(Title of the Thesis)*

EXAMINING THE LONG-TERM ECOLOGICAL CONSEQUENCES OF GOLD MINING OPERATIONS, URBANIZATION, AND CLIMATIC CHANGES ON SUB-ARCTIC LAKES NEAR YELLOWKNIFE (NORTHWEST TERRITORIES, CANADA) USING DIATOMS AS PALEOENVIRONMENTAL INDICATORS

Branaavan Sivarajah

A thesis submitted to the Department of Biology

In conformity with the requirements for

the degree of Doctor of Philosophy

Queen’s University

Kingston, Ontario, Canada

(October, 2020)

Abstract

The mineral resource potential of Canada’s North has been recognized since the early 20th century with mines operating across all three territories. However, the long-term biological consequences of Northern mining operations and associated anthropogenic activities on surrounding freshwaters have not been investigated extensively. The sub-Arctic lakes around the

City of Yellowknife (Northwest Territories) provide an important opportunity to fill this knowledge gap by examining the long-term cumulative impacts of past gold mining operations, land-use changes related to urbanization, and regional climatic changes on aquatic biota. In this thesis, diatom-based (Bacillariophyceae) paleolimnological approaches were used to assess the long-term biological responses to the multiple environmental stressors mentioned above through space and time. A spatial survey of diatom assemblages from the surface sediment of 33 lakes around Yellowknife indicated that assemblage composition was strongly influenced by variables related to lake-water nutrient concentrations and ionic composition in the water column, while metal(loid) contaminants (i.e. antimony, arsenic) associated with gold mining activities played a secondary role. Analyses of diatom assemblages from ten well-dated sediment cores from lakes along a gradient of anthropogenic activities (i.e. mining and urbanization) recorded notable shifts over the last ~200 years. The high species turnover in the lakes closest to the mines and the city were indicative of metal(loid) pollution from past gold mining activities (e.g. roaster stack emissions, accidental spills from tailings pond) and eutrophication from land-use changes (e.g. sewage disposal, developments in the catchment). The impacts of climate-mediated changes were also apparent in both the urban and remote lakes. The synergistic effects of warming and nutrient enrichment led to striking changes at Niven and Jackfish lakes. Recent diatom assemblage composition in all lakes was substantially different from pre-disturbance periods, ii

which suggests that the sub-Arctic lakes around Yellowknife have crossed critical limnological thresholds in response to both anthropogenic activities and climatic changes. The long-term perspectives gained from this research contributes to the growing body of knowledge on biological responses to Northern mining contamination and urbanization in a warming world.

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Co-Authorship

Chapter 2 was co-authored by Jennifer B. Korosi (York University), Jules M. Blais (University of Ottawa), and John P. Smol (Queen’s University). The chapter represents original work that was completed as part of my PhD thesis. I participated in the study design and field work, analyzed diatom data, conducted all statistical analyses, and was the primary author of the manuscript. This chapter has been published separately in Hydrobiologia.

Sivarajah, B., Korosi, J.B., Blais, J.M., and Smol, J.P. 2019. Multiple environmental variables influence diatom assemblages across an arsenic gradient in 33 subarctic lakes near abandoned gold mines. Hydrobiologia 841:133-151.

Chapter 3 was co-authored by Cynthia L. Cheney (University of Ottawa), Madi Perrett (Queen’s University), Linda E. Kimpe (University of Ottawa), Jules M. Blais (University of Ottawa), and John P. Smol. This chapter represents original work that was completed as part of my PhD thesis. I designed the study, collected the diatom and sedimentary chlorophyll-a data from Alexie Lake, conducted all statistical analysis, and was the primary author of the manuscript. This chapter has been published separately in Polar Biology.

Sivarajah, B., Cheney, C.L., Perrett, M., Kimpe, L.E., Blais, J.M., and Smol, J.P. 2020. Regional gold mining activities and recent climate warming alter diatom assemblages in deep sub-Arctic lakes. Polar Biology 43:305-317.

Chapter 4 was co-authored by Brigitte Simmatis (Queen’s University), Elizabeth J. Favot (Queen’s University), Michael J. Palmer (Aurora Research Institute), and John P. Smol (Queen’s University). This chapter represents original work that was completed as part of my PhD thesis. I designed the study, conducted the field work, completed the sedimentary diatom and cladoceran analyses, and was the primary author of the manuscript. This chapter has been submitted for publication.

Sivarajah, B., Simmatis, B., Favot, E.J., Palmer, M.J., and Smol, J.P. (In Review) Eutrophication and climate warming lead to unprecedented cyanobacterial blooms in a Canadian sub-Arctic lake.

Chapter 5 was co-authored by John Smol (Queen’s University). This chapter represents original work that was completed as part of my PhD thesis. I designed the study, participated in the field work, collected the diatom data from seven lakes (Alexie, BC-02, BC-17, BC-43, Jackfish, Niven, and Vee), conducted all statistical analyses, and was the primary author of the manuscript.

Appendix A was co-authored by Jesse C. Vermaire (Carleton University), and John P. Smol (Queen’s University). This study was an extension of my PhD thesis and this research was conducted during my PhD. I designed the study, collected the cladoceran data, performed statistical analyses, and was the primary author of the manuscript. This manuscript has been submitted for publication. iv

Sivarajah, B., Vermaire, J.C., and Smol, J.P. (In Review) Assessing the potential environmental factors affecting Cladocera assemblage composition in arsenic- contaminated lakes near abandoned silver mines.

Appendix B was co-authored by Neal Michelutti (Queen’s University), Xiaowa Wang (Environment and Climate Change Canada), Christopher Grooms (Queen’s University), and John P. Smol (Queen’s University). This study was an extension of my PhD thesis and this research was conducted during my PhD. I co-designed the study with John P. Smol, participated in the field work, analyzed the water chemistry data, and was the primary author of the manuscript. This manuscript has been submitted for publication.

Sivarajah, B., Michelutti, N., Wang, X., Grooms, C., and Smol, J.P. (In Review) Limnology of lakes near Canada’s northernmost mine in Little Cornwallis Island, Nunavut.

Acknowledgements

Science is a team effort, and I have had the absolute pleasure of being a part of several wonderful teams over the last 4 years and learn from a diverse group of people! First, I would like to thank John Smol and my fellow PEARLites (past and present) for creating a fun, supportive, and productive environment to do science! John, thanks for accepting me as an undergraduate student and then guiding me throughout my graduate degrees, it is “legendary supervision” indeed! I am grateful for the opportunities and mentorship you have provided me to pursue Arctic research, and your passion to communicate science inspires all of us to be more engaging scientists. This diatom-based thesis would not have been possible if I didn’t know what diatoms were, and for that reason I am forever thankful to Kat Rühland, who introduced me to the microscopic world of diatoms! Kat, your excitement and willingness to share the breadth of knowledge about diatoms and paleolimnology have been so important to my research training. I would also like to thank Jenny Korosi and Neal Michelutti for giving me excellent advice on scientific writing and reading many drafts of my funding applications. Many thanks to my committee members Drs. Shelley Arnott and Melissa Lafrenière for providing constructive feedback and encouragement throughout my degree. I am also grateful to Dr. Brian Cumming for always saying yes to use his office computer software and sharing important insights regarding numerical approaches in paleolimnology.

PEARL is a very special place to do research, because of the people, both past and present. I am extremely thankful for the excellent guidance and mentorship provided by the research associates (Kat Rühland, Neal Michelutti, Josh Kurek, Adam Jeziorski, Carsten Meyer-Jacob, Kate Laird) and technical staff (Chris Grooms and John Glew) throughout my time at PEARL, especially when I started as a 3rd year undergraduate student! Many thanks to the past and present graduate students (and visiting students) at PEARL with whom I have had the privilege of doing science, attending conferences around the world, go on exciting adventures in the field, celebrate accomplishments, getting through challenging days, and, most importantly, sharing numerous laughs around the Secchi table. Thank you for making my time at PEARL an exciting one! Specifically, I would like to extend my sincere appreciation to Andrew Labaj, Emily Stewart, Jamie Summers, Clare Nelligan, Liz Favot, Moumita Karmakar, Joeline Lim, Brigitte Simmatis, Cale Gushulak, Robin Valleau, Kathryn Hargan, Katie Griffiths, Rachel Melnik-Proud, Cécilia Barouillet, Nell Libera, Katherine Moir, Mackenzie Waller, Max Boreux, Matt Duda, Madi Perrett, Graham Mushet, Kristen Coleman, Guy Thierry Tenkouano, Monica Garvie Fisher, and Janae Rampone for their friendship, encouragement, and advice.

This thesis became a possibility because of the many collaborators at PEARL, Queen’s Geology (Drs. Heather Jamieson and Christopher Schuh) and elsewhere. I am grateful to Drs. Jennifer Korosi and Joshua Thienpont (York University) for leading the first field season during the winter of 2014, which laid the groundwork for the Yellowknife project! I would also like to thank Dr. Jules Blais at the University of Ottawa and members of his lab (Cynthia Cheney, Mija Azdajic, Linda Kimpe, Dave Eickmeyer, Laurent Gallant) who helped me with field work in Yellowknife and the High Arctic, and coordinated the sedimentary metal(loid) analyses. Many thanks are also due to Dr. Jesse Vermaire at Carleton University for introducing me to the limnological legacies of past silver mining operations around Cobalt, Ontario and inviting me to vi

join the project. Specifically, I would like to thank Amanda Little and Christina Frendo for welcoming me into the project and introducing me to world of smoothie bowls during our field adventures in Cobalt. I am also grateful to Mike Palmer (Aurora Research Institute, Yellowknife) for discussing potential project ideas, sharing his expertise, and helping me with field work logistics. Many thanks to Xiaowa Wang (Environment and Climate Change Canada) for coordinating the water chemistry analyses for the High Arctic lakes, and to Ajay Persaud (York University) and Brigitte Simmatis for providing excellent field assistance in 2019. Thank you to Dr. Andrew Paterson, Carmen Pereira, and Anna DeSellas for hosting me at the Dorset Environmental Science Centre for multiple internships and allowing me to tag along on the winter field sampling programs. I would like to thank Dr. Jonathan Holmes for hosting me at the Environmental Change Research Center (University College London) and teaching me ostracod- based paleolimnological approaches. I am also thankful to the members of the ECRC (Lucy Roberts, Chiara Bancone, Joanna Tindall, Janet Hope), who made my visit to the UK an adventurous one!

Many thanks to the funding organization who made my doctoral research possible. Specifically, the Natural Sciences and Engineering Research Council of Canada (Alexander Graham Bell Canada Graduate Scholarship and Michael Smith Foreign Study Supplement), W. Garfield Weston Foundation (Doctoral award for Northern Research), Polar Knowledge Canada (Northern Scientific Training Program), and Queen’s University (Arthur B. McDonald Prize and Dean’s Doctoral Travel Award).

Thank you to all my friends who have been an incredible part of this journey and always encouraging me to take a break from work. In particular, thanks to Sinthu Sivanesan, Anoodth Naushan, Andrew Labaj, Sharmini Parasivam, Shakira Azan, Zoya Islam, Jennifer Hosek, Sarah Hasnain, and Erin Neville for their endless encouragement and excellent advice. Many thanks to Julie French, Joanne Surette, Arunima Khanna, Catherine Landon, and Stu Grunwall for their support and chats during my time at Queen’s. I am also grateful to the QTSA crew (Sinthu Sivanesan, Tharsi Sundaralingam, Geeththana Sriharigalan, Kamallesh Canjimavadivel, Dawn David, Tehani Guruge, Kirusika Balasinham) for all the fun times in Kingston and Toronto! Thank you to Jeeth Panchadcharam and Niventhini Indrajith for always inviting me over and having the best discussions about politics, education, economics, and life over great food! Many thanks to Justine Grob, Alex Voinot, Benjamin Amann, Cécile Wehrell, George Semple, Nicolas Hili and Carsten for all the Kingston running and food adventures, and encouraging me to play Mölkky, even when I could never win! Especially, thanks to Carsten for introducing me to the wonderful world of trail running and always reminding me that it’s never a bad time to run, especially after a long day at work.

I would like to thank my family, the Kanagaratnams’ and the Selliahs’, who have provided unwavering support ever since I started school. Specifically, I would like to thank Kailas, Uma, Lakshmana, Devaki, Vithiya, Ranji, and Ammammah, I am indebted to your kindness, support, and care. I am particularly grateful for Sarika and Sayanthan, who are the best kind of siblings anyone could ask for. Finally, I would like to thank my parents, Usha and Sivarajah, for believing in me and encouraging me to pursue science. This thesis would not have been possible without you. Thank you!

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Dedication

This thesis is dedicated to the memories of Mohaneswary Balasubramanium & John R. Glew who were incredible teachers, mentors & friends

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Table of Contents

Abstract ...... ii Co-Authorship...... iv Acknowledgements ...... vi Dedication ...... viii List of Figures ...... xiv List of Tables ...... xv List of Abbreviations ...... xvi Chapter 1 General Introduction...... 1 1.1 Mining in Canada and the Northwest Territories ...... 1 1.2 Environmental damage from gold mining operations in Yellowknife ...... 3 1.3 Consequences of past gold mining activities on aquatic ecosystems around Yellowknife...... 5 1.4 The impacts of multiple environmental stressors on the lakes around Yellowknife ...... 6 1.5 Tracking the cumulative impacts of multiple long-term biological consequences using paleolimnology ...... 8 1.6 Diatom are excellent indicators of multiple environmental stressors ...... 10 1.7 Thesis objectives ...... 11 1.8 References ...... 15 Chapter 2 Multiple environmental variables influence diatom assemblages across an arsenic gradient in 33 subarctic lakes near abandoned gold mines ...... 22 2.1 Abstract ...... 22 2.2 Introduction ...... 23 2.3 Study area...... 25 2.4 Methods...... 27 2.4.1 Sample collection ...... 27 2.4.2. Diatom preparation and identification ...... 28 2.4.3 Data screening for ordination analyses ...... 30 2.4.4 Ordination analysis ...... 31 2.4.5 Additional statistical analysis ...... 32 2.5 Results ...... 33 2.5.1 Limnological characteristics of lakes around Yellowknife ...... 33 2.5.2 Relationships between diatom assemblages and water chemistry parameters from arsenic- contaminated lakes around Yellowknife...... 35 ix

2.6 Discussion ...... 39 2.6.1 Limnology of Yellowknife lakes ...... 39 2.6.2 Diatom assemblages from arsenic-contaminated lakes around Yellowknife ...... 40 2.6.3 The influence of multiple environmental variables on diatom assemblages in Yellowknife lakes ...... 46 2.7 Acknowledgements ...... 49 2.8 References ...... 49 Chapter 3 Regional gold mining activities and recent climate warming alter diatom assemblages in deep sub-Arctic lakes ...... 65 3.1 Abstract ...... 65 3.2 Introduction ...... 66 3.3 Study area...... 69 3.4 Materials and Methods ...... 71 3.4.1 Sediment coring and chronology ...... 71 3.4.2 Element analysis ...... 72 3.4.3 Diatom analysis ...... 73 3.4.4 Sedimentary chlorophyll-a ...... 73 3.4.5 Temperature data ...... 74 3.4.6 Data analysis ...... 74 3.5 Results ...... 75 3.5.1 Radiometric dating ...... 75 3.5.2 Element concentrations in sediments ...... 75 3.5.3 Diatom assemblage changes at Grace and Alexie lakes ...... 76 3.5.4 Past trends in whole-lake primary production ...... 78 3.5.5 Temperature data ...... 79 3.6 Discussion ...... 79 3.6.1 Geochemical signals of mining operations and land-use changes ...... 79 3.6.2 Biological responses to multiple environmental stressors at Alexie and Grace lakes ...... 81 3.6.3 Changes in the limnology of deep sub-Arctic lakes around Yellowknife ...... 84 3.7 Acknowledgements ...... 86 3.8 References ...... 86 Chapter 4 Eutrophication and climate warming lead to unprecedented cyanobacterial blooms in a Canadian sub-Arctic lake ...... 100 4.1 Abstract ...... 100 x

4.2 Introduction ...... 101 4.3 Materials and Methods ...... 104 4.3.1 Study area ...... 104 4.3.2 Sediment coring and dating ...... 106 4.3.3 Spectral inferences of trends in whole-lake primary production and cyanobacteria ...... 107 4.3.4 Subfossil analysis of diatoms, cladocerans, and chironomids ...... 109 4.3.5 Monitoring data ...... 110 4.4 Results ...... 111 4.4.1 Paleoecology of Jackfish Lake ...... 111 4.4.2 Past trends in local meteorology and ice phenology ...... 112 4.4.3 Regional trends in primary production and cyanobacterial abundance ...... 113 4.5 Discussion ...... 114 4.5.1 Jackfish Lake was impacted by multiple environmental stressors during the 20th century ..... 114 4.5.2 The unprecedented 21st century cyanobacterial blooms are a cumulative response to nutrient enrichment and warming ...... 119 4.5.3 Regional trends in primary production and cyanobacteria ...... 122 4.5.4 Outlook for (sub-) Arctic lake water quality and occurrence of cyanobacterial blooms ...... 123 4.6 Acknowledgments ...... 124 4.7 References ...... 125 Chapter 5 Long-term diatom responses to metal(loid) pollution, urbanization, and climatic changes in 10 sub-Arctic lakes around Yellowknife, Northwest Territories, Canada ...... 139 5.1 Abstract ...... 139 5.2 Introduction ...... 140 5.3 Study Area ...... 143 5.4 Materials and Methods ...... 146 5.4.1 Sediment coring and dating ...... 146 5.4.2 Laboratory analyses ...... 147 5.4.3 Statistical analyses ...... 147 5.5 Results ...... 149 5.5.1 Diatom assemblage changes at BC-02, BC-17, BC-43, and Vee lakes ...... 149 5.5.2 Regional changes in diatom assemblages ...... 151 5.6 Discussion ...... 154 5.6.1 Magnitude of diatom assemblage changes vary among Yellowknife lakes ...... 154 5.6.2 Differences in the timing of diatom assemblage changes among sites ...... 155 xi

5.6.3 Diverse diatom responses to multiple environmental stressors in Yellowknife lakes ...... 156 5.6.4 Limnological changes in the lakes around Yellowknife and biological recovery ...... 162 5.7 Acknowledgements ...... 164 5.8 References ...... 164 Chapter 6 General Summary and Conclusions ...... 182 6.1 Major findings ...... 183 6.2 Significance and recommendations for lake management ...... 185 6.3 Future research ...... 186 6.4 References ...... 187 Appendix A Assessing the potential environmental factors affecting cladoceran assemblage composition in arsenic-contaminated lakes near abandoned silver mines ...... 189 A.1 Abstract ...... 189 A.2 Introduction ...... 190 A.3 Study area ...... 193 A.4 Material and Methods ...... 194 A.4.1 Sample Collection ...... 194 A.4.2 Laboratory analyses ...... 195 A.4.3 Numerical analyses...... 196 A.5 Results ...... 198 A.5.1 Limnology of lakes around Cobalt, Ontario ...... 198 A.5.2 Cladocera assemblages from contaminated and uncontaminated lakes ...... 199 A.5.3 Changes in Cladocera assemblages since pre-industrial time ...... 201 A.6 Discussion ...... 202 A.6.1 Environmental determinants of recent limnological characteristics ...... 202 A.6.2 Spatial distribution of cladoceran...... 203 A.6.3 Temporal changes in cladoceran assemblages...... 205 A.7 Conclusions ...... 209 A.8 Acknowledgements ...... 209 A.9 References ...... 210 Appendix B Limnological characteristics of lakes near Canada’s northernmost mine, Little Cornwallis Island, Nunavut ...... 228 B.1 Abstract ...... 228 B.2 Introduction ...... 228 B.3 Materials and Methods ...... 230 xii

B.4 Results ...... 232 B.5 Discussion ...... 234 B.6 Acknowledgements ...... 237 B.6 References ...... 237 Appendix C Supporting information for Chapter 2 ...... 249 Appendix D Supporting information for Chapter 3 ...... 254 Appendix E Supporting information for Chapter 4 ...... 257 Appendix F Supporting information for Chapter 5 ...... 272 Appendix G Raw diatom data from Chapter 1 ...... 281 Appendix H Raw diatom data for Alexie Lake ...... 293 Appendix I Raw diatom data for BC-02 ...... 297 Appendix J Raw diatom data for BC-17 ...... 300 Appendix K Raw diatom data for BC-43 ...... 302 Appendix L Raw diatom data for Jackfish Lake ...... 304 Appendix M Raw diatom data for Vee Lake ...... 309

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List of Figures

Figure 2.1: A map of the study area of lakes around Yellowknife, Northwest Territories ...... 59 Figure 2.2: PCA of measured environmental variables ...... 60 Figure 2.3: Histograms of the dominant diatom taxa from the Yellowknife area ...... 61 Figure 2.4: Relationships between the relative abundances of most common taxa, and depth ...... 62 Figure 2.5: Results of the CCA (site scores) ...... 63 Figure 2.6: Results of the CCA (species scores) ...... 64 Figure 3.1 Location of the study sites around Yellowknife ...... 93 Figure 3.2 Aerial photographs of Grace Lake and the surrounding area ...... 94 Figure 3.3 Sedimentary metal(loid) concentrations of Grace and Alexie lakes ...... 95 Figure 3.4 Sedimentary diatom data from Grace Lake ...... 96 Figure 3.5 Sedimentary diatom data from Alexie Lake ...... 97 Figure 3.6 Results of Principal component analysis (PCA) ordination ...... 98 Figure 3.7 The mean annual air temperature data from Yellowknife and Hay River ...... 99 Figure 4.1 A map of the study area ...... 135 Figure 4.2 Biostratigraphy of Jackfish Lake ...... 136 Figure 4.3 Monitoring data from Yellowknife ...... 137 Figure 4.4 Comparison of whole lake primary production and cyanobacterial abundance ...... 138 Figure 5.1 A map of all study lakes ...... 175 Figure 5.2 Diatom stratigraphies of BC-02, BC-43, Vee, and BC-17 ...... 177 Figure 5.3 Diatom species compositional turnover...... 179 Figure 5.4 Trends in the most common taxa ...... 180 Figure 5.5 Trends in other common taxa ...... 181

xiv

List of Tables

Table 2.1: Results of ANOSIM...... 58 Table 2.2: Results of species richness and Hill’s N2 diversity ...... 58 Table 3.1: Limnological and environmental information collected for Grace and Alexie lakes ...... 92 Table 4.1: Limnological characteristics of the 9 study lakes around Yellowknife, Northwest Territories, Canada ...... 134 Table 5.1: Limnological and environmental information collected for the 10 study lakes from Yellowknife ...... 173 Table 5.2: Summary of the DCCA...... 174

List of Abbreviations

ANOSIM – Analysis of similarities

As – Arsenic

BDL – Below detection limit

CCA – Canonical correspondence analysis

CONISS – Constrained incremental sum of squares

CRS – Constant rate of supply

DCCA – Detrended canonical correspondence analysis

DCA – Detrended correspondence analysis

DOC – Dissolved organic carbon

ICP-MS – Inductively coupled plasma-mass spectroscopy

Na+ – Sodium

NT – Northwest Territories

PCA – Principal component analysis

Pb – Lead

RDA – Redundancy analysis

Sb - Antimony

SQRT – Square root transformed

TN – Total Nitrogen

TP – Total Phosphorus

VRS Chl-a – Visible reflectance spectroscopy of chlorophyll-a

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Chapter 1

General Introduction

1.1 Mining in Canada and the Northwest Territories

Canada is a Northern nation rich in natural resources (e.g. potash, uranium, gemstones, cobalt, diatoms, gold, titanium), which has made the country a global leader in mining production and innovation (Mining Association of Canada 2018). Natural resource extraction operations occur in all ten provinces and the three territories (Natural

Resources Canada 2019). The mining sector is an important contributor to Canada’s economy as it creates jobs and forms the basis for revenue generation in several remote regions, often referred as “company towns” or simply “mining towns”. Between 2010 and 2018, minerals produced in Canada were consistently valued at more than $40 billion annually, with metals bringing the highest production value (Natural Resources Canada

2019). Not surprisingly, gold is the most valued metal (and commodity) produced in

Canada, and the production has increased from ~100 tonnes in 2010 to more than 180 tonnes in 2018 (Natural Resources Canada 2020). However, gold mining is not a new industry in Canada; in fact, there is a long history associated with gold production, including a 50-year sub-Arctic operation that now requires ~ $1 billion for site clean-up and remediation.

The mineral resource potential in Canada’s North has been recognized for a long time and large-scale mining operations have been undertaken in all three territories. In the

Northwest Territories alone, more than 80 mines of varying size and duration have operated since the beginning of the 20th century (Silke 2009; Northwest Territories

Industries, Tourism and Investment 2017). The exploration and extraction of precious metals, such as gold and silver, laid the foundation for mining operations in the

Northwest Territories during the first three decades of the 20th century (Nassichuk 1987;

Silke 2009). However, during World War II, due to a shortage of labor and materials, precious metal mining activities declined in the territory, while exploration and extraction for strategic minerals (e.g. tungsten, tantalum, lithium) gained interest (Nassichuk 1987;

Wolfe 1998). Once the war ended precious and base metal mining activities regained momentum and several operations were established across the Northwest Territories

(Nassichuk 1987; Silke 2009). More recently (post-1990s), the focus of mining operations in the territory has shifted towards diamonds (Hall 2013).

Even though resource extraction operations have been taking place for nearly a century in the Northwest Territories, the long-term environmental consequences of both active and abandoned mines are not thoroughly understood. Specifically, freshwater sampling protocols intended to assess the potential damage to climatically sensitive aquatic habitats and food web interactions have only recently been mandated by the territorial government (e.g. Galloway et al. 2015; Palmer et al. 2015). Hence, very little long-term ecological information is available on the impacts of early resource extraction activities. This thesis examines the long-term ecological consequences of past gold mining activities on the lakes around Yellowknife, Northwest Territories, within the context of multiple environmental stressors.

1.2 Environmental damage from gold mining operations in Yellowknife

Yellowknife, capital of the Northwest Territories, is situated on Chief Drygeese

Territory, which has been the traditional home of the Wıìlıìdeh Yellowknives Dene for centuries (Wıìlıìdeh Yellowknives Dene 1997). The gold deposits, which are hosted in arsenopyrite within the Yellowknife Greenstone belt, were first identified by the

Geological Survey of Canada in the late-1890s (Coleman 1957; Wolfe 1998). However, geological mapping of the Yellowknife area and detailed explorations around the 1920s led to the establishment of gold mining operations in the 1930s (Wolfe 1998). The first gold brick was poured at Con Mine (Consolidated Mining and Smelting Company) in

1938 and mining operations occupied a prominent position in the early development of

Yellowknife as a Northern urban center (Wolfe 1998). The discovery of new ores just outside of Yellowknife and increased availability of labor by the end of World War II revived gold production in the Yellowknife area in the late-1940s and operations began at the Giant Mine by the Giant Yellowknife Gold Mines Limited in 1948 (Nassichuk 1987;

Wolfe 1998; Silke 2009).

The commencement of mining operations at Giant Mine marked the beginning of a new era in Arctic mining as it was one of the earliest, large-scale natural resource extraction operation that lasted for over five decades. However, the extensive gold ore processing and lack of strict emission controls during the early years at Giant Mine resulted in arsenic (As) contamination of the surrounding terrestrial and aquatic environments (Jamieson 2014). This was mainly because gold ores found around Giant are predominantly hosted in large grains of sulfide minerals such as arsenopyrite and pyrite (Coleman 1957; Indian and Northern Affairs Canada 2007). When the arsenopyrite

and pyrite ores were roasted, a pre-leaching oxidation process prior to cyanidation, large quantities of As vapor, As-bearing iron oxides and sulfur dioxide were released into the environment via roaster stacks (Walker et al. 2005; Palmer et al. 2015). The As vapor precipitated into solid-phase arsenic trioxide (As2O3) in the atmosphere and was deposited around Yellowknife (Walker et al. 2005). The emission of toxic As2O3 into the environment between 1949 and 1999 totaled more than 20,000 tonnes with greater amounts (~7.3 tonnes per day!) being released during the early years (1948 - 1951) of operation (summarized by Jamieson 2014). However, the death of a Dene boy in 1951 due to acute arsenic poisoning led to the implementation of a series of emission control measures (Jamieson 2014). An electrostatic precipitator was installed in 1951 that dramatically reduced the amount of As2O3 entering the environment through roaster stacks (~5.5 vs. 1.5 tonnes per day in 1952 and 1958, respectively) (Indian and Northern

Affairs Canada 2007; Jamieson 2014). Further reductions in As2O3 emissions became possible by the construction of a baghouse and underground storage facilities (Indian and

Northern Affairs Canada 2007; Jamieson 2014). These emission controls substantially reduced the amount of As2O3 that entered the environment (~0.01 – 0.9 tonnes per day).

Presently, 237,000 tonnes of As2O3 is stored in the underground chambers and a ~$1 billion remediation effort is underway, paid for by the federal government, as the Giant

Mine property has been abandoned by its previous owners. However, the ~$1 billion remediation plan only encompasses the Giant Mine lease property and not the surrounding environment that has received ~20,000 tonnes of As2O3 over a period of five decades.

1.3 Consequences of past gold mining activities on aquatic ecosystems around

Yellowknife

Arsenic concentrations are elevated in terrestrial (e.g. Hocking et al. 1978;

Hutchinson et al. 1982; St-Onge 2007) and aquatic (e.g. Moore et al. 1979; Mudroch et al. 1989; de Rosemond et al. 2008; Galloway et al. 2015) environments around the City of Yellowknife. Specifically, recent (since 2010) regional assessments of lakes within and around the City of Yellowknife have reported that As concentrations are still exceeding the Canadian water quality guidelines for the protection of aquatic life (5 µg/L;

Canadian Council of Ministers of the Environment 1999) and drinking water (10 µg/L;

Health Canada 2014; e.g. Palmer et al. 2015, 2019; Galloway et al. 2015; Houben et al.

2016). Both Palmer et al. (2015) and Houben et al. (2016) have shown that As concentrations of lakes declined with increasing distance from the Giant Mine point source and generally reached low levels (< 10 µg/L) beyond a distance of 17 km.

Furthermore, Van den Berghe et al. (2018) observed As2O3 particles in sediments from three lakes around Yellowknife, thus providing further evidence that higher concentrations of As are due to anthropogenic activities rather than local geology. The observation of elevated As concentrations in lakes around Yellowknife 10-15 years after roasting had ceased at Giant is concerning because many of these lakes are heavily used by local residents and tourists for recreational and fishing purposes.

Much of the previous freshwater biological research efforts have focused on the

Back Bay and Yellowknife Bay of Great Slave Lake, as these areas received mine effluents from Giant Mine via Baker Creek (e.g. Moore 1981; Mudroch et al. 1989; de

Rosemond et al. 2008). Specifically, sediment core analyses from these embayments have

reported increases in sedimentary metal(loid) concentrations (arsenic, antimony, copper, lead, and zinc) corresponding to the timing of Giant Mine operations (Mudroch et al.

1989; Andrade et al. 2010; Pelletier et al. 2020). Similarly, geochemical analysis of sediment cores from several lakes around Yellowknife have also recorded substantial increases in metal(loid)s when the mines were in operation (Dirszowsky and Wilson

2016; Thienpont et al. 2016; Schuh et al. 2018; Cheney et al. 2020). More recently

Cheney et al. (2020) conducted a regional examination of 20 lakes and used a palaeotoxicity model to demonstrate that sedimentary concentrations of arsenic and antimony exceeded the interim sediment quality guidelines at several lakes during the mining era.

Furthermore, investigations on fish from Yellowknife and Back bays have reported elevated levels of total As concentrations in the body tissues of various fish species (de Rosemond et al. 2008; Cott et al. 2016). Limnological studies of “smaller” lakes around the City of Yellowknife have also recorded higher concentrations of As in the water column, sediments, zooplankton, phytoplankton and macrophytes in the contaminated lakes (Wagemann et al. 1978; Moore et al. 1979; Caumette et al. 2011).

More recently, Nasser et al. (2016) examined the effects of arsenic pollution on lacustrine arcellinina (testate amoebae) in the lakes around Yellowknife.

1.4 The impacts of multiple environmental stressors on the lakes around

Yellowknife

Gold mining operations around Yellowknife was the most prominent source of employment during the 1950s and 1960s, when Yellowknife was a “small mining town”,

and it laid the foundation for population growth in the area (Wolfe 1998). However, the rapid growth in population began after 1967, when Yellowknife was designated the capital of the Northwest Territories and became an important centre for territorial government administration (Wolfe 1998). The increase in population throughout the 20th century, especially after 1967, led to notable land-use changes associated with urbanization in this region, which inevitably impacted the local lakes (e.g. Dirszowsky and Wilson 2016; Gavel et al. 2018; Stewart et al. 2018). Therefore, in addition to elevated As concentrations, the some lakes within the city limits have also been impacted by nutrient enrichment from local land-use changes. For example, raw sewage was routed into Niven Lake from 1948 to 1981, which has led to the eutrophication of this site

(Stewart et al. 2018). Presently, Niven has elevated As concentrations and supports prolific macrophyte growth due to high nutrient availability (Koch et al. 2000). Similarly,

Frame Lake has also eutrophied during the 20th century as it was impacted by infrastructure development in the catchment (Dirszowsky and Wilson 2016; Gavel et al.

2018;). Very recently (post-2013) annual cyanobacterial algal blooms have been reported at Jackfish Lake; this lake is a source of cooling water for the Northwest Territories

Power Corporation plant located on its northern shores. Furthermore, a series of small lakes (Meg-Keg-Peg lake chain) near Con Mine received accidental spills of salt-rich tailings from Pud Lake (a small shallow lake where tailings from the mine were routed)

(Bright et al. 1996). Another nearby lake, Kam Lake, also received accidental spills from

Pud Lake, in addition to receiving raw sewage briefly during the 1970s (Bright et al.

1996).

Furthermore, there is unequivocal scientific evidence that climate warming poses significant threat to the functioning of Northern freshwaters and the impacts of recent climatic changes have been tracked using direct monitoring records and proxy data preserved in lake sediments (Smol et al. 2005; Duguay et al. 2006; Smol and Douglas

2007; Vincent et al. 2013). Previous investigations of a wide spectrum of Northern lakes suggest that the ice-cover duration has declined, and stratification and mixing regimes have also changed (Smol et al. 2005; Mueller et al. 2009; Thienpont et al. 2013; Griffiths et al. 2017). Similar to other Northern regions, climate warming is also impacting the functioning of freshwaters around Yellowknife (Mullan et al. 2017; Stewart et al. 2018).

The regional air temperature has warmed by 2.7 oC between the mid-1940s and 2016, and regional warming has accelerated since the mid-to late-1950s (Mullan et al. 2017;

Sivarajah et al. 2020, In Review). Direct monitoring records of three lakes in this region suggest that the ice-cover duration has decreased since the mid-20th century (Benson et al. 2013) and is consistent with reports from other sub-Arctic and Arctic regions (Duguay et al. 2006).

1.5 Tracking the cumulative impacts of multiple long-term biological consequences using paleolimnology

Previous limnological research around Yellowknife has provided considerable information on the history of As contamination and biological responses to elevated As.

However, these studies usually provide information from a specific time period (snapshot), whilst long-term (>50 years) continuous monitoring data that assesses the cumulative effects of As contamination and other stressors on aquatic ecosystems around

Yellowknife are not abundant. The lack of long-term monitoring data, specifically from the pre-mining era, poses challenges for identifying the consequences of long-term metal(loid) exposure on biological organisms in these sub-Arctic lakes. Fortunately, natural archives, such as lake sediments, preserve the remains of many sensitive biological indicators, and faithfully record past concentrations of contaminants such as

As over long-periods of time. Hence, the study of lake sediment cores (paleolimnology) can be used to reconstruct the missing long-term environmental data.

Paleolimnology has been widely used in Canada and around the world to assess the impacts of mining and other regional stressors on temperate lake ecosystems (Smol

2008). For example, the early paleolimnological assessments of many lakes around the

Sudbury region gathered a plethora of important information on the biological responses to metal contamination (copper and nickel) and acidification (e.g. Dixit et al. 1992).

However, recent paleolimnological investigations of this region have expanded this research by examining biological responses to industrial contamination and recovery within the context of multiple environmental stressors such as urbanization and climatic changes (Tropea et al. 2010, 2011; Labaj et al. 2015; Sivarajah et al. 2017). Unlike temperate regions, there are fewer paleolimnological research programs focusing on the effects of industrial activities within the context of multiple stressors in Canada’s North

(sub-Arctic and Arctic environments) where major mining operations have been undertaken in the past century. To fill these data gaps, the overall goals of this thesis are to use paleolimnological approaches to: 1) establish the baseline limnological conditions of Yellowknife lakes prior to anthropogenic activities; 2) determine how biota responded

to mining contamination, urbanization, and climatic changes; and 3) assess if there are any signs of biological recovery from mining pollution in warming world.

1.6 Diatom are excellent indicators of multiple environmental stressors

Diatoms (Bacillariophyceae) are unicellular siliceous algae, occupying a key position in aquatic food webs as they are important for global primary production and aquatic energy cycling. Diatoms are also excellent limnological indicators as they rapidly respond to changes in chemical (nutrients, pH, metal(loid) contamination) and physical

(thermal properties in lakes, habitat availability) conditions in freshwater ecosystems

(e.g. Dixit et al. 1991; Cumming et al. 1992; Battarbee et al. 2001; Smol and Douglas

2007; Hall and Smol 2010; Rühland et al. 2015). In paleolimnological investigations, diatoms are the most commonly used paleo-bioindicator groups, because the siliceous frustules preserve well in lake sediments, are identifiable to species level, and different species have well-defined optima and tolerances for various limnological parameters

(Smol 2008).

Due to their sensitivity to industrial pollution, diatoms are widely used to monitor ecosystem health and are regularly used in laboratory-based ecotoxicological experiments

(Morin et al. 2008; Barral-Ferga et al. 2016). Previous paleolimnological assessments have shown that diatoms are affected by metal(loid) contamination (aluminum, copper, nickel) in, for example, temperate (e.g. Sudbury, Dixit et al. 1992) and subalpine (e.g.

Lago d’ Orta, Italy, Cattaneo et al. 1998, 2008) lakes. Generally, declines in planktonic

(pelagic) diatom species (e.g. Discostella stelligera), increases in the abundance of pollution-tolerant benthic taxa (e.g. Achnanthidium minutissimum), and low diatom

species diversity (e.g. Lake Orijärvi in Finland), are observed in lakes that have been metal(loid) contaminated (Salonen et al. 2006; Cattaneo et al. 2008).

Diatoms are also extensively used to track the impacts of eutrophication and climate-mediated changes in lakes around the world (reviewed by Hall and Smol 2010;

Rühland et al. 2015). Furthermore, a growing number of studies are using diatom-based paleolimnological approaches to understand the cumulative impacts of multiple environmental stressors on aquatic ecosystems (e.g. Tropea et al. 2010; Nelligan et al.

2016; Sivarajah et al. 2018). Specifically, studies on temperate lakes have used diatom- based approaches to understand the cumulative impacts of industrial contamination and recovery within the context of urbanization and/or recent climate warming (Tropea et al.

2011; Sivarajah et al. 2017). However, similar regional assessments have not been undertaken in sub-Arctic Canada, even though this region has a long history of mining activities and has warmed significantly since the mid-20th century. Hence, in this thesis, I am using diatom assemblage changes as my primary indicators to assess how sub-Arctic lakes around Yellowknife have responded to multiple environmental stressors during the

20th and early-21st centuries.

1.7 Thesis objectives

This thesis aims to determine the long-term ecological consequences of metal(loid) pollution from past gold mining activities around Yellowknife within the context of multiple environmental stressors, such as land-use changes related to urbanization (e.g. raw sewage input, development in the catchment, run-off from waste disposal site) and climate-mediated changes to lake properties (e.g. longer open-water

duration, stronger thermal stratification). To achieve this goal, I have used modern limnological data, long-term meteorological records collected at the Yellowknife airport, and diatom-based paleolimnological approaches, along with other sedimentary proxies

(brief descriptions of all proxies and justification for the use of each proxy are provided in each data chapter) in this thesis.

Chapter 2: Previous investigations of temperate lakes have examined the relationships between modern limnological variables and surface sediment diatom assemblages (i.e. flora from the most recent years) from lakes where metal concentrations vary greatly (e.g. aluminum, nickel) (Dixit et al. 1991; Cumming et al. 1992). However, the sensitivity of diatoms to As contamination has not been extensively documented in lakes at the landscape level, and data are especially sparse from sub-Arctic regions. In

Chapter 2, my colleagues and I assess the impacts of As on diatom taxa by examining surface sediment diatom assemblages from 33 sub-Arctic lakes along a large As gradient

(~1.5 µg/L – 2780 µg/L), using the Giant Mine as the center of the majority of emissions.

To achieve this goal, we used multivariate ordination approaches (i.e. principal correspondence analysis, detrended correspondence analysis and canonical correspondence analysis) to discern the relationship between surface-sediment diatom samples and a suite of limnological variables from 33 lakes around Yellowknife. The data from this chapter is used to: 1) determine the relative importance of As and other environmental variables on the recent diatom assemblages; 2) identify indicator diatom taxa that may be present (or absent) when As concentrations are high (> 10 µg/L); and 3) assess if diatom species diversity (Hill’s N2) and richness are affected by high As

concentrations. The results from this study were used to interpret the down-core diatom assemblage changes from other lakes in the region (Chapters 3, 4, and 5).

Chapter 3: The deep (> 10 m) sub-Arctic lakes around Yellowknife support large- bodied cold-water fish species that have cultural significance to community members and play an important role in regional economic activities (e.g. tourism). However, the cumulative impacts of gold mining activities, urbanization, and recent warming have not been investigated in deep sub-Arctic lakes previously. In this study, we used diatom- based paleolimnological approaches to track the history of multiple environmental stressors on two deep lakes around Yellowknife. To disentangle the impacts of urbanization and mining pollution from regional climate warming, we selected one deep urban lake (Grace Lake) and another lake that was far from any anthropogenic activities

(Alexie Lake). In addition to examining sedimentary diatom assemblages, we also examined past trends in whole lake primary production using visible near-infrared reflectance spectroscopy (VNIRS) inferences of algal pigments in lake sediments.

Chapter 4: Recently, unprecedented algal blooms have been reported at Jackfish

Lake in Yellowknife, which has raised concerns among lake users and managers. In this chapter, my colleagues and I examined a variety of paleolimnological proxies from a well-dated sediment core from Jackfish Lake and combined it with the local meteorological records to provide long-term environmental context to recent cyanobacterial algal blooms. Specifically, we used VNIRS inferences of algal pigments in lake sediments to track past trends in whole lake primary production and cyanobacterial abundance. We then used sedimentary diatoms, cladocerans, and chironomids to track the impacts of mining activities, nutrient enrichment from land-use

changes, zooplankton predation changes, and the cumulative impacts of nutrient enrichment and climatic changes on the limnology of Jackfish Lake. Finally, we assessed past trends in whole-lake primary production and cyanobacterial abundance from 8 other lakes in the region to place the recent trends in observed at Jackfish into regional context.

Chapter 5: Previous paleolimnological case studies have highlighted the importance of site-specific limnological characteristics on the biological responses to metal(loid) contamination in the lakes around Yellowknife. In this chapter, we synthesized down-core diatom data from 10 lakes around Yellowknife and provided a regional perspective on limnological changes in the region over the last ~ 200 years. The

10 lakes were strategically chosen to capture the diverse chemical and physical characteristics of lakes around Yellowknife. The diatom profiles from four of the lakes had not been published previously, while the data from six lakes have already been published in previous case studies (Thienpont et al. 2016; Stewart et al. 2018; Sivarajah et al. 2020, Chapter 3) or are under consideration for publication with peer-reviewed journals (Perrett et al. In Review; Sivarajah et al. In Review, Chapter 4). In addition to examining species specific shifts at the regional level, I have used detrended canonical correspondence analysis to summarize the past changes in diatom assemblages and quantify species turnover as beta diversity. This landscape-scale paleolimnological investigation will also provide insights on how multiple environmental stressors influence the timing of biological changes at the landscape level, assess the relative importance of various stressors on different sites at a regional scale, and identify lakes that may be most susceptible to environmental perturbations.

Chapter 6: This thesis concludes with a brief overall conclusion and recommendations for future research.

Appendices A and B: The manuscripts presented in Appendices A and B are an extension of this thesis project and focus on the impacts of past mining activities in temperate (abandoned silver mines in Cobalt, Ontario) and High-Arctic (Polaris Mine,

Little Cornwallis Island, Nunavut) lakes. In Appendix A I have examined the cladoceran responses to mining pollution in a suite of lakes around the Town of Cobalt (northeastern

Ontario), where silver mining activities during the early 20th century resulted in the contamination of several lakes in the region. Similar to the Yellowknife lakes, the Cobalt sites also have high levels of arsenic in the surface waters. Appendix B is a short note on the present limnology of lakes near Canada’s northernmost mine (Polaris Mine) in Little

Cornwallis Island. Specifically, we compared the water chemistry variables of three lakes near the mine to those from a suite of sites near Resolute Bay in Cornwallis Island.

1.8 References

Andrade CF, Jamieson HE, Kyser TK, Praharaj T, Fortin D (2010) Biogeochemical redox cycling of arsenic in mine-impacted lake sediments and co-existing pore waters near Giant Mine, Yellowknife Bay, Canada. Appl Geochem 25: 199–211.

Barral-Farga L, Morin S, Rovira MDM, Urrea G, Magellan K, Guasch H (2016) Short- term arsenic exposure reduces diatom cell size in biofilm communities. Environ Sci Pollut Res 23:4257–4270.

Battarbee RW, Jones VJ, Flower RJ, Cameron NG, Bennion H, Carvalho L, Juggins S (2001) Diatoms. In: Smol JP, Birks HJB, Last WM (eds) Tracking environmental change using lake sediments. Vol 3: Terrestrial, Algal and Siliceous Indicators. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Benson B, Magnuson J, Sharma S (2013) Global Lake and River Ice Phenology Database, Version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. 15

Bright DA, Dodd M, Reimer KJ (1996) Arsenic in subarctic lakes influenced by gold mine effluent: the occurrence of organoarsenicals and hidden arsenic. Sci Total Environ 180: 165–182.

Canadian Council of Ministers of the Environment (CCME) (1999) Canadian sediment quality guidelines for the protection of aquatic life Arsenic. In: Canadian environmental quality guidelines, 1999, Canadian Council of Ministers of the Environment, Winnipeg.

Cattaneo A, Asioli A, Comoli P, Manca M (1998) Organisms’ response in a chronically polluted lake supports hypothesized link between stress and size. Limnol Oceanogr 43: 1938–1943.

Cattaneo A, Couillard Y, Wunsam S (2008) Sedimentary diatoms along a temporal and spatial gradient of metal contamination. J Paleolimnol 40: 115–127.

Caumette G, Koch I, Estrada E, Reimer KJ (2011) Arsenic speciation in plankton organisms from contaminated lakes: Transformations at the base of freshwater food chain. Environ Sci Technol 45: 9917–9923.

Cheney CL, Eccles KM, Kimpe LE, Thienpont JR, Korosi JB, Blais JM (2020) Determining the effects of past gold mining using a sediment palaeotoxicity model. Sci. Total Environ. 718: 137308.

Coleman LC (1957) Mineralogy of the Giant Yellowknife gold mine, Yellowknife, NWT. Econ Geol 52: 400–425.

Cott PA, Zajdlik BA, Palmer MJ, McPherson MD (2016) Arsenic and mercury in lake whitefish and burbot near the abandoned Giant Mine on Great Slave Lake. J Great Lakes Res 42: 223–232.

Cumming BF, Smol JP, Kingston JC, Charles DF, Birks HJB, Camburn KE, Dixit SS, Uutala AJ, Selle AR (1992) How much acidification has occurred in Adirondack region lakes (New York, USA) since pre-industrial times? Can J Fish Aquat Sci 49: 128–141. de Rosemond S, Xie Q, Liber K (2008) Arsenic concentration and speciation in five freshwater fish species from Back Bay near Yellowknife, NT, Canada. Environ Monit Assess 147: 199–210.

Dirszowsky RW, Wilson KM (2016) Biogeochemical evidence of eutrophication and metal contamination of Frame Lake, City of Yellowknife, Northwest Territories, Canada. Environ Earth Sci 75:76

Dixit AS, Dixit SS, Smol JP (1991) Multivariable environmental inferences based on diatom assemblages from Sudbury (Canada) lakes. Freshw Biol 26: 251–266.

Dixit AS, Dixit SS, Smol JP (1992) Long-term trends in lake water pH and metal concentrations inferred from diatoms and chrysophytes in three lakes near Sudbury. Ontario. Can J Fish Aquat Sci 49(Suppl. 1): 17–24.

Duguay CR, Prowse TD, Bonsal BR, Brown RD, Lacroix MP, Ménard P (2006) Recent trends in Canadian lake ice cover. Hydrol Process 20: 781–801.

Galloway JM, Palmer M, Jamieson HE, Patterson RT, Nasser N, Falck H, Macumber AL, Goldsmith SA, Sanei H, Normandeau P, Hadlari T, Roe HM, Neville LA, Lemay D (2015) Geochemistry of lakes across ecozones in the Northwest Territories and implications for the distribution of arsenic in the Yellowknife region. Part 1: Sediments. Geol Surv Canada, Open File 7908: 49pp.

Gavel MJ, Patterson RT, Nasser NA, Galloway JM, Hanna BW, Cott PA, Roe HM, Falck H (2018) What killed Frame Lake? A precautionary tale for urban planners. PeersJ. https://doi.org/10.7717/peerj .4850

Griffiths K, Michelutti N, Sugar M, Douglas MSV, Smol JP (2017) Icecover is the principal driver of ecological change in High Arctic lakes and ponds. PLoS ONE 12(3):e0172989.

Hall R (2013) Diamond mining in Canada’s Northwest Territories: A colonial continuity. Antipode 45:376–393.

Hall RI, Smol JP (2010) Diatoms as indicators of lake eutrophication. – In: Smol JP, Stoermer EF(eds): The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge.

Health Canada (2014) Guidelines for Canadian Drinking Water Quality—Summary Table. Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario.

Hocking D, Kuchar P, Plambeck JA, Smith RA (1978) The impact of gold smelter emissions on vegetation and soils of a sub-Arctic forest-tundra transition ecosystem. J Air Pollut Control Assoc 28: 133–137.

Houben AJ, D’Onofrio R, Kokelj SV, Blais JM (2016) Factors affecting elevated arsenic and methyl mercury concentrations in small shield lakes surrounding gold mines near the Yellowknife, NT, (Canada) region. PLoS ONE 11: e0150960.

Hutchinson TC, Aufreiter S, Hancock RGV (1982) Arsenic pollution in the Yellowknife area from gold smelter activities. J Radioanal Chem 71: 59–73.

Indian and Northern Affairs Canada (2007) Giant Mine Remediation Plan. Giant Mine remediation team. Department of Indian Affairs and Northern Development. 260 pp.

Jamieson HE (2014) The legacy of arsenic contamination from mining and processing of refractory gold ore at Giant Mine, Yellowknife, Northwest Territories, Canada. Rev Mineral and Geochem 79: 533–551.

Koch I, Wang L, Feldmann J, Andrewes P, Reimer KJ, Cullen WR (2000) Antimony species in environmental samples. Int J Environ An Ch 77: 111–131.

Labaj AL, Kurek J, Jeziorski A, Smol JP (2015) Elevated metal concentrations inhibit biological recovery of Cladocera in previously acidified boreal lakes. Freshw Biol 60: 347–359.

Mining Association of Canada (2018) Facts and figures of the Canadian Mining Industry. The Mining Associate of Canada 109 pp.

Moore JW, Sutherland DJ, Beaubien VA (1979) Algal and invertebrate communities in three subarctic lakes receiving mine wastes. Water Res 13: 1193–1202.

Moore JW (1981) Influence of water movements and other factors on distribution and transport of heavy metals in a shallow bay (Canada). Arch Environm Contam Toxicol 10: 715–724.

Morin S, Duong TT, Dabrin A, Coynel A, Herlory O, Baudrimont M, Delmas F, Durrieu G, Schäfer J, Winterton P, Blanc G, Coste M (2008) Long-term survey of heavy-metal pollution, biofilm contamination and diatom community structure in the Riou Mort watershed, South-West France. Environ Pollut 151:532–542.

Mudroch A, Joshi SR, Sutherland D, Mudroch P, Dickson KM (1989) Geochemistry of sediments in the Back Bay and Yellowknife Bay of the Great Slave Lake. Environ Geol Water Sci 14: 35–42.

Mullan D, Swindles G, Patterson T, Galloway J, Macumber A, Falck H, Crossley L, Chen J, Pisaric M (2017) Climate change and the long-term variability of the World’s busiest heavy haul ice road. Theor Appl Climatol 129:1089–1108

Mueller, DR, Van Hove P, Antoniades D, Jeffries MO, Vincent WF (2009) High Arctic lakes sentinel ecosystems: Cascading regime shifts in climate, ice cover, and mixing. Limnol Oceanogr 54: 2371–2385.

Nasser NA, Patterson RT, Roe HM, Galloway JM, Falck H, Palmer MJ, Spence C, Sanei H, Macumber AL, Neville LA (2016) Lacustrine arcellinina (testate amoebae) as bioindicators of arsenic contamination. Microb Ecol 72: 130–149.

Nassichuk WW (1987) Forty years of Northern non-renewable natural resource development. Arctic 40: 274–284.

Natural Resources Canada (2019) Mineral and the economy. https://www.nrcan.gc.ca/our-natural-resources/minerals-mining/minerals-and- economy/20529.

Natural Resources Canada (2020) Gold facts. https://www.nrcan.gc.ca/our-natural- resources/minerals-mining/gold-facts/20514.

Nelligan C, Jeziorski A, Rühland KM, Paterson AM, Smol JP (2016) Managing lake trout lakes in a warming world: a paleolimnological assessment of nutrients and lake production at three Ontario sites. Lake and Reserv Manag 32:315–328.

Northwest Territories Industry, Tourism and Investment (NWT ITI) (2017) A guide to mineral deposits. 187pp.

Palmer MJ, Galloway, JM, Jamieson HE, Patterson RT, Falck H, Kokelj SV (2015) The concentration of arsenic in lake waters of the Yellowknife area; Northwest Territories Geological Survey, NWT Open File 2015-06. 25pp.

Palmer MJ, Chételat J, Richardson M, Jamieson HE, Galloway JM (2019) Seasonal variation of arsenic and antimony in surface waters of small subarctic lakes impacted by legacy mining pollution near Yellowknife, NT, Canada. Sci. Total Environ. 684: 326– 339.

Pelletier N, Chételat J, Cousens B, Zhang S, Stepner D, Muir DCG, Vermaire, JC (2020) Lead contamination from gold mining in Yellowknife Bay (Northwest Territories), reconstructed using stable lead isotopes. Environ Pollut 259: 113888.

Perrett M, Sivarajah B, Cheney CL, Korosi JB, Kimpe LE, Blais JM, Smol JP (In Review) Salinization and metal(loid) contamination by gold mine tailings from Consolidated Mine (Yellowknife, Canada) impact aquatic biota. (In Review with Environmental Pollution ENVPOL_2020_4502).

Rühland KM, Paterson AM, Smol JP (2015) Lake diatom responses to warming: reviewing the evidence. J Paleolimnol 54: 1–35.

Salonen V-P, Tuovinen N, Valpola S (2006) History of mine drainage impact on Lake Orijärvi algal communities, SW Finland. J Paleolimnol 35: 289–303.

Schuh CE, Jamieson HE, Palmer MJ, Martin AJ (2018) Solid-phase speciation and post- depositional mobility of arsenic in lake sediments impacted by ore roasting at legacy gold mines in the Yellowknife area, Northwest Territories, Canada. Appl Geochem 91:208– 220.

Silke R (2009) The operational history of mines in the Northwest Territories, Canada. An Historical Research Project.

Sivarajah B, Rühland KM, Smol JP (2017) Are diatoms recovering to pre-acidification assemblages in a warming world? Revisiting Killarney Provincial Park lakes (Sudbury, Canada). Fundam Appl Limnol 190: 13–28.

Sivarajah B, Paterson AM, Rühland KM, Köster D, Karst-Riddoch T, Smol JP (2018) Diatom response to 20th century shoreline development and climate warming in three embayments of Georgian Bay, Lake Huron. J Great Lakes Res 44: 1339–1350.

Sivarajah B, Cheney CL, Perrett M, Kimpe LE, Blais JM, Smol JP (2020) Regional gold mining activities and recent climate warming alter diatom assemblages in deep sub- Arctic lakes. Polar Biol 43: 305–317.

