culture Phormidium

autumnale

Y, T± Y, T± Harland et al. (2013)

Lake Erken,

Sweden

Gloeotrichia

echinulate

C Hyenstrand et al. (2001)

Lake Erken,

Sweden

Gloeotrichia

echinulate

C N Karlsson-Elfgren et al.

(2005)

Culture Microcystis novacekii Y, T+ Li et al. (2009)

Culture Microcystis

aeruginosa

N, T± Y, T- N, T± Y, T± Lukac and Aegerter (1993)

Lake 227, ELA

Aphanizomenon

schindlerii

Y

Molot et al. (2010)

17

Anabaena flos-aquae,

Synechococcus

Y

Culture Anacystis nidulans Y N Peschek (1979)

Culture Microcystis

aeruginosa

N Y, T+ Polyak et al. (2013)

Culture Synechocystis Y Salomon and Keren (2011)

Culture Microcystis

aeruginosa

T- Sevilla et al. (2008)

Laurentian Great

Lakes

Total cyanophyta C Sorichetti et al. (2014)

Culture Anabaena

oscillarioides

C ter Steeg et al. (1986)

Culture Microcystis

aeruginosa

T+ Utkilen and Gjolme (1995)

Clear Lake,

California

Aphanizomenon flos-

aquae

C Wurtsbaugh and Horne

(1983)

Culture Microcystis

aeruginosa

Y, T- Yeung et al. (2016)

Lake Taihu, China Total cyanophyta N Y, C Y, C N N Zhang et al. (2019)

Microcystis

aeruginosa

N Y, C C N N

1.15 Knowledge gaps Given the ability of cyanobacteria to form blooms and produce toxins, they are of particular

importance to catchment managers. While a large number of studies demonstrate trace metal

limitation of primary productivity in freshwater (see review by Downs et al. (2008) and more

recent studies such as Harpole et al. (2011) and Corman et al. (2010)), relatively few studies

assess the effect specifically on freshwater cyanobacteria. Cyanobacteria have particular trace

metal requirements and metal uptake strategies (Wever et al. 2008; Molot et al. 2010). Therefore,

metals may stimulate growth in the cyanobacterial community but decrease eukaryotic

phytoplankton productivity. It is important to differentiate between the cyanobacterial response

and the response of the eukaryotic algal community. Further, understanding how cyanobacteria

compete with other phytoplankton groups under different trace metal and macronutrient regimes

has received little attention – although Molot et al. (2010, 2014) and Sorichetti et al. (2014) do

provide conceptual models for iron-mediated bloom formation and community dynamics.

18

Of the 27 studies presented in Table 1, 12 focus on metal interactions with Microcystis spp. This

may be as Microcystis is the most common bloom-forming genera (Wiegand and Pflugmacher

2005; Zurawell et al. 2005; Pearson et al. 2010; Mowe et al. 2015; Omidi et al. 2018) and is

therefore central to many catchment management plans (Paerl et al. 2011b; Stroom and

Kardinaal 2016) and axenic cultures are readily available. However, cyanobacteria are a diverse

group, with upwards of 150 genera (Likens 2009), and literature skewed towards Microcystis

will not reflect the overall cyanobacterial community. Similarly, microcystin dominates the

literature in studies of environmental regulation of cyanotoxin synthesis (Omidi et al. 2018).

However, there is a high degree of structural variation in bioactive, toxic compounds released by

cyanobacteria (Downing et al. 2015) which suggests that the factors stimulating cyanotoxin

production and their biological role may be unique to each compound. This body of knowledge

must be expanded by the addition of other cyanobacterial species and cyanotoxins to better

understand the role of metals in the growth of cyanobacteria and provide insight into species

specific responses.

Iron is by far the most commonly examined trace metal and most frequently observed metal to

effect cyanobacterial growth, as evident in Table 1. Of the 18 studies which examine irons effect

on cyanobacterial growth, 15 observed limitation or colimitation. While all trace metals

examined in this review have a demonstrated capacity to limit cyanobacterial growth to some

degree, they have not received the same attention. For example, cobalts effect on freshwater

cyanobacterial growth has only been examined in 5 studies, of which 1 showed limitation.

Similarly, the influence of iron availability on microcystin production has received considerable

attention following the early paper by Lukac and Aegerter (1993), whose results first suggested

an iron-chelating role of microcystin. Since this preliminary study, other research has further

examined this relationship, such as Alexova et al. (2011) and Yeung et al. (2016). Other trace

metals have received much less attention, or in some cases none.

Culture-based experiments form most of the literature on cyanobacterial-metal interactions

(~63% of the studies from Table 1). While culture experiments often demonstrate unambiguous

relationships between a single species growth and a given micronutrient, as demonstrated by

19

Fujii et al. (2016), it is also important to examine these relationships under field conditions

which take into account environmentally relevant concentrations of trace metals, particularly as

selective pressures and behaviours of culture raised organisms can differ to those in natural

systems (Cole et al. 1993). Nutrient amendment bioassays are a useful tool in bridging the gap

between culture and field studies, and have been used effectively in studies such as Wever et al.

(2008) and Zhang et al. (2019). However Nogueira et al. (2014) outlines how the incubation

time, sample volume and pre-filtration process of small-scale mesocosms may alter how

representative the system is of the original community.

It is unclear how regularly cyanobacterial blooms are limited by trace metals in natural systems.

Field monitoring studies examining trace metal fluxes and cyanobacterial bloom dynamics, such

as in Baptista et al. (2014), are an important missing piece in the literature and must be extended

to include a greater variety of systems and locations to link and validate the results of culture and

bioassay studies. We also require a better understanding of the quantity of trace metals required

to support cellular functions of cyanobacteria. This information would allow the development of

a model that predicts scenarios where trace metals may become limiting. These gaps in the

literature demonstrate a need for further study to fully understand how cyanobacteria and their

toxins are influenced by trace metals.

1.16 Scope and need for this study It is imperative to understand the environmental factors that cause cyanobacterial blooms, as well

as factors that influence the toxicity and severity of these events. This thesis examines the

importance of micronutrient trace metals for cyanobacterial growth and their role in bloom

formation and toxin production. This research aims to provide a more comprehensive conceptual

framework of the causes of cyanobacterial blooms and the conditions that sustain them. I seek to

build upon the current state of knowledge regarding nutrient requirements of cyanobacteria. A

combination of in situ microcosms, long-term field monitoring and laboratory culture

experiments have been employed that, when combined, provide valuable information that may

aid the management of harmful algal blooms in freshwater systems.

20

1.17 Layout of Chapters

Chapter 1 This chapter addressed the current state of knowledge regarding the relationship

between cyanobacteria growth, toxin production and trace metals. This chapter has been

published as a review paper in the journal Toxins.

Chapter 2 This chapter sought to identify the prevalence of micronutrient limitation in a

variety of freshwater systems in NSW by undertaking nutrient amendment bioassays at seven

sites. This chapter is under revision in the journal Aquatic Ecology.

Specific aims were:

To understand the extent of micronutrient limitation and/or colimitation of cyanobacterial

growth in some Australian freshwater systems.

To understand how the phytoplankton community changes in response to various

micronutrient and macronutrient regimes and to observe which conditions favour

cyanobacteria.

To determine the limiting nutrient for phytoplankton growth at an assemblage and genus

level.

Chapter 3 This chapter built on the results from Chapter 2 which showed that two of the

seven sites investigated showed signs of micronutrient limitation of cyanobacteria growth. One

of these, Mannus Lake, was selected for an in-depth monitoring study of micronutrient

availability over a period of 18 months. We examined various sources of dissolved

micronutrients and interactions between cyanobacterial growth and micronutrient availability.

Other factors that influence cyanobacterial growth, such as macronutrients, thermal stratification

and light availability were also monitored to identify the causes of excessive cyanobacterial

growth in the lake.

Specific aims were:

To quantify the micronutrient inflows from upstream creeks, the catchment, and within-

dam sources.

To determine if/when any micronutrients become a limiting factor for cyanobacterial

growth.

21

To determine the factors causing dense recurring cyanobacterial blooms in Mannus Lake.

Chapter 4 This chapter focuses on a key bloom-forming species, Microcystis aeruginosa

grown under culture conditions. Microcystis sp. regularly reached bloom proportions in Mannus

Lake. M. aeruginosa was grown in culture conditions with varying concentrations of the

micronutrients iron, cobalt, copper, manganese and molybdenum to assess which micronutrients

limited growth over a 60-day period. The effect of micronutrient limitation on toxin production

was also assessed.

Specific aims were:

To assess the effect of micronutrient limitation on the growth of M. aeruginosa.

To determine the effect of micronutrient limitation on the cell size of M. aeruginosa.

To determine whether micronutrient limitation effects microcystin-LR production.

To measure intracellular micronutrient concentrations to investigate their uptake and

retention in cyanobacterial cells.

Chapter 5 This chapter builds upon chapter 4 which found limitation of Microcystis

aeruginosa growth by iron, cobalt and manganese. Cobalt was selected for further investigation

as the importance of this micronutrient to cyanobacteria is severely understudied. Cobalt

availability was modified under culture conditions to assess how depletion influences growth

across a range of concentrations. Subsequently, a similar experiment was performed in which

cobalamin (vitamin b12) was removed from the media to determine whether cobalamin is

bioavailable to M. aeruginosa. This also provides insight into the cause of cobalt limitation of

growth. A monitoring study was also undertaken to determine typical background concentrations

of cobalt in various freshwater systems.

Specific aims were:

To determine the cobalt concentrations required to sustain M. aeruginosa growth.

To determine whether M. aeruginosa can uptake and utilise cobalamin.

To identify typical cobalt concentrations in Australian freshwaters to assess whether

cobalt is likely to be at limiting concentrations for cyanobacterial growth.

22

Chapter 6 In this chapter, results of chapters 2, 3, 4, and 5 are discussed and overall

conclusions are drawn.

23

Chapter 2: Micronutrients as growth limiting factors in

cyanobacterial blooms; a survey of freshwaters in South

East Australia

2.1 Abstract

The role of trace metal micronutrients in limiting cyanobacterial growth and structuring the

phytoplankton community is becoming more evident. However, little is known regarding the

extent of micronutrient limitation in freshwaters or which micronutrient conditions favour

potentially-toxic cyanobacteria. To assess how freshwater phytoplankton respond to

micronutrient and macronutrient additions, we conducted nutrient amendment bioassays at seven

sites across South Eastern-Australia. Sites were variable in cyanobacterial cell densities and

phytoplankton community compositions. At two sites, Mannus Lake and Burrendong Dam,

micronutrient additions (iron, cobalt, copper, manganese, molybdenum and zinc) increased

cyanobacterial growth, suggesting micronutrient limitation. Both sites had cyanobacterial blooms

present at the onset of the experiment, dominated by Chrysosporum ovalisporum at Mannus

Lake and Microcystis aeruginosa at Burrendong Dam. This suggests that micronutrients may be

an important regulator of the severity of cyanobacterial blooms and may become limiting when

there is high competition for nutrient resources. The addition of the micronutrient mixture

resulted in a higher proportion of cyanobacteria compared to the control and a lower diversity

community compared to phosphorus additions, indicating that micronutrients can not only

influence cyanobacterial biovolume but also their ability to dominate the phytoplankton

community. This reinforces that micronutrient requirements of phytoplankton are often species

specific. As micronutrient enrichment is often overlooked when assessing nutrient-constraints on

cyanobacterial growth, this study provides valuable insight into the conditions that may influence

cyanobacterial blooms and the potential contribution of micronutrients to eutrophication.

24

2.2 Introduction

Freshwater phytoplankton communities are highly variable in space and time, and respond

rapidly to changes in their physical, chemical and biological environment (Varol and Sen 2018).

Highly diverse, low biomass phytoplankton communities are indicative of healthy freshwater

systems (Shao et al. 2019). Conversely, high biomass, less diverse phytoplankton communities

often dominated by bloom-forming cyanobacteria tend to persist in systems anthropogenically

modified through increased nutrients or flow restriction (Dignum et al. 2005; O’Neil et al. 2012;

Mitrovic et al. 2003; Bormans et al. 2004). Many cyanobacteria can produce biologically active

secondary metabolites, known as cyanotoxins, which can have severe ecological, economic and

human health impacts (Bowling 1994; Falconer 2001; Bormans et al. 1997; Rastogi et al. 2015;

Pearson et al. 2010).

Optimal growth of phytoplankton depends on the availability of several key nutrients. Among

these, phosphorus (P) and nitrogen (N) are required in the largest quantities and can be a growth-

limiting factor in freshwater systems (Paerl and Otten 2013). The role of these macronutrients in

stimulating cyanobacterial blooms is well documented (Schindler et al. 2016; Paerl and Otten

2013; Mueller and Mitrovic 2014; Hunt and Matveev 2005). Micronutrient trace metals also play

key roles in a multitude of biological processes and are cofactors in numerous cyanobacterial

proteins (Baptista and Vasconcelos 2006; Facey et al. 2019a). There is emerging evidence they

can influence cyanobacterial growth alone or in combination with macronutrients (Lukac and

Aegerter 1993; Downs et al. 2008; Molot et al. 2010; Harland et al. 2013; Polyak et al. 2013;

Sorichetti et al. 2014).

The availability of macronutrients and micronutrients play a key role in structuring the

phytoplankton community (Vyverman et al. 2007). Nutrient requirements within the

phytoplankton community are highly variable, leading to interspecific competition for nutrient

resources (Sourisseau et al. 2017). As different phytoplankton groups have distinct nutrient

requirements and means of nutrient acquisition, the addition of a nutrient can cause differential

responses in different segments of the phytoplankton community. This process, termed

‘community colimitation’, can cause an alteration to the overall structure of the community

(Arrigo 2005). For example, Molot et al. (2014) proposed that iron regulates the ability of

25

cyanobacteria to compete with eukaryotic algae and cyanobacterial dominance can be supressed

in P-loaded systems by reducing Fe2+ availability. Further, the growth of heterocystous

cyanobacteria will likely be more dependent on molybdenum availability than non-heterocystous

cyanobacteria due to its role in the assimilation of inorganic nitrogen (Glass et al. 2012).

While the importance of trace metal micronutrients for phytoplankton growth is becoming more

evident, little is known about the extent of micronutrient limitation in freshwaters or how

increased concentrations of micronutrients may alter phytoplankton community structure.

Identifying how phytoplankton communities, particularly those that include toxin-producing

cyanobacteria, respond to different macronutrient and micronutrient regimes is crucial to making

informed, effective catchment management decisions. Our research had two aims, firstly, to

understand the extent of micronutrient limitation and/or colimitation of cyanobacterial growth in

some South Eastern Australian freshwater systems. Secondly, to understand how various

phytoplankton communities change in response to micronutrient amendments and to observe

which conditions favour cyanobacteria. We hypothesise that (1) micronutrients will be a limiting

factor of cyanobacterial growth in some freshwater systems and (2) changes in phytoplankton

community structure will occur with increased micronutrient concentrations. We chose to use a

mixture of iron, cobalt, copper, manganese, molybdenum and zinc as these are required by some

or all phytoplankton at a biochemical level (Facey et al. 2019a).

2.3 Materials and Methods 2.3.1 Study sites

Seven sites were selected across New South Wales and Victoria, Australia. Study sites were

chosen because they were known to have varying levels of cyanobacteria present in warmer

months. They comprised of lakes, rivers and creeks and are summarised in Table 2.1. Sampling

occurred between the months of November to February when water temperatures and light

intensities were not limiting.

26

Table 2.1: Summary of study sites, sampling dates and locations. 2

Site Date Coordinates Description Approx max

depth (m)

Surrounding land

uses

Hunter River at Morpeth Nov-2017 -32.724, 151.651 Upper estuary 2.5 Grazing, cropping

Windeyers Creek Nov-2017 -32.779, 151.738 Small free flowing stream 1 Grazing, residential,

industrial

Mannus Lake Feb-2018 -35.812, 147.976 Shallow artificial reservoir 6

Grazing, plantation

forests, native

forestry

Lake Lyell Jan-2020 -33.516, 150.077 Large dam 10

Nature

conservation,

residential, grazing

Burrendong Dam Jan-2020 -32.685, 149.146 Large dam 10 Grazing, managed

resource protection

Murray River at Mildura Jan-2020 -34.176, 142.165 Weir pool 6 Urban, grazing

Murray River at Euston Jan-2020 -34.582, 142.745 Weir pool 6 Grazing, nature

conservation

2.3.2 Microcosm enrichment assays

In situ nutrient enrichment microcosms were conducted to determine which nutrients were

limiting phytoplankton growth and to test for any nutrient-driven changes in community

composition after a seven-day incubation period, similar to Mueller and Mitrovic (2014).

Approximately 60 L of surface water was filtered through a 63 μm plankton net into a large

plastic container. Water was filtered to exclude zooplankton grazers. 1.0 L clear PET bottles

were filled from the container, leaving some air space at the top. Nutrient additions were

conducted according to the six treatments outlined in Table 2.2. All treatments were conducted in

triplicate.

Following the nutrient additions, bottles were mixed by rotation and tied together in random

order. They were suspended at the same depth within the euphotic zone using floats

(approximately 90% surface irradiance). Concentrations of nitrate and phosphate were selected

27

to alleviate any macronutrient limitation while remaining within levels typically found in natural

Australian systems. They resembled those used by Mueller and Mitrovic (2014) (500 µg/L N,

200 µg/L P), as they effectively stimulated growth and had no toxic effects. Trace metal

additions resembled the concentrations of the cyanobacterial growth medium, MLA (Bolch and

Blackburn 1996) and were low enough to avoid any toxic effects. Samples for dissolved

micronutrients, nitrate/phosphate, physiochemistry, chlorophyll a and phytoplankton

enumeration were taken in triplicate from the filtered water at the onset of the experiment.

Nitrate/phosphate and micronutrient samples were also taken from surrogate bottles with added

nutrients and micronutrients to determine the total concentration of the addition plus the ambient

concentration. Samples for chlorophyll a and phytoplankton enumeration were taken after 7 days

from each sample bottle.

Table 2.2: Summary of treatments and nominal concentrations of the target nutrient additions. 3

Treatment Salt Concentration (µg/L)

Control - - Nitrogen (N) KNO3 500 Phosphorus (P) KH2PO4 300 Nitrogen + Phosphorus (NP) KNO3 500

KH2PO4 300 Metals (M) CoCl2.6H20 2

CuSO4.5H2O 2 FeCl3.6H2O (+ Na2EDTA.2H2O) 200 MnCl2.4H2O 100 Na2MoO4.2H2O 3 ZnSO4.7H20 3

Nitrogen + Phosphorus + Metals (NPM) KNO3 500 KH2PO4 300 CoCl2.6H20 2 CuSO4.5H2O 2 FeCl3.6H2O (+ Na2EDTA.2H2O) 200 MnCl2.4H2O 100 Na2MoO4.2H2O 3 ZnSO4.7H20 3

2.3.4 Nutrient sampling and analysis In the field, 50mL of water sample was filtered through a prerinsed 0.45 μm cellulose acetate

syringe filter (Sartorius) and frozen immediately. Bioavailable nitrate and phosphate

concentrations were determined photometrically using Flow Injection Analysis on a QuikChem

8500 Lachat nutrient analyser. For analysis, frozen samples were slowly thawed to room

temperature. Filtered reactive phosphorus (FRP) was measured by the reduction of ascorbic acid

28

using the molybdate blue method (Murphy and Riley 1962). Nitrate and nitrite (NOx) was

determined following reduction by a cadmium column using the sulphanilamide method (APHA,

1998).

2.3.5 Trace metal micronutrient analysis

In the field, 25 mL of water sample was filtered through a 0.45 μm cellulose acetate syringe filter

(Sartorius) prerinsed with 50 mL of 10% nitric acid followed by 100 mL milli-Q water. Samples

were collected in 50 mL falcon tubes and refrigerated. Falcon tubes had been soaked overnight in

an acid bath (10% nitric acid v/v) and rinsed repeatedly with Milli-Q water. Within 24 h of

collection, samples were acidified with ultra-pure nitric acid to 0.2% v/v. The concentrations of

dissolved micronutrients in the filtered solution were analysed by inductively coupled atomic

emission spectrometry (ICP-AES) (Varian 730 ES). The spectrometer was operated according to

the standard operating procedures outlined by the manufacturer. The instruments were calibrated

using matrix-matched standards. At least 10% of samples were conducted in duplicate to ensure

the precision of the analyses. To check for potential matrix interferences at least 10% of samples

had spike recoveries performed.

2.3.6 Phytoplankton identification and enumeration

200 mL grab samples were taken at the beginning of the experiment (day 0) from the large

container and from individual microcosms on day 7 after homogenization by mixing and

preserved with Lugol’s Iodine solution (~0.25% v/v). Samples were identified and enumerated at

200 times magnification using a light microscope (Olympus BX41) and Sedgwick-Rafter

counting chamber. If required, samples were concentrated 5x prior to counting by settling in 50

mL measuring cylinders for 24 hours. The upper 40 mL was removed after checking all

phytoplankton had settled and were no longer present in the upper layer. Phytoplankton taxa

were identified to a genus level using identification literature by (Prescott 1978), except for

potentially toxic cyanobacteria which were identified to species. Counting precision was

29

performed to ±10% with at least 100 units of the dominant taxa counted following Hötzel and

Croome (1999). Biovolumes were calculated using the most appropriate conversion factors from

Newcombe (2012) and Olenina et al. (2006).

2.3.7 Chlorophyll a analysis

200 mL of sample water was filtered on site via vacuum filtration onto GFC glass fibre filters

(Whatman) and frozen for preservation. Chlorophyll a was analysed according to (Mueller and

Mitrovic 2014). The glass fibre filters were extracted in 10 mL 90% ethanol heated in a 75°C

water bath for 10 minutes. Unwanted filtered material was removed by centrifuging at 3000 rpm

for 10 minutes. The supernatant was analysed immediately using a Varian Cary 50 Bio UV

Spectrophotometer at wavelengths 665 nm and 750 nm.

2.3.8 Statistical analysis

All statistical analyses were carried out using the software R Version 1.2.1335 (R Core Team,

2018). Phytoplankton biovolume, cyanobacterial biovolume and chlorophyll a were analysed

with a One-Way analysis of variance (ANOVA) with a significance level of α = 0.05 using the

car package (Fox and Weisberg 2019). Tukey’s pairwise comparison was used to determine

differences within treatments. The Levene statistic was used to test homogeneity of variance.

Community analyses (nMDS, PERMANOVA, SIMPER and Inverse Simpson Diversity Index)

were performed using the vegan package (Oksanen et al. 2019). A square root transformation

was performed on the community data prior to the nMDS and SIMPER to reduce the influence

of extreme values and plots were created using ggplot2 (Wickham 2016). Inverse Simpson

Diversity was measured in terms of biovolume (Behl et al. 2011) and used algal data identified to

the genus level as this is a useful resolution for assessing changes in community structure

(Nielsen et al. 1998).

2.4 Results

30

The effect of nutrient additions on phytoplankton communities was highly variable based on

locations. Limitation by either macronutrients or micronutrients are indicated by increases in the

biovolume of some or all groups within the phytoplankton community (Table 4). At two

locations that had cyanobacterial dominance and high cell concentrations (Burrendong Dam and

Mannus Lake), the micronutrient mixture stimulated cyanobacterial growth, suggesting that one

or multiple trace metals were limiting cyanobacterial growth. This was not observed at the other

bloom sites on the Murray River at Mildura and Euston (Appendix A, Figure A2), both of which

had very low nitrogen and phosphorus concentrations at the beginning of the experiments (Table

2.3). Nitrogen, phosphorus or a combination of the two (co-limitation) regularly limited

phytoplankton growth, as observed at Morpeth, Windeyers Creek, Lake Lyall, Burrendong Dam,

Mildura and Euston (Table 2.4).

Table 2.3: Ambient concentrations of dissolved macronutrients and micronutrients. All values are in µg/L, n=3. 4

Parameter Windeyers Ck Mannus Lake Burrendong Dam

Mildura Euston Morpeth Lake Lyell

NOx 401.950 ± 27.330

90.905 ± 0.138 61.200 ± 2.620 8.468 ± 0.160 2.915 ± 0.568 139.600 ± 5.141 62.067 ± 6.257

FRP 25.900 ± 3.148 7.884 ± 0.105 1.770 ± 0.067 1.878 ± 0.090 1.786 ± 0.217 13.867 ± 1.118 8.867 ± 0.268

Cobalt <5 <5 <5 <5 <5 <5 <5

Copper <2 3.879 ± 0.101 <2 <2 <2 <2 3.977 ± 0.054

Iron 295.340 ± 2.752 496.667 ± 7.20 14.600 ± 3.285 16.889 ± 0.787 32.486 ± 10.436 11.076 ± 0.952 29.281 ± 2.225

Manganese 83.643 ± 0.090 206.667 ± 2.722 74.450 ± 0.579 0.783 ± 0.091 2.893 ± 1.062 19.705 ± 1.056 48.656 ± 0.715

Molybdenum 1.196 ± 0.034 <1 <1 <1 <1 1.733 ± 0.237 11.632 ± 0.064

Zinc 16.417 ± 1.982 5.453 ± 0.442 14.289 ± 6.813 1.414 ± 0.250 17.791 ± 1.935 34.643 ± 0.479 4.634 ± 0.042

Table 2.4: Summary of results from seven nutrient amendment bioassays across South-Eastern Australia. Limiting nutrients are any nutrient treatments that had a greater chlorophyll or total biovolume than the control. 5

Site Date Bloom present at onset?

Dominant taxa Limiting nutrient/s

Notable changes in community

Morpeth Nov-2017 No Green algae, Diatoms N+P Minimal changes

Windeyers Creek

Nov-2017 No Diatoms P P: dinophyceae+, diatoms-

Mannus Lake

Feb-2018 Yes Cyanobacteria (Chrysosporum ovalisporum)

M M: cyanobacteria+, P: green algae+

Lake Lyall Jan-2020 No Euglenoids N+P N+P: green algae+

31

Burrendong Dam

Jan-2020 Yes Cyanobacteria (Microcystis aeruginosa)

N, M M: cyanobacteria+, P: green algae+

Mildura Jan-2020 Yes Cyanobacteria (Aphanocapsa sp., Dolichospermum crassum)

N, P P: cyanobacteria+, N: cyanobacteria-

Euston Jan-2020 Yes Cyanobacteria (Aphanocapsa sp., Dolichospermum crassum)

P P: cyanobacteria+

Cyanobacterial biovolume was strongly influenced by the addition of micronutrients at

Burrendong Dam and Mannus Lake (Figure 2.1). At Mannus Lake there was no significant

difference between cyanobacterial biovolume in the control (C), nitrogen (N treatment),

phosphorus (P treatment) or nitrogen+phosphorus (NP treatment) treatments (One-Way

ANOVA: p-value > 0.05). However, in the micronutrient treatments (M and NPM)

cyanobacterial biovolume was significantly greater than the control and all other treatments

(One-Way ANOVA: NPM vs Control p-value = 0.024, M vs Control p-value = 0.007). Similarly,

at Burrendong Dam the addition of micronutrients alone increased cyanobacterial biovolume

relative to the control (p-value = 0.046). Nitrogen alone also had a stimulatory effect on

cyanobacteria relative to the control (p-value = 0.023). There was no significant difference

between chlorophyll a results across the different treatments at either Burrendong Dam (One-

Way ANOVA: p-value = 0.193) or Mannus Lake (p-value = 0.45) (Appendix A, Figure A1).

* *

* *

* *

*

32

Figure 2.1: Total phytoplankton and cyanobacterial biovolume in Mannus Lake and Burrendong Dam microcosms. Asterisk represents significant difference compared to the control (One-Way ANOVA, p-value < 0.05). The nutrient concentrations added for each treatment are listen in Table 2.2. Error bars are standard error of the mean, n=3.2

At Mannus Lake the phytoplankton community composition was also significantly affected by

macronutrient additions (Figure 2.2) (PERMANOVA: p-value = <0.001). In the P treatment,

growth of green algae was stimulated. However, phosphorus additions did not cause a significant

change in cyanobacterial biovolume relative to the control (One-Way ANOVA: p-value =

0.964). Conversely, the addition of the micronutrient mixture (M), even in the presence of

phosphorus (NPM), increased the growth of cyanobacteria, which was already dominant. There

was a clear distinction between phytoplankton communities at Mannus Lake in treatments with

the metal mixture and those without it (Figure 2.2). SIMPER analysis demonstrated that the

largest contributor to the differences between all treatments was Chryosporum ovalisporum. The

increase in C. ovalisporum in the M treatment contributed up to ~95% of dissimilarity compared

to the control, while the reduction in C. ovalisporum in the NP treatment contributed 71% of the

dissimilarity compared to the control. Mougeotia and Dictyosphaerium were the key genera of

green algae that responded to phosphorus addition in the P and NP treatments. Similarly, at

Burrendong Dam the M treatment had a higher proportion of cyanobacteria compared to the

control while the phosphorus addition favored a reduction in the proportion of cyanobacteria and

a higher diversity community (Figure 2.2). SIMPER analysis demonstrated that Microcystis and

Radiocystis were the largest contributors to differences between all treatments, while

Scenedesmus, Cryptomonas and Chlamydomonas were the largest non-cyanobacterial responders

to the NP addition compared to the control.

33

Figure 2.2: Proportion of community made up of several key phytoplankton groups at Mannus Lake and Burrendong Dam (left). Shannon Diversity Index (middle) and nMDS plots (right) illustrating differences in phytoplankton community structure between treatments. A square root transformation was performed on the community data for nMDS. Stress < 0.2. Error bars are standard error of the mean, n=3.3

2.5 Discussion In situ nutrient bioassays were conducted at seven locations throughout South Eastern Australia

to assess the extent of trace metal micronutrient limitation of cyanobacterial growth and to

identify how increased micronutrient availability influences phytoplankton community structure.

Of the seven freshwater systems examined, two exhibited signs of metal limitation of

cyanobacterial growth: Mannus Lake and Burrendong Dam. At Mannus Lake, a dense

cyanobacterial bloom had established which was dominated by the heterocystous cyanobacteria

Chrysosporum ovalisporum, a producer of the toxin cylindrospermopsin (Shaw et al. 1999;

Quesada et al. 2006; Yilmaz et al. 2008; Fadel et al. 2014). Cyanobacterial biovolume

significantly increased in treatments containing the metal mixture (NPM and M treatments)

(Figure 2.1) primarily driven by increased growth of the bloom forming C. ovalisporum. The

addition of nitrogen and phosphorus alongside micronutrients (NPM treatment) did not increase

34

the effect size as there were no significant differences to the phytoplankton response to

micronutrients alone. Interestingly, the Murray River at Euston and Mildura experiments were

also undergoing a bloom of a filamentous, nitrogen-fixing cyanobacteria, Dolichospermum

crassum, but the response from the metal addition was not observed at either location on the

Murray River. Instead, phosphorus was the limiting factor for cyanobacterial growth at Mildura,

and to a lesser extent at Euston (Appendix A, Figure A2). A similar study conducted by Sterner

et al. (2004) found phosphorus limitation at Lake Superior and did not observe any limitation of

algal growth by micronutrient trace metals (manganese, iron or zinc). However, Sterner proposed

that the system was on the cusp of micronutrient limitation but suggests this may have been

clouded by the simultaneous limitation of phosphorus (North et al. 2007).

C. ovalisporum is often dominant in low nitrogen concentrations where heterocystous

cyanobacteria have an advantage over other phytoplankton (Fadel et al. 2014). Nitrogen fixation

requires high levels of iron (Sterner et al. 2004; Molot et al. 2014), molybdenum (Paerl et al.

2006; ter Steeg et al. 1986) and cobalt (Rodriguez and Ho 2015), as the N2 fixing enzyme

nitrogenase contains metal cofactors. This causes heterocystous cyanobacteria to require some

trace metals in higher amounts than other phytoplankton (Schoffman et al. 2016) and may make

them more prone to micronutrient limitation (Kustka et al. 2002; Molot et al. 2010). Romero et

al. (2013) observed significant increases in nitrogen fixation upon addition of both iron and

molybdenum and suggested co-limitation involving trace metals is common in lakes. A similar

phenomenon may have caused the increase in C. ovalisporum growth in Mannus Lake upon the

addition of the trace metal micronutrient mixture. C. ovalisporum had already established a

dense bloom so it is possible that nutrient constraints were beginning to come into effect. Given

that iron was relatively available at the onset of the Mannus Lake experiment (Table 2.3),

molybdenum and cobalt are more likely to be the limiting micronutrients. Both were below

detection limit.

At Burrendong Dam, which was dominated by the microcystin-producing genera Microcystis

aeuginosa and Radiocystis sp. (Vieira et al. 2003; Rastogi et al. 2015), the micronutrient

treatment (M) had a slightly higher proportion of cyanobacteria than the control, and the NPM

treatment had a higher but insignificant proportion of cyanobacteria than the NP treatment. This

indicates that cyanobacteria may be more successful competitors in the phytoplankton

35

community with higher micronutrient concentrations. The addition of micronutrients alone (M)

and nitrogen (N) stimulated cyanobacterial growth relative to the control. Although the NPM

treatment was higher, it was not statistically different (p-value >0.05) to the control (Figure 2.1).

M. aeruginosa and Radiocystis remained dominant under all treatments. The large stimulatory

effect of nitrogen on cyanobacteria at Burrendong Dam was not observed at Mannus Lake where

the heterocystous Chrysosporum ovalisporum dominated. It has been suggested that reduced

nitrogen input will cause an increase in the proportion of N2 fixing cyanobacteria (Schindler et

al. 2008). The relatively low availability of NOx at the onset of the Mannus Lake experiment

was likely a contributing factor to the dominance of C. ovalisporum and given its ability to fix

atmospheric nitrogen, nitrate is unlikely to limit C. ovalisporum growth. Conversely, Microcystis

and Radiocystis depend on dissolved nitrogen for growth, which had become limiting by the

onset of the Burrendong Dam experiment.

As Microcystis and Radiocystis, the dominant cyanobacterial genera at Burrendong Dam, are

non-nitrogen fixing, the limitation of growth by micronutrients in this system was unlikely to be

related to nitrogen fixation. Iron is also required for the reduction of nitrate to ammonia prior to

assimilation (via nitrate and nitrite reductase) (Schoffman et al. 2016). Sub-optimal iron

availability appears to be able to limit nitrate uptake in natural waters (DiTullio et al. 1993). At

low iron concentrations, and without the presence of highly bioavailable ammonia, the

phytoplankton community can be co-limited by iron and nitrogen (Schoffman et al. 2016; Saito

et al. 2008). For example, North et al. (2007) suggested that iron enrichment reduced nitrogen

limitation by allowing NO3 assimilation in nutrient enrichment bioassays. However, this is not

supported by our results as the addition of nitrate alone in the N treatment stimulated

cyanobacterial growth at Burrendong Dam, suggesting there was sufficient Fe in the ambient

water to allow for nitrate reduction and assimilation. The simultaneous limitation of the

community by nitrate and micronutrient trace metals at Burrendong Dam, combined with the

lack of response in the NP and NPM treatments, is difficult to elucidate.

These results demonstrate that micronutrient trace metals can stimulate cyanobacterial growth in-

situ and may act as an important regulator of the severity of cyanobacterial blooms. This study

joins a growing list that have observed an important role of micronutrients in structuring

phytoplankton communities and increasing cyanobacterial growth in physically and chemically

36

diverse freshwater systems. For example, Downs et al. (2008) noted a stimulation of the

cyanobacterium Anabaena flos-aquae upon addition of cobalt, copper, manganese and a trace

metal mixture, while a number of studies have observed iron limitation of cyanobacteria growth

(Wever et al. 2008; Fujii et al. 2016; Harland et al. 2013; Molot et al. 2010).

2.5.1 Phosphorus-driven changes in community structure

In both experiments the addition of phosphorus promoted higher diversity in the phytoplankton

community composition. Green algae, diatoms and dinoflagellates made up a larger proportion

of the community in P and NP treatments (Figure 2.2). This trend is surprising as the addition of

P decreases the N:P ratio, which is generally expected to favour cyanobacterial growth (Tew et

al. 2014; Li et al. 2018). However, the opposite effect was observed at both Burrendong Dam

and Mannus Lake. The change in community composition may indicate that the systems were

also phosphorus limited at the time and green algae and diatoms were able to respond faster to

the sudden phosphorus pulse due to their faster growth rate compared to cyanobacteria (Lürling

et al. 2013; Deng et al. 2014). Alternatively, each species is likely to have different nutrient

requirements and therefore some species can be nutrient limited whereas others are not (Baptista

and Vasconcelos 2006; Mueller and Mitrovic 2014). This may explain why phosphorus

limitation was not evident when assessing total phytoplankton biomass. This trend is particularly

evident in Burrendong Dam where phosphorus concentrations were very low. Interestingly,

when the phosphorus addition (P or NP) was coupled with the metal mixture (NPM) the

communities were composed of a notably higher proportion of cyanobacteria, particularly at

Mannus Lake. This suggests that micronutrients impart a competitive advantage to cyanobacteria

over other components of the phytoplankton community even under high phosphorus conditions.

This may be because of specific micronutrient requirements of cyanobacteria or a result of a

more efficient metal uptake system (Baptista and Vasconcelos 2006; Sunda 2012), for example

via the production of metallophores (Kraemer et al. 2015).

2.5.2 Implications for management and research

Micronutrient trace metals appear to be an important regulator of the severity of cyanobacterial

blooms in some freshwater systems. Improving our understanding of how specific micronutrients

37

influence phytoplankton community structure and cyanobacterial growth could be an important

aspect of catchment management plans and may be critical to securing freshwater resources into

the future. In both micronutrient limited sites, high-density cyanobacterial blooms had

established by the onset of the experiment. Limiting micronutrient inputs may help to reduce the

severity of such blooms. All the micronutrients used in this study are common additions to many

fertilizers (Molina et al. 2009). Over application of fertilizers and subsequent runoff may be a

significant source of trace metals in freshwater systems as well as N and P. This risk could be

minimized through more targeted application of fertilizers or by increasing vegetation in the

riparian zone to act as a buffer for micronutrient inflows, which are already effective measures

for reducing macronutrient inflows (Aguiar et al. 2015).

Many trace metals (such as Co, Cu, Fe, Mn and Zn) can be released from sediments under anoxic

conditions caused by thermal stratification (Shipley et al. 2011). These micronutrients can

become available to cyanobacteria who may vertically migrate to nutrient-rich hypolimnial

waters (Molot et al. 2014; Bormans et al. 1999; Wagner and Adrian 2009), particularly in

shallow reservoirs such as Mannus Lake. Further, when the water column mixes after periods of

thermal stratification upwelling occurs, increasing the availability of dissolved micronutrients in

surface waters. Breaking down or supressing the formation of thermal stratification via

maintaining high flow velocities in rivers or by installing mixers (such as fans or bubble plumes)

are commonly used to manage blooms in systems where cyanobacterial buoyancy mechanisms

are a primary driver of their dominance (Mitrovic et al. 2011; Visser et al. 2016; Bormans et al.

2016). These mixers may also be effective in reducing sediment-derived dissolved

micronutrients in systems prone to cyanobacterial blooms by preventing anoxic conditions at the

water-sediment interface.

2.5.3 Conclusion

This study has provided insight into the extent of micronutrient limitation of cyanobacterial

growth in Australian freshwater systems and how the phytoplankton community changes in

response to trace metal additions. We hypothesised that micronutrients will be a limiting factor

of cyanobacterial growth in some freshwater systems. Two sites out of seven exhibited signs of

micronutrient limitation. Both of these sites had high cyanobacterial biovolume at the onset of

38

the bioassays, suggesting that micronutrients may become limiting during high competition for

nutrient assimilation during bloom events. This suggests that micronutrient trace metals can

regulate the severity of cyanobacterial blooms in some freshwater systems. Micronutrients also

influenced phytoplankton community structure, supporting our second hypothesis. At both sites

showing micronutrient limitation of cyanobacteria, the addition of the trace metal mixture

resulted in higher proportion of cyanobacteria compared to the control, suggesting that

micronutrients can not only influence cyanobacterial biovolume but also their ability to compete

with other phytoplankton. These results may have important implications for the management of

micronutrients and cyanobacterial blooms in freshwater systems.

39

Chapter 3: The role of nutrients, micronutrients and

thermal stratification in promoting cyanobacterial blooms:

A case study of Mannus Lake, New South Wales

3.1 Abstract

Nutrient dynamics play a key role in structuring the phytoplankton community and regulating the

growth of harmful cyanobacteria. However, our understanding of dynamics between

micronutrients and cyanobacterial growth is limited. Over two summer periods we performed

regular monitoring of Mannus Lake, a small freshwater reservoir in South-Eastern Australia that

regularly undergoes dense cyanobacterial blooms. We sought to understand the causes of the

bloom events, with particular emphasis on understanding micronutrient dynamics within the

system and their role in bloom formation. High density blooms of Chrysosporum ovalisporum

occurred in both summers during periods of persistent thermal stratification. During these

periods there was strong evidence of sediment release of dissolved micronutrients (Ca, Co, Fe,

Mn, Mo, Mg) into the hypolimnial water which appeared to be utilized by C. ovalisporum in

some instances. A strong correlation between Co, Fe and Mn was observed in the hypolimnial

water which further supports micronutrient release via reduction of Mn and/or Fe

(oxyhydr)oxides in the sediments. In both summers, following the C. ovalisporum blooms cf.

Microcystis sp. dominated the phytoplankton community under less stratified conditions. The

two creeks that flow into Mannus Lake did not appear to contribute a large portion of the lake’s

micronutrient supply. While there was no direct evidence of micronutrient limitation of

cyanobacterial growth, multivariate community analyses indicated some positive relationships

between the availability of the micronutrients Co and Mg (and potentially Mo and Ca) with the

growth of the bloom forming cyanobacteria at Mannus Lake. Micronutrients likely play a

secondary role in regulating bloom severity when biovolume is very high.

40

3.2 Introduction

Cyanobacterial blooms are a primary management concern in freshwaters, largely due to the

production of a range of harmful secondary metabolites known as cyanotoxins (Landsberg

2002b; Baptista and Vasconcelos 2006; Pearl and Fulton 2006; Drobac et al. 2013). Cyanotoxins

have been implicated in animal illness and death, as well as a range of human health conditions,

ranging from skin irritation to cancer, liver damage and neurodegenerative diseases (Carmichael

2001; Ou et al. 2012; Holland and Kinnear 2013). Freshwater resources such as drinking water

and irrigation reservoirs can be rendered unusable during and after bloom events, causing

additional economic damage (Bowling 1994; Bormans et al. 1997; Falconer 2001).

Cyanobacterial blooms are complex events and are often driven by a combination of multiple

factors (Heisler et al. 2008; O’Neil et al. 2012). Understanding how these factors interact and

influence bloom dynamics under field conditions is essential for effective freshwater

management strategies.

Thermal stratification, the formation of distinct layers in the depth profile of a water body, is

often a trigger for cyanobacterial blooms (Bormans et al. 1997; Mitrovic et al. 2003; Mitrovic et

al. 2011). Cyanobacteria possess gas vacuoles which allow them to regulate their vertical

position in the water column (Bormans et al. 2001; Carey et al. 2012). When turbulent mixing is

reduced under thermal stratification, cyanobacteria can maintain position within the upper layer

(epilimnion) where light availability is highest. This is particularly advantageous in turbid water

where light penetration is low, allowing cyanobacteria to effectively ‘shade out’ photosynthetic

competitors (Paerl and Otten 2013). In addition to influencing phytoplankton community

dynamics, thermal stratification can also affect nutrient availability, such as nitrogen and

phosphorus, and also micronutrients such as iron, cobalt, molybdenum and many others (Molot

et al. 2014). Anoxic conditions in bottom hypolimnial waters under periods of extended

persistent thermal stratification can promote sediment release of nitrogen and phosphorus

(Hickey and Gibbs 2009; Loh et al. 2013; Müller et al. 2016) and essential micronutrients

(Baldwin and Williams 2007; Baptista et al. 2014). Some cyanobacteria appear to migrate into

the nutrient-rich hypolimnial water to access this source (Ganf and Oliver 1982; Molot et al.

2014) and vertical mixing of the water column through high wind or inflow events can ‘upwell’

41

these nutrients into the surface waters (Corman et al. 2010) and can stimulate cyanobacterial

blooms (Bormans et al. 2005).

The role of phosphorus and nitrogen in stimulating cyanobacterial blooms is well documented

(Dignum et al. 2005; Pearl and Fulton 2006). Far less is known about the role of micronutrient

trace metals in cyanobacterial bloom formation, although there is growing evidence that

micronutrients can limit cyanobacterial growth and regulate the severity of blooms. For example,

iron is most regularly observed to limit or co-limit cyanobacterial growth, as demonstrated in

several culture experiments (Lukac and Aegerter 1993; Li et al. 2009; Harland et al. 2013; Fujii

et al. 2016) and field studies (Hyenstrand et al. 2001; Karlsson-Elfgren et al. 2005; Wever et al.