Sivarajah, B, Simmatis B, Favot, EJ, Palmer MJ, Smol JP (In Review) Eutrophication and climate warming lead to unprecedented cyanobacterial blooms in a Canadian sub- Arctic lake. (In Review with Harmful Algae HARALG-D-20-00222).

Smol JP (2008) Pollution of lakes and rivers: a paleoenvironmental perspective, 2nd ed. Blackwell Publishing, Oxford, UK.

Smol JP, Wolfe AP, Birks HJB, Douglas MSV, Jones VJ, Korhola A, Pienitz R, Rühland K, Sorvari S, Antoniades D, Brooks SJ, Fallu MA, Hughes M, Keatley BE, Laing TE, Michelutti N, Nazarova L, Nyman M, Paterson AM, Perren B, Quinlan R, Rautio M, Saulnier-Talbot E, Siitonen S, Solovieva N, Weckstrom J (2005) Climate-driven regime shifts in the biological communities of arctic lakes. Proc Natl Acad Sci USA 102:4397– 4402.

Smol JP, Douglas MSV (2007) From controversy to consensus: making the case for recent climate change in the Arctic using lake sediments. Front Ecol Environ 5: 466–474.

St-Onge SM (2007) Impacts of arsenic and sulphur dioxide contamination from mining activities on forest health near Yellowknife, NWT. M.Sc. Thesis, Carleton University.

Stewart EM, Hargan KE, Sivarajah B, Kimpe LE, Blais JM, Smol JP (2018) A paleoenvironmental study tracking eutrophication, mining pollution, and climate change in Niven Lake, the first Sewage Lagoon of Yellowknife (Northwest Territories). Arctic 71:201–217.

Thienpont JR, Rühland KM, Pisaric MFJ, Kokelj SV, Kimpe LE, Blais JM, Smol JP (2013) Biological responses to permafrost thaw slumping in Canadian Arctic lakes. Freshw Biol 58:337–353.

Thienpont JR, Korosi JB, Hargan KE, Williams T, Eickmeyer DC, Kimpe LE, Palmer MJ, Smol JP, Blais JM (2016) Multi-trophic level response to extreme metal contamination from gold mining in a subarctic lake. Proc R. Soc B 283:20161125.

Tropea AE, Paterson AM, Keller W, Smol JP (2010) Sudbury sediments revisited: Evaluating limnolgical recovery in a multiple-stressor environment. Water Air Soil Pollut 210: 317–333.

Tropea AE, Paterson AM, Keller W, Smol JP (2011) Diatoms as indicators of long-term nutrient enrichment in metal-contaminated urban lakes from Sudbury, Ontario. Lake and Reserv Manag 27: 48–60.

Van den Berghe MD, Jamieson HE, Palmer MJ (2018) Arsenic mobility and characterization in lakes impacted by gold ore roasting, Yellowknife, NWT, Canada. Environ Pollut 234:630–641

Vincent WF, Pienitz R, Laurion I, Walter Anthony K (2013) Climate impacts on Arctic lakes. In Goldman, C.R., Kumagai, M. and Robarts, R.D. (eds). Climatic Change and Global Warming of Inland Waters: Impacts and Mitigation for Ecosystems and Societies, John Wiley & Sons, Ltd, Chichester, U.K pp. 27-42.

Wagemann R, Snow NB, Rosenberg DM, Lutz A (1978) Arsenic in sediments, water and aquatic biota from lakes in the vicinity of Yellowknife, Northwest Territories, Canada. Arch Environ, Contam Toxicol 7: 169–191.

Walker SR, Jamieson HE, Lanzirotti A, Andrade CF, Hall GEM (2005) The speciation of arsenic in iron oxides in mine wastes from the Giant Gold Mine, N.W.T.: application of synchrotron micro-XRD and micro-Xanes at the grain scale. Can Mineral 43: 1205–1224.

Wıìlıìdeh Yellowknives Dene (1997) Wıìlıìdeh Yellowknives Dene: a history. Dettah: Yellowknives Dene First Nation Elders Advisory Council.

Wolfe SA (1998) Living with frozen ground: A field guide to permafrost in Yellowknife, Northwest Territories. Geol Surv Canada. Miscellaneous Report 64: 77 pp.

Chapter 2

Multiple environmental variables influence diatom assemblages across

an arsenic gradient in 33 subarctic lakes near abandoned gold mines

2.1 Abstract

We examined surface sediment diatom assemblages from 33 subarctic lakes around the historic gold mines in Yellowknife, Northwest Territories (Canada), where lake-water As concentrations ([As]) still range between 1.5 µg/L and 2780 µg/L, even though the roasting of the arsenopyrite-bearing gold ores ceased in 1999. Water chemistry variables related to gold mining pollution (arsenic, antimony and sulphate) declined with increasing distance from the mines. The diatom assemblages varied substantially in species composition across our study lakes with the planktonic

Discostella stelligera/pseudostelligera complex dominating in deep lakes where [As] were less than 5 µg/L. However, in lakes that exceeded the guideline for the protection of aquatic life ([As] > 5 µg/L), opportunistic benthic diatoms (small fragilarioid taxa and

Achnanthidium minutissimum) were generally present in higher abundances. The subtle differences in diatom species richness and diversity among lakes with varying [As] suggest these indices may not be affected strongly by long-term arsenic pollution.

Furthermore, canonical correspondence analysis of the diatom assemblage and environmental data indicated that nutrient and ionic compounds explained most of the variation in the assemblages, while mining-related variables played a limited role. Our results indicate that multiple limnological variables (nutrients, ions, and contaminants) and subarctic climatic conditions are important factors shaping the diatom assemblage

composition in lakes impacted by mining activities and land-use changes near

Yellowknife.

2.2 Introduction

Diatoms (Bacillariophyceae) are excellent indicators of water quality as they rapidly respond to changes in chemical (nutrients, pH, metal concentrations) and physical

(thermal structure, habitat availability) conditions in freshwater ecosystems (Smol and

Stoermer 2010). Furthermore, their siliceous cell valves are typically well preserved in most lake sediments making them ideal paleolimnological indicators to study a variety of environmental stressors, such as acidification, heavy metal pollution, and eutrophication

(e.g. Dixit et al., 1992; Hall and Smol, 1996; Cattaneo et al., 2008; Fernández et al.,

2018). Previous studies have examined the relationships between varying metal concentrations (e.g. aluminum, nickel) and diatom assemblages in acidified temperate lakes (e.g. Dixit et al., 1991; Cumming et al., 1992) and other lakes that have been affected by metal mining operations (Ruggiu et al., 1998; Salonen et al., 2006; Leppänen et al., 2017). However, their sensitivity and response to arsenic (As) contamination has not been extensively documented in lakes from climatically sensitive subarctic regions where mining is considered to be a major growth industry (Northwest Territories

Industry, Tourism and Investment 2014).

The lakes around Yellowknife, Northwest Territories, present an important opportunity to assess the influence of As on diatom assemblages, as these lakes have some of the highest As concentrations ([As]) in Canada (e.g. Palmer et al., 2015; Houben et al., 2016; Fig. 2.1), with many exceeding the federal water quality guidelines for the

protection of aquatic life (5 μg/L; CCME, 2001) and drinking water (10 μg/L; Health

Canada, 2006). The gold mining operations around the City of Yellowknife, beginning in the mid-20th century, resulted in the emission of more than 20,000 tonnes of arsenic trioxide into the surrounding environment (Silke, 2009; Jamieson, 2014) and led to the atmospheric deposition of mining-related contaminants into nearby lakes (Palmer et al.

2015). Much of the arsenic trioxide was released from Giant Mine (Fig. 2.1) where the arsenopyrite-bearing gold ores were roasted between 1949 and 1999 and during this period it was the largest producer of gold in the region. Recent limnological surveys and geochemical analyses of lake sediments demonstrate the exceptionally high [As] are a legacy of the 20th century gold mining operations around Yellowknife, and not a result of the underlying geology (Palmer et al., 2015; Houben et al., 2016; Thienpont et al., 2016;

Galloway et al., 2018; Schuh et al., 2018; Van den Berghe et al., 2018). Furthermore, the concentrations of As, antimony (Sb), and sulfate in the lake waters were inversely correlated to distance from the mine roaster stacks and corroborate that the increased concentrations of contaminants are a result of roasting arsenopyrite ores to extract gold

(Houben et al., 2016). In addition to the aerial deposition of contaminants, lakes within the City of Yellowknife were also impacted by local land-use changes, such as urbanization (Dirszowsky and Wilson, 2016), raw sewage input (Stewart et al., 2018), and accidental spills from mine tailings (Bright et al., 1994). Although mining operations ceased in the early-2000s, the [As] in many lakes is still high but the effects of prolonged exposure to elevated [As] on key aquatic primary producers, such as diatoms, are poorly understood at a regional scale.

Previous studies in laboratory settings have reported that the acute toxicity of arsenic may lead to a decline in the relative abundance of green algae to cyanobacteria and reduce the photosynthetic efficiency in diatoms (Tuulaikhuu et al. 2015).

Furthermore, short-term field experiments have shown that the total abundance of diatoms on biofilms decreases when exposed to high arsenic concentrations (Barral-Fraga et al. 2018). A reduction in cell size and differences in community composition of biofilms have also been noted as a response to high arsenic concentrations (Rodriguez-

Castro et al. 2015; Barral-Fraga et al. 2016). Here we examined diatom distributions along a gradient of lake-water arsenic concentrations ([As]) by analyzing surface- sediment assemblages and measured environmental variables from 33 subarctic lakes around Yellowknife where algal communities have been exposed to high [As] for more than 50 years. The lakes were strategically chosen to cover a large gradient of [As] (~1.5

µg/L – 2780 µg/L) along a ~40 km radius from the city (Fig. 2.1). The goals of this exploratory study were to assess: 1) the potential influence of lake water [As] on diatom assemblage composition, and 2) if other measured environmental variables were important in structuring the assemblages.

2.3 Study area

Yellowknife (Fig. 2.1) is situated on the northern shores of Great Slave Lake, within the Slave Structural Province of the Canadian Shield (Wolfe, 1998; Kerr &

Wilson, 2000). The Yellowknife area contains pockets of discontinuous permafrost and experiences a continental subarctic climate (Wolfe, 1998). Based on the meteorological data collected at the Yellowknife airport from 1942 to 2016, the mean annual air

temperature ranged between -1 oC and -7 oC, while the mean annual precipitation varied between 195 mm and 507 mm (data retrieved in April 2017 from - http://www.ec.gc.ca/dccha-ahccd/). Yellowknife and the surrounding area are located within the Great Slave Uplands and Lowlands of Taiga Shield High Boreal ecoregion

(Ecosystem Classification Group, 2008). The regional vegetation is characterized by the presence of black spruce [Picea mariana (Mill.) Britton, Sterns & Poggenburg], jack pine

(Pinus banksiana Lamb.), paper birch (Betula papyrifera Marshall), trembling aspen

(Populus tremuloides Michx), and white spruce [Picea glauca (Moench) Voss] with rock lichen occurring on exposed bedrock (Ecosystem Classification Group, 2008). The

Wıìlıìdeh Yellowknives Dene First Nations have used lakes around the Yellowknife area to travel, hunt, and fish for many centuries, as this area has traditionally supported excellent fisheries (Wıìlıìdeh Yellowknives Dene, 1997). However, recent biological surveys from the freshwaters near the mines have recorded elevated [As] in the tissues of fish species and this may be of concern to human health (e.g. Cott et al., 2016).

Regardless of the high arsenic concentrations in many lakes around this area, they continue to be used for recreational purposes.

The study lakes were generally small (mean: 0.95 km2; median: 0.36 km2; range:

0.01 km2 – 6.28 km2), shallow (mean: 4.6 m; median: 3.5 m; range: 0.5 m – 21 m), and underlain by Precambrian granitoids, metasedimentary bedrocks, or volcanic rocks (Kerr

& Wilson, 2000; Houben et al., 2016). Ice-cover data were not available for all lakes in this study, however the long-term ice phenological data available from 1956 to 1994 for one of the study lakes (Long Lake) and the Back Bay of the Great Slave Lake suggests that these lakes are ice-covered for most of the year (range: 196 – 237 days; Benson et al.

2013). Detailed limnological characteristics are provided in Appendix Table C.1 and discussed in subsequent sections. Most of the study lakes were circumneutral to alkaline and the concentrations of total arsenic in the lake water declined with increasing distance from Giant and Con mines. The lakes were divided into 5 groups based on varying total

[As] (i.e. [As] > 1000 µg/L; 1000 µg/L > [As] > 100 µg/L; 100 µg/L > [As] > 10 µg/L;

10 µg/L > [As] > 5 µg/L; [As] < 5 µg/L) to better visualize the As gradient in our study lakes (Fig. 2.1). Two of the categories were based on the guidelines for the protection aquatic life and drinking water quality in Canada (i.e. 10 µg/L > [As] > 5 µg/L; [As] < 5

µg/L), and the other three categories were delineated to ensure the rest of the 25 lakes with > 10 µg/L of total [As] were not clumped into one large group as it may lead to the loss of valuable ecological information. However, this coding method was not used in the statistical analyses as there were notable differences in the number of samples within each category.

2.4 Methods

2.4.1 Sample collection

Surface sediments and water chemistry variables were collected from our study lakes (Fig. 2.1) between March 2014 and July 2016. Due to logistical challenges, some lakes in this study were sampled during the ice-on season while others were sampled after ice melt, with the latter the more common approach when working in subarctic regions.

The water and sediment samples were usually obtained from the middle of the lake as we did not have detailed bathymetry for many of the remote study lakes. Hence, the depth of lakes reported in this study were measured at the coring site. Sediment cores were

retrieved using an Uwitec gravity corer and sectioned using a modified Glew (1988) vertical extruder at 0.5 cm increments. The water sampling bottles were triple rinsed with lake water and then filled completely right before retrieving the sediment cores. Water chemistry variables were measured at the Taiga Environmental Laboratory in

Yellowknife, Northwest Territories following analytical protocols based on Standard US

Environmental Protection Agency methods (Taiga Environmental Laboratory is a

Canadian Association for Laboratory Accreditation Incorporated certified facility).

2.4.2. Diatom preparation and identification

The top 0.5 cm of sediments were analyzed for siliceous microfossils following standard protocols described by Battarbee et al. (2001). However, for ten lakes, surface sediments were no longer available from the top 0.5 cm (as they were used for other analyses); therefore, subsequent intervals (0.5-1.0 cm or 1.0-1.5 cm) had to be used.

Approximately 0.2 g of wet sediments or 0.02 g of dry sediments from each sample were treated with strong acids (50:50 molar ratio of concentrated nitric and sulfuric acids) and placed in a hot-water bath for ~2 hours to accelerate the breakdown of organic material.

Samples were then allowed to settle for ~24 hours prior to removing the supernatant, followed by rinsing with de-ionized water. This rinsing procedure was repeated about six times to dilute the strong acids. Once the samples reached circumneutral pH, aliquots of the slurries were plated onto glass cover slips and allowed to air dry before mounting them on microscope slides using Naphrax®. A minimum of 500 diatom valves were enumerated and identified for each sample at 1000× magnification using a Leica DMR light microscope fitted with differential interference contrast optics. Diatom taxonomy was based on an assortment of texts, including Krammer & Lange Bertalot (1986, 1988,

1991a, b), Lange-Bertalot & Moser (1994), Camburn & Charles (2000), Fallu et al.

(2000), Håkansson (2002), and Rühland et al. (2003a). Staurosira construens var. venter

(Ehrenberg) Hamilton and Staurosirella pinnata (Ehrenberg) D. M. Williams & Round were both observed in our data set. However, due to challenges in differentiating valves when present in girdle view, we grouped these taxa together for statistical analyses as they have similar optima for critical limnological variables such as dissolved inorganic carbon, dissolved organic carbon, total nitrogen, pH, total phosphorus, and maximum depth (Hall & Smol, 1996; Rühland & Smol, 2002; Schmidt et al. 2004). The diatom assemblage composition was presented as percent abundance relative to the total number of valves counted in a sample, and all species were included in statistical analyses.

We examined the relative abundances of planktonic and benthic taxa in the diatom assemblages from the 33 lakes by grouping taxa with similar habitat preferences.

As mentioned in Philibert & Prairie (2002), the classification of diatom taxa as benthic and planktonic species is a difficult task because different diatom surveys have reported the same taxa from diverse habitats. Therefore, we used a variety of sources including published literature (e.g. Philibert & Prairie, 2002; Round et al., 1990), online resources

(e.g. Diatoms of North America), and discussions with researchers who have worked with diatom flora from the Canadian Arctic extensively to best determine the habitat preference for various taxa. In summary, we classified species from the following genera as benthic: Achnanthes sensu lato, Amphora, Amphipleura, Brachysira, cymbelloid taxa,

Caloneis, Cocconeis, Denticula, Diploneis, Diatoma, Eunotia, Epithemia, Frustulia, benthic fragilarioid taxa, Gomphonema, Gyrosigma, naviculoid taxa, Neidium,

Hantzschia, Nitzschia, Rhopalodia, Pinnularia, Stauroneis and Surirella. The following

genera were classified as planktonic: Aulacoseira, Asterionella, Cyclotella sensu lato, pennate fragilarioid taxa, Melosira, Stephanodiscus, and Tabellaria.

2.4.3 Data screening for ordination analyses

A total of 47 environmental variables was measured as part of this limnological survey. However, many water chemistry variables contained concentrations that were below the detection limits (BDL). Therefore, we only used environmental variables that had measurements above the detection limits in at least 90% of the lakes (~30 lakes)

(sensu Karst-Riddoch et al., 2009). Furthermore, if an environmental variable had concentrations BDL in 3 lakes or less (< 10%), then it was replaced with the value of the detection limit (BDL at 1 site – chloride, total antimony, total iron; BDL at 2 sites – uranium; BDL at 3 sites - sulphate). Values for total phosphorus and dissolved phosphorus were not included in the analysis because measurements of phosphate are

3- often interfered with by arsenate (AsO4 ) in lakes with high [As] (e.g. Stewart et al.,

2018).

The chemical and physical variables from the 33 lakes were examined for normality using the Shapiro-Wilk test and appropriate transformations were applied, using the vegan (Oksanen et al., 2018) and analogue (Simpson and Oksanen, 2018) packages in the R Studio statistical software environment (R Core team, 2018). While assessing the environmental variables for normality, we noted that the two lakes (Keg and

Peg) that received tailings from Pud Lake near the abandoned Con Mine had exceptionally high values for certain ions and specific conductivity. The anomalous values from Keg and Peg lakes prevented these variables to be normally distributed, even after transformations of the data. Therefore, Keg and Peg lakes were removed from

further statistical analysis related to ordinations as these sites may have excess leverage on normalized ordination techniques.

Latitude (in decimal degrees) was the only environmental parameter that was normally distributed, and so all other environmental variables had to be transformed to meet the assumption of normality in the 31 lakes data set. Total nitrogen (TN), dissolved organic carbon (DOC), total alkalinity, specific conductivity, total dissolved solids, calcium (Ca2+), chloride (Cl-), magnesium (Mg2+), potassium (K+), sodium (Na+),

2- sulphate (SO4 ), total metals (aluminum (Al), antimony (Sb), arsenic (As), barium (Ba), lithium (Li), manganese (Mn), rubidium (Rb), and uranium (U)), distance from Giant

Mine, distance from Con Mine, coring depth, and surface area were all log (base 10) transformed and used for subsequent ordination analyses. Lake-water pH was not normally distributed in our data set. Therefore, we back transformed the pH (pH = - log(H+)) to the concentration of hydrogen ions (i.e. 10-pH). Then the resulting values were square-root transformed to ensure the concentration of H+ were normally distributed

(hereafter SQRT[H+]). After applying the appropriate transformations for the 25 environmental variables, a Pearson correlation matrix was generated in the R Studio software environment using the Hmisc package (Harrell, 2018) to assess the covariance among the chemical and physical parameters.

2.4.4 Ordination analysis

First, we performed a principal component analysis (PCA) with the 25 environmental variables to assess the distribution of the 31 sites along the environmental gradients (Fig. 2.2) using the statistical program CANOCO version 5.0 (ter Braak &

Šmilauer, 2012). The detrended correspondence analysis (DCA) of the full diatom

species data revealed a gradient length > 2 standard deviation units, suggesting that a unimodal technique, such as canonical correspondence analysis (CCA), was a suitable method to explore the relationship between diatom species and environmental data

(Birks, 2010).

A series of CCAs was run constrained to each environmental variable to assess if the selected variable explained a significant portion of the species data by examining the significance (P < 0.05) of the primary axes using 999 Monte Carlo permutation tests.

Then we used the Pearson correlation matrix of the environmental variables in conjunction with the results from the 25 CCAs to reduce the number of explanatory environmental parameters by identifying groups of variables that were highly correlated

(correlation coefficients > 0.6) and/or did not explain a significant portion of the variation in the diatom assemblages (P > 0.05). After removing the environmental variables that did not explain a significant portion of the species data, two groups of highly correlated variables were identified. Representative variables that explained the most variation in the diatom assemblage from each group were objectively selected using the forward selection option in CANOCO, following methods described by Blanchet et al. (2008).

We elected to forward select the representative variables from each group to ensure the most parsimonious set of explanatory variables that were not correlated with each other were chosen objectively and included in the final CCA. However, these selected representatives were highly correlated to many variables within their groups and therefore they represent a group of explanatory variables in the CCA and the interpretations in the subsequent sections will be made within this context.

2.4.5 Additional statistical analysis

Analysis of similarities (ANOSIM; Clarke, 1993) was used to determine if the diatom assemblages differed significantly (P < 0.05) among three a priori defined groups

([As] > 100 µg/L, n = 12; 100 µg/L > [As] > 10 µg/L, n = 13; [As] < 10 µg/L, n = 8). The

ANOSIM was based on the Bray-Curtis similarity matrix and the diatom assemblages were square-root transformed to stabilize the variance in the data using the statistical program PRIMER V6 (Clarke and Gorley, 2006). In ANOSIM, an R statistic of 0 suggests that the assemblages are not different among the two groups, while R > 0 (and closer to 1) suggests that there are differences in diatom assemblages among the two groups. However, R statistic can be less than 0 when there is higher variation in diatom assemblages within a group. Additionally, diatom species richness and Hill’s N2 diversity for all 33 sites were calculated using the vegan (Oksanen et al., 2018) and rioja

(Juggins, 2017) packages available for the R software environment (R Core Team, 2018).

Prior to calculating richness and diversity, the diatom data from each of the sites were rarefied to a common sum of 500 diatom valves per sample.

2.5 Results

2.5.1 Limnological characteristics of lakes around Yellowknife

The study sites spanned relatively large physical and chemical limnological gradients (Appendix Table C.1). For example, the surface areas and coring depths of the lakes ranged from 0.01 km2 to 6.28 km2 and 0.5 m to 21 m, respectively (Appendix Table

C.1). Specifically, environmental parameters associated with gold mining operations, such as arsenic (range: 1.5 – 2780 µg/L; median: 41.7 µg/L), antimony (range: 0.1 – 49.8

µg/L; median: 1.1 µg/L), and sulphate (range: 1 – 2920 mg/L; median: 11 mg/L), had

large gradients in this data set (Appendix Table C.1). The apparent large variation in the ionic strength of the water (e.g. calcium, chloride, sodium, specific conductivity) was influenced by the high values recorded at two lakes that were impacted by mine tailings

(Keg and Peg lakes; Appendix Table C.1). As mentioned previously, it was not possible to meet the assumption of normality of environmental variables related to ions due to the high values from Keg and Peg; therefore, these lakes were not included in subsequent ordination analyses.

Axes 1 and 2 of the PCA captured 62% of the total variation in the environmental data from the 31 lakes (Fig. 2.2). Axis 1 was primarily influenced by TN and DOC, as well as ionic composition (total alkalinity, specific conductivity, total dissolved solids,

Ca2+, Cl-, Mg2+, K+, Na+), while axis 2 mainly reflected a gradient of physical properties

(surface area and depth) and gold mining-related variables (distance from the mines, As,

2- Sb, SO4 ) (Fig. 2.2). Many environmental variables in our 31-lake data set were strongly correlated (correlation coefficients > 0.6) to each other, as identified by the Pearson correlation matrix (Appendix Table C.2). Surface area of lakes and SQRT [H+] were the only two variables that were not correlated to any other environmental parameters in this data set. The principal component analysis (PCA) highlighted the strong relationships among the 25 environmental variables (Fig. 2.2) because highly correlated variables were grouped together (positively correlated) or distributed in opposite directions if negatively correlated. Chemical parameters, including nutrients, ionic composition, and some metals not related to mining activities in this area (e.g. Ba, Li, Mn, Rb), were positively correlated and plotted in the top left quadrant of the biplot (Fig. 2.2). The arrows

representing environmental variables along axis 1 and axis 2 were perpendicular to each other suggesting minimal correlations among these parameters.

Generally, the concentrations of mining-related water chemistry parameters (As,

2- Sb, SO4 ) declined with increasing distance from the mines (Figs. 2.1 & 2.2) and were strongly negatively correlated (correlation coefficients > 0.6; Appendix Table C.2).

Furthermore, the PCA also captured the large [As] gradient of this data set, as lakes with very low [As] were plotted in the top right quadrant, while high [As] lakes were plotted in the bottom left quadrant, and lakes with intermediate [As] were plotted between the two areas (Fig. 2.2). Although not strong, a negative correlation (r = 0.54) was observed between As and depth in this data set. Due to logistical restraints, the water samples used in this study were collected from both ice-on and ice-free seasons and we acknowledge using samples from the same time window would have been preferable. Nonetheless, the patterns we observed in the water chemistry variables were consistent with previously published studies from the Yellowknife area (e.g. Houben et al., 2016; Palmer et al.,

2015), which have also shown that the concentrations of contaminants associated with

2- arsenopyrite ore processing (As, Sb, SO4 ) were higher in lakes closer to the mines.

2.5.2 Relationships between diatom assemblages and water chemistry parameters from arsenic-contaminated lakes around Yellowknife.

Dominant diatom taxa in this 33-lake dataset included the planktonic Discostella stelligera (Cleve & Grunow) Houk and Klee / Discostella pseudostelligera (Hustedt)

Houk & Klee complex, Achnanthidium minutissimum (Kützing) Czarnecki, and benthic fragilarioid species (S. construens var. venter, S. pinnata, Staurosira construens

Ehrenberg, Pseudostaurosira brevistriata (Grunow) Williams & Round, P. brevistriata type) (Fig. 2.3). However, there were a few lakes where the diatom assemblages were 35

dominated or co-dominated by Stephanodiscus taxa (Jackfish Lake), Navicula cryptotenella Lange-Bertalot (BC-18), Denticula kuetzingii Grunow (Range Lake),

Cocconeis placentula Ehrenberg (Moose Lake), and Fragilaria mesolepta Rabenhorst

(Niven Lake) (Fig. 2.3). Due to the high number of taxa in this dataset, we are only discussing some of the dominant species in subsequent sections.

Generally, in very shallow (< 1 m) and highly arsenic-contaminated lakes the diatom assemblages were dominated by A. minutissimum while benthic fragilarioid taxa were dominant in shallow and deeper As-contaminated lakes (Fig. 2.4). In deep lakes where arsenic concentrations were lower than the guideline for the protection of aquatic life (< 5 µg/L) the assemblages were dominated by the D. stelligera complex (Fig. 2.4).

Furthermore, benthic diatom taxa were more common in arsenic-contaminated shallow and deep lakes while planktonic taxa were dominant in deep lakes with very low arsenic concentrations in this data set (Fig. 2.4). Clearly, there are some exceptions to these generalizations, because, as discussed later, the diatom assemblages in lakes around

Yellowknife were also influenced by other limnological variables related to nutrients and ions that may play an important role.

The diatom assemblage composition was diverse in this data set (Fig. 2.3); however, the a priori divided groups (i.e. [As] > 100 µg/L; 100 µg/L > [As] > 10 µg/L;

[As] < 10 µg/L) were not always significantly different from each other with respect to diatom distributions (Table 2.1). There were statistical differences in the diatom assemblages between lakes with [As] > 100 µg/L and 100 µg/L > [As] > 10 µg/L, however these were not substantial (R = 0.159). The diatom species richness and diversity (Hill’s N2) ranged from ~ 10 to ~ 47 and ~ 1.6 to ~ 13.4, respectively, and

varied substantially across the 33 study lakes (Table 2.2). Additionally, the differences in species richness and diversity among lakes with varying [As] were subtle (Table 2.2). For example, the mean diatom species richness of lakes with > 100 µg/L of [As] was ~ 24 while the lakes with < 100 µg/L of [As] was ~ 30. Species diversity (Hill’s N2) was marginally higher in lakes with < 10 µg/L of [As] (Table 2.2).

Most environmental variables were highly correlated (R > 0.6) in this data set as identified by the Pearson correlation matrix (Appendix Table C.2). A series of CCAs, constrained to each environmental variable individually, identified that the following 21 environmental variables explained a significant portion of the variations in the diatom assemblages (by examining the significance (P < 0.05) of the primary axes of CCAs), TN

(9.3%), depth (8.2%), DOC (7.4%), Na+ (7.1%), latitude (6.7%), Li (6.7%), Sb (6.6%), distance from Giant Mine (6.6%), Mg2+ (6.6%), As (6.4%), Rb (6.4%), total dissolved solids (6.3%), Ba (6.2%), total alkalinity (6.1%), Cl- (6.1%), U (6.1%), specific

+ 2+ 2- conductivity (6%), K (6%), Mn (5.3%), Ca (5%), and SO4 (4.9%). Only four environmental variables (i.e. Al, distance from Con Mine, surface area, SQRT[H+]) did not explain a significant portion of the species data when they were the sole explanatory variable and therefore removed from subsequent analyses.

The environmental variables that explained significant amounts of variation in the diatom assemblages clustered into two groups of correlated variables (i.e. each variable was correlated with one or more variables within the group). Generally, variables related to ions (Ca2+, Mg2+, K+, Na+, Cl-, specific conductivity, total alkalinity, total dissolved solids), TN, and DOC were correlated with some metals (Ba, Li, Mn, Rb), latitude, and

2- depth. Furthermore, environmental variables related to mining (As, Sb, SO4 , distance

from Giant Mine) were correlated with each other and with U. The forward selection procedure in CANOCO identified three environmental variables to represent the two groups. TN and Na+ were chosen from the nutrient and ion group to represent a suite of variables mentioned above and Sb was chosen from the mining related variables. We note that the selected variables (TN, Na+, Sb) represent a group of variables and subsequent interpretations should be viewed in this context. For example, when we use Sb as a representative variable to track industrial pollution, it allows us to track the potential influence of all the correlated mining parameters, including As.

The three representative environmental variables (TN, Na+, and Sb) were then used to perform the final CCA that explained ~22.9% of the variation in the diatom assemblages when constrained to the first three axes of the CCA. Furthermore, the variance inflation factors for these three variables were very low and ranged between 1.1 and 1.4, suggesting that correlations among these variables were near negligible.

Conventionally, diatom-environment relationships are presented in two-dimensional figures by examining CCA Axis 1 vs. Axis 2 (Fig. 2.5a) scores or Axis 1 vs. Axis 3 (Fig.

2.5b). However, here we also display the results of the CCA by plotting Axis 1, Axis 2 and Axis 3 samples scores within the same ordination space (Fig. 2.5c) to examine if the underlying patterns in the distribution of sites could be presented more clearly. In Fig.

2.5a (Axis 1 vs. Axis 2), two sites with very low [As] plotted with sites where relatively high [As] were present (in the right quadrant along Axis 1). However, when Axis 1 and

Axis 3 were plotted within the same ordination space (Fig. 2.5b), sites with similar [As] generally clustered together. Similarly, in our three-dimensional CCA plot (Fig. 2.5c), sites with similar [As] grouped around each other. Therefore, Axis 3 is likely tracking the

gradient of mining pollution, while Axes 1 and 2 are primarily tracking the influences of variables related to nutrients and ions. In addition to the separation of sites along Axis 3 of the CCA, lakes with relatively high [As] plotted in the same direction as Sb

(representative variable for mining pollution) in Figs 5b and 5c, suggesting that the inclusion of the third axis was necessary to appropriately present the distribution of sites in this data set.

Due to the high number of diatom taxa present in this data set, we only plotted the sample scores of the 23 most common diatom taxa (present in at least two samples in >

5% abundance). Similar to the CCA biplot of the site scores (Fig. 2.5), the plot with Axis

1 vs. Axis 3 was the most informative in exploring the distribution of diatom taxa along a gradient of mining pollution (Fig. 2.6b). Benthic D. kuetzingii (11), Encyonopsis descripta (Hustedt) Krammer (5), and P. brevistriata type (14) plotted in the upper right quadrant, while A. minutissimum (1), Brachysira neoexilis Lange-Bertalot (4),

Encyonopsis microcephala (Grunow) Krammer (6), Navicula radiosa Kützing (16), N. cryptotenella (17), Navicula cryptocephala Kützing (18), Navicula pupula Kützing (21),

Nitzschia frustulum (Kützing) Grunow (22), and Nitzschia fonticola Grunow (23) plotted in the right quadrant and around the center of the biplot (Fig. 2.6b).

2.6 Discussion

2.6.1 Limnology of Yellowknife lakes

The range of arsenic concentrations reported in this study (range: 1.5 µg/L - 2780

µg/L (mean = 287 µg/L; median = 41.7 µg/L)) as well as from previous limnological assessments from the Yellowknife area (e.g. Palmer et al. 2015; Houben et al. 2016)

represents one of the largest gradients of lake water arsenic concentrations in Canada and likely around the world. The strong negative correlations between distance from Giant

Mine and arsenic (r = - 0.82), and antimony (r = - 0.86) is consistent with the observations by Palmer et al. (2015) and Houben et al. (2016), who have shown that the exceptionally high concentrations of these contaminants in the lakes around Yellowknife were a result of arsenopyrite ore processing activities at Giant Mine.

Other chemical variables also had large environmental gradients; however, this was often influenced by two lakes (Keg and Peg). Keg and Peg lakes are part of a chain of lakes that begin with Pud Lake near Con Mine. Therefore, when Pud Lake received mine tailings from Con Mine, the mine effluent migrated to the downstream lakes (Pud –

Meg – Keg – Peg) via connecting streams (Bright et al., 1994). Mining activities ceased at Con Mine in 2004 (Silke, 2009); however, the concentrations of ionic compounds associated with tailings (specific conductivity, total dissolved solids, Ca2+, Cl-, Mg2+,

+ 2- Na , SO4 ) were exceptionally high when these lakes were sampled in 2014 (Appendix

Table C.1). Even though mining activities ended around the turn of the 21st century in the

Yellowknife area, the high concentrations of mining-related contaminants (e.g. As and

Sb) in many lakes in the region and ionic compounds at Keg and Peg suggest that these subarctic lakes are far from reaching any state of chemical recovery.

2.6.2 Diatom assemblages from arsenic-contaminated lakes around Yellowknife

The diatom assemblage composition was diverse in our dataset and the lack of a clear separation in assemblage composition (Table 2.1) or species richness and diversity among the a priori groups of lakes is likely a result of the high variability within each group. The mean Hill’s N2 diversity of 5.4 in this study was lower than the means (range: 40

6.72 - 13.88) reported from other diatom surveys from the Canadian Arctic (e.g.

Michelutti et al., 2003; Antoniades et al., 2005; Keatley et al., 2009). Recently, Barral-

Farga et al. (2016) noted low diatom species richness in biofilms exposed to As in a short-term (13 days) laboratory-based study. Similarly, multi-year limnological surveys of lakes from Canada (Austin et al., 1985) and a paleolimnological analysis of a lake in

Finland (Leppänen et al., 2017) have also shown that diatom species richness and diversity generally decreased with metal input. However, no changes in the diversity and richness were observed in a diatom survey of streams impacted by metal pollution in

Cornwall and Wales, United Kingdom (Hirst et al., 2002). Furthermore, diatom species diversity did not significantly differ between biofilms exposed to different As concentrations in Anllóns River, a system impacted by historical mining activities

(Barral-Fraga et al. 2018). Based on our data, diatom species richness and diversity may not always clearly respond to metal contamination, and this is likely due to site specific limnological factors such as top-down biotic interactions and other abiotic factors including physio-chemical parameters. Furthermore, paleolimnological investigations from northern latitudes have provided evidence that diatom species diversity is partly determined by changes in ice-cover and length of the growing season (e.g. Griffiths et al.,

2017). Therefore, subarctic lakes in the Yellowknife area, where the lakes are ice-covered for most of the year (196 – 237 days), changes in ice phenology may be influencing the algal diversity strongly and the response to metal pollution could be muted.

Although diatom teratology and size reduction have previously been reported from freshwaters affected by metal pollution (e.g. Ruggiu et al., 1998; Cattaneo et al.,

2004; Morin et al., 2008; Tolotti et al., 2019), we only observed teratological forms in

one lake (Pocket Lake) and in very low abundances. The general absence of teratological forms has been recorded in other aquatic ecosystems affected by industrial activities (e.g.

Hamilton et al., 2015; Thienpont et al., 2016). The key drivers of teratology are not completely known. However, in contrast to morphological deformities of diatoms, a more consistent measure of metal contamination includes shifts in the community structure of diatom assemblages (Morin et al., 2012). For example, paleolimnological assessments of metal-contaminated systems have reported a shift from assemblages dominated by planktonic diatoms to benthic forms when metal load increased (e.g. Salonen et al., 2006;

Cattaneo et al., 2008; Thienpont et al., 2016). Similarly, in our study, the diatom assemblages were generally dominated by benthic fragilarioid taxa and A. minutissimum in the shallow and deeper lakes that exceeded the concentration of As for the protection of aquatic life (i.e. [As] > 5 µg/L; Figs. 2.3 & 2.4). Coincidentally, the lakes where As concentrations were below the levels set for the protection of aquatic life were also lakes that were generally deeper, and the planktonic D. stelligera complex often dominated these assemblages (Figs. 2.3 & 2.4). However, there were exceptions to this generalization (i.e. D. stelligera was dominant in David, YK-42, BCR-07A, Vee and

Grace lakes, where [As] were greater than 5 µg/L) and these will be discussed more fully below.

The presence and/or dominance of a variety of benthic taxa in our data set (A. minutissimum, small fragilarioid taxa, N. radiosa, N. cryptotenella, N. cryptocephala, E. descripta, D. kuetzingii, Amphora pediculus Grunow, and small naviculoid taxa) is consistent with previous diatom surveys of subarctic lakes from Canada (e.g. Pienitz &

Smol, 1993; Rühland & Smol, 2002) and elsewhere (e.g. Weckström et al., 1997; Karst-

Riddoch et al., 2009). However, some of these benthic taxa have also been reported from metal-contaminated systems around the world. For example, A. minutissimum, an opportunistic taxon, has been observed in many freshwaters with exceptionally high metalloid concentrations (Ruggiu et al., 1998; Ivorra et al., 2000; Szabó et al., 2005;

Salonen et al., 2006), including a heavily As-contaminated lake in China (Chen et al.,

2015). Furthermore, laboratory-based experiments have also shown that A. minutissimum can tolerate high [As] (e.g. Rodriguez Castro et al., 2015; Barral-Fraga et al., 2016). In this data set, D. kuetzingii and E. descripta were only present in lakes where As concentrations were well above the guideline for the protection of aquatic life (i.e. 5

µg/L) suggesting these taxa may also tolerate elevated As concentrations. A previous, detailed paleolimnological investigation of one of the most As-contaminated lake in our study (Pocket Lake: [As] = 2070 µg/L) recorded an increase in benthic naviculoid taxa

(N. radiosa, N. cryptotenella, and N. cryptocephala) and D. kuetzingii at the expense of planktonic D. stelligera when Giant Mine was in operation (Thienpont et al., 2016).

Furthermore, benthic fragilarioid taxa were very common in many lakes where [As] were greater than 5 µg/L. Although benthic fragilarioid taxa have not been commonly associated with metal pollution, our study provides some indication that these opportunistic taxa, which are ubiquitously present in many High Arctic and subarctic environments, may tolerate high arsenic concentrations in lakes around Yellowknife.

Previous diatom surveys of metal-contaminated lakes have reported that planktonic cyclotelloid taxa (including the D. stelligera/pseudostelligera complex) and other planktonic forms are sensitive to metal pollution (e.g. Ruggiu et al.,1998; Cattaneo et al., 2008). In our data set, Cyclotella ocellata Pantocsek, an oligotrophic planktonic

diatom, was exclusively observed in deeper lakes (range: 7.7 – 21 m) with [As] < 5 µg/L, suggesting that this taxon may be sensitive to As contamination in this region.

Furthermore, as mentioned previously, at Pocket Lake, the D. stelligera/pseudostelligera complex was replaced by benthic taxa in response to severe As contamination (Thienpont et al., 2016). In our survey, the D. stelligera/pseudostelligera complex was dominant in deep lakes with [As] < 5 µg/L; however, its dominance in 5 lakes with relatively high

[As] (David – 184 µg/L, YK-42 – 170 µg/L, BCR-07A – 61 µg/L, Vee – 34 µg/L, Grace

– 16 µg/L) may suggest that multiple environmental factors may be influencing its distribution in this region. Generally, the D. stelligera/pseudostelligera complex has been associated with deeper lakes in the Canadian subarctic (Rühland et al., 2003a) and the

Siberian Arctic (Laing et al., 1999); similarly, in our study, this species complex and a few other planktonic taxa occurred primarily in deep lakes (Fig. 2.4). However, these planktonic taxa were also present in shallow lakes in this survey, including some of the highly As-contaminated lakes (e.g. 77% relative abundance in YK-42: depth – 2.6 m,

[As] – 170 µg/L; Fig. 2.4). Furthermore, the relative abundances of the D. stelligera/pseudostelligera complex have increased in subarctic lakes as a result of recent warming (e.g. Rühland et al., 2003b, 2008, 2015; Rühland & Smol, 2005); therefore, the high relative abundances of this taxon in some of the As-contaminated lakes may also be a recent response to changing climatic conditions such as longer growing season and stronger thermal stratification. Detailed diatom-based paleolimnological analyses of lakes in the region are necessary to better understand the underlying mechanisms that are leading to the high abundances of D. stelligera/pseudostelligera complex in lakes where

[As] > 5µg/L.

At moderately As-contaminated Jackfish and Niven lakes ([As] at Jackfish = ~75

µg/L and Niven = 42 µg/L), Stephanodiscus taxa (mostly Stephanodiscus medius- minutulus type) and F. mesolepta dominated the assemblages, respectively. These taxa have been associated with nutrient enrichment in many previous diatom assessments from the Arctic (e.g. Moser et al., 2002) and elsewhere (e.g. Tropea et al., 2011; Nelligan et al., 2016). In addition to receiving As via atmospheric emissions, these lakes have also been impacted by local land-use changes. For example, Niven Lake was Yellowknife’s first sewage lagoon from 1948 to 1981, and in a detailed paleolimnological assessment of this lake, Stewart et al. (2018) noted that eutrophic Stephanodiscus taxa increased subtly at the time of raw sewage input. However, the eutrophic F. mesolepta increased to dominance after ~1990 (i.e. well after the cessation of sewage inputs), suggesting that

Niven Lake may be subjected to internal nutrient loading, as this shallow lake currently undergoes hypolimnetic anoxia during summer months.

Similar to the diatom assemblages from many other As-contaminated lakes in this study, the assemblage composition of the two lakes that received mine tailings from the abandoned Con Mine (Keg and Peg lakes) were also dominated by benthic fragilarioid taxa (specifically P. brevistriata type). However, notable abundances of Achnanthes thermalis (Rabenhorst) Schoenfeld and Navicula incertata Lange-Bertalot were also observed in these lakes. Although rare, these taxa have been noted in saline environments

(e.g. Gell et al., 2002) or during periods of high salinity (e.g. Last et al., 1998). Therefore, it is not surprising that these two taxa were present, exclusively in the two lakes that had the highest concentrations for total dissolved solids, specific conductivity, sulphate, aluminum and other ionic compounds (Ca2+, Cl-, Mg2+, K+, Na+) in our data set.

2.6.3 The influence of multiple environmental variables on diatom assemblages in Yellowknife lakes

The proportion of variation explained by the environmental variables (TN, Na+, and Sb) was relatively low (~22.9%), but similar to those reported from other Arctic and subarctic regions (e.g. Michelutti et al., 2006; Antoniades et al., 2009). The relatively low explained variation suggests that other environmental variables not measured (e.g. top- down biotic interactions, ice phenology) may also be important determinants of diatom assemblages in lakes around Yellowknife. Total nitrogen and Na+ explained the highest amount of variation in the diatom assemblages in the final CCA (Fig. 2.5) and it is consistent with previous diatom-based limnological surveys from high latitudes (e.g.

Douglas & Smol, 1995; Weckström & Korhola, 2001; Rühland & Smol, 2002; Tammelin et al., 2017). However, following TN and Na+, Sb also explained a significant portion of the variation, suggesting that environmental variables related to mining pollution also influenced the diatom assemblages of lakes around Yellowknife.

Benthic taxa that were exclusively present in lakes with > 10 µg/L of As (i.e. D. kuetzingii, E. descripta, and P. brevistriata type) plotted in the upper right quadrant where Sb was also high (Fig. 2.6b), suggesting that these taxa are able to tolerate high metal pollution. Meanwhile, planktonic C. ocellata plotted in the lower left quadrant, furthest away from Sb (Fig. 2.6b), indicating that this taxon is very sensitive to metal contamination. Furthermore, benthic taxa such as A. minutissimum, B. neoexilis, E. microcephala, N. radiosa, N. cryptotenella, N. cryptocephala, N. pupula, N. frustulum, and N. fonticola have been observed in other subarctic lakes where TN and DOC concentrations were generally high and they have relatively high optima for these parameters (Pienitz & Smol, 1993; Rühland & Smol, 2002). In the Yellowknife area, 46

these taxa may be influenced by environmental variables related to nutrients and mining pollution as they plotted along axis 1 in the right quadrant and in between TN and Sb

(Fig. 2.6b). Small benthic taxa such as P. brevistriata, S. pinnata-S. construens var. venter complex and Navicula minima Grunow plotted along the first axis in the left quadrant and near Na+ (Fig. 2.6b), indicating these taxa may be strongly influenced by ionic composition. This is consistent with a previous survey of diatoms from subarctic lakes in forested catchments where small benthic fragilarioid and naviculoid taxa were generally present in alkaline lakes where ionic concentrations were higher (Pienitz &

Smol, 1993). The D. stelligera/pseudostelligera complex plotted close to the center of the biplot (Fig. 2.6b) suggesting that this taxon complex has a broad distribution in this data set and occurs across a wide range of lakes around Yellowknife.

The CCA highlights that the diatom assemblages in the subarctic lakes around

Yellowknife are structured by multiple limnological parameters, with mining pollutants playing a limited role. This is in contrast to the findings of surface sediment diatom surveys from metal-contaminated lakes in temperate regions, such as Sudbury (Ontario), where relationships were identified between metal concentrations in the water column and surface sediment diatom assemblages that led to the development and application of inference models to reconstruct past metal concentrations (Dixit et al., 1991, 1992).

However, in the Sudbury region, heavy metal contamination was often accompanied by sharp decreases in pH (Dixit et al., 1992), whereas lakes around Yellowknife were not acidified due to the presence of carbonates that provided the necessary buffering capacity

(Jamieson, 2014). Our results are similar to those reported from Arctic lakes near

Noril’sk smelters in Russia, where diatom assemblages were dominated by opportunistic

benthic fragilarioid taxa and were not severely affected or altered by high metal contamination (Laing et al., 1999; Michelutti et al., 2001). The almost identical response of algal assemblages from lakes around Yellowknife and Noril’sk are likely due to two reasons: first, these regions experience similar climatic conditions; and second, the underlying geology prevented acidification of these lakes. The role of local climatic conditions on diatom assemblages of lakes around the circumpolar Arctic has been investigated extensively, and many studies have concluded that the cold conditions and relatively long ice-cover lead to assemblages dominated by benthic diatoms, specifically higher abundances of opportunistic taxa, such as small benthic fragilarioids (Smol, 1988).

Therefore, the responses of the diatom flora to metal contamination are likely partly influenced by local climatic conditions and catchment characteristics in northern latitudes.

Although some overall patterns are apparent, our diatom survey highlights some of the complexities of the biological responses to stressors linked to mining activities in climatically sensitive subarctic lakes. Since mining activities continue to increase in subarctic and Arctic regions (Northwest Territories Industry, Tourism and Investment

2014), a better understanding of how key ecological indicators, such as diatoms, respond to mining-related stressors will be required. Furthermore, spatial surveys, such as this study, will be necessary to place paleolimnological assessments into appropriate environmental and historical contexts.

2.7 Acknowledgements

We would like to thank Brian Cumming, Matthew Duda, Neal Michelutti,

Kathleen Rühland, Jamie Summers, and Emily Stewart for various discussions. We also gratefully acknowledge field assistance by Mija Azdajic, Cynthia Cheney, Kristen

Coleman, Dave Eickmeyer, Kirsten Maitland, Mike Palmer, Christopher Schuh, Emily

Stewart, and Joshua Thienpont. Water chemistry analysis by Judy Mah and the Taiga

Environmental Laboratory are also acknowledged. This manuscript benefitted greatly from the constructive feedback provided by two anonymous reviewers. This study was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC)

Strategic Grant and Polar Continental Shelf Program grants to Jules Blais and John Smol, as well as an NSERC Post Graduate Scholarship D, Northern Scientific Training

Program, W. Garfield Weston Scholarship for Northern Research (doctoral) and an

Arthur B. McDonald Prize to Branaavan Sivarajah.

2.8 References

Antoniades, D., M. S. V. Douglas & J. P. Smol, 2005. Benthic diatom autecology and inference model development from the Canadian High Arctic Archipelago. Journal of Phycology 41:30–45.

Antoniades, D., M. S. V. Douglas & J. P. Smol, 2009. Biogeographic distributions and environmental controls of stream diatoms in the Canadian Arctic Archipelago. Botany 87:443–454.

Austin, A., J. Deniseger & M. J. R. Clark, 1985. Lake algal populations and physico- chemical changes after 14 years input of metallic mining wastes. Water Research 19:299–308.

Barral-Farga, L., S. Morin, M. D. M. Rovira, G. Urrea, K. Magellan & H. Guasch, 2016. Short-term arsenic exposure reduces diatom cell size in biofilm communities. Environmental Science and Pollution Research 23:4257–4270.

Barral-Fraga, L., D. Martiñá-Prieto, M. T. Barral, S. Morin & H. Guasch, 2018. Mutula interaction between arsenic and biofilm in a mining impacted river. Science of the Total Environment 636:985–998.

Battarbee, R. W., V. J. Jones, R. J. Flower, N. G. Cameron, H. Bennion, L. Carvalho & S. Juggins, 2001. Terrestrial, algal, and siliceous indicators. In Smol, J. P., H. J. B. Birks & W. M. Last (eds), Tracking Environmental Change Using Lake Sediments 3: Terrestrial, Algal, and Siliceous Indicators. Kluwer Academic Publishers, Dordrecht, The Netherlands:155–202.

Benson, B., J. Magnuson, & S. Sharma, 2013. Global Lake and River Ice Phenology Database, Version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center.

Birks, H. J. B., 2010. Numerical methods for the analysis of diatom assemblage data. In Smol, J. P. & E. F. Stoermer (eds), The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge, United Kingdom: 23–54.

Blanchet, F. G., P. Legendre & D. Borcard, 2008. Forward selection of explanatory variables. Ecology 89:2623–2632.

Bright, D. A., B. Coedy, W. T. Dushenko & K. J. Reimer, 1994. Arsenic transport in a watershed receiving gold mine effluent near Yellowknife, Northwest Territories, Canada. Science of the Total Environment 155:237–252.

Camburn, K. E. & D. F. Charles, 2000. Diatoms of low-alkalinity lakes in the Northeastern United States. The Academy of Natural Sciences of Philadelphia, Philadelphia, United States of America.

CCME (Canadian Council of Ministers of the Environment) 2001. Canadian water quality guidelines for the protection of aquatic life: Arsenic. Updated In: Canadian environmental quality guidelines, 1999, Canadian Council of Ministers of the Environment, Winnipeg, Canada.

Cattaneo, A., Y. Couillard, S. Wunsam & M. Courcelles, 2004. Diatom taxonomic and morphological changes as indicators of metal pollution and recovery in Lac Default (Québec, Canada). Journal of Paleolimnology 32:163–175.

Cattaneo, A., Y. Couillard & S. Wunsam, 2008. Sedimentary diatoms along a temporal and spatial gradient of metal contamination. Journal of Paleolimnology 40:115–127.

Chen, G., H. Shi, J. Tao, L. Chen, Y. Liu, G. Lei, X. Liu & J. P. Smol, 2015. Industrial arsenic contamination causes catastrophic changes in freshwater ecosystems. Scientific Reports 5:17419

Clarke, K. R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18: 117–143.

Clarke, K. R. & R. N. Gorley, 2006. PRIMER v6: User Manual/Tutorial (Plymouth Routines in Multivariate Ecological Research). PRIMER-E, Plymouth.

Cott, P. A., B. A. Zajdilk, M. J. Palmer & M. D. McPherson, 2016. Arsenic and mercury in lake whitefish and burbot near the abandoned Giant Mine on Great Slave Lake. Journal of Great Lakes Research 42: 223–232.

Cumming, B. F., J. P. Smol, J. C. Kingston, D. F. Charles, H. J. B. Birks, K. E. Camburn, S. S. Dixit, A. J. Uutala & A. R. Selle, 1992. How much acidification has occurred in Adirondack region lakes (New York, USA) since preindustrial times? Canadian Journal of Fisheries and Aquatic Sciences 49:128–141.

Dirszowsky, R. W. & Wilson, K. M., 2016. Biogeochemical evidence of eutrophication and metal contamination of Frame Lake, City of Yellowknife, Northwest Territories, Canada. Environmental Earth Science 75:76.

Dixit, A. S., S. S. Dixit & J. P. Smol, 1992. Long-term trends in lake water pH and metal concentrations inferred from diatoms and chrysophytes in three lakes near Sudbury, Ontario. Canadian Journal of Fisheries and Aquatic Sciences 49(Suppl.1):17–24.

Dixit, S. S., A. S. Dixit & J. P. Smol, 1991. Multivariable environmental inferences based on diatom assemblages from Sudbury (Canada) lakes. Freshwater Biology 26:251–266.

Douglas, M. S. V. & J. P. Smol, 1995. Periphytic diatom assemblages from high Arctic ponds. Journal of Phycology 31:60–69.

Ecosystem Classification Group, 2008. Ecological regions of the Northwest Territories: Taiga Shield. Department of Environment and Natural Resources, Government of the Northwest Territories, Yellowknife, NT, Canada.

Fallu, M.-A., N. Allaire & R. Pienitz, 2000. Freshwater diatoms from northern Québec and Labrador (Canada): Species-environment relationships in lakes of boreal forest, forest-tundra and tundra regions. Bibliotheca Diatomologica Band 45. Berlin, Stuttgart.

Fernández, M. R., G. Martín, J. Corzo, A. de la Linde, E. Gracía, M. López & M. Sousa, 2018. Design and testing of a new diatom-based index for heavy metal pollution. Archives of Environmental Contaminants and Toxicology 74:170–192.

Galloway, J. M., G. T. Swindles, H. E. Jamieson, M. Palmer, M. B. Parsons, H. Sanei, A. L. Macumber, R. T. Patterson & H. Falck, 2018. Organic matter control on the distribution of arsenic in lake sediments impacted by ~65 years of gold ore processing in subarctic Canada. Science of the Total Environment 622-623:1668–1679.

Gell, P. A., I. R. Sluiter & J. Fluin, 2002. Seasonal and interannual variations in diatom assemblages in Murray River connected wetlands in north-west Victoria, Australia. Marine and Freshwater Research 53:981–992.

Glew, J.R., 1988. A portable extruding device for close interval sectioning of unconsolidated core samples. Journal of Paleolimnology 1:235–239.

Griffiths, K., N. Michelutti, M. Sugar, M. S. V. Douglas & J. P. Smol, 2017. Ice-cover is the principal driver of ecological change in High Arctic lakes and ponds. PLoS ONE 12:e0172989.

Håkansson, H., 2002. A compilation and evaluation of species in the general Stephanodiscus, Cyclotestephanos and Cyclotella with a new genus in the family Stephanodiscaceae. Diatom Research 17:1–139.

Hall, R. I. & J. P. Smol, 1996. Paleolimnological assessment of long-term water-quality changes in south-central Ontario lakes affected by cottage development and acidification. Canadian Journal of Fisheries and Aquatic Sciences 53:1–17.

Hamilton, P. B., I. Lavoie, S. Alpay & K. Ponader, 2015. Using diatom assemblages and sulfur in sediments to uncover the effects of historical mining on Lake Arnoux (Quebec, Canada): a retrospective of economic benefits vs. environmental debt. Frontiers in Ecology and Evolution 3:99.