2008; Molot et al. 2010; Sorichetti et al. 2014; Yeung et al. 2016). Other trace metals such as

cobalt, copper, manganese and molybdenum have also exhibited the capacity to limit the growth

of cyanobacteria (Downs et al. 2008; Glass et al. 2010; Harland et al. 2013; Polyak et al. 2013;

Zhang et al. 2019), however these metals have received much less attention than iron (Facey et

al. 2019a). Currently, the role of micronutrients in bloom formation is rarely considered in

freshwater management. Understanding micronutrient fluxes in freshwater systems and how

these relate to cyanobacterial blooms and phytoplankton community structure may be an

important consideration when investigating bloom triggers in lakes and reservoirs, and

developing effective management options.

Mannus Lake is a reservoir frequently subjected to cyanobacterial blooms. A previous study at

this location demonstrated that a micronutrient mixture (containing iron, cobalt, molybdenum,

manganese, copper and zinc) greatly increased cyanobacterial biovolume during a high-density

bloom in February 2018 (Chapter 2). This study investigates micronutrient levels in the Mannus

Lake system, as well as other factors that commonly influence cyanobacterial blooms, such as

macronutrients, thermal stratification and light availability. Understanding the factors causing

cyanobacterial blooms in this reservoir may provide broader insight into their causes and the role

of micronutrients. Further, few studies examine the sources of micronutrients in freshwater

systems. This study aims to (1) identify the causes of cyanobacterial blooms in Mannus Lake; (2)

quantify the micronutrient inflows from upstream creeks, the catchment, and within-dam sources

and (3) identify the causes of changes in phytoplankton community structure. We hypothesise

that (1) the availability of some dissolved micronutrients may limit cyanobacterial growth during

42

dense blooms where there is high competition for nutrient uptake; (2) thermal stratification and

subsequent anoxia of benthic sediments will be a significant source of dissolved nutrients and

micronutrients at Mannus Lake; (3) high inflow events from upstream creeks will be significant

sources of dissolved nutrients and micronutrients at Mannus Lake.

3.3 Materials and Methods 3.3.1 Study sites

Mannus Lake (35°48’S, 147°58’E) is a shallow artificial reservoir in South-Eastern NSW,

Australia with a capacity of ~2350 ML. The system has lentic characteristics, with an

approximate area of 0.65 km2 and a maximum depth of ~6 m. Upstream of the dam are a tight

network of streams originating from elevated margins of the catchment which eventually

combine and flow into two larger watercourses Munderoo Creek and Mannus Creek which

eventually enter Mannus Lake. Anthropogenic disturbance in the catchment is moderate, with

land use primarily composed of grazing, plantation forest and native forest. The geological

setting is largely foliated granite, leucogranite, adamellite, granodiorite and tonalite. The Mannus

system has high economic value for the area, where there is a dependence on water extraction for

pasture, cereals, vineyards and fruit. The lake is commonly used for recreational activity and

fishing.

43

Figure 3.1: Location of Mannus Lake and study sites. (1.) Mannus Lake outlet, (2.) Mannus Lake mid-dam, (3.) Mannus Creek, (4.) Munderoo Creek.4

Two study sites were selected on Mannus Lake, as well as a site each on the inflowing Mannus

Creek and Munderoo Creek. Site 1, the dam outlet, is the deepest (~6 m) and most protected.

Whereas Site 2, mid-dam, is shallower (~4 m) and more exposed. Mannus Creek and Munderoo

Creek were sampled upstream of the dam. Both sites are shallow (typically ~1 m) (Figure 3.1).

Mannus Lake

Mannus Ck

Munderoo Ck

Mannus Ck

1.

2.

3.

1.

4.

44

3.3.2 Sample collection

Thermistor chains were installed at both dam sites in December 2018 to monitor thermal

stratification at 30-minute intervals. Temperature loggers (HOBO Pendant UA-001-64) were

placed at the following depths: Surface, 0.5 m, 1 m, 1.5 m, 2 m, 3 m, 4 m, 5 m, 6 m. From

October 2019 dissolved oxygen loggers (D-OptoLogger, Zebra-Tech) were installed alongside

the thermistor chain at both sites. Oxygen loggers were placed just below the surface and above

the sediment-water interface. Routine monitoring of phytoplankton, dissolved nutrients,

dissolved micronutrients, secchi depth and phytoplankton was conducted approximately

fortnightly throughout the warmer months (October to the end of May) and was reduced to

approximately monthly through cooler months (June to September). A depth profile of

physicochemical data (dissolved oxygen, pH, conductivity, temperature) with 1 m intervals was

collected on each sampling trip using a multiparameter water quality sonde probe.

At the dam sites, duplicate samples for nutrients (NOx, ammonia and filtered reactive phosphate),

dissolved micronutrients, turbidity and chlorophyll a were collected directly from the surface

water. Nutrient and micronutrient samples were also collected from the bottom water (~0.5 m

above the sediments) using a prerinsed Van Dorn sampler. Duplicate depth integrated

phytoplankton samples were taken from the top 1.5 m and preserved with Lugol’s iodine. Grab

samples were taken from 10cm below the surface of inflowing creeks due to their shallow depth.

Discharge data was only available for Mannus Creek from the upstream gauging station at

Yarramundi.

3.3.3 Nutrient sampling and analysis

Water samples (50mL) were filtered through a pre-rinsed 0.45 μm cellulose acetate syringe filter

(Sartorius) and frozen immediately. Filtered Reactive Phosphate (FRP) and nitrate and nitrite

(NOx) concentrations were determined photometrically using Flow Injection Analysis on a

QuikChem 8500 Lachat nutrient analyser using the operating conditions recommended by the

manufacturer. For analysis, samples were slowly warmed to room temperature. FRP was measured

by the reduction of ascorbic acid using the molybdate blue colorimetric method (Murphy and Riley

1962). NOx was determined following reduction by a cadmium column using the sulphanilamide

45

method (APHA, 1998). Ammonia concentrations were measured with a handheld colorimeter

(Hanna) on site using an adaptation of the Nessler method (Jeong et al. 2013).

3.3.4 Micronutrient analysis

Micronutrient samples were prepared by filtering 25 mL of water sample through a 0.45 μm

cellulose acetate syringe filter (Sartorius) prerinsed with 50 mL of 10% nitric acid followed by

100 mL milli-Q water. Samples were collected in 50 mL falcon tubes presoaked in 10% nitric

acid. Following collection, samples were immediately refrigerated and acidified with ultra-pure

nitric acid to 0.2% v/v and refrigerated within 2 days. The concentration of metals in the filtered

solution was analysed with a combination of inductively coupled atomic emission spectrometry

(ICP-AES) (Varian 730 ES) and inductively coupled plasma mass spectrometry (ICP-MS)

(Agilent 7500 CE). The spectrometers were operated according to the standard operating

procedures outlined by the manufacturer. The instruments were calibrated using matrix-matched

standards. At least 10% of samples were conducted in duplicate to ensure the precision of the

analyses. To check for potential matrix interferences at least 10% of samples had spike

recoveries performed.

3.3.5 Phytoplankton identification and enumeration

Phytoplankton were identified and enumerated at 200 times magnification using a light

microscope (Olympus BX41) and Sedgwick-Rafter counting chamber. If required, samples were

concentrated 5x prior to counting by settling in 50 mL measuring cylinders for 24 hours. The

upper 40 mL was removed after checking all phytoplankton had settled and were no longer

present in the upper layer. Phytoplankton taxa were identified to a genus level. Counting

precision was performed to ±10% with at least 100 units of the dominant taxa counted following

Hötzel and Croome (1999).

3.3.6 Chlorophyll a extraction and analysis

200 mL of sample water was filtered on site via vacuum filtration onto GFC glass fibre filters

(Whatman) and frozen for preservation. Chlorophyll a was analysed according to the method

described by (Mueller and Mitrovic 2014). The glass fibre filters were extracted in 10 mL 90%

ethanol heated in a 75°C water bath for 10 minutes. Unwanted filtered material was removed by

46

centrifuging at 3000 rpm for 10 minutes. The supernatant was analysed immediately using a

Varian Cary 50 Bio UV Spectrophotometer at wavelengths 665 nm and 750 nm.

3.3.7 Statistical analysis

Temperature profiles were plotted in SigmaPlot 12.5 using the average weekly water temperature

at each depth. Regression analysis was performed with car (Fox and Weisberg 2019) and plots

were created using ggplot2 (Wickham 2016). Redundancy analysis was performed using

CANOCO version 4.5 to assess environmental factors that influenced phytoplankton density and

community structure (Braak and Šmilauer, 2002). The explanatory environmental variables were

selected using automatic forward selection. The selected variables were nutrient concentrations

(NOx, FRP, NH3), micronutrient concentrations (iron, manganese, cobalt, magnesium), thermal

stratification (measured as the difference between the average daily temperature at the surface

and the bottom with a lag factor of seven days), Secchi depth and average daily surface water

temperature and discharge. Calcium and molybdenum were removed from the analysis due to

their strong correlation with magnesium. Monte-Carlo permutation (999 permutations without

restriction) was used to assess the significance of canonical axis and environmental variables on

the phytoplankton community at the Outlet site. The 12 genera which were present in the highest

average biovolume throughout the study were selected for analysis.

3.4 Results

Cyanobacteria regularly dominated the phytoplankton community during the warmer months in

both 2018-19 and 2019-20 (Figure 3.2). In both years two distinct cyanobacterial blooms

occurred separated by a brief period in which a highly diverse phytoplankton community

persisted, composed of diatoms, chlorophyta, euglenoids, among others. The summer blooms

occurred between approximately December – February and were dominated by the potentially

toxic filamentous cyanobacterium Chrysosporum ovalisporum. This bloom reached the highest

biovolumes, at times ~80 mm3/L (Figure 3.3). The second cyanobacterial bloom occurred

between approximately February – May and was dominated by cf. Microcystis sp.. This late-

season bloom occurred both years and was smaller in magnitude compared to the C. ovalisporum

bloom, although at times it reached biovolumes as high as 10 mm3/L. Cyanobacteria were rarely

observed from July until the end of October, during which time dinoflagellates, cryptomonads

47

and diatoms were common. In the Mannus Creek and Munderoo Creek inflows, cyanobacterial

biovolume remained low throughout the study period with very minimal C. ovalisporum and cf.

Microcystis sp. present (Appendix B, Figure B2).

Figure 3.2: Composition of key phytoplankton community groups throughout the study period at Outlet (top) and mid-dam (bottom).5

48

Figure 3.3: Time series data illustrating the biovolumes of total cyanobacteria and two key bloom-forming genera: Chrysosporum ovalisporum and cf. Microcystis sp. at the outlet (top) and mid-dam (bottom) sites.6

Persistent thermal stratification occurred between approximately October until late March, and

during much of this time Chrysosporum ovalisporum was the dominant taxon in the

phytoplankton community (Figure 3.4). The outlet site underwent the most significant

stratification due to its greater depth whereas at the shallower mid-dam site diel mixing could

occur more frequently. Mixing events occurred sporadically in summer (Figure 3.4; Appendix B,

Figure B3) but the lake rapidly reverted to a persistently stratified state until late March when

stratification was less pronounced, after which the phytoplankton community was generally

dominated by cf. Microcystis sp. (Figure 3.3). While stratification did begin to reform for short

periods of time during the cf. Microcystis bloom, it was punctuated by a series of mixing events

that prevented strong, persistent stratification from forming. Discharge into the dam from

upstream Mannus Creek was low throughout the summer months, particularly in 2019-20

(Figure 3.5).

49

Figure 3.4: Temperature profiles from the outlet (left) and mid-dam (right). Thermal stratification is evident when there is a strong vertical colour gradient. Plotted from weekly average temperatures at each depth.7

Figure 3.5: Discharge from Mannus Creek, measured at the Yarramundi gauging station upstream of Mannus Lake.8

50

Chrysosporum ovalisporum dominated the phytoplankton community under thermally stratified

conditions. This species demonstrated the ability to utilise buoyancy mechanisms to migrate to

the surface waters of Lake Mannus. The highest concentrations of C. ovalisporum were found at

1 m depth, gradually declining with depth to the bottom (Figure 3.6).

Figure 3.6: Vertical cell concentrations of Chrysosporum ovalisporum at 1 m intervals during thermal stratification on 16th December 2019 at the outlet site.9

During both the C. ovalisporum and cf. Microcystis sp. blooms the dissolved oxygen

concentration of surface waters fluctuated greatly, reaching very high levels throughout the

afternoon when photosynthesis was occurring, but reducing in the night and early morning when

algal respiration dominated (Figure 3.7). Following the breakdown of the cf. Microcystis sp.

bloom in April 2020, these large diurnal fluctuations ceased. Anoxia regularly developed in the

hypolimnion during the persistent thermal stratification period and was anoxic (or close to

anoxic) for all sampling visits from the 11th December 2018 to the 31 January 2019 (Appendix

B, Table B1). Following a series of mixing events in early February 2019 (Figure 3.4; Appendix

B, Figure B3), hypolimnial oxygen levels increased by mid-February, coinciding with the onset

of the cf. Microcystis bloom (Figure 3.3). Similarly, in summer 2019/20 bottom waters were

51

anoxic in sampling trips from mid-December until early February when a small mixing event

occurred and dissolved oxygen levels increased.

Figure 3.7: Dissolved oxygen concentrations in the surface and bottom waters from the outlet site.10

FRP concentrations were generally within the range of a mesotrophic reservoir throughout much

of the study period at all sites, although several higher concentration spikes occurred at the outlet

site (Figure 3.8). Some of these corresponded with inflow events from upstream creeks which

had elevated FRP concentrations, such as January 2019 and July 2019 (Figure 3.10). Others,

such as April 2019, corresponded with mixing events following a period of persistent thermal

stratification (Figure 3.4). NOx concentrations were more variable throughout the year and at all

sites they were high throughout much of the winter period and decreased in summer during the

blooms. Ammonia was at high concentrations during the 2018/19 bloom events, particularly in

the hypolimnion. Munderoo Creek and Mannus Creek generally had lower ammonia

concentrations compared to the lake sites.

Iron, cobalt and manganese concentrations followed similar trends within the dam throughout the

study period (Figure 3.8; Figure 3.9). All three elements were elevated during December-January

52

2018/19 and showed evidence of sedimentrelease during periods of persistent stratification at the

outlet site. Magnesium, calcium and molybdenum also followed similar trends to each other, and

were notably higher in the summer period than the winter period at the dam sites. Munderoo

Creek had consistently higher concentrations of iron, cobalt, molybdenum, magnesium and

calcium compared to Mannus Creek (Figure 3.10). The pH was circumneutral to alkaline at the

outlet site at all sampling dates, ranging from ~6.8 to ~10 and was generally lower in the bottom

water than the surface water. Maximum pH in the surface water coincided with the dense

cyanobacterial bloom in summer 2018-19, whereas the minimum occurred in winter months

(Figure 3.11).

53

Figure 3.8: Filtered nutrient and micronutrient concentrations at the outlet site throughout the study period. Samples were taken from the bottom water (blue) and surface water (red). Error bars are standard error of the mean.11

54

Figure 3.9: Filtered nutrient and micronutrient concentrations at the Pontoon site throughout the study period. Samples were taken from the bottom water (blue) and surface water (red). Error bars are standard error of the mean.12

55

Figure 3.10: Filtered nutrient and micronutrient concentrations at the upstream creeks Mannus Creek (blue line) and Munderoo Creek (red line). Error bars are standard error of the mean.13

56

Figure 3.11: pH of surface and bottom waters measured at the outlet site.14

The relationships between the concentration of dissolved nutrients/micronutrients in the surface

waters and the corresponding biovolume of the two dominant cyanobacterial genera are

displayed in Figure 3.12. Notably, the highest concentrations of FRP, NOx and ammonia often

did not correspond to higher cyanobacterial growth. High biomass could be maintained under

low FRP and NOx water column concentrations. No growth of cf. Microcystis or Chrysosporum

was observed below 0.1 ug/L molybdenum. Similarly, higher cyanobacterial growth was

observed during periods of higher magnesium and calcium concentrations. In both cases, there

were differences in the response of the two genera to micronutrient concentrations, where cf.

Microcystis sp. was only observed under higher concentrations compared to C. ovalisporum.

Both C. ovalisporum and cf. Microcystis sp. were present at a wide range of phosphorus, NOx

and iron concentrations.

57

Figure 3.12: scatter plots displaying relationship between dissolved nutrients/micronutrients and biovolume of Chrysosporum ovalisporum (red circles) and cf. Microcystis sp. (blue squares).15

The relationships between various dissolved micronutrients and macronutrients measured at the

outlet site are illustrated in Figure 3.13. Notably, in the surface water there were strong positive

correlations between many dissolved micronutrients, such as molybdenum, magnesium and

calcium. Manganese and cobalt were also strongly correlated. In the bottom waters there were

58

strong positive relationships between cobalt and both iron and manganese, as well as between

calcium and molybdenum.

Figure 3.13: Correlation plots illustrating the R values of various dissolved nutrients and micronutrients in the surface water (left) and bottom water (right). Red circles indicate a positive relationship ≥0.7 and blue circles indicate a negative relationship ≤-07.16

Redundancy analysis was performed to assess the relationships between various environmental

factors and the phytoplankton community at the outlet site. Redundancy analysis explained 83%

of variation in the phytoplankton community structure and density at the outlet site (Figure 3.13).

The first canonical axis explained 81.7% of variation. Thermal stratification (with a 7-day lag)

had the largest effect on phytoplankton community structure (RDA: p-value = 0.006, λ = 0.23)

and showed a strong positive relationship with Chrysosporum ovalisporum. To a lesser extent

cobalt concentration was also positively associated with C. ovalisporum biovolume (RDA: p-

value = 0.042, λ = 0.05). cf. Microcystis was not strongly influenced by stratification and was

positively correlated with magnesium (and likely calcium and molybdenum which were removed

from the analysis due to their strong relationship with magnesium). Secchi depth had a

significant effect on the phytoplankton community (RDA: p-value = 0.010, λ = 0.19), and had a

positive association with diatoms and chlorophyta (along with NOx and NH3). Iron (RDA: p-

59

value = 0.002, λ = 0.15) and manganese (RDA: p-value = 0.018, λ = 0.07) also had a significant

effect on the structure of the phytoplankton community.

Figure 3.14: The ordination diagram for redundancy analysis (RDA) results at the outlet site. Stratification refers to the difference between surface water and bottom water temperature with a 7-day lag period. Temperature refers to the daily average surface water temperature. Species are 1. Chrysosporum sp., 2. cf. Microcystis sp., 3. Dolichospermum sp., 4. Fragillaria sp., 5. Aulacoseira sp., 6. Trachelomonas sp., 7. Peridinium sp., 8. Chroomonas sp., 9. Synedra sp., 10. Cosmarium sp., 11. Cyclotella sp., 12. Cryptomonas sp..17

3.5 Discussion

This study aims to describe the environmental factors influencing cyanobacterial blooms and

phytoplankton community structure at Mannus Lake, NSW, Australia. Micronutrients were a

focus of this study as they were previously demonstrated to favour community dominance of

60

cyanobacteria and had a positive effect on cyanobacterial biovolume in-situ (Chapter 2).

However, the role of micronutrients is complex and requires a detailed analysis of other

physicochemical parameters that influence or interact with micronutrient availability and algal

growth such as thermal stratification, light availability, macronutrients and the water balance of

Mannus Lake (combined effect of catchment rainfall, base flow contributions, evaporation

losses, downstream flow and discharge into the lake from the inflowing creeks).

Potentially toxic cyanobacteria regularly dominated Mannus Lake during the summer periods of

2018-19 and 2019-20. High-density blooms were apparent throughout the reservoir, forming

extensive scums and far exceeding recreational guidelines. In both years and bloom periods, the

phytoplankton community was dominated by Chrysosporum ovalisporum in early summer when

persistent thermal stratification was evident, followed by cf. Microcystis sp. later in the season

when mixing events were common and thermal stratification was not as persistent. The inflowing

Munderoo Creek and Mannus Creek had cyanobacteria in low concentrations, indicating that

blooms were not being transported from upstream and were forming within the reservoir

(Appendix B, Figure B2).

3.5.1 Nutrient and micronutrient dynamics in upstream creeks

Nutrient and micronutrient concentrations were measured in the inflowing Mannus Creek and

Munderoo Creek to ascertain their contribution to the nutrient composition of Mannus Lake. The

micronutrients that were chosen for examination were iron, cobalt, molybdenum, manganese,

magnesium and calcium as their role as micronutrients or essential cofactors has been defined

(Raven et al. 1999; Cavet et al. 2003; Vrede and Tranvik 2006; Glass et al. 2010; Shi et al. 2013;

Rodriguez and Ho 2015). (Micro)nutrients were generally low in both creeks, not notably higher

than those in Mannus Lake. The creeks are unlikely to contribute a large proportion of the lakes

total (micro)nutrient influx under normal flow conditions. Many (micro)nutrients (such as

phosphorus, iron, cobalt and molybdenum) were notably higher in Munderoo Creek than Mannus

Creek (Figure 3.10), possibly due to a high level of organic matter in Munderoo Creek.

However, Munderoo Creek rarely reached high discharge rates, based on anecdotal observations.

Unfortunately, no gauging stations are present on Munderoo Creek to obtain accurate long-term

flow data, but waters were visibly stagnant during the study period. High inflow events occurred

61

from upstream Mannus Creek, for example in July 2019 and June 2020. Discharge was recorded

as high as ~585 ML/Day, representing approximately 25% of the lake’s total capacity. Given the

scale of these inflows relative to the capacity of the dam, a part flushing of the lake likely

occurred. This is supported by the rapid decrease in calcium and magnesium concentrations in

Mannus Lake following high flows (Figure 3.8; Figure 3.9), likely caused by dilution effects.

Alternatively, NOx tended to increase during high flow events, suggesting mobilization of

terrestrial sources from the catchment. Generally, Mannus Creek is unlikely to be a large source

of (micro)nutrients given the low concentrations observed in this study. This evidence suggests

that in-dam processes are predominantly responsible for regulating (micro)nutrient availability.