Harrell, F. E., 2018. Hmisc: Harrell Miscellaneous. R package version 4.1-1.

Health Canada, 2006. Guidelines for Canadian Drinking Water Quality: Guideline Technical Document — Arsenic. Water Quality and Health Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada.

Hirst, H. I. Jüttner & S. J. Ormerod, 2002. Comparing the responses of diatoms and macroinvertebrates to metals in upland streams of Wales and Cornwall. Freshwater Biology 47:1752–1765.

Houben, A. J., R. D’Onofrio, S. V. Kokelj & J. M. Blais. 2016. Factors affecting elevated arsenic and methyl mercury concentrations in small shield lakes surrounding gold mines near the Yellowknife, NT, (Canada) region. PLoS ONE 11:e0150960.

Ivorra, I., S. Bremer, H. Guasch, M. H. S. Kraak & W. Admiraal, 2000. Differences in the sensitivity of benthic microalgae to Zn and Cd regarding biofilm development and exposure history. Environmental Toxicology and Chemistry 19:1332–1339.

Jamieson, H. E., 2014. The legacy of arsenic contamination from mining and processing refractory gold ore at Giant Mine, Yellowknife, Northwest Territories, Canada. Reviews in Minerology and Geochemistry 79:533–551.

Juggins, S., 2017. rioja: Analysis of Quaternary Science Data. R package version 0.9- 15.1.

Karst-Riddoch, T. L., H. J. Malmquist & J. P. Smol, 2009. Relationships between freshwater sedimentary diatoms and environmental variables in Subarctic Icelandic lakes. Fundamental and Applied Limnology 175:1–28.

Keatley, B. E., M. S. V. Douglas, J. M. Blais, M. L. Mallory & J. P. Smol. 2009. Impacts of seabird-derived nutrients on water quality and diatom assemblages from Cape Vera, Devon Island, Canadian High Arctic. Hydrobiologia 621:191–205.

Kerr, D. E. & P. Wilson, 2000. Preliminary surficial geology studies and mineral exploration consideration in the Yellowknife area, Northwest Territories. Geological Survey of Canada, Current Research 2000-C3.

Krammer, K. & H. Lange-Bertalot, 1986. Bacillariophyceae. 1. Teil: Naviculaceae. In Ettl, H., J. Gerloff, H. Heynig, & D. Mollenhauer (eds), Süsswasserflora von Mitteleuropa. Band 2/1. Stuttgart: Gustav Fischer Verlag.

Krammer, K. & H. Lange-Bertalot, 1988. Bacillariophyceae. 2. Teil: Bacillariaceae Epithemiaceae, Surirellaceae. In: Ettl, H., J. Gerloff, H. Heynig, & D. Mollenhauer (eds), Süsswasserflora von Mitteleuropa. Band 2/2. Stuttgart: Gustav Fischer Verlag.

Krammer, K. & H. Lange-Bertalot, 1991a. Bacillariophyceae. 3. Teil: Centrales, Fragilariaceae, Eunotiaceae. In: Ettl, H., J. Gerloff, H. Heynig, & D. Mollenhauer (eds), Süsswasserflora von Mitteleuropa. Band 2/3. Stuttgart: Gustav Fischer Verlag.

Krammer, K. & H. Lange-Bertalot, 1991b. Bacillariophyceae. 4. Teil: Achnanthaceae, Kritische-Ergänzungen zu Navicula (Lineolatae) und Gomphonema, Gesamtliteraturverzeichnis. In: Ettl, H., G. Gärtner, J. Gerlof, H. Heynig, & D. Mollenhauer (eds), Süsswasserflora von Mitteleuropa. Band 2/4. Stuttgart: Gustav Fischer Verlag.

Lange-Bertalot, H. & G. Moser, 1994. Brachysira. Bibliotheca Diatomologica Band 29. Berlin, Stuttgart.

Last, W. M., R. E. Vance, S. Wilson & J. P. Smol, 1998. A multi-proxy limnologic record of rapid early-Holocene hydrologic change on northern Great Plains, southwestern Saskatchewan, Canada. The Holocene 8:503–520.

Leppänen, J. J., J. Weckström & A. Korhola, 2017. Multiple mining impacts induce widespread changes in ecosystem dynamics in a boreal lake. Scientific Reports 7: 10581.

Laing, T. E., R. Pienitz & J. P. Smol, 1999. Freshwater diatom assemblages from 23 lakes near Norilsk, Siberia: A comparison with assemblages from other circumpolar treeline regions. Diatom Research 14:285–305. 53

Michelutti, N., T. E. Laing & J. P. Smol, 2001. Diatom assessment of past environmental changes in lakes located near the Noril’sk (Siberia) smelters. Water Air Soil Pollution 125:231–241.

Michelutti, N., A. J. Holtham, M. S. V. Douglas & J. P. Smol, 2003. Periphytic diatom assemblages from ultra-oligotrophic and UV transparent lakes and ponds on Victoria Island and comparison with other diatom surveys in the Canadian Arctic. Journal of Phycology 39:465–480.

Michelutti, N., J. P. Smol & M. S. V. Douglas, 2006. Ecological characteristics of modern diatom assemblages from Axel Heiberg Island (High Arctic Canada) and their applications to paleolimnological inference models. Canadian Journal of Botany 84: 1695–1713.

Morin, S., T. T. Duong, A. Dabrin, A. Coynel, O. Herlory, M. Baudrimont, F. Delmas, G. Durrieu, J. Schäfer, P. Winterton, G. Blanc & M. Coste, 2008. Long-term survey of heavy-metal pollution, biofilm contamination and diatom community structure in the Riou Mort watershed, South-West France. Environmental Pollution 151:532–542.

Morin, S., A. Cordonier, I. Lavoie, A. Arini, S. Blanco, T. T. Duong, E. Tornés, B. Bonet, N. Carcoll, L. Faggiano, M. Laviale, F. Pérès, E. Becares, M. Coste, A. Feutet- Mazel, C. Fortin, H. Guasch & S. Sabater. 2012. Consistency in diatom responses to metal-contaminated environments. In Guasch, H., A. Ginebreda & A. Geiszinger (eds), Emerging and priority pollutants in rivers: Bringing science into river management plans. Springer-Verlag, Berlin:117-146.

Moser, K. A., J. P. Smol, G. M. MacDonald & C. P. S. Larsen, 2002. 19th century eutrophication of a remote boreal lake: a consequence of climate warming? Journal of Paleolimnology 28:269–281.

Nelligan, C., A. Jeziorski, K. M. Rühland, A. M. Paterson & J. P. Smol, 2016. Managing lake trout lakes in a warming world: a paleolimnological assessment of nutrients and lake production at three Ontario sites. Lake and Reservoir Management 32:315–328.

Northwest Territories Investment, Tourism and Investment, 2014. Economic Outlook 2014-2015: Investment and Economic Analysis. Government of Northwest Territories.

Oksanen, J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O’Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, E. Szoecs & H. Wagner, 2018. vegan: Community Ecology Package. R package version 2.5-2.

Palmer, M. J., J. M. Galloway, H. E. Jamieson, R. T. Patterson, H. Falck & S. V. Kokelj, 2015. The concentration of arsenic in lake waters of the Yellowknife area. Northwest Territories Geological Survey, NWT Open File 2015-06.

Philibert, A. & Y. T. Prairie, 2002. Is the introduction of benthic species necessary for open-water chemical reconstruction in diatom-based transfer functions? Canadian Journal of Fisheries and Aquatic Sciences 59:938–951.

Pienitz, R. & J. P. Smol, 1993. Diatom assemblages and their relationship to environmental variables in lakes from the boreal forest-tundra ecotone near Yellowknife, Northwest Territories, Canada. Hydrobiologia 269⁄270:391–404.

R Core Team, 2018. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.

Rodriguez Castro, M. C., G. Urrea & H. Gausch, 2015. Influences of the interaction between phosphate and arsenate on periphyton’s growth and its nutrient uptake capacity. Science of the Total Environment 503/504:122–132.

Round, F. E., R. M. Crawford & D. G. Mann. 1990. The Diatoms: Biology & Morphology of the Genera. Cambridge University Press, Cambridge, United Kingdom.

Ruggiu, D., A. Luglié, A. Cattaneo & P. Panzani, 1998. Paleoecological evidence for diatom response to metal pollution in Lake Orta (N. Italy). Journal of Paleolimnology 20:333–345.

Rühland, K. M. & J. P. Smol, 2002. Freshwater diatoms from the Canadian Arctic treeline and development of paleolimnological inference models. Journal of Phycology 38:249–64.

Rühland, K. M., J. P. Smol & R. Pienitz, 2003a. Ecology and spatial distributions of surface-sediment diatoms from 77 lakes in the subarctic Canadian treeline region. Canadian Journal of Botany 81:57–73.

Rühland, K., A. Priesnitz & J. P. Smol, 2003b. Evidence for recent environmental changes in 50 lakes across the Canadian Arctic treeline. Arctic Antarctic and Alpine Research 35: 110–123.

Rühland, K. & J. P. Smol, 2005. Diatom shifts as evidence for recent subarctic warming in a remote tundra lake, NWT, Canada. Palaeogeography, Palaeoclimatology, Palaeoecology 226: 1–16.

Rühland, K.M., A.M. Paterson & J.P. Smol, 2008. Hemispheric-scale patterns of climate- related shifts in planktonic diatoms from North American and European lakes. Global Change Biology 14:2740-2754.

Rühland, K.M., A.M. Paterson & J.P. Smol, 2015. Diatom assemblage responses to warming: reviewing the evidence. Journal of Paleolimnology 54: 1-35.

Salonen, V-P., N. Tuovinen & S. Valpola, 2006. History of mine drainage impact on Lake Orijärvi algal communities, SW Finland. Journal of Paleolimnology 35:289–303.

Schmidt, R., Kamenik, C., Lange-Bertalot, H., & R. Klee, 2004. Fragilaria and Staurosira (Bacillariophyceae) from sediment surfaces of 40 lakes in the Austrian Alps in relation to environmental variables, and their potential for palaeoclimatology. Journal of Limnology 63:171–189.

Schuh, C. E., H. E. Jamieson, M. J. Palmer & A. J. Martin, 2018. Solid-phase speciation and post-depositional mobility of arsenic in lake sediments impacted by ore roasting at legacy gold mines in the Yellowknife area, Northwest Territories, Canada. Applied Geochemistry 91:208–220.

Silke, R., 2009. The operational history of mines in the Northwest Territories, Canada. An Historical Research Project. Simpson, G. L. & J. Oksanen, 2018. analogue: Analogue matching and Modern Analogue Technique transfer function models. R package version 0.17-1.

Smol, J. P., 1988. Paleoclimate proxy data from freshwater arctic diatoms. Verhandlungen des Internationalen verein Limnologie 23:837–44.

Smol, J.P. & E.F. Stoermer [Editors], 2010. The Diatoms: Applications for the Environmental and Earth Sciences. 2nd edition. Cambridge University Press. Cambridge. 667 pp.

Stewart, E. M., K. E. Hargan, B. Sivarajah, L. E. Kimpe, J. M. Blais & J. P. Smol, 2018. A paleoenvironmental study tracking eutrophication, mining pollution, and climate change in Niven Lake, the first sewage lagoon of Yellowknife (Northwest Territories). Arctic 71:201–217.

Szabó, K., K. T. Kiss, G. Taba & É. Ács, 2005. Epiphytic diatoms of the Tisza River, Kisköre Reservoir and some oxbows of the Tisza River after the cyanide and heavy metal pollution in 2000. Acta Botanica Croatica 64:1–46.

Tammelin, M., T. Kauppila & M. Viitasalo, 2017. Factors controlling recent diatom assemblages across a steep local nutrient gradient in central-eastern Finland. Hydrobiologia 799:309–325. ter Braak, C. J. F. & P. Šmilauer, 2012. CANOCO reference manual and user's guide: Software for ordination, version 5.0. Microcomputer Power, Ithaca, New York, United States of America.

Thienpont, J. R., J. B. Korosi, K. E. Hargan, T. Williams, D. C. Eickmeyer, L. E. Kimpe, M. J. Palmer, J. P. Smol & J. M. Blais, 2016. Multi-trophic level response to extreme metal contamination from gold mining in a subarctic lake. Proceedings of the Royal Society B 283: 20161125. 56

Tolotti, R., S. Consani, C. Carbone, G. Vagge, M. Capello & L. Cutroneo, 2019. Benthic diatom community response to metal contamination from an abandoned Cu mine: Case study of the Gromolo Torrent (Italy). Journal of Environmental Sciences 75:233–246.

Tropea, A. E., A. M. Paterson, W. Keller & J. P. Smol, 2011. Diatoms as indicators of enrichment in metal-contaminated urban lakes from Sudbury, Ontario. Lake Reservoir Management 27:48–60.

Tuulaikhuu, B-A., A.M. Romani & H. Guasch, 2015. Arsenic toxicity effects on microbial communities and nutrient cycling in indoor experimental channels mimicking a fluvial system. Aquatic Toxicology 166:72–82.

Van den Berghe, M. D., H. E. Jamieson & M. J. Palmer, 2018. Arsenic mobility and characterization in lakes impacted by gold ore roasting, Yellowknife, NWT, Canada. Environmental Pollution 234:630–641.

Weckström, J., A. Korhola & T. Blom, 1997. The relationship between diatoms and water temperature in thirty subarctic Fennoscandian lakes. Arctic Alpine Research 29:75–92.

Weckström, J. & A. Korhola, 2001. Patterns in the distribution, composition and diversity of diatom assemblages in relation to ecoclimatic factors in Arctic Lapland. Journal of Biogeography 28:31–46.

Wıìlıìdeh Yellowknives Dene, 1997. Weledeh Yellowknives Dene: a history. Dettah: Yellowknives Dene First Nation Elders Advisory Council.

Wolfe, S. A., 1998. Living with frozen ground: A field guide to permafrost in Yellowknife, Northwest Territories. Geological Survey of Canada Miscellaneous Report 64.

Table 2.1: Results of ANOSIM

Results of the analysis of similarity (ANOSIM) showing differences in diatom assemblages from the a priori groupings of lakes with varying arsenic concentrations

(µg/L). Bold values represent significant differences.

[As] > 100 100 > [As] > 10 [As] < 10 (n = 12) (n = 13) (n = 8) [As] > 100 100 > [As] > 10 0.159 [As] < 10 0.111 0.058 Global R = 0.12

Table 2.2: Results of species richness and Hill’s N2 diversity

Results of the diatom species richness and diversity (Hill’s N2) from the a priori groupings of lakes with varying arsenic concentrations (µg/L).

Species Richness Species Diversity Min Max Mean Median Min Max Mean Median [As] > 100 10.4 43 23.5 21.9 1.6 9.1 5.1 4.8 (n = 12) 100 > [As] > 10 16.9 47 30.5 29.8 2.1 13.4 5.5 5.0 (n = 13) [As] < 10 22 41.8 30.1 29.3 2.2 9.8 5.9 6.3 (n = 8)

Figure 2.1: A map of the study area of lakes around Yellowknife, Northwest Territories

The shaded asterisk indicates Giant Mine while the clear asterisk denotes Con Mine. The Yellowknife Airport is indicated by an airplane. The shapes of the symbols represent the concentration of arsenic in the water column and the number of lakes in each category are included. The inset map shows the location of Yellowknife within Canada.

Figure 2.2: PCA of measured environmental variables

The distribution of the 31 lakes along the gradients of the measured environmental variables using principal component analysis. The shapes of the symbols represent the concentration of arsenic in the water column.

Figure 2.3: Histograms of the dominant diatom taxa from the Yellowknife area

The sites are arranged according to As concentrations. The colors of the individual bars represent the varying arsenic concentrations at the site (red – > 1000 µg/L; orange – 100 - 1000 µg/L; yellow – 10 - 100 µg/L; light green – 5 - 10 µg/L; dark green - < 5 µg/L). 61

Figure 2.4: Relationships between the relative abundances of most common taxa, and depth

Relationships between Achnanthidium minutissimum, benthic fragilarioid taxa, the Discostella stelligera complex, and sum of all planktonic and benthic taxa with log transformed depth from lakes with varying arsenic concentrations. The shapes of the symbols represent the concentration of arsenic in the water column and a conversion table for the log depth is provided in the figure.

Figure 2.5: Results of the CCA (site scores)

Canonical correspondence analysis (CCA) showing the distribution of sites with total nitrogen (TN), sodium (Na+), and antimony (Sb) in the ordination space. CCA axis 1 λ = 0.33, CCA axis 2 λ = 0.20, CCA axis 3 λ = 0.11.

Figure 2.6: Results of the CCA (species scores)

Canonical correspondence analysis (CCA) showing the distribution of the most common species (> 5% in at least 2 samples) with total nitrogen (cross), sodium (plus), and antimony (hexagon) in the ordination space. CCA axis 1 λ = 0.33, CCA axis 2 λ = 0.20, CCA axis 3 λ = 0.11. The diatom codes are as follows, 1) Achnanthidium minutissimum, 2) Amphora pediculus, 3) Asterionella formosa, 4) Brachysira neoexilis, 5) Encyonopsis descripta, 6) Encyonopsis microcephala, 7) Cyclotella ocellata, 8) Discostella stelligera/pseudostelligera complex 9) Lindavia michiganiana, 10) Cocconeis placentula, 11) Denticula kuetzingii, 12) Fragilaria nanana, 13) Pseudostaurosira brevistriata, 14) Pseudostaurosira brevistriata type, 15) S.pinnata-S.construens var. venter complex, 16) Navicula radiosa, 17) Navicula cryptotenella, 18) Navicula cryptocephala, 19) Navicula minima, 20) Navicula seminulum, 21) Navicula pupula, 22) Nitzschia frustulum, 23) Nitzschia fonticola.

Chapter 3

Regional gold mining activities and recent climate warming alter diatom

assemblages in deep sub-Arctic lakes

3.1 Abstract

Previous paleolimnological investigations have examined the effects of gold mining operations, local land-use changes, and regional climate warming on the aquatic biota from shallow lakes in Yellowknife, Northwest Territories, Canada. However, the long-term impacts of these multiple environmental stressors on the biota of deeper lakes that support large-bodied fish species have not been investigated. In this study, we examined multiple sedimentary proxies from two deep lakes around Yellowknife to assess the long-term effects of metalloid contamination, development of the city, and recent warming over the past ~ 200 years. The sedimentary metalloid profiles tracked the influence of mining operations and local land-use changes in the Yellowknife area and there were some similarities in the diatom responses to multiple stressors across the two lakes. However, the increases in sedimentary metalloid concentrations, eutrophic diatom taxa, and whole lake primary production were more pronounced at Grace Lake relative to

Alexie, likely because Grace is located nearer to the gold mines, as well as local city development. The overall lake primary production and the relative abundance of the planktonic diatom Discostella stelligera increased at both sites suggesting that some of the biological changes may be influenced by changes in thermal stratification, as has been documented in a wide spectrum of lakes across the Northern Hemisphere. Furthermore, the diatom assemblage changes in these deep lakes differed from those observed from

shallow lakes in the region, suggesting that site specific limnological characteristics will influence the biological responses to multiple environmental stressors through time.

3.2 Introduction

The acceleration of mining operations to meet the global demand for metals after the Industrial Revolution and the two World Wars has led to the contamination of many aquatic ecosystems globally (e.g. Wong et al. 1999). For example, in Canada the exploration and extraction of precious metals such as gold and silver increased rapidly during the 20th century and eventually led to the metalloid contamination of surrounding ecosystems (e.g. Sprague et al. 2016). The consequences of metalloid contamination on aquatic ecosystems can be catastrophic as biota from multiple trophic levels may be affected (e.g. Chen et al. 2015; Leppänen et al. 2017). A notable example of 20th century metal mining operations and subsequent environmental damage occurred around the City of Yellowknife in the Northwest Territories, Canada, where gold mining operations led to

2- the arsenic (As), antimony (Sb), and sulphate (SO4 ) contamination of numerous sub-

Arctic lakes (Palmer et al. 2015; Houben et al. 2016). In addition to mining pollution, lakes within the City of Yellowknife were also affected by land clearance, establishment and growth of the city, and other municipal activities (e.g. Dirszowsky and Wilson 2016;

Gavel et al. 2018). Furthermore, similar to many northern regions, Yellowknife has also undergone marked increases in air temperature (e.g. Mullan et al. 2017). The aquatic biological responses to the interactive effects of anthropogenic activities in watersheds and climate can be complex (Smol 2010) and have led to limnological surprises in this area. For instance, at Yellowknife’s first sewage lagoon, Niven Lake, the legacy of

eutrophication was exacerbated by warmer temperatures in recent decades (Stewart et al.

2018). Therefore, understanding the cumulative impacts on aquatic organisms, within the context of multiple stressors, is necessary to determine appropriate remediation plans and postulate realistic recovery targets for lakes around Yellowknife and other Northern regions.

Previous and on-going paleolimnological research around Yellowknife have provided the much-needed long-term perspectives on the cumulative effects of multiple stressors (e.g. Dirszowsky and Wilson 2016; Gavel et al. 2018; Stewart et al. 2018;

Thienpont et al. 2016). However, most of these studies have focused on shallow lakes (<

10-m maximum depth) as these are the dominant water bodies in this region (Palmer et al. 2015). While these paleolimnological studies have been informative, the cumulative long-term effects of multiple stressors on the biota of deeper (> 10-m maximum depth) sub-Arctic lakes, which often support important fisheries, have not been extensively investigated in this region. Indeed, the deep sub-Arctic lakes around Yellowknife contain suitable habitat to support large-bodied and cold-water fish populations (e.g. lake trout -

Salvelinus namaycush) that have cultural significance to local indigenous peoples and play an important role in regional economic activities (e.g. sports fisheries, tourism)

(Healey and Woodall 1973; Stewart 1997). Meanwhile, shallow lakes in the region do not support similar fish species as they do not have suitable thermal habitats and/or were affected by anthropogenic activities, or simply freeze to the bottom in winter (e.g.

Stewart 1997; Gavel et al. 2018). Recently, land-use changes associated with industrial development has been identified as a potential threat to the habitat of large-bodied and late-maturing fish species in Canada’s North (Cott et al. 2015). For example, increased

human activities, such as highway constructions, logging, contamination, and fishing pressure, can negatively impact the habitat quality and population sizes of fish species

(Cott et al. 2015). Furthermore, investigations of temperate lakes have indicated that the depletion of hypolimnetic dissolved oxygen, often induced by anthropogenic activities and subsequent eutrophication, is an important environmental stressor affecting aquatic habitats that support large-bodied fish species (Evans et al. 1996). Recent climate warming has also been suggested as an emerging stressor altering the habitats and phenology of cold-water fish species in temperate lakes (Guzzo and Blanchfield 2017).

Whilst previous paleolimnological studies have assessed the cumulative impacts of land- use changes and climate warming on deep lakes that support large-bodied, cold-water fish species in temperate regions (e.g. Nelligan et al. 2016), relatively little is known about the long-term impacts of multiple stressors on their sub-Arctic counterparts.

In this paleolimnological investigation, we examined dated sediment records from two deep sub-Arctic lakes (> 10 m) in the Yellowknife area that support large-bodied fish species. Since we were interested in comparing the effects of multiple environmental stressors, we selected one lake near the City of Yellowknife and the mines (Grace Lake) and another lake that is ~ 20 km away from these activities (Alexie Lake). We used sedimentary metal(loid) analysis to track the history of mining pollution, while subfossil diatom (Bacillariophyceae) assemblages and visible reflectance of chlorophyll-a from the sediments were examined to infer the biological effects of multiple environmental stressors. In this study, we specifically selected algal-based paleolimnological techniques to track the history of eutrophication and impacts of recent warming on the limnology of these sub-Arctic lakes as these proxies have been used extensively to investigate the

consequences of nutrient enrichment and climatic changes on temperate lakes that support large-bodied fish species (e.g. Nelligan et al. 2016). However, future investigations of zooplankton based paleolimnological studies can provide more insights on the long-term changes in the fish communities of these lakes.

3.3 Study area

The gold mining activities in Yellowknife commenced around the late-1930s but were briefly interrupted by World War II and resumed soon after the war ended when an increased labour force became available. The gold mining operations lasted for over five decades at Giant (Giant Yellowknife Gold Mines Limited; 1948-2004) and Con

(Consolidated Mining and Smelting Company; 1938-2003) mines (Wolfe 1998; Silke

2009). When the gold-bearing arsenopyrite and pyrite ores around Yellowknife were roasted, large quantities of As-bearing iron oxides and sulfur dioxide were released into the environment via roaster stacks (Coleman 1957; Walker et al. 2005; INAC 2007;

Palmer et al. 2015). The As vapour precipitated into solid-phase arsenic trioxide (As2O3) in the atmosphere and was deposited on the terrestrial and aquatic ecosystems around

Yellowknife (Walker et al. 2005; Schuh et al. 2018; Van Den Berghe et al. 2018). It has been estimated that ~ 20,000 tonnes of As2O3 were introduced into the surrounding environment from gold mining activities at Giant between 1948 and 1999 with greater amounts being released during the early years (1948 - 1951) (summarized by Jamieson

2014). As a mitigation procedure, an electrostatic precipitator was installed in 1951 that dramatically reduced the amount of As2O3 entering the environment through roaster stacks (~5.5 vs. 1.5 tonnes per day in 1952 and 1958, respectively) (INAC 2007;

Jamieson 2014). Further reductions in As2O3 emissions became possible by the construction of a baghouse and underground storage facilities in 1951 (INAC 2007;

Jamieson 2014). Cumulatively, these emission controls substantially reduced the amount of As2O3 that entered the environment between 1959 and 1999 (varied between 0.01 and

0.9 tonnes per day). Nonetheless, recent surveys of many lakes in the region recorded As concentrations that exceeded the suggested guidelines for the protection of aquatic life (<

5 µg/L) and drinking water (< 10 µg/L) quality (Palmer et al. 2015).

Grace and Alexie lakes are located within the Slave Structural Province of the

Canadian Shield. Grace Lake is underlain by granitoid bedrock, while Alexie lies in the transition zone of granitoid and metasedimentary rock formations (Henderson 1985).

Both lakes are relatively deep (Alexie – 21 m; Grace – 16 m) and have very similar pH and alkalinity, however Alexie is substantially larger than Grace (surface area of Alexie is ~ 4.56-km2, whilst Grace is ~ 0.63 km2) (Table 3.1), hence there are likely differences in the volume of these two lakes. Alexie has an inlet on the northwest corner that connects it to Chitty Lake and has an outflow on the southeast corner. Grace Lake has an inlet on the eastern part of the lake and an outflow on the west that connects it to Kam

Lake. The predominant wind direction in the Yellowknife area is from east to west and

Alexie is located to the northeast of Giant and Con mines, while Grace is located to the south of the mines (Fig. 3.1). The present-day contaminant concentrations in the water are higher at Grace, as this site is closer to the mining activities and the city (Table 3.1 and Fig. 3.1). For example, the present As concentration in the water column at Grace (16

µg/L) is ~ 10 times higher than Alexie (1.5 µg/L), and exceeds the guidelines for the protection of aquatic life and drinking water quality.

Grace Lake experienced substantial anthropogenic disturbances during the 20th and 21st centuries as it is located within the City of Yellowknife and near Con Mine.

Specifically, development around Grace Lake occurred as early as 1937 with the construction of the Con Mine (~ 3.7 km away), and a road was built over the outflow from Grace Lake to Kam Lake between 1937 and 1950 (Fig. 3.2). Furthermore, the population of Yellowknife increased substantially around the mid-20th century, and so to meet the growing demand of wood for construction, logging became an important form of land clearance activity in the area (St-Onge 2007). Very recently (post-2010) some houses have been built along the shores of Grace Lake, and the lake is used for recreational purposes by residents and tourists visiting the city. Grace Lake supports a diverse fish population that includes burbot (Lota lota), northern pike (Esox lucius), lake whitefish (Coregonus clupeaformis), lake cisco (Coregonus artedi), ninespine stickleback

(Pungitius pungitius), and walleye (Sander vitreus) (Stewart 1997). Unlike Grace, Alexie

Lake is located ~ 20 km from major anthropogenic activities and is considered relatively pristine in terms of shoreline development. Importantly, Alexie also supports a diverse large-bodied fish population that includes species such as burbot, lake trout (Salvelinus namaycush), northern pike, and lake whitefish (e.g. Cott et al. 2011; Callaghan et al.

2016; Guzzo et al. 2016).

3.4 Materials and Methods

3.4.1 Sediment coring and chronology

The high-resolution sediment cores were retrieved through the ice from Grace and

Alexie lakes in April 2014 using a Uwitec gravity corer from the middle of the lakes to

ensure the sediments are integrative and representative of past environmental information from multiple habitats in the lake. A modified Glew (1988) extruder was used to section the samples at 0.5-cm intervals to the base of the core. The samples were frozen for transportation to the Laboratory for the Analysis of Natural and Synthetic Environmental

Toxicants at the University of Ottawa. Select sediment intervals were freeze-dried and analyzed for 210Pb isotopes using an Ortec high-purity Germanium gamma spectrometer

(Oak Ridge, Tennessee) at the University of Ottawa to radiometrically date the cores.

Certified reference materials obtained from International Atomic Energy Association

(Vienna, Austria) were used for efficiency corrections, and results were analyzed using

ScienTissiME (Barry’s Bay, ON, Canada). The sediment core chronologies were developed following standard protocols described by Appleby (2001). Briefly, the supported activities of 210Pb were determined by measuring the concentrations of 214Pb in the sediments, and then the unsupported activities of the 210Pb and the constant rate of supply model (CRS) were used to determine the 210Pb dates.

3.4.2 Element analysis

Approximately 0.5 g of freeze-dried sediments from select intervals were analyzed at SGS Laboratory (Lakefield, Ontario; Canadian Association for Laboratory

Accreditation Inc. (CALA) accredited facility) for total metalloid concentrations using

USEPA method 305B Aqua Regia Hot Plate Digest, followed by Inductively Coupled

Plasma-Mass Spectroscopy (ICP-MS) analysis. This method releases all elements that are not bound in silicate structures and are therefore potentially available to be mobile. All arsenic (As), antimony (Sb), and lead (Pb) concentrations presented herein are reported as total metalloid concentrations.

3.4.3 Diatom analysis

Subfossil diatom analyses were performed following procedures described in

Sivarajah et al. (2019). A minimum of 350 diatom valves were identified and enumerated from each sample using a Leica DMR light microscope fitted with differential interference contrast optics under 1000x magnification. We used the photomicrographs presented in Krammer & Lange-Bertalot (1986 –1991), Håkansson (2002) and Rühland et al. (2003a) to identify the diatom taxa, and then verified the taxonomy with recent publications and online databases, such as Diatoms of North America

(https://diatoms.org/) to ensure the most updated names of species were used. The diatom taxa are expressed as percent abundance relative to the total number of diatom valves enumerated in a sample.

3.4.4 Sedimentary chlorophyll-a

Visible reflectance spectroscopy of chlorophyll-a (VRS Chl-a) is a rapid and non- destructive method to examine past trends in overall lake primary production (Michelutti et al. 2010) and has been used to assess past changes in trophic status in lakes across

Canada (Michelutti and Smol 2016). Since chlorophyll-a, isomers of chlorophyll-a, and the main degradation products of chlorophyll-a (i.e., pheophytin-a and pheophorbide-a) have similar visible spectroscopic properties, the VRS Chl-a method tracks both primary chlorophyll-a and its main diagenetic products in sediments (Michelutti et al. 2010).

Freeze-dried sediments were sieved through a 125-μm mesh to remove the effects of particle size and transferred to a glass vial. The samples were then scanned on a FOSS

NIR System model 6500 rapid content analyzer, and the spectra in the range of 650 –

700-nm were analyzed to infer past trends in whole-lake primary production (Michelutti et al. 2010).

3.4.5 Temperature data

The long-term mean annual air temperature record for Yellowknife, NT (Climate

Station ID: 2204101) was retrieved from the Adjusted and Homogenized Canadian

Climate Database. The Yellowknife air temperature record was mostly continuous and contained data between 1944 and 2016, with one year (1945) of missing data. In addition, we used a longer record from Hay River, NT (Climate Station ID: 2202401) where air temperature data are available to the late-19th century (1896 - 2016), but with 14 years of data missing (1897, 1899, 1902, 1908, 1909, 1917, 1918, 1922, 1927, 1943-1946, 1963).

The Hay River climate station is located on the southern shores of Great Slave Lake, ~

200 km south of Yellowknife (Fig. 3.1). The temperature records from both sites had very similar trends through time, however, the absolute values were generally lower at

Yellowknife as it is more northerly.

3.4.6 Data analysis

Stratigraphic zones of the diatom assemblages were determined using constrained incremental sum of squares (CONISS) (Grimm 1987), and the broken stick model

(Bennett 1996) was used to identify the important zones for each core using the R software environment (R Core Team, 2018) using ‘rioja’ (Juggins 2017) and ‘vegan’

(Oksanen et al. 2018) software packages. Since the diatom species data had gradient lengths of < 2 standard deviation units (based on detrended correspondence analysis), linear ordination methods such as principal component analyses (PCA) were deemed appropriate to assess diatom assemblage changes through time (Birks 2010). Specifically,

we examined the sedimentary diatom data from both lakes within the same ordination space using PCA (e.g. Sivarajah et al. 2017). This approach allowed us to determine if there were differences in diatom assemblages across both lakes and track the trajectories of changes in composition through time.

3.5 Results

3.5.1 Radiometric dating

Both Alexie and Grace lakes approached background 210Pb activities around 12 cm (Appendix Figure D.1). The CRS sedimentation rates were similar at both lakes around the early 20th century (Grace Lake: 0.0031 g/cm2/year ±0.0022; Alexie Lake:

0.0021 g g/cm2/year ±0.0017). However, the CRS sedimentation rates increased and fluctuated substantially during the mid- to late-20th century at Grace Lake, while rates remained low and only subtly increased at Alexie (Appendix Figure D.1). For example, between the 1940s and 2014, the average sedimentation rates at Grace and Alexie were

0.0137 and 0.0041 g/cm2/year, respectively.

3.5.2 Element concentrations in sediments

There were notable differences in the elemental profiles of both study lakes (Fig.

3) with sedimentary metalloid concentrations generally higher at Grace than Alexie, even before the mining operations began. Coincident with the onset of regional gold mining operations, the concentration of mining-related metalloids, particularly arsenic and antimony, increased markedly around the 1930s-1940s at Grace Lake and reached maximum concentrations around the 1960s. However, the concentrations of As and Sb declined sharply after the 1960s and the present-day concentrations are only slightly

higher than the pre-industrial period. Although lead concentrations of Grace Lake followed similar trajectories as arsenic and antimony until about the 1960s, the decline after the 1960s was more gradual. The metalloid concentrations at Alexie Lake also increased during the period of mining, however, the changes were subtler compared to

Grace Lake. In fact, when plotted on the same graph, the changes in metalloid concentrations at Alexie were not clearly distinguishable from background concentrations

(Fig. 3.3). We therefore provided the metalloid profiles of Alexie Lake separately in

Appendix Figure D.2 to highlight the trends through time. We also explored the concentrations of metalloids in relation to titanium to determine watershed influence,

(Appendix Figure D.3) and the trends remained similar to those presented in Fig. 3.3, indicating that the watershed inputs had little impact on the higher sedimentary metalloid concentrations during the mining era.

3.5.3 Diatom assemblage changes at Grace and Alexie lakes

The diatom assemblages of both study lakes were dominated by planktonic taxa throughout the last ~200 years. Specifically, Discostella stelligera (Cleve and Grunow)

Houk and Klee was a common taxon in both lakes and increased in the most recent sediments (Figs. 3.4 and 3.5). The CONISS analysis, in conjunction with the broken stick model, identified two important zones in the diatom profiles of both lakes (Figs. 3.4 and

3.5) and the biggest split in assemblage composition occurred around the mid-1950s and early-1960s at Grace and Alexie lakes, respectively. However, the diatom assemblage changes were subtle at Alexie relative to the sharp changes that occurred at Grace Lake

(Figs. 3.4, 3.5, and 3.6).

The diatom assemblages of Grace Lake were dominated by D. stelligera throughout the entire sedimentary record (Fig. 3.4). Although CONISS identified the major assemblage change around the mid-1950s (~ 7 – 8 cm), where the relative abundances of D. stelligera and Stephanodiscus taxa (S. medius Håkansson and S. minutulus (Kützing) Round) increased sharply, the changes in diatom assemblages were evident as early as the mid- to late-1940s. These assemblage changes included a decrease in the relative abundances of many planktonic taxa (Asterionella formosa Hassall,

Aulacoseira subarctica (Müller) Haworth, Pantocsekiella ocellata (Pantocsek) Kiss &

Ács, Fragilaria nanana Lange-Bertalot, Fragilaria tenera (W Smith) Lange-Bertalot,

Tabellaria flocculosa str. IIIp Koppen) and small fragilarioid species (mostly Staurosira construens var. venter (Ehrenberg) Hamilton, Staurosirella pinnata (Ehrenberg) Williams and Round), with increases in planktonic Stephanodiscus taxa around the 1940s at Grace

Lake.

The pre-disturbance diatom assemblages of Alexie Lake were co-dominated by D. stelligera and P. ocellata (Fig. 3.5). As further identified by CONISS, after the 1960s (~

6 – 7 cm) the relative abundances of D. stelligera and small fragilarioid taxa (S. construens var. venter, S. pinnata, Staurosira construens Ehrenberg, Pseudostaurosira brevistriata (Grunow) Williams and Round) increased, while P. ocellata decreased. In contrast to Grace Lake, the relative abundances of A. subarctica, and Stephanodiscus taxa

(S. medius and S. minutulus) did not change substantially through time. Similarly, taxa such as Achnanthidium minutissimum (Kützing) Czarnecki, Nitzschia taxa (N. frustulum

(Kützing) Grunow and N. fonticola Grunow), and Encyonopsis taxa (E. microcephala

(Grunow) Krammer and E. descripta (Hustedt) Krammer) occurred in < 10% relative

abundances and did not display a directional trend throughout the sedimentary record of

Alexie Lake.

The general direction of trajectories of the diatom assemblages through time, tracked by PCA axis 2 (Fig. 3.6 a and b), were similar across both lakes. In the PCA ordination space, the pre-1940s samples from Grace Lake clustered together in the top- left quadrant while samples from post-1940s sediments plotted in the bottom-left quadrant (Fig. 3.6a). However, consistent with the diatom assemblage changes observed in the profiles, the changes in diatom composition at Alexie were subtler and the major shift occurred after the 1970s (Fig. 3.6b). Although both study lakes shared similar diatom taxa, the samples from the two lakes plotted separately along the PCA axis 1, highlighting the differences in the relative importance of each taxon in these lakes through time (Fig 6a). For example, taxa that were present in higher abundances in the early sediments plotted in the top quadrant (e.g. A. subarctica, P. ocellata, T. flocculosa str. IIIp) while taxa that were present in higher abundances in the recent sediments plotted in the bottom quadrant (e.g. D. stelligera, Stephanodiscus taxa).

3.5.4 Past trends in whole-lake primary production

Trends in whole-lake primary production (inferred through VRS Chl-a) increased in both Grace and Alexie lakes (Figs. 3.4 and 3.5). The increase at Alexie was gradual through time with higher concentrations recorded in the post-1960s sediments (Fig. 3.5).

The whole-lake primary production at Grace began to increase after the 1940s and fluctuated during the second half of the 20th century. After the initial increase in VRS

Chl-a between the 1940s and 1960s, it decreased briefly from ~1960 to ~1970, after

which it increased until ~2000. Afterwards, the whole-lake primary production at Grace decreased subtly between 2000 and 2005 and then increased to the surface of the core.

3.5.5 Temperature data

Similar to previous investigations from Arctic and sub-Arctic environments, the annual air temperature records showed marked increases in Yellowknife and Hay River

(Fig. 3.7). Specifically, the longer record from Hay River displayed a continuous increase in annual air temperature throughout the observational period (1896 - 2016). The two- segmented piecewise linear breakpoint analysis identified notable shifts in the air temperature records around 1956 and 1966 at Yellowknife and Hay River, respectively

(Fig. 3.7). To examine if the differences in breakpoints were influenced by the length of the air temperature records, we applied the two segmented piecewise linear breakpoint analysis on a section of the record from Hay River (1943 to 2016 where data were missing from 1943 to 1946 and 1963). This analysis identified a breakpoint around 1961 for the shorter annual air temperature record from Hay River.

3.6 Discussion

3.6.1 Geochemical signals of mining operations and land-use changes

The sedimentary biological and geochemical proxies at both lakes were generally stable prior to the onset of mining activities around Yellowknife (i.e. pre-1940s). The higher background concentrations of As at Grace relative to Alexie is likely linked to the underlying bedrock in the region, as well as Grace Lake’s close proximity to the vein of arsenic enriched arsenopyrite ore that made this area a prosperous location for gold mining operations (Cousens et al. 2006). The sedimentary metalloid profiles from Grace 79

Lake (Fig. 3.3) clearly tracked the influence of mining in the region, as the As and Sb concentrations increased sharply during the early phase of gold mining (~ 1930s -

~1970s), consistent with previous geochemical analyses from other lakes that are closer to the mine and the city (e.g. Dirszowsky and Wilson 2016; Thienpont et al. 2016).

Unlike Grace, the increase in metalloid concentrations at Alexie were not pronounced and the differences in the magnitude of changes across the two sites highlight the influence of distance from the mines. The introduction of emission controls and the construction of the baghouse at Giant Mine to store the As2O3 in 1958 was also tracked by the notable and sharp declines in the concentrations of As and Sb after the 1960s.

The sedimentary Pb concentrations followed similar increasing patterns as As and

Sb, with Pb concentrations declining gradually after the 1960s. Previous paleolimnological investigations, including one from the City of Yellowknife (Frame

Lake; Dirszowsky and Wilson 2016), have noted that the sedimentary Pb concentrations tracked multiple anthropogenic activities but especially the use of leaded gasoline (Siver and Wozniak 2001). Hence, the gradual decline after the 1960s is tracking both the reduced atmospheric deposition of Pb from mining activities and use of leaded fuel.

Furthermore, the CRS sedimentation rates at both lakes increased during the mid- to late-

20th century, with pronounced changes and fluctuations occurring at Grace Lake relative to Alexie Lake (where the sedimentation rates remained low). The increases in average

CRS sedimentation rates at Grace between the 1940s and 2014, relative to early 20th century, suggests that local land-use changes (e.g. construction of the road adjacent to the lake and increased traffic, logging in the region, housing development on the shores) may have influenced the sedimentation rates of lakes in the City of Yellowknife. Meanwhile,

the lack of catchment disturbances around Alexie led to minimal changes in sedimentation rates over the same time frame.

3.6.2 Biological responses to multiple environmental stressors at Alexie and Grace lakes

Disentangling the effects of multiple environmental stressors on aquatic biota can be challenging as stressors may interact, thus leading to complex biological responses

(Smol 2010). In the case of Yellowknife, three major environmental stressors impacted the region after the late-1930s: 1) metalloid contaminants from gold mining operations

(late-1930s to early-2000s); 2) land-use changes related to the development of the city

(late-1930s to the present); and 3) marked increases in regional air temperature (~ post-

1960s to the present). Although the onset of these stressors occurred as early as the

1930s, the major shift in diatom assemblages at Grace and Alexie occurred after the mid-

20th century (Figs. 3.4 and 3.5). Similar to the trends in sedimentary metalloid profiles, larger diatom assemblage changes were observed in Grace Lake (Fig. 3.6) as it was closer to the mines and the city, thus receiving higher metalloid contaminants and nutrients from land-use changes in the area. The lake water As concentrations at Grace was 16.1 µg/L in

2014; however, metalloid concentrations in the water column were likely higher in the past, as we observed clear increases in the concentrations of As and Sb in the sediments during the mining era (Fig. 3.3). Hence, the higher concentrations of metalloids may have had direct impacts on the algal assemblages. For instance, the loss or decline in the abundance of several planktonic diatom taxa (A. formosa, F. nanana, F. tenera, T. flocculosa str. IIIp, P. ocellata) at Grace after the 1950s were likely responses to mining activities, as similar changes have been observed in other metal-contaminated lakes (e.g.

Austin et al. 1985; Cattaneo et al. 2004; Leppänen et al. 2019). Since Alexie Lake was 81

not contaminated to the same extent as Grace, the diatom assemblage changes in response to mining were subtle.

Interestingly, metal-sensitive planktonic taxa and some small fragilarioid species at Grace Lake were replaced by Stephanodiscus taxa and D. stelligera during the second half of the 20th century (Fig. 3.4). Limnological and paleolimnological assessments from temperate and Arctic regions have shown that Stephanodiscus taxa often indicate eutrophication (e.g. Lotter 1998; Hadley et al. 2010; Nelligan et al. 2016). For example, in a paleolimnological assessment of a macrophyte-dominated shallow lake (Niven Lake,

Yellowknife), Stewart et al. (2018) suggested that the subtle increase in the relative abundance of Stephanodiscus taxa was a response to raw sewage input from 1948 to

1981. While there was no documented evidence of sewage disposal into Grace Lake during the 20th century, the land-use changes around Grace began as early as the 1930s with the construction of Con Mine less than 4 km away. Then, a road was constructed sometime between 1937 and 1950 near the outflow (Fig. 3.2) that likely increased human activity around the lake. Additionally, the population of the city also grew rapidly as a direct response to the economic prosperity with Yellowknife becoming the capital of the

Northwest Territories in 1967. Hence, urbanization during the mid- to late-20th century likely resulted in higher nutrient inputs to lakes within the city as observed in previous studies (e.g. Dirszowsky and Wilson 2016; Gavel et al. 2018). Although we are not aware of any monitoring data on the historic nutrient inputs to Grace, we can infer that the increased sedimentation rates after the 1940s likely brought in additional nutrients from the landscape to Grace Lake which promoted algal production. Furthermore, the concomitant increases in eutrophic Stephanodiscus taxa and VRS-Chl-a at Grace after the

1940s suggests that the higher nutrient inputs from the local land-use changes may have contributed to the rise in whole-lake primary production. Meanwhile, the relative abundances of Stephanodiscus taxa at Alexie did not change substantially over the past ~

200 years, as there were no catchment development and anthropogenic sources of nutrients to the lake. Despite the lack of changes in Stephanodiscus taxa, the whole-lake primary production increased at Alexie Lake too, suggesting that factors other than nutrient additions may also be influencing algal production in the lakes around

Yellowknife. Unlike Grace, the increase in sedimentary chlorophyll-a (and its main diagenetic products) at Alexie was gradual throughout the record with greater increases occurring after the 1960s when temperatures became notably warmer. The rising temperatures likely led to declining ice-cover, and longer and warmer growing seasons that provided ideal conditions for increases in whole-lake primary production, as has been noted in many other Arctic (e.g. Griffiths et al. 2017) and temperate lakes (e.g.

Nelligan et al. 2016; Summers et al. 2016; Paterson et al. 2017; Sivarajah et al. 2017).

The relative abundance of D. stelligera, a small-celled centric planktonic taxon that dominates the diatom assemblages in many deep lakes around the world, increased at both of our study lakes. Investigations of acidified and metal-contaminated lakes have identified D. stelligera as a taxon sensitive to pollution (Battarbee and Charles 1987;

Cattaneo et al. 2008), including a severely As-contaminated shallow lake close to Giant

Mine where D. stelligera complex decreased to negligible abundances while benthic naviculoid and Denticula taxa increased in response to severe metalloid contamination

(Thienpont et al. 2016). However, its increase across both of our deep sites during the warmest years was consistent with trends observed in the paleolimnological records of

lakes across the Northern Hemisphere where planktonic diatoms (mostly D. stelligera) have increased in response to recent warming mediated by changes to lake thermal properties (Sorvari et al. 2002; Rühland et al. 2008, 2015). Specifically, warmer air- temperatures often lead to longer ice-free periods with stronger thermal stratification which provides competitive advantages to small-celled centric diatoms, such as D. stelligera, that have high surface area to volume ratios and low sinking velocities

(Ptacnik et al. 2003) and are able to remain in the photic zone longer relative to larger and heavier diatoms (Rühland et al. 2015). Furthermore, the increase in D. stelligera in response to warming was more pronounced in stratified deep lakes in the Canadian Arctic

(Rühland et al. 2003b). Therefore, we conclude that climate-mediated changes to lake thermal properties may be stronger drivers of D. stelligera abundance relative to metalloid pollution from mining activities in these deep sub-Arctic lakes.

3.6.3 Changes in the limnology of deep sub-Arctic lakes around Yellowknife

The sedimentary geochemical and biological proxies from Alexie and Grace suggest that the limnology of these lakes that support large-bodied fish species have changed markedly since the mid-20th century in response to multiple environmental stressors. The degree of metal(loid) contamination and eutrophication varied between the two lakes as there were notable differences in distance to anthropogenic activities.

Although both lakes received metal(loid) contaminants in the past (Alexie to a lesser extent), the recent decreases in sedimentary metal(loid) concentrations provide some indications of chemical recovery from gold mining operations. The recent biological assemblages at both lakes were substantially different relative to the ones from pre- mining and mining eras. This is consistent with some of the earlier work done on shallow 84

lakes in the Yellowknife area (e.g. Thienpont et al. 2016; Stewart et al. 2018) and other industrially impacted lakes (e.g. Summers et al. 2016; Sivarajah et al. 2017) where recent warming has likely altered limnological conditions sufficiently that returning to pre- industrial conditions is no longer tenable.

Although speculative, some of the paleolimnological changes observed in this study may provide insights into the potential long-term impacts of multiple environmental stressors on the habitat of large-bodied fish species. For instance, the increased sediment inputs due to anthropogenic activities, such as road construction and housing development, may lead to changes in water clarity and impede visual abilities of fish during foraging and mate selection (Cott et al. 2015). Furthermore, the increases in primary production across both lakes may have indirect consequences on the fish habitat as the decomposition of large quantities of algal biomass consume oxygen in the cold- hypolimnion where many large-bodied fish species take refuge. The concomitant increase in D. stelligera at our study sites suggest that the lake thermal properties (i.e. less ice- cover, longer periods of thermal stratification) have been changing in these lakes and could eventually limit the optimal oxy-thermal habitat for cold-water fish species such as lake trout (Guzzo and Blanchfield 2017), especially in lakes that may be subjected to eutrophication from increasing urbanization. Hence, special consideration should be given to the management of sub-Arctic lakes that support large-bodied fish populations as they have important cultural and economic significance to local communities.

3.7 Acknowledgements

Water chemistry analysis by Judy Mah and the Taiga Environmental Laboratory, and field work by Kristen Coleman, Dave Eickmeyer, Jennifer Korosi, and Joshua

Thienpont are greatly acknowledged. The authors thank Profs. Piepenburg and Van de

Vijver along with two anonymous reviewers for providing constructive feedback on our manuscript that improved the clarity and quality. This study was funded by a Natural

Sciences and Engineering Research Council of Canada (NSERC) Strategic Grant

(STPGP 493786-16) and Polar Continental Shelf Program grants to Jules M. Blais and

John P. Smol, as well as an NSERC Alexander Graham Bell Canada Graduate

Scholarship D, Northern Scientific Training Program, and a W. Garfield Weston

Scholarship for Northern Research (doctoral) to Branaavan Sivarajah.

3.8 References

Appleby PG (2001) Chronostratigraphic techniques in recent sediments. In: Last WM, Smol JP, (eds) Tracking environmental change using lake sediments Vol 1: Basin analysis, coring and chronological techniques. Dordrecht: Kluwer Academic Publishers, pp. 172–203

Austin A, Deniseger J, Clark MJR (1985) Lake algal populations and physico-chemical changes after 14 years input of metallic mining wastes. Water Res 19:299–308

Battarbee RW, Charles DF (1987) The use of diatom assemblages in lake sediments as a means of assessing the timing, trends, and causes of lake acidification. Prog Phys Geog 11:552–580

Bennett KD (1996) Determination of the number of zones in a biostratigraphical sequence. New Phytol 132: 155–170

Birks HJB (2010) Numerical methods for the analysis of diatom assemblage data. In Smol JP, Stoermer EF (eds) The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge, United Kingdom, pp. 23–54

Callaghan DT, Blanchfield PJ, Cott PA (2016) Lake trout (Salvelinus namaycush) spawning habitat in a northern lake: The role of wind and physical characteristics on habitat quality. J Great Lakes Res 42: 299–307

Cattaneo A, Couillard Y, Wunsam S, Courcelles M (2004) Diatom taxonomic and morphological changes as indicators of metal pollution and recovery in Lac Default (Québec, Canada). J Paleolimnol 32:163–175

Cattaneo A, Couillard Y, Wunsam S (2008) Sedimentary diatoms along a temporal and spatial gradient of metal contamination. J Paleolimnol 40:115–127

Chen G, Shi H, Tao J, Chen L, Liu Y, Lei G, Liu X, Smol JP (2015) Industrial arsenic contamination causes catastrophic changes in freshwater ecosystems. Sci Rep 5:17419

Coleman LC (1957) Mineralogy of the Giant Yellowknife Gold Mine, Yellowknife, N.W.T. Econ Geol 52: 400–425

Cott PA, Johnston TA, Gunn JM (2011) Food web position of burbot relative to lake trout, northern pike, and lake whitefish in four sub-Arctic boreal lakes. J Appl Ichthyol 27(Supp 1): 49–56

Cott PA, Schein A, Hanna BW, Johnston TA, MacDonald DD, Gunn JM (2015) Implications Of linear developments on northern fishes. Environ Rev 23:177–190

Cousens B, Falck H, Ootes L, Jackson V, Mueller W, Corcoran P, Finnigan C, van Hees E, Facey C, Aalcazar A (2006) Regional correlations, tectonic settings, and stratigraphic solutions in the Yellowknife greenstone belt and adjacent areas from geo-chemical and Sm-Nd isotopic analyses of volcanic and plutonic rocks; in Gold in the Yellowknife Greenstone Belt, Northwest Territories: In Anglin CD, Falck H, Wright DF, Ambrose EJ (eds) Results of the EXTECH III Multidisciplinary Research Project. Geological Association of Canada, Mineral Deposits Division, Special Publication 3. pp. 70–94

Dirszowsky RW, Wilson KM (2016) Biogeochemical evidence of eutrophication and metal contamination of Frame Lake, City of Yellowknife, Northwest Territories, Canada. Environ Earth Sci 75:76

Evans DO, Nicholls KH, Allen YC, McMurtry MJ (1996) Historical land use, phosphorus loading, loss of fish habitat in Lake Simcoe, Canada. Can J Fish Aquat Sci 53:194–218

Gavel MJ, Patterson RT, Nasser NA, Galloway JM, Hanna BW, Cott PA, Roe HM, Falck H (2018) What killed Frame Lake? A precautionary tale for urban planners. PeersJ:10.7717/peerj.4850

Glew JR (1988) A portable extruding device for close interval sectioning of unconsolidated core samples. J Paleolimnol 1:235–239 87

Griffiths K, Michelutti N, Sugar M, Douglas MSV, Smol JP (2017) Ice-cover is the principal driver of ecological change in High Arctic lakes and ponds PLoS ONE 12(3):e0172989

Grimm EC (1987) CONISS: A FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. Comput Geosci 13:13–35

Guzzo MM, Blanchfield PJ, Chapelsky AJ, Cott PA (2016) Resource partitioning among top-level piscivores in a sub-Arctic lake during thermal stratification. J Great Lakes Res 42:276–285

Guzzo MM, Blanchfield PJ (2017) Climate change alters the quantity and phenology of lake trout (Salvelinus namaycush) in small Boreal Shield lakes. Can J Fish Aquat Sci 74:871–884

Hadley K R, Douglas MSV, McGhee RH, Blais JM, Smol JP (2010) Ecological influences of Thule Inuit whalers on high Arctic pond ecosystems: A comparative paleolimnological study from Bathurst Island (Nunavut, Canada). J. Paleolimnol 44: 85– 93

Håkansson H (2002) A compilation and evaluation of species in the general Stephanodiscus, Cyclotestephanos and Cyclotella with a new genus in the family Stephanodiscaceae. Diatom Res 17:1–139

Healey MC, Woodall WL (1973) Limnological surveys of seven lakes near Yellowknife, Northwest Territories. Fisheries Research Board of Canada. Technical Report 407

Henderson JB (1985) Geology of the Yellowknife-Hearne Lake Area, District of Mackenzie: A segment across an Archean basin. Geological Survey of Canada. Memoir 414

INAC (Indian and Northern Affairs Canada) (2007) Giant Mine remediation plan. Report of the Giant Mine Remediation Project Team. Department of Indian Affairs and Northern Development. Yellowknife, Northwest Territories

Jamieson HE (2014) The legacy of arsenic contamination from mining and processing refractory gold ore at Giant Mine, Yellowknife, Northwest Territories, Canada. Rev Minerol Geochem 79:533–551

Juggins S (2017) rioja: Analysis of Quaternary Science Data. R package version 0.9-15.1

Krammer K, Lange-Bertalot H (1986) Bacillariophyceae. 1. Teil: Naviculaceae. In Ettl H, Gerloff J, Heynig H, Mollenhauer D (eds) Süsswasserflora von Mitteleuropa. Band 2/1. Stuttgart: Gustav Fischer Verlag

Krammer K, Lange-Bertalot H (1988) Bacillariophyceae. 2. Teil: Bacillariaceae Epithemiaceae, Surirellaceae. In: Ettl H, Gerloff J, Heynig H, Mollenhauer D (eds) Süsswasserflora von Mitteleuropa. Band 2/2. Stuttgart: Gustav Fischer Verlag

Krammer K, Lange-Bertalot H (1991a) Bacillariophyceae. 3. Teil: Centrales, Fragilariaceae, Eunotiaceae. In Ettl H, Gerloff J, Heynig H, Mollenhauer D (eds),Süsswasserflora von Mitteleuropa. Band 2/3. Stuttgart: Gustav Fischer Verlag

Krammer K, Lange-Bertalot H (1991b) Bacillariophyceae. 4. Teil: Achnanthaceae, Kritische-Ergänzungen zu Navicula (Lineolatae) und Gomphonema, Gesamtliteraturverzeichnis. In Ettl H, Gärtner G, Gerloff J, Heynig H, Mollenhauer D (eds) Süsswasserflora von Mitteleuropa. Band 2/4. Stuttgart: Gustav Fischer Verlag

Leppänen, JJ, Luoto TP, Weckström J (2019) Spatio-temporal impact of salinated mine water on Lake Jormasjärvi, Finland. Environ Pollut 247:1078–1088

Leppänen, JJ, Weckström J, Korhola A (2017) Multiple mining impacts induce widespread changes in ecosystem dynamics in a boreal lake. Sci Rep 7:10581

Lotter AF (1998) The recent eutrophication of Baldeggersee (Switzerland) as assessed by fossil diatom assemblages. Holocene 8:395–405

Michelutti N, Blais JM, Cumming BF, Paterson AM, Rühland K, Wolfe AP, Smol JP (2010) Do spectrally inferred determinations of chlorophyll a reflect trends in lake trophic status? J Paleolimnol 43:205–217

Michelutti N, Smol JP (2016) Visible spectroscopy reliably tracks trends in paleo- production. J Paleolimnol 56:253–265

Oksanen J, Blanchet FG, Friendly M, et al (2018) vegan: Community Ecology Package. R package version 2.5-2

Palmer MJ, Galloway JM, Jamieson HE, Patterson RT, Falck H, Kokelj SV (2015) The concentration of arsenic in lake waters of the Yellowknife area. Northwest Territories Geological Survey, NWT Open File 2015-06

Paterson AM, Rühland KM, Anstey CV, Smol JP (2017) Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada. Lake Res Manage 33: 403–414

Ptacnik R, Diehl S, Berger S (2003) Performance of sinking and nonsinking phytoplankton taxa in a gradient of mixing depths. Limnol Oceanogr 48:1903–1912

R Core Team, 2018. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna

Rühland K, Smol JP, R. Pienitz (2003a) Ecology and spatial distributions of surface- sediment diatoms from 77 lakes in the subarctic Canadian treeline region. Canadian Journal of Botany 81: 57–73

Rühland K, Priesnitz A, Smol JP (2003b) Evidence for recent environmental changes in 50 lakes across the Canadian Arctic treeline. Arct Antarct Alp Res 35:110–123

Rühland KM, Paterson AM, Smol JP (2008) Hemispheric-scale patterns of climate- related shifts in planktonic diatoms from North American and European lakes. Glob Change Biol 14:2740–2754

Rühland KM, Paterson AM, Smol JP (2015) Diatom assemblage responses to warming: reviewing the evidence. J Paleolimnol 54:1–35

Silke R (2009) The operational history of mines in the Northwest Territories, Canada. An Historical Research Project

Sivarajah B, Korosi JB, Blais JM, Smol JP (2019) Multiple environmental variables influence diatom assemblages across an arsenic gradient in 33 subarctic lakes near abandoned gold mines. Hydrobiologia 841:133–151

Sivarajah B, Rühland KM, Smol JP (2017) Are diatoms recovering to pre-acidification assemblages in a warming world? Revisiting Killarney Provincial Park lakes (Ontario, Canada). Fundam Appl Limnol 190:13–28

Siver PA, Wozniak JA (2001) Lead analysis of sediment cores from seven Connecticut lakes. J Paleolimnol 26:1–10 90

Smol JP (2010). The power of the past: using sediments to track the effects of multiple stressors on lake ecosystems. Frewater Biol 55 (Suppl 1):43–59

Sorvari S, Korhola A, Thompson R (2002) Lake diatom response to recent Arctic warming in Finnish Lapland. Glob Change Biol 8: 153–163

Sprague DD, Michel FA, Vermaire JC (2016) The effects of migration on ca. 100-year- old arsenic-rich mine tailings in Cobalt, Ontario, Canada. Environ Earth Sci 75:405

Stewart DB (1997) A review of the status and harvests of fish stocks in the North Slave area, Northwest Territories. Central and Arctic Region Department of Fisheries and Oceans, Winnipeg, Manitoba

Stewart EM, Hargan KE, Sivarajah B, Kimpe LE, Blais JM, Smol JP (2018) A paleoenvironmental study tracking eutrophication, mining pollution, and climate change in Niven Lake, the first sewage lagoon of Yellowknife (Northwest Territories). Arctic 71:201–217

St-Onge SM (2007) Impacts of arsenic and sulphur dioxide contamination from mining activities on forest health near Yellowknife, NWT. MSc Thesis, Carleton University

Summers JC, Kurek J, Kirk JL, Muir DCG, Wang X, Wiklund JA, Cooke CA, Evans MS, Smol JP (2016) Recent warming, rather than industrial emissions of bioavailable nutrients, is the dominant driver of lake primary production shifts across the Athabasca Oil Sands Region. PLoS ONE 11(5): e0153987

Thienpont JR, Korosi JB, Hargan KE, Williams T, Eickmeyer DC, Kimpe LE, Palmer MJ, Smol JP, Blais JM (2016) Multi-trophic level response to extreme metal contamination from gold mining in a subarctic lake. Proc R Soc B 283:20161125

Van den Berghe MD, Jamieson HE, Palmer MJ (2018) Arsenic mobility and characterization in lakes impacted by gold ore roasting, Yellowknife, NWT, Canada. Environ Pollut 234:630–641

Walker SR, Jamieson HE, Lanzirotti A, Andrade CF, Hall GEM (2005) The speciation of arsenic in iron oxides in mine wastes from the Giant Gold Mine, N.W.T.: Application of synchrotron micro-xrd and micro-xanes at the grain scale. Can Mineral 43:1205–1224

Wolfe SA (1998) Living with frozen ground: A field guide to permafrost in Yellowknife, Northwest Territories. Geological Survey of Canada Miscellaneous Report 64

Wong, HKT, Gauthier A, Nriagu JO (1999) Dispersion and toxicity of metals from abandoned gold mine tailings at Goldenville, Nova Scotia, Canada. Sci Total Environ 228:35–47

Table 3.1: Limnological and environmental information collected for Grace and Alexie lakes

Total arsenic (Total As), total antimony (Total Sb), sulphate, pH, alkalinity, and total nitrogen were single point measurements from April 2014.

Limnological parameter Grace Alexie Latitude 62° 40'35.32" N 62° 25'14.44" N Longitude 114° 06'05.77" W 114° 25'51.83" W Coring depth (m) 16 21 Surface area (km2) 0.63 4.56 Distance from Giant Mine (km) 10.23 23.52 Distance from Con Mine (km) 3.66 29.97 Total As (µg/L) 16.1 1.5 Total Sb (µg/L) 0.6 < 0.1 Sulphate (mg/L) 15 4 pH 7.42 7.69 Alkalinity (mg/L) 59.4 59.2 Total nitrogen (mg/L) 0.75 0.51

Figure 3.1 Location of the study sites around Yellowknife a) The red star indicates the location of Yellowknife, Northwest Territories on a map of Canada. b) Map with the locations of meteorological stations and the two lakes in relation to Great Slave Lake. c) Map of the Yellowknife area with the municipal boundary (indicated by red line), mines and the two study lakes.

Figure 3.2 Aerial photographs of Grace Lake and the surrounding area

Photographs from 1937 (29 June 1937), 1950 (18 August 1950), and 1970 (2 September 1970). The original photographs were obtained from National Air Photo Library - Natural Resources Canada. A star and a circle were used to identify Grace and Kam lakes in all three photographs. The path of the road was traced with a black line and a triangle was used to identify Con Mine in photographs from 1950 and 1972.

Figure 3.3 Sedimentary metal(loid) concentrations of Grace and Alexie lakes

Metal concentrations (weight per gram dry weight) in the sediments from Grace and Alexie lakes scaled by 210Pb estimated years. The dates beyond background 210Pb levels were extrapolated using a second-order polynomial. These dates have been identified with an asterisk (*) and should be interpreted with caution. 95

Figure 3.4 Sedimentary diatom data from Grace Lake

Diatom assemblage profiles scaled by core depth (with 210Pb estimated years plotted secondarily) showing relative abundances of the most common diatom taxa from Grace Lake, Yellowknife, Northwest Territories. For clarity of display, species from the same genera that exhibited similar trends through time were grouped together. CONISS zones are delineated with a horizontal broken line. The sedimentary VRS Chl-a (mg g^-1 dry weight) are also included in the stratigraphy.

Figure 3.5 Sedimentary diatom data from Alexie Lake

Diatom assemblage profiles scaled by core depth (with 210Pb estimated years plotted secondarily) showing relative abundances of the most common diatom taxa from Alexie Lake, Yellowknife, Northwest Territories. For clarity of display, species from the same genera that exhibited similar trends through time were grouped together. CONISS zones are delineated with a horizontal broken line. The sedimentary VRS Chl-a (mg g^-1 dry weight) are also included in the stratigraphy.

Figure 3.6 Results of Principal component analysis (PCA) ordination

Axis 1 (λ=0.41) and axis 2 (λ=0.17) sample scores for the species and sites from Grace and Alexie lakes plotted within the same ordination space (a). The PCA axis scores for the most abundant species (i.e., > 5% in at least one sample from the two sites) are presented. The 210Pb dates for select samples are included to highlight the major shifts in diatom composition through time. The dates beyond background 210Pb levels have been italicized and these dates should be interpreted with caution. The PCA axis 1 and 2 samples scores for Alexie Lake has been plotted separately (b) to present the trends through time clearly.

Figure 3.7 The mean annual air temperature data from Yellowknife and Hay River

Yellowknife (Fig. 3.7a) and Hay River (Fig. 3.7b). A two segmented piecewise linear regression of the mean annual air temperate data identified major breakpoints in the records around 1956 and 1966 at Yellowknife and Hay River, respectively (shown with black dotted lines in the figure). The grey dotted line indicates a breakpoint around 1961 when the Hay River record was shortened to represent the same time frame as the Yellowknife record.

Chapter 4

Eutrophication and climate warming lead to unprecedented

cyanobacterial blooms in a Canadian sub-Arctic lake

4.1 Abstract

Cyanobacterial blooms have been increasing in frequency but are often considered an issue restricted to temperate and tropical lakes. Here we report one of the first occurrences of annual cyanobacterial (Planktothrix spp.) blooms in a sub-Arctic lake from Yellowknife (Northwest Territories, Canada) and provide a long-term context for the recent blooms using local meteorological data and multi-proxy paleolimnological analyses. Multiple co-occurring regional (gold mining emissions and climatic change) and local (land clearance and urbanization) stressors have impacted Jackfish Lake during the 20th and early-21st centuries, which have led to biological responses across multiple trophic levels. The unprecedented post-2013 cyanobacterial blooms were likely a cumulative response to nutrient enrichment and complex climate-mediated (e.g. rising temperature, declining windspeed) changes to lake thermal properties. A regional analysis of eight lakes around Yellowknife revealed that reduced ice cover duration and longer growing seasons have led to an increase in whole-lake primary production, whilst urban lakes were also fertilized by nutrients from local land-use changes, including development in the catchment. Our findings suggest that nutrient-enriched sub-Arctic lakes, akin to their low-latitude counterparts, are now vulnerable to cyanobacterial blooms in a warming world.

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4.2 Introduction

Cyanobacteria are an important component of the algal communities in freshwater ecosystems, but the formation of cyanobacterial blooms are of concern because they can deteriorate water quality, reduce aesthetic appeal, and lead to serious socioeconomic repercussions (Steffensen, 2008). Nuisance (and sometimes toxic) cyanobacterial blooms of planktonic Planktothrix spp. (previously Oscillatoria spp.), Dolichospermum spp.

(previously Anabaena spp.), and Gleotrichia spp. have increased in frequency across a wide spectrum of temperate and tropical lakes (Winter et al., 2011; Posch et al., 2012;

Mowe et al., 2015; Pick, 2016), including in sites where cyanobacterial blooms have not been reported in the past (Carey et al., 2008; LeBlanc et al., 2008; Favot et al., 2019). In these low-latitude systems, cyanobacterial blooms are often associated with nutrient enrichment from heavy land-use changes (e.g. agricultural activities, urbanization) and, more recently, climate-mediated limnological changes (e.g. warmer water temperatures, stronger thermal stratification) (Kosten et al., 2012; Pick, 2016; Huisman et al., 2018;

Favot et al., 2019; Simmatis et al., 2020). Planktonic cyanobacterial blooms, however, are rarely reported from sub-Arctic and Arctic lakes (Gu and Alexander, 1993; Einarsson,

2004), and only anecdotal reports exist from the Canadian Arctic (Welch and Legault,

1986; Pick 2016). In cold Arctic lakes, picocyanobacteria (e.g. Synechococcus spp.) and benthic mat-forming cyanobacteria (e.g. Dichothrix spp., Tolypothrix spp.) are dominant, whereas bloom-forming planktonic species are virtually absent (Vincent and Quesada,

2012; de los Ríos et al., 2015). Yet, recent incubation studies have indicated that the observed and predicted increases in air temperatures in Arctic regions may alter lake

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properties to make conditions favorable for bloom-forming cyanobacteria to thrive

(Przytulska et al., 2017).

The City of Yellowknife, capital of the Northwest Territories, is surrounded by lakes and experiences a continental sub-Arctic climate where lakes are ice-covered for more than six months of the year. In 2013, “rust-coloured sludge” was reported at

Jackfish Lake (formerly Stock Lake), which surprised community members and scientists because similar phenomena had not been previously observed in this region (Canadian

Broadcasting Corporation (CBC), 2015). Investigations by the Department of the

Environment and Natural Resources (Government of Northwest Territories) identified this new occurrence as cyanobacterial blooms of Planktothrix spp. and such blooms now reoccur annually in the lake (CBC, 2015; Golder Associates, 2019). Since this is a new phenomenon for this region, the Chief Medical Officer of Health of the territory has recommended wearing gloves when handling fish from the lake (CBC, 2015). Lakes in the Yellowknife area support valuable recreational and subsistence fisheries, similar to many sub-Arctic Canadian lakes. In particular, the Wıìlıìdeh Yellowknives Dene have used lakes in the region for several centuries for subsistence fisheries (Wıìlıìdeh

Yellowknives Dene, 1997) and recreational fishing is an important source of tourist revenue. Because of this, community members expressed concerns about how the potential future proliferation of cyanobacterial blooms may affect culturally and economically important fishing practices in this region. Interestingly, in September of

2013, surface blooms of what appear to be cyanobacteria (based on photographic record) have also been reported anecdotally from Yellowknife Bay of Great Slave Lake (Pick,

2016).

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We postulated that the recent occurrence of Planktothrix spp. blooms at Jackfish

Lake was linked to a complex response to changing limnological conditions in sub-Arctic

Canada. However, as in many regions, there is an absence of necessary multi-decadal monitoring to answer critical management questions, such as 1) have similar blooms occurred in the past at Jackfish or other lakes in the area? and 2) what are the environmental triggers for the recent annual cyanobacterial blooms in this sub-Arctic lake? Cyanobacterial blooms in temperate and tropical lakes are often the result of a complex combination of chemical (e.g. increases in nutrients), physical (e.g. reduced mixing, increases in temperature), and/or biological (e.g. changes in herbivore pressure and/or competition) factors acting in concert (Rigosi et al., 2014), and the interactions among these factors change through time as a result of various environmental perturbations. This further necessitates the use of long-term multi-proxy environmental records to understand the potential causative mechanisms of recent cyanobacterial blooms and to determine appropriate management strategies (Smol 2019) and paleolimnological assessments can provide the missing long-term monitoring data (Smol

2008).

Here, we used a variety of paleolimnological approaches that combined multiple biological proxies preserved in the sediments of Jackfish Lake, along with local meteorological data, to provide a long-term perspective on past interactions between chemical, physical, and biological processes in this sub-Arctic lake. Specifically, subfossil remains of biota representing three distinct groups in aquatic food webs, diatoms (Bacillariophyceae; primary producers), cladocerans (Cladocera; primary

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consumers) and chironomids (Chironomidae; primary consumers/detritivores), were examined from a well-dated sediment core from Jackfish Lake. These aquatic organisms respond to variety of limnological stressors rapidly and leave well-preserved remains in lake sediments, which makes them excellent paleo-biological indicators of past environmental changes (Stewart et al., 2014; Thienpont et al., 2016; Sivarajah et al.,

2019). In this study, these three indicators were strategically chosen to track the impacts of past gold mining activities in Yellowknife (all three indicators), nutrient enrichment from land-use changes around the city (diatoms), changes in zooplankton predation

(cladocerans), and cumulative impacts of eutrophication and climate change (all three indicators) on Jackfish Lake. We also examined trends in primary production of Jackfish and eight other lakes in the region over the past ~ 200 years to provide regional context to the recent cyanobacterial blooms at Jackfish Lake.

4.3 Materials and Methods

4.3.1 Study area

Yellowknife is situated on Chief Drygeese Territory in the Northwest Territories,

Canada and has been the traditional home of the Wıìlıìdeh Yellowknives Dene for centuries (Wıìlıìdeh Yellowknives Dene, 1997). It is one of the northern-most cities in

Canada (Figure 4.1) and has a population of ~ 20,000 people. The city is located within the Slave Structural Province of the Canadian Shield in the discontinuous permafrost zone, where permafrost is typically limited to areas with thick peat or fine-grained mineral soil deposits (Wolfe, 1998). The Taiga Shield High Boreal Ecoregion that surrounds this region is characterized by the presence of jack pine, trembling aspen,

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paper birch, and mixed spruce forests with lichen and moss understories (Wolfe, 1998;

Ecosystem Classification Group, 2008).

The development of Yellowknife as a northern population hub was initiated in the early-1930s when large-scale gold mining operations began, which lasted for more than a half century. The gold mining industry brought economic prosperity to Yellowknife but left a complex environmental legacy as the roasting of arsenopyrite ore at the Giant and

Con mines between 1938 and 1999 released contaminants including: arsenic (As), antimony (Sb), lead (Pb), mercury (Hg), and sulfate (SO4) across the region (Jamieson,

2014). Although much of the roaster stack emissions took place prior to 1958, elevated concentrations of many of elements continue to persist in lake waters and sediments across the region (Palmer et al., 2015). In addition to mining pollution, lakes within

Yellowknife city limits (e.g. Jackfish, Niven, Kam, Grace) were also impacted by land clearance and urbanization (Stewart et al., 2018; Sivarajah et al., 2020). For example, raw sewage was disposed into Niven Lake from 1948 to 1981 and briefly into Kam Lake from 1975 to 1981 (Bright et al., 1994; Stewart et al., 2018). Furthermore, the population of Yellowknife increased rapidly after 1967 when it became the capital of the Northwest

Territories. A solid waste disposal site was commissioned north of Jackfish Lake in 1974 to facilitate the municipal needs of the growing population and this site drains into

Jackfish Lake (Figure 4.1). Although most of the power supply for Yellowknife comes from the Snare and Bluefish hydroelectric plants, a diesel-powered generating facility was built adjacent to Jackfish Lake in 1969 (Figure 4.1). This facility draws water from the lake to cool the generators and subsequently discharges the warmed water back into the lake. Unlike several urban lakes in Yellowknife, the remote lakes (Alexie, BC-02,

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BC-43, Pocket, and YK-40) in this study have not been impacted by cultural eutrophication (Figure 4.1); however, these lakes have been affected by varying degrees of atmospheric deposition of contaminants from mining activities (Cheney et al 2020).

The limnological characteristics of Jackfish and the other study lakes have been published in Sivarajah et al. (2019) and are provided in Table 1. Water chemistry measurements reported herein are discrete measurements obtained during lake sampling.

Since some of the lakes were sampled during the winter, the formation of lake-ice may have led to elevated levels of some water chemistry variables relative to the open-water period due to solute-exclusion and/or increased sediment efflux of redox sensitive elements (Palmer et al., 2019). Measurements of total phosphorus (TP) concentrations in the water column are often used to determine the trophic status of lakes and TP levels are commonly used to predict the probability of cyanobacterial blooms in lakes (Beaulieu et al., 2013). However, in Yellowknife the measurements of TP in the water column should be interpreted with caution as there is suspected interference of arsenate in the measurements of phosphates (Stewart et al., 2018). Hence, we rely on the taxonomic composition of diatom assemblages to determine the present trophic status of Jackfish

Lake, because many diatom taxa have well-defined optima for diverse nutrient conditions

(Hall and Smol, 2010).

4.3.2 Sediment coring and dating

Sediment cores were retrieved from the middle of the lakes using gravity corers

(Glew (1989), UwitecTM) since it is usually the deepest point and detailed bathymetric maps were not available for some of the remote sites. The short gravity cores were extruded at 0.5-cm contiguous intervals using a Glew (1988) type extruder. The freeze-

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dried sediments of select intervals from each core were analyzed for 210Pb, 214Pb, and

214Bi using gamma spectroscopy and chronologies were developed using the unsupported concentrations of 210Pb and the constant rate of supply model (Appleby, 2001) in

ScienTissiME (Barry’s Bay, ON, Canada). Dates beyond background 210Pb concentrations (generally dates before 1900 CE) were extrapolated using a second-order polynomial equation and should be interpreted with caution. Results of the sediment core dating are provided in Appendix Figures E.1, E.2 and E.3.

4.3.3 Spectral inferences of trends in whole-lake primary production and cyanobacteria

Visible near-infrared reflectance spectroscopy (VNIRS) inferences of algal pigments in lake sediments is a rapid and non-destructive technique to reconstruct past trends in whole-lake production (VNIRS Chl-a; Michelutti et al., 2010) and cyanobacterial abundance (VNIRS Cyano; Favot et al., 2020). The VNIRS Chl-a (which includes chlorophyll-a, and its derivatives and degradation products) method has been successfully applied to a variety of lake sediment cores with diverse trophic histories, including lakes with known eutrophication histories, where this method tracks past trends in whole-lake primary production (Michelutti and Smol, 2016). The VNIRS Cyano technique, however, is a newly developed method that shows promise in inferring overall trends in cyanobacterial abundance and has been shown to be in good agreement with well-established methods such as high-performance liquid chromatography (HPLC)

(Favot et al., 2020). Briefly, freeze-dried sediments were sieved through a 125-µm mesh to remove the effects of particle size and then transferred to glass cuvettes, which were then scanned using a FOSS NIR System Model 6500 Rapid Content Analyzer. The

VNIRS Chl-a was inferred by a linear equation that includes the area under the 107

absorbance peak from 650 – 700 nm (Michelutti et al., 2010). The VNIRS Cyano was calculated using a two-component partial least squares regression model which uses spectral reflectance data from 400 – 2498 nm (excluding 650 – 700 nm) (Favot et al.,

2020).

This VNIRS Cyano model was built using a series (n=15) of sediment matrix inoculations with increasing concentrations of a cyanobacterial mixture, and then correlating the spectral signature to the parts per thousand addition of cyanobacteria. An important caveat of the spectrally inferred cyanobacteria model is that carotenoid pigments sourced from several major classes of algae (e.g. cyanobacteria, diatoms, chlorophytes) have reflectance maxima at close wavelengths on the electromagnetic spectrum, and so the resulting inferences may be confounded by groups other than cyanobacteria. Furthermore, the cyanobacterial mixture that was used to build the VNIRS

Cyano model was mostly composed of blue-green algae rich in phycocyanin (e.g.

Microcystis and Anabaena species), with < 10% of Planktothrix species, which have the characteristic red-color (phycoerythrin). Hence, this could be a potential limitation of applying this model to Jackfish Lake where the blooms are usually dominated by

Planktothrix species. It is stressed that this inference model be used only to compare cyanobacterial production in a relative sense, through time, rather than absolute concentrations. Despite these limitations, in most cases results from this model correlate well with temporal trends in cyanobacterial abundance produced by the traditional HPLC approach (Favot et al., 2020). Since we were interested in inferring past trends in primary production and cyanobacterial abundance, we have calculated the Z-scores of the inferred values to make meaningful comparisons among sites. The absolute values are provided

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in Appendix Figure E.11, and for Jackfish Lake in Figure 4.2. The sedimentary VNIRS

Chl-a profiles of Pocket (Thienpont et al., 2016), Niven (Stewart et al., 2018), Grace

(Sivarajah et al., 2020), Alexie (Sivarajah et al., 2020), and Kam (Perrett et al., In

Review) lakes have been published in previous case studies and are included here to assess regional trends.

4.3.4 Subfossil analysis of diatoms, cladocerans, and chironomids

The subfossil remains of biological organisms were isolated from the freeze-dried sediments of Jackfish Lake following standard procedures. Siliceous diatom valves were separated from the organic sediment matrix using 50:50 M mixture of sulfuric and nitric acids and heated at ~ 80 oC for ~ 2 hours in a hot water bath. After the slurries cooled and settled, the supernatant was removed and refilled with deionized water. This rinsing procedure was completed until the slurries reached circumneutral pH. Then a well mixed aliquot of the diatom slurry was pipetted onto glass cover slips and allowed to air dry before being permanently mounted onto glass microscope slides using Naphrax®. A minimum of 400 diatom valves were enumerated from each sample using a Leica DMR light microscope fitted with differential interference contrast optics at ×1000 magnification. The diatom valves were identified using an assortment of taxonomic guides (Krammer and Lange-Bertalot, 1986-1991; Camburn and Charles, 2000; Fallu et al., 2000; Håkansson, 2002; Rühland et al., 2003). Since diatom taxonomy has been updated since the publications of some of these studies, we verified the nomenclature with more recent publications and online databases, such as Diatoms of North America.

The chitinous remains of chironomids and cladocerans were isolated from the sediments separately using 5% and 10% potassium hydroxide (KOH) solutions,

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respectively. The sediment-KOH mixtures were heated at ~ 80 oC for ~ 20 minutes and then rinsed with deionized water through a 100-µm mesh for chironomids and a 36-µm sieve for cladocerans. For chironomid analyses, the resulting slurries were transferred to a

Bogorov tray where chironomid head capsules were carefully handpicked using forceps under a dissecting microscope and placed on microscope coverslips. Then the coverslips were permanently mounted onto glass microscope slides using Entellan®. The cladoceran slurries were transferred to a glass vial, and few drops of safranin and ethanol were added to each sample to stain and preserve, respectively. Well-mixed aliquots of cladoceran slurries were pipetted to glass microscope slides and glycerine jelly was used to mount the samples on the slides with glass coverslips. The recommended minimums of 70 cladoceran individuals (Kurek et al., 2010), and 40 chironomid head capsules (Quinlan and Smol, 2001) were met for each sample analyzed. A variety of taxonomic guides were used to identify sub-fossil cladoceran remains (Szeroczyńska and Sarmaja-Korjonen,

2007; Korosi and Smol, 2012a,b) using a Nikon YS100 microscope at ×100 – ×400 magnification. Chironomid head capsules were identified using a Leica DM2500 LED microscope set up for brightfield illumination at ×200 – ×400 magnification and taxonomy was based on Brooks et al. (2007). For each biological proxy, the data are expressed as percent abundance relative to the total number of specimens enumerated in a sample.

4.3.5 Monitoring data

Long-term meteorological data from Yellowknife (Climate Station ID:

2204100/2204101) from the mid-1940s to 2016 were used to assess past changes in climatic parameters including: air temperature, precipitation, and wind speed (retrieved

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from the Adjusted and Homogenized Canadian Climate Database, Environment and

Climate Change Canada, 2017). Lake ice-cover data for our study lakes were not available; therefore, we used available ice-cover data (duration of ice-cover and ice-free seasons) from three other lakes in Yellowknife as confirmatory evidence of regional ice phenologies available through the Global Lake and River Ice Phenology Database,

National Snow and Ice Data Center (Benson et al., 2013). Past trends in lake ice-cover and meteorological data were summarized using Mann-Kendall trend tests to determine if the observed long-term trends were significant (p < 0.05), and the Theil-Sen slope estimator to calculate overall changes. The long-term meteorological record is missing data from a few years, details of which are provided in Appendix Tables E.1 and E.2, along with the results of the Mann-Kendall trend tests and Theil-Sen slope estimators

(Appendix Figures E.7, E.8, E.9 and E.10). Population estimates for Yellowknife were compiled from a variety of sources listed in Appendix Table E.3.

4.4 Results

4.4.1 Paleoecology of Jackfish Lake

Prior to the 20th century, the biota from all three aquatic groups, whole-lake primary production (VNIRS Chl-a) and cyanobacterial abundance (inferred from VNIRS

Cyano) remained stable (Figure 4.2) in Jackfish Lake. In the early-20th century the sedimentation rate increased in the lake (Appendix Figure E.1) and the relative abundances of Stephanodiscus taxa (diatoms) increased subtly, while the VNIRS Chl-a and VNIRS Cyano decreased (Figure 4.2). The most notable change across all three bioindicator groups occurred after the 1940s. This shift was characterized by sharp increases

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in the relative abundances of Stephanodiscus taxa, at the expense of other planktonic diatoms (Discostella stelligera and Aulacoseira spp.; Figure 4.2). Similarly, D. longispina replaced Bosmina species as the dominant Cladocera taxa in the lake around the 1940s (Figure 4.2). The relative abundances of Cricotopus species and Procladius

(chironomids) increased briefly at the same time (Figure 4.2). Profundal chironomid taxa, such as Micropsectra radialis, Segentia species, and Chironomus anthracinus, have declined in relative abundance since the mid-20th century while Tanytarsus species and

Corynoneura species have increased over the same period (Figure 4.2). The relative abundances of littoral Cladocera taxa have increased since the mid-20th century at

Jackfish Lake (mostly Alona species, and subtle increases in Chydorus brevilabris)

(Figure 4.2). In the most recent sediments of Jackfish Lake (~ post-2013), both VNIRS

Chl-a and VNIRS Cyano have increased to unprecedented levels (Figure 4.2). Notable decreases in benthic fragilarioid diatoms and further increases in Stephanodiscus taxa were also observed in the ~ post-2013 sediments (Figure 4.2). The relative abundances of

Chironomus plumosus (chironomid) have also decreased in the recent sediments.

4.4.2 Past trends in local meteorology and ice phenology

Mean air temperature has increased across all seasons over the past ~ 70 years in

Yellowknife (Figure 4.3; Appendix Figure E.7). The largest increase in air temperature was observed during winter (+4.9 oC), followed by spring (+2.7 oC), summer (+1.7 oC), and autumn (+1.4 oC) (Appendix Figure E.7). In the most recent two decades, summer air temperature has been consistently higher than the time-series average (Figure 4.3). Mean rainfall has also increased in summer (+34 mm), autumn (+15 mm) and to a lesser degree in spring (+4 mm) between 1943 and 2012 (Figure 4.3; Appendix Figure E.8). Contrary

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to changes observed in air temperature and rainfall, mean annual windspeed in

Yellowknife has declined between 1953 and 2014 (Figure 4.3; Appendix Figure E.9).

The greatest decline in mean seasonal wind speed was observed during summer (-10.8 km h-1), followed by autumn (-10.1 km h-1), spring (-9.3 km h-1), and winter (-9.3 km h-1) as has been observed in many other regions across Canada (Wan et al. 2010). The ice phenology records from three lakes in Yellowknife were shorter (mid-1950s to 1994) compared to the meteorological data and did not include observations from the last two decades, which were relatively warmer than the previous five decades (Figure 4.3;

Appendix Figure E.10). Despite the short lake-ice records, an overall lengthening of the ice-free season (range: 5 – 7 days) has been observed between the mid-1950s and 1994

(Appendix Figure E.10). Since air temperature exerts a strong influence on lake-ice formation (Williams and Steffan, 2006; Dibike et al., 2012), marked warming in the last two decades, specifically in the spring (Appendix Figure E.7), has likely led to further reductions in ice-cover duration in Yellowknife.

4.4.3 Regional trends in primary production and cyanobacterial abundance

Similar to Jackfish Lake, whole lake primary production has increased across all the study lakes (both urban and remote sites) between ca. 1800 and the present (Figure

4.4; Appendix Figures E.10 and E.11). Specifically, the high levels of primary production recorded in the most recent years were unprecedented within the context of the last ~200 years (Figure 4.4; Appendix Figures E.10 and E.11). Unlike trends in whole lake primary production, trends in cyanobacterial abundance over the last ~ 200 years has varied across sites and no coherent trend was observed between the urban and remote lakes

(Figure 4.4; Appendix Figures E.10 and E.11). 113

4.5 Discussion

Our paleolimnological record tracked the known Planktothrix spp. blooms in

Jackfish Lake as the inferred primary production and cyanobacterial abundances (VNIRS

Chl-a and Cyano, respectively) increased sharply in sediments after 2013 (Figure 4.2).

The high levels of primary production and cyanobacterial abundance were unprecedented within the context of the last two centuries and were likely a complex response in the 21st century to multiple environmental stressors that have been impacting this lake since the mid-20th century. The nature of biological changes indicate that the chemical (nutrient pollution) and physical (thermal properties) conditions of this lake have been altered and, as discussed below, shifted the limnological characteristics of the lake to be more conducive to the development of Planktothrix spp. blooms.

4.5.1 Jackfish Lake was impacted by multiple environmental stressors during the 20th century

The sedimentary bioindicators, representing three distinct groups of aquatic organisms (algae, zooplankton, and insects), changed markedly during the 20th century

(Figure 4.2). The earliest, albeit minor, limnological changes occurred during the early-

20th century when land-use changes (e.g. logging, construction of infrastructure, mineral exploration) occurred around Yellowknife. These activities likely resulted in higher allochthonous inputs to the lake, which was tracked by elevated sedimentation rates.

Hence, the apparent declines in sedimentary concentrations of VNIRS Chl-a and Cyano were likely a result of the dilution of these algal pigments from the higher sedimentation rates during this period (Figure 4.2). The early land-use changes may have also slightly increased nutrient delivery to the lake that resulted in the subtle rise in eutrophic 114

Stephanodiscus (diatoms) taxa (Reynolds et al., 2002; Dong et al., 2008; Hall and Smol,

2010) at Jackfish Lake (Figure 4.2).

The most notable and abrupt shift in the biological proxies occurred after the

1940s when metalloid-sensitive planktonic primary producers and consumers decreased in relative abundance (Figure 4.2). Specifically, the abrupt declines in metalloid-sensitive planktonic diatoms (Discostella stelligera, and Aulacoseira spp.) (Ruggiu et al., 1998) and cladocerans (Bosmina spp.) (Passino and Novak, 1984; Tenkouano et al., 2019) are consistent with the impacts of atmospheric deposition of metalloid pollutants (arsenic and antimony) from roaster stack emissions from local gold mines (Thienpont et al., 2016).

Similarly, the sharp increase in arsenic-tolerant planktonic Daphnia spp. (Cladocera;

Passino and Novak, 1984; Thienpont et al., 2016) and brief increases in metalloid- tolerant Cricotopus spp. and Procladius spp. (chironomids; Ilyashuk et al., 2003;

Brodersen and Quinlan, 2006) during the early mining years provide further evidence that mining activities impacted the biota of Jackfish Lake across multiple trophic levels.

Gold mining was not the only stressor that affected Jackfish Lake around the mid-

20th century. For example, the sharp increase in the abundance of eutrophic

Stephanodiscus taxa in the mid-1940s suggests that nutrient concentrations had also increased concomitantly (Figure 4.2). Similar increases in eutrophic Stephanodiscus taxa were observed in other lakes around Yellowknife as anthropogenic activity increased

(population growth and urbanization) with gold mine establishment and as the city was declared capital of the Northwest Territories in 1967 (Stewart et al., 2018; Sivarajah et al., 2020). In addition to the impacts of urbanization that have been discussed in previous investigations (Gavel et al., 2018; Stewart et al., 2018), run-off from the nearby solid

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waste disposal site (Figure 4.1) was likely an important point source of nutrients to

Jackfish Lake after the mid-1970s (Figure 4.3). The impacts of the solid waste disposal sites on nearby water resources are complex and, in the cases of Arctic and sub-Arctic lakes, generally undocumented. For instance, precipitation can flow overland as surface run-off and bring a variety of pollutants into freshwaters. However, precipitation could also percolate through solid waste materials and introduce a variety of organic and inorganic contaminants to groundwater (Kjeldsen, 1993; Eggen et al., 2010), which would eventually discharge to surface waters and can be retained in the subsurface for long periods (on decadal scales). Since rainfall has increased in the summer and autumn in Yellowknife (Figure 4.3; Spence et al. 2011), it is likely that the accelerated delivery of excess nutrients in the form of additional run-off from the solid waste disposal site has gradually enriched Jackfish Lake over the course of forty years.

Similar to other sub-Arctic regions, air temperatures in Yellowknife have increased across all seasons over the last 70 years, which has had direct and indirect consequences on the functioning of aquatic ecosystems around the city (Stewart et al.,

2018; Sivarajah et al., 2020). For example, warmer conditions, particularly during spring and autumn, have likely resulted in earlier ice-off and later ice-on in Yellowknife lakes and lengthening of the open water growing seasons. Unlike most lakes in Yellowknife,

Jackfish also receives warm water discharges from the powerplant which results in ice- free conditions near discharge stations during winter months (Golder Associates, 2019).

The warm water discharge could also accelerate the melting of lake-ice at Jackfish in the spring relative to other lakes in the region. Warming spring temperatures and earlier ice- off can lead to earlier phytoplankton blooms, which could favor fast growing Daphnia

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spp. as observed in other seasonally ice-covered lakes (Adrian et al., 2006). Hence, the gradual increase in primary production (inferred from VNIRS Chl-a) from the 1940s to the early-21st century at Jackfish (Figure 4.2) were a cumulative response to longer growing seasons (Paterson et al., 2017) and higher nutrient availability from progressive eutrophication. Additionally, the abrupt increase in the metalloid-tolerant planktonic

Daphnia spp. (Cladocera) (Passino and Novak, 1984) after the 1940s (Figure 4.2) may have been triggered by mining pollution; however, the continued success of these efficient primary consumers is likely due to increases in food availability. Similar responses of daphniid taxa have been observed in temperate lakes where metalloid pollution and eutrophication have acted synergistically favoring these planktonic grazers

(Rogalski et al., 2017; Tenkouano et al., 2019). The longer open-water seasons have also likely led to the development of complex (and sometimes new) littoral habitats which may favor biota with diverse habitat requirements in Arctic lakes (Smol and Douglas,

2007), including littoral chironomids (e.g. Corynoneura spp., Tanytarsus spp.) and cladocerans (e.g. Chydorus brevilabris, Alona spp.) that have increased gradually in

Jackfish Lake (Figure 4.3).

Limnological conditions of Jackfish Lake were monitored from May to December in 2018 and the dissolved oxygen concentrations ([DO]) in May (after spring mixing),

September (after autumn mixing), and December (early ice-cover) ranged between 8 – 12 mg L-1 throughout the entire water column (Golder Associates, 2019). However, during the peak of thermal stratification (July and August), only the top 4 m of the water column was well oxygenated ([DO] ~ 10 mg L-1), while the deepest parts of the lake (> 6 m) were anoxic (~0 – 1 mg L-1), and hypoxia (< 4 mg L-1) was observed at sites that were < 5 m

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(Golder Associates, 2019). Not surprisingly, the benthic invertebrate communities were dominated by anoxia-tolerant Chironomus taxa (chironomids; Broderson and Quinlan,

2006) in 2018, and specifically the assemblage from the middle of the lake (i.e. deepest sampling point) was composed exclusively of Chironomus spp. (Golder Associates,

2019). Hence, profundal chironomid assemblages are tracking the hypolimnetic [DO] at

Jackfish and our paleolimnological chironomid assessment can be used to extend the temporal perspective back in time. Indeed, the abundances of profundal chironomid taxa indicative of oxygenated conditions (e.g. Sergentia spp., Micropsectra spp.; Brodersen and Quinlan, 2006) began declining around the mid-20th century along with hypoxia- tolerant Chironomus anthracinus (Brodersen and Quinlan, 2006) (Figure 4.2). However, anoxia-tolerant C. plumosus (Brodersen and Quinlan, 2006; Stewart et al., 2018) persisted into the 21st century and only declined to negligible abundances in the most recent sediments (Figure 4.2). Collectively, these changes in profundal chironomid taxa suggest that, although seasonal hypoxia has occurred previously in this lake, hypolimnetic [DO] have worsened in recent years.

The decrease in hypolimnetic [DO] may be an indirect consequence of increased primary production as the bacterial decomposition of excess organic matter likely increased biological oxygen demand near the sediment-water interface, as has been observed in other eutrophied Arctic lakes (O’Brien et al., 2005; Lienesch et al., 2005). In fact, the recently observed cyanobacterial blooms may have exacerbated the already high biological oxygen demand in the hypolimnion leading to anoxic conditions and declines in chironomid taxa known to survive in low oxygen conditions (e.g. C. plumosus).

Furthermore, co-occurring declines in wind speeds and increases in air temperature may

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have intensified the depletion of hypolimnetic [DO] (Snorthiem et al., 2017). Since lake thermal stability is influenced by changes in air temperature and wind speed (Adrian et al., 2009), long-term increases in summer air temperature and monotonic declines in summer wind speeds (Figure 4.3; Appendix Figures E.7 and E.9) may have strengthened the thermal stability of this lake over the past five decades. The warming of the lake may have been further exacerbated by the warm discharges from the powerplant. The strong thermal barrier has likely reduced the diffusion of DO by preventing mixing of the oxygen-saturated epilimnion with the oxygen-poor hypolimnion. An unfortunate consequence of prolonged periods of hypolimnetic anoxia is internal phosphorus (P) loading from the sediments (Nürnberg et al., 2013; Tammeorg et al., 2017) as observed in the bottom waters of Jackfish during the summer of 2018 (Golder Associates, 2019). A recent synthesis of internal P loading in temperate lakes across Canada identified that eutrophic alkaline lakes with hypolimnetic anoxia may be more susceptible to internal P loading from the sediments, especially in a warming climate (Orihel et al., 2017), and

Jackfish meets these criteria. Our paleolimnological (i.e. inferences of hypolimnetic [DO] based on assemblage changes in profundal chironomid taxa) and regional meteorological data suggest that the ideal hypolimnetic conditions for internal P loading at Jackfish may have gone undetected for several years before blooms occurred.

4.5.2 The unprecedented 21st century cyanobacterial blooms are a cumulative response to nutrient enrichment and warming

In temperate regions, blooms of Planktothrix spp., as with most cyanobacterial blooms, occur in eutrophic waters. However, bloom formation is influenced by multiple environmental variables and often requires a “perfect storm” of appropriate conditions to form. For instance, previous investigations have reported that Planktothrix spp. blooms 119

typically form in the metalimnion (Nürnberg et al., 2003) under high nutrient (Almodóvar et al., 2004; Posch et al., 2012), low light (Reynolds et al., 2002; Walsby and Jüttner,

2006), and warm (Posch et al., 2012; Rigosi et al., 2014) conditions. The sustained increase in the abundance of eutrophic Stephanodiscus taxa since the 1940’s provides evidence that nutrient levels are rising in the lake. This is likely from a combination of external (i.e. run-off from waste disposal site) and internal (i.e. P loading from the sediments) sources of nutrients, which are now allowing Planktothrix spp. to thrive at

Jackfish Lake in a warming climate. Autumn mixing (in September) helps to redistribute nutrients in the bottom waters (from internal P loading in the summer) throughout the entire water column, and has led to elevated phytoplankton abundance and biomass that is near-exclusively composed of Planktothrix spp. (95% - 100%) (based on 2018 data,

Golder Associates, 2019). Moreover, during the 2018 monitoring program, phytoplankton assemblages during the open-water season were dominated by

Planktothrix spp. and they outcompeted all other phytoplankton groups during well mixed (May and September) and thermally stratified (July and August) periods (Golder

Associates, 2019).

Increases in nutrients alone would not likely have led to the development of the recent blooms had warming-mediated changes to lake thermal properties not occurred.

For example, summer air temperatures were higher on average during the 2010s relative to the previous six decades, which would have provided a competitive advantage to cyanobacteria that are known to thrive in warm, nutrient-rich waters (Kosten et al., 2012;

Posch et al., 2012; Beaulieu et al., 2013; Rigosi et al., 2014). Specifically, under strongly stratified thermal conditions, cyanobacteria can regulate buoyancy and migrate along the

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water column to regions with optimal light conditions, which makes them successful relative to other phytoplankton groups in eutrophic lakes (Jöhnk et al., 2008; Taranu et al., 2012). In addition, the use of lake-water to cool the generators at the Jackfish Lake power plant has likely resulted in additional warming of water near the discharge area

(Golder Associates, 2019) and, in fact, the highest phytoplankton biomass (dominated by cyanobacteria) were recorded near the discharge stations in August and September 2018

(Golder Associates, 2019). Therefore, it is possible that the rising air temperatures during the open-water period and discharge from the power plant may be acting synergistically creating warm conditions for Planktothrix spp. to thrive at eutrophic Jackfish Lake.

Collectively, our paleolimnological and long-term meteorological data provide insights into the roles of past changes in nutrients and temperature, and how these stressors may have interacted with each other, leading to recent blooms. We have also considered the role of changes in zooplankton predation (i.e. top-down effects) as a potential causative mechanism. Clearly, a major shift in the cladoceran assemblage was observed at Jackfish when gold mining activities began in Yellowknife and pre-dated the observed blooms by ~ 60 years. However, this shift did not result in the complete collapse of the pelagic grazers; in fact, the bosminids were replaced by efficient daphniid grazers, which are known to co-exist with Planktothrix spp. as they can selectively avoid cyanobacteria (Kurmayer and Jüttner, 1999). This selective avoidance by a keystone planktonic grazer may have partly contributed to the success of cyanobacteria in recent years when eutrophication and warming provided ideal conditions for them to proliferate.

Although, the switch between bosminids and daphniids was primarily driven by mining

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pollution during the mid-20th century, the impacts of altered grazing patterns at Jackfish

Lake have likely manifested six decades later through complex food web interactions.

4.5.3 Regional trends in primary production and cyanobacteria

Similar to Jackfish, whole-lake primary production (inferred from VNIRS Chl-a) has increased across a wide spectrum of urban and remote sites around Yellowknife

(Figure 4.4). Although this is consistent with observations from remote Arctic lakes where warming-mediated lengthening of growing season has led to increases in primary production (Michelutti et al., 2005; Griffiths et al., 2017), the lakes around the City of

Yellowknife (i.e. Jackfish, Grace, Kam, Niven) were also influenced by local anthropogenic activities. Hence, cultural eutrophication from land-use changes (e.g. logging, catchment development, raw sewage inputs) have likely contributed to the increases in primary production (Stewart et al., 2018; Sivarajah et al., 2020) and cyanobacterial abundance at the urban lakes (Figure 4.4). For instance, both primary production and cyanobacterial abundance increased concomitantly at Niven Lake with raw sewage inputs (late-1940s to mid-1980s; Appendix Figures E.10 and E.11). This is consistent with previous investigations where eutrophication often leads to dominance by phytoplankton (particularly cyanobacteria) relative to macrophytes in shallow lakes

(Scheffer and van Nes, 2007). As expected, following the cessation of sewage inputs, both overall primary production and cyanobacterial abundance decreased sharply

(Appendix Figures E.10 and E.11). In the most recent years, however, whole-lake primary production has increased at Niven (Stewart et al., 2018) while cyanobacteria have remained low (Figure 4.4; Appendix Figures E.10 and E.11). Hence, the recent increases in primary production are likely driven by climate-mediated lengthening of the

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growing season which may favor a variety of primary producers (e.g. diatoms) in this macrophyte dominated system. While some of the urban and remote sites have recorded an increase in cyanobacterial abundance (inferred from VNIRS-Cyano), to our knowledge nuisance cyanobacterial blooms have not been reported from any of these sites. The lack of algal blooms at these other urban sites suggests that nutrient-rich run- off from the waste disposal site and warm water discharge from the power plant are key factors promoting the recent algal blooms at Jackfish, because these factors are not present at our other study lakes.

4.5.4 Outlook for (sub-) Arctic lake water quality and occurrence of cyanobacterial blooms

Eutrophication of Arctic lakes is not a novel problem: in fact, there are several examples of nutrient enrichment as a result of sewage inputs and municipal wastes

(Schindler et al., 1974; Michelutti et al., 2007; Stewart et al., 2014), including Niven

Lake from Yellowknife (Stewart et al., 2018). In the past, however, biological responses to eutrophication were somewhat mitigated by the cold climatic conditions (e.g. longer ice-cover periods) (Douglas and Smol, 2000; Michelutti et al., 2007; Antonaides et al.,

2011) and nuisance cyanobacterial blooms, a common repercussion of nutrient enrichment in temperate and tropical regions, were not previously reported (Schindler et al., 1974). Hence, the recent blooms of Planktothrix spp. at Jackfish Lake can be considered a novel response to eutrophication and climate warming within the context of northern lakes. These blooms suggest that eutrophic Arctic lakes are now vulnerable to planktonic blooms of cyanobacteria as climate-mediated shifts in thermal properties are making nutrient-enriched systems more susceptible to unprecedented blooms.

Specifically, warmer water temperatures, longer open-water growing seasons, and 123

stronger thermal stability are acting concertedly with chemical conditions (i.e. increased nutrient availability) and are likely providing a competitive advantage to cyanobacteria over other phytoplankton groups. An increase in the occurrence of cyanobacterial blooms could have negative implications to the functioning of northern lakes both directly (e.g. poor quality food source for zooplankton, alteration of light climate for other phytoplankton) and indirectly (e.g. increased decomposition of algal biomass could deplete [DO] in the hypolimnion where large-bodied cold water fish, e.g. salmonids, take refuge). These limnological changes can impede the use of important ecosystem services, such as fisheries for subsistence and tourism, in northern communities. Therefore, it is imperative to employ mitigation strategies that will reduce the amount of nutrients entering northern lakes and initiate regular monitoring programs of eutrophic lakes, especially sites that have been impacted by known anthropogenic activities (e.g. history of sewage input and waste disposal).

4.6 Acknowledgments

We thank the Jules Blais, David Eickmeyer, Cynthia Cheney, and Mija Azdajic of the University of Ottawa and Jennifer Korosi and Emily Stewart of York University for assistance in the field. Additionally, many thanks to Joshua Thienpont, Emily Stewart, and Madi Perrett for sharing the published VNIRS Chl-a data. We would also like to thank David Jessiman (Department of Environment and Natural Resources (ENR),

Government of the Northwest Territories (GNWT)), Robin Staples (ENR, GNWT),

Andrea Czarnecki (ENR, GNWT), and Matt Miller (Northwest Territories Power

Corporation) for providing logistical advice and sharing information regarding Jackfish

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and other lakes in Yellowknife. Funding for this research was provided by the Natural

Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants

Program (John Smol), Northern Supplements (John Smol), and Alexander Graham Bell

Canada Graduate Scholarship (Branaavan Sivarajah); W. Garfield Weston Scholarship for Northern Research - Doctoral (Branaavan Sivarajah), Polar Knowledge Canada’s

Northern Scientific Training Program (Branaavan Sivarajah).

4.7 References

Adrian, R., Wilhelm, S., Gerten, D., 2006. Life-history traits of lake plankton species may govern their phenological response to climate warming. Glob. Chang. Biol. 12, 652– 661.

Adrian, R., O’Reilly, C. M., Zagarese, H., Baines, S. B., Hessen, D. O., Keller, W., Livingstone, D. M., Sommaruga, R., Straile, D., van Donk, E., Weyhenmeyer, G., Winder, M., 2009. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283– 2297.

Almodóvar, A., Nicola, G. G., Nuevo, M., 2004. Effects of a bloom of Planktothrix rubescens on the fish community of a Spanish reservoir. Limnetica 23, 167–178.

Antonaides, D., Michelutti, N., Quinlan, R., Blais, J. M., Bonilla, S., Douglas, M. S. V., Pienitz, R., Smol, J. P., Vincent, W. F., 2011. Cultural eutrophication, anoxia, and ecosystem recovery in Meretta Lake, High Arctic Canada. Limnol. Oceanogr. 56, 639– 650.

Appleby, P. G., 2001. Chronostratigraphic techniques in recent sediments. In Last, W. M., Smol, J. P., (Eds.), Tracking Environmental Change Using Lake Sediments. Vol. 1: Basin Analysis, Coring, and Chronological Techniques. Kluwer: Dordrecht, pp 171−176.

Benson, B., Magnuson, J., Sharma, S., 2013. Global Lake and River Ice Phenology Database, Version 1. NSIDC: National Snow and Ice Data Center: Boulder.

Beaulieu, M., Pick, F., Gregory-Eaves, I., 2013. Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set. Limnol. Oceanogr. 58, 1736–1746.

125

Bright, D. A., Coedy, B., Dushenko, W. T., Reimer, K. J., 1994. Arsenic transport in a watershed receiving gold mine effluent near Yellowknife, Northwest Territories, Canada. Sci. Total. Environ. 155, 237–252.

Brodersen, K., Quinlan, R., 2006. Midges as palaeoindicators of lake productivity, eutrophication and hypolimnetic oxygen. Quaternary Sci. Rev. 25, 1995–2012.

Brooks, S.J., Langdon, P.G., Heiri, O., 2007. The identification and use of Paleoarctic Chironomidae larvae in palaeoecology. Technical Guide No. 10. London (UK): Quaternary Research Association.

Camburn, K. E., Charles, D. F., 2000. Diatoms of low-alkalinity lakes in the Northeastern United States. The Academy of Natural Sciences of Philadelphia, Philadelphia, United States of America.

Canadian Broadcasting Corporation North, Algae bloom coats Yellowknife’s Jackfish Lake with sludge. 2015, https://www.cbc.ca/news/canada/north/algae-bloom-coats- yellowknife-s-jackfish-lake-with-sludge-1.3075172

Carey, C. C., Weathers, K. C., Cottingham, K. L., 2008. Gleotrichia echinulate blooms in an oligotrophic lake: helpful insights from eutrophic lakes. J. Plankton Res. 30, 893–904.

Cheney, C. L., Eccles, K. M., Kimpe, L. E., Thienpont, J. R., Korosi, J. B., Blais, J. M., 2020. Determining the effects of past gold mining using a sediment palaeotoxicity model. Sci. Total Environ. 718, 137308. de los Ríos, A., Ascaso, C., Wierzschos, J., Vincent, W. F., Quesada, A., 2015. Microstructure and cyanobacterial composition of microbial mats from the High Arctic. Biodivers. Conserv. 24, 841–863.

Dibike, Y., Prowse, T., Bonsal, B., de Rham, L., Saloranta, T., 2012. Simulation of North American lake-ice cover characteristics under contemporary and future climate conditions. Int. J. Climatol. 32, 695–709.

Dong, X., Bennion, H., Battarbee, R., Yang, X., Yang, H., Liu, E., 2008. Tracking eutrophication in Taihu Lake using the diatom record: potential and problems. J. Paleolimnol. 40, 413–429.

Douglas, M. S. V., Smol, J. P., 2000. Eutrophication and recovery in the High Arctic: Meretta Lake (Cornwallis Island, Nunavut, Canada) revisited. Hydrobiologia 431, 193– 204.

126

Eggen, T., Moeder, M., Arukwe, A., 2010. Municipal landfill leachates: a significant source for new and emerging pollutants. Sci. Total Environ. 408, 5147–5157.

Einarsson, Á., Stefánsdóttir, G., Jóhannesson, H., Ólafsson, J. S., Gíslason, G. M., Wakana, I., Gudbergsson, G., Gardarsson, A., 2004. The ecology of Lake Myvatn and the River Lexá: Variation in space and time. Aquat. Ecol. 38, 317–348.