3.5.2 Nutrient and micronutrient dynamics within Mannus Lake

The concentrations of many dissolved nutrients and micronutrients increased upon the

senescence of the large C. ovalisporum bloom, likely due to the breakdown of cells and release

of intracellular contents. Trends in FRP, NOx, cobalt, manganese, magnesium, calcium and

molybdenum concentrations all showed probable evidence of liberation of nutrients from lysing

cells, denoted by a notable increase co-occurring with bloom breakdown. Given that mixing

events coincided with, and likely caused, the decline in C. ovalisporum, upwelling of

hypolimnial nutrients may also be responsible for this increase, although nutrient data suggests

this was minimal in the 2019/20 summer as there were minimal differences between nutrient

concentrations in the surface and hypolimnial waters (Figure 3.8; Figure 3.9). This suggests an

important role of cyanobacteria in (micro)nutrient cycling in freshwater lakes that undergo large

blooms. Some cyanobacteria can access certain (micro)nutrients which are not available to other

phytoplankton. For example, heterocystous cyanobacteria are able to assimilate dissolved

atmospherically-derived nitrogen and so are unlikely to be limited by the availability of fixed

nitrogen (but nitrogen fixation may be resource limited by trace metals) (Kerby et al. 1987).

High efficiency uptake systems, luxury uptake and buoyancy regulation also allow cyanobacteria

to access (micro)nutrients such as phosphorous and iron from hypolimnial waters and transport it

to surface waters via vertical migration (Cottingham et al. 2015). For example Chrysosporum

flos-aquae can release phosphorus into surface waters during diurnal vertical migrations

(Jacobsen 1994). The production of metallophores, biogenic ligands that facilitate the uptake of

metals, are another mechanism by which cyanobacteria can access otherwise non-bioavailable

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resources. When blooms breakdown, these resources are released into the water column where

they become available to the rest of the ecosystem (including other cyanobacteria). This may

have been another factor that promoted the development of the cf. Microcystis bloom. However,

given that these events coincided with mixing of the water column, inflows and weakening

thermal stratification; it is difficult to untangle these multiple potential nutrient sources.

Phosphorus levels were moderate throughout the study, generally between 0-35 ug/L and high

cyanobacterial biomass was regularly observed at FRP concentrations <10 ug/L. This suggests

that much of the available phosphorus was assimilated into cyanobacterial cells but also that

these blooms can be sustained by relatively low levels of dissolved phosphorus. NOx levels were

quite variable through the year and were generally higher during non-bloom periods compared to

bloom periods, which may indicate denitrification or utilization by phytoplankton. As observed

with FRP, C. ovalisporum and cf. Microcystis could maintain high biomass even when dissolved

NOx concentrations were low (Figure 3.12). As C. ovalisporum can assimilate nitrogen via a

reduction of atmospheric N2, it is unsurprising that they can persist under low NOx

concentrations. Conversely, cf. Microcystis is non-nitrogen fixing but may have been utilizing

ammonia, a highly bioavailable form of nitrogen (Blomqvist et al. 1994), and has been shown to

grow under low nitrogen concentrations (Mitrovic et al. 2000).

These results reinforce that cyanobacteria can form extremely dense blooms even in mesotrophic

lakes. Cottingham et al. (2015) models the effect of cyanobacterial nitrogen fixation and

liberation of hypolimnial nutrients on freshwater lakes. When cyanobacteria access these nutrient

sources they can initiate a positive feedback loop that accelerates anoxia of lake sediments and

further nutrient release. This can cause a shift from a low/moderate nutrient state to a high

nutrient state. According to this model, there is a risk that the proliferation of cyanobacteria in

Mannus Lake may facilitate a change in the lake system in which the current nutrient conditions

will worsen and transition to a turbid, eutrophic state which may exacerbate bloom events.

High biovolumes of C. ovalisporum and cf. Microcystis sp. were observed during periods of low

iron, cobalt and manganese concentrations (Figure 3.12), which may indicate that these

micronutrients were being utilised and incorporated into cyanobacterial cells. This is reinforced

by notable decreases in concentration during the peak of the C. ovalisporum bloom, particularly

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in summer 2018-19 and an increase in concentrations after bloom senescence, possibly due to

release from cells. These micronutrients may have been the cause of the metal limitation of C.

ovalisporum growth that was observed in February 2017 (Chapter 2). Interestingly, C.

ovalisporum and cf. Microcystis sp. biovolume was low below 0.1 ug/L molybdenum (Figure

3.12), which may indicate a threshold concentration required for optimal growth.

3.5.3 Nutrient release from anoxic sediments at Mannus Lake

A major source of internal nutrient loading is via the release from enriched sediments. Anoxia,

low pH and/or bacterial catalysis can stimulate the release of phosphorus, nitrogen and

micronutrient trace metals (Baldwin and Williams 2007; Özkundakci et al. 2011; Shipley et al.

2011; Müller et al. 2016). At Mannus Lake, anoxic conditions were common and persistent in

the hypolimnial waters during periods of stratification, influencing the redox state of the

sediments and subsequently, (micro)nutrient release (Figure 3.7, Appendix B, Table B1). The

release of (micro)nutrients from Mannus Lake sediments is supported by a distinctly higher

concentration within the hypolimnial waters than surface waters during periods of stratification

and anoxia. This was observed for cobalt, manganese, iron, magnesium, molybdenum, calcium

and ammonia (Figure 3.8; Figure 3.9). This is particularly clear during December-February

2018/19 when thermal stratification was well established, and anoxic conditions were present in

the hypolimnial water (Figure 3.4; Appendix B, Table B1). Furthermore, there were strong

correlations between the concentration of some dissolved micronutrients in the hypolimnial

water. For example, cobalt, iron and manganese concentration were positively correlated in the

hypolimnial waters (Figure 3.13). This is likely caused by the reductive dissolution of cobalt-

bound manganese and/or iron (oxyhydr)oxides in the sediments into porewater. This has

previously been observed by Lienemann et al. (1997) and Wang et al. (2016) and is particularly

common under anoxic conditions (Heggie and Lewis 1984). Although, given that the half-life of

Fe2+ is in the order of minutes at pH 8.0 in the presence of dissolved oxygen, the

circumneutral/alkaline conditions observed in the bottom waters during stratification would have

likely caused rapid precipitation of any dissolved Fe2+ (Appendix B, Table B1) (Pham and Waite

2008; Shipley et al. 2011).

64

Alternatively, there was no evidence of phosphorus release from anoxic sediments which is

surprising given that there was evidence of sediment-derived iron release (Figure 3.8). A major

pathway for sediment-derived phosphorus dissolution and release is the reduction of iron

oxyhydroxides under anoxic conditions (Amirbahman et al. 2003; Loh et al. 2013). Iron and

phosphorus inputs from lake sediments can be independent from each other, as observed by

Müller et al. (2016) who found that phosphorus release in Grahamstown Dam, Australia was

largely controlled by dissolved organic carbon and that the reduction of iron minerals was not a

major contributor. Like Mannus Lake, Grahamstown Dam did not have large external nutrient

inputs, which may be a defining characteristic of systems with uncoupled iron and phosphorus

release. Alternatively, P may be primarily absorbed to Al hydroxides which are redox-insensitive

(Loh et al. 2013)

Sediment-derived (micro)nutrients are typically concentrated within the dense hypolimnial layer

or at the water-sediment interface during persistent stratification (Xue et al. 1997), however

mixing events can cause upwelling of the nutrient rich hypolimnial waters – thereby increasing

availability to cyanobacteria (Mitrovic et al. 2001; Bormans et al. 2005; Paerl et al. 2011;

Özkundakci et al. 2011; Molot et al. 2014). Following the mixing event in February 2019, the

differences between surface and bottom concentrations were decreased and often the surface

availability increased, indicating upwelling of hypolimnial water. This trend was also observed

in the following summer, although to a lesser extent. These upwelling events that increased

surface (micro)nutrient concentrations, combined with a change in the stability of the water

column were likely factors that caused the switch in community dominance from C. ovalisporum

to cf. Microcystis sp. – which increased notably following these mixing events. The potential for

hypolimnetic upwelling to stimulate cyanobacterial growth has been observed in the Fitzroy

impoundment near Rockhampton, Australia, where a cyanobacterial bloom was partially

attributed to upwelling of nutrient-rich hypolimnial water from anoxic sediments (Bormans et al.

2005). However, the redundancy analysis does not provide a clear explanation for the cause of

the cf. Microcystis bloom. A positive relationship was detected between cobalt concentration and

cf. Microcystis biovolume, which may indicate a role of hypolimnial upwelling, however other

hypolimnial-derived nutrients were not strongly related to cf. Microcystis biovolume, suggesting

65

they were present at concentrations that did not limit cyanobacterial growth. There was only a

relatively weak positive relationship between thermal stratification and cf. Microcystis observed.

3.5.4 Thermal stratification as a driver of change in phytoplankton community structure

Thermal stratification was a primary driver of C. ovalisporum biovolume and community

dominance. C. ovalisporum consistently appeared following the establishment of persistent

thermal stratification and occurred at the highest densities between approximately December to

early February when the water column was very stable. This is reinforced by the results of the

redundancy analysis (Figure 3.14) which showed a strong relationship between thermal

stratification and C. ovalisporum biovolume. Bormans et al. (1997) observed a similar

phenomenon in the Murrumbidgee River, where persistent stratification for a period >14 days

stimulated the establishment of Dolichospermum blooms. The success of C. ovalisporum under

stratified conditions is likely linked to its production of gas vesicles which provide the ability to

regulate their vertical position in the water column during periods of thermal stratification (Carey

et al. 2012). Buoyancy regulation by C. ovalisporum has previously been observed in a similar

system by Porat et al. (2001). This is particularly important in turbid environments where vertical

migration provides access to illuminated surface waters, and may also allow access to

(micro)nutrient rich hypolimnial waters (Ganf and Oliver 1982). Secchi depths were generally

low during periods of high C. ovalisporum biovolume – ranging from 0.2 to 0.7 m at the dam

sites (Appendix B, Table B1). C. ovalisporum was concentrated in the top 2 m of the water

column during stratification (Figure 3.6). Cell concentrations decreased substantially in the lower

4 m, reinforcing its ability to maintain a vertical position at a depth that provides an advantage

against other phytoplankton in the competition for light (Reynolds et al. 1987; Porat et al. 2001).

Stratification broke down and re-established several times throughout the 2019-20 summer

(Figure 3.4, Appendix B, Figure B3). Given that there were no significant inflows during this

period (Figure 3.5), mixing events were likely driven by wind action or isolated rain and storm

events. These events had a large effect on the phytoplankton community. For example, the

mixing of the water column that occurred in mid-late January 2020 coincided with the dramatic

reduction in C. ovalisporum numbers observed in counts from the 6th of February 2020 (199,773

± 35,516 cells/mL on 21st of January, 8,718 ± 1,378 cells/mL on 6th of February at the outlet site)

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(Figure 3.3; Appendix B, Figure B3). Following this mixing event, the water column re-stratified

and C. ovalisporum biovolume increased again, as evident by the small peak in biovolume

around the 2nd of March 2020 (Figure 3.3). This reaffirms that C. ovalisporum is predominantly

successful under stratified conditions. Upon the breakdown of stratification, the C. ovalisporum

bloom rapidly decreases in magnitude. Interestingly, in both summers this resulted in a short

period of high algal diversity (comprising a mixture of diatoms, cryptomonads, chlorophytes and

others). Redundancy analysis (Figure 3.2) suggests that many of these species are positively

related to ammonia, NOx and molybdenum and are negatively related to thermal stratification.

This diverse phytoplankton community was rapidly replaced by a dense cf. Microcystis sp.

bloom, which reached biovolumes as high as 10 mm3/L.

Given the strong relationship between thermal stratification and C. ovalisporum, and lack of

clear evidence of micronutrient limitation of cyanobacterial growth, micronutrients likely play a

secondary role in regulating bloom severity. At Mannus Lake this may only occur when

biovolume is extremely high, as seen in the nutrient enrichment bioassays (Chapter 3.2). Other

factors, such as persistent thermal stratification, which allows C. ovalisporum to exploit the

surface water light environment, appears to play a larger role in stimulating blooms and

structuring the phytoplankton community at these times. The upwelling of (micro)nutrients from

enriched hypolimnial waters may be an important source for the late season cf. Microcystis

bloom, although the shift in community structure is likely a combined effect of nutrient changes,

breakdown of stratification and a changing light environment.

3.5.5 Management implications

Chrysosporum ovalisporum is a relatively new bloom forming cyanobacteria in Australian

waters. It was first observed in Queensland in 1999 (Shaw et al. 1999) but has spread rapidly,

now appearing frequently in the Murray-Darling Basin in South-Eastern Australia (Crawford et

al. 2017). The dominance of C. ovalisporum in Mannus Lake is concerning given its ability to

reach very high densities, particularly under the mesotrophic conditions. Further, C. ovalisporum

can produce the cyanotoxin cylindrospermopsin in Australia and overseas (Shaw et al. 1999;

Quesada et al. 2006; Yilmaz et al. 2008; Messineo et al. 2010; Fadel et al. 2014).

Cylindrospermopsin is an alkaloid that inhibits glutathione, protein synthesis and cytochrome

67

P450 (Runnegar et al. 2002; Van Apeldoorn et al. 2007; Pearson et al. 2010). Further,

Microcystis is widespread and can produce a diverse group of heptapeptides, microcystins, that

preferentially accumulate in the liver where they inhibit catalytic subunits of protein

phosphatases-1 and -2A, cause acute hepatotoxicosis and may promote cancer (Carmichael 2001;

Van Apeldoorn et al. 2007; Pearson et al. 2010; Facey et al. 2019b). Given the potential toxicity

of these taxa combined with the increasing prevalence of C. ovalisporum in Australian systems,

and the widespread nature of Microcystis blooms, it is imperative to develop effective

management strategies to minimize their impact on human health and freshwater ecosystems.

As Chrysosporum ovalisporum blooms regularly occurred with the establishment of strong

persistent thermal stratification, mechanical mixing of the lake may prevent these blooms from

forming. This was effective in a Lake Dalbang, Korea, where artificial destratification through

aeration was effective at shifting the summer phytoplankton community from cyanobacteria

dominated to diatom dominated (Heo and Kim 2004). Artificial destratification at Mannus Lake

should prevent C. ovalisporum from utilising buoyancy regulation to outcompete other

phytoplankton for light. Mixing would likely assist in maintaining oxygenation throughout the

water column and sediments, preventing the reduction of oxidized species into soluble reduced

species and subsequent release into overlying water. This may also assist in suppressing bloom

formation. Although, there is a risk that preventing stratification through artificial mixing could

improve the competitive advantage of Microcystis over other phytoplankton, resulting in earlier

bloom onset and increased biovolume.

Management of nutrient inputs is another technique regularly used to control cyanobacterial

blooms, either through reducing sediment-derived phosphorus or by controlling nutrient sources

in the catchment (Paerl 2018; Li et al. 2018). At Mannus Lake, sediments did not appear to be a

major contributor to phosphorus availability and dense cyanobacterial blooms occurred even at

moderate phosphorus concentrations. Similarly, NOx levels were quite variable through the year

at the lake sites and were low during the 2019/20 bloom of C. ovalisporum. As C. ovalisporum

can assimilate nitrogen via a reduction of atmospheric N2, managing external NOx may not be

effective as this may enhance the environmental niche in which it is successful. Ammonia and

NOx reductions may be more effective at decreasing growth of Microcystis and other non-

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heterocystous cyanobacteria, although this may depend on another method for reducing C.

ovalisporum.

While micronutrient management is rarely considered in bloom mitigation, the observation of

micronutrient regulation of the Mannus Lake Chyrosporum ovalisporum bloom (Chapter 2)

suggests that reducing micronutrient availability may contribute to limiting the severity of this

bloom. Further research is needed in this area to identify which micronutrients were a limiting

factor and to ascertain whether micronutrient concentrations can be reduced to levels which

prevent cyanobacterial bloom formation. Upon further research, some effective management

techniques may be akin to those employed to manage phosphorus and nitrogen inputs. For

example, application of a sediment capping agent which prevent nutrient release from anoxic

sediments or managing micronutrient inputs from the catchment. Artificial mixing of the

reservoir would likely assist in reducing micronutrient loading from sediments in addition to

phosphorus and ammonia.

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Chapter 4: The influence of micronutrients on Microcystis aeruginosa growth and toxin production

4.1 Abstract Microcystis aeruginosa is a widespread cyanobacteria capable of producing hepatotoxic

microcystins. Understanding the environmental factors that influence it’s growth and toxin

production is essential to managing the negative effects on freshwater systems. Some

micronutrients are important cofactors in cyanobacterial proteins and can influence

cyanobacterial growth when availability is limited. However, micronutrient requirements are

often species specific, and can be influenced by substitution between metals or by luxury uptake.

In this study, M. aeruginosa was grown in modified growth media that individually excluded

some micronutrients (cobalt, copper, iron, manganese, molybdenum) to assess the effect on

growth, toxin production, morphology and iron accumulation. M. aeruginosa growth was limited

when iron, cobalt and manganese were excluded from the growth media, whereas the exclusion

of copper and molybdenum had no effect on growth. The limitation of growth by cobalt was

particularly interesting given the presence of cobalamin in the growth media, which suggests that

M. aeruginosa cannot utilise this form of cobalamin or the cobalt contained within the

cobalamin. Intracellular microcystin-LR concentrations were variable and were at times elevated

in treatments undergoing growth limitation by cobalt. A novel relationship in freshwater

cyanobacteria was observed in which intracellular iron was notably higher in treatments grown

in cobalt-deplete media, possibly due to inhibition or competition for transporters, or due to irons

role in detoxifying reactive oxygen species (ROS).

4.2 Introduction Cyanobacterial blooms are common in freshwater systems, and threaten anthropogenically and

environmentally important resources (Sciuto and Moro 2015). A primary driver of

cyanobacterial blooms is high nutrient concentrations, or eutrophication (Heisler et al. 2008).

While the link between phosphorous and nitrogen and cyanobacterial growth is well established

70

(Dignum et al. 2005; Pearl et al. 2006; North et al. 2007; Paerl and Otten 2013; Mueller and

Mitrovic 2014) there are instances where seemingly favourable conditions do not instigate a

bloom, suggesting the importance of additional factors (Bowling 1994). There is growing

evidence that micronutrients can regulate cyanobacterial growth and can act as a limiting factor

(Baptista and Vasconcelos 2006; North et al. 2007; Downs et al. 2008). Up to a third of all

microbe proteins contain a metal cofactor (Huertas et al. 2014), and as such, trace metals are

clearly vital to maintaining cellular functions. In cyanobacteria, metals play a variety of roles,

but are most often associated with photosynthetic electron transport in the thylakoids and the

assimilation of macronutrients nitrogen and phosphorus (Raven et al. 1999; Cavet et al. 2003;

Sunda 2006). Their capacity to limit cyanobacterial growth has been demonstrated by in situ

nutrient enrichment bioassays (Auclair 1995; Vrede and Tranvik 2006; Downs et al. 2008;

Wever et al. 2008; Zhang et al. 2019) and culture studies (Lukac and Aegerter 1993; Li et al.

2009; Molot et al. 2010; Polyak et al. 2013; Fujii et al. 2016). Given the importance of trace

metals as micronutrients as well as their toxicity at high concentrations (Baptista and

Vasconcelos 2006), maintaining a balance in intracellular trace metal quotas is essential for

optimal cellular metabolism (for a comprehensive review of trace metal uptake and transport

pathways see Cavet et al. (2003)). However, little is known about how cyanobacteria respond to

extended periods of low metal availability.

Microcystis aeruginosa is among the most common and widely distributed bloom-forming

cyanobacterial species found in freshwater systems (Zurawell et al. 2005; Mowe et al. 2015;

Harke et al. 2016). M. aeruginosa can produce hepatotoxic microcystins, a group of cyclic

heptapeptides with >250 isomers of varying toxicities (Schatz et al. 2007; Zilliges et al. 2011;

Neilan et al. 2013; Klein 2016; Bouaïcha et al. 2019). Microcystins often accumulate in the liver

and inhibit protein phosphatases PP1 and PP2A, damage membrane integrity, promote oxidative

stress and cause tumours (Codd et al. 2005; Schatz et al. 2007; Schmidt et al. 2014; Facey et al.

2019b). The environmental conditions conducive to increased cyanobacterial toxin production

are still widely debated. There is some evidence that the rate of microcystin production is linked

with various physical and chemical factors, for example macronutrients (Pimentel and Giani

2014), light (Song et al. 1998; Wiedner et al. 2003), pH and temperature (Song et al. 1998;

Neilan et al. 2013). While other studies indicate that toxin production is simply related to cell

71

division and growth (Orr and Jones 1998; Gouvêa et al. 2008). Neilan et al. (2013) reasoned that

while there is a strong correlation between microcystin production and growth rate, a more

complex relationship with certain nutrients and physiochemical conditions exists. An early study

by Lukac and Aegerter (1993) observed that microcystin production was stimulated in response

to suboptimal iron availability and suggested microcystin may assist in the acquisition of metal

ions. Since then numerous studies have examined the relationship between microcystin

production and iron, some of which support the findings of Lukac and Aegerter (1993), for

example Alexova et al. (2011), Yeung et al. (2016) and Sevilla et al. (2008). While others found

a positive relationship between iron concentration and microcystin production (Utkilen and

Gjolme 1995; Amé and Wunderlin 2005; Li et al. 2009). Other trace metals have received

significantly less attention, or in some cases none.

Identifying the environmental conditions stimulating cyanotoxin production and driving the

increase in cyanobacterial blooms is essential to developing effective management strategies

aimed at protecting the ecological and economic value of freshwater systems. The role of

micronutrients in M. aeruginosa bloom formation and toxicity has received little attention,

except for iron. In this study, we aim to determine the importance of certain micronutrients (iron,

cobalt, copper, manganese and molybdenum) for the optimal growth of M. aeruginosa by

excluding them from culture media. Specifically, we aim to determine the effect of low levels of

various micronutrients on (1) M. aeruginosa growth rate (2) cell volume (3) production of the

cyanotoxin microcystin-LR and (4) intracellular accumulation of iron.

4.3 Materials and Methods 4.3.1 Microcystis culturing conditions Batch culture experiments were performed using the toxic Microcystis aeruginosa MASH01-

AO5 (Australian National Algae Culture Collection, Hobart, Tasmania, Australia). Axenic

cultures were maintained in MLA media (Bolch and Blackburn 1996) in an environmental

chamber (Labec, HC-50 environmental chamber). Incubation was at 22°C under 20–25

µmoles/m2/s light with a 14–10 h light-to-dark cycle throughout the long-term maintenance of

the cultures as well as the duration of the experiment.

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4.3.2 Culture media At the onset of the experiment, cells were subcultured into triplicate 700mL sterile plastic culture

flasks (Corning), which had previously been soaked overnight in an acid bath (10% HNO3 (v/v))

and rinsed repeatedly with Milli-Q water. Flasks contained 600mL of filter sterilised MLA

media, modified as described below. Table 4.1 summarises the concentrations of trace metals in

unmodified MLA media (control treatment).