Environment and Climate Change Canada (ECCC). 2017. Historical climate data: Yellowknife, Northwest Territories (Climate Station ID: 2204100/2204101). Available from https://climate-change.canada.ca/climate-data/#/adjusted-station-data.

Fallu, M.-A., Allaire, N., Pienitz, R., 2000. Freshwater diatoms from northern Québec and Labrador (Canada): Species-environment relationships in lakes of boreal forest, forest-tundra and tundra regions. Bibliotheca Diatomologica Band 45. Berlin, Stuttgart.

Favot, E. J., Rühland, K. M., DeSellas, A. M., Ingram, R., Paterson, A. M., Smol, J. P., 2019. Climate variability promotes unprecedented cyanobacterial blooms in a remote, oligotrophic Ontario lake: evidence from paleolimnology. J. Paleolimnol. 62, 31–52.

Favot, E. J., Hadley, K. R., Paterson, A. M., Michelutti, N., Watson, S. B., Zastepa, A., Hutchinson, N. J., Vinebrooke, R. D., Smol, J. P., 2020. Using visible near-infrared reflectance spectroscopy (VNIRS) of lake sediments to estimate historical changes in cyanobacterial production: potential and challenges. J. Paleolimnol. https://doi.org/10.1007/s10933-020-00140-2

Gavel, M.J., Patterson, R.T., Nasser, N.A., Galloway, J.M., Hanna, B.W., Cott, P.A., Roe, H.M., Falck, H., 2018. What killed Frame Lake? A precautionary tale for urban planners. PeerJ https://doi.org/10.7717/peerj .4850

Glew, J. R., 1988. A portable extruding device for close interval sectioning of unconsolidated core samples. J. Paleolimnol. 1, 235–239.

Glew, J. R., 1989. A new trigger mechanism for sediment samples. J. Paleolimnol. 2, 241–243.

Griffiths, K., Michelutti, N., Sugar, M., Douglas, M. S. V., Smol, J. P., 2017. Ice-cover is the principal driver of ecological change in High Arctic lakes and ponds. PLoS ONE 12, e0172989.

Gu, B., Alexander, V., 1993.Dissolved nitrogen uptake by a cyanobacterial bloom (Anabaena flos-aquae) in a subarctic lake. Appl. Environ. Microbiol. 59, 422–430.

Golder Associates Limited, 2019. 2018 Environmental Monitoring Report Jackfish Lake Generating Facility. Yellowknife, Northwest Territories. http://registry.mvlwb.ca/Documents/MV2019L1-0001/MV2019L1-0001%20-

127

%20NTPC%20-%20Jackfish%20-%20New%20Water%20Licence%20Application%20- %20Feb26-19.pdf

Håkansson, H., 2002. A compilation and evaluation of species in the general Stephanodiscus, Cyclotestephanos and Cyclotella with a new genus in the family Stephanodiscaceae. Diatom Res. 17, 1–139.

Hall, R. I., Smol, J. P., 2010. Diatoms as indicators of lake eutrophication. In The Diatoms: Applications for the Environmental and Earth Sciences, Smol, J. P., Stoermer, E. F., (Eds.), Cambridge University Press: United Kingdom, pp. 122–151.

Huisman, J., Codd, G. A., Pearl, H. W., Ibelings, B. W., Verspagen, J. M. H., Visser, P. M., 2018. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483.

Ilyashuk, B., Ilyashuk, E., Dauvalter, V., 2003. Chironomid responses to long-term metal contamination: a paleolimnological study in two bays of Lake Imandra, Kola Peninsula, northern Russia. J. Paleolimnol. 30, 217–230.

Jamieson, H. E., 2014. The legacy of arsenic contamination from mining and processing refractory gold ore at Giant Mine, Yellowknife, Northwest Territories, Canada. Rev. Mineral. Geochem. 79, 533–551.

Jöhnk, K. D., Huisman, J., Sharples, J., Sommeijer, B., Visser, P., Stroom, J. M., 2008. Summer heatwaves promote blooms of harmful cyanobacteria. Glob. Chang. Biol. 14, 495–512.

Kjeldsen, P., 1993. Groundwater pollution source characterization of an old landfill. J. Hydrol. 142, 5121–5127.

Korosi, J. B., Smol, J. P. 2012a An illustrated guide to the identification of cladoceran subfossils from lake sediments in northeastern North America: Part 1- the Daphniidae, Leptodoridae, Bosminidae, Polyphemidae, Holopedidae, Sididae, and Macrothricidae. J. Paleolimnol. 48, 571−586.

Korosi, J. B., Smol, J. P., 2012b. An illustrated guide to the identification of cladoceran subfossils from lake sediments in northeastern North America: Part 2 − the Chydoridae. J. Paleolimnol. 48, 587−622.

Kosten, S., Huszar, V. M., Bécares, E., Costa, L. C., van Donk, E., Hansson, L.-A., Jeppesen, E., Kruk, C., Lacerot, G., Mazzeo, N., Meester, L. D., Moss, B., Lürling M., Nõges, T., Romo, S., Scheffer, M., 2012. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob. Chang. Biol. 18, 118–126.

Krammer, K., Lange-Bertalot, H., 1986. Bacillariophyceae. 1. Teil: Naviculaceae. In Ettl, H., Gerloff, J., Heynig, H., Mollenhauer, D., (Eds.), Süßwasserflora von Mitteleuropa. Band 2/1. Gustav Fischer Verlag, Germany. 128

Krammer, K., Lange-Bertalot, H., 1988. Bacillariophyceae. 2. Teil: Bacillariaceae Epithemiaceae, Surirellaceae. In Ettl, H., Gerloff, J., Heynig, H., Mollenhauer, D. (Eds.), Süßwasserflora von Mitteleuropa. Band 2/2. Gustav Fischer Verlag, Germany.

Krammer, K., Lange-Bertalot, H., 1991a. Bacillariophyceae. 3. Teil: Centrales, Fragilariaceae, Eunotiaceae. In Ettl, H., Gerloff, J., Heynig, H., Mollenhauer, D., (Eds.), Süßwasserflora von Mitteleuropa. Band 2/3, Gustav Fischer Verlag, Germany.

Krammer, K., Lange-Bertalot, H., 1991b. Bacillariophyceae. 4. Teil: Achnanthaceae, Kritische Ergänzungen zu Achnanthes s.l., Navicula s. str. Gomphonema. In Ettl, H., Gärtner, G., Heynig, H., Mollenhauer, D., (Eds.), Süßwasserflora von Mitteleuropa. Band 2/4. Gustav Fischer Verlag, Germany.

Kurek, J., Korosi, J. B., Jeziorski, A., Smol, J. P., 2010. Establishing reliable minimum count sizes for cladoceran subfossils sampled from lake sediments. J. Paleolimnol. 44, 603−612.

Kurmayer, R., Jüttner, F., 1999. Strategies for the co-existence of zooplankton with the toxic cyanobacterium Planktothrix rubescens in Lake Zürich. J. Plankton Res. 21, 659– 683.

LeBlanc, S., Pick, F. R., Hamilton, P. B., 2008. Fall cyanobacterial blooms in oligotrophic-to-mesotrophic temperate lakes and the role of climate change. Verh. Internat. Verein. Limnol. 30, 90–94.

Lienesch, P. W., McDonald, M. E., Hershey, A. E., O’Brien, W. J., Bettez, N. D., 2005. Effects of a whole-lake, experimental fertilization on lake trout in a small oligotrophic arctic lake. Hydrobiologia 548, 51–66.

Michelutti, N., Wolfe, A., Vinebrooke, R. D., Rivard, B., Briner, J. P., 2005. Recent primary production increases in arctic lakes. Geophys. Res. Lett. 32, L19715.

Michelutti, N., Hermanson, M. H., Smol, J. P., Dillon, P. J., Douglas, M. S. V., 2007. Delayed response of diatom assemblages to sewage inputs in an Arctic lake. Aquat. Sci. 69, 523–533.

Michelutti, N., Blais, J. M., Cumming, B. F., Paterson, A. M., Rühland, K.M., Wolfe, A. P., Smol, J. P., 2010. Do spectrally inferred determinations of chlorophyll a reflect trends in lake trophic status. J. Paleolimnol. 43, 205–217.

Michelutti, N., Smol, J. P., 2016. Visible spectroscopy reliably tracks trends in paleo- production. J. Paleolimnol. 56, 253–265.

129

Mowe, M. A. D., Mitrovic, S. M., Lim, R. P., Furey, A., Yeo, D. C. J., 2015. Tropical cyanobacterial blooms: a review of prevalence, problem taxa, toxins and influencing environmental factors. J. Limnol. 74, 205–224.

Nürnberg, G., LaZerte, B. D., Olding, D. D., 2003. An artificially induced Planktothrix rubescens surface bloom in a small kettle lake in southern Ontario compared to blooms world-wide. Lake and Reserv. Manag. 19, 307–322.

Nürnberg, G., Molot, L. A., O’Connor, E., Jarjanazi, H., Winter, J., Young, J., 2013. Evidence for internal phosphorus loading, hypoxia and effects on phytoplankton in partially polymictic Lake Simcoe, Ontario. J. Great Lakes Res. 39, 259–270.

O’Brien, W. J., Barfield, M., Bettez, N., Hershey, A. E., Hobbie, J. E., Kipphut, G., Kling, G., Miller, M. C., 2005. Long-term response and recovery to nutrient addition of a partitioned arctic lake. Freshwater Biol. 50, 731–741.

Orihel, D. M., Baulcj, H. M., Casson, N. J., North, R. L., Parson, C. T., Seckar, D. C. M., Venkiteswaran, J. J., 2017. Internal phosphorus loading in Canadian fresh waters: a critical review and data analysis. Can. J. Fish. Aquat. Sci. 74, 2005–2029.

Palmer, M. J., Galloway, J. M., Jamieson, H. E., Patterson, R. T., Flack, H., Kokelj, S. V. 2015. The concentration of arsenic in lake waters of the Yellowknife area, NWT Open File 2015-06. Northwest Territories Geological Survey, Government of the Northwest Territories.

Palmer, M. J., Chételat, J., Richardson, M., Jamieson, H. E., Galloway, J. M., 2019. Seasonal variation of arsenic and antimony in surface waters of small subarctic lakes impacted by legacy mining pollution near Yellowknife, NT, Canada. Sci. Total Environ. 684, 326–339.

Passino, D. R. M., Novak, A. J., 1984. Toxicity of arsenate and DDT to the cladoceran Bosmina longirostris. Bull. Environ. Contam. Toxicol. 33, 325−329.

Perrett, M., Sivarajah, B., Cheney, C. L., Korosi, J. B., Kimpe, L. E., Blais, J. M., Smol, J. P. (In Review) Salinization and metal(loid) contamination by gold mine tailings from Consolidated Mine (Yellowknife, Canada) impact aquatic biota. (In Review with Environmental Pollution ENVPOL_2020_4502).

Paterson, A.M., Rühland, K.M., Anstey, C.V., Smol, J.P., 2017. Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada. Lake Reserv. Manag. 33, 403–414.

Pick, F. R., 2016. Blooming algae: a Canadian perspective on the rise of toxic cyanobacteria. Can. J. Fish. Aquat. Sci. 73, 1149–1158.

130

Posch, T., Köster, O., Salcher, M. M., Pernthaler, J., 2012. Harmful filamentous cyanobacteria favoured by reduced water turnover with lake warming. Nat. Clim. Change 2, 809–813.

Przytulska, A., Bartosiewicz, M., Vincent, W. F., 2017. Increased risk of cyanobacterial blooms in northern high-latitude lakes through climate warming and phosphorus enrichment. Freshwater. Biol. 62, 1986–1996.

Quinlan, R., Smol, J.P., 2001. Setting minimum head capsule abundance and taxa deletion criteria in chironomid-based inference models. J Paleolimnol. 26, 327–342.

Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L., Melo, S., 2002. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428.

Rigosi, A., Carey, C. C., Ibelings, B. W., Brookes, J. D., 2014. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Oceangr. 59, 99–114.

Rogalski, M. A., Leavitt, P. R., Skelly, D. K., 2017. Daphniid zooplankton assemblage shifts in response to eutrophication and metal contamination during the Anthropocene. Proc. R. Soc. B. 284, 20170865.

Ruggiu, D., Luglié, A., Cattaneo, A., Panzani, P., 1998. Paleoecological evidence for diatom response to metal pollution in Lake Orta (N. Italy). J Paleolimnol. 20, 333–345.

Rühland, K. M., Smol, J. P., Pienitz, R., 2003. Ecology and spatial distributions of surface-sediment diatoms from 77 lakes in the subarctic Canadian treeline region. Can. J. Bot. 81, 57–73.

Scheffer, M., van Nes, E., 2007. Shallow lake theory revisited: various alternative regimes driven by climate, nutrients, depth and lake size. Hydrobiologia 584, 455–466.

Schindler, D. W., Kalff, J., Welch, H. E., Brunskill, G. J., Kling, H., Kritsch, N., 1974. Eutrophication in the High Arctic – Meretta Lake, Cornwallis Island (75o N Lat.). J. Fish. Res. Board. Can. 31, 647–662.

Simmatis, B., Nelligan, C., Rühland, K. M., Jeziorski, A., Castro, V., Paterson, A. M., Smol, J. P., 2020. Tracking ~ 200 years of water quality in Muskrat Lake, a eutrophic lake trout lake in Ontario (Canada) with cyanobacterial blooms. Lake Reserv. Manag. DOI: 10.1080/10402381.2020.1755749

Sivarajah, B., Korosi, J. B., Blais, J. M., Smol, J. P., 2019. Multiple environmental variables influence diatom assemblages across an arsenic gradient in 33 subarctic lakes near abandoned gold mines. Hydrobiologia 841, 133–151.

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Sivarajah, B., Cheney, C. L., Perrett, M., Kimpe, L.E., Blais, J. M., Smol, J.P., 2020. Regional gold mining activities and recent climate warming alter diatom assemblages in deep sub-Arctic lakes. Polar Biol. 43, 305–317.

Smol, J. P., 2008. Pollution of lakes and rivers: a paleoenvironmental perspective, 2nd ed. Blackwell Publishing, Oxford, UK.

Smol, J. P., 2019. Under the radar: long-term perspectives on ecological changes in lakes. Proc. R. Soc. B 286, 20190834.

Smol, J. P., Douglas, M. S. V., 2007. From controversy to consensus: making the case for recent climate change in the Arctic using lake sediments. Front. Ecol. Environ. 5, 466– 474.

Snorthiem, C. A., Hanson, P. C., McMahon, K. D., Read, J. S., Carely, C. C., Dugan, H. A., 2017. Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake. Ecol Modell. 343, 39–53.

Spence, C., Kokelj, S. V., Kokelj, S. A., McCluskie, M., Hedstrom, N., 2011. Evidence of a change in water chemistry in Canada’s subarctic associated with enhanced winter streamflow. J. Geophys. Res. Biogiosci. 120, 113–127.

Steffensen, D. A., 2008. Economic cost of cyanobacterial blooms. In Cyanobacterial Harmful Algal Blooms: State of the Science and Research Needs, Hundell, H. K. Ed., Spring: New York, pp 855–865.

Stewart, E. M., McIver, R., Michelutti, N., Douglas, M. S. V., Smol, J. P., 2014. Assessing the efficacy of chironomid and diatom assemblages in tracking eutrophication in High Arctic sewage ponds. Hydrobiologia 721, 251–268.

Stewart, E. M., Hargan, K.E., Sivarajah, B., Kimpe, L.E., Blais, J. M., Smol, J.P., 2018. A paleoenvironmental study tracking eutrophication, mining pollution, and climate change in Niven Lake, the first sewage lagoon of Yellowknife (Northwest Territories). Arctic, 71, 201–217.

Szeroczyńska, K., Sarmaja-Korjonen, K., 2007. Atlas of subfossil Cladocera from central and northern Europe. Friends of the Lower Vistula Society, Świecie.

Tammeorg, O., Möls, T., Niemistö, J., Holmroos, H., Horppila, J., 2017 The actual role of oxygen deficit in the linkage of the water quality and benthic phosphorus release: Potential implications for lake restoration. Sci. Total Environ. 599-600, 732–738.

Tenkouano, G.-T., Cumming, B. F., Jamieson, H. E., 2019 Geochemical and ecological changes within Moira Lake (Ontario, Canada): A legacy of industrial contamination and remediation. Environ. Poll. 247, 980–988.

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Thienpont, J. R., Korosi, J. B., Hargan, K. E., Williams, T., Eickmeyer, D. C., Kimpe, L. E., Palmer, M. J., Smol, J. P., Blais, J. M., 2016 Multi-trophic level response to extreme metal contamination from gold mining in a subarctic lake. Proc. R. Soc. B. 283, 20161125.

Taranu, Z. E., Zurawell R. W., Pick, F., Gregory-Eaves, I., 2012. Predicting cyanobacterial dynamics in the face of global change: the importance of scale and environmental context. Glob. Chang. Biol. 18, 3477–3490.

Vincent, W. F., Quesada, A., 2012 Cyanobacteria in high latitude lakes, rivers and seas. In Ecology of Cyanobacteria II: Their diversity in space and time, Whitton, B. A., (Ed.), Springer Science pp 371–385.

Walsby, A. E., Jüttner, F., 2006. The uptake of amino acids by the cyanobacterium Planktothrix rubescens is simulated by light at low irradiances. FEMS Microbiol. Ecol. 58, 14–22.

Welch, H. E., Legault, J. A., 1986. Precipitation chemistry and chemical limnology of fertilized and natural lakes at Saqvaqjuac, N.W.T. Can. J. Fish. Aquat. Sci. 43, 1104– 1134.

Wıìlıìdeh Yellowknives Dene, 1997. Wıìlıìdeh Yellowknives Dene: A History. Yellowknives Dene First Nation Elders Advisory Council, Dettah.

Williams, S. G., Steffan, H. G., 2006. Modeling of lake ice characteristics in North America using climate, geography, and lake bathymetry. J. Cold Reg. Eng. 20, 140–167.

Winter, J. G., DeSellas, A. M., Fletcher, R., Heintsch, L., Morley, A., Nakamoto, L., Utsumi, K., 2011. Algal blooms in Ontario, Canada: increases in reports since 1994. Lake Reserv. Manag. 27, 107–114.

Wolfe, S. A., 1998. Living with frozen ground: A field guide to permafrost in Yellowknife, Northwest Territories. Geological Survey of Canada Miscellaneous Report 64.

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Table 4.1: Limnological characteristics of the 9 study lakes around Yellowknife, Northwest Territories, Canada

All water chemistry measurements were taken at the time of sampling and have been published previously in Sivarajah et al. (2019).

Lake Alexie BC 02 BC 43 Grace Jackfish Kam Niven Pocket YK 40 July July July July March July Sampling date April 2014 April 2014 April 2014 2016 2016 2015 2015 2014 2016 Latitude 62.67648 62.65564 62.50851 62.42068 62.46703 62.42242 62.46131 62.50897 62.36656 (decimal degrees) Longitude -114.102 -114.386 -114.175 -114.431 -114.392 -114.401 -114.369 -114.374 -114.134 (decimal degrees) Distance to Giant Mine 23.52 16.96 9.75 10.23 4.25 9.25 4.62 0.94 19.08 (km) Coring depth (m) 21 7.7 5 16 7.5 7.5 1 2 4.6 Surface area (km2) 4.56 0.23 0.4 0.63 0.52 2.16 0.09 0.05 0.35 pH 7.69 7.69 8.64 7.42 9.07 7.55 9.98 7.71 8.01 Dissolved organic 13.4 10.8 33.7 19.7 9.7 17 28.2 25 26.1 carbon (mg L-1) Total Alkalinity 59.2 26.5 222 59.4 94.2 107 89.6 191 70.2 (mg L-1) Specific conductance 139 68 410 187 383 400 569 494 144 (µS cm-1) Total nitrogen 510 430 1870 750 760 880 2000 2480 1360 (µg L-1) Total arsenic 1.5 3.3 16.4 16.1 74.5 238 41.7 2070 7.9 (µg L-1) Total antimony <0.1 0.3 0.3 0.6 1.5 22.6 1.9 49.8 0.2 (µg L-1)

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Figure 4.1 A map of the study area

(Top) The locations of all lakes included in this study, with lakes near the city identified by red circles and remote lakes indicated by green squares. The location of Yellowknife is identified by a red star in the inset map. (Bottom) The locations of the Solid Waste Disposal Site and Jackfish Lake Power Plant (PP) in relation to Jackfish Lake.

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Figure 4.2 Biostratigraphy of Jackfish Lake

Stratigraphic profiles of diatoms, cladocerans, and chironomids along with VNIRS Chl-a and VNIRS-Cyano scaled by core depth (with CRS 210Pb dates plotted secondarily) from Jackfish Lake. The solid black line represents the onset of regional gold mining activities. The dashed dark grey line indicates the cessation of mining operations. The dotted light grey line identifies the onset of algal blooms at this lake. 136

Figure 4.3 Monitoring data from Yellowknife

A) Past population trends in the City of Yellowknife. Long-term meteorological data, B) mean annual summer rainfall (MSR), C) mean annual summer air temperature (MSAT), and E) mean summer wind speed (MSWS) collected at the Yellowknife Airport climate station. D) Past changes in ice-free days at Long Lake in Yellowknife, Northwest Territories. The Thiel-Sen slopes (annual change) and significance levels from the Mann- Kendall trends tests are included for the meteorological and ice-free days data. The grey dashed lines in B) and C) represent the mean of the time-series data. 137

Figure 4.4 Comparison of whole lake primary production and cyanobacterial abundance

Standardized (Z-scores) trends in past whole lake primary production (VNIRS Chl-a) and cyanobacterial abundance (VNIRS-Cyano) among Jackfish, and other urban (Niven, Kam, Grace) and remote (Pocket, Alexie, BC-02, BC-43, YK-40) lakes around Yellowknife.

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Chapter 5

Long-term diatom responses to metal(loid) pollution, urbanization, and climatic changes in 10 sub-Arctic lakes around Yellowknife, Northwest

Territories, Canada

5.1 Abstract

The lakes around Yellowknife (Northwest Territories) have been impacted by multiple environmental stressors throughout the 20th and early-21st centuries. Previous paleolimnological case studies have provided important perspectives on how site-specific characteristics have influenced long-term biological responses to multiple environmental stressors at these sub-Arctic lakes. Here, we have synthesized diatom assemblage data from 10 lake sediment cores from the Yellowknife area and used a landscape-scale paleolimnological approach to investigate the cumulative impacts of past gold mining activities, urbanization, and climate warming on aquatic biota. Our investigations indicated that diatom species turnover (measured using detrended canonical correspondence analysis) was highest at lakes closer to the city and mines, because these sites were severely impacted by land-use changes (e.g. sewage disposal, run-off from waste disposal sites) and roaster stack emission from the Giant Mine. However, these sites also recorded notable shifts that are indicative of climate-mediated changes to lake thermal properties. The inclusion of remote sites was useful to help disentangle the impacts of climate-mediated changes from the impacts of anthropogenic activities in the lakes near the mines and the city. Our investigation suggests that the diatom assemblages of the lakes around Yellowknife have changed markedly over the last ~200 years and

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there are no signs of biological recovery since the cessation of mining activities around the turn of the 21st century. The recent biological changes observed in the sub-Arctic lakes around Yellowknife are indicative of climate-mediated changes to lake thermal properties and the biota in the urban lakes are also influenced by the legacies of past land- use changes.

5.2 Introduction

Environmental changes during the 20th and 21st centuries in high latitude regions have led to important alterations in the fundamental physical and chemical processes of

Northern lakes, often resulting in the reorganization of biological assemblages (Smol et al. 2005; Mueller et al. 2006; Vincent et al. 2011, 2013; Roberts et al. 2017; St. Pierre et al. 2019). There is unequivocal evidence that climate-induced changes (e.g. reduced ice- cover, stronger thermal stratification, permafrost thaw slumps, longer growing seasons) are important drivers of biological shifts in Northern freshwaters (e.g. Michelutti et al.

2005; Smol et al. 2005; Thienpont et al. 2013). A growing number of studies, however, report that the cumulative impacts of climatic changes and other stressors (e.g. metal(loid) contamination, sewage disposal, persistent organic pollutants) on the biota of

Northern lakes pose a greater threat to the functioning and integrity of aquatic ecosystems

(e.g. Antoniades et al. 2011; Ahonen et al. 2018; Stewart et al. 2018; Hudelson et al.

2019; Zubova et al. 2020). With projected increases in air-temperature and precipitation

(Prowse et al. 2009) as well as proposed land-use changes (Cott et al. 2015), it is likely that the further perturbations of Northern freshwaters could impede the provision of important ecosystem services such as subsistence fisheries, tourism, and recreational

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activities. Hence, there is a need to better understand the long-term cumulative effects of multiple environmental stressors on the biota of Northern lakes. Unfortunately, direct long-term limnological monitoring data are not available for most Northern regions, making it difficult to establish baseline limnological conditions and assess biological responses to environmental stressors (Smol 2019). This knowledge gap can be addressed by analysing proxies stored in lake sediments (paleolimnology), which can provide useful insights into past limnological changes and biological responses (Smol 2008).

The sub-Arctic lakes around the City of Yellowknife (Northwest Territories,

Canada; Figure 5.1) provide an important opportunity to assess the long-term biological responses to multiple environmental stressors, as this region: 1) has warmed by ~2.7 oC between 1942 and 2016 (Appendix Figure E.7); 2) was impacted by one of the largest gold mining operations in Canada’s North for more than 50 years; and 3) has experienced major land-use changes in response to urban development (Jamieson 2014; Nasser et al.

2016; Stewart et al. 2018; Cheney et al. 2020). Although previous paleolimnological case studies have provided useful insights on how lake-specific characteristics have influenced the biological responses to the aforementioned stressors (Thienpont et al. 2016; Gavel et al. 2018; Stewart et al. 2018; Sivarajah et al. 2020, In Review; Perrett et al. In Review), the long-term cumulative effects of multiple environmental stressors have not been extensively investigated at the landscape level. As with most Northern regions, the lakes around Yellowknife vary greatly in limnological characteristics (e.g. size, depth, nutrients, contaminants), catchment disturbances, and distance from mines and urban development (Table 5.1; Figure 5.1). Therefore, a regional assessment of detailed paleolimnological records is necessary to compare the long-term biological responses to

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multiple environmental stressors at the landscape level. A landscape-level paleolimnological approach, such as the one described here, will help researchers better understand when the biota of Yellowknife lakes began responding to stressors and to determine if there are consistencies in the nature and timing of biological changes across the region. Furthermore, a regional paleolimnological synthesis will help to infer the relative importance of individual stressors at sites with different physical and chemical characteristics through time and identify which lakes are most susceptible to environmental perturbations. In addition to quantifying and comparing the magnitude of biological changes to multiple stressors, this landscape-scale approach provides an opportunity to gain better insights on how climatic changes may influence the trajectory of regional biological recovery from past anthropogenic activities (i.e. mining activities and urbanization).

In this study, we compiled paleolimnological records from 10 lakes (4 new records – BC-02, BC-17, BC-43, Vee; 6 published in previous case studies – Alexie,

Grace, Jackfish, Kam, Niven, Pocket; Thinepont et al. 2016; Stewart et al. 2018;

Sivarajah et al. 2020, In Review, Perrett et al. In Review) that are representative of the diverse limnological characteristics (e.g. depth, contaminant concentrations, size, land- use history) observed around Yellowknife (Table 5.1). The biological responses to past limnological changes were examined using sub-fossil remains of diatoms

(Bacillariophyta, siliceous unicellular algae). Diatoms are one of the most commonly used group of paleolimnological bio-indicators, because they rapidly respond to a variety of environmental stressors, leave identifiable remains in lake sediments, and have well- defined optima for various limnological variables (Smol and Stoermer 2010). In addition

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to examining species-specific shifts, we have summarized the past changes in diatom assemblages and quantified species turnover using detrended canonical correspondence analysis (DCCA; ter Braak 1986). We have restricted our paleolimnological analyses to the past ~200 years to ensure sedimentary records from all lakes represent similar timeframes (i.e. ~1800s to present) and contain enough samples from the pre-disturbance period to establish reasonable baseline limnological conditions.

5.3 Study Area

The Yellowknife area experiences a continental sub-Arctic climate (Wolfe 1998), where the mean annual air temperature has been below the freezing point between 1942 and 2016 (range: -7oC to -1oC; Environment and Climate Change Canada, 2017) and lakes are ice-covered for more than 6 months of the year (Benson et al. 2013). The study lakes are located within the Slave Structural Province of the Canadian Shield (Wolfe

1998), and the bedrock geology of each site varies between volcanic, granitoid, sedimentary, and mafic intrusives (Palmer et al. 2015). The study region is situated within the Great Slave Uplands and Lowlands of Taiga Shield High Boreal ecoregion, with the vegetation characterized by the presence of mixed spruce forests, jack pine, trembling aspen, paper birch and understories of moss and lichen (Ecosystem

Classification Group 2008).

Yellowknife has been the traditional home of the Wıìlıìdeh Yellowknives Dene for centuries (Wıìlıìdeh Yellowknives Dene 1997); however, with the discovery of gold- bearing arsenopyrite ores and subsequent establishment of mines around the 1930s, a series of notable land-use changes occurred in this region. The establishment of gold

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mines in Yellowknife, specifically the Giant (1948 - 2004) and Con (1938 - 2003) mines, marked a new era in Arctic mining as these large-scale resource extraction operations lasted for several decades. They also led to significant contamination of the surrounding environment (e.g. arsenic, antimony, lead, mercury), leaving complex ecological legacies

(Jamieson 2014; Palmer et al. 2015; Houben et al. 2016; Thienpont et al. 2016). Due to poor environmental regulations, roaster stack emissions during the early mining years

(i.e. 1949 to 1951) at Giant Mine led to the release of ~8000 tonnes of toxic arsenic trioxide (As2O3) into the environment, and a total of ~21000 tonnes of As2O3 was released between 1949 and 1999 (Jamieson 2014). Although roaster stack emissions were reduced after the construction of a baghouse in 1958, recent limnological surveys around

Yellowknife have reported some of the highest arsenic concentrations in Canada with the concentrations of contaminants (arsenic, antimony, sulphate) decreasing with increasing distance from Giant Mine roaster stack (Galloway et al. 2018; Palmer et al. 2019; Cheney et al. 2020).

The establishment of large-scale gold mining activities led to the development of

Yellowknife as a northern population hub after the 1930s. The city has expanded since then, especially after 1967, when it became the capital of the Northwest Territories.

Urbanization has also impacted several lakes within the city limits (Gavel et al. 2018).

For instance, disposal of raw sewage into Niven Lake (Stewart et al. 2018) and runoff from waste disposal into Jackfish Lake (Sivarajah et al. In Review) have eutrophied these sites. In addition to metal(loid) pollution and eutrophication, the lakes around

Yellowknife have also been impacted by changing climatic conditions (Appendix Figures

E.7, E.8, E.9 and E.10; Stewart et al. 2018; Sivarajah et al. 2020). Specifically, marked

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increases in winter and spring air temperatures have led to a shortening of ice-cover duration and a lengthening of open water growing seasons. Furthermore, the increases in summer air temperature have likely led to the development of longer periods of thermal stratification in these sub-Arctic lakes. In addition to notable warming, the regional wind speeds have also declined, which could further intensify thermal stratification during the summer months (Appendix Figure E.9; Sivarajah et al. In Review).

The study lakes were strategically chosen to incorporate both urban and remote lakes with diverse limnological characteristics. Specifically, we ensured that a large gradient in lake water arsenic concentrations ([As]) reported from this region was represented, with [As] ranging between 1.5 and 2070 µg/L (Table 5.1; Palmer et al. 2015;

Houben et al. 2016). Both Pocket and BC-17 are shallow lakes (Table 5.1), which are close to the roaster stack of the Giant Mine at ~1 km and ~3 km, respectively (Figure

5.1), and have the highest [As], 2070 and 750 µg/L. Pocket Lake is located within the

Giant Mine lease boundary (Figure 5.1), and a previous paleolimnological investigation has reported remarkable shifts in the biota, including the functional loss of planktonic zooplankton and diatom taxa, as a response to severe metal(loid) contamination

(Thienpont et al. 2016). Hence, in this regional synthesis, Pocket Lake was selected to represent the worse case scenario with respect to the impacts of Giant Mine activities on aquatic biota and place the changes observed in other lakes into an appropriate regional environmental context. Based on a recent regional paleolimnological survey of sedimentary metal(loid) concentrations, all of our study lakes are located within the area that received atmospheric deposition of pollutants during the mining years, including remote sites such as Alexie Lake (Cheney et al. 2020). The degree of contamination

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decreased with increasing distance from the Giant roaster stack and an overall decline in sedimentary metal(loid) concentrations were apparent in the recent sediments of most lakes (Cheney et al. 2020). Kam Lake, however, also received accidental spills of salt- rich mine tailings from Pud Lake, because mine effluents from Con Mine were routed into Pud (Perrett et al. In Review). The study lakes also vary greatly in depth (range: 0.5

– 21 m) and size (surface area range: 0.05 – 4.56 km2). The detailed limnological characteristics of the 10 study lakes are provided in Table 5.1.

5.4 Materials and Methods

5.4.1 Sediment coring and dating

The sediment cores were collected between 2014 and 2016 from the middle of the lakes, as the bathymetry for most of the remote lakes were not available. Gravity corers

(Glew (1989) and UwitecTM) were used to retrieved high-resolution sediment cores, which were then vertically extruded at 0.5 cm intervals using a Glew (1988) extruder.

The sediment cores were radiometrically dated at the Paleoecological Environmental

Assessment and Research Laboratory at Queen’s University and the Laboratory for the

Analysis of Natural and Synthetic Environmental Toxicants at the University of Ottawa following standard protocols outlined by Schelske et al. (1994) for 210Pb dating by gamma spectroscopy. The core chronologies were developed using the constant rate of supply (CRS) model described by Appleby (2001) using the program ScienTissiMe

(Barry’s Bay, Ontario, Canada). The dates beyond background 210Pb concentrations were extrapolated by fitting a second-order polynomial equation through the CRS dates. Since the dates beyond background 210Pb concentrations (generally dates before 1900 CE) are

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best estimates based on the established 210Pb chronologies, they should be interpreted with caution. The results of the sediment core chronologies for all lakes are presented in

Appendix Figures F.1, F.2 and F.3.

5.4.2 Laboratory analyses

The siliceous diatom valves were isolated from the sediment matrix following protocols described in Sivarajah et al. (2019). The diatom valves were identified at ×1000 times magnification using a Leica DMR light microscope fitted with differential interference contrast optics, and the photomicrographs presented in Kramer and Lange-

Bertalot (1986, 1988, 1991a & b), Camburn and Charles (2000), Fallu et al. (2000),

Håkansson (2002), and Rühland et al. (2003) were used for taxonomy. Since diatom taxonomy has been updated since the publication of many of these resources, we verified the nomenclature with latest publications and online databases to ensure the most recent taxonomy are presented in the manuscript. In this study, the diatom taxa are expressed as percent abundance relative to the total number of valves counted in a sample. The diatom stratigraphic profiles for the four new sediment records are presented in the Results section. The previously published stratigraphic profiles from the other six lakes are included in Appendix Figures F.4 – F.9, and we have summarized the patterns of the most common diatoms from the 10 lakes in the Results section.

5.4.3 Statistical analyses

All statistical analyses were conducted on the full diatom data set. Constrained incremental sum of squares (CONISS, Grimm 1987) was used to identify stratigraphic zones, and the important zones in the profiles were identified using a broken stick model

(Bennett 1996). The CONISS and broken stick model analyses were performed in R

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statistical software (R Core Team 2019) using ‘rioja’ (Juggins 2017) and ‘vegan’

(Oksanen et al. 2019) packages.

Detrended canonical correspondence analysis (DCCA, ter Braak 1986), the constrained form of detrended correspondence analysis, is a direct ordination technique that is used to estimate species compositional turnover within paleoecological sequences

(Smol et al. 2005; Birks 2007). DCCA with detrending by segments and non-linear rescaling provide sample scores that are scaled in standard deviation (SD) units of compositional change or species turnover as beta-diversity (Birks 2007). Since sedimentary biological data are preserved in a known stratigraphic order, the DCCAs can be constrained to sample age (e.g. Smol et al. 2005; Thienpont et al. 2013) or sample depth (e.g. Felde et al. 2020). The scaling of sample scores in SD units of species turnover has made DCCA an attractive statistical technique to elegantly compare beta- diversity changes through time across multiple sites (Birks 2007; Thienpont et al. 2013;

Kahlert et al. 2020) and among different sedimentary proxies (Smol et al. 2005). In addition to the estimation of species turnover in SD units, the DCCA axis 1 sample scores

(also scaled in SD units) can be plotted to visualize and compare beta-diversity trends through time and among different sites (Birks 2007; Felde et al. 2020). For this investigation, we have limited our analyses to the last ~200 years to enable direct comparison among the 10 sites around Yellowknife and compare beta-diversity changes in our study lakes to those reported from previous Arctic investigations (Smol et al. 2005;

Thienpont et al. 2013; Kahlert et al. 2020). The reference value for beta-diversity (i.e. >

1.0 SD), established by Smol et al. (2005), was used to determine if the changes observed in our study lakes were ecologically important. Furthermore, we have conducted two sets

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of DCCAs, one constrained to sample-age and another to sample-depth, to investigate if constraining by age or depth lead to notable differences in species turnover in SD units.

The DCCAs were performed on square-root transformed diatom data (i.e. to equalize the variance among taxa), with no downweighing of rare taxa, detrending by segments, and non-linear rescaling as has been recommended in previous investigations (Smol et al.

2005; Birks 2007; Birks 2010; Felde et al. 2020). The DCCAs were performed in

CANOCO 5.0 (ter Braak and Šmilauer 2012).

5.5 Results

5.5.1 Diatom assemblage changes at BC-02, BC-17, BC-43, and Vee lakes

Consistent with previous paleolimnological investigations from Yellowknife, the diatom assemblages at BC-02, BC-43 and Vee have changed markedly over the last ~200 years (Figure

5.2). The CONISS analysis with the broken stick model identified two important zones in the biostratigraphies (Figure 5.2). Unlike BC-02, BC-43 and Vee, the diatom assemblages from BC-

17 have undergone relatively subtle changes over the last ~200 years and no important zones were identified (Figure 5.2).

The most important shift in the diatom assemblages of BC-02 occurred around the late-1960s (Figure 5.2). Prior to the late-1960s the diatom assemblages at BC-02 were co- dominated (~10 - 20%) by planktonic species (Discostella stelligera, Aulacoseira taxa,

Pantocsekiella ocellata) and benthic Staurosira construens var. venter, while other benthic taxa occurred in ~10% abundances (Achnanthidium minutissimum,

Pseudostauroseira brevistriata, small naviculoid taxa – Navicula submuralis, Navicula kuelbsii, Sellophora seminulum) (Figure 5.2). After the 1960s, however, the relative

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abundances of D. stelligera increased (~30%), and the relative abundances of planktonic

P. ocellata and several benthic taxa (S. construens var. venter, small naviculoid taxa, P. brevistriata) decreased (Figure 5.2).

The important split in the biostratigraphy of BC-43 was identified around the mid-

1930s (Figure 5.2). Prior to this period, the diatom assemblages at BC-43 were dominated by Amphora pediculus (> 40%) and other benthic species (Psammothidium curtissimum,

A. minutissimum, Cocconeis placentula) occurred in notable abundances (< 20%; Figure

5.2). After the 1930s, the relative abundances of A. pediculus and C. plecentula decreased while Nitzschia fonticola, Nitzschia frustulum, and Encyonema silesiacum increased briefly (Figure 5.2). Between the mid-1930s and early-1950s, the abundances of A. pediculus and C. plecentula continued to decline along with P. curtissimum, and A. minutissimum (Figure 5.2). In the post-1950s sediments benthic fragilarioid taxa (S. construens var. venter, P. brevistriata, Staurosirella pinnata) dominated the diatom assemblages, and the abundances of N. fonticola, N. frustulum and E. silesiacum declined to negligible abundances (Figure 5.2). Recovery in the abundances of A. pediculus was observed in the most recent sediments (~post 1990s), but this taxon has not reached pre- disturbance levels (Figure 5.2).

Unlike BC-02 and BC-43, where CONISS identified the notable shifts in diatom assemblages around the mid-20th century, the most important diatom shift at Vee Lake occurred around the turn of the 20th century (Figure 5.2). However, several other diatom assemblage changes were observed at this lake throughout the 20th century (Figure 5.2).

Prior to the late-1930s, the diatom assemblages were dominated by S. pinnata (> 20%), and several other benthic taxa occurred in 10 – 15% abundance (e.g., P. brevistriata, A.

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pediculus, Amphora lybica, Platessa conspicua, P. curtissimum, Achnanthidium exiguum,

Planothidium lanceolatum, Stauroneis anceps, Stauroneis phoenicenteron) (Figure 5.2).

During the late-1930s and early-1940s, the relative abundances of Asterionella formosa and Fragilaria capucina increased briefly and then decreased abruptly around the late-

1950s. Beginning around the early-1960s, the relative abundance of benthic fragilarioid taxa (P. brevistriata, S. pinnata) increased while Stauroneis taxa and Lindavia michiganiana declined to negligible abundances (Figure 5.2). Around the late-1980s the relative abundances of D. stelligera, L. michiganiana, and pennate planktonic taxa

(Fragilaria tenera, Fragilaria nanana) increased abruptly, along with A. minutissimum.

The most recent shift around the turn of the 20th century was characterized by notable decreases in the abundances of benthic fragilarioid taxa, while planktonic D. stelligera, L. michiganiana, F. tenera and F. nanana continued to increase (Figure 5.2).

None of the CONISS zones identified in the biostratigraphy of BC-17 were deemed important by the broken stick model. However, subtle changes in the diatom assemblages were discernable. For example, around the 1950s the relative abundances of

Brachysira neoexilis, Encyonopsis microcephala, and D. stelligera declined, while A. minutissimum increased (Figure 5.2). Additionally, around the late-1970s, the relative abundance of Encyonopsis descripta increased (Figure 5.2). In the most recent sediments

(~post-2000), the relative abundances of Brachysira neoexilis, Encyonopsis microcephala and D. stelligera increased, and A minutimssim declined (Figure 5.2).

5.5.2 Regional changes in diatom assemblages

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The DCCAs of the diatom data (constrained to both sample depth and age) suggest that the species turnover over the last ~200 years at 8 lakes around Yellowknife were ecologically important, as the beta-diversity at these sites were greater than the reference value (1.0 SD units) established by Smol et al. (2005) (Figure 5.3; Table 5.2).

The greatest amount of species turnover occurred at the lakes closer to the mines and the city (Pocket, Kam, Niven, Vee, Grace, BC-43, Jackfish) (Figure 5.3). The species turnover at the remote sites (Alexie, BC-02) were smaller relative to lakes closer to the city and the mines (Figure 5.3). Consistent with the subtle assemblage changes observed at BC-17 (Figure 5.2), the species turnover at BC-17 was lowest in the region (Figure

5.3). We did not observe substantial differences in species turnover when the diatom data were constrained to sample-depth vs. sample-age (Table 5.2).

Based on CONISS with the broken stick model, the diatom assemblages of seven lakes (Alexie, BC-02, BC-43, Grace, Jackfish, Kam, Pocket) underwent notable changes around the mid-20th century (Figure 5.3). At Niven and Vee lakes, the important shift in diatom assemblages were observed around the turn of the 20th century, and no significant changes were observed at BC-17 (Figures 5.2, 5.3). At Kam and Pocket lakes, a second important shifts in diatom assemblages were also observed around the turn of the 20th century (Figure 5.3).

The baseline (pre-disturbance) diatom assemblages varied greatly among the 10 study lakes as these sites have very different limnological characteristics (Figure 5.2;

Appendix Figures F.4 – F.9). The pre-disturbance diatom assemblages of BC-17, BC-43,

Jackfish, Kam, Niven, Pocket and Vee lakes were dominated by benthic taxa, such as N. cryptotenella, N. cryptocephala, E. descripta, E. microcephala, B. neoexilis, S.

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construens var. venter, and S. pinnata, while at Alexie, BC-02 and Grace both benthic and planktonic (D. stelligera and P. ocellata) taxa co-dominated the assemblages

(Figures 5.2, 5.4, 5.5; Appendix Figures F.4 – F.9). During the 20th century the relative abundances of several diatom taxa have changed substantially (Figures 5.2, 5.4, 5.5;

Appendix Figures F.4 – F.9) and the patterns in taxon-specific changes were coherent among most lakes, however, there were some differences among sites (Figures 5.4, 5.5).

The relative abundance of planktonic D. stelligera began increasing around the mid-20th century in the deeper lakes (Alexie, BC-02, Grace), and declined to negligible abundances at Pocket Lake (severely metal(loid) polluted) and Jackfish (an urban lake)

(Figure 5.4; Appendix Figures F.4 – F.9). At the shallower Vee Lake, the relative increase in D. stelligera began much later (~late-1980s) (Figures 5.2, 5.4). Unlike D. stelligera, the relative abundance of planktonic P. ocellata has decreased at all three deeper lakes during the 20th century, even though it was present in notable abundances at these sites during the 19th century (Figure 5.4). Planktonic Stephanodiscus taxa, key indicators of eutrophication, have increased at the urban lakes (Jackfish and Grace) during the 20th century (Figure 5.4).

The relative abundance of benthic S. pinnata increased during the mid to late-20th century at Kam, Jackfish, Vee, and BC-43 (Figures 5.2, 5.4). However, its relative abundance has decreased around the turn of the 20th century at Kam, Jackfish, and Vee lakes (Figures 5.2, 5.4). A. minutissimum was observed at all study lakes, and its abundance increased at BC-17 and Pocket during the mining era when this taxon declined at BC-43 (Figures 5.2, 5.4). In the recent sediments (post-1990s), the relative abundances of A. minutissimum decreased at BC-17, Niven, and Pocket lakes, but increased in Vee

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Lake (Figures 5.2, 5.4). Notable abundances of N. cryptotenella and N. cryptocephala were only observed at Pocket Lake, and these taxa increased in relative abundance during the mining era and then declined after the ~1990s. At Pocket Lake, the relative abundance of E. decripta declined sharply when mining activities began in this region; however, at BC-17 this taxon increased during the mining era (Figure 5.5). The relative abundances of E. microcephala and B. neoexilis followed similar trends through time at

BC-17, because these taxa decreased during the mining era at BC-17 and some recovery is apparent in the most recent sediments (Figures 5.2, 5.5). The relative abundances of E. microcephala and B. neoexilis have decreased in the post-2000 sediments of Niven. The relative abundance of S. construens var. venter has decreased at BC-02 since the mid-20th century and over the same period it has increased at BC-43. Meanwhile, in Jackfish Lake

S. construens var. venter decreased between the beginning of the 20th century up until the

~1970s, then increased between 1980s and 2010s, after which it declined (Figure 5.4).

5.6 Discussion

5.6.1 Magnitude of diatom assemblage changes vary among Yellowknife lakes

The diatom assemblages of the Yellowknife lakes have changed markedly over the last ~200 years, and the timing and nature of shifts suggest that these biological changes were responses to multiple environmental stressors. The diatom species turnover measured as beta-diversity in the study lakes (range: 0.98 – 2.31 SD) were comparable to those reported by Smol et al. (2005; range: 0.7 – 2.85 SD) and Thienpont et al. (2013; range: 1.17 – 2.59 SD). Unlike the remote Arctic lakes examined by Smol et al. (2005) and Thienpont et al. (2013), where biological shifts were driven by climate-mediated 154

changes to lake thermal properties and local landscape, the lakes around Yellowknife were impacted by both climatic changes and anthropogenic activities during the 20th and early-21st centuries. Hence, the high species turnover (> 1.5 SD) observed at sites near the mines and the city were a cumulative biological response to resource extraction operations, urbanization, and climatic changes (e.g. Stewart et al. 2018; Sivarajah et al.

2020). For example, the greatest amount of species turnover was observed at Pocket Lake

(2.31 SD units), which is located within the lease boundary of Giant Mine and has been heavily impacted by mining activities (Thienpont et al. 2016). This was followed by Kam

Lake (2.11 SD), which received saline mine tailings from Con Mine and pollutants from roaster stack emissions. The species turnover at Niven Lake was also high (2.02 SD), as the limnology of this lake has been substantially altered the disposal of raw sewage between 1948 and 1981 (Stewart et al. 2018). Similarly, other lakes closer to the mine and the city also recorded high species turnover over the last ~200 years, except for BC-

17 – discussed in more detail below. As expected, the species turnover at Alexie and BC-

02 were lower (1.11 and 0.99 SD) likely because these remote lakes are located furthest away from the mines and the city. The sedimentary assemblage changes observed at these remote lakes were likely driven by climate-associated changes to lake thermal properties as we discuss later.

5.6.2 Differences in the timing of diatom assemblage changes among sites

Diatom responses to multiple environmental stressors can be complex, because the timing and nature of assemblage changes are often dependent on the magnitude of the impact caused by a stressor (or a combination of stressors) and site-specific physio- chemical characteristics (Heino and Soininen 2006; Rühland et al. 2015; Kuefner et al. 155

2020). Hence, changes in diatom assemblages to the same environmental stressors can vary substantially across the landscape, as we observed in the lakes around Yellowknife.

For example, CONISS with the broken stick model identified important diatom assemblage changes around the mid-20th century at most lakes in Yellowknife, and this time period is characterized by increases in anthropogenic activities (i.e. land-use changes related to urbanization and gold mining operations) and a notable shift in the regional air temperature record (Sivarajah et al. 2020). However, a second important shift was observed in the diatom records of Pocket and Kam lakes around the turn of the 20th century when Niven and Vee recorded the largest shift in the sediment records. The turn of the 20th century coincides with the warmest decades on the meteorological record of

Yellowknife (Stewart et al. 2018; Sivarajah et al. 2020). Therefore, it is likely that the recent diatom assemblage changes observed around this period were a threshold-type response to warming-mediated changes to lake thermal properties and the legacy of past land-use changes (Stewart et al. 2018; Perrett et al. In Review; Sivarajah et al. 2020, In

Review).

5.6.3 Diverse diatom responses to multiple environmental stressors in Yellowknife lakes

Previous investigations of freshwaters impacted by mining pollution have reported a range of responses by diatoms, including the occurrences of teratological forms (Cantonati et al. 2014; Lavoie et al. 2019), increase in metal(loid) tolerant taxa

(e.g. A. minutissimum: Ivorra et al. 2000; Ruggiu et al. 1998; Morin et al. 2008), and decreases in planktonic taxa (e.g. D. stelligera: Cattaneo et al. 2008; Tuovinen et al.

2012; Leppänen et al. 2019). Teratological forms were not common in the lakes around

Yellowknife (e.g. Thienpont et al. 2016), and were only observed in the surface 156

sediments of one lake, in very low abundances (Sivarajah et al. 2019). In Yellowknife lakes, however, we observed notable shifts in the diatom assemblage composition through time when the gold mines were in operation.

The most striking mining pollution-induced diatom shifts occurred at Pocket

Lake, where the relative abundance of planktonic D. stelligera decreased to negligible abundances while benthic metal(loid)-tolerant Navicula taxa (Austin et al. 1985) increased (Thienpont et al. 2016). Similarly, declines in planktonic D. stelligera were also observed at BC-17 and Jackfish as a response to mining pollution (Figure 5.2;

Sivarajah et al. In Review). However, the magnitude of diatom changes observed at BC-

17 were much smaller relative to Pocket Lake, even though BC-17 is located only 2 km away from Pocket Lake (Figure 5.3). The sedimentary metal(loid) concentrations at BC-

17 increased sharply when Giant Mine was in operation and then declined (Cheney et al.

2020); however, the increases were not as dramatic as Pocket (Thienpont et al. 2016). For instance, the highest sediment arsenic concentration at BC-17 was ~3500 µg/g dry weight

(Cheney et al. 2020), whereas at Pocket it was more than 30000 µg/g dry weight

(Thienpont et al. 2016). Similar trends were also observed for other metal(loid)s

(Thienpont et al. 2016; Cheney et al. 2020). The differences in the magnitude of contamination could have led to the diverse trends in metal(loid) tolerant E. descripta

(Sivarajah et al. 2019) and Navicula taxa at BC-17 and Pocket. It is likely that Navicula taxa can tolerate severe metal(loid) pollution, hence substantial increases in N. cryptocephala and N. cryptotenella were only observed at Pocket and not at BC-17 or any of the other study lakes (Figure 5.5). Unlike Navicula taxa, E. descripta is probably tolerant of moderate levels of metal(loid) contamination; therefore, it decreased markedly

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at Pocket when the lake received exceptionally high amounts of contaminants. However, the relative abundance of E. descripta began increasing in the most recent decades at

Pocket when contaminant concentrations and Navicula taxa declined. Since BC-17 was not contaminated to the same degree as Pocket, the increase in E. descripta and A. minutissimum at the expense of other benthic (E. microcephala and B. neoexilis) and planktonic (D. stelligera) taxa was likely indicative of the moderate level of contamination of this lake relative to Pocket. Furthermore, the increase in the relative abundance of metal(loid)-tolerant benthic A. minutissimum at these shallow lakes during the mining era is consistent with our earlier regional survey of surface sediment diatom assemblages (i.e. flora accumulated in recent years) from Yellowknife, where this taxon dominated the diatom assemblages of very shallow lakes (i.e. depth < 1m) with high arsenic concentrations (Sivarajah et al. 2019).

The relative abundance of benthic fragilarioid taxa (P. brevistriata, S. pinnata, and S. construens var. venter) increased at BC-43, Jackfish, Kam, and Vee lakes when

Giant and Con mines were in operation. Although benthic fragilarioid taxa are not commonly associated with mining pollution, spatial surveys of recent diatom assemblages from Yellowknife and arsenic-polluted lakes in Cobalt, Ontario, have recorded high abundances of these taxa in a variety of lakes where arsenic concentrations exceeded the guidelines for the protection of aquatic life (5µg/L, CCME 1999; Sivarajah et al. 2019; Little et al. 2020). Like other metal(loid) tolerant species (e.g. A. minutissimum, Navicula taxa and E. descripta), benthic fragilarioid taxa also have a wide distribution in Northern Lakes and are able to dominate (or co-dominate) diatom assemblages across a broad range of limnological conditions. In fact, these opportunistic

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benthic fragilarioid taxa were the most common diatoms observed in the metal-polluted

Arctic lakes around the Norlisk smelters in Russia (Laing et al. 1999; Michelutti et al.

2001). Rather than toxicity from metal(loids), Michelutti et al. (2001) suggested that the success of benthic fragilarioids in these polluted systems were related to changes in the watershed induced by mining activities (e.g. changes to water transparency from increased erosion which could influence light availability for benthic diatom taxa). In

Yellowknife, the increases in benthic fragilarioid taxa generally occurred at lakes that were impacted by both contaminants from roaster stack emissions and local disturbances.

For example, Kam Lake received accidental spills of salt-rich tailings that originated from Con Mine (Perrett et al. In Review), while Jackfish Lake was impacted by run-off from nearby waste disposal site (Sivarajah et al. In Review). Similarly, the boat launch at

Vee Lake is regularly used to access nearby Walsh Lake, and small-scale ore extraction activities (Ranney Hill and Gold Lake mines) took place in the vicinity of the lake (Silke

2009). Although there are no signs of land-use changes around BC-43, ore extraction activities at the nearby Ptarmigan Mine (~1 km) may have had an influence on the limnology of this lake. Thus, it is possible that the diatom response to metal(loid) contamination from roaster stack emissions at these lakes were influenced by local disturbances, which may have favored benthic fragilarioid taxa.

The impacts of local land-use changes and associated nutrient enrichment of freshwaters were clearly tracked by eutrophic Stephanodiscus taxa at Jackfish and Grace lakes. These planktonic diatom species increased from trace abundances to dominant or co-dominant species during the mid-20th century at Grace and in the most recent decade at Jackfish Lake (Figure 5.4; Sivarajah et al. 2020, In Review). Although Niven received

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raw sewage from 1948 to 1981, the increase in Stephanodiscus taxa was rather subtle, because Niven is a very shallow lake and planktonic Stephanodisucs taxa are often reported from the pelagic zone of deeper eutrophic lakes such as Grace Lake (Stewart et al. 2018; Sivarajah et al. 2020).

In our spatial survey, D. stelligera was dominant in deeper lakes with arsenic concentrations below the guideline for the protection of aquatic life (Sivarajah et al.

2019). These ecological interpretations are consistent with the sedimentary record of

Pocket Lake (Thienpont et al. 2016). However, D. stelligera was also observed in notable abundances at five lakes (BCR-07A, David, Grace, Vee, YK-42) where arsenic concentrations were high, and we suspected that these occurrences were influenced by factors other than metal(loid) pollution (Sivarajah et al. 2019). The detailed paleolimnological investigation of two of those sites (Grace and Vee) and other lakes from this region (Alexie, BC-02, BC-17, Kam) provides evidence that the abundance of

D. stelligera has increased at these lakes after the mid-20th century and this is likely related to climate-mediated changes to lake thermal properties (this study and Sivarajah et al. 2020). Large-scale paleolimnological surveys (Rühland et al. 2008), direct monitoring data (Winder et al. 2009), and field experiments (Saros et al. 2016) have demonstrated that the increase in this small cyclotelloid taxon is at least partly related to changing ice-cover and/or open-water stratification patterns induced by recent climate warming (Rühland et al. 2015). Specifically, centric (Discostella taxa) and pennate (F. nanana and F. tenera as observed at Vee and Kam lakes) planktonic diatoms with high surface area to volume ratios have low sinking velocities (Ptacknik et al. 2003), which provides a competitive advantage to these species under thermally stratified conditions,

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over taxa that require turbulent conditions (Winder et al. 2009; Winder and Sommer

2012; Rühland et al. 2015; Kuefner et al. 2020). The earlier increases in planktonic D. stelligera at Alexie, BC-02 and Grace lakes suggest that the diatom response to warming in deeper lakes preceded the response of shallower sites such as Vee (Sivarajah et al.

2020). Furthermore, the abrupt increase in D. stelligera and pennate planktonic diatoms around the late-1980s at the shallower Vee Lake appear to be a threshold-type response warming rather than the gradual increase observed at the deeper lakes.

Meanwhile, the relative abundances of P. ocellata, a small-centric diatom, has declined regionally throughout the 20th century at deeper remote (Alexie and BC-02) and urban (Grace) lakes (Figure 5.4). The decrease at Grace could be a response to metal(loid) pollution from regional mining activities, as this planktonic taxon is generally absent in lakes with high contaminant concentrations (Sivarajah et al. 2019). However, the decrease in P. ocellata at the remote sites suggests that other factors may also influence the abundance of this taxon in Yellowknife. Previous investigations have indicated that P. ocellata blooms in the spring as it thrives in well-mixed, nutrient-rich, and low light conditions (Schlegel and Scheffler 1999; Malik and Saros 2016). However, it is also known to dominate diatom assemblages in oligotrophic lakes under strongly stratified conditions (Winder and Hunter 2008; Winder et al. 2009). Furthermore, the recent increases in the relative abundance of P. ocellata has been associated with stronger stratification in a variety of lakes, including lakes in the Laurentian Great Lakes region, as this small taxon can compete well during prolonged periods of stratification (Liu et al.

2017; Reavie et al. 2017; Sivarajah et al. 2018). Since P. ocellata occurs under a wide spectrum of environmental conditions, detailed investigations of this taxon within the

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context of sub-Arctic Canada could provide better understanding of the ecological preferences of this taxon and explore the causes for its decline across multiple lakes.

5.6.4 Limnological changes in the lakes around Yellowknife and biological recovery

Collectively, our diatom data from the 10 lakes provide strong evidence that the limnology of Yellowknife lakes have changed markedly during the 20th and early-21st centuries. Specifically, the increases in metal(loid)-tolerant diatom taxa in the lakes closer to the mines and the city highlight the long-term biological consequences of gold mining pollution at a regional scale. The location of lakes in relation to the Giant Mine roasters stack and the presence of local stressors have strongly influenced diatom response to mining contamination. The urban lakes have also experienced eutrophication from local land-use changes (Stewart et al. 2018; Sivarajah et al. 2020, In Review).

The diatoms have also responded to climate-mediated changes to lake thermal properties. Specifically, the inclusion of Alexie and BC-02 has allowed us to partly disentangle the impacts of gold mining and urbanization from climatic changes. The increase in planktonic taxa at several sites suggest the ice-cover and stratification patterns have changed in response to regional warming (Sivarajah et al. 2020, In Review).

Furthermore, the lengthening of the open-water period has resulted in higher annual primary production (Stewart et al. 2018; Perrett et al. In Review; Sivarajah et al. 2020, In

Review), while the longer and/or stronger thermal stratification has led to an increase in planktonic D. stelligera in some of the lakes. Climatic changes have also exacerbated the impacts of eutrophication at some of the urban lakes. For example, chironomid-based paleolimnological investigations of Niven and Jackfish lakes have shown that hypolimnetic anoxia is becoming increasingly common in this region (an indirect 162

consequence of enhanced thermal stratification) and thus making these lakes vulnerable to the effects of internal phosphorus loading (Stewart et al. 2018; Sivarajah et al. In

Review). In fact, the most recent diatom assemblages of these two lakes are dominated by eutrophic Fragilaria mesolepta (Niven Lake) and Stephanodiscus taxa (Jackfish Lake).

Previous studies have postulated that internal phosphorus loading likely has a role to play in the proliferation of these taxa (Stewart et al. 2018; Sivarajah et al. In Review).

The recent diatom assemblages of our study lakes are remarkably different from pre-disturbance composition and there are no signs of biological recovery towards pre- disturbance conditions. At the regional scale, the recent diatom assemblages are likely influenced by climate-mediated changes and land-use changes related to urbanization.

Future warming will likely continue to alter the thermal stratification patterns at these lakes. Although it has been widely recognized that metal pollution in this region is an important concern, this regional synthesis (and previous paleolimnological case studies) provide strong evidence that the urban lakes around the city are also susceptible to the effects of eutrophication. The combined effects of eutrophication and climate-mediated changes could lead to unprecedented ecological phenomena, such as the recent cyanobacterial algal blooms at Jackfish Lake. Therefore, developing an understanding on how limnological stressors interact in complex ways could help with the management of these sub-Arctic lakes. Furthermore, many of our study lakes have crossed important limnological thresholds related to climatic changes and eutrophication; therefore, recovery from metal(loid) pollution (decreases in the concentration of contaminants in the water column) will likely not lead to pre-disturbance limnological conditions. Moving forward, regular monitoring efforts could inform lake managers and community members

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about further changes to the limnology of the sub-Arctic lakes that provide important ecosystem services.

5.7 Acknowledgements

We would like to thank Joshua Thienpont and Madi Perrett for sharing the diatom data for Pocket Lake, Kam Lake, and Grace Lake. We also gratefully acknowledge field assistance by Mija Azdajic, Cynthia Cheney, Kristen Coleman, Dave Eickmeyer, Emily

Stewart, and Joshua Thienpont. Many thanks to Professor H. John B. Birks (University of

Bergen, University College London) for encouraging us to apply the DCCA technique and giving excellent guidance on how to conduct the analysis in CANOCO. Many thanks are also due to Robin Valleau and Clare Nelligan for reading an earlier draft of this manuscript and providing constructive feedback. Funding for this research provided by a

Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic Grant and Polar Continental Shelf Program grants to Jules Blais (University of Ottawa) and

John Smol, as well as an NSERC Alexander Graham Bell Canada Graduate Scholarship,

W. Garfield Weston Scholarship for Northern Research (doctoral) and Northern

Scientific Training Program to Branaavan Sivarajah.

5.8 References

Antonaides D, Michelutti N, Quinlan R, Blais JM, Bonilla S, Douglas MSV, Pienitz R, Smol JP, Vincent WF (2011) Cultural eutrophication, anoxia, and ecosystem recovery in Meretta Lake, High Arctic Canada. Limnol Oceanogr 56: 639–650.

Ahonen SA, Hayden B, Leppännen JJ, Kahilainen KK (2018) Climate and productivity affect total mercury concentration and bioaccumulation rate of fish along a spatial gradient of subarctic lakes. Sci Total Environ 637–638: 1586–1596.

164

Appleby PG (2001) Chronostratigraphic techniques in recent sediments. In: Last WM, Smol JP (eds) Tracking Environmental Change Using Lake Sediments. Vol 1: Basin Analysis, Coring, and Chronological Techniques. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Austin A, Deniseger J, Clark MJR (1985) Lake algal populations and physico-chemical changes after 14 years input of metallic mining wastes. Water Res 19:299–308.

Bennett KD (1996) Determination of the number of zones in a biostratigraphical sequence. New Phytol 132: 155–170.

Benson B, Magnuson J, Sharma S (2013) Global Lake and River Ice Phenology Database, Version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center.

Birks HJB (2007) Estimating the amount of compositional change in late-Quaternary pollen-stratigraphical data. Veget Hist Archaeobot 16:197–202.

Camburn KE, Charles DF (2000) Diatoms of low alkalinity lakes in the Northeastern United States. The Academy of Natural Sciences of Philadelphia Special Publication, Philadelphia, USA.

Cantonati M, Angeli N, Virtanen L, Wojtal AZ, Gabrieli J, Falasco E, Lavoie I, Morin S, Marchetto A, Fortin C, Smirnova S (2014) Achnanthidium minutissimum (Bacillariophyta) valve deformities as indicators of metal enrichment in diverse widely- distributed freshwater habitats. Sci Total Environ 475: 201–215.

Cattaneo A, Couillard Y, Wunsam S (2008) Sedimentary diatoms along a temporal and spatial gradient of metal contamination. J Paleolimnol 40: 115–127.

Cheney CL, Eccles KM, Kimpe LE, Thienpont JR, Korosi JB, Blais JM (2020) Determining the effects of past gold mining using a sediment palaeotoxicity model. Sci Total Environ 718: 137308.

Cott PA, Schein A, Hanna BW, Johnston TA, MacDonald DD, Gunn JM (2015) Implications Of linear developments on northern fishes. Environ Rev 23:177–190.

165

Ecosystem Classification Group (2008) Ecological regions of the Northwest Territories: Taiga Shield. Department of Environment and Natural Resources, Government of the Northwest Territories, Yellowknife, NT, Canada.

Environment and Climate Change Canada (ECCC) (2017) Historical climate data: Yellowknife, Northwest Territories (Climate Station ID: 2204100/2204101). Available from https://climate-change.canada.ca/climate-data/#/adjusted-station-data.

Fallu M-A, Allaire N, Pienitz R (2000) Freshwater diatoms from northern Québec and Labrador (Canada): Species-environment relationships in lakes of boreal forest, forest- tundra and tundra regions. Bibliotheca Diatomologica Band 45. Berlin, Stuttgart.

Felde VA, Flantua SGA, Jenks CR, Benito BM, de Beaulieu J-L, et al. (2020) Compositional turnover and variation in Eemian pollen sequences in Europe. Veget Hist Archaeobot 29:101–109.

Galloway JM, Swindles GT, Jamieson HE, Palmer M, Parsons MB, Sanei H, Macumber AL, Patterson RT, Falck H (2018) Organic matter control on the distribution of arsenic in lake sediments impacted by ~65 years of gold ore processing in subarctic Canada. Sci Total Environ 622-623: 1668–1679.

Gavel MJ, Patterson RT, Nasser NA, Galloway JM, Hanna BW, Cott PA, Roe HM, Falck H (2018) What killed Frame Lake? A precautionary tale for urban planners. PeersJ:10.7717/peerj.4850

Glew JR (1988) A portable extruding device for close interval sectioning of unconsolidated core samples. J Paleolimnol 1:235–239

Glew JR (1989) A new trigger mechanism for sediment samples. J Paleolimnol 2: 241– 243.

Grimm EC (1987) CONISS: A FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. Comput Geosci 13:13–35

Håkansson H (2002) A compilation and evaluation of species in the general Stephanodiscus, Cyclotestephanos and Cyclotella with a new genus in the family Stephanodiscaceae. Diatom Res 17:1–139

Heino J, Soininen J (2006) Regional occupancy in unicellular eukaryotes: a reflection of niche breadth, habitat availability or size-related dispersal capacity? Freshw Biol 51:672– 685.

166

Hudelson KE, Muir DCG, Drevnick PE, Köck G, Iqaluk D, Wang X, Kirk JL, Barst BD, Grgicak-Mannion A, Shearon R, Fisk AT (2019) Temporal trends, lake-to-lake variation, and climate effects on Arctic char (Salvelinus alpinus) mercury concentrations from six High Arctic lakes in Nunavut, Canada. Sci Total Environ 678: 801–812.

Juggins S (2017) rioja: Analysis of Quaternary Science Data, R package version (0.9-21).

Ivorra N, Bremer S, Guasch H, Kraak MHS, Admiraal W (2000) Differences in the sensitivity of benthic microalgae to Zn and Cd regarding biofilm development and exposure history. Environ Toxic Chem 19: 1332–1339.

Kahlert M, Rühland KM, Lavoie I, Keck F, Saulnier-Talbot E, Bogan D, Brua RB, Campeau S, Christoffersen KS, Culp JM, Karjalainen SM, Lento J, Schneider SC, Shaftel R, Smol JP (2020) Biodiversity patterns of Arctic diatom assemblages in lakes and streams: Current reference conditions and historical context for biomonitoring. Freshw Biol DOI: 10.1111/fwb.13490.

Kuefner W, Hofmann A, Ossyssek S, Dubois N, Geist J, Raeder U (2020) Composition of highly diverse diatom community shifts as response to climate change: A down core study of 23 central European mountain lakes. Ecol Indic 117: 106590.

Laing TE, Pienitz R, Smol JP (1999) Freshwater diatom assemblages from 23 lakes near Norilsk, Siberia: A comparison with assemblages from other circumpolar treeline regions. Diatom Res 14: 285–305.

167

Lavoie I, Morin S, Laderriere V, Paris L-E, Fortin C (2019) Assessment of diatom assemblages in close proximity to mining activities in Nunavik, Northern Quebec (Canada). Environments 6: 74.

Leppänen, JJ, Luoto TP, Weckström J (2019) Spatio-temporal impact of salinated mine water on Lake Jormasjärvi, Finland. Environ Pollut 247:1078–1088

Little AJ, Sivarajah B, Frendo C, Sprague DD, Smol JP, Vermaire JC (2020) The impacts of century-old, arsenic-rich mine tailings on multi-trophic level biological assemblages in lakes from Cobalt (Ontario, Canada). Sci Total Environ 709: 136212.

Liu J, Rühland KM, Chen J, Xu Y, Chen S, Chen Q, Huang W, Xu Q, Chen F, Smol JP (2017) Aerosol-weakened summer monsoons decrease lake fertilization on the Chinese Loess Plateau. Nat Clim Change 7: 190–194.

Malik HI, Saros JE (2016) Effects of temperature, light and nutrients on five Cyclotella sensu lato taxa assessed with in situ experiments in arctic lakes. J Plankton Res 38: 431– 442.

Michelutti N, Laing TE, Smol JP (2001) Diatom assessment of past environmental changes in lakes located near the Noril’sk (Siberia) smelters. Water Air Soil Poll 125:231–241.

Michelutti N, Wolfe AP, Vinebrooke RD, Rivard B, Briner JP (2005) Recent primary production increases in arctic lakes. Geophys Res Lett 32: L19715.

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2019) vegan: Community Ecology Package. R package version 2.5-6.

Prowse, TD, Furgal C, Bonsal BR, Edwards TWD (2009) Climatic conditions in Northern Canada: past and future. Ambio 38: 257–265.

Ptacnik R, Diehl S, Berger S (2003) Performance of sinking and nonsinking phytoplankton taxa in a gradient of mixing depths. Limnol Oceanogr 48:1903–1912

R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Reavie ED, Sgro GV, Estepp ER, Bramburger AJ, Shaw Chraïbi VL, Pillsbury RW, Cai M, Stow CA, Dove A (2017) Climate warming and changes in Cyclotella sensu lato in the Laurentian Great Lakes. Limnol Oceanogr 62, 768–783.

Roberts KE, Lamoureux SF, Kyser TK, Muir DCG, Lafrenière MJ, Iqaluk D, Pieńkowski AJ, Normandeau A (2017) Climate and permafrost effects on the chemistry and ecosystems of High Arctic Lakes. Sci Rep 7: 13292.

Rühland K, Smol JP, R. Pienitz (2003) Ecology and spatial distributions of surface- sediment diatoms from 77 lakes in the subarctic Canadian treeline region. Canadian Journal of Botany 81: 57–73

Rühland KM, Paterson AM, Smol JP (2008) Hemispheric-scale patterns of climate- related shifts in planktonic diatoms from North American and European lakes. Glob Change Biol 14: 2740–2754

Rühland KM, Paterson AM, Smol JP (2015) Lake diatom responses to warming: reviewing the evidence. J Paleolimnol 54: 1–35.

Ruggiu D, Luglié A, Cattaneo A, Panzani P (1998) Paleoecological evidence for diatom response to metal pollution in Lake Orta (N. Italy). J Paleolimnol 20: 333–345.

Saros JE, Northington RM, Anderson DS, Anderson NJ (2016) A whole-lake experiment confirms a small centric diatom species as an indicator of changing lake thermal structure. Limnol Oceano Lett 1: 27–35.

Schelske CL, Peplow A, Brenner M, Spencer CN (1994) Low-background gamma counting: applications for 210Pb dating of sediments. J Paleolimnol 10: 115–128.

169

Schlegel I, Scheffler W (1999) Seasonal development and morphological variability of Cyclotella ocellata (Bacillariophyceae) in the eutrophic Lake Dagow (Germany). Int Rev Gesamten Hydrobiol 84: 469–478.

Silke R (2009) The operational history of mines in the Northwest Territories, Canada. An Historical Research Project.

Smol JP (2008) Pollution of lakes and rivers: a paleoenvironmental perspective, 2nd ed. Blackwell Publishing, Oxford, UK.

Smol JP, Stoermer EF (2010) The diatoms: applications for the environmental and earth sciences, 2nd edn. Cambridge University Press, Cambridge, UK.

Smol JP (2019) Under the radar: long-term perspectives on ecological changes in lakes. Proc R Soc B 286: 20190834.

St. Pierre KA, St. Louis VL, Lehnherr I, Schiff SL, Muir DCG, Poulin AJ, Smol JP, Talbot C, Ma M, Findlay DL, Findlay WJ, Arnott SE, Gardner AS (2019) Contemporary limnology of the rapidly changing glacierized watershed of the world’s largest High Arctic lake. Sci Rep 9: 4447.

Stewart EM, Hargan KE, Sivarajah B, Kimpe LE, Blais JM, Smol JP (2018) A paleoenvironmental study tracking eutrophication, mining pollution, and climate change 170

in Niven Lake, the first Sewage Lagoon of Yellowknife (Northwest Territories). Arctic 71:201–217. ter Braak, CJF (1986) Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67: 1167–1179. ter Braak CJF, Šmilauer P (2012) CANOCO reference manual and user's guide: Software for ordination, version 5.0. Microcomputer Power, Ithaca, New York, USA.

Thienpont JR, Rühland KM, Pisaric MFJ, Kokelj SV, Kimpe LE, Blais JM, Smol JP (2013) Biological responses to permafrost thaw slumping in Canadian Arctic lakes. Freshw Biol 58:337–353.

Tuovinen N, Weckström K, Salonen V-P (2012) Impact of mine drainage on diatom communities of Orijäarvi and Määrjärvi, lakes in SW Finland. Boreal Env Res 17: 437– 446.

Vincent WF, Callaghan TV, Dahl-Jensen D, Johansson M, Kovacs KM, Michel C, Prowse T, Reist JD, Sharp M (2011) Ecological implications of changes in the Arctic cryosphere. Ambio 40: 87–99.

Wıìlıìdeh Yellowknives Dene (1997) Wıìlıìdeh Yellowknives Dene: a history. Dettah: Yellowknives Dene First Nation Elders Advisory Council.

Winder M, Hunter DA (2008) Temporal organization of phytoplankton communities linked to physical forcing. Oecologia 156:179–192.

Winder M, Reuter JE, Schadlow SG (2009) Lake warming favours small-sized planktonic diatom species. Proc R Soc B 276:427–435.

Winder M, Sommer U (2012) Phytoplankton response to a changing climate. Hydrobiologia 698:5–16.

Wolfe SA (1998) Living with frozen ground: A field guide to permafrost in Yellowknife, Northwest Territories. Geol Surv Canada. Miscellaneous Report 64: 77 pp.

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Zubova EM, Kashulin NA, Dauvalter VA, Denisov DB, Valkova SA, Vandysh OI, Slukovskii ZI, Terentyev PM, Cherepanov AA (2020) Long-term environmental monitoring in an Arctic lake polluted by metals under climate change. Environments 7: 34.

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Table 5.1: Limnological and environmental information collected for the 10 study lakes from Yellowknife

The water chemistry measurements were taken at the time of sampling and have been published in Sivarajah et al. (2019).

Lake Alexie BC-02 BC-17 BC-43 Grace Jackfish Kam Niven Pocket Vee Urban No No No No Yes Yes Yes Yes No Partly Latitude (dd) 62.676 62.656 62.500 62.509 62.421 62.467 62.422 62.461 62.509 62.553 Longitude (dd) -114.102 -114.386 -114.421 -114.175 -114.431 -114.392 -114.401 -114.369 -114.374 -114.348 Coring depth (m) 21 7.7 0.5 5 16 7.5 7.5 1 2 5.2 Surface area (km2) 4.56 0.23 0.16 0.4 0.63 0.52 2.16 0.09 0.05 0.77 Distance to Giant (km) 23.52 16.96 3.1 9.75 10.23 4.25 9.25 4.62 0.94 5.79 Distance to Con (km) 29.97 24.64 7.91 13.1 3.66 3.98 1.97 2.82 8.38 13.61 pH 7.69 7.69 8.25 8.64 7.42 9.07 7.55 9.98 7.71 8 Total nitrogen (mg/L) 0.51 0.43 2.59 1.87 0.75 0.76 0.88 2 2.48 1.51 Dissolved organic carbon (mg/L) 13.4 10.8 35.8 33.7 19.7 9.7 17 28.2 25 20.6 Total alkalinity (mg/L) 59.2 26.5 68.8 222 59.4 94.2 107 89.6 191 131 Specific conductivity (µS/cm) 139 68 165 410 187 383 400 569 494 323 Total dissolved solids (mg/L) 88 63 179 287 128 243 246 379 330 220 Calcium (mg/L) 13.8 8.6 19.1 29.9 21 35.1 46.8 35.6 57 42.4 Chloride (mg/L) 3.1 1.6 2.1 3.8 9.4 43.5 33 110 1.7 13.1 Magnesium (mg/L) 6.2 2.2 11.1 34 7 12.1 12.5 19.7 26.3 12.1 Potassium (mg/L) 2.5 0.6 2.5 15.6 1.9 3.6 3.9 3.4 5 2.4 Sodium(mg/L) 4.1 2.1 5.5 19.3 6.6 22.2 15.2 52.6 6.7 7.4 Sulphate (mg/L) 4 5 13 4 15 31 41 35 66 14 Aluminum (µg/L) 4.5 14.8 101 11.7 27.5 6.5 28.2 20.1 10.1 41.7 Antimony (µg/L) 0.1 0.3 12.3 0.3 0.6 1.5 22.6 1.9 49.8 1 Arsenic (µg/L) 1.5 3.3 750 16.4 16.1 74.5 238 41.7 2070 34.2 Barium (µg/L) 11.2 6.5 31.6 47.9 9.9 25.6 33.6 14.2 79.6 27.2 Iron (µg/L) 7 11 45 21 28 5 56 23 78 35 Lithium (µg/L) 5.9 2.6 5.6 22.9 4.5 6.6 6.6 10.7 10.7 6.4 Manganese (µg/L) 2.4 21 28.9 69.7 7.7 9 23.4 22.5 598 14

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Table 5.2: Summary of the DCCA

Results of the detrended canonical correspondence analyses (DCCA) of the diatom assemblage data from the 10 lakes around Yellowknife. Total inertia, compositional turnover (in standard deviation (SD) units), and the eigen values of DCCA axis 1 samples scores are provided.

Depth Dates Lake Inertia Compositional Eigen value of Compositional Eigen value of turnover (SD) Axis 1 turnover (SD) Axis 1 Alexie 1.30 1.11 0.11 1.11 0.11 BC-02 0.97 0.99 0.09 0.99 0.10 BC-17 0.77 0.98 0.09 1.01 0.08 BC-43 0.94 1.70 0.23 1.66 0.22 Grace 1.72 1.77 0.24 1.71 0.24 Jackfish 1.28 1.60 0.21 1.56 0.21 Kam 1.95 2.11 0.33 1.87 0.30 Niven 1.34 2.02 0.28 1.79 0.25 Pocket 1.08 2.31 0.34 2.28 0.32 Vee 2.29 1.89 0.24 1.91 0.24

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Figure 5.1 A map of all study lakes

The study lakes are indicated with black circles in relation to the Giant Mine (grey shaded area), the City of Yellowknife (beige shaded area). The red star in the inset maps show the location of Yellowknife in a map of Canada.

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Figure 5.2 Diatom stratigraphies of BC-02, BC-43, Vee, and BC-17

Diatom assemblage profiles scaled by core depth (with 210Pb estimated years plotted secondarily) showing relative abundances of the most common diatom taxa from of BC-02, BC-43, Vee, and BC-17. For clarity of display, some species that exhibited similar trends through time were grouped together. The most important CONISS zones are delineated with grey horizontal dotted lines.

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Figure 5.3 Diatom species compositional turnover

Diatom species compositional turnover (estimated by detrended canonical correspondence analysis (DCCA) in standard deviation (SD) units) over the last ~ 200 years for the 10 lakes mapped in a map of Yellowknife, Northwest Territories. The size of the circles indicates the magnitude of beta-diversity changes at each location. The DCCA axis 1 samples scores for each site are plotted against years estimated with 210Pb dates. The most important CONISS zones are delineated with grey horizontal dashed (first zone) and dotted (second zone identified in the biostratigraphies of Pocket and Kam lakes) lines.

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Figure 5.4 Trends in the most common taxa

Past trends in the relative abundances of Staurosirella pinnata, Discostella stelligera, Stephanodiscus taxa, Achnanthidium minutissimum, and Pantocsekiella ocellata at the 10 study lakes around Yellowknife plotted against years estimated with 210Pb dates. 180

Figure 5.5 Trends in other common taxa

Past trends in the relative abundances of Navicula cryptotenella and Navicula cryptocephala, Encyonopsis descripta, Brachysira neoexilis, Encyanopsis microcephala, and Staurosira construens var. venter at the 10 study lakes around Yellowknife plotted against years estimated with 210Pb dates.

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Chapter 6

General Summary and Conclusions

The limnological consequences of mining activities have been extensively studied in temperate regions of Canada (Keller et al. 2006; Dixit et al. 1991, 1992), which have been supplemented by paleolimnological investigations within a multiple stressor framework (e.g. Tropea et al. 2011;

Labaj et al. 2015; Sivarajah et al. 2017). However, there was a paucity in similar types of long- term data for Arctic and sub-Arctic lakes, even though mines have operated in Canada’s Northern regions since the early-20th century. Hence, one of the primary objectives of this thesis was to fill this knowledge gap using paleolimnological techniques.

Paleolimnological studies of strategically selected lakes around the City of

Yellowknife (Northwest Territories) provided an important opportunity to examine the long-term effects of Northern mining activities on local aquatic biota within the context of multiple environmental stressors. Gold mining operations brought economic prosperity to Yellowknife and laid the foundation for this region to become a Northern urban centre

(Wolfe 1998). However, past mining activities have left complex environmental legacies as the contaminants associated with arsenopyrite ore processing are still very high in local freshwater ecosystems (Jamieson 2014; Palmer et al. 2015), with water-column arsenic concentrations exceeding guideline for the protection of aquatic life (5µg/L;

CCME 1999; Palmer et al. 2015; Houben et al. 2016). In fact, the high arsenic concentrations reported from some Yellowknife area lakes (e.g. Peg Lake: 2780 µg/L,

Pocket Lake: 2070 µg/L) are amongst the highest concentrations reported in Canada. In addition to mining pollution, the urban lakes have been impacted by land-use changes related to the development of the city (e.g. sewage disposal, development in the 182

catchment). Furthermore, like many other Northern regions, the area been impacted by climate-mediated changes to the physical properties of the lake (e.g. reduced ice-cover).

6.1 Major findings

Chapter 2: Analysis of surface sediment diatom assemblages from 33 sub-Arctic lakes indicated that the biotic responses to mining were complex and strongly influenced by multiple environmental variables. The recent diatom floras were primarily influenced by factors related to nutrient concentrations and ionic composition. Despite the presence of an exceptionally large arsenic concentration gradient (1.5 – 2780 µg/L), mining-related variables played a limited role in structuring the recent diatom assemblages. While this spatial study provided new insights concerning the environmental variables that may be influencing assemblage composition ~ 15 years after mining activities ceased (i.e. period of chemical recovery), detailed paleolimnological studies were necessary to examine the long-term relationships between limnological variables (e.g. contaminants, nutrients) and biota.

Chapter 3: The two relatively deep (> 10 m) sub-Arctic lakes examined in this study contain large-bodied fish species such as Burbot (Lota lota), Northern Pike (Esox lucius), and Lake Whitefish (Coregonus clupeaformis), which are important for subsistence fisheries and recreational activities (e.g. revenue from tourism). The main factors driving recent diatom assemblage changes at both lakes were indicative of regional warming. However, the impacts of urbanization (i.e. nutrient enrichment) and gold mining activities (i.e. metal(loid) contamination) were also striking at Grace Lake, as it is located closer to the city and the mines. These findings indicated that the

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cumulative impacts of these stressors could deteriorate the water quality of lakes that supports habitat for cold-water fish species. Warming-mediated changes to lake thermal properties (e.g. longer open-water duration, stronger stratification) have already been occurring, and further impacts from eutrophication could limit the optimal oxy-thermal habitat for large-bodied fish species.

Chapter 4: A multiproxy paleolimnological assessment at Jackfish Lake revealed that recently reported cyanobacterial algal blooms were unprecedented within the context of the last ~200 years, and that bloom formation may be a cumulative response to eutrophication and climate-mediated limnological changes (prolonged stratification and weaker mixing). All three sedimentary bioindicator groups examined (i.e. diatoms, cladocerans, chironomids) tracked the impacts of mining pollution, while the increase in eutrophic diatoms further tracked the long-term eutrophication of this lake. The changes in sedimentary chironomids suggested that hypolimnetic anoxia was likely occurring, as had also been inferred from another nearby site affected by cultural eutrophication (Niven

Lake, Stewart et al. 2018). Since internal phosphorus loading can occur with hypolimnetic anoxia, it is likely that the nutrient budgets at this lake are influenced by both external (i.e. run-off from waste disposal site) and internal processes. The recent algal blooms at Jackfish Lake highlight how mine-impacted lakes are also susceptible to the unintended negative consequences of eutrophication and climate change in the sub-

Arctic. Regional increases in whole lake primary production recorded in a wide spectrum of lakes further supports the conclusion that climate-mediated changes have extended the open-water growing duration of these sub-Arctic lakes.

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Chapter 5: This regional synthesis of diatom assemblage changes from 10 lakes around the City of Yellowknife show that species turnovers over the last ~200 years were higher at lakes closer to the mines and city. The cumulative impacts of multiple environmental stressors varied across the landscape. Specifically, the impacts of metal(loid) pollution were strongly dependent on proximity to the mine. Although warming had impacted all of the lakes, its effects exacerbated the impacts of eutrophication in the urban lakes. In all study lakes, the recent diatom assemblages were substantially different compared to those from the mining or pre-mining eras, and there were no signs of biological recovery to pre-disturbance conditions.

6.2 Significance and recommendations for lake management

The findings from this thesis provide strong evidence that sub-Arctic lakes are vulnerable to the cumulative impacts of multiple environmental stressors at the landscape level. Specifically, the long-term perspectives gained from this paleolimnological research contribute to the growing body of knowledge on biological responses to

Northern mining contamination within the context of multiple environmental stressors

(e.g. urbanization and climatic changes). Furthermore, the nature of diatom assemblage changes suggests that the sub-Arctic lakes around Yellowknife have crossed critical limnological thresholds in response to both anthropogenic activities and climatic changes.

An extreme example of this threshold-type response was the unprecedented cyanobacterial blooms reported from Jackfish Lake, which demonstrate that such blooms are no longer limited to eutrophic lakes in temperate and tropical regions, because warming has made eutrophic Northern lakes susceptible to nuisance algal blooms.

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The paleolimnological assessments of lakes within the city limits provided useful information for urban lake management. For example, nutrient loading from external and internal sources should be monitored, and external nutrient inputs should be controlled to avoid unprecedented cyanobacterial blooms, especially with continued warming. This is particularly important for sub-Arctic lakes that support large-bodied fish populations as they have important cultural and economic significance. To that end, the findings of this thesis have been regularly communicated with aquatic scientists from the Government of the Northwest Territories and community members.

6.3 Future research

This thesis used diatom-based paleolimnological approaches to examine the cumulative impacts of past gold mining activities, urbanization, and climate warming on sub-Arctic lakes using primarily diatom assemblage changes. However, important insights could also be gained by examining past trends in mid-trophic level biota such as

Cladocera and chironomids. The chironomid assemblage changes at Niven and Jackfish lakes suggest that hypolimnetic anoxia could be an important environmental stressor impacting the lakes in this region. Hence, regional chironomid investigations could provide important information regarding long-term trends in hypolimnetic anoxia. The chironomid-based investigations should be complemented with direct monitoring of water column oxygen, temperature, and nutrient profiles. Similarly, Cladocera-based investigations could help us to gain insights on how primary consumers have responded to multiple limnological changes and top-down impacts (e.g. predation). Sampling of modern diatom assemblages from planktonic and littoral habitats would also contribute

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important data concerning the distribution of various species and provide a better understanding of the habitat preferences of certain benthic diatom species. Similarly, biweekly monitoring of phytoplankton would provide useful insights regarding algal succession and under-ice primary production dynamics in sub-Arctic regions.

Specifically, the phytoplankton monitoring could be a useful technique to understand and manage cyanobacterial algal blooms at Jackfish Lake. Since Yellowknife is an urban region, future research should also consider the potential impacts of emerging stressors, such as microplastic pollution, and how these stressors may affect biological communities. Finally, the utility of underused paleolimnological indicators, such as

Ostracoda, would provide a more holistic overview of ecosystem changes.

6.4 References

Dixit AS, Dixit SS, Smol JP (1991) Multivariable environmental inferences based on diatom assemblages from Sudbury (Canada) lakes. Freshw Biol 26: 251–266.

Keller W, Heneberry J, McLachlan E, MacPhee S (2006) Data report: 25 years of extensive monitoring acidified lakes in the Sudbury area 1981 to 2005 pp 152.

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Labaj AL, Kurek J, Jeziorski A, Smol JP (2015) Elevated metal concentrations inhibit biological recovery of Cladocera in previously acidified boreal lakes. Freshw Biol 60: 347–359.

Tropea AE, Paterson AM, Keller W, Smol JP (2011) Diatoms as indicators of long-term nutrient enrichment in metal-contaminated urban lakes from Sudbury, Ontario. Lake and Reserv Manag 27: 48–60.

Wolfe SA (1998) Living with frozen ground: A field guide to permafrost in Yellowknife, Northwest Territories. Geol Surv Canada. Miscellaneous Report 64: 77 pp.

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Appendix A Assessing the potential environmental factors affecting cladoceran assemblage composition in arsenic-contaminated lakes near abandoned silver mines

A.1 Abstract

Silver mining has a long history in Cobalt (Ontario, Canada), and it has left a complex environmental legacy where many lakes are contaminated with arsenic-rich mine tailings. We examined subfossil Cladocera remains in the surface sediments of 22 lakes in the abandoned mining region to assess which environmental variables may be influencing the recent assemblage structure. Further, using a “top-bottom” paleolimnological approach, we compared the recent and pre-industrial assemblages from a subset of 16 lakes to determine how cladoceran composition has changed between the two periods. The recent cladoceran assemblages were strongly structured by differences in lake depth, with pelagic taxa (mostly bosminids) more abundant in large deeper lakes and, not surprisingly, littoral Chydoridae species more common in small shallower lakes.

Furthermore, the relative contribution of pelagic species has increased through time, while species diversity decreased in both mine-impacted and unimpacted lakes. In particular, the relative abundance of bosminids has increased in most lakes, while littoral

Alonella and Alona species have declined. Although some of these observations were likely related to toxicity from mining pollution, they may also be linked to changes in lake morphometry from the disposal of mine tailings. Furthermore, the increase in bosminids at the unimpacted lakes is likely indicative of regional stressors such as warming-mediated changes to lake properties. Our findings are consistent with a growing

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number of environmental assessments that highlight the complexities in biological responses to mining pollution within the context of multiple stressors.

A.2 Introduction

Cladocera (Branchiopoda) are widely distributed in freshwaters around the world and occupy key intermediate positions in aquatic food webs. These mid-trophic zooplankters are efficient grazers and play an important role in aquatic energy cycling.

Cladocera are also sensitive to changes in chemical (e.g. metals), physical (e.g. depth, habitat), and biological (e.g. fish predation) conditions, which have made them excellent biological indicators of limnological change (Amsinck et al. 2006; Davidson et al. 2010;

Korosi and Smol 2011; Leppänen 2018). In paleolimnological investigations, the well- preserved chitinous remains of Cladocera are often used to infer past ecological conditions and to assess the effects of various environmental stressors (Korhola and

Rautio 2001). For example, subfossil cladoceran assemblages and size structures have been used to assess the impacts of metal contamination (Leppänen 2018), lakewater calcium decline (Jeziorski and Smol 2016), eutrophication (Hann et al. 1994), climatic changes (Armstrong and Kurek 2019), and fish predation (Davidson et al. 2010). Many cladoceran taxa (e.g. Daphnia spp., Ceriodaphnia spp.) have also been used as model organisms in laboratory-based in-vitro experiments to determine the toxicity of environmental contaminants to aquatic biota (Sarma and Nandini 2006), and therefore

Cladocera are often included as biological indicators in environmental monitoring programs of mining operations (Environment Canada 2012). In addition to the in-vitro toxicity studies, in-situ examinations of Cladocera from mine-impacted lakes have

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reported changes in assemblage structure (Doig et al. 2015; Leppänen et al. 2017a), with decreases in diversity and richness (Labaj et al. 2015; Winegardner et al. 2017), and, in extreme cases, functional loss (Thienpont et al. 2016).

In this study, we analyzed subfossil cladoceran remains from temperate lakes in the vicinity of abandoned silver mines near Cobalt (Ontario) where arsenic (As) levels in some lakes have exceeded the guidelines for the protection of aquatic life (5 µg/L;

Canadian Council of Ministers of the Environment 2001) and drinking water (10 µg/L;

Heath Canada 2006) by orders of magnitude (Sprague and Vermaire 2018a). Silver was discovered around Cobalt in 1903 and by 1905 there were 16 mines in the area that continued production well into the 1930s and some small operations persisted until the

1980s (Dumaresq 1993). The hard-rock mining at the Cobalt camps produced a substantial amount of waste rock and tailings that were rich in As, cobalt, nickel and mercury (Dumaresq 1993). However, due to the lack of environmental regulations for the disposal of mine waste at the time, waste rock was piled around the local landscape, while the tailings were discharged into surrounding waterbodies (Dumaresq 2007).

Although mine waste has not been produced for many decades in the Cobalt area, recent limnological surveys have reported significantly higher levels of As in the water column and sediments of lakes impacted by historical mine tailings (Dumaresq 1993; Sprague and Vermaire 2018a; Little et al. 2020). These surveys and detailed As mobility studies suggest that the tailings in the local waterbodies are sources of As in the Cobalt area

(Percival et al. 2004; Kwong et al. 2007). Furthermore, the deposition of tailings into some lakes have led to the reduction of the lakes’ surface areas (Dumaresq 1993;

Patterson et al. 1996).

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Previous in-vitro toxicological experiments have indicated that the toxicity of As is influenced by environmental factors, such as microbes in the host water (He et al.

2009) and other contaminants (Sales et al. 2015; Li et al. 2016). Furthermore, paleolimnological investigations of severely As-polluted lakes from China and sub-Arctic

Canada have noted marked shifts in assemblage composition and declines in the total abundances of Cladocera with mining activities (Chen et al. 2015; Thienpont et al. 2016).

Here, we used two different paleolimnological approaches in an attempt to better understand which environmental factors were influencing the recent cladoceran assemblages in the As-contaminated temperate lakes around Cobalt and how these assemblages have changed since pre-industrial times. First, we examined the cladoceran remains from the surface sediments of 22 lakes (10 impacted and 12 unimpacted by mining activities) and compared assemblage composition to a suite of measured environmental variables to determine the influence of multiple limnological parameters in structuring the recent cladoceran assemblages. Second, we used a “top-bottom paleolimnological approach” (Smol 2008) to determine if the cladoceran assemblages have changed through time by comparing the assemblages in the surface sediments (top) to those of pre-industrial sediments (bottom) of 16 lakes (6 impacted and 10 unimpacted by mining activities). The top-bottom approach provides a snapshot of changes in biological composition between two time periods (i.e. recent and pre-industrial periods); however, this method also has limitations such as the timing of change cannot be determined and the bottom sediment from all lakes may not be from the same time period

(Smol 2008). Despite this limitation, this method has been an effective paleolimnological

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tool to assess regional environmental change in Arctic and temperate lakes (Jeziorski et al. 2015; Leppänen et al. 2017b; Armstrong and Kurek 2019).

A.3 Study area

The Town of Cobalt is in northeastern Ontario near Lake Timiskaming, which forms part of the border between the Canadian provinces of Ontario and Québec (Fig.

A.1). The local vegetation of this area is characterized by a mixture of coniferous and deciduous species within the Lake Temagami Ecoregion of the Ontario Shield (Crins et al. 2009). This region experiences a humid and cool climate (Crins et al. 2009) where mean annual air temperature and total annual precipitation ranged between 0.8 oC and 4.6 oC, and 685 mm and 1229 mm, respectively (data collected at Earlton Climate Station

(ID: 6072230) between 1939 and 2004 and retrieved from Adjusted and Homogenized

Canadian Climate Data; https://climate.weather.gc.ca/). Detailed descriptions of the local geology are provided in Dumaresq (1993), Percival et al. (2004), and Sprague and

Vermaire (2018 a,b). Briefly, the study area is located within the Southern Structural

Province of the Canadian Shield and is overlain by Proterozoic sediments of the Cobalt

Group, which are intruded by the Proterozoic Nipissing diabase that contain veins rich in silver, arsenic, cobalt, and nickel.

Most of the study lakes are around the Town of Cobalt, however a few lakes were sampled around the mines in Silver Center, ~ 20 km southwest of Cobalt (Fig. A.1). The study lakes are underlain by Nipissing diabase, granite, basalt/rhyolite, Gowganda, or

Lorraine formations (Sprague and Vermaire 2018b) and alkaline (median pH 8.2) except for Ice Chisel Lake that had a pH of 4.7. Unfortunately, fish presence/absence data are

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not available for all lakes. Detailed limnological characteristics are provided in

Supplement A.1 and discussed in more detail in Sprague and Vermaire (2018a). During the silver mining years, only a few study lakes received As-rich tailings directly from the nearby mining camps (e.g. Brady, Cart, Cobalt, Crosswise, Fourclaim, and Tooth).

However, the migration of tailings through the local landscape has resulted in the contamination of more lakes over the last century (e.g. Ibsen, Kirk, New Lake, and

Maidens; Sprague et al. 2016; Sprague and Vermaire 2018a).

A.4 Material and Methods

A.4.1 Sample Collection

The study lakes were sampled during the summers of 2015 and 2017 as part of regional limnological survey; the details of site selection are provided in Sprague and Vermaire

(2018a) and Little et al. (2020). Water samples for chemical analyses were obtained from a depth of ~ 20 cm below the surface and the samples collected for metal analyses were preserved with 0.5% HNO3. Then water samples were refrigerated and transported to

Caduceon Environmental Laboratories in Ottawa (Ontario), a Canadian Association for

Laboratory Accreditation certified facility, for chemical analyses. Since detailed bathymetry for the study lakes were not available, the sediments were retrieved from the middle of the lakes, which was often the deepest point of the study lakes where material from a variety of aquatic habitats accumulate continuously (Sprague and Vermaire

2018a; Little et al. 2020). A Glew (1989) gravity corer was used to retrieve sediment cores and extruded using a Glew (1988) extruder at 1 cm contiguous intervals. Sediment

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samples were refrigerated and transported to Carleton University, Ottawa, Ontario, and were stored at 4 oC.

A.4.2 Laboratory analyses

We analyzed the surface sediments (0 – 1 cm) for Cladocera remains following standard protocols described by Korhola and Rautio (2001) with the exception of Cobalt and Green lakes where sediments from the 1 – 2 cm interval were processed as the surface sediments were used up completely for other analyses. Briefly, sediments were deflocculated in 10% potassium hydroxide (KOH) for ~ 20 minutes on a hotplate at ~ 70 oC. The samples were then rinsed through a 38-µm sieve with deionized water to remove fine particles and the resulting residue was retained in glass vials. A few drops of safranin-glycerol and ethanol were added to stain the Cladocera remains and preserve the samples, respectively. Aliquots of the samples were pipetted on to microscope slides and covered with glass cover slips using glycerin jelly. In addition to the surface sediments, we also processed bottom sediments (> 20 cm) of 16 lakes for our top-bottom paleolimnological analysis following the same laboratory methods as described above.

Although we did not radiometrically date all the sediment cores for this study, the 210Pb analysis of a few cores (i.e. Ibsen, Green, Oxbow) confirm that sediments below ~ 20 cm represented the pre-industrial time periods in this region. The dating profiles for the three lakes are provided in Supplement A.2. However, due to differences in sedimentation rates among lakes, the time represented by the deeper samples may vary among lakes. We could not examine the pre-industrial sediments of 6 lakes because older material were not available as the sediment cores were relatively short.

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Cladoceran remains were identified from each sample at 200× – 400× magnification using a Leica DMR light microscope under bright-field optics and the counting effort exceeded the recommended minimum of 70 individuals (range: 95 to 206; mean: 126; median: 114; Kurek et al. 2010). The photomicrographs presented in

Szeroczyńska and Sarmaja-Korjonen (2007) and Korosi and Smol (2012a, b) were used to identify the sub-fossil cladoceran remains. Since Cladocera are disarticulated in the sediments, we tabulated the different body parts (e.g. carapaces, headshields, post- abdominal claws) separately and the most common remains from each taxon were used to quantify the number of individuals for each species (Korhola and Rautio 2001). Bosminid

(i.e. Bosmina species or Eubosmina species) headshields were not differentiated to the species level because there were challenges in locating the lateral head pores due to the high amounts of clastic materials from the contaminated lakes. All attempts were made to differentiate between the remains of Alona circumfimbriata and Alona guttata when enumerating the samples; however, due to challenges in differentiating the headshields of these taxa (the most common remain from these taxa), we grouped them as the Alona circumfimbriata-guttata complex (sensu Labaj et al. 2018).

A.4.3 Numerical analyses

Cladocera assemblage composition is presented as relative abundances (i.e. percent abundance of each species relative to the total number of individuals from a sample) and all taxa were included in subsequent numerical analyses. Cladoceran species richness and Hill’s N2 diversity were calculated in the R software environment (R Core

Team 2019) using the vegan (Oksanen et al. 2019) and rioja (Juggins 2017) statistical packages, and the data were rarified to 95 individuals per sample, as that was the lowest

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count. The change in the percent relative abundance of the most common cladoceran taxa between the pre-industrial (bottom) and recent (top) sediments were calculated (% top -

% bottom) and plotted along with the changes in Hill’s N2 diversity and species richness.

The relative abundances of pelagic and littoral cladoceran taxa were compared by grouping taxa with similar habitat preferences. Specifically, species belonging to

Daphnia, Ceriodaphnia, Holopedium, Leptodora and bosminids were classified as pelagic, while taxa in the following genera Eurycercus, Acroperus, Alonopsis,

Camptocercus, Kruzia, Alona, Leydigia, Graptoleberis, Rynchotalona, Monospilus,

Alonella, Disparalona, Pleuroxus, Chydorus, Paralona, Acantholeberis, Ophryoxus,

Latona, and Sida crystallina were grouped as littoral (Hann 1981; Amsinck et al. 2006;

Korosi and Smol 2011; Griffiths et al. 2019). Since earlier investigations have identified lake depth as an important variable influencing the relative proportion of pelagic vs. littoral cladoceran taxa, we used Pearson product moment correlations to assess any underlying patterns linked to depth and habitat preferences.

Prior to conducting ordination analyses, we prescreened the data and removed chemical variables where measurements were below the detection limits in more than

10% of the lakes. During the pre-screening procedure, we noted that Ice Chisel Lake was an acidic lake as it had anomalously low pH (4.7), while all other study lakes were neutral to alkaline. Hence, Ice Chisel Lake was deemed an outlier in this dataset and removed from normalized ordination analyses. Shapiro-Wilk tests were used to assess the normality of the environmental variables from the 21 lakes in R software environment.

Alkalinity, magnesium (Mg2+), and pH were normally distributed while other environmental parameters required transformations to achieve normality. Square-root-

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transformation was applied to conductivity, total dissolved solids (TDS) and calcium

+ 2- (Ca ), while sulphate (SO4 ), As and surface area were log-transformed, and aluminum

(Al) and depth were log (x+1)-transformed. Next, a Pearson correlation matrix was generated to assess the correlation among the normalized environmental parameters using the Hmisc package (Harrell 2019) in R.

A principal component analysis (PCA) with the 11 normalized environmental parameters from the 21 lakes was performed to visualize the distribution of our study lakes along measured environmental gradients using the statistical program CANOCO version 5.0 (ter Braak & Šmilauer 2012). A detrended correspondence analysis (DCA) of the square-root-transformed cladoceran data revealed a gradient length of 1.76, therefore a linear direct ordination method (redundancy analysis - RDA) was deemed appropriate to assess the relationships between the environmental variables and biological data (Birks

2010). An RDA with forward selection of explanatory variables (Blanchet et al. 2008) was performed to determine which measured environmental variable(s) explained the most amount of variation in the cladoceran assemblage. The DCAs and RDA were performed in R using the vegan package.

A.5 Results

A.5.1 Limnology of lakes around Cobalt, Ontario

The study lakes are generally small (range: 2.74 – 102.18 ha; median: 18.35 ha) and shallow (range: 1.2 – 19.2 m; median: 6.4 m; Fig. A.2; Supplement A.1). However, the impacted lakes (median depth: 8.4 m) are generally deeper than the unimpacted lakes

2- (median depth: 4.0 m). The concentrations of As, nickel (Ni), and SO4 were higher in

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lakes that received direct and migrated mine tailings compared to the unimpacted lakes

2- (Fig. A.2). The As, Ni, and SO4 concentrations varied between 0.4 – 972 µg/L, 0.5 –

10.2 µg/L, and 1 – 10 mg/L, respectively (Fig. A.2; Supplement A.1). Most study lakes were circumneutral to alkaline (range: 6.9 – 9.2; median: 8.2) and the Ca2+ concentrations

(range: 3.5 – 49 mg/L; median: 20.7 mg/L) were generally high, with the exception of Ice

Chisel Lake that had a pH of 4.7 and [Ca2+] of 0.36 mg/L (Fig. A.2; Supplement A.1).

Interestingly, Ice Chisel Lake also had the lowest values for alkalinity, Mg2+, sodium

(Na+), TDS, and conductivity in this data set (Supplement A.1). We could not normalize the concentrations for Na+ and chloride (Cl-) in this dataset as relatively high values were recorded at Green, Clear, Cobalt, and Maidens lakes (Supplement A.1).

The PCA and Pearson correlation of the water chemistry variables highlighted the high correlation among the variables associated with the ionic strength of the water in our study lakes. Specifically, alkalinity, Ca2+, Mg2+, conductivity, and TDS were highly correlated (r > 0.8) and plotted along axis 1 of the PCA biplot (Supplement A.3; Fig.

A.3). Lakes with varying physical characteristics (i.e. depth and surface area) and metal concentrations (As and Al) spread along axis 2 of the PCA (Fig. A.3). The axes 1 and 2 of the PCA explained 64% of the variation in the environmental parameters and some separation was apparent among the contaminated and uncontaminated lakes (Fig. A.3).

Generally, deep lakes impacted by mining activities plotted in the bottom quadrant while unimpacted shallower lakes with lower As concentrations plotted in the upper quadrant

(Fig. A.3).

A.5.2 Cladocera assemblages from contaminated and uncontaminated lakes

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The recent Cladocera assemblages from the lakes around Cobalt were dominated by bosminids (range: 7 – 94%; median: 58%) except for a severely contaminated lake

(Cart) and an uncontaminated site (Nicol) where Chydorus brevilabris was dominant

(Fig. A.4). Pelagic Daphnia taxa were not very common in this dataset as they only occurred in 10 lakes and the D. pulex complex was only observed in two impacted lakes

(Fig. A.4). Unlike the D. pulex complex, the D. longispina complex was observed in both impacted and unimpacted lakes, however it was present in higher abundances (> 15%) at two unimpacted lakes (Fig. A.4). Several littoral taxa, such as C. brevilabris, Alona circumfimbriata-guttata complex, and Alonella nana, were present in most lakes and in notable abundances (Fig. A.4). Subtle differences in median cladoceran species richness and diversity were observed among the three lake groups (Table A.1). Although the median values for these indices were generally higher in uncontaminated lakes and sites that received migrated tailings, there were substantial variation in the range (i.e. minimum and maximum) within each group (Table A.1). Pelagic taxa constituted a larger portion of the assemblages in lakes that received tailings directly (median: 76%), while their relative abundances were lower in lakes impacted by migrated tailings (median:

66%) and uncontaminated ones (median: 56%; Table A.1). The Pearson product moment correlation revealed a positive relationship between lake depth and pelagic taxa (r = 0.42; p = 0.05) while a negative relationship was observed between lake depth and littoral taxa

(r = - 0.42; p = 0.05). The RDA with forward selection identified lake depth as the only variable explaining a significant portion of the variation in the cladoceran assemblages from Cobalt. The total variation in the cladoceran dataset was 20.2 (inertia) and depth explained 14% of this variation (2.8).

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A.5.3 Changes in Cladocera assemblages since pre-industrial time

Bosminids have increased in abundance between the two time periods in ten lakes

(Fig. A.5). At < 1% abundance, Alona intermedia and Leptodora kindtii were only observed in the top sediments of 5 lakes. Generally, the relative abundances of the A. circumfimbriata-guttata complex, A. nana, Alona quadrangularis, and the Daphnia longispina complex have decreased between the two periods (Fig. A.5). The relative abundance of C. brevilabris remained stable across most lakes with the exception of

Nicol Lake (unimpacted site) where it increased by 37% (Fig. A.5). Overall, cladoceran species diversity and richness have decreased between the two periods (n = 16; median diversity: recent = 2.8, pre-industrial = 3.4; median richness: recent = 11.6, pre-industrial

= 12.5) (Fig. A.5; Table A.2). Although species diversity and richness have decreased in

10 and 9 lakes, respectively, the magnitude of change between the pre-industrial period and the recent period varied notably among lakes (Table A.2). The median decreases in species diversity and richness were generally greater in lakes impacted by mining pollution (n = 6; diversity: recent = 2.2, pre-industrial = 3.2; richness: recent = 11.3, pre- industrial = 12.3; Table A.2) relative to unimpacted lakes (n = 10; median diversity: recent = 3.0, pre-industrial = 3.4; median richness: recent = 12.0, pre-industrial = 12.5;

Table A.2). The relative abundances of pelagic taxa have increased in most lakes between the recent and pre-industrial periods while the relative contributions of littoral taxa has decreased (Table A.2). Particularly, the decrease in littoral taxa were more pronounced in impacted lakes (median abundance: recent = 30%; pre-industrial = 54.5%) where pelagic taxa have increased (median abundance: recent = 70%; pre-industrial = 45.6%). The

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switch from littoral to pelagic taxa between the two periods were moderate at the unimpacted sites (Table A.2).

A.6 Discussion

A.6.1 Environmental determinants of recent limnological characteristics

The Cobalt silver rush took place during the first three decades of the 20th century, although some short-lived smaller operations persisted intermittently (Percival et al.

2004). Nonetheless, more than a century has passed since the initial disposal of As-rich mine tailings began in the region. Yet, when the lakes in this region were sampled in

2015, the mine-impacted sites still had some of the highest [As] in Canada and exceeded the guidelines for the protection of aquatic life (5 µg/L; CCME 2001) and drinking water quality (10 µg/L; Health Canada 2006). The high [As] reported in our study lakes that received tailings are comparable to those from the Yellowknife area where lakes were impacted by the atmospheric deposition of As from regional gold mining activities

2- (Palmer et al. 2015; Sivarajah et al. 2019). The concentrations for Ni, K, and SO4 were also high in the lakes that received direct mine waste and migrated tailings in Cobalt.

Even though the majority of the mining activities ended more than eight decades ago, the persistent high concentrations of contaminants in the impacted lakes suggest that the mine tailings on the landscape are still important sources of contaminants (Percival et al.