Table 4.1: The composition of unmodified MLA algal growth media. Salts in bold text indicate those examined in this experiment.6

Nutrient/salt Final concentration (mg L-1)

K2HPO4 34.80

NaNO3 170.00

NaHCO3 16.80

CaCl2 29.40

Mg.SO4.7H2O 49.10

H3BO3 2.40

CoCl2.6H2O 0.01

CuSO4.5H2O 0.01

FeCl3.6H2O 1.58

Na2EDTA.2H2O 4.56

MnCl2.4H2O 0.36

Na2MoO4.2H2O 0.006

ZnSO4.7H2O 0.022

Thiamine HCl 0.10

Biotin 5 x 10-4

Cyanocobalamin (B12) 5 x 10-4

In each experimental treatment one micronutrient was excluded from the growth media.

Experimental treatments are summarised below. pH ranged between 7.4 and 7.7 in the growth

media for all treatments.

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a) Control - filter sterilised MLA medium

b) MLA media without CoCl2.6H2O

c) MLA media without CuSO4.5H2O

d) MLA media without FeCl3.6H2O

e) MLA media without FeCl3.6H2O and Na2EDTA.2H2O

f) MLA media without MnCl2.4H2O

g) MLA media without Na2MoO4.2H2O

For each treatment an inoculum of M. aeruginosa was centrifuged at 3500 RPM for 10 minutes

and the supernatant removed. The pellet was resuspended in the appropriate medium for the

treatment. The centrifugation and resuspension steps were repeated to ensure there was no carry

over of original media to the experimental cultures. A cell count was conducted on the washed,

resuspended cells to calculate the volume of inoculum required to achieve an initial cell density

of 104 cell/mL. Once transferred a cell count was performed using a haemocytometer and

cultures were maintained in the conditions outlined above. On day 31, 104 cell/mL of M.

aeruginosa were reinoculated into freshly prepared media to extend the exposure to the

experimental conditions and assess the extent of luxury uptake and storage of micronutrients.

4.3.3 Sampling Every 2-4 days cell counts were conducted via optical density (680nm using Varian Cary 50 Bio

UV Spectrophotometer). The relationship between M. aeruginosa cell count and absorbance at

680nm was previously determined (R2 = 0.98) (Appendix C, Figure C1). Manual cell counts

were performed periodically using a haemocytometer to ensure manual cell counts were closely

aligned with the optical density results.

The nutrient composition of the culture media was sampled on day 0 and 31 by filtering 25 mL

of culture material through a 0.45 μm cellulose acetate syringe filter (Sartorius) prerinsed with 50

mL of 10% nitric acid followed by 100 mL milli-Q water. Samples were collected in acid

washed 50 mL falcon tubes and refrigerated. Within 24 h of collection, samples were acidified

with ultra-pure nitric acid to 0.2% v/v. Samples for intracellular microcystin-LR were taken from

the inoculum on day 0 and days 20, 31, 50 and 60 from the experimental flasks. 10 mL of culture

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material from each replicate was filtered onto a Whatmans GF/C filter paper which was then

stored in a -80 °C freezer. Intracellular iron was also sampled on day 0 from the inoculum and

days 10, 20, 31, 40, 50 and 60 from the experimental flasks. Samples were prepared by

transferring a volume of culture material corresponding to ~5 x 107 cells (or ~0.97 pg dry weight)

into acid washed, pre-weighed 50 mL falcon tubes. Transfers were performed immediately

following a cell count. The culture material was centrifuged at 3000 RPM for 10 minutes to form

a pellet. The supernatant was removed after ensuring the absence of cells by pipetting 1 mL of

solution into a Sedgewick rafter counting chamber for observation using a light microscope

(Olympus BX41). Samples were frozen at -20 °C.

4.3.4 Solution nutrient determination The concentration of nutrients (P, Co, Cu, Fe, Mn, Mo) in the filtered solution was analysed with

a combination of inductively coupled atomic emission spectrometry (ICP-AES) (Varian 730 ES)

and inductively coupled plasma mass spectrometry (ICP-MS) (Agilent 7500 CE). The

spectrometer was operated according to the standard operating procedures outlined by the

manufacturer. The instruments were calibrated using matrix-matched standards. At least 10% of

samples were conducted in duplicate to ensure the precision of the analyses. To check for

potential matrix interferences at least 10% of samples had spike recoveries performed.

4.3.5 Intracellular iron sample preparation and analysis The falcon tubes were weighed to determine the volume of any overlying solution before freeze

drying at 0.1 mbar and -80 °C until all liquid was sublimated from the samples. The dried pellet

was submerged in 500 μL distilled nitric acid and microwaved at 80 °C with a 30 min holding

time (CEM Mars 6). Samples were diluted with 4.5 mL Milli-Q water and transferred to 5 mL

acid washed vials for analysis via ICP-MS and ICP-AES. Analysis was performed using the

instrument procedure outlined above.

4.3.6 Microcystin-LR method Filter papers were freeze dried (Martin Christ, alpha 2-4 LD plus) at 0.1 mbar and -80°C until all

liquid was sublimated from the samples. Extraction was performed with 4 x 2 mL washes of

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75% (v/v) aqueous methanol solution (methanol ≥ 99.9%, Sigma-Aldrich, Castle Hill, NSW)

before sonication in a sonicator bath (Unisonics) for 15 min and centrifugation (Hettich, Rotanta

460R) at 3000 RPM for 5 mins. The supernatant was transferred to a new 10mL centrifuge tube

and dried using a dry block heater (Ratek) at 40°C under nitrogen. If any liquid remained it was

removed by freeze drying. The sample was reconstituted in 1 mL of 10% methanol. A final

filtration step was performed by transferring the sample to a microcentrifuge tube with 0.2 μM

nylon filter (Costar, Spin-X) and centrifuging at 6500 for 5 min (Eppendorf, Centrifuge 5424 R).

The samples were transferred into 2 mL amber vials (Supelco, Sigma-Aldrich, Castle Hill,

NSW).

The LC-MS analysis was performed on Thermo Scientific™ Q EXACTIVE™ high resolution

mass-spectrometer equipped with an electrospray ionization source. The following source

parameters were used in all experiments: a capillary temperature of 272 0C, a spray voltage of

3.5 kV, an auxiliary gas heater temperature of 425 0C, a sheath gas and an auxiliary gas flow rate

of 54 and 14 (arbitrary units). The mass spectrometer was operated in negative ion mode

scanning across the range of m/z 100- 1150. Thermo Xcalibur software (version 3.0.63, Thermo

Fisher Scientific, Inc.) was used for the data analysis.

Chromatographic separation was performed on a Thermo Scientific™ ACCELA™ UPLC

system. LC-MS analysis was performed by the method published by Turner et al. (2018).

Separation was performed by an Acquity UPLC BEH Shield RP18 1.7um, 2.1 x 50 mm column

at temperature 40 0C. Mobile phases were A (ultrapure water + 0.025% formic acid) and B

(acetonitrile + 0.025% formic acid). The LC-MS gradient and flow rate are shown in Table 4.2.

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Table 4.2: LC-MS gradient and flow rate for microcystin-LR analysis.7

Time (min) A% B% μL/min

0.00 98.0 2.0 600.0

0.50 75% 25.0 600.0

1.50 75% 25.0 600.0

3.00 60% 40.0 600.0

4.00 50% 50.0 600.0

4.10 5% 95.0 600.0

4.50 5% 95.0 600.0

5.00 98% 2.0 600.0

100% 0.0 600.0

4.3.7 Cell volume Culture material was placed on a haemocytometer and photographed through a compound

microscope (Olympus BX41). Images were processed using ImageJ software. Cells were

measured when the treatment exhibited signs of growth limitation and were compared to the

control. Treatments that did not exhibit growth limitation were measured at the completion of the

experiment and compared to the control.

4.3.8 Growth rate Specific growth rate was determined according to the following equation.

Growth rate (day -1) = 𝐿𝑛 𝐶2−𝐿𝑛 𝐶1

(𝑇2−𝑇1)

Where C1 is the concentration of cells at time T1. C2 is the concentration of cells at time T2.

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4.3.9 Data analysis A Kruskal-Wallis ANOVA on ranks with Dunn’s test was used to investigate differences in cell

volume. Iron quota was analysed with a One-Way ANOVA and Tukey’s pairwise comparison

was used to determine differences between treatments. Tests were performed using SigmaPlot

12.5 with a significance level of α = 0.05. The PERMANOVA (with Euclidian distances) was

performed using the software PRIMER 7.0. Plots were created using the software R Version

1.2.1335 (R Core Team, 2018).

4.4 Results There were notable decreases in the growth of Microcystis aeruginosa when depleted of iron,

cobalt and manganese (Figure 4.1). Iron starved cultures demonstrated the most severe signs of

growth limitation, becoming limited after 12 days of growth. Without the presence of both iron

and EDTA, M. aeruginosa growth reduced further and was negligable. Cobalt deplete cultures

showed signs of growth limitation after 24 days and manganese after 30 days. There were no

significant differences in growth between treatments starved of copper or molybdenum

compared to the control. Similar trends were observed in specific growth rate (Figure 4.2). There

were no significant differences observed between the control, copper and molybdenum. Iron and

cobalt depletion both caused significant reductions in specific growth rate. Manganese depletion

had a minor effect on growth rate in the first transfer, however after this, growth rate was

significantly decreased compared to the control.

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Figure 4.1: Microcystis aeruginosa growth through time under variable micronutrient conditions. (A) Transfer 1 and (B) Transfer 2. Error bars are standard error of the mean.18

Figure 4.2: Specific growth rate in treatments exposed to depletion of different micronutrients across two transfers. Asterisk denotes significant difference relative to the control of the same transfer (One-way ANOVA: p-value < 0.05).19

* *

*

*

* * *

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There were significant differences between Microcystis aeruginosa cell volume when exposed to

different micronutrient conditions (Kruskal-Wallis ANOVA, p-value <0.01). Figure 4.3

illustrates changes in cell volume under different treatments. Cell volume was measured when

treatments began showing decreased growth compared to the control, or otherwise at the

completion of the experiment. Treatments that exhibited signs of growth limitation were

significantly smaller than control cells. Iron and cobalt depletion caused a ~25% reduction in cell

volume (μ = 22.03 ± 1.27 μm and μ = 20.54 ± 1.06 μm respectively). Manganese deprivation

appeared to have a large effect on cell volume, which decreased ~52% compared to the control

(μ = 17.66 ± 1.14 μm).

Figure 4.3: Scatterplot of cell volume relative to cells in the control treatment. Cell volume was measured once the treatment exhibited a growth limitation and compared to the control cell volume at the same time point. Asterisk denotes significant difference relative to the control. Error bars are ± standard error of the mean.20

Intracellular microcystin-LR (MC-LR) concentrations fluctuated through time and with

treatment (Figure 4.4). The concentration of intracellular MC-LR in the -Fe treatment was

significantly lower than the control after 20 days (PERMANOVA: p-value 0.002) whereas in the

*

*

*

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-Co treatment microcystin-LR concentration was significantly higher than the control after 31

days (PERMANOVA: p-value 0.014). The decreased intracellular microcystin-LR concentration

in the -Fe and the elevated concentration in the -Co treatment both corresponded with notable

limitation of growth in their respective treatments. There were no other statistically significant

differences between treatments and the control at any time points.

Figure 4.4: Changes in intracellular microcystin-LR cell quotas throughout the experiment. Error bars are standard error of the mean. Asterisks denote significant difference to control at same time point (PERMANOVA: p-value < 0.05).21

The intracellular iron quota in the treatment starved of cobalt was much higher than the control

and all other treatments after 31 days of growth (One-way ANOVA: p-value 0.001) (Figure 4.5).

The iron deplete treatment exhibited significantly lower intracellular iron concentration

compared to all other treatments. There were no significant differences between treatments -Cu, -

Mn and -Mo treatments compared to each other or to the control.

*

*

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Figure 4.5: Differences in the intracellular quota of iron in treatments depleted of different micronutrients after 31 days. Samples from the FeEDTA treatment had insufficient sample mass for analysis so are excluded. Error bars are standard error of the mean. Asterisk denotes significant difference compared to the control (One-way ANOVA: p-value < 0.05). A log10 transformation was performed to satisfy the assumptions of parametric statistical analyses.22

4.5 Discussion Microcystis aeruginosa was grown in batch cultures under different trace metal conditions to

assess how micronutrient deprivation affects growth, cell morphology, toxin production and iron

regulation. The removal of several trace metals had demonstrable effects on growth (Figure 4.1),

confirming they are required by Microcystis aeruginosa for optimal cellular functioning. The

growth of M. aeruginosa in cultures depleted of iron, cobalt and manganese exhibited decreased

maximum cell density and growth rate compared to the control treatment grown in trace metal

replete conditions. As hypothesised, iron limitation induced the most pronounced effects, with

severe limitation of growth observable after 12 days of exposure to iron-deplete conditions. This

result is not surprising given the wide range of iron-requiring functions in cyanobacteria, for

example, iron is required as a cofactor of many enzymes, detoxifies reactive oxygen species

(ROS) and has a direct role in electron transport (Raven et al. 1999; North et al. 2007; Li et al.

2009; Alexova et al. 2011). Further, growth limitation of cyanobacteria by iron has previously

been observed in both culture studies (Lukac and Aegerter 1993; Li et al. 2009; Harland et al.

*

*

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2013; Fujii et al. 2016; Yeung et al. 2016) and field conditions (Wever et al. 2008; Zhang et al.

2019). Chlorosis was evident within 12 days of iron starvation, perhaps indicating nitrogen

colimitation, as suggested by Sherman and Sherman (1983). This is likely due to the role of iron

in nitrate reduction and assimilation (Schoffman et al. 2016).

Due to the low solubility of iron in oxygenated, circumneutral waters (Molot et al. 2014), a

chelator or ligand, such as ethylenediaminetetraacetic acid (EDTA), is often added to increase

iron’s bioavailability in culture experiments (Bolch and Blackburn 1996). Given that many

cultured cyanobacterial genera are effectively grown in media containing chelating agents it is

apparent that they are capable of utilizing chelated iron, however unchelated inorganic iron is

often reported as the preferred form for phytoplankton and the most bioavailable (Lis et al.

2015b). Chelated iron still plays an important role in cyanobacterial growth, as observed by

Lange (1971) who noted that the addition of chelated iron regularly enhanced growth of culture

grown cyanobacteria. Uptake of this form of iron relies on reductive or siderophore-assisted

pathways (Lis et al. 2015b). In the present study the absence of the chelator EDTA along with

iron (Treatment E) had a large negative impact on M. aeruginosa growth. Growth was negligible

following day 0 and was significantly less than eliminating iron alone (Treatment D). In

Treatment D, EDTA may have chelated some trace levels of iron contamination in the day 0

solution, causing the small degree of growth observed. By day 10 iron concentration was below

detection limit in Treatment D (<1 μg/L) and growth rapidly plateaued.

Cobalt deficiency also had a large negative effect on the growth of M. aeruginosa. The

physiological role of cobalt in freshwater cyanobacteria is severely understudied, however its

importance was noted by Downs et al. (2008) who observed an increase in primary productivity

upon addition of cobalt during a bloom of Anabaena flos-aquae. Further, some marine

cyanobacteria (eg Prochlorococcus, Trichodesmium and Synechococcus) appear to have an

absolute cobalt requirement (Sunda & Huntsman, 1995; Saito et al., 2002; Rodriguez & Ho,

2015). Cobalt is predominantly linked with cobalamin (vitamin B12), required for transfer of

methyl groups and rearrangement reactions in cellular metabolism (Huertas et al. 2014;

Rodriguez and Ho 2015; Helliwell et al. 2016). Cyanocobalamin was present in the culture media

for all treatments as it is a part of the vitamin mix in MLA media (Bolch and Blackburn 1996).

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Growth was supressed despite the presence of cobalamin in the media, suggesting that M.

aeruginosa lacks the appropriate transporters to acquire cobalamin from its surroundings and

therefore the cyanocobalamin added in MLA media is not bioavailable to M. aeruginosa. The

cobalt associated with cyanocobalamin likely cannot be utilised for other means. These results

may indicate that cobalamin requirements differ between cyanobacterial species. This is

supported by Helliwell et al. (2016) who found that two strains of Microcystis, along with the

vast majority of cyanobacteria, lack the full suite of genes required for the synthesis of

cobalamin. Instead, many genera synthesise pseudocobalamin, a structural variation of the form

added to MLA media. Cells exposed to cobalt depletion may have been limited due to the

inability to synthesise pseudocobalamin combined with the non-bioavailability of

cyanocobalamin. Alternatively, Rodriguez & Ho (2015) conducted batch cultures experiments

using Trichodesmium with varying concentrations of Co and cobalamin. Like the present study,

low cobalt concentrations appeared to limit Trichodesmium growth. Upon addition of cobalamin,

growth was elevated, indicating Trichodesmium can utilise cobalamin and acquire its biological

demand for cobalamin from the surrounding media. However cultures used by Rodriguez and Ho

(2015) were not axenic, therefore other bacteria may have influenced Co dynamics.

Interestingly, intracellular iron quotas after 31 days were much higher in the cobalt deplete

treatment compared to the control and all other treatments (Figure 4.5). As expected, in the iron

deplete treatment intracellular iron quota was negligible due to the exclusion of iron from the

growth medium. To our knowledge, the trend of higher intracellular iron during cobalt-

deficiency has not been previously observed in cyanobacteria. However a similar negative

relationship has been observed in higher plants in which high cobalt concentrations induce Fe

deficiency by reducing absorption and inhibiting transport (Blaylock et al. 1986; Wallace and

Abouzamzam 1989; Gopal et al. 2003). This may also indicate that Co transporters also bind to

Fe. When there are low Co concentrations more of these binding sites may be used for Fe or,

alternatively, more transporters may be produced in response to low Co which can then be

utilised by Fe. We can also speculate that iron was selectively transported into cells undergoing

cobalt deficiency due to its role in the detoxification of reactive oxygen species (ROS) (Latifi et

al. 2009) that may have been produced due to the lack of cobalt in the growth media. However,

ROS were not measured in this experiment. Further, the manganese deplete treatment was also

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showing signs of growth limitation after 31 days but the increase in iron quota was not apparent

in this treatment. The growth limitation in the -Mn treatment was not as severe as the limitation

observed in the -Co treatment, so the response may have been obscured. In future studies it may

be valuable to focus upon Fe kinetics during periods of cobalt limitation to understand this

relationship.

Manganese plays a crucial role in photosynthesis and growth. In cyanobacteria, Mn plays a

similar role to iron, as it is a crucial component of PSII, where four Mn atoms form the core of

the water-splitting site. It may also scavenge and detoxify ROS (Wolfe-Simon et al. 2005).

Consistent with other studies (Salomon and Keren 2011; Hernández-Prieto et al. 2012), iron

limitation has a more severe effect on phytoplankton growth compared to manganese due to its

induction of extensive protein degradation in both PSII and PSI. This is illustrated by the

relatively long period taken for manganese limitation to become apparent compared to iron

(Figure 4.1).

As expected, molybdenum had no effect on Microcystis aeruginosa growth rate. Molybdenum is

important to heterocystous cyanobacteria for the assimilation of inorganic nitrogen (ter Steeg et

al. 1986; Glass et al. 2010) and does not appear to be required by the non-heterocystous M.

aeruginosa. More surprisingly, copper deficiency did not limit M. aeruginosa growth over 60

days. Copper is reportedly necessary for phytoplankton growth given that it is a component of

the thylakoid membrane (Cavet et al. 2003; Sunda 2006), as well as cytochrome oxidase and

plastocyanin in the electron-transport chain (Raven et al. 1999; Burnat et al. 2009). However,

Sunda (2012) reinforces that cellular trace metal concentrations and requirements differ among

phytoplankton species. Further, some copper-containing proteins (such as plastocyanin and

Cu/Zn-SOD) are readily substituted for iron-containing proteins (cytochrome c6 and Fe-SOD)

(Sunda 2012). These substitutions may reduce the copper requirement of M. aeruginosa and

prolong the period within which an intracellular copper store can sustain cellular functioning and

optimal growth in copper-depleted conditions.

Treatments undergoing a growth limitation (-Co, -Fe, -Mn) had a significantly smaller cell

volume than control cells (ANOVA: p-value <0.05) (Figure 4.3). Whereas there were no

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differences between the cell volume of the control treatment and treatments not showing growth

limitation (-Cu, -Mo). Given that all treatments undergoing growth limitation exhibited a

decrease in cell volume relative to the control, it is likely a general morphological response to an

environmental stressor. This has been previously observed by Yeung et al. (2016), who found

that iron limitation caused a decrease in cell size of Microcystis aeruginosa grown in continuous

culture. The reduction in cell volume may be part of a reversable downregulation of

physiological rates where cell growth and metabolism are decreased in response to stress caused

by micronutrient deficiency (González et al. 2018). A similar process has been observed in

cyanobacteria during nitrogen starvation in which a dormant, chlorotic state is established until

favourable nutrient conditions are attained (Spät et al. 2018).

It has previously been proposed that microcystins function as a siderophore, a biogenic ligand

that assists in the acquisition of iron by facilitating their transport across the cell membrane

(Lukac and Aegerter 1993; Utkilen and Gjolme 1995; Kraemer et al. 2015). Siderophores

increase iron bioavailability via a reduction of the less-bioavailable ferric iron (Fe3+) to form

ferrous iron (Fe2+) (Wilhelm, Maxwell, and Trick 1996; Pearl et al. 2006; Alexova et al. 2011;

Martínez-Ruiz and Martínez-Jerónimo 2016). However, Klein et al. (2013) showed that Fe3+

forms weaker complexes with microcystin-LR than is typical of other siderophores, and

proposed that microcystins are more likely to regulate iron via intracellular processes or by

acting as a shuttle across the cell membrane. Recent studies suggest that nutrient acquisition

systems like siderophores may exist for other metallic nutrients (Kraemer et al. 2015).

Microcystins do form complexes with some metal ions besides iron, such as Zn2+, Cu2+, and

Mg2+ (Humble et al. 1997; Saito et al. 2008), however these have yet to be thoroughly studied in

relation to microcystin-LR production. Our results show no stimulation of microcystin-LR

production upon iron limitation, as would be expected if it was functioning as an iron-scavenging

siderophore released under stress. This is consistent with findings by (Li et al. 2009) and (Amé

and Wunderlin 2005). Similarly, intracellular microcystin-LR was not significantly higher than

the control in -Cu, -Mo, or -Mn at any time points. In the -Co treatment, microcystin-LR was

significantly higher than the control on day 31, when growth limitation was most severe. If a

more generalised relationship existed – for example stimulated in response to oxidative stress,

microcystin-LR concentration would likely have also been increased by iron and manganese

86

limitation. The relationship between cobalt and microcystin-LR production has not been

examined in depth, however these results may provide preliminary evidence of a role of cobalt

deficiency in regulating microcystin-LR production.

This study enhances our understanding of cyanobacterial-metal interactions by demonstrating the

importance of iron, cobalt and manganese for optimal growth. Interestingly, the absence of

copper (a component of the thylakoid membrane and proteins in the electron-transport chain

(Raven et al. 1999; Burnat et al. 2009; Facey et al. 2019a) did not appear to impact growth rate in

Microcystis aeruginosa. This may indicate the substitution of copper-containing proteins (such

as plastocyanin and Cu/Zn-SOD) with iron-containing proteins (such as cytochrome c6 and Fe-

SOD). A novel relationship was observed between iron internalisation and cobalt deficiency.

Intracellular iron was significantly higher in cobalt deficient cultures compared to the control and

all other treatments. This may due to the role of iron in the detoxification of ROS. Further, there

was some evidence of cobalt-mediated microcystin-LR production, which requires further

investigation.

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Chapter 5: Assessing the importance of cobalt for freshwater cyanobacteria

5.1 Abstract Micronutrients play key roles in numerous metabolic processes in cyanobacteria. However, our

understanding of whether the micronutrient cobalt influences the productivity of freshwater

systems or the occurrence of cyanobacterial blooms is limited. This study aimed to quantify the

concentration of Co necessary for optimal cyanobacterial growth by exposing Microcystis

aeruginosa to a range of Co concentrations under culture conditions. Extended exposure to

concentrations below ~0.06 μg/L resulted in notable inhibition of M. aeruginosa growth. A clear

negative relationship was observed between Co concentration in solution and intracellular Fe

quota of M. aeruginosa, possibly due to reduced transport of Fe at higher Co concentrations. In a

separate experiment, cyanocobalamin concentration had no effect on M. aeruginosa growth

when there was a surplus of Co in the growth media. This indicates that cyanocobalamin and any

Co within the structure of cyanocobalamin is non-bioavailable to M. aeruginosa, instead they

likely rely on the synthesis of a structural variant – pseudocobalamin, which may have

implications for the wider algal community. To evaluate the likelihood of Co limitation of

cyanobacterial growth under field conditions, a survey of 10 freshwater reservoirs was

conducted. Four of the ten sites had dissolved Co concentrations below the 0.06 μg/L threshold

value. All four of these sites rarely undergo cyanobacterial blooms, strengthening evidence of the

potential for Co to limit growth.

5.2 Introduction The importance of the macronutrients nitrogen (N) and phosphorus (P) in freshwater

cyanobacterial bloom formation is well established (Dignum et al. 2005; Paerl and Fulton 2006).

We know far less about the role of micronutrients in determining the structure and productivity

of cyanobacterial communities. The importance and role of the micronutrient cobalt (Co) for

freshwater cyanobacteria is particularly understudied. Cobalt’s biological significance is often

88

associated with its ability to substitute for other micronutrients (Intwala et al. 2008); such as for

zinc in the enzyme carbonic anhydrase (Quigg 2016). Cobalt is also a component of cobalamin

(vitamin B12), a diverse group of corrinoids involved in the transfer of methyl groups and

rearrangement reactions in cellular metabolism (Healey 1973; Huertas et al. 2014; Rodriguez and

Ho 2015; Helliwell et al. 2016).

There is some evidence that Co can influence marine cyanobacteria distribution and productivity

(Panzeca et al. 2006; Koch et al. 2011; Huertas et al. 2014; Helliwell et al. 2016; Nef et al. 2019)

as well as nitrogen fixation (Healey 1973; Rodriguez and Ho 2015). Some marine cyanobacteria

(e.g. Prochlorococcus, Trichodesmium and Synechococcus) appear to have an absolute cobalt

requirement (Sunda and Huntsman 1995; Saito et al. 2002; Rodriguez and Ho 2015). However,

micronutrient requirements often differ between marine and freshwater cyanobacteria (Quigg

2016). The importance of Co in freshwater systems has also been observed to some extent. For

example, Downs, Schallenberg and Burns (2008) noted a stimulation of primary productivity

upon addition of cobalt during a bloom of the freshwater heterocystous cyanobacteria Anabaena

flos-aquae in Lake Waihola, New Zealand.

Rodriguez and Ho (2015) exposed Trichodesmium to varying concentrations of Co and

cobalamin under culture conditions and observed that low cobalt concentrations appeared to

reduce Trichodesmium growth. Upon addition of cobalamin, growth was elevated. These results

indicate cobalt requirement for cobalamin synthesis and suggest that Trichodesmium can acquire

its biological demand for cobalamin from the surrounding media. Interestingly, this does not

support the results of our previous experiment on Microcystis aeruginosa (Chapter 4) as growth

was supressed despite the presence of cobalamin in the media. This indicates that either M.

aeruginosa lacks the appropriate transporters to acquire cobalamin from its surroundings or that

cobalt is required for a currently unknown role.

Ji and Sherrell (2008) observed that Microcystis sp. subjected to phosphorus limitation exhibited

an increase in both cellular Co and alkaline phosphatase (APase) activity. When cyanobacteria

are subjected to extended phosphorus deficiency extracellular APase is excreted to catalyse the

hydrolysis of dissolved organic phosphorus when the preferred inorganic phosphorus is limited

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(Pandey and Tiwari 2003; Ji and Sherrell 2008). The dominant phosphatase in Microcystis may

require cobalt, as reported for other prokaryotes, and may be accumulated upon phosphate

deficiency due to the upregulated activity of APase (Ji and Sherrell 2008). In our previous study

(Chapter 4), phosphate was in surplus, so a stimulation of APase activity (and subsequent Co

requirement) is unlikely to have occurred. However, the findings of Ji and Sherrell (2008) may

indicate a wider role for cobalt in phosphorous uptake or transport.

Currently little is known regarding typical cobalt concentrations required for optimal freshwater

cyanobacterial growth and the availability of cobalt in Australian freshwaters. Furthermore,

some interesting trends in the intracellular accumulation of Fe at different Co concentrations

observed in Chapter 3 require further investigation. This study aims to (1) determine what cobalt

concentrations limit the growth of the toxic cyanobacteria Microcystis aeruginosa (2) investigate

the biochemical function of cobalt in Microcystis aeruginosa (3) perform a survey of cobalt

concentrations in East-Australian freshwaters.

5.3 Materials and Methods 5.3.1 Microcystis culturing conditions Batch culture experiments were performed using the toxic Microcystis aeruginosa MASH01-

AO5 (Australian National Algae Culture Collection, Hobart, Tasmania, Australia). Axenic

cultures were maintained in MLA media (Bolch and Blackburn 1996) in an environmental

chamber (Labec, HC-50 environmental chamber). Incubation was at 22°C under 20–25

µmoles/m2/s light with a 14–10 h light-to-dark cycle throughout the long-term maintenance of

the cultures as well as the duration of the experiment.

5.3.2 Culture media

At the onset of the experiment, cells were subcultured into triplicate 700mL sterile plastic culture

flasks (Corning) at an initial cell density of 105 cells/mL. Flasks had previously been soaked

overnight in an acid bath (10% HNO3) and repeatedly rinsed with Milli-Q water. An inoculum of

105 cells/mL was added to each flask containing 600mL of filter sterilised MLA growth media

(Bolch and Blackburn 1996) modified as described below.

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Experiment 1

M. aeruginosa was grown in MLA media with modified cobalt composition. The composition of

MLA growth media is outlined in Chapter 4. Standard MLA media contains cobalt in surplus and

was used as the control. Three additional treatments were assessed, they are detailed below:

a) Control - filter sterilised MLA medium containing 2.48 μg/L Co as CoCl.6H2O.

b) MLA media without any addition of Co.

c) MLA media with 1% of standard MLA concentration of Co.

d) MLA media with 10% of standard MLA concentration of Co.

Experiment 2

A separate experiment was conducted under similar conditions to the above in which Microcystis

aeruginosa was grown with and without the presence of cyanocobalamin (0.05 μg/L) to assess

whether M. aeruginosa requires an external source of cobalamin for optimal growth or if it can

be synthesised in the presence of sufficient Co.

5.3.3 Transfers

On day 0 an inoculum of M. aeruginosa was pelletised via centrifugation at 3500 RPM for 10

minutes. Previous tests have shown that this process does not lyse M. aeruginosa cells. The

supernatant was removed and the pellet washed with treatment media. The pelletisation and

washing steps were repeated before the pellet was resuspended in treatment media. A cell count

was performed to determine the volume required for transfer of 105 cells/mL to the culture flasks.

For experiment 2 only, on day 35 cultures were transferred into new culture flasks containing

fresh experimental media using the pelletisation method described above. Cells were

reinoculated at 105 cell/mL to extend the exposure to the experimental conditions.

5.3.4 Culture experiment sampling Every 2-4 days cell counts were estimated via optical density (680nm using Varian Cary 50 Bio

UV Spectrophotometer). The relationship between M. aeruginosa cell count and absorbance at

91

680nm was previously determined (R2 = 0.98) (Appendix C, Figure C1). Manual cell counts

were performed periodically using a haemocytometer to ensure manual cell counts were closely

aligned with the optical density results.

The dissolved nutrient composition of the culture media was sampled on day 0, 10, 20 and 30 by

filtering 25 mL of culture material through a 0.45 μm cellulose acetate syringe filter (Sartorius)

prerinsed with 50 mL of 10% nitric acid followed by 100 mL milli-Q water. Samples were

collected in acid washed 50 mL falcon tubes and refrigerated. Within 24 h of collection, samples

were acidified with ultra-pure nitric acid to 0.2% v/v. Intracellular iron was also sampled on day

0 from the inoculum and days 10, 20, 30 from the experimental flasks. Samples were prepared

by transferring a volume of culture material corresponding to ~5 x 107 cells (or ~0.97 pg dry

weight) into acid washed, pre-weighed 50 mL falcon tubes. Transfers were performed

immediately following a cell count. The culture material was centrifuged at 3000 RPM for 10

minutes to form a pellet. The supernatant was removed after ensuring the absence of cells by

pipetting 1 mL of solution into a Sedgewick rafter counting chamber for observation using a

light microscope (Olympus BX41). Samples were frozen at -20 °C.

5.3.5 Solution nutrient determination The concentration of dissolved nutrients (P, Co, Cu, Fe, Mn, Mo) in the filtered solution was

analysed with a combination of inductively coupled atomic emission spectrometry (ICP-AES)

(Varian 730 ES) and inductively coupled plasma mass spectrometry (ICP-MS) (Agilent 7500

CE). The spectrometer was operated according to the standard operating procedures outlined by

the manufacturer. The instruments were calibrated using matrix-matched standards. At least 10%

of samples were conducted in duplicate to ensure the precision of the analyses. To check for

potential matrix interferences at least 10% of samples had spike recoveries performed.

5.3.6 Intracellular iron sample preparation and analysis Samples were freeze dried at 0.1 mbar and -80 °C until all liquid was sublimated from the

samples. The dried pellet was submerged in 500 μL distilled nitric acid and microwaved at 80 °C

with a 30 min holding time (CEM Mars 6). Samples were diluted with 4.5 mL Milli-Q water and

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transferred to 5 mL acid washed vials for analysis via ICP-MS and ICP-AES. Analysis was

performed using the instrument procedure outlined above.

5.3.7 Field evaluation of cobalt concentrations

Ten sites were sampled to assess the availability of cobalt in a variety of freshwater systems in

South-Eastern Australia. A description of study sites is provided in Table 5.1.

Table 5.1: Summary of study sites.8

Site Coordinates Geological setting Altitude

(m)

Capacity

(ML)

Cyanobacterial

blooms

Level of

anthropogenic

disturbance in

catchment

Land use in

catchment

Mannus

Lake

-35.81809,

147.98329

Largely foliated

granite,

leucogranite,

adamellite,

granodiorite,

tonalite.

487 2,350 Common Moderate

Grazing,

plantation

forests, native

forestry

Carcoar

Dam

-33.60478,

149.19042

Volcanics; granite

and diorite 852 35,800 Common High

Grazing,

cropping

Lake Lyell -33.52685,

150.07971

Granite and

granodiorite 785 34,500 Moderate Low/moderate

Nature

conservation,

residential,

grazing

Wentworth

Falls Lake

-33.70505,

150.36908

Multi-coloured

chert sandstone,

quartzose

sandstone, shale and

claystone

880 300 Rare Low/moderate

Marsh/wetland,

residential,

nature

conservation

Glenbrook

Lagoon

-33.75593,

150.61692

Hawkesbury

Sandstone – quartz

sandstone with

some shale

209 168 Rare Moderate

Nature

conservation,

residential

93

Lake Albert -35.16636,

147.36932

Gravel, sand, silt,

clay 189 4,000 Common High

Residential,

cropping

Wyangala

Dam

-33.96170,

148.96002

Granite and diorite;

grey and black slate

and quartz

greywacke

386 1,217,000 Moderate Moderate Grazing

Blowering

Reservoir

-35.44750,

148.28265

Quartz feldspar

porphyry with

minor slate,

greywacke,

sandstone, quartzite,

tuff, andeśite

366 1,613,741 Rare Low

Nature

conservation,

native forest,

grazing

Burrinjuck

Dam

-34.99482,

148.60628

Mainly

conglomerate, grit,

shale, sandstone and

minor limestone

345 1,026,000 Moderate Low Grazing, nature

conservation

Lake

Jindabyne

-36.38914,

148.64423

Largely massive

intrusions 902 688,287 Rare Low

Nature

conservation,

grazing

Water samples were obtained from the shore using a PVC sampling pole with a 1L acid washed

Nalgene bottle fixed to the end. The bottle was rinsed once with site water which was discarded

away from the sampling location. Samples were taken at ~0.5 m depth. 100 mL of sample water

was filtered through a 0.45 μm cellulose acetate syringe filter (Sartorius) pre-rinsed with 50 mL

of 10% nitric acid followed by 100 mL milli-Q water. Samples were collected in 125 mL sample

bottles pre-soaked in 10% nitric acid followed by repeated rinsing with milli-Q water. Field

blanks were prepared at four sites by following the above procedure using milli-Q water. After

sample collection, physicochemical measurements (pH, dissolved oxygen, conductivity and

temperature) were taken using a multiparameter water quality sonde probe. All samples were

refrigerated immediately following collection and were acidified with ultra-pure nitric acid to

0.2% v/v in a trace metal-clean room within 7 days of collection.

Cobalt in the filtered solution was analysed with a combination of inductively coupled atomic

emission spectrometry (ICP-AES) (Varian 730 ES) and inductively coupled plasma mass

94

spectrometry (ICP-MS) (Agilent 7500 CE). The spectrometers were operated according to the

standard operating procedures outlined by the manufacturer. The instruments were calibrated

using matrix-matched standards. At least 10% of samples were conducted in duplicate to ensure

the precision of the analyses. To check for potential matrix interferences at least 10% of samples

had spike recoveries performed.

5.3.8 Dissolved organic carbon Samples for dissolved organic carbon (DOC) were collected in the field using the procedure

outlined above. Samples were stored in the 1L Nalgene bottle used for filtered cobalt sample

collection and refrigerated. DOC samples were acidified to 0.5% v/v HCl within a week of

sampling, filtered to 0.45 μm using a cellulose acetate syringe filter and purged with oxygen gas

for 20 min to remove inorganic carbon. Analysis was performed by high-temperature

combustion with a Shimadzu TOC-LCSH Total Organic Carbon Analyser using the procedures

recommended by the manufacturer.

5.3.9 PO4-P determination

Samples for dissolved PO4-P were collected in the field using the procedure outlined above.

Samples were stored in the 1L Nalgene bottle used for filtered cobalt sample collection and

refrigerated. Prior to analysis, samples were filtered to 0.45 μm using a cellulose acetate syringe

filter. PO4-P was measured by the reduction of ascorbic acid using the molybdate blue

colorimetric method (Murphy and Riley 1962; APHA 1998). Analysis was performed using a

SEAL AQ400 Discrete Analyser.

5.3.10 Data Analysis Differences in Fe quotas between treatments and through time were analysed with a Two-Way

ANOVA with Tukey pairwise comparison. Tests were performed using SigmaPlot 12.5 with a

significance level of α = 0.05. The Levene statistic was used to test homogeneity of variance.

Plots were created using the software R Version 1.2.1335 (R Core Team, 2018).

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5.4 Results 5.4.1 Cobalt limitation experiment There were notable decreases in the growth of Microcystis aeruginosa when exposed to

decreasing cobalt concentrations (Figure 5.1). Cultures that had no added cobalt demonstrated

the most severe signs of growth limitation. The cell concentration was notably less than the

control after 13 days and maximum cell concentration occurred after 18 days, after which growth

decreased. Similarly, the Co 1% treatment showed signs of limitation after 13 days of growth,

but reached a higher maximum cell concentration, which occurred on day 24. The cell

concentrations in the Co 10% treatment were similar to the control until day 28, after which they

showed minor signs of limitation.

Figure 5.1: Microcystis aeruginosa growth through time under variable trace metal conditions. Error bars are standard error of the mean.23

The relationship between cobalt concentration and the percentage growth inhibition compared to

the control is illustrated in Figure 5.2. Growth was severely inhibited (>40% inhibition) after

sustained (>10 day) exposure to concentrations below ~0.06 μg/L cobalt in the -Co and Co 1%

96

treatments. After 10 days, growth was less impacted even at similar concentrations. Growth was

minimally impacted even after 30 days of exposure to ~0.25 μg/L.

Figure 5.2: Relationship between cobalt concentration in the culture media and the percentage growth inhibition compared to the control.24

There were significant differences in the intracellular Fe quota between treatments and through

time (Two-Way ANOVA, p-value <0.05) (Figure 3). Intracellular Fe quota was similar across

treatments after 10 days, but after 20- and 30-days variations between treatments became

evident. After 20- and 30-days the -Co treatment had the largest intracellular Fe quota and was

notably different to the control. There was a trend of increasing Fe quota with decreasing Co

concentration in solution at these time points. Tukey Multiple Comparison indicated significant

differences between the -Co and control treatments after 20 and 30 days (p-value <0.05),

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however there were no statistically significant differences between the Co 1% and Co 10%

treatments compared to the control or any other treatments.

Figure 5.3: Differences in the intracellular quota of iron in treatments exposed to varying cobalt concentrations. Error bars are standard error of the mean. Asterisks represent significant difference compared to the control.25

5.4.2 Cobalamin experiment There were no notable differences between the cell densities of M. aeruginosa in cobalamin

deplete cultures compared to a control at any time points over a 70 day period (Figure 5.4).

Similarly, there were no differences in growth rate or maximum cell density reached at the

conclusion of the experiment.

*

*

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Figure 5.4: Microcystis aeruginosa growth through time with and without the addition of vitamin B12. Error bars are standard error of the mean.26

5.4.3 Field survey

Dissolved cobalt concentrations measured in various freshwater systems in Eastern-Australia are

presented in Table 5.2, along with a summary of the site and physicochemical characteristics.

Dissolved cobalt concentrations ranged from 0.019 ± 0.001 μg/L at Glenbrook Lagoon to 0.144

± 0.004 μg/L at Lake Albert. Four sites had dissolved cobalt concentration below the threshold

value for M. aeruginosa growth of 0.06 μg/L outlined in Figure 2.

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Table 5.2: Dissolved cobalt concentrations and physicochemical parameters measured at 0.5 m depth at 10 freshwater sites in NSW, Australia.9

Site pH

Dissolved

oxygen

(mg/L)

Conductivity

(μS/cm)

Temperature

(0C)

Dissolved

organic

carbon

(mg/L)

PO4-P (μg/L)

Dissolved cobalt

concentration

(μg/L)

Mannus Lake

7.7 6.5 103 20.7 13.0 8 0.228 ± 0.000

Carcoar Dam

8.6 9.6 381 21.9 12.0 5 0.562 ± 0.006

Lake Lyell 8.6 9.5 477 19.8 5.3 <2 0.072 ± 0.002

Wentworth Falls Lake

8.0 8.6 38 25.3 4.8 3 0.022 ± 0.002

Glenbrook Lagoon

7.7 6.2 139 22.61 8.0 <2 0.019 ± 0.001

Lake Albert 8.2 7.8 480 22.7 13.0 18 0.144 ± 0.004

Wyangala Dam

8.0 7.9 244 21.4 9.6 <2 0.091 ± 0.001

Blowering Reservoir

7.6 8.9 34 22.0 2.0 <2 0.026 ± 0.001

Burrinjuck Dam

8.4 9.1 169 22.5 6.4 3 0.071 ± 0.000

Lake Jindabyne

7.9 11.8 27 21.3 2.3 <2 0.016 ± 0.001

5.5 Discussion Microcystis aeruginosa was grown in batch cultures in media composed of varying Co

concentrations to assess effects on growth and to provide insight into the biochemical role of Co

in freshwater cyanobacteria. Consistent with the findings of Chapter 4, Co concentration had

significant effects on Microcystis aeruginosa growth (Figure 5.1). This indicates that cobalt is an

essential nutrient, given that growth will cease in its absence and is optimal in its presence, and

its role could not be replaced by any of the other major micronutrients present in the growth

media.

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5.5.1 Cobalamin

Cobalt is important in biological systems as it can often metabolically substitute for zinc in many

enzymes, such as carbonic anhydrase (Xu et al. 2008; Sunda 2012). Given that zinc is the

preferred substrate in these enzymes (Intwala et al. 2008; Quigg 2016) and was available in

surplus in the growth media, this relationship is unlikely to have caused the negative growth

effects observed from low cobalt concentrations. Co is also a component of cobalamin

derivatives (e.g. vitamin B12), required for transfer of methyl groups and rearrangement reactions

in cellular metabolism (Huertas et al. 2014; Rodriguez and Ho 2015; Helliwell et al. 2016). A

possible explanation for the growth limitation observed in treatments exposed to low Co

concentrations is that there was not sufficient Co to synthesise pseudocobalamin. Given that

cyanocobalamin was present in the growth media, this suggests that the synthetic form of

cobalamin (cyanocobalamin) added to MLA growth media is not bioavailable to Microcystis

aeruginosa and therefore is not an essential component of growth media used for the culturing of

M. aeruginosa. This is supported by recent findings by Helliwell et al. (2016) who found that

two strains of Microcystis, along with the vast majority of cyanobacteria, lack the full suite of

genes required for the synthesis of cobalamin. Instead, many genera synthesise

pseudocobalamin, a structural variation of the form added to MLA media. In Experiment 2, no

differences in growth were observed in the presence/absence of cobalamin over 70 days when an

excess of Co was present in the growth media. This provides further evidence that M. aeruginosa

is synthesizing pseudocobalamin in the presence of sufficient Co.