2004; Sprague and Vermaire 2018a). Furthermore, the present limnology of lakes around

Cobalt are also likely impacted by emerging limnological stressors such as run-off from road-salt application. For example, the Na+ and Cl- concentrations at 4 study lakes were relatively higher than most other lakes in the region (Supplement A.1), likely due to road- 202

salt inputs from the Trans-Canada Highway 11b (Green, Clear, Cobalt) and Ontario

Highway 567 (Maidens).

A.6.2 Spatial distribution of cladoceran.

Bosminids, the most common pelagic zooplankton in lakes across Canada (Pinel-

Alloul et al. 2013), dominated the cladoceran assemblages in Cobalt and, similar to many previous regional surveys, water depth was identified as an important variable structuring the recent assemblages (DeSellas et al. 2008; Korosi and Smol 2011; Yatigammana and

Cumming 2017; Griffiths et al. 2019). The strong relationship between cladoceran assemblages and depth exist because lake depth facilitates the availability and complexity of various littoral and pelagic habitats, which have a direct impact on the cladoceran assemblage composition. Specifically, pelagic bosminids and daphniid taxa are often more common in larger and deeper lakes where there are considerable amounts of open- water habitat (Amsinck et al. 2006; Griffiths et al. 2019). Meanwhile Chydoridae taxa, such as Alona, and Chydorus species, often thrive in small and shallow lakes with considerable amounts of macrophyte cover that provides complex littoral habitats (Korosi and Smol 2011; Adamczuk 2014). This relationship between habitat preference of cladocerans and depth was captured by a positive correlation between sum of all pelagic cladocerans and depth (r = 0.42, p = 0.05; a negative relationship was observed between sum of all littoral taxa and depth, r = - 0.42, p = 0.05) in our study.

Despite the presence of a large arsenic gradient, [As] was not identified as an important variable influencing the recent cladoceran assemblages around Cobalt.

Although this observation was initially unexpected, it was consistent with spatial assessments of diatom-based investigations of As-contaminated lakes in Yellowknife,

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Northwest Territories (Sivarajah et al. 2019) and Cobalt (Little et al. 2020), where a combination of chemical and physical variables influenced the composition of recent assemblages in lakes around abandoned mines. Yet, downcore paleolimnological (i.e. temporal changes) examinations of As-contaminated lakes have revealed substantial changes in cladoceran assemblage composition through time (e.g. Chen et al. 2015;

Thienpont et al. 2016; Tenkouano et al. 2019). For instance, the relative abundances of C. brevilabris and the D. pulex complex have increased in As-contaminated lakes, at least initially following metal contamination (Thienpont et al. 2016; Tenkouano et al. 2019). In our spatial survey, pelagic D. pulex complex was only observed at two deep contaminated sites (Cobalt – 10.4 m and Maidens – 9.4 m) where Na+ and Cl- concentrations were also high. Meanwhile littoral C. brevilabris was observed in most of the study lakes but reached highest relative abundances in the most contaminated shallow site (Cart Lake – As = 972 µg/L). However, C. brevilabris also co-dominated the assemblages at shallow and uncontaminated Nicol Lake where arsenic concentrations were low (0.7 µg/L). Since these two taxa are known to tolerate a variety of environmental pollutants (e.g. metals and road salt; Manca and Comoli 1995; Labaj et al.

2015; Valleau et al. 2020) and have been observed in a broad spectrum of lakes (Griffiths et al. 2019), it is likely that the presence of these taxa in some of the contaminated lakes around Cobalt are influenced by a combination of limnological parameters (e.g. depth, concentrations of ions) and mining legacies.

Lake-water pH and Ca+ concentrations have also been identified as important drivers of cladoceran assemblages in the soft-water lakes in Ontario where many lakes have acidified (e.g. Walseng et al. 2003; DeSellas et al. 2008; Jeziorski and Smol 2016).

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In this study, however, most lakes (with the exception of Ice Chiesel Lake) were alkaline and had [Ca+] well above (range: 3.5 – 49 mg/L; median: 20.7 mg/L) the critical thresholds that may induce changes in assemblage composition (Jeziorski and Smol

2016) and reduce population growth rates (Arnott et al. 2017). Furthermore, at the acidic

Ice Chiesel Lake (pH = 4.7), where Ca2+concentrations were low (0.36 mg/L), assemblages were also dominated by bosminids and thus assemblage composition was not markedly different from the alkaline lakes in our dataset (Fig. A.4). However, as expected, daphniid taxa with high Ca2+ requirements were not present at Ice Chiesel

Lake. Previous investigations have also suggested that predation by fish and other invertebrates are important factors influencing cladoceran assemblages (Manca et al.

2008; Davidson et al. 2010); however, other studies have indicated that physio-chemical limnological parameters play a more significant role in shaping assemblages (e.g.

Amsinck et al. 2006; DeSellas et al. 2008; Kurek et al. 2011). Since we do not have information on zooplanktivorus fish and macroinvertebrates for all the study lakes, we have only focused on the physio-chemical parameters that shape the cladoceran assemblages in these lakes.

A.6.3 Temporal changes in cladoceran assemblages.

The cladoceran species diversity and richness in the pre-industrial period were similar across all study lakes (Table A.2) and the values were comparable to those reported from other parts of Ontario (Jeziorski et al. 2015). However, diversity (and richness, to a certain extent) has decreased in most lakes in the region through time (Fig.

A.5; Table A.2). Specifically, the magnitude of decline was greater in lakes that were impacted by mining pollution (Table A.2), which was consistent with the lower median

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richness and diversity observed in the surface sediment cladoceran assemblages of mine impacted sites (Table A.1). The decreases in Hill’s N2 diversity were generally synchronous with overall increases in pelagic taxa (specifically, with the increases in the relative abundance of bosminids) between the pre-industrial and recent time periods across multiple study lakes (Fig. A.5). Interestingly, the aforementioned changes were not specific to either the impacted or unimpacted groups, and in some lakes we observed subtle increases in species richness and diversity, along with an increase in littoral taxa.

These observations may suggest that the cladoceran changes recorded in these lakes were potentially influenced by both regional and site-specific stressors as we discuss below.

In Cobalt, we observed decreases in several littoral taxa (Alona species and

Alonella species), while one pelagic group (bosminids) increased in abundance, which resulted in the decrease in Hill’s N2 diversity in four of the six mine-impacted lakes.

Earlier paleolimnological assessments of mine-impacted lakes have suggested that metal(loid) contamination results in the loss of several pollution-sensitive species and increases in the relative abundances of a few generalist and/or pollution-tolerant taxa, thus leading to lower diversity (e.g. Manca and Comoli 1995; Leppänen et al. 2017a;

Winegardner et al. 2017). Toxicity of mining contaminants may explain the decrease in some of the littoral taxa, as the decreases in taxa such as A. nana and the A. circumfimbriata-guttata complex have been reported from other metal-contaminated lakes (Thienpont et al. 2016). However, the pelagic bosminids that replaced these littoral taxa in the contaminated lakes are known to be sensitive to As pollution (Passino and

Novak 1984; Chen et al. 2015). This suggests that factors beyond metal(loid) toxicity may be influencing the cladoceran responses to past silver mining activities in Cobalt.

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The physical characteristics of some of the Cobalt lakes have been altered by the disposal of mine waste (Dumaresq 1993; Patterson et al. 1996). For instance, the length of Crosswise Lake (an impacted lake) has been reduced by about 200 to 300 m as a result of tailings deposition into the lake (Dumaresq 1993; Patterson et al. 1996). Our top- bottom analysis of this lake revealed that the littoral cladoceran taxa have decreased from

47% in pre-industrial sediments to 5% in the recent sediments. The shortening of

Crosswise Lake has likely led to substantial reductions in the littoral habitat and contributed, at least in part, to the subsequent decline in the relative abundance of littoral taxa and indirectly favored pelagic taxa, such as bosminids. Similar increases in pelagic taxa were also observed at two other lakes where tailings were deposited directly (Brady and Fourclaim) and one lake where mine tailings migrated into the lake from the local landscape (Ibsen Pond).

At the landscape level, the overall increase in pelagic bosminids in the recent sediments of both mine-impacted and unimpacted lakes may suggest that regional factors may also be contributing to the increase of this group. Previous paleolimnological investigations have indicated that increases in bosminids may be indicative of climate- mediated changes to declining ice conditions and lake thermal properties in Ontario

(Jeziorski et al. 2015; Hargan et al. 2016) and elsewhere (Leppänen et al. 2017b;

Armstrong and Kurek 2019). Similar to many other parts of Ontario, the annual air temperature around Cobalt has increased (Supplement A.4). For instance, warmer temperatures and associated lengthening of ice-free periods may alter the phenology and increase the production of pelagic cladoceran taxa through direct physiological mechanisms and/or indirect pathways such as higher food availability in the pelagic zone

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due to increases in planktonic production during longer open-water periods (Adrian et al.

2006; Carter and Schindler 2012; Nevalainen and Luoto 2012; Nevalainen et al. 2014;

Hargan et al. 2016). Furthermore, in two uncontaminated sites (Clear and Goodwin) we observed a switch among pelagic taxa where the relative abundances of the large-bodied

D. longispina complex decreased while small-bodied bosminids increased. Similar shifts have been reported in temperate lakes from New Brunswick (Canada), where Bosmina species were more abundant in recent sediments (Armstrong and Kurek 2019) because warming-mediated increases in metabolism often favors small-bodied taxa that have lower energy requirement (Moore and Folt 1993).

Although climate-mediated limnological changes and mining-induced alterations in morphometry could explain the increase in pelagic bosminids in most lakes around

Cobalt, there were some exceptions. For example, the aforementioned trends were not observed at two lakes that received migrated tailings (New Lake, and Maidens); in fact, at these lakes the relative abundances of bosminids decreased subtly between the two time periods. This may be because these two lakes were not as severely impacted by mining activities as they have lower [As] (proxy for the degree of mining impacts) relative to other mine-impacted lakes analyzed in this study. Interestingly however, the cladoceran assemblages in these two lakes have also been resilient to climate mediated changes as the abundances of most taxa have not changed markedly. Furthermore, at three unimpacted lakes, the relative abundances of bosminids have decreased notably (North

Pickeral, Nicol, and Frog). As we discussed earlier, these trends may be due to limnological changes that were not measured in this study. For example, long-term

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changes in nutrient concentrations and predation by invertebrates and planktivorous fish could explain the observed changes.

A.7 Conclusions

Our regional survey suggests that the cladoceran assemblages in the Cobalt area are primarily structured by differences in lake depth, while site-specific limnological characteristics, including those related to past mining activities, may also have important roles in shaping the recent cladoceran compositions. As with most regional biological surveys, the responses to historical mining activities and recent climate warming are not uniform across all sites, which further emphasizes the importance of considering site- specific limnological characteristics when assessing the impacts of mining pollution. Our findings are consistent with a growing number of limnological assessments that show that biological responses to mining contamination are influenced by a variety of environmental parameters (Sivarajah et al. 2019; Little et al. 2020).

A.8 Acknowledgements

The authors thank Dale Sprague for collecting the sediment cores and water samples during the 2015 field season, and Amanda Little and Christina Frendo for conducting the field sampling in 2017. Many thanks to Robin Valleau and Andrew Labaj for providing training on Cladocera taxonomy. We would also like to thank two journal reviewers and the associate editor for providing constructive feedback on an earlier draft of this manuscript. This study was funded by the Natural Sciences and Engineering

Research Council of Canada (NSERC) grants awarded to Jesse Vermaire (RGPIN-2015-

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05257) and John Smol (RGPIN-2017- 04548), as well as an NSERC Alexander Graham

Bell Canada Graduate Scholarship (Doctoral) to Branaavan Sivarajah.

A.9 References

Adamczuk M (2014) Niche separation by littoral-benthic Chydoridae (Cladocera, Crustacea) in a deep lake – potential drivers of their distribution and role in littoral- pelagic coupling. J Limnol 73:490–501.

Adrian R, Wilhelm S, Gerten D (2006) Life-history traits of lake plankton species may govern their phenological response to climate warming. Glob Change Biol 12:652–661.

Amsinck SL, Strzelczak A, Bjerring R, Landkildehus F, Lauridsen TL, Christoffersen K, Jeppesen E (2006) Lake depth rather than fish planktivory determines cladoceran community structure in Faroese lakes – evidence from contemporary data and sediments. Freshwater Biol 51:2124–2142.

Armstrong Z, Kurek J (2019) Sensitivity and response of low-nutrient lakes to post twentieth century environmental change in New Brunswick, Canada. J Paleolimnol 61:85–98.

Arnott SE, Azan S, Ross A (2017) Calcium decline reduces population growth rates of zooplankton in field mesocosms. Can J Zool 95:323–333.

Birks HJB (2010) Numerical methods for the analysis of diatom assemblage data. In Smol, JP, Stoermer EF (eds) The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge:23–54.

Blanchet FG, Legendre P, Borcard D (2008) Forward selection of explanatory variables. Ecology 89:2623–2632.

Canadian Council of Ministers of the Environment, CCME (2001) Canadian Water Quality Guidelines for the Protection of Aquatic Life: Arsenic. Updated in: Canadian Environmental Quality Guidelines, 1999. Canadian Council of Ministers of the Environment, Winnipeg.

Carter JL, Schindler DE (2012) Responses of zooplankton populations to four decades of climate warming in lakes of Southwestern Alaska. Ecosystems 15:1010–1026.

Chen G, Shi H, Tao J, Chen L, Liu Y, Lei G, Liu X, Smol JP (2015) Industrial arsenic contamination causes catastrophic changes in freshwater ecosystems. Sci Rep 5:17419.

210

Crins WJ, Gray PA, Uhlig PWC, Wester MC (2009) The ecosystem of Ontario, Part 1: Ecozones and ecoregions. Ontario Ministry of Natural Resources, Peterborough, Ontario, Inventory, Monitoring and Assessment, SIB TER IMA TR-01, pp1–71.

Davidson TA, Sayer CD, Perrow M, Bramm M, Jeppesen E (2010) The simultaneous inference of zooplanktivorous fish and macrophyte density from sub-fossil cladoceran assemblages: a multivariate regression tree approach. Freshwater Biol 55:546–564.

DeSellas AM, Paterson AM, Sweetman JN, Smol JP (2008) Cladocera assemblages from the surface sediments of south-central Ontario (Canada) lakes and their relationships to measured environmental variables. Hydrobiologia 600:105–119.

Doig LE, Schiffer ST, Liber K (2015) Reconstructing the ecological impacts of eight decades of mining, metallurgical, and municipal activities on a small Boreal lake in Northern Canada. Integr Environ Assess Manag 11:490–501.

Dumaresq CG (1993) The occurrence of arsenic and heavy metal contamination from natural and anthropogenic sources in the Cobalt area of Ontario. MSc Dissertation, Carleton University.

Dumaresq C (2007) Canada’s mining heritage – balancing the heritage preservation with the environment, health and safety. Mining and the Environment IV Conference, Sudbury, Ontario, Canada.

Environment Canada (2012) Metal mining technical guidance for environmental effects monitoring, Government of Canada, pp 1–550.

Glew JR (1988) A portable extruding device for close interval sectioning of unconsolidated core samples. J Paleolimnol 1:235–239.

Glew JR (1989) A new trigger mechanism for sediment samplers. J Paleolimnol 2:241– 243.

Griffiths K, Winegardner AK, Beisner BE, Gregory-Eaves I (2019) Cladoceran assemblage changes across the Eastern United States as recorded in the sediments from the 2007 National Lakes Assessment, USA. Ecol Indic 96:368–382.

Hann BJ (1981) Occurrence and distribution of littoral Chydoridae (Crustacea, Cladocera) in Ontario, Canada, and taxonomic notes on some species. Can J Zool 59:1465–1474.

Hann BJ, Leavitt PR, Chang PSS (1994) Cladocera community response to experimental eutrophication in Lake 227 as recorded in laminated sediments. Can J Fish Aquat Sci 51:2312–2321.

211

Hargan KE, Nelligan C, Jeziorski A, Rühland KM, Paterson AM, Keller W, Smol JP (2016) Tracking the long-term responses of diatoms and cladocerans to climate warming and human influences across lakes of the Ring of Fire in the Far North of Ontario, Canada. J Paleolimnol 56:153–172.

Harrell FE (2019) Hmisc: Harrell Miscellaneous. R package version 4.3-0.

He W, Megharaj M, Naidu R (2009) Toxicity of tri- and penta-valent arsenic, along and in combination, to the cladoceran Daphnia carinata: the influence of microbial transformation in natural waters. Environ Geochem Health 31:133–141.

Health Canada (2006) Guidelines for Canadian Drinking Water Quality: Guideline Technical Document – Arsenic. Water Quality and Health Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa.

Jeziorski A, Keller B, Dyer RD, Paterson AM, Smol JP (2015) Differences among modern-day and historical cladoceran communities from the ‘‘Ring of Fire’’ lake region of northern Ontario: identifying responses to climate warming. Fundam Appl Limnol 186:203–216.

Jeziorski A, Smol JP (2016) The ecological impacts of lakewater calcium decline on softwater boreal ecosystems. Environ Rev 25:245–253.

Juggins S (2017) rioja: Analysis of Quaternary Science Data. R package version 0.9-21.

Korhola A, Rautio M (2001) Cladocera and other brachiopod crustaceans. In: Smol JP, Birks HJB, Last WM (eds) Tracking environmental change using lake sediments: Zoological indicators. Kluwer, Dordrecht, pp 5–41.

Korosi JB, Smol JP (2011) Distribution of cladoceran assemblages across environmental gradients in Nova Scotia (Canada) lakes. Hydrobiologia 663:83–99.

Korosi JB, Smol JP (2012a) An illustrated guide to the identification of Cladoceran subfossils from lake sediments in northeastern North America: part 1—the Daphniidae, Leptodoridae, Bosminidae, Polyphemidae, Holopedidae, Sididae, and Macrothicidae. J Paleolimnol 48:571–586.

Korosi JB, Smol JP (2012b) An illustrated guide to the identification of Cladoceran subfossils from lake sediments in northeastern North America: part 2—the Chydoridae. J Paleolimnol 48:587–622

Kurek J, Korosi JB, Jeziorski A, Smol JP (2010) Establishing reliable minimum count sizes for cladoceran subfossils sampled from lake sediments. J Paleolimnol 44:603–612.

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Kurek J, Weeber RC, Smol JP (2011) Environment trumps predation and spatial factors in structuring cladoceran communities from Boreal Shield lakes. Can J Fish Aquat Sci 68:1408–1419.

Kwong YTJ, Beauchemin S, Hossain MF, Gould WD (2007) Transformation and mobilization of arsenic in the historic Cobalt mining camp, Ontario, Canada. J Geochem Explor 92:133–150.

Labaj AL, Kurek J, Jeziorski A, Smol JP (2015) Elevated metal concentrations inhibit biological recovery of Cladocera in previously acidified boreal lakes. Freshwater Biol 60:347–359.

Labaj AL, Michelutti N, Smol JP (2018) Cladocera in shallow lakes from the Ecuadorian Andes show little response to recent climate change. Hydrobiologia 822:203–216.

Leppänen JJ (2018) An overview of Cladoceran studies conducted in mine water impacted lakes. Int Aquat Res 10:207–221.

Leppänen JJ, Weckström J, Korhola A (2017a) Multiple mining impacts induce widespread changes in ecosystem dynamics in a boreal lake. Sci Rep 7:10581.

Leppänen JJ, Siitonen S, Weckström J (2017b) The stability of cladoceran communities in sub-arctic NW Finnish Lapland lakes. Polar Biol 40:2211–2223.

Li M, Lua Z, Yan Y, Wang Z, Chi Q, Yan C, Xing B (2016) Arsenate accumulation, distribution, and toxicity associated with titanium dioxide nanoparticles in Daphnia magna. Envrion Sci Technol 50:9636–9643.

Manca M, Comoli P (1995) Temporal variations of fossil Cladocera in the sediments of Lake Orta (N. Italy) over the last 400 years. J Paleolimnol 14:113–122.

Manca M, Vijverberg J, Polishchuk LV, Voronov DA (2008) Daphnia body size and population dynamics under predation by invertebrate and fish predators in Lago Maggiore: an approach based on contribution analysis. J Limnol 67:15–21.

Moore M, Folt C (1993) Zooplankton body size and community structure: effects of thermal and toxicant stress. Trends Ecol Evol 8:178–183.

Nevalainen L, Luoto TP (2012) Faunal (Chironomidae, Cladocera) responses to post- Little Ice Age climate warming in high Austrian Alps. J Paleolimnol 48:711–724

213

Nevalainen L, Ketola M, Korosi JB, Manca M, Kurmayer R, Koinig K, Psenner R, Luoto TP (2014) Zooplankton (Cladocera) species turnover and long-term decline of Daphnia in two high mountain lakes in the Austrian Alps. Hydrobiologia 722:75–91

Palmer MJ, Galloway JM, Jamieson HE, Patterson RT, Falck H, Kokelj SV (2015) The Concentration of Arsenic in Lake Waters of the Yellowknife Area. Northwest Territories Geological Survey, NWT Open File 2015-06.

Passino DRM, Novak AJ (1984) Toxicity of arsenate and DDT to the cladoceran Bosmina longistris. Bull Environ Contam Toxicol 33:325–329.

Patterson RT, Barker T, Burbidge SM (1996) Arcellaceans (Thecamoebians) as proxies of arsenic and mercury contamination in Northeastern Ontario lakes. J Foramin Res 26:172–183.

Percival JB, Kwong YTJ, Dumaresq CG, Michel FA (2004) Transport and attenuation of arsenic, cobtalt and nickel in an alkaline environment (Cobalt, Ontario). Geological Survey of Canada, Open File 1680.

Pinel-Alloul B, André A, Legendre P, Cardille JA, Patalas K, Salki A (2013) Large-scale geographic patterns of diversity and community structure of pelagic crustacean zooplankton in Canadian lakes. Global Ecol Biogeogr 22:784–795.

R Core Team (2018) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.

Sales SCM, Rietzler AC, Ribeiro MM (2015) Arsenic toxicity to cladocerans isolated and associated with iron: implications for aquatic environments. An acad Bras cienc 88:539– 548.

Sarma SSS, Nandini S (2006) Review of recent ecotoxicological studies on Cladocerans. J Environ Sci Heal B 41:1417–1430.

Smol JP (2008) Pollution of lakes and rivers: A paleoenvironmental perspective. Blackwell Publishing, Malden:383.

214

Sprague DD, Vermaire JC (2018a) Legacy arsenic pollution of lakes near Cobalt, Ontario, Canada: Arsenic in lake water and sediment remains elevated nearly a century after mining activity has ceased. Water Air Soil Pollut 229:87,

Sprague DD, Vermaire JC (2018b) The landscape-scale relationship between lake sediment geochemistry and catchment bedrock composition from the Temagami and Gowganda areas of Northeastern Ontario, Canada. Environ Earth Sci 77:463.

Sprague DD, Michel FA, Vermaire JC (2016) The effects of migration on ca. 100-year- old arsenic-rich mine tailings in Cobalt, Ontario, Canada. Environ Earth Sci 75:405.

Szeroczyńska K, Sarmaja-Korjonen K (2007) Atlas of subfossil Cladocera from central and northern Europe. Friends of the Lower Vistula Society, Świecie

Tenkouano G-T, Cumming BF, Jamieson HE (2019) Geochemical and ecological changes within Moira Lake (Ontario, Canada): A legacy of industrial contamination and remediation. Environ Pollut 247:980–988. ter Braak CJF, Šmilauer P (2012) CANOCO reference manual and users guide: software for ordination (version 5.0). Microcomputer Power, Ithaca, pp 1–496

Valleau RE, Paterson AM, Smol JP (2020) Effects of road salt on Cladocera assemblages in shallow Precambrian Shield lakes in south-central, Ontario, Canada. Freshwater Science DOI: 10.1086/711666.

Walseng B, Yan ND, Schartau AK (2003) Littoral microcrustacean (Cladocera and Copepoda) indicators of acidification in Canadian Shield lakes. AMBIO 32:208–213.

Winegardner AK, Salter N, Aebishcer S, Pientiz R, Derry AM, Wing B, Beisner BE, Gregory-Eaves I (2017) Cladoceran diversity dynamics in lakes from a northern mining region: responses to multiple stressors characterized by alpha and beta diversity. Can J Fish Aquat Sci 74:1654–1667.

Yatigammana, S. K. & B. F. Cumming, 2017. Cladocera assemblages from reservoirs in Sri Lanka and their relationship to measured limnological variables. Lakes and Reservoirs: Research and Management 22:247–261.

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Table A.1: Summary of the cladoceran species richness, diversity, and habitat preferences from 22 lakes

Summary of the cladoceran species richness, diversity (Hill’s N2), and habitat preferences (pelagic vs. littoral) for lakes that received direct and migrated tailings, and unimpacted sites

Species richness Species diversity Min. Max. Mean Median Min. Max. Mean Median Direct tailings 6.25 15.40 10.26 10.44 1.22 3.85 2.12 2.03 (n=6) Migrated tailings 10.70 17.49 13.03 11.97 1.71 4.32 2.97 2.92 (n=4) Unimpacted 7.62 16.00 12.11 12.01 1.27 3.70 2.78 2.78 (n=12)

Pelagic Littoral Min. Max. Mean Median Min. Max. Mean Median Direct tailings 7.41 94.66 64.06 75.90 5.34 92.59 35.94 24.10 (n=6) Migrated tailings 35.64 77.88 61.54 66.31 22.12 64.36 38.46 33.69 (n=4) Unimpacted 38.24 91.06 60.08 56.37 8.94 61.76 39.92 43.63 (n=12)

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Table A.2: Summary of the top-bottom cladoceran species richness, diversity, and habitat preferences from 16 lakes

Summary of the cladoceran species richness, diversity (Hill’s N2), and habitat preferences (pelagic vs. littoral) from the impacted and unimpacted lakes between the recent and pre-industrial periods

Species richness Species diversity

Pre- Pre- Pre- Pre- Recent Recent Recent Recent industrial industrial industrial industrial range median range median range median range median

Impacted 10.8- 6.3-17.5 11.3 12.3 1.2 - 4.3 2.2 1.8 - 5.1 3.2 (n=6) 13.7

Unimpacted 7.6-16.0 12.0 9.8-18.0 12.5 1.3 - 3.7 3.0 1.5 - 4.0 3.4 (n=10)

Pelagic Littoral

Pre- Pre- Pre- Pre- Recent Recent Recent Recent industrial industrial industrial industrial range median range median range median range median

Impacted 35.6- 23.0- 19.4- 70 45.6 5.3-64.4 30 54.4 (n=6) 94.7 80.6 77.0

Unimpacted 38.2- 22.2- 15.0- 55.9 50.2 8.9-61.8 44.1 49.8 (n=10) 91.1 85.0 77.8

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Fig. A.1 Map of the study area of lakes around Cobalt, Ontario, Canada

The black line delineates the border between the provinces of Ontario and Québec. The lakes that received direct tailings are represented by red squares (n = 6) and the lakes impacted by migrated tailings are indicated by yellow triangles (n = 4). The unimpacted sites are represented by green circles (n = 12). The red star in the inset map indicates the location of Cobalt within Canada

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Fig. A.2 Boxplots summarizing and comparing measured limnological variables

Boxplots summarizing and comparing measured limnological variables among lakes that received direct (“cont”, n = 4) and migrated (“migr”, n = 6) tailings, and unimpacted sites (“uncon”, n = 12). Concentrations of arsenic and nickel are reported in µg/L, and alkalinity, potassium, calcium and sulphate are reported in mg/L. Depth and surface area are reported in ha and m, respectively

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Fig. A.3 PCA of limnological variables from Cobalt, Ontario

The distribution of the 21 lakes along the gradients of the measured environmental variables using principal component analysis (PCA). Lakes that received direct tailings are represented by red squares while the lakes impacted by migrated tailings are indicated by orange triangles. The unimpacted sites are represented by green circles

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Fig. A.4 Histograms of common Cladocera taxa Histograms of Cladocera taxa (i.e. taxa that occurred in > 5% abundance in at least one lake) from the surface sediments of lakes in Cobalt. The sites are arranged according to lakewater As concentrations. Lakes that received direct tailings are represented by red bars while the lakes impacted by migrated tailings are indicated by orange bars. The unimpacted sites are represented by green bars

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Fig. A.5 Histograms of the differences in the relative abundances of the most common Cladocera taxa from top and bottom samples Histograms of the differences in the relative abundances of the most common Cladocera taxa (i.e. taxa that occurred in > 5% abundance in at least one sample) from recent (top) and pre-mining (bottom) sediments from 16 lakes around Cobalt, Ontario. Lakes that received direct tailings are represented by red bars while the lakes impacted by migrated tailings are indicated by orange bars. The unimpacted sites are represented by green bars

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Supplement A.1: Selected limnological variables for the 22 lakes sampled around Cobalt, Ontario, Canada. The data presented here were point measurements taken in the summer of 2015.

Latitude Longitude Coring depthSurface areaAlkalinity Total DissolvedConductivity Solids pH

decimal decimal m ha mg CaCO3/L mg/L µS/cm degrees degrees Cart 47.38259 -79.6838 1.2 4.53 76 83.4 165 8.89 Tooth 47.18434 -79.509 17.2 39.5 28 34 68.1 7.66 Fourclaim 47.19714 -79.513 4.3 11.1 42 47.9 95 8.2 Cobalt 47.39039 -79.6877 10.4 18.4 129 186 366 8.34 Ibsen 47.35121 -79.6534 4.5 2.74 70 78.4 161 8.75 Brady 47.36064 -79.6483 2.4 5.74 100 112 220 8.7 Crosswise 47.3846 -79.6409 7.4 95.2 66 69.7 141 8.1 New 47.34464 -79.661 19.2 64.06 41 47.2 97.5 8 Kirk 47.36591 -79.627 11.9 26.9 66 70 141 8.06 Maidens 47.20121 -79.4831 9.4 30.88 28 48.8 105 7.84 Silver 47.33966 -79.6374 1.2 6.55 60 64 132 8.75 North Pickeral 47.36671 -79.7092 2.0 18.3 74 82.3 161 8.7 South Pickeral 47.36065 -79.7073 2.7 7.7 70 77.4 152 8.52 Green 47.38631 -79.7136 5.4 3.81 59 93.6 190 8.47 Clear 47.39429 -79.7129 15.2 21 68 126 253 8.05 IceChiesel 47.31163 -79.6887 15.2 5.98 5 2.6 5.29 4.7 Goodwin 47.32684 -79.5961 7.6 41.1 72 75.8 153 8.28 Mary Ann 47.33105 -79.6413 1.3 3.1 86 89.8 181 7.2 Frog 47.32765 -79.6535 12.8 4.97 58 63.6 129 6.9 Nicol 47.3445 -79.5983 2.1 41.82 65 66.5 135 9.17 Oxbow 47.17497 -79.4951 1.6 29.88 6 12.6 27 7.15 Pine 47.26776 -79.6255 12.8 102.18 43 54.6 116 8.13

Min 47.17497 -79.7136 1.22 2.74 5 2.6 5.29 4.7 Max 47.39429 -79.4831 19.20 102.18 129 186 366 9.17 Median 47.34457 -79.6448 6.37 18.35 65.5 69.85 141 8.165 Mean 47.32249 -79.6312 7.63 26.61 59.64 72.10 145.18 8.03

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Chloride Sulphate Calcium MagnesiumSodium Potassium Aluminum Arsenic Nickel mg/L mg/L mg/L mg/L mg/L mg/L µg/L µg/L µg/L

Cart 0.5 6 21 6.91 1.8 0.9 20 972 5.3 Tooth 0.5 4 9.24 1.92 0.8 0.3 20 635 0.8 Fourclaim 0.5 4 14.5 2.09 0.9 0.7 20 542 3.6 Cobalt 22.1 10 49 9.22 17.4 1 70 495 10.2 Ibsen 0.5 3 24.9 6.24 2.3 0.2 70 142 7.8 Brady 0.6 7 35.2 6.87 0.9 0.6 20 140 2.1 Crosswise 0.5 3 20.7 5.06 0.8 0.3 40 82.4 2.5 New 0.5 3 13.9 3.81 0.7 0.2 30 36.8 2.9 Kirk 0.5 3 21.1 4.88 0.7 0.3 30 34 1.3 Maidens 10.6 4 11.5 4.25 2 0.3 20 23.6 1.3 Silver 0.5 2 19.3 5.32 0.8 0.3 50 7.3 1.5 North Pickeral 0.6 6 26.3 3.75 0.8 0.2 30 5 0.9 South Pickeral 0.6 5 24.4 3.58 0.7 0.2 40 3.3 0.8 Green 17.6 3 20.1 4.4 12.6 0.4 30 2.9 0.7 Clear 30.9 5 24.8 3.87 19.9 0.4 10 2 0.5 IceChiesel 0.5 1 0.36 0.12 0.2 0.3 20 1.7 0.5 Goodwin 0.5 3 23 5.47 0.7 0.2 20 1.4 1 Mary Ann 0.5 2 26.7 6.63 0.9 0.3 40 0.9 0.7 Frog 0.6 2 19.1 4.89 0.9 0.2 40 0.7 0.6 Nicol 0.5 1 19.8 5.04 0.6 0.3 60 0.7 7.4 Oxbow 0.5 3 3.52 0.91 0.8 0.2 70 0.7 0.5 Pine 0.5 3 18.7 4.21 2 0.3 70 0.4 0.7

Min 0.5 1 0.36 0.12 0.2 0.2 10 0.4 0.5 Max 30.9 10 49 9.22 19.9 1 70 972 10.2 Median 0.5 3 20.4 4.64 0.85 0.3 30 6.15 1.15 Mean 4.12 3.77 20.32 4.52 3.15 0.37 37.27 142.26 2.44

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Supplement A.2: Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Oxbow Lake, Green Lake and Ibsen Pond. Bottom panel – Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Oxbow Lake, Green Lake and Ibsen Pond.

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Supplement A.3: Pearson correlation matrix of the environmental variables (following transformations described in text) from the 21 lakes analyzed in the ordination analysis.

Alkalinity LogSulphateSQRTCa Magnesium Log(x+1)Al LogAs SQRTTDS SQRTConductivityLog(x+1)DepthLogSurfaceAreapH Alkalinity 1 LogSulphate 0.379298 1 SQRTCa 0.974345 0.39326 1 Magnesium 0.872543 0.204601 0.836066 1 Log(x+1)Al 0.048385 -0.34047 0.039009 0.175409 1 LogAs 0.213811 0.562431 0.163786 0.227572 -0.26045 1 SQRTTDS 0.92732 0.446083 0.949417 0.795666 -0.11273 0.172329 1 SQRTConductivity 0.918878 0.429797 0.945499 0.802433 -0.10358 0.157253 0.999081 1 Log(x+1)Depth -0.14564 0.122094 -0.04367 -0.12997 -0.22533 0.099872 0.040042 0.051504 1 LogSurfaceArea -0.36241 -0.07904 -0.31969 -0.34574 0.017551 -0.10395 -0.32994 -0.32312 0.51287 1 pH 0.407802 0.155565 0.431619 0.34415 0.006429 0.276834 0.390573 0.386528 -0.36039 -0.09614 1 Abbreviations LogSulphate – Log transformed sulphate SQRTTDS – Square root transformed total dissolved solids SQRTCa – Square root transformed calcium SQRTConductivity – Square root transformed conductivity Log(x+1)Al – Log (x+1) transformed aluminum Log(x+1)Depth – Log (x+1) transformed depth LogAs – Log transformed arsenic LogSurfaceArea – Log transformed surface area

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Supplement A.4: Mean annual air temperature data for Earlton, Ontario (Climate Station ID: 6072230). Data retrieved from Adjusted and Homogenized Canadian Climate Data (https://climate.weather.gc.ca/). The record was mostly continuous, however, data from 1947 and 1952 were missing.

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Appendix B Limnological characteristics of lakes near Canada’s northernmost mine, Little Cornwallis Island, Nunavut

B.1 Abstract

We compared modern chemical limnological characteristics of three lakes near the world’s northernmost base metal (lead-zinc) mine, Polaris Mine, which operated between 1981-

2002 on Little Cornwallis Island (Nunavut, Canada), to a suite of sites from Resolute Bay

(Qausuittuq), Cornwallis Island. Although both study regions are underlain by broadly similar geology and experience nearly identical climatic conditions, present-day water chemistry variables differed markedly between sites on the two islands. Specifically, the lakes near the

Polaris Mine recorded substantially higher concentrations of zinc and lead, as well as several other heavy metals (cadmium, molybdenum, nickel, selenium, uranium, vanadium), relative to the sites on Cornwallis Island. Although the Polaris Mine closed in 2002, elevated levels of heavy metals in our 2017 survey are likely a legacy of contamination from prior operations.

B.2 Introduction

Canada’s North is rich in natural resources and it has a long history of mining activities (Silke 2009; Keeling and Sandlos 2015). For example, the discovery of lead- zinc ores on Little Cornwallis Island (Nunavut, High Arctic Canada) in the 1960s led to the establishment of the Polaris Mine (75.39° N, 96.94° W) in the early-1980s (Dewing et al. 2006). This site became the world’s northernmost base metal mine, with underground operations lasting for two decades (1981-2002; Donald 2005; Dewing et al. 2006). The operational history of the Polaris Mine has been presented in earlier issues of Arctic by

Nassichuk (1987, Volume 40, Issue 4) and by Dewing et al. (2006, Volume 59, Issue 4).

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Annually, Polaris Mine produced about 250,000 to 300,000 tonnes of wet zinc and lead concentrates (Nassichuk 1987) that were stored on site prior to being shipped to Belgium for smelting (Donald 2005). However, 15 million tonnes of mine tailings from the concentrator were dewatered and routed to the bottom of nearby Garrow Lake (Dewing et al. 2006; Donald 2005), a naturally meromictic lake with an anoxic hypolimnion that is separated from the upper mixolimnion due to a strong salinity gradient (Ouellet et al.

1989; Markager et al. 1999). Garrow Lake was strategically selected as a tailings storage site because its meromictic nature would presumably contain the waste metals in the hypolimnion with little danger of mixing into surface waters (Donald 2005). However, there were at least two instances of a breach in the tailings line (1985 and 1989), which discharged mine wastes into the upper layer of the lake and resulted in elevated zinc concentrations (Gartner Lee Ltd. 2001). The mining operations ended in 2002 and surface remediation activities were subsequently undertaken (Donald 2005).

Here, we compare present-day water chemistry variables from the directly impacted Garrow Lake and two other lakes located near the former mine site (Fig. B.1) to data from a suite of lakes and ponds near the Polar Continental Shelf Program (PCSP) base at Resolute Bay (Cornwallis Island, Nunavut). Our goal was to assess whether the modern limnology of lakes located near the abandoned Polaris Mine differed from those on nearby Cornwallis Island, which has no history of mining activities. Both study islands have broadly similar surface geology and climatic conditions (Thorsteinsson

1958). Comparative limnological surveys such as these are useful tools to understand the past impacts of anthropogenic disturbances in Arctic regions, which are under increasing

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pressure from resource extraction projects, but where long-term monitoring data are sparse (e.g. Houben et al. 2016; Palmer et al. 2019; Sivarajah et al. 2019).

B.3 Materials and Methods

Water samples were collected from Garrow Lake (maximum depth = 49 m; surface area = 4.1 km2; Ouellet et al. 1989) and two other nearby sites (unofficial names

Little Cornwallis 1 and 2) near the former Polaris Mine site on July 31, 2017 (Fig. B.1).

Little Cornwallis 1 is a small lake (surface area = 0.03 km2), located adjacent to the aircraft landing strip, while Little Cornwallis 2 is a larger lake (surface area = 1.68 km2) about 2 km north of Garrow Lake (Fig. B.1). We also collected water samples from 10 lakes and ponds near Resolute Bay on Cornwallis Island during the same sampling season between late-July to early-August, 2017. We followed field sampling protocols that have been routinely used and described in our previous limnological surveys of lakes and ponds in the Canadian High Arctic to ensure consistency and comparison among all sites (e.g. Michelutti et al. 2002; Antoniades et al. 2003; Keatley et al. 2007; Michelutti et al. 2007). The water samples for this investigation were collected from ~30 cm depth near the shoreline and kept cool (~ 4 oC) in the field and during transportation. Littoral zone sampling is common for High Arctic summer limnological surveys, as ice conditions are typically precarious for mid-lake sampling. Specific conductivity and pH were measured using handheld meters at the base camp in Resolute Bay shortly after sample collection. The water samples were then analyzed for a variety of water chemistry variables related to metals, cations, anions and nutrients at the National Laboratory for

Environmental Testing in Burlington (Ontario, Canada) following standard protocols

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outlined in Environment Canada Manual of Analytical Methods (1994 a, b) and are described in Michelutti et al. (2019).

In this limnological survey, we measured more than 50 water chemistry variables for each of the 13 study sites. Prior to conducting numerical analyses, we removed variables that contained concentrations below detection limits (BDL) in more than half the lakes, following similar procedures used in Antoniades et al. (2003) and Michelutti et al. (2019). This resulted in the elimination of chlorophyll-corrected, ammonia, and several metal(loid)s (beryllium, bismuth, germanium, indium, niobium, platinum, scandium, silver, thallium, tin and zirconium) from further numerical treatments.

Parameters that had concentrations BDL in 5 lakes or less (BDL at 1 site – cadmium,

BDL at 2 sites – tungsten, BDL at 3 sites – gallium and titanium, BDL at 4 sites – cesium, BDL at 5 sites – tellurium and zinc) were replaced with the value of the detection limit (Michelutti et al. 2019; Sivarajah et al. 2019). Next, we screened all water chemistry variables for normality using the Shapiro-Wilk test in R statistical software environment

(R Core Team 2019). Particulate organic carbon (POC), particulate organic nitrogen

(PON), pH, total dissolved nitrogen (TDN), soluble reactive phosphorus (SRP), dissolved inorganic carbon (DIC), and silica were normally distributed. However, many other variables had skewed distributions in this dataset, and some were log-transformed

(fluoride, sulfate, total dissolved phosphorus (TDP), calcium, aluminum, antimony, barium, boron, cadmium, cerium, chromium, cobalt, copper, gallium, iron, lanthanum, lithium, manganese, molybdenum, tungsten, uranium, vanadium, and yttrium), while others were square-root-transformed (total Kjeldahl dissolved nitrogen (TKDN), total phosphorus (TP), arsenic). Certain variables could not be normalized using log or square-

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root transformations as exceptionally high concentrations were recorded in lakes around the Polaris Mine site (Table B.1).

A preliminary principal component analysis (PCA) was conducted with all the normal and transformed variables (n = 33) to explore the distribution of the 13 sites in ordination space using the R statistical environment. Since strong correlations among multiple variables were apparent in the preliminary PCA (Supplement B.1), a Pearson correlation matrix was generated in the R statistical environment to identify positively correlated variables and to remove variables that were redundant (Supplement B.2). For example, TDN was chosen to represent TKDN (correlation coefficient (r) = 0.97) and

POC tracked PON (r = 0.89) as these variables were strongly correlated with each other.

A final PCA of the 13 sites was conducted with the remaining 21 variables.

B.4 Results

The concentrations of several variables related to the ionic strength of the water

(e.g., conductivity, chloride, magnesium, sodium, potassium) and certain metals (e.g., lead, nickel, selenium, strontium, zinc) were higher in the lakes near the Polaris Mine

(Table B.1). In particular, the concentrations of lead and zinc were exceptionally high at

Garrow and Little Cornwallis 1 (the lake near the landing strip) (Fig. B.2). Since the variables listed in Table 1 could not be normalized using various transformations, they were not included in the PCA. This was due to the higher levels recorded at Garrow Lake and Little Cornwallis 1 compared to Little Cornwallis 2 and the 10 sites in Resolute Bay

(Table B.1). The initial PCA of all the normalized variables (Fig. B.3) allowed us to identify notable differences in the limnology of lakes in Little Cornwallis Island and

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Resolute Bay, and it was apparent that several variables were highly correlated

(Supplement B.1). The Pearson correlation identified several strong positive relationships between water chemistry variables (Supplement B.2). Since sulfate was strongly correlated with boron (r = 0.92), cobalt (r = 0.81), lithium (r = 0.89) and uranium (r =

0.88), it was chosen to represent these variables. Similarly, manganese, aluminum, silica, and molybdenum were chosen to represent iron, gallium, fluoride, and antimony, respectively (r values in Supplement B.2) and cerium was selected to represent lanthanum (r = 0.99) and yttrium (r = 0.96). Nonetheless, our reduction of water chemistry variables in the final PCA (Fig. B.3) did not alter the distribution of sites and variables in the PCA ordination space as both PCAs resulted in very similar biplots (Fig.

B.3; Supplement B.1). Similarly, axes 1 and 2 explained similar amounts of variation in the water chemistry in both PCAs (Fig. B.3; Supplement B.1).

Axis 1 of the PCA was primarily influenced by the large gradient of metal(loid) concentrations and ionic strength of the waters, with the three Little Cornwallis sites plotted along this axis. Not surprisingly, the most impacted Garrow Lake plotted farthest away from all other sites along axis 1. Specifically, the Little Cornwallis Island lakes had higher concentrations for boron, cadmium, calcium, lithium, molybdenum, sulfate, uranium, and vanadium relative to the sites near Resolute Bay (Fig. B.3; Supplement

B.3). Meanwhile, axis 2 of the PCA more closely tracked differences in nutrient concentrations (Fig. B.3). For instance, R12 and R13 (unofficial names, from Stewart et al. 2014) plotted separately from other Resolute Bay sites in the PCA as these ponds had relatively high concentrations of POC, TDP, TDN, TKDN, and DIC (Fig. B.3;

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Supplement B.3). All lakes included this survey had pH > 7 (Supplement B.3), however the lakes near Resolute Bay were slightly more alkaline (Fig. B.3).

B.5 Discussion

Concentrations of lead and zinc recorded in Garrow Lake (lead = 9.78 µg/L; zinc

= 53.6 µg/L) and Little Cornwallis 1 (lead = 6.06 µg/L; zinc = 152 µg/L) were some of the highest levels reported from the Canadian High Arctic Archipelago. In contrast, the study lakes and ponds near Resolute Bay had concentrations below 1 and 0.2 µg/L for zinc and lead, respectively (Fig. B.2). Moreover, previous limnological surveys of hundreds of High Arctic freshwaters from diverse geological regions have only rarely recorded values for Pb and Zn that exceeded detection levels (Hamilton et al. 2001;

Michelutti et al. 2002; Antoniades et al. 2003; Dranga et al. 2018). In fact, limnological data from Garrow Lake in the early-1980s recorded lower values for lead and zinc relative to our 2017 data (Stewart and Platford 1986; Ouellet et al. 1989). The present levels of zinc at Garrow Lake (53.6 µg/L) are lower than the levels measured after tailings spill into the surface layer (410 µg/L in 1991; Gartner Lee Ltd. 2001), nonetheless they are an order of magnitude higher than the levels measured in other High

Arctic lakes. Past mining activities appear to have left a legacy of elevated Pb and Zn concentrations in the Little Cornwallis Island study lakes.

The Canadian Water Quality Guideline (CWQG) for lead is dependent on hardness (as calcium carbonate) of the water; specifically, when hardness is > 180 mg/L the CWQG guideline for lead is 7 µg/L (Canadian Council of Ministers of the

Environment (CCME) 2008). Both Little Cornwallis 1 and Garrow had water hardness

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levels well above 180 mg/L (374 mg/L and 437 mg/L, respectively); however, the threshold of 7 µg/L of lead in the water was only exceeded at Garrow (9.78 µg/L) while the lead concentration at Little Cornwallis 1 (6.06 µg/L) was just below the guideline.

The long-term CWQG for zinc is influenced by hardness, dissolved organic carbon and pH (CCME 2018). The zinc concentration at Little Cornwallis 1 (152 µg/L) was twice as high relative to the level determined by the CWQG (76 µg/L). However, we could not determine if the zinc level at Garrow Lake (53.6 µg/L) exceeded the CWQG, as the hardness of the water was beyond the range of the equation used for the guideline

(applicable over a range of 23.4 mg/L to 399 mg/L) and the dissolved organic carbon measurements for this lake was interfered with by the exceptionally high chloride levels

(~1000 mg/L; Table B.1).

Mine tailings are often composed of a variety of metal(loid)s even when a single metal(loid) dominates the composition (e.g. Leppänen et al. 2017; Perrett et al. In

Review). At the Polaris Mine, for example, the tailings were rich in zinc, yet we recorded higher levels of other heavy metals (e.g., cadmium, molybdenum, nickel, selenium, uranium, vanadium) in the Little Cornwallis Island lakes relative to the ones from

Resolute Bay. The concentrations of the aforementioned heavy metals were nonetheless low, near (or below) 1 µg/L in the Little Cornwallis Island lakes. However, they were still several times greater than those recorded on Cornwallis Island lakes and ponds with no mining operations (Table B.1; Supplement B.3). Specifically, the concentrations for cadmium, nickel, and selenium were more than 30, 15, and 10-times higher, respectively, at Garrow relative to median values from the Resolute Bay study sites. Furthermore, at the meromictic Garrow Lake, variables related to ionic concentrations were higher

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relative to other lakes in this study (Table B.1), as was reported from previous limnological surveys of this lake (Stewart and Platford 1986; Ouellet et al. 1989).

Interestingly, elevated levels of salinity-related variables were also recorded at Little

Cornwallis 1. However, based on the available information, it is not possible to assert if

Little Cornwallis 1 is naturally saline, or whether other factors (e.g. marine inputs or mining activities) elevated the salinity.

A growing number of investigations are identifying the long-term environmental consequences of anthropogenic activities on remote High Arctic aquatic ecosystems. For example, R12 and R13 in Resolute Bay received sewage from above ground pipes and utilidors between 1949 and 1979 (Stewart et al. 2014). More than 30 years have passed since the cessation of sewage inputs into R12 and R13, yet these sites had some of the highest concentrations of variables related to nutrients (e.g. TP, TDP, TDN, TKDN) and production (e.g. POC) in our survey. Similarly, when we sampled the Polaris Mine study lakes in 2017, 15 years had passed since the cessation of ore extraction activities, and there were almost no visible remnants of past mining operations on the surface (e.g. waste rock, infrastructure), as has been reported in previous publications (Donald 2005;

Green 2015). However, the elevated levels of lead and zinc in the waters of nearby lakes suggest that past mining activities have potentially left long-term limnological legacies.

Future studies should consider examining the biological impacts associated with past mining activities on Little Cornwallis Island lakes. Cumulatively, these data will assist in managing mine-impacted waters in the Arctic, which is critical given that biota in these lakes are already under stress from other global change stressors (e.g. climate change) and there may be cumulative or even synergistic effects.

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B.6 Acknowledgements

We thank Bruce Donald from Teck Resources Limited for assisting us with the coordination of the sampling of the sites in Little Cornwallis Island. Many thanks to Dave

Eickmeyer, Jules Blais, Linda Kimpe, and Lauren Gallant from University of Ottawa for helping with the sampling of the Resolute Bay lakes during out 2017 field season. We are also grateful to the logistical staff of the Polar Continental Shelf Program (PCSP),

Natural Resources Canada in Resolute Bay. Financial support by the Natural Sciences and Engineering Research Council of Canada (Discovery Grant and Northern

Supplements to John Smol, Alexander Graham Bell Canada Graduate Scholarship to

Branaavan Sivarajah), Northern Scientific Training Program (Branaavan Sivarajah),

Queen’s University Graduate Dean’s Doctoral Field Travel Grant (Branaavan Sivarajah), and PCSP (John Smol and Jules Blais) are greatly acknowledged.

B.6 References

Antoniades, D., Douglas, M.S.V., and Smol, J.P. 2003. Comparative physical and chemical limnology of two Canadian High Arctic regions: Alert (Ellesmere Island, NU) and Mould Bay (Prince Patrick Island, NWT). Archiv für Hydrobioligie 158:485 – 516.

Canadian Council of Ministers of the Environment. 2008. Canadian Water Quality Guidelines. Ottawa, Canada.

Canadian Council of Ministers of the Environment. 2018. Scientific criteria document for the development of the Canadian water quality guidelines for the protection of aquatic life: zinc. Canadian Council of Ministers of the Environment, Winnipeg, Manitoba.

Dewing, K., Sharp, R.J., and Muraro, T. 2006. Exploration history and mineral potential of the Central Arctic Zn-Pb district, Nunavut. Arctic 59:415 – 427.

Donald, B.J. 2005. Polaris Mine: a case study of reclamation in the High Arctic. Proceedings of the British Columbia Mine Reclamation Symposium, University of British Columbia. DOI: 10.14288/1.0042473

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Dranga, S.A., Hayles, S., Gajewski, K. 2018. Synthesis of limnological data from lakes and ponds across Arctic and Boral Canada. Arctic Science 4: 167 – 185.

Environment Canada Manual of Analytical Methods. 1994a. Major ions and nutrients, Vol. 1. Burlington (ON): National Laboratory for Environmental Testing, Canadian Centre for Inland Waters.

Environment Canada Manual of Analytical Methods. 1994b. Trace metals, Vol. 2. Burlington (ON). National Laboratory for Environmental Testing, Canadian Centre for Inland Waters.

Hamilton, P.B., Gajewski, K., Atkinson, D.E., Lean, and D.R.S. 2001. Physical and chemical limnology of 204 lakes from the Canadian Arctic Archipelago. Hydrobiologia 457:133 – 148.

Houben, A.J., D’Onofrio, R., Kokelj, S.V., Blais, J.M. 2016. Factors affecting elevated arsenic and methyl mercury concentrations in small shield lakes surrounding gold mines near the Yellowknife, NT, (Canada) region. PLoS ONE 11:e0150960.

Gartner Lee Limited. 2001. Polaris Mine Decommissioning and Reclamation Plan, Volume 1 of 4. GLL 20-935.

Green, H. 2015. “There is no memory of it here”: Closure and memory of the Polaris Mine in Resolute Bay, 1973-2012. In: Keeling, A., and Sandlos, J., eds. Mining and Communities in Northern Canada, History, Politics, and Memory. University of Calgary Press. 315 – 340.

Keatley, B.E., Douglas, M.S.V., and Smol, J.P. 2007. Limnological characteristics of a High Arctic oasis and comparisons across Northern Ellesmere Island. Arctic 60:294 – 308.

Keeling, A., and Sandlos, J. 2015. Mining and Communities in Northern Canada, History, Politics, and Memory. University of Calgary Press

Leppänen, J.J., Weckström, J., and Korhola, A. 2017. Multiple mining impacts induce widespread changes in ecosystem dynamics in a boreal lake. Scientific Reports 7:10581.

Markager, S., Vincent, W.F., and Tang, E.P.Y.1999. Carbon fixation by phytoplankton in high Arctic lakes: Implications of low temperature for photosynthesis. Limnology and Oceanography 44:597 – 607.

Michelutti, N., Douglas, M.S.V., Lean, D.R.S., and Smol, J.P. 2002. Physical and chemical limnology of 34 ultra-oligotrophic lakes and ponds near Wynniatt Bay, Victoria Island, Arctic Canada. Hydrobiologia 482:1 – 13.

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Michelutti, N., Douglas, M.S.V., and Smol, J.P. 2007. Evaluating diatom community composition in the absence of marked limnological gradients in the high Arctic: a surface sediment calibration set from Cornwallis Island (Nunavut, Canada). Polar Biology 30:1459 – 1473.

Michelutti, N., Tapia, P.M., Labaj, A.L., Groom, C., Wang, X., and Smol, J.P. 2019. A limnological assessment of the diverse waterscape in the Cordillera Vilcanota, Peruvian Andes. Inland Waters 64:121 – 135.

Nassichuk, W.W. 1987. Forty years of Northern non-renewable natural resource development. Arctic 40: 274 – 284.

Ouellet, M., Dickman, M., Bisson, M., and Pagé, P. 1989. Physico-chemical characteristics and origin of hypersaline meromictic Lake Garrow in the Canadian High Arctic. Hydrobiologia 172:215 – 234.

Palmer, M. J., Chételat, J., Richardson, M., Jamieson, H. E., and Galloway, J. M. Seasonal variation of arsenic and antimony in surface waters of small subarctic lakes impacted by legacy mining pollution near Yellowknife, NT, Canada. Science of the Total Environment 684:326 – 339.

Perrett, M., Sivarajah, B., Cheney, C., Korosi, J.B., Kimpe, L.E., Blais, J.M., and Smol, J.P. (In Review Environmental Pollution) Salinization and metal(loid) contamination by gold mine tailings from Consolidated Mine (Yellowknife, Canada) impact aquatic biota.

R Core Team, 2019. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Silke, R., 2009. The operational history of mines in the Northwest Territories, Canada. An Historical Research Project.

Sivarajah, B., Korosi, J.B., Blais, J.M., and Smol, J.P. (2019) Multiple environmental variables influence diatom assemblages across an arsenic gradient in 33 subarctic lakes near abandoned gold mines. Hydrobiologia 841:133 – 151.

Stewart, K.M., and Platford, R.F. 1986. Hypersaline gradients in two Canadian High Arctic lakes. Canadian Journal of Fisheries and Aquatic Sciences 43:1795 – 1803.

Stewart, E.M., McIver, R., Michelutti, N., Douglas, M.S.V., and Smol, J.P. 2014. Assessing the efficacy of chironomid and diatom assemblages in tracking eutrophication in High Arctic sewage ponds. Hydrobiologia 721(1):251 – 268.

Thorsteinsson, R. 1958. Cornwallis and Little Cornwallis Islands, District of Franklin Northwest Territories. Memoir 294, Geological Survey of Canada, Ottawa, Canada.

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Table B.1: Limnological and environmental details of the study lakes.

The locations (latitude and longitude in decimal degrees) of the three lakes on Little Cornwallis Island along with measurements of water chemistry variables that could not be normalized using log or square-root transformations. The median values from the ten Resolute Bay sites are provided for comparison. (Lat. – latitude, Long. – Longitude,

- - - Conduc. – conductivity, Cl – chloride, NO2 / NO3 as N – nitrate/nitrite as nitrogen, Mg – magnesium, Na – sodium, K – potassium, Cs – cesium, Pb – lead, Ni – nickel, Rb – rubidium, Se – selenium, Sr – strontium, Ti – titanium, Zn – zinc).

NO - / Lat. Long. 2 Conduc. Cl- NO - Mg Na K Lake (dd) (dd) 3 (µS/cm) (mg/L) as N (mg/L) (mg/L) (mg/L) North West (mg/L) Garrow 75.3964 96.8263 3080 1000 0.106 79 626 18.5

L Cornwallis 1 75.3915 96.9223 957 102 0.07 29.5 76.6 5.89

L Cornwallis 2 75.4241 96.8484 321 24 0.024 9.62 13.3 1.07 Resolute Bay 283 15.9 0.008 8.54 11.65 0.575 median

Cs Pb Ni Rb Se Sr Ti Zn (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) Garrow 0.022 9.78 1.84 4.78 0.38 722 2.1 53.6

L Cornwallis 1 0.003 6.06 1.55 0.709 0.32 296 0.3 152

L Cornwallis 2 0.004 0.424 0.97 0.334 0.38 1130 0.5 6.5 Resolute Bay 0.001 0.044 0.11 0.109 0.035 73 0.2 0.2 median

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Figure B.1: Map showing the study location in Little Cornwallis Island.

The open circle in the top-right inset map (a) indicates the location of Cornwallis and Little Cornwallis islands on a partial map of North America. The star in the main map identifies the location of Resolute Bay (Qausuittuq) on Cornwallis Island. LCI and BHI refer to Little Cornwallis Island and Baillie-Hamilton Island, respectively. The red rectangle in the main map identifies the location of the study area on Little Cornwallis Island. The black dots in the bottom-left inset map (b) identifies the study lakes (LC1 – Little Cornwallis 1; LC2 – Little Cornwallis 2) and the black triangle identifies the location of Polaris Mine.

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Figure B.2: Concentrations of zinc and lead presented as histograms for lakes in Little Cornwallis Island.

Concentrations of zinc and lead presented as histograms for Garrow, Little Cornwallis 1 (LC1), Little Cornwallis 2 (LC2), and the median of the Resolute Bay (Res. Bay Median) sites are included for comparison. The y-axis for both elements has been scaled differently and the concentrations for each site have been reported right above the bars.

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Figure B.3: A PCA of the limnological variables from lakes in Little Cornwallis Island and Cornwallis Island.

A principal component analysis biplot summarizing the variability in the water chemistry parameters of 13 sites from Little Cornwallis Island (red triangles) and Cornwallis Island (blue circles). RM, RR, RC, and Lchar are codes for Meretta Lake, Resolute Lake, Char Lake and Little Char Lake, respectively. Further details regarding the Resolute Bay sites are provided in Michelutti et al. (2007) and Stewart et al. (2014).

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Supplement B.1: A principal component analysis biplot with all 33 normal(ized) variables of the 13 sites from Little Cornwallis Island and Cornwallis Island (Nunavut, Canada). RM, RR, RC, and Lchar are codes for Meretta Lake, Resolute Lake, Char Lake and Little Char Lake, respectively.

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Supplement B.2: A Pearson correlation matrix of the 33 water chemistry variables. The abbreviation for the codes are as follows, POC – Particulate organic carbon; PON – Particulate organic nitrogen; LogF – Log transformed fluoride; LogSulfate – Log transformed sulfate; TDN – Total dissolved nitrogen; SQRTTKDN – Square-root transformed total Kjedahl dissolved nitrogen; SRP – Soluble reactive phosphorus; SQRTTP – Square-root transformed total phosphorus; LogTDP – Log transformed total dissolved phosphorus; DIC – Dissolved inorganic carbon; LogCa – Log transformed calcium; LogAl – Log transformed aluminum; Log Sb – Log transformed antimony; SQRTAs – Square-root transformed arsenic; LogBa – Log transformed barium; LogB – Log transformed boron; LogCd – Log transformed cadmium; LogCe – Log transformed cerium; LogCr – Log transformed chromium; LogCo – Log transformed cobalt; LogCu – Log transformed copper; LogGa – Log transformed gallium; LogFe – Log transformed iron; LogLa – Log transformed lanthanum; LogLi – Log transformed lithium; LogMn – Log transformed manganese; LogMo – Log transformed molybdenum; LogW – Log transformed tin; LogU – Log transformed uranium; LogV – Log transformed vanadium; LogY – Log transformed yttrium

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POC

PON

LogF

LogSulfate

TDN

SQRTTKDN

SRP

SQRTTP

LogTDP

DIC

LogCa

Silica

LogAl

LogSb SQRTAs pH 1.00 POC 0.02 1.00 PON 0.15 0.89 1.00 LogF -0.38 -0.16 0.02 1.00 LogSulfate -0.62 -0.37 -0.32 0.79 1.00 TDN -0.44 0.70 0.68 0.35 0.21 1.00 SQRTTKDN -0.36 0.78 0.72 0.22 0.09 0.97 1.00 SRP 0.22 0.37 0.58 0.11 -0.32 0.18 0.20 1.00 SQRTTP -0.29 0.24 0.20 0.08 -0.12 0.49 0.42 -0.06 1.00 LogTDP -0.12 0.75 0.77 0.26 -0.14 0.74 0.71 0.58 0.49 1.00 DIC 0.01 0.70 0.58 0.03 -0.39 0.48 0.58 0.42 0.35 0.65 1.00 LogCa -0.77 0.09 0.01 0.74 0.70 0.52 0.44 -0.04 0.21 0.37 0.29 1.00 Silica -0.22 0.06 0.21 0.81 0.46 0.35 0.30 0.29 0.25 0.33 0.35 0.54 1.00 LogAl -0.25 -0.71 -0.81 0.12 0.34 -0.55 -0.56 -0.53 -0.04 -0.66 -0.28 0.20 0.14 1.00 LogSb -0.53 0.35 0.40 0.43 0.27 0.69 0.60 -0.08 0.77 0.49 0.30 0.51 0.53 -0.05 1.00 SQRTAs -0.39 0.30 0.29 0.56 0.32 0.73 0.64 -0.04 0.74 0.67 0.39 0.63 0.51 -0.11 0.75 1.00 LogBa -0.70 0.11 0.15 0.27 0.39 0.47 0.44 -0.16 0.41 0.06 0.07 0.48 0.37 0.23 0.76 0.34 LogB -0.67 -0.46 -0.45 0.77 0.92 0.21 0.06 -0.32 0.15 -0.07 -0.33 0.70 0.49 0.42 0.34 0.48 LogCd -0.85 -0.16 -0.22 0.41 0.68 0.28 0.16 -0.50 0.35 -0.06 -0.25 0.64 0.22 0.38 0.65 0.39 LogCe -0.27 -0.73 -0.68 0.46 0.70 -0.35 -0.43 -0.55 -0.13 -0.63 -0.56 0.27 0.32 0.80 0.08 -0.02 LogCr -0.44 -0.33 -0.29 0.52 0.48 0.01 -0.04 -0.25 0.25 -0.25 0.02 0.40 0.67 0.54 0.47 0.19 LogCo -0.67 -0.32 -0.34 0.79 0.81 0.24 0.12 -0.34 0.27 -0.03 -0.02 0.80 0.66 0.54 0.54 0.54 LogCu -0.18 0.15 0.17 0.35 0.02 0.16 0.12 0.20 0.33 0.19 0.48 0.33 0.64 0.20 0.52 0.21 LogGa -0.50 -0.57 -0.65 0.36 0.52 -0.22 -0.28 -0.35 0.08 -0.39 -0.16 0.50 0.33 0.87 0.17 0.12 LogFe -0.33 -0.65 -0.63 0.44 0.43 -0.27 -0.35 -0.44 0.30 -0.38 -0.12 0.36 0.45 0.83 0.30 0.25 LogLa -0.29 -0.71 -0.67 0.44 0.68 -0.33 -0.42 -0.54 -0.10 -0.64 -0.53 0.27 0.33 0.81 0.12 -0.03 LogLi -0.73 -0.41 -0.37 0.75 0.89 0.27 0.13 -0.30 0.27 -0.05 -0.30 0.70 0.52 0.43 0.49 0.51 LogMn -0.22 -0.72 -0.64 0.41 0.45 -0.29 -0.38 -0.47 0.22 -0.38 -0.29 0.26 0.30 0.74 0.20 0.28 LogMo -0.65 0.14 0.18 0.53 0.62 0.68 0.57 -0.24 0.55 0.31 -0.11 0.53 0.44 -0.04 0.81 0.70 LogW 0.08 -0.30 -0.47 -0.29 -0.08 -0.38 -0.42 -0.64 0.02 -0.39 -0.30 -0.15 -0.54 0.26 -0.20 -0.15 LogU -0.68 -0.14 -0.17 0.53 0.88 0.38 0.31 -0.51 -0.02 -0.15 -0.38 0.58 0.26 0.20 0.39 0.32 LogV -0.51 -0.50 -0.38 0.51 0.55 0.02 -0.11 -0.39 0.48 -0.19 -0.24 0.44 0.44 0.62 0.61 0.43 LogY -0.42 -0.66 -0.60 0.47 0.70 -0.22 -0.33 -0.46 0.01 -0.54 -0.51 0.33 0.36 0.75 0.24 0.03

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LogBa LogB LogCd LogCe LogCr LogCo LogCu LogGa LogFe LogLa LogLi LogMn LogMo LogW LogU LogV LogY pH POC PON LogF LogSulfate TDN SQRTTKDN SRP SQRTTP LogTDP DIC LogCa Silica LogAl LogSb SQRTAs LogBa 1.00 LogB 0.35 1.00 LogCd 0.74 0.68 1.00 LogCe 0.30 0.67 0.53 1.00 LogCr 0.53 0.56 0.54 0.60 1.00 LogCo 0.51 0.88 0.73 0.69 0.75 1.00 LogCu 0.38 0.09 0.22 0.19 0.60 0.48 1.00 LogGa 0.42 0.62 0.53 0.79 0.55 0.75 0.41 1.00 LogFe 0.34 0.60 0.49 0.73 0.79 0.76 0.41 0.78 1.00 LogLa 0.35 0.65 0.55 0.99 0.64 0.70 0.25 0.80 0.75 1.00 LogLi 0.53 0.97 0.77 0.69 0.62 0.88 0.16 0.63 0.62 0.68 1.00 LogMn 0.23 0.61 0.39 0.69 0.55 0.64 0.08 0.66 0.90 0.66 0.59 1.00 LogMo 0.70 0.65 0.75 0.28 0.40 0.59 0.09 0.17 0.23 0.28 0.75 0.26 1.00 LogW -0.29 -0.05 0.19 0.18 -0.06 -0.04 -0.19 0.02 0.18 0.19 -0.07 0.12 -0.22 1.00 LogU 0.56 0.76 0.77 0.56 0.41 0.65 -0.11 0.33 0.25 0.55 0.78 0.28 0.77 -0.02 1.00 LogV 0.64 0.66 0.74 0.69 0.72 0.76 0.33 0.66 0.85 0.71 0.77 0.80 0.59 0.11 0.48 1.00 LogY 0.48 0.69 0.65 0.96 0.66 0.73 0.31 0.81 0.73 0.98 0.75 0.61 0.38 0.14 0.58 0.77 1.00

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Supplement B.3: Measured water chemistry variables that were included in the principal component analysis. The median values from the ten Resolute Bay sites are provided for comparison.

Lake Ca Silica Al Sb As Ba B Cd Ce Cr Co (mg/L) (mg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) Garrow 44.7 0.57 79.9 0.089 0.18 46.40 244.0 0.132 0.274 0.21 0.094 LCornwallis1 101.0 0.63 16.0 0.078 0.22 24.90 154.0 0.088 0.041 0.07 0.092 LCornwallis2 39.6 0.63 17.9 0.122 0.12 138.00 21.7 0.048 0.052 0.12 0.029

RR (Resolute Lake) 15.1 0.31 10.5 0.015 0.08 8.25 20.5 0.003 0.037 0.05 0.011 RM (Meretta Lake) 22.9 0.36 4.9 0.030 0.15 9.18 10.8 0.002 0.015 0.04 0.011 RC (Char Lake) 36.2 0.71 108.0 0.027 0.10 15.40 33.8 0.007 0.215 0.21 0.068 Lchar 37.0 0.71 27.5 0.007 0.10 9.47 39.2 0.001 0.029 0.09 0.022 R1 25.6 0.15 6.6 0.016 0.07 12.50 5.5 0.003 0.004 0.04 0.008 R4 31.6 0.21 3.4 0.020 0.15 12.60 21.9 0.002 0.008 0.03 0.015 R3 27.0 0.18 67.8 0.036 0.15 21.40 11.0 0.006 0.013 0.06 0.016 R5 31.6 0.35 9.5 0.032 0.13 11.80 17.0 0.012 0.014 0.09 0.018 R12 (received sewage) 40.8 0.96 12.3 0.126 0.24 20.70 24.9 0.004 0.021 0.08 0.035 R13 (received sewage) 37.8 0.81 2.1 0.155 0.20 29.10 24.6 0.006 0.003 0.15 0.028

Median Resolute Bay 31.6 0.36 10.0 0.029 0.14 12.55 21.2 0.004 0.015 0.07 0.017

Lake pH POC PON F Sulfate TDN TKDN SRP TP TDP DIC (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) Garrow 7.79 0.127 0.011 0.050 180.00 0.217 0.186 0.0002 0.0116 0.0009 10.3 LCornwallis1 7.77 0.276 0.028 0.080 298.00 0.283 0.262 0.0005 0.0053 0.0045 19.5 LCornwallis2 8.06 0.334 0.039 0.040 51.60 0.280 0.331 0.0005 0.0044 0.0014 18.8

RR (Resolute Lake) 8.79 0.194 0.024 0.030 15.30 0.078 0.077 0.0005 0.0027 0.0010 6.6 RM (Meretta Lake) 8.97 0.256 0.035 0.040 8.06 0.188 0.176 0.0006 0.0061 0.0023 17.4 RC (Char Lake) 8.57 0.170 0.017 0.050 34.00 0.092 0.094 0.0003 0.0029 0.0006 20.6 Lchar 8.51 0.195 0.022 0.050 39.40 0.130 0.158 0.0008 0.0013 0.0013 20.6 R1 8.36 0.341 0.032 0.020 1.95 0.161 0.171 0.0008 0.0043 0.0023 22.9 R4 8.44 0.350 0.033 0.030 18.80 0.328 0.397 0.0004 0.0038 0.0021 20.8 R3 8.42 0.239 0.020 0.020 2.51 0.131 0.151 0.0003 0.0102 0.0014 23.3 R5 8.41 0.362 0.030 0.030 17.40 0.216 0.257 0.0002 0.0045 0.0016 21.0 R12 (received sewage) 8.56 0.398 0.040 0.050 11.70 0.304 0.354 0.0007 0.0121 0.0064 29.2 R13 (received sewage) 8.25 0.372 0.044 0.050 9.21 0.346 0.395 0.0008 0.0121 0.0061 28.8

Median Resolute Bay 8.48 0.299 0.031 0.035 13.50 0.175 0.174 0.0006 0.0044 0.0019 20.9 Lake Cu Ga Fe La Li Mn Mo W U V Y (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) Garrow 0.61 0.033 140.0 0.139 19.70 4.57 1.110 0.006 0.8110 0.726 0.144 LCornwallis1 0.52 0.013 37.2 0.017 7.28 2.20 0.731 0.003 0.6570 0.199 0.024 LCornwallis2 0.88 0.007 22.0 0.030 2.23 0.83 0.806 0.001 0.7010 0.219 0.042

RR (Resolute Lake) 0.16 0.002 15.2 0.015 0.90 1.37 0.328 0.002 0.2020 0.063 0.016 RM (Meretta Lake) 0.27 0.001 16.1 0.008 0.76 0.95 0.281 0.008 0.1340 0.110 0.012 RC (Char Lake) 4.81 0.030 148.0 0.101 1.62 3.79 0.188 0.005 0.2020 0.187 0.065 Lchar 0.22 0.006 29.9 0.014 1.85 1.43 0.195 0.001 0.2060 0.061 0.015 R1 1.08 0.003 6.5 0.003 0.35 0.13 0.139 0.004 0.0723 0.025 0.007 R4 0.15 0.002 6.1 0.004 0.92 0.43 0.383 0.002 0.3510 0.023 0.006 R3 0.26 0.009 68.8 0.007 0.66 4.52 0.245 0.005 0.1060 0.175 0.009 R5 0.24 0.001 15.3 0.008 0.91 0.46 0.407 0.011 0.4260 0.050 0.009 R12 (received sewage) 2.58 0.007 26.1 0.011 1.69 0.80 0.594 0.001 0.1670 0.098 0.015 R13 (received sewage) 1.79 0.001 26.9 0.002 1.53 1.00 0.638 0.001 0.1720 0.124 0.005

Median Resolute Bay 0.27 0.003 21.1 0.008 0.92 0.98 0.305 0.003 0.1870 0.081 0.011 248

Appendix C Supporting information for Chapter 2

Table C.1: Selected limnological variables for the 33 lakes sampled around Yellowknife, Northwest Territories, Canada. The data presented here were point measurements taken when the sediment cores were retrieved between March 2014 and July 2016.

Latitude Longitude Distance toDistance Giant Mine toDepth Con Mine Surface areaDissolved organicTotal nitrogen carbon pH Decimal Decimal Lake km km m km2 mg/L mg/L degrees degrees Alexie Lake 62.67648 -114.102 23.52 29.97 21 4.56 13.4 0.51 7.69 Batwing Lake 62.68025 -114.169 22.05 28.96 8 0.22 35.4 2.69 7.13 Duckfish Lake 62.66088 -114.448 18.24 25.84 3 6.28 58.3 3.7 7.29 Frame Lake 62.45712 -114.389 5.35 2.78 5 0.93 42.8 4.02 7.59 Grace Lake 62.42068 -114.431 10.23 3.66 16 0.63 19.7 0.75 7.42 Kam Lake 62.42242 -114.401 9.25 1.97 7.5 2.16 17 0.88 7.55 Keg Lake 62.40597 -114.398 11.09 3.22 1 0.41 45.2 3.77 6.94 Long Lake 62.47439 -114.43 4.69 5.17 6 1.15 12.1 0.6 7.63 Lower Martin Lake 62.51312 -114.419 3.27 9.18 7 1.35 30.3 2.48 7.29 Moose Lake 62.22893 -113.796 42.2 37.25 1 3.68 130 6.64 7.36 Peg Lake 62.3904 -114.41 12.86 5.08 1 0.11 41.9 2.68 7.08 Pocket Lake 62.50897 -114.374 0.94 8.38 2 0.05 25 2.48 7.71 Tees Lake 62.55828 -114.041 17.69 21.98 6 0.17 16.7 0.67 7.52 Vital Lake 62.59334 -114.432 10.74 17.91 10 1.52 15.9 0.7 7.44 YK-O4 62.43778 -114.115 14.58 13.12 4 0.02 150 6.68 6.93 BCR07a 62.54303 -114.353 4.52 12.23 1 0.01 25.7 1.54 8.26 David Lake 62.5432 -114.378 4.55 12.22 2 0.13 37.2 3.03 8.25 Jackfish Lake 62.46703 -114.392 4.25 3.98 7.5 0.52 9.7 0.76 9.07 Upper Martin Lake 62.53041 -114.441 5.04 11.35 3.5 3.05 14.3 0.82 7.83 Niven Lake 62.46131 -114.369 4.62 2.82 1 0.09 28.2 2 9.98 Vee Lake 62.55253 -114.348 5.79 13.61 5.2 0.77 20.6 1.51 8 Range 62.44787 -114.425 7.03 2.97 1 0.19 36 2.27 8.27 BC 02 62.65564 -114.386 16.96 24.64 7.7 0.23 10.8 0.43 7.69 BC 14 62.52516 -114.419 3.92 10.6 0.5 0.2 76.4 5.8 7.81 BC 17 62.49985 -114.421 3.1 7.91 0.5 0.16 35.8 2.59 8.25 BC 18 62.51794 -114.395 2.47 9.46 0.8 0.17 59.4 4.19 8.81 BC 20 62.50542 -114.388 1.52 8.04 0.5 0.36 52.2 3.78 9.76 BC 24 62.54668 -114.745 20.43 22.85 2.9 0.011 19.4 0.84 7.2 BC 43 62.50851 -114.175 9.75 13.1 5 0.4 33.7 1.87 8.64 BC 59 62.41601 -114.136 15.15 12.15 6.7 0.57 44 2.02 8.76 Octopus 62.3748 -114.451 15.23 7.53 0.9 0.71 33.1 1.91 9.48 YK 40 62.36656 -114.134 19.08 14.21 4.6 0.35 26.1 1.36 8.01 YK 42 (Handle) 62.4921 -114.396 2.17 6.65 2.6 0.21 32.9 2.58 8.45

Maximum 62.68025 -113.796 42.2 37.25 21 6.28 150 6.68 9.98 Minimum 62.22893 -114.745 0.94 1.97 0.5 0.01 9.7 0.43 6.93 Median 9.25 10.6 3.5 0.36 32.9 2.02 7.71 Mean 10.68 12.45 4.62 0.95 37.85 2.38 7.97 249

Dissolved organicTotal nitrogen carbon pH Total alkalinitySpecific conductivityTotal dissolvedCalcium solid Chloride MagnesiumPotassium Sodium Sulphate

Lake mg/L µS/cm mg/L mg/L mg/L mg/L mg/L mg/L mg/L

Alexie Lake 59.2 139 88 13.8 3.1 6.2 2.5 4.1 4 Batwing Lake 76 159 120 17.5 0.8 7.2 2.2 3.3 1 Duckfish Lake 118 262 248 33.8 6.1 8.1 3.5 8.7 5 Frame Lake 308 1210 820 120 110 52.2 18 56.4 174 Grace Lake 59.4 187 128 21 9.4 7 1.9 6.6 15 Kam Lake 107 400 246 46.8 33 12.5 3.9 15.2 41 Keg Lake 168 22900 17400 3690 7780 257 56 1820 2920 Long Lake 104 463 268 37.3 68.1 14.6 3.7 30.8 17 Lower Martin Lake 98.6 230 180 27.4 6.6 8.6 2.6 6.7 6 Moose Lake 544 1270 870 96.6 93.8 62.9 27.1 78.1 1 Peg Lake 179 13200 9040 1850 4150 137 29.1 973 1190 Pocket Lake 191 494 330 57 1.7 26.3 5 6.7 66 Tees Lake 76.3 182 112 18.8 4.5 7.3 3.4 7.5 6 Vital Lake 46 116 94 13.7 4.1 4.1 1.2 3.7 3 YK-O4 307 610 540 53.2 4.3 35.2 15.1 31.4 1 BCR07a 118 285 210 34.3 9.4 11.6 2.4 11.5 14 David Lake 115 381 313 42.2 12.4 22.3 3.8 9.2 67 Jackfish Lake 94.2 383 243 35.1 43.5 12.1 3.6 22.2 31 Upper Martin Lake 48.8 123 87 14.9 4.1 4.7 1.3 3.5 5 Niven Lake 89.6 569 379 35.6 110 19.7 3.4 52.6 35 Vee Lake 131 323 220 42.4 13.1 12.1 2.4 7.4 14 Range 110 458 330 48 16.9 23 8 13.3 99 BC 02 26.5 68 63 8.6 1.6 2.2 0.6 2.1 5 BC 14 121 284 319 37.8 2.6 16 4.8 9 20 BC 17 68.8 165 179 19.1 2.1 11.1 2.5 5.5 13 BC 18 150 288 268 26.7 3.5 21.1 7.7 10.7 2 BC 20 121 271 276 23.6 3.7 21.9 6.3 10.9 22 BC 24 27.6 59.5 62 6.1 0.7 4.1 1.8 2.2 1 BC 43 222 410 287 29.9 3.8 34 15.6 19.3 4 BC 59 447 826 564 43.2 29.2 47.9 26.4 62.4 4 Octopus 97.9 198 173 17.1 3.7 13.7 2.7 12.9 2 YK 40 70.2 144 121 15.6 1.4 8.1 3.5 6.1 2 YK 42 (Handle) 129 273 213 32.5 5.2 16 4.3 7.6 11

Maximum 544 22900 17400 3690 7780 257 56 1820 2920 Minimum 26.5 59.5 62 6.1 0.7 2.2 0.6 2.1 1 Median 110 285 246 33.8 5.2 13.7 3.6 9.2 11 Mean 140.28 1434.26 1054.27 200.29 380.07 28.72 8.37 100.62 145.48

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Sulphate Aluminum Antomony Arsenic Barium Lithium ManganeseRubidium Uranium

Lake µg/L µg/L µg/L µg/L µg/L µg/L µg/L µg/L

Alexie Lake 4.5 0.1 1.5 11.2 5.9 2.4 4.6 0.2 Batwing Lake 5.4 0.1 2.6 46.8 7 42.7 5.8 0.4 Duckfish Lake 14.9 0.3 4.8 68.2 12 240 9.9 2.1 Frame Lake 9.7 5.1 343 177 22.9 1560 18 5.4 Grace Lake 27.5 0.6 16.1 9.9 4.5 7.7 2.1 0.6 Kam Lake 28.2 22.6 238 33.6 6.6 23.4 2.8 1.3 Keg Lake 468 31.9 300 269 73.7 621 86.8 1 Long Lake 3.9 1.3 41.7 37.3 5.4 14.5 4.3 0.7 Lower Martin Lake 12.1 1.7 61.4 28.9 5.2 139 5.3 0.4 Moose Lake 34.2 0.2 7.4 340 55.8 2970 5.9 0.8 Peg Lake 435 7.2 2780 257 46.5 3640 39.3 0.6 Pocket Lake 10.1 49.8 2070 79.6 10.7 598 8.6 4.8 Tees Lake 9.2 0.1 2.9 23.4 8.8 3.6 4.3 0.8 Vital Lake 9.3 0.3 4.8 9.4 3.2 13 3 0.4 YK-O4 84.5 0.3 117 116 33.2 1170 9.1 0.1 BCR07a 49.9 1.1 60.9 24.5 8.1 10.7 2.7 0.7 David Lake 54.2 5.6 184 50.4 6.1 139 5 0.8 Jackfish Lake 6.5 1.5 74.5 25.6 6.6 9 2.3 0.5 Upper Martin Lake 14.2 1 25.8 10.8 3.1 51.1 2.6 0.3 Niven Lake 20.1 1.9 41.7 14.2 10.7 22.5 2.2 0.6 Vee Lake 41.7 1 34.2 27.2 6.4 14 2.4 0.4 Range 16.4 1.1 38.9 40.7 15.9 19.4 7.3 1.9 BC 02 14.8 0.3 3.3 6.5 2.6 21 2 1 BC 14 515 17.7 1060 74.4 11.3 170 11.1 3 BC 17 101 12.3 750 31.6 5.6 28.9 5.4 0.9 BC 18 20.7 6.4 246 71.7 18.4 50.8 14.2 0.4 BC 20 98.6 12.2 697 50.2 14.5 32.6 10.2 5 BC 24 26.8 0.2 17.2 10.6 2.9 9.8 4.7 0.1 BC 43 11.7 0.3 16.4 47.9 22.9 69.7 15 0.2 BC 59 4.5 0.1 42.9 83.1 51.5 24.9 5.3 0.3 Octopus 15 0.2 11.8 29 14.3 28.8 1.8 0.2 YK 40 14.6 0.2 7.9 22.7 6.9 22.5 4.8 0.1 YK 42 (Handle) 31.7 7.5 170 35.7 7.9 215 8.8 1.1

Mean 67.08788 5.824242 287.0818 65.57879 15.6697 363.1818 9.624242 1.124242 Maximum 515 49.8 2780 340 73.7 3640 86.8 5.4 Minimum 3.9 0.1 1.5 6.5 2.6 2.4 1.8 0.1

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Table C.2: Pearson correlation matrix of the environmental variables (following transformations described in text) from the 31 lakes analyzed in the ordination analysis. (full versions for the abbreviations follow in the next page)

LogTN LogDOC LogTalk LogSpecCondSqurtantilogpHLogTDissSolidLogCa LogCl LogMg LogK LogNa LogSO24LogAlt LogSbt LogAst LogBat LogLit LogMnt LogRbt LogUt LogDistGiantLogDistConLogDepthLogSurfareaLatdd LogTN 1 LogDOC 0.94 1 LogTalk 0.70 0.69 1 LogSpecCond 0.57 0.52 0.91 1 SqurtantilogpH 0.03 0.16 -0.07 -0.14 1 LogTDissSolid 0.72 0.67 0.92 0.97 -0.14 1 LogCa 0.60 0.51 0.86 0.96 -0.05 0.93 1 LogCl 0.08 0.05 0.51 0.76 -0.21 0.66 0.71 1 LogMg 0.71 0.67 0.94 0.93 -0.24 0.95 0.85 0.54 1 LogK 0.65 0.69 0.92 0.85 -0.09 0.87 0.74 0.45 0.93 1 LogNa 0.44 0.45 0.82 0.92 -0.21 0.89 0.80 0.82 0.85 0.81 1 LogSO24 0.01 -0.20 0.11 0.39 -0.40 0.34 0.49 0.48 0.27 0.07 0.23 1 LogAlt 0.51 0.51 0.08 0.02 -0.05 0.19 0.13 -0.15 0.15 0.05 -0.03 0.11 1 LogSbt 0.32 0.09 0.14 0.26 -0.32 0.32 0.39 0.13 0.28 0.07 0.07 0.68 0.48 1 LogAst 0.49 0.31 0.36 0.41 -0.30 0.50 0.48 0.13 0.50 0.32 0.25 0.57 0.53 0.91 1 LogBat 0.83 0.79 0.90 0.80 0.08 0.87 0.81 0.34 0.86 0.86 0.66 0.08 0.19 0.23 0.42 1 LogLit 0.70 0.77 0.91 0.80 -0.12 0.84 0.69 0.38 0.89 0.94 0.79 -0.05 0.08 -0.05 0.21 0.84 1 LogMnt 0.78 0.73 0.66 0.58 0.28 0.67 0.64 0.18 0.61 0.56 0.41 0.03 0.28 0.30 0.40 0.78 0.55 1 LogRbt 0.67 0.63 0.53 0.38 0.09 0.49 0.40 -0.10 0.55 0.65 0.23 0.05 0.17 0.28 0.40 0.69 0.54 0.59 1 LogUt 0.29 0.13 0.21 0.35 -0.17 0.38 0.48 0.27 0.26 0.14 0.15 0.72 0.24 0.66 0.52 0.35 0.13 0.33 0.33 1 LogDistGiant -0.22 0.03 -0.09 -0.17 0.46 -0.21 -0.26 -0.05 -0.22 0.01 -0.01 -0.62 -0.28 -0.86 -0.82 -0.09 0.09 -0.14 -0.24 -0.52 1 LogDistCon 0.00 0.15 -0.11 -0.39 0.38 -0.34 -0.40 -0.53 -0.30 -0.14 -0.39 -0.72 -0.05 -0.59 -0.56 -0.02 -0.03 0.03 0.07 -0.35 0.52 1 LogDepth -0.62 -0.54 -0.23 -0.21 0.43 -0.37 -0.21 0.02 -0.39 -0.24 -0.20 -0.15 -0.65 -0.47 -0.54 -0.38 -0.34 -0.31 -0.28 -0.35 0.42 0.12 1 LogSurfarea -0.16 -0.14 0.07 0.09 0.03 0.01 0.11 0.32 -0.06 0.01 0.10 -0.02 -0.36 -0.15 -0.33 0.04 0.00 0.03 -0.08 0.10 0.25 0.03 0.36 1 Latdd -0.31 -0.36 -0.53 -0.57 0.18 -0.56 -0.48 -0.50 -0.60 -0.57 -0.66 -0.04 -0.16 -0.10 -0.26 -0.43 -0.53 -0.34 0.02 0.06 -0.03 0.39 0.28 -0.04 1

252

Abbreviations LogTN – Log transformed total nitrogen LogDOC – Log transformed dissolved organic carbon LogTalk – Log transformed total alkalinity LogSpecCond – Log transformed specific conductivity SqurtantilogpH – Squareroot transformed concentrations of hydronium (H+) ions LogTDissSolid – Log transformed total dissolved solids LogCa – Log transformed calcium LogCl – Log transformed chloride LogMg – Log transformed magnesium LogK – Log transformed potassium LogNa – Log transformed sodium LogSO24 – Log transformed sulphate LogAlt – Log transformed total aluminum LogSbt – Log transformed total antimony LogAst – Log transformed total arsenic LogBat – Log transformed total barium LogLit – Log transformed total lithium LogMnt – Log transformed total manganese LogRbt – Log transformed total rubidium LogUt – Log transformed total uranium LogDistGiant – Log transformed distance from Giant Mine LogDistCon – Log transformed distance from Con Mine LogDepth – Log transformed depth LogSurfarea – Log transformed surface area Latdd – Latitude in decimal degrees

253

Appendix D Supporting information for Chapter 3

Figure D.1: Dating profiles for Grace and Alexie lakes. Top panels (a & b) - Radiometric dating analysis showing the total 210Pb (black circles), and average 214Pb (grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Grace (left) and Alexie (right) lakes. Bottom panels (c & e) - Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Grace and Alexie lakes. Bottom panels (d & f) - The CRS-derived sedimentation rates (g cm^-2 year^-1) and associated standard errors (using horizontal error bars) plotted against core depth for Grace and Alexie lakes.

Grace Lake Alexie Lake

0 0 2 2 4 4 6 6 8 8 10 10 12 12 14 14 16 16 18 18

20 20

Sediment core (cm) depth Sediment core (cm) depth 22 22 24 24

0 200 400 600 800 1000 1200 1400 0 500 1000 1500 2000 2500 3000 210 214 -1 210 214 -1 Pb & Pb activity (Bq kg ) Pb & Pb activity (Bq kg )

0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7

8 8

Sediment core depth (cm) Sediment core depth (cm) 9 9 10 10

0.00 0.01 0.02 0.03 0.00 0.01 0.02 0.03 1900192019401960198020002020 1900192019401960198020002020 CRS sedimentation CRS sedimentation CRS dates (g/cm2/year) CRS dates (g/cm2/year)

254

Figure D.2: Metal concentrations (weight per gram dry weight) in the sediments from Alexie Lake scaled by 210Pb estimated years. The dates beyond background 210Pb levels were extrapolated using a second-order polynomial. These dates have been identified with an asterisk (*) and should be interpreted with caution.

Arsenic Antimony Lead

2020 2020 2000 2000 1980 1980 1960 1960 1940 1940

1920 1920 Year(CE) 1900 1900 1880 1880

Year (CE) Year 1860 1860 1840 1840 1820 1820 1800 1800 1780 Alexie 1780 1760 1760

0 10 20 30 400 1 2 3 0 5 10 15 Metal concentrations (µg/g)

255

Figure D.3: Sedimentary metal concentrations from Grace and Alexie lakes in relation to titanium. The scaled by 210Pb estimated years. The dates beyond background 210Pb levels were extrapolated using a second-order polynomial. These dates have been identified with an asterisk (*) and should be interpreted with caution. As:Ti Sb:Ti Pb:Ti

2020 2020 2000 2000 1980 1980 1960 1960 1940 1940 1920 1920 1900 1900 1880 1880

Year (CE) Year 1860 1860 1840 1840 1820 1820 1800 1800 1780 Grace 1780 Alexie 1760 1760

0 1 2 3 4 0.0 0.1 0.2 0.0 0.1

Ratio of metals in relation to titanium

256

Appendix E Supporting information for Chapter 4

Monitoring records Table E.1: Long-term meteorological records from Yellowknife Airport (Climate Station ID: 2204100/2204101) were retrieved from the Adjusted and Homogenized Canadian Climate Database. The air temperature, wind speed, and rainfall records were mostly continuous and contained only a few years from the 1940s where measurements were missing. The length of records for each meteorological parameter varied, therefore the length of records and the missing years are provided in the table below. Last Missing years Beginnin year Parameter g of Summe Autum include Annual Winter Spring record r n d Air 1942, 1942 2016 1942 1942 1942 1945 temperature 1945 Wind speed 1953 2014 None None None None None

Rainfall 1943 2012 None 1943 None None None

Table E.2: Lake ice-cover data for Jackfish Lake nor for the other study lakes were not available. Therefore, we assessed regional lake ice-cover dynamics from three sites around Yellowknife where data were available, namely Long Lake, Frame Lake and Back Bay of Great Slave Lake (Benson et al. 2013). Similar to the meteorological data, the ice-cover data were also mostly continuous between the mid-1950s and the early- 1990s. List of years where ice-cover data were not available for the three sites in Yellowknife are provided below. Beginning Last year Missing years Lake of record included Ice-free Ice-cover 1974, 1982, 1983, 1955, 1974, 1983, Long Lake 1955 1994 1984, 1985 1984,1985 1956, 1973, 1983, Frame Lake 1956 1994 1973, 1982, 1993 1994 Back Bay, 1982, 1983, 1984, 1983, 1984, 1985, Great Slave 1957 1994 1985, 1993 1994 Lake

257

Population of Yellowknife Table E.3: The population of Yellowknife between 1939 and 2016 were compiled from a variety of sources. The table below provides year, population, and source of the information. Year Population Source 1939 1000 Ostergaard 1976 1941 1410 Ostergaard 1976 1945 3000 Ostergaard 1976 1951 2724 Ostergaard 1976 1956 3100 Ostergaard 1976 1961 3245 Ostergaard 1976, Bourne 1963 1962 3250 Bourne 1963 1964 3787 Ostergaard 1976 1966 3741 Ostergaard 1976 1970 6000 Northwest Territories Community Data 1971 1971 6122 Ostergaard 1976 1973 7500 Ostergaard 1976 1974 8100 Ostergaard 1976 1975 8100 Canada North Almanac 1975 1976 9000 Canada North Almanac 1976 1977 9950 Canada North Almanac 1977 1979 9918 Northwest Territories Databook 1981 1980 9731 Northwest Territories Databook 1982-83, 1982 1981 9483 Northwest Territories Databook 1986-1987, 1986 1985 11077 Northwest Territories Databook 1986-1987, 1986 1986 11753 Northwest Territories Databook 1990/91, 1990 1988 13011 Northwest Territories Databook 1990/91, 1990 1991 15179 Statistics Canada 1996 1996 17275 Statistics Canada 1996 2001 16541 Statistics Canada 2007 2006 18700 Statistics Canada 2007 2011 19234 Statistics Canada 2017 2016 19569 Statistics Canada 2017

Details of references

Bourne, L. S., 1963. Yellowknife, N.W.T.: A Study of its Urban and Regional Economy. Northern Co-ordination and Research Centre, Department of Northern Affairs and National Resources, Ottawa, Ontario, Canada.

258

Canada North Almanac, 1975. Research Institute of Northern Canada, Yellowknife, Northwest Territories, Canada.

Canada North Almanac, 1976. Wood, D. G., (Ed.), Research Institute of Northern Canada, Yellowknife, Northwest Territories, Canada.

Canada North Almanac, 1977. Koring, P., (Ed.), Research Institute of Northern Canada, Yellowknife, Northwest Territories, Canada.

Northwest Territories Community Data, 1971. Department of Industry and Development, Government of the Northwest Territories, Canada.

Northwest Territories Databook 1981, 1981. Devine, M., (Ed.), Outcrop Ltd. The Northern Publishers, Yellowknife, Northwest Territories, Canada.

Northwest Territories Databook 1982-83, 1982. Devine, M., (Ed.), Outcrop Ltd. The Northern Publishers, Yellowknife, Northwest Territories, Canada.

Northwest Territories Databook 1986-1987, 1986. Heming, R., (Ed.), Outcrop Ltd. The Northern Publishers, Yellowknife, Northwest Territories, Canada.

Northwest Territories Databook 1990/91, 1990. Outcrop Ltd. The Northern Publishers, Yellowknife, Northwest Territories, Canada.

Ostergaard, P. E., 1976. Quality of Life in a Northern City: A Social Geography of Yellowknife, N.W.T. Master of Arts Thesis, Department of Geography, The University of British Columbia.

Statistics Canada, 1996. Population and Dwelling Counts, 1991 and 1996 Censuses. Statistics Canada Catalogue No. 93-357-XPB. https://www150.statcan.gc.ca/n1/c1996- r1996/4129976-eng.htm.

Statistics Canada, 2007. 2006 Community Profiles. 2006 Census. Statistics Canada Catalogue No. 92-591-XWE. Ottawa. https://www12.statcan.gc.ca/census- recensement/2006/dp-pd/prof/92-591/index.cfm?Lang=E.

Statistics Canada. Census Profile, 2017. 2016 Census. Statistics Canada Catalogue No. 98-316-X2016001. Ottawa. http://www12.statcan.gc.ca/census-recensement/2016/dp- pd/prof/index.cfm?Lang=E.

259

Figure E.1: Dating profile of Jackfish Lake Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Jackfish Lake in Yellowknife, Northwest Territories. Bottom panel (left) - Estimated 210Pb ages (using Constant Rate of Supply (CRS) model) with associated standard errors (using horizontal error bars) plotted against core depth for Jackfish Lake. Bottom panel (right) – The CRS-derived sedimentation rates (g/cm2/year) and associated standard errors (using horizontal error bars) plotted against core depth.

260

Figure E.2: Dating profile of study lakes. Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Alexie Lake, BC-02, BC-43, and YK-40. Bottom panel – Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Alexie Lake, BC-02, BC-43, and YK-40.

261

Figure E.3: Dating profile of study lakes. Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Grace Lake, Kam Lake, Niven Lake, Pocket Lake. Bottom panel – Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Grace Lake, Kam Lake, Niven Lake, Pocket Lake.

262

Figure E.4: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa (2% in at least 2 samples) from Jackfish Lake, Yellowknife, Northwest Territories. The solid black line identifies the beginning of regional gold mining era, while the grey dashed line indicates the end of gold mining operations. The light grey dotted line indicates the onset of algal blooms at Jackfish Lake.

263

Figure E.5: Cladoceran stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa (2% in at least 2 samples) from Jackfish Lake, Yellowknife, Northwest Territories. The sum of all planktonic and littoral taxa is also included in the figure. The solid black line identifies the beginning of regional gold mining era, while the grey dashed line indicates the end of gold mining operations. The light grey dotted line indicates the onset of algal blooms at Jackfish Lake.

264

Figure E.6: Chironomid stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa (5% in at least 2 samples) from Jackfish Lake, Yellowknife, Northwest Territories. The sum of all profundal and littoral taxa are also included in the figure. The solid black line identifies the beginning of regional gold mining era, while the grey dashed line indicates the end of gold mining operations. The light grey dotted line indicates the onset of algal blooms at Jackfish Lake.

265

Figure E.7: The mean annual and seasonal air temperature data collected at the Yellowknife Airport climate station. The Thiel-Sen slopes (annual change) and significance levels from the Mann-Kendall trends tests are also included.

266

Figure E.8: The mean annual and seasonal rainfall data collected at the Yellowknife Airport climate station. The Thiel-Sen slopes (annual change) and significance levels from the Mann-Kendall trends tests are also included.

267

Figure E.9: The mean annual and seasonal windspeed data collected at the Yellowknife Airport climate station. The Thiel-Sen slopes (annual change) and significance levels from the Mann-Kendall trends tests are also included.

268

Figure E.10: Past changes in ice-cover duration for three lakes in Yellowknife. The panels on the left show the past trends in the duration of ice-free season and the right panel shows the duration of ice-on season at the three lakes. The Thiel-Sen slopes (annual change) and significance levels from the Mann-Kendall trends tests are also included.

269

Figure E.11: Past changes in whole lake primary production (VNIRS inferences of sedimentary Chl-a) and cyanobacterial abundance (VNIRS inferences of sedimentary cyanobacterial pigments) for lakes around Yellowknife, Northwest Territories.

270

Figure E.12: Standardized (Z-scores) trends in past whole lake primary production (VNIRS Chl-a) and cyanobacteria abundance (VNIRS-Cyano) of lakes around Yellowknife, Northwest Territories.

271

Appendix F Supporting information for Chapter 5

Figure F.1: Dating profile of study lakes. Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Alexie Lake, BC-02, and BC-43. Bottom panel – Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Alexie Lake, BC-02, and BC-43.

272

Figure F.2: Dating profile of study lakes. Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Grace Lake, Kam Lake, Niven Lake, Pocket Lake. Bottom panel – Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Grace Lake, Kam Lake, Niven Lake, Pocket Lake.

273

Figure F.3: Dating profile of study lakes. Top panel - Radiometric dating analysis showing 210Pb (black circles), and average 214Pb (dashed grey vertical line) activities from selected sedimentary intervals (Becquerels per kilogram) plotted against core depth for Jackfish Lake, BC-17, and Vee Lake. Bottom panel – Estimated 210Pb ages (using Constant Rate of Supply model) with associated standard errors (using horizontal error bars) plotted against core depth for Jackfish Lake, BC-17, and Vee Lake.

274

Figure F.4: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa from Alexie Lake, Yellowknife, Northwest Territories.

275

Figure F.5: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa from Grace Lake, Yellowknife, Northwest Territories.

276

Figure F.6: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa from Jackfish Lake, Yellowknife, Northwest Territories.

277

Figure F.7: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa from Kam Lake, Yellowknife, Northwest Territories.

278

Figure F.8: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa from Niven Lake, Yellowknife, Northwest Territories.

279

Figure F.9: Diatom stratigraphic profile scaled by core depth (CRS dates plotted secondarily) showing relative abundances of the most common taxa from Pocket Lake, Yellowknife, Northwest Territories.

280

Appendix G Raw diatom data from Chapter 1

Taxa Long Batwing YKO-4 Duckfish Tees Vital Lower MartinNiven Peg Jackfish BC-43 Kam Grace Range Frame Alexie Achnanthes thermalis 112 Achnanthidium minutissimum 12 107 65 70 2 40 194 12 1 19 10 4 15 107 59 19 Achnanthidium exiguum 9 2 Karayevia clevei 2 2 Platessa conspicua 1 1 Planothidium lanceolatum 3 1 2 16 14 Planothidium delicatulum 8 15 6 Psammothidium marginulatum 2 10 1 Psammothidium curtissimum 2 2 2 33 15 Psammothidium subatomoides 19 Rossithidium pussillum 9 2 3 4 2 3 1 3 2 Rossithidium petersenii 1 Achnanthes ricula 2 Karayevia suchlandtii 1 Psammothidium cf. bioretii Achnanthes acares Aulacoseira subarctica 12 1 30 Aulacoseira perglabra complex Aulacoseira ambigua 2 Aulacoseira cf. nygardii Amphora pediculus 5 2 12 114 1 3 5 Amphora libyca 3 25 3 2

281

Taxa Long Batwing YKO-4 Duckfish Tees Vital Lower MartinNiven Peg Jackfish BC-43 Kam Grace Range Frame Alexie Amphora ovalis Amphora commutata 7 Amphora veneta Asterionella formosa 25 5 35 42 45 Amphipleura pellucida Brachysira neoexilis 44 2 68 1 23 1 33 5 Brachysira sp. BC-14 Brachysira brebissonii 1 2 1 Brachysira vitrea 2 Cymbella cistula 1 Cymbopleura amphicephala Cymbopleura naviculiformis 5 Cymbopleura lapponica 1 18 8 Cymbopleura lata Delicata delicatula 4 6 2 Encyonema neogracile 1 Encyonema silesiacum 5 4 2 7 6 1 4 Encyonopsis descripta 4 2 Encyonopsis falaisensis Encyonopsis microcephala 2 22 2 31 8 45 2 1 17 5 13 Encyonopsis cesatii Cymbopleura incerta Navicymbula cf. pusilla Cymbella tumida Reimeria sinuata 2 Cyclotella bodanica var. lemanica 3 10 5 2 6 1 1 Cyclotella ocellata 30 29 Cyclotella tripartita type 26 Cyclotella tripartita 14 Cyclotella cf. comensis 7 Discostella stelligera complex 10 156 104 289 335 50 1 24 321 1 196 Lindavia michiganiana 6 41 52 3 21 9 8 1 4 1 24 24 Lindavia antiqua Lindavia rossii 2 Discostella cf. stelligera 282

Taxa Long Batwing YKO-4 Duckfish Tees Vital Lower MartinNiven Peg Jackfish BC-43 Kam Grace Range Frame Alexie Cyclotella cf. meneghiniana 2 Caloneis silicula 6 2 6 Caloneis bacillum 1 Cocconeis placentula 3 98 3 11 1 Cocconeis pediculus 3 Denticula kuetzingii 2 14 2 265 2 Denticula kuetzingii var. rurichae Denticula valida Diploneis ovalis 1 Diploneis marginestriata 2 1 1 3 Diatoma tenuis 13 2 4 2 Diatoma moniliformis 6 Eunotia bidentula 8 Eunotia bilunaris 16 Eunotia faba 17 Eunotia formica 1 Eunotia incisa Eunotia soleirolii Eunotia unknown girdle YK-40 Eunotia unknown girdle BC-18 Epithemia argus 25 Epithemia sorex 1 Epithemia adnata 2 Epithemia smithii 3 Frustulia rhomboides Fragilaria capucina 8 17 5 22 13 5 1 Fragilaria mesolepta 198 Fragilaria nanana 5 10 11 30 4 Fragilaria tenera 3 2 3 10 4 Fragilaria crotonensis Fragilaria parasitica 6 Fragilaria constricta 2 Pseudostaurosira brevistriata 26 12 15 2 72 250 20 6 51 Pseudostaurosira brevistriata type 412 37 43 Staurosira construens 27 7 2 15 5 8 283

Taxa Long Batwing YKO-4 Duckfish Tees Vital Lower MartinNiven Peg Jackfish BC-43 Kam Grace Range Frame Alexie Staurosira construens var. venter 148 47 3 3 46 50 254 12 78 6 Staurosirella pinnata 267 2 97 57 4 7 2 81 25 58 22 7 182 8 Synedra ulna 1 2 1 Fragilaria vaucheriae Pseudostaurosira cf. pseudoconstruens 6 Synedra cyclopum Gomphonema acuminatum 2 3 Gomphonema parvulum 15 2 Gomphonema pumilum 12 2 4 Gomphonema cf. gracile Gomphonema olivaceum Gyrosigma acuminatum 1 1 Melosira arentii 1 Navicula angusta Navicula submuralis 34 11 2 3 2 2 Navicula pseudoventralis 14 11 2 Navicula seminuloides 27 2 Navicula minima 4 44 1 18 3 4 2 Navicula pupula 2 8 39 1 7 1 12 5 2 6 1 6 Navicula kuelbsii 10 1 2 2 5 3 Navicula schmassmannii 2 2 Navicula radiosa 24 10 20 1 1 42 4 4 6 Navicula cryptotenella 20 5 32 5 11 15 2 3 2 2 28 14 5 Navicula cryptocephala 4 8 18 6 12 4 13 7 3 4 3 Navicula pseudocutiformis 2 1 Navicula subminuscula 1 Navicula jaagii 4 2 9 Navicula halophila 1 4 Navicula tusculus 2 2 Navicula libonensis Navicula seminulum 9 7 1 2 Navicula lapidosa 1 Navicula gastrum 2 1 Navicula peregrina 5 3 Navicula cocconeiformis 1 284

Taxa Long Batwing YKO-4 Duckfish Tees Vital Lower MartinNiven Peg Jackfish BC-43 Kam Grace Range Frame Alexie Navicula digitoradiata var. minima Navicula pseudolanceolata 2 Navicula subrotundata Navicula striolata Navicula absoluta Navicula cuspidata 1 Navicula incertata 32 Navicula rhyncocephala 1 Navicula pseudotuscula Kobayasiella subtilissima Navicula disjuncta Navicula capitata Navicula elginensis 1 Navicula gerlofii Navicula stroemii Navicula tridentula Navicula soehrensis var. hassiaca Navicula ventralis Navicula jarnefeltii Cavinula cf. pusio 1 Navicula crucicula Navicula vulpina Navicula detenta Navicula cf. bryophila Neidium ampliatum 2 2 1 Hantzschia spp. BC-18 Nitzschia frustulum 6 20 28 20 15 1 4 4 7 3 6 2 Nitzschia fonticola 33 1 30 2 110 4 10 7 14 2 32 8 Nitzschia pura 6 2 4 3 2 Nitzschia palea 4 20 7 2 6 1 1 2 Nitzschia inconspicua 11 Nitzschia fossilis 2 Nitzschia gracilis 2 11 Nitzschia dissipata 3 6 2 1 2 3 Nitzschia linearis 3 2 285

Taxa Long Batwing YKO-4 Duckfish Tees Vital Lower MartinNiven Peg Jackfish BC-43 Kam Grace Range Frame Alexie Nitzschia paleaeformis 2 Nitzschia perminuta 2 2 Nitzschia valdestriata Nitzschia angustata 4 Nitzschia amphibia 1 2 Nitzschia microcephala Nitzschia heufleriana Rhopalodia gibba Pinnularia interrupta 16 2 Pinnularia microstauron Pinnularia gibba 2 2 Melosira numnuloides 1 Stephanodiscus spp. Vee Lake Stephanodiscus parvus 5 6 2 Stephanodiscus parvus type 7 6 Stephanodiscus minutulus type 9 7 Stephanodiscus medius 1 1 28 3 6 15 Stephanodiscus alpinus Stephanodiscus hantzschii 8 11 3 Stephanodiscus minutulus 22 6 6 Stephanodiscus cf. medius Stephanodiscus hantzschii-minutulus 3 Stephanodiscus medius-minutulus 132 Stephanodiscus cf. minutulus 7 Stauroneis phoenicenteron 1 4 1 1 Stauroneis tackei 2 Stauroneis anceps Stauroneis smithii Stauroneis cf. recondita Surirella linearis 1 Tabellaria flocculosa str. III 1 3 2 Tabellaria flocculosa str. IV 1 Tabellaria flocculosa str. IIIp 4 Tabellaria fenestrata Tabellaria quadriseptata 1 286

Taxa Moose BC-02 Keg Pocket YK-40 BC-59 Octopus YK-42 Vee David BC-17 Upper MartinBC-14 BC-24 BCR-07a BC-20 BC-18 Achnanthes thermalis 51 Achnanthidium minutissimum 27 25 9 2 74 25 239 33 71 56 137 70 242 44 85 214 103 Achnanthidium exiguum 1 Karayevia clevei 1 Platessa conspicua 14 18 3 Planothidium lanceolatum 1 16 1 6 Planothidium delicatulum Psammothidium marginulatum 1 Psammothidium curtissimum 12 2 3 17 6 Psammothidium subatomoides Rossithidium pussillum 7 4 2 2 5 Rossithidium petersenii Achnanthes ricula Karayevia suchlandtii Psammothidium cf. bioretii 1 Achnanthes acares 14 Aulacoseira subarctica Aulacoseira perglabra complex 126 Aulacoseira ambigua Aulacoseira cf. nygardii 1 Amphora pediculus 22 56 19 11 2 Amphora libyca 1 1 2

287

Taxa Moose BC-02 Keg Pocket YK-40 BC-59 Octopus YK-42 Vee David BC-17 Upper MartinBC-14 BC-24 BCR-07a BC-20 BC-18 Amphora ovalis 3 35 Amphora commutata Amphora veneta 6 Asterionella formosa 2 54 33 Amphipleura pellucida 2 Brachysira neoexilis 1 1 9 3 30 63 2 37 2 4 53 21 Brachysira sp. BC-14 1 Brachysira brebissonii 2 2 Brachysira vitrea 5 Cymbella cistula 2 3 4 Cymbopleura amphicephala 2 Cymbopleura naviculiformis 2 Cymbopleura