Cobalamin and its structural variants can only be synthesized de novo by certain prokaryotes,

including some cyanobacteria; however, the majority of microalgal species require it for growth

(Watanabe and Bito 2018; Nef et al. 2019). Pseudocobalamin produced by M. aeruginosa and

many other cyanobacteria is much less bioavailable that cobalamin – although some species are

capable of converting pseudocobalamin to a bioavailable form (Helliwell et al. 2016). The

implication of this is that the form of cobalamin produced, as well as the concentration of Co in

surface waters, likely influence phytoplankton productivity and community composition.

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5.5.2 Cobalt requirements – linking culture experiments and natural systems

There was a clear relationship between Co concentration in the media and the percentage

inhibition of growth compared to the control. The trend suggests a threshold value of ~0.06 ug/L,

below which M. aeruginosa growth was severely inhibited. Time is also a factor as M.

aeruginosa cells were less effected by low Co availability in the solution after 10 days compared

to 30 days, even at similar concentrations. This is likely due to luxury uptake of Co sustaining

cell growth for ~10 days by maintenance of an adequate Co quota inside the cell causing a delay

in the response (Droop 1973; Saito et al. 2008; Sunda 2012). Alternatively, this could be the

result of the overall Co pool being shared between a larger number of cells later in the

experiment. A similar study by Holm‐Hansen et al. (1954) assessed the cobalt requirements of

the cyanobacteria Nostoc muscorum and found that growth was optimal above 0.40 μg/L

although some growth was observed at concentrations as low as 0.002 μg/L.

Co concentrations can be quite variable and depend on the systems geology, hydrology sediment

composition, land use practices and other anthropogenic activity (Neal et al. 1996; Nagpal et al.

2004; Kim et al. 2006). For example Neal et al. (1996) compared trace metal concentrations in

major rivers draining into the Humber estuary, England and found that dissolved Co

concentrations were notably higher in urban locations compared to rural. In one of the rural sites

the minimum dissolved Co concentration observed over a 12-month period was 0.07 μg/L. As

observed in Chapter 3, anoxic benthic sediments can provide an intermittent source of Co to

overlying waters upon the reduction of Co-bound Mn and/or Fe (oxyhydr)oxides. However,

given that Co concentrations are more often reported within the context of potentially toxic

levels, such as over application of fertilizers or wastewater from industries (Vetrimurugan et al.

2017), there is limited information on the concentrations of Co in freshwaters without

contamination from industry and urban settlements.

In the present study, Co concentrations were surveyed in 10 freshwater lakes and reservoirs of

varying inflow characteristics, algal bloom history, geology and anthropogenic disturbance and

compared to the threshold value calculated in the culture experiment. To our knowledge this is

the first survey of dissolved Co concentrations in Australia. A wide range of Co concentrations

were observed, ranging from 0.019 ± 0.001 μg/L to 0.144 ± 0.004 μg/L. Interestingly, the four

102

sites in the study that rarely undergo cyanobacterial blooms (Lake Jindabyne, Glenbrook

Lagoon, Wentworth Falls Lake and Blowering Dam) were below the 0.06 μg/L threshold,

perhaps indicating the capacity for Co to limit the formation or severity of cyanobacterial

blooms. On the other hand, systems that undergo blooms were all above the 0.06 μg/L threshold

value. For example, Lake Albert, a highly anthropogenically influenced reservoir which

frequently undergoes dense blooms had the highest concentration, likely reflecting runoff from

an urban environment. PO4-P concentrations followed a similar trend to those of Co, where sites

that rarely bloom had the lowest concentrations. However PO4-P concentrations were all below

those typically necessary for the proliferation of algae (Walker and Havens, 1995; Zeng et al.,

2016). This may be due to a significant portion of P being particulate or temporal variation in P

concentrations. At low P concentrations there is possible colimitation of phytoplankton growth

by both P and Co.

5.5.3 Cobalt and intracellular iron

Consistent with the results of Chapter 4, there was a clear negative relationship between Co

concentration in media and intracellular Fe quotas after 20 and 30 days (Figure 5.3). There were

no notable differences between treatments after 10 days, likely due to luxury uptake of Co

allowing cells to maintain sufficient Co quotas for optimal cellular functioning (Sunda 2012),

thereby causing a delay in response in Fe quota. Alternatively, the large increase in Fe

availability when cells were transferred on day 0 may have allowed the rapid accumulation of Fe

in all treatments. This study supports and expands upon the relationship demonstrated in Chapter

3 by increasing the frequency of sampling and assessing a gradient of Co concentrations.

However, the cause of this relationship is yet to be clarified.

High levels of Co induce Fe deficiency in plants by reducing absorption and inhibiting transport

(Blaylock et al. 1986; Wallace and Abouzamzam 1989), potentially by displacing ions in binding

sites (Gopal et al. 2003). A similar phenomenon may be occurring in M. aeruginosa where at

high cobalt concentrations (in this case the control treatment), Co binds non-specifically with

coordination sites normally occupied by Fe, displacing Fe and reducing intracellular

concentrations. Cells appeared healthy and grew rapidly in the control treatment in the presence

of the highest Co concentrations (2.48 μg/L), indicating that decreased Fe quotas were not a

103

limiting factor. However, given this negative relationship, Fe deficiency will likely occur sooner

in the presence of higher Co concentrations. We can also speculate that siderophores required for

the assimilation of chelated iron are non-specifically binding other metals, including cobalt.

Braud et al. (2009) observed that pyochelin, a major siderophore produced by Pseudomonas

aeruginosa, also binds Co2+ which inhibited the uptake of Fe in vivo. Cells may have been able

to accumulate greater amounts of intracellular iron when competition for the siderophore-

mediated uptake pathway was reduced under low cobalt conditions.

5.5.4 Significance and future direction Consistent with Chapter 4, growth of M. aeruginosa was decreased by exposure to low Co

concentrations under culture conditions. Extended exposure (>10 days) to Co concentrations

below 0.06 μg/L resulted in significant inhibition of growth. Field concentrations of Co were

assessed, and we observed concentrations well below this threshold value, and were exclusively

at sites that are not known to undergo cyanobacterial blooms. These results indicate that Co

limitation of cyanobacterial growth may occur in freshwater reservoirs.

While we have provided evidence of growth-limiting concentrations of Co, PO4-P concentrations

were generally aligned to Co in this study, indicating phosphorus limitation may have also been

occurring. Given that phosphorus and Co have different sources, it is unlikely they will always

be strongly correlated (Chapter 3). However, future studies would benefit from analysing the

availability of macronutrients and physicochemical parameters over a larger temporal scale to

fully understand the limiting factors in each system. Further, our threshold value was based only

on the growth response of M. aeruginosa. Other cyanobacterial taxa, including nitrogen fixing

species, will likely have different requirements for Co. Future studies should assess a broad

range of cyanobacterial species to account for these differences.

As evidenced by the negative growth impacts of low Co even in the presence of cyanocobalamin,

M. aeruginosa appears to be unable to assimilate cyanocobalamin and unable to repurpose the

Co held within cyanocobalamin. This is reinforced by the results of the cyanocobalamin culture

experiment in which the availability of cyanocobalamin did not affect the growth of M.

aeruginosa when grown in the presence of sufficient Co. It appears that dissolved Co is required

to produce the structural variant of cobalamin – pseudocobalamin. The variants of cobalamin are

104

not necessarily functionally exchangeable and pseudocobalamin appears to be far less

bioavailable (Helliwell et al. 2016). Given the important role of cyanobacteria in the production

of cobalamin for utilization by eukaryotic algae, future studies should assess to what extent

pseudocobalamin is bioavailable to these taxa, including further examination of the role of

eukaryotic taxa capable of remodelling pseudocobalamin (Helliwell et al. 2016).

There was a clear negative relationship between the concentration of Co in solution and the

intracellular Fe quota after more than 10 days of growth. To our knowledge this is the first time

this relationship has been observed in cyanobacteria, although a similar relationship has been

observed in higher plants in which Fe transport was inhibited by high Co concentrations. Future

studies may seek to examine Co and Fe transport pathways and whether any competition or

inhibition is occurring. Alternatively, ROS could be measured to assess if Fe is being assimilated

for their detoxification.

5.5.5 Conclusion Extended exposure (>10 days) to Co concentrations below ~0.06 μg/L resulted in significant

inhibition of the growth of Microcystis aeruginosa under culture conditions. We also observed

that M. aeruginosa can maintain optimal growth without the presence of cobalamin in the growth

media. When combined with the growth limiting effect of low Co, these experiments provide

evidence that cyanocobalamin is non-bioavailable to M. aeruginosa and instead

pseudocobalamin is being produced when there is sufficient Co available. Ten freshwater lakes

and reservoirs were sampled for Co concentration. Four had Co concentrations below the 0.06

μg/L threshold value calculated under culture conditions. Interestingly, none of these sites

regularly undergo cyanobacterial blooms, providing evidence that Co may limit cyanobacterial

growth in field environments.

105

Chapter 6: General discussion and conclusion

The aim of this thesis was to improve our understanding of the importance of micronutrient trace

metals for cyanobacteria, and in particular to understand their role in bloom formation and toxin

production. To achieve this, I undertook a combination of lab-based culture experiments, in-situ

microcosm experiments and a long-term monitoring study involving 18-months of regular

sampling at Mannus Lake. Initially, in situ microcosms were performed at seven sites in South-

Eastern Australia to test how a micronutrient mixture would impact cyanobacterial growth and

phytoplankton community structure. These experiments indicated that micronutrients may be an

important regulator of the severity of cyanobacterial blooms, and micronutrient limitation of

cyanobacterial growth may be more prevalent than previously anticipated. The results of the

microcosm study provided a proof of concept and informed the subsequent culture and

monitoring studies in Chapters 3, 4 and 5, which investigated specific micronutrient

requirements of M. aeruginosa, provided threshold requirements of the micronutrient cobalt and

investigated micronutrient dynamics under field conditions. Combined, these studies provide

valuable information that may aid the management of harmful algal blooms in freshwater

systems.

6.1 Effect of micronutrient inputs on cyanobacterial growth and community dominance in situ

6.1.1 Response of cyanobacteria to micronutrient inputs

Cyanobacterial blooms are an increasing problem in anthropogenically modified systems (Paerl

and Otten 2013). The structure of phytoplankton communities and their proportion of potentially

toxic cyanobacteria is influenced by environmental factors, including the availability of

macronutrients (nitrogen and phosphorus) (Vyverman et al. 2007; Arrigo 2005; Molot et al.

2014; Sourisseau et al. 2017). I determined that in some systems the addition of micronutrients

can also influence the phytoplankton community, favouring cyanobacterial dominance and

causing an increase in cyanobacterial biovolume.

106

Of seven sites assessed, two showed signs of cyanobacterial limitation by micronutrients –

Mannus Lake and Burrendong Dam. At the onset of the Mannus Lake experiment the

phytoplankton community was dominated by the potentially toxic heterocystous cyanobacteria

Chrysosporum ovalisporum. Cyanobacteria biovolume increased significantly when

micronutrients alone (Treatment M) and nitrogen, phosphorus and micronutrients (Treatment

NPM) were added and was driven mainly by growth of C. ovalisporum. Similarly, at Burrendong

Dam the non-diazotrophic genera Microcystis aeruginosa and Radiocystis sp. dominated the

phytoplankton community and were present in high numbers at the onset of the experiment. The

addition of micronutrients alone (M) and nitrogen (N) stimulated cyanobacterial growth relative

to the control. M. aeruginosa and Radiocystis remained dominant under all treatments and were

the primary drivers of the differences between treatments.

Both sites that displayed evidence of micronutrient limitation of cyanobacterial growth were

undergoing dense cyanobacterial blooms at the onset of the experiments. This suggests that

micronutrients may become limiting during high competition for nutrient assimilation during

bloom events, indicating that micronutrient trace metals can regulate the severity of

cyanobacterial blooms in some freshwater systems. This has important implications for

management of freshwater systems as decreasing micronutrient inputs may help to reduce the

severity of such blooms.

6.1.2 Changes in phytoplankton community structure driven by micronutrient inputs

The micronutrient requirements of cyanobacteria differ to other phytoplankton (Baptista and

Vasconcelos 2006). At Mannus Lake the addition of micronutrients caused notable changes in

the phytoplankton community structure and appeared to influence the ability of cyanobacteria to

compete with other phytoplankton. The NPM treatment had a higher proportion of cyanobacteria

than the NP treatment, whereas in the phosphorus (P) and nitrogen + phosphorus (NP)

treatments, cyanobacterial biovolume was much lower than the micronutrient treatments and

diversity was higher. Similarly, at Burrendong Dam cyanobacteria were more dominant in the M

treatment compared to the P and NP treatments. This indicates that cyanobacteria may be more

successful competitors in the phytoplankton community when there is a high availability of

107

dissolved micronutrients. This may be due to a more efficient metal uptake system (Baptista and

Vasconcelos 2006; Sunda 2012), for example, via the production of metallophores (Kraemer et

al. 2015) or due to the high micronutrient requirements of heterocystous cyanobacteria (such as

C. ovalisporum) for atmospheric nitrogen assimilation.

6.2 Sources of micronutrients and their role in the formation of cyanobacterial blooms.

Chapter 2 demonstrated that cyanobacterial blooms can increase in severity upon micronutrient

input in some systems. One of the systems in which cyanobacterial growth was stimulated was

Mannus Lake, a small (~2350 ML) artificial reservoir in South-Eastern New South Wales.

Mannus Lake regularly undergoes dense cyanobacterial blooms in the summer months. Chapter

3 seeks to understand the causes of these blooms and to examine micronutrient sources and

interactions in the Mannus Lake system, building upon the findings of Chapter 2. The role of

micronutrients in bloom formation was a particular focus.

6.2.1 Sources of micronutrients in Mannus Lake

Many trace metals (such as Co, Cu, Fe, Mn and Zn) can be released from sediments under

hypolimnial anoxic conditions caused by thermal stratification (Shipley et al. 2011). These

dissolved micronutrients can become available to cyanobacteria who may vertically migrate to

nutrient-rich hypolimnial waters (Bormans et al. 1999; Wagner and Adrian 2009; Molot et al.

2014), particularly in shallow reservoirs such as Mannus Lake. Further, when the water column

mixes after periods of thermal stratification, upwelling occurs, increasing the availability of

dissolved micronutrients in surface waters (Corman et al. 2010).

Persistent thermal stratification and subsequent anoxia below the thermocline was evident in

both summers, but was strongest in the summer of 2018/19. During the period of strong

persistent stratification there was notably higher dissolved concentrations of the micronutrients

Ca, Co, Fe, Mg, Mn and Mo in the hypolimnial water compared to surface water. This trend

suggests that sediments are likely a significant source of micronutrients in Mannus Lake. There

was a strong correlation between the concentrations of Co, Fe and Mn in the hypolimnial water,

108

most likely caused by the reduction of Co-bound Mn and/or Fe (oxyhydr)oxides in sediments.

Interestingly, the pH in the hypolimnial water at Mannus Lake was largely circumneutral, and

never dropped below pH 6. As the solubility of some micronutrients (such as Fe and Mn) is pH

dependent and decreases with increasing alkalinity (Balistrieri et al. 1992), they may have been

rapidly recycled back to the sediments. In the summer of 2019/20, mixing events occurred more

frequently and micronutrient release from sediments was less apparent.

Mannus Creek and Munderoo Creek, located upstream of Mannus Lake, did not appear to

contribute a significant portion of the Lake’s micronutrient supply. Micronutrient concentrations

remained low throughout the study, with some exceptions at Munderoo Creek during low flow

periods. High volume inflows into the Lake from Mannus Creek would have caused a rapid

turnover or ‘flushing’ of Mannus Lake and also had a clear dilution effect on the concentrations

of Ca, Mg and Mo.

6.2.2 Causes of recurring cyanobacterial blooms in Mannus Lake The causes of cyanobacterial blooms in freshwater systems are diverse, complex, and often

multi-faceted (Paerl and Otten 2013). Understanding these causes is essential for developing

effective management strategies and requires in depth monitoring studies over an extended

period which are tailored to the system of interest. At Mannus Lake, there was a strong

relationship between the biovolume of Chrysosporum ovalisporum and thermal stratification. C.

ovalisporum was concentrated in the surface layers of the lake, suggesting it was utilising

buoyancy to gain access to light. Multivariate redundancy analysis (RDA) indicated that

persistent thermal stratification was the strongest driver of C. ovalisporum growth and C.

ovalisporum appeared to thrive even under moderate nitrogen and phosphorus concentrations.

Later in the season, cf. Microcystis sp. dominated the phytoplankton community under less

stratified conditions. There was no clear positive association between cf. Microcystis sp.

biovolume with stratification, nitrogen or phosphorus, making it difficult to elucidate the main

driver of these smaller bloom events.

It was not clear whether micronutrients were a primary cause of the blooms at Mannus Lake, but

higher dissolved micronutrient concentrations did co-occur with the commencement of the C.

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ovalisporum bloom in December 2018, and its breakdown corresponded with decreased

micronutrient availability in February 2019, particularly Co, Fe and Mn. When these

micronutrient concentrations increased shortly after, cyanobacterial biovolume once again

increased. Further, RDA analysis indicated that Co concentration significantly influenced the

phytoplankton community and was positively correlated with C. ovalisporum biovolume. The

biovolume of cf. Microcystis sp. was positively correlated with Mg concentration (and likely Ca

and Mo, which were removed from the analysis due to collinearity). These results may indicate

that the bloom was caused by anoxia along the water/sediment boundary which led to increased

availability of a metal in a reduced oxidation state needed by cyanobacteria. Species specific

cellular demand for micronutrients combined with different micronutrient uptake capacities (for

example the capacity to transport metals with certain oxidation states) may be the driver of

changes in phytoplankton community structure.

6.3 Impact of low micronutrient availability on Microcystis aeruginosa in culture Chapters 2 and 3 examined the role of micronutrients under field conditions. While these studies

are important to understand how micronutrients influence the structure of the phytoplankton

community and their role in bloom formation, it can be difficult to observe the influence of

micronutrients in isolation. Laboratory culture studies provide a valuable tool for assessing the

importance of individual micronutrients under controlled conditions. I grew a strain of

Microcystis aeruginosa in modified MLA growth media, composed of reduced concentrations of

micronutrients (Co, Cu, Fe, Mn and Mo) to assess how low micronutrient availability influences

cyanobacterial growth.

The growth of M. aeruginosa in cultures depleted of Fe, Co and Mn exhibited decreased

maximum cell density and growth rate compared to the control treatment grown in micronutrient

replete conditions. This confirms these elements are required by Microcystis aeruginosa for

optimal cellular functioning and could not be replaced by another element in the growth media.

Reduced Fe concentration had the most severe impact on growth, which is to be expected given

its role as a cofactor of many enzymes and in electron transport (Raven et al. 1999; North et al.

2007; Li et al. 2009; Alexova et al. 2011). Further, growth limitation of cyanobacteria by iron

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has previously been observed in both culture studies (Lukac and Aegerter 1993; Li et al. 2009;

Harland et al. 2013; Fujii et al. 2016; Yeung et al. 2016) and field conditions (Wever et al. 2008;

Zhang et al. 2019). M. aeruginosa growth decreased when exposed to low Co and Mn

availability. Cobalt’s ability to limit growth of freshwater cyanobacteria and its biochemical

function has received much less attention in the literature. Therefore, I chose to examine Co in

closer detail.

6.3.1 Quantifying the cobalt requirement of Microcystis aeruginosa and links to natural systems.

There is some evidence that Co can influence marine cyanobacteria distribution and productivity

(Panzeca et al. 2006; Koch et al. 2011; Huertas et al. 2014; Helliwell et al. 2016; Nef et al.

2019). However, micronutrient requirements often differ between marine and freshwater

cyanobacteria (Quigg 2016). Cobalt is a component of cobalamin, a group of corrinoids involved

in the transfer of methyl groups and rearrangement reactions in cellular metabolism (Healey

1973; Huertas et al. 2014; Rodriguez and Ho 2015; Helliwell et al. 2016) and can substitute for

other micronutrients in some enzymes (Quigg 2016). There have been few attempts to quantify

the Co requirements of cyanobacteria and to identify whether Co concentrations can limit

cyanobacterial growth in situ.

I grew Microcystis aeruginosa under various concentrations of Co to assess the concentration at

which inhibition of growth occurred. Extended exposure (>10 days) to Co concentrations below

0.06 μg/L resulted in significant inhibition of growth. The limited inhibition of growth after 10

days is evidence of luxury uptake of Co by M. aeruginosa. A similar study by Holm‐Hansen et

al. (1954) assessed the cobalt requirements of the cyanobacteria Nostoc muscorum and found that

growth was optimal above 0.40 μg/L although some growth was observed at concentrations as

low as 0.002 μg/L. This difference may be due to increased Co requirements of heterocystous

cyanobacteria (Rodriguez and Ho 2015). Although, given the improvements in trace metal clean

protocols in recent decades, this early study may not present an accurate value (Jiann et al.

2016).

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The relevance of the 0.06 μg/L threshold value largely depends on the range of Co

concentrations occurring in natural systems. I measured cobalt concentrations at ten freshwater

lakes and reservoirs in NSW using ultra-trace level sampling and analysis techniques to assess

whether Co exists in sub-optimal levels natural systems. Of the ten sites, four had Co

concentrations below this threshold value. Interestingly, all four of these sites rarely undergo

cyanobacterial blooms, whereas the other six sites were above this threshold and do undergo

blooms. This includes Mannus Lake (Chapter 3), which regularly undergoes dense

cyanobacterial blooms. The Co concentrations at Mannus Lake were well above 0.06 μg/L for

the duration of the monitoring study, indicating that Co was not a limiting factor. It is possible

that the four low-bloom sites were limited by phosphorus, temperature or another environmental

variable, however, this study provides support for the ability of Co to act as a limiting factor of

cyanobacterial growth in situ.

6.3.2 Cobalamin

Given cobalt’s function as a component of cobalamin, it was surprising to observe growth

limitation by low Co concentrations in Chapter 4. Cyanocobalamin was added to the growth

media as a part of the vitamin mixture and was present in the –Co treatment. This suggests that

the form of cobalamin (cyanocobalamin) added to MLA growth media is not bioavailable to

Microcystis aeruginosa MASH01-AO5. This is supported by recent findings by Helliwell et al.

(2016) who found that two strains of Microcystis, along with the vast majority of cyanobacteria,

lack the full suite of genes required for the synthesis of cobalamin. Instead, many genera

synthesise pseudocobalamin, a structural variation of the form added to MLA media.

Subsequently, I conducted an experiment in which I grew M. aeruginosa in the absence of

cyanocobalamin but with sufficient Co in solution. There was no difference in growth rate

compared to a control with added cyanocobalamin. This provides further evidence that M.

aeruginosa is synthesizing pseudocobalamin in the presence of sufficient Co. Pseudocobalamin

is less bioavailable than cobalamin to eukaryotic algae (Helliwell et al. 2016), so this may have

implications for other organisms that rely on cyanobacteria for cobalamin synthesis.

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6.3.3 Iron/cobalt interactions

Micronutrients often interact with one another through substitution in enzymes or competition

for binding sites (Sunda 1989). In Chapter 4, I observed a strong interaction between Fe and Co,

in which low Co concentrations caused a significant increase in intracellular Fe quota after 31

days. In Chapter 5, I measured intracellular iron at more frequent intervals and at different Co

concentrations. Once again, I observed a clear negative relationship between Co concentration

and intracellular Fe after 20 and 30 days of exposure. The cause of this relationship is not clear,

and there is limited information on Co/Fe interactions in cyanobacteria. However, a similar

negative relationship exists in higher plants, in which higher Co concentrations cause a reduction

in Fe transport and subsequently Fe deficiency (Blaylock et al. 1986; Wallace and Abouzamzam

1989). I suggest that at high Co concentrations, Co may bind non-specifically with coordination

sites normally occupied by Fe, or alternatively, to non-specific metallophores produced by M.

aeruginosa. This may displace Fe and reduce intracellular concentrations. In the current study,

the highest Co concentrations were in the control treatment (Chapter 4, Chapter 5), and cells

appeared healthy. However even at this Co concentration, Fe deficiency may occur more quickly

compared to lower levels of Co. If Co concentrations are elevated above the highest level in this

study (2.48 μg/L Co), severe Fe limitation will likely become apparent.

6.4 Influence of micronutrients on cyanotoxin production

Cyanotoxin production, particularly microcystin, has been widely studied as a function of

various physiochemical properties in an attempt to understand their possible functions (Lukac

and Aegerter 1993; Wiedner et al. 2003; Gouvêa et al. 2008; Neilan et al. 2013; Polyak et al.

2013). One suggested function of microcystin is that it acts as a metallophore, assisting in the

acquisition of trace metals (particularly iron). As such, Lukac and Aegerter (1993) suggest that

microcystin may be produced in response to low iron concentrations.

In Chapter 4, I examined production of microcystin-LR by Microcytis aeruginosa under culture

conditions. My results show no stimulation of microcystin-LR production upon iron limitation,

as would be expected if it was functioning as an iron-scavenging siderophore. This is consistent

with findings by Li et al. (2009) who noted a positive relationship between iron concentration

113

and microcystin content and Amé and Wunderlin (2005) who observed that protein biosynthesis

was increased by higher iron additions, but not specifically microcystin. Similarly, intracellular

microcystin-LR was not significantly higher than the control in -Cu, -Mo, or -Mn at any time

points. In the -Co treatment, microcystin-LR was significantly higher than the control on day 31,

when growth limitation was most severe. The relationship between cobalt and microcystin-LR

production has not been examined in depth, however these results may provide preliminary

evidence of a role of cobalt deficiency in regulating microcystin-LR production.

6.5 Further research 6.5.1 Greater spatial and temporal variation of monitoring

The environmental factors influencing cyanobacterial bloom are often specific to each system.

Monitoring of Mannus Lake revealed some interesting data on the sources of micronutrients and

the causes of recurring blooms. Investigating other freshwater systems both within South-East

Australia and beyond over longer timescales will be crucial in achieving a more holistic

understanding of the importance of micronutrients for cyanobacteria. Further microcosm

experiments, such as in Chapter 2, would also be valuable in determining the true extent of

micronutrient limitation of cyanobacterial growth.

Similarly, the survey of cobalt concentrations in 10 freshwater reservoirs demonstrated that

limitation of cyanobacterial growth by cobalt is feasible in Australian systems. A greater number

of reservoirs should be surveyed, incorporating a range of geologies, catchment land uses and

phytoplankton communities. An investigation of rivers, particularly in sections that are likely to

undergo cyanobacterial blooms such as weir pools, would also be valuable. Further, this research

would benefit from temporal monitoring of a wider range of nutrient and physicochemical

parameters to assess whether low cobalt concentrations are causing growth limitation or another

factor that may be co-occurring. Seasonal changes in nutrient availability and limitation should

also be analysed.

114

Fine-scale event sampling would be valuable to determine the role of inflow events in

micronutrient dynamics. This may include sampling systems with different catchment uses and

flood plain characteristics during flood events as well as at low flows.

6.5.2 Nutrient release from sediments

In Chapter 3, I observed elevated micronutrient concentrations in the hypolimnial water of

Mannus Lake during periods of persistent thermal stratification. There was also a strong

correlation between the concentrations of Fe, Co and Mn in the hypolimnial water, likely due to

the reduction of Co bound Fe/Mn oxyhydr(oxides) in the sediments. However, a study which

focuses on the sediments themselves is required to more accurately quantify micronutrient

releases from sediments and to gain a more accurate insight into what conditions cause these

releases. For example, Müller et al. (2016) conducted sediment incubation experiments under

oxic and anoxic conditions to determine the causes of nutrient release from sediments of a

shallow polymictic reservoir. It may be interesting to test the capacity for cyanobacterial to

utilise buoyancy mechanisms to vertically migrate to nutrient rich hypolimnial water as

described by Molot et al. (2014).

6.5.3 Micronutrient speciation and bioavailability Micronutrient limitation can occur even when total metal supply is high. The speciation of the

metal in solution controls its bioavailability, and therefore its status as a limiting nutrient (Sunda

and Huntsman 1998). Future studies may wish to measure the various forms in any given system,

their bioavailability and the factors influencing speciation. This is also an important

consideration when seeking to determine threshold concentrations of micronutrients for optimal

growth. The accuracy of these measurements could be improved by relating intracellular

micronutrient quotas to cyanobacterial growth, as described by Droop (1973).

6.5.4 Expansion of batch culture experiments The batch culture experiments undertaken in Chapters 4 and 5 utilised Microcystis aeruginosa

MASH01-AO5. Given that nutrient requirements are often species specific (Kangro et al. 2007),

115

the 0.06 μg/L Co threshold calculated in Chapter 5 may not be applicable to other species. Future

studies should address this by increasing the number of species tested. It would be particularly

valuable to compare heterocystous taxa to non-heterocystous, given the high requirements of

iron, cobalt and molybdenum for nitrogen fixation (Healey 1973; Rodriguez and Ho 2015).

Micronutrient requirements could also be tested under varying nitrogen concentration to

understand any interactions with the availability of macronutrients or rate of nitrogen fixation.

It would also be valuable to expand the range of micronutrients tested. Given that there was

some association between the concentrations of Mg, Ca and Mo with cyanobacterial growth in

Chapter 3, they could be tested under similar conditions to the culture experiments in Chapter 4

and 5. This would make possible the determination of threshold values of these micronutrients,

along with Fe, Mn and macronutrients.

6.5.5 Influence of cobalt on iron transport

In Chapter 4 and Chapter 5, a novel relationship was observed in which reduced cobalt

availability increased the intracellular Fe quota of Microcystis aeruginosa. While we could

speculate about the causes of the relationship, further studies could improve our understanding of

this phenomenon by examining Co and Fe transport pathways to assess whether any competition

or inhibition is occurring. An increased concentration range of cobalt, including toxic levels,

could be used and assess if any deleterious effects are due to the prevention of Fe uptake and

subsequent Fe limitation. Alternatively, ROS could be measured to test if Fe is being assimilated

for detoxification.

6.6 Management recommendations

Nutrient pollution is common in anthropogenically modified systems and can lead to the

proliferation of cyanobacterial blooms. Traditionally, management of these blooms has focused

on reducing macronutrients, for example by limiting sediment-derived phosphorus or by

controlling nutrient sources in the catchment (Paerl 2018; Li et al. 2018). While trace metal

management is rarely considered in bloom mitigation, my research has provided strong evidence

116

for the ability of micronutrients to limit cyanobacterial growth under field conditions. This

management could be utilised in systems where reducing macronutrient availability is not

feasible. Some effective management techniques may be akin to those employed to manage

phosphorus and nitrogen inputs. For example, application of a sediment-capping agent to prevent

nutrient release from anoxic sediments or managing micronutrient inputs from the catchment.

Artificial mixing of the reservoir would likely assist in reducing micronutrient loading from

sediments in addition to phosphorus and nitrogen.

However, for these management techniques to be successful I recommend the inclusion of

micronutrients into monitoring plans, particularly Fe, Co, Mg and Mn as these have

demonstrated capacity to limit M. aeruginosa growth in culture or were correlated with the

growth of a bloom forming cyanobacteria in field conditions. With regular monitoring, the

suitability of micronutrient-based management techniques can be assessed, and potentially

introduce an alternative strategy for managing cyanobacterial blooms.

6.7 Conclusions Micronutrient trace metals appear to be vital for the growth of freshwater cyanobacteria and play

a role in structuring the broader phytoplankton community. In situ nutrient bioassays at Mannus

Lake and Burrendong Dam demonstrated that micronutrients can be a limiting factor for

cyanobacterial growth under field conditions. This may be pertinent during high-density bloom

events when there is high competition for nutrient resources. Micronutrient additions resulted in

a higher proportion of cyanobacteria in the community and appeared to favour cyanobacteria

over other phytoplankton. These results indicate that micronutrients may regulate the severity of

blooms in some freshwater systems.

A monitoring study was performed on Mannus Lake over 18 months to observe micronutrients

dynamics in the system and to understand the role of micronutrients in recurring cyanobacterial

blooms. Persistent thermal stratification and subsequent anoxia below the thermocline

corresponded with notably higher concentrations of the micronutrients Ca, Co, Fe, Mg, Mn and

117

Mo in the hypolimnial water compared to surface water. This trend suggests that sediments are

likely an important and significant source of dissolved micronutrients in Mannus Lake.

While thermal stratification appeared to be the primary driver of cyanobacterial biovolume at

Mannus Lake, micronutrients may have also played an important role. Cobalt, iron and

manganese generally correlated with C. ovalisporum biovolume during the 2018-19 bloom.

Further, RDA analysis indicated that Co concentration significantly influenced the

phytoplankton community and was positively correlated with C. ovalisporum biovolume. The

biovolume of cf. Microcystis sp. was positively correlated with Mg concentration, and likely Ca

and Mo which were removed from the analysis due to colinearity.

I grew a strain of Microcystis aeruginosa in modified MLA growth media, composed of reduced

concentrations of micronutrients (Co, Cu, Fe, Mn and Mo) to assess how low micronutrient

availability influences cyanobacterial growth. The growth of M. aeruginosa in cultures depleted

of Fe, Co and Mn exhibited decreased maximum cell density and growth rate compared to the

control treatment grown in micronutrient replete conditions. This confirms these elements are

required by M. aeruginosa for optimal cellular functioning and could not be replaced by another

element in the growth media. An interesting trend was observed in which low Co availability

caused an increase in intracellular Fe, possibly due to non-specifically binding of Co with

coordination sites normally occupied by Fe, or alternatively, to non-specific metallophores

produced by M. aeruginosa.

To quantify the cobalt requirements of Microcystis aeruginosa I conducted further culture

experiments in which M. aeruginosa was exposed to various concentrations of Co. Extended

exposure (>10 days) to Co concentrations below 0.06 μg/L resulted in significant inhibition of

growth. Ultra-trace level sampling and analysis techniques were used to assess Co concentrations

at ten freshwater lakes and reservoirs in NSW to determine whether sub-optimal levels occur in

natural systems. Of the ten sites sampled, four had Co concentrations below the 0.06 μg/L

threshold value. Interestingly, all four of these sites rarely undergo cyanobacterial blooms,

whereas the other six sites were above this threshold and do undergo blooms. This provides

evidence for the potential of Co to limit cyanobacterial growth in freshwater systems.

118

Taking Chapters 2 to 5 as separate studies, they all individually contribute valuable insights into

their respective sub-disciplines within aquatic ecology. When viewed together, the research

contributes to a more holistic understanding of the causes of cyanobacterial blooms and the

environmental factors that influence the phytoplankton community. The results provide a strong

case to increase monitoring of micronutrients in freshwater systems and to consider bloom

management strategies that target micronutrient reductions.

119

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Appendix A

Figure A1: Chlorophyll a results from nutrient amendment experiments at Mannus Lake (A), Burrendong Dam (B), Murray River at Euston (C), Murray River at Mildura (D), Hunter River at Morpeth (E), Windeyers Creek (F), Lake Lyell (G). Asterisk represents significant difference compared to the control (One-way ANOVA, p-value <0.05). Error bars are standard error of the mean, n=3.27

* * *

*

147

Figure A2: Total phytoplankton and cyanobacterial biovolume at different sites. Asterisk represents significant difference compared to the control (One-Way ANOVA, p-value < 0.05). Error bars are standard error of the mean, n=3.28

*

*

* *

*

* *

*

*

148

Figure A3: Proportion of community made up of several key phytoplankton groups (left) at different sites. Shannon Diversity Index (middle) and nMDS plots (right) illustrating differences in phytoplankton community structure between treatments. A square root transformation was performed on the community data for nMDS. Stress < 0.2. Error bars are standard error of the mean, n=3.29

149

Table A1: Summary statistics of phytoplankton and chlorophyll a data.10

Measure F statistic P-value

Mannus Lake Cyanobacteria biovolume F5,12 = 13.240 <0.001

Chlorophyll a F5,12 = 1.016 0.450

Total phytoplankton biovolume F5,12 = 8.530 0.001

Burrendong Dam Cyanobacteria biovolume F5,12 = 9.129 <0.001

Chlorophyll a F5,12 = 0.193 0.193


Murray River at Mildura

Cyanobacteria biovolume F5,12 = 24.610 <0.001

Chlorophyll a F5,12 = 21.200 <0.001

Total phytoplankton biovolume F5,12 = 15.290 <0.001

Murray River at Euston Cyanobacteria biovolume F5,12 = 13.990 <0.001

Chlorophyll a F5,12 = 10.010 <0.001


Hunter River at Morpeth

Cyanobacteria biovolume F5,12 = 0.584 0.712

Chlorophyll a F5,11 = 1.153 0.390


Windeyers Ck Cyanobacteria biovolume F5,12 = 8.268 0.001

Chlorophyll a F5,12 = 0.682 0.646


Lake Lyell

Cyanobacteria biovolume F5,12 = 1.552 0.246

Chlorophyll a F5,12 = 1.154 0.386


150

Table A2: Output of SIMPER analysis showing the three genera contributing the most to differences between treatments11

Site Treatments Genera Cumulative contribution

Burrendong Dam C, N Microcystis Aulacosiera Dolichospermum

0.841 0.878 0.903

C, P Microcystis Nitzschia Dolichospermum

0.591 0.661 0.718

C, NP Microcystis Scenedesmus Cryptomonas

0.494 0.729 0.797

C, NPM Microcystis Radiocystis Cryptomonas

0.483 0.631 0.701

C, M Microcystis Radiocystis Nitzschia

0.729 0.863 0.911

N, P Microcystis Dolichospermum Aulacosiera

0.827 0.853 0.877

N, NP Microcystis Scenedesmus Cryptomonas

0.700 0.8362 0.874

N, NPM Microcystis Radiocystis Cryptomonas

0.721 0.775 0.812

N, M Microcystis Radiocystis Nitzschia

0.395 0.622 0.732

P, NP Microcystis Scenedesmus Cryptomonas

0.439 0.691 0.752

P, NPM Microcystis Radiocystis Nitzschia

0.588 0.697 0.742

P, M Microcystis Radiocystis Nitzschia

0.739 0.821 0.882

NP, NPM Microcystis Scenedesmus Radiocystis

0.538 0.763 0.803

NP, M Microcystis Scenedesmus Radiocystis

0.644 0.790 0.843

NPM, M Microcystis Nitzschia Radiocystis

0.682 0.758 0.808

Mannus Lake C, N Chrysosporum Scenedesmus Mougeotia

0.687 0.745 0.7887

C, P Chrysosporum Mougeotia Scenedesmus

0.477 0.760 0.844

C, NP Mougeotia Chrysosporum Scenedesmus

0.405 0.710 0.828

151

C, NPM Chrysosporum Mougeotia Scenedesmus

0.804 0.858 0.898

C, M Chrysosporum Mougeotia Scenedesmus

0.886 0.927 0.942

N, P Chrysosporum Mougeotia Scenedesmus

0.496 0.787 0.835

N, NP Mougeotia Chrysosporum Scenesmus

0.441 0.726 0.832

N, NPM Chrysosporum Mougeotia Oocystis

0.833 0.878 0.911

N, M Chrysosporum Mougeotia Scenedesmus

0.894 0.930 0.934

P, NP Mougeotia Chrysosporum Scenedesmus

0.417 0.655 0.786

P, NPM Chrysosporum Mougeotia Oocystis

0.780 0.906 0.931

P, M Chrysosporum Mougeotia Scenedesmus

0.849 0.930 0.943

NP, NPM Chrysosporum Mougeotia Scenedesmus

0.673 0.850 0.898

NP, M Chrysosporum Mougeotia Scenedesmus

0.741 0.890 0.930

NPM, M Chrysosporum Mougeotia Oocystis

0.686 0.797 0.850

152

Table A3: The five most dominant genera on Day 0 of each experiment12

Site Genera Contribution to total algal biovolume on Day 0 (%)

Burrendong Dam Microcystis Cryptomonas Nitzschia Radiocystis Cyclotella

83.7 7.1 2.1 1.9 1.1

Mannus Lake Chrysosporum Scenedesmus Trachelomonas Cryptomonas Mallomonas

72.3 5.0 4.2 3.1 2.7

Lake Lyell Trachelomonas Euglena Peridinium Cymbella Oscillatoria

23.7 16.3 14.6 9.4 8.0

Euston Aphanocapsa Aulacoseira Staurastrum Dolichospermum Peridinium

69.8 7.7 4.9 3.5 2.7

Mildura Aphanocapsa Dolichospermum Aulacoseira Nitzschia Mougeotia

46.7 10.7 9.5 5.0 3.0

Morpeth Aulacoseira Cryptomonas Peridinium Navicula Scenedesmus

32.6 12.4 9.9 7.5 6.6

Windeyers Creek Synedra Aulacoseira Cocconeis Peridinium Mallomonas

34.2 23.8 10.7 7.9 5.4

153

Appendix B

Figure B1: Chlorophyll a concentrations at the dam sites (top) and upstream of the dam (bottom).30

154

Figure B2: Cyanobacterial biovolume upstream of Mannus Lake.31

Figure B3: High temporal resolution temperature data from the outlet site.32

155

Table B1: Summary of physicochemical data measured at the outlet site.13

Date Secchi Depth (cm)

Turbidity (NTU)

pH

Surface Bottom

Dissolved oxygen (mg/L)

Surface Bottom 10-Dec-18 70 9.93 7.67 10.2 0.0

21-Dec-18 20 21.4 7.81 7.37 0.7 0.0

10-Jan-19 15 9.81 7.39 18.8 0.0

31-Jan-19 120 8.1 7.27 2.6 0.0

13-Feb-19 100 9.12 8.12 8.8 4.7

20-Feb-19 90 9.7 7.82 10.8 0.0

10-Mar-19 80 12.5 9.46 7.24 10.8 0.0

02-Apr-19 100 9.07 8.83 10.9 8.6

16-Apr-19 100 9.37 7.72 11.8 4.9

6-May-19 90 8.0 7.5 7.1 5.5

26-Jun-19 50

22-Jul-19 30 7.82 7.4 8.3 8.3

05-Aug-19 40 7.07 6.71 10.5 6.3

16-Sep-19 75 14.9

03-Oct-19 100 11.7 7.95 7.36 11.3 3.8

21-Oct-19 100 7.42 6.91 9.9 7.0

28-Oct-19 100

14-Nov-19 90

04-Dec-19 60 18.6 8.59 8.24 11.0 8.7

17-Dec-19 15 42 9.16 7.34 14.1 0.9

21-Jan-20 70 57 7.32 6.94 5.3 0.0

06-Feb-20 80 11.9 8.01 7.01 9.8 0.0

13-Feb-20 50 8.17 6.98 10.2 1.6

02-Mar-20 40 22 8.46 7.16 10.4 0.3

17-Mar-20 80 16 8.35 7.06 10.6 1.8

01-Apr-20 60 23 7.73 7.21 9.1 2.8

21-Apr-20 50 45 8.05 7.64 10.6 8.6

07-May-20 40 26 7.14 7.16 8.9 6.1

25-May-20 65 21 7.02 7.12 7.3 6.9

23-Jun-20 15 58 7.1 7.0 8.0 7.6

156

Appendix C

Figure C1: Relationship between Microcystis aeruginosa cell count and A680.33

Table C1: concentrations of some macronutrients and micronutrients of interest in MLA media on day 0.14

Treatment Co (μg/L) Cu (μg/L) Fe (μg/L) Mn (μg/L) Mo (μg/L) P (mg/L)

Control 2.52 ± 0.23 3.25 ± 0.10 302.57 ± 0.07 98.67 ± 0.30 2.86 ± 0.04 5.35 ± 0.00

-Co <2 3.64 ± 0.38 298.53 ± 1.39 95.71 ± 0.45 2.99 ± 0.18 5.33 ± 0.04

-Cu 2.73 ± 0.27 <2 297.58 ± 2.19 97.57 ± 0.81 2.97 ± 0.17 5.34 ± 0.04

-Fe 2.94 ± 0.14 3.13 ± 0.20 <1 98.06 ± 0.31 2.77 ± 0.17 5.41 ± 0.01

-FeEDTA 2.59 ± 0.24 2.06 ± 0.05 3.18 ± 1.57 96.53 ± 1.57 2.80 ± 0.14 5.36 ± 0.07

-Mn 2.59 ± 0.15 4.91 ± 0.44 298.36 ± 0.14 0.77 ± 0.14 2.84 ± 0.11 5.30 ± 0.05

-Mo 2.64 ± 0.27 3.77 ± 0.13 304.87 ± 0.67 98.10 ± 0.67 <1 5.41 ± 0.02

importance of micronutrients for freshwater cyanobacterial

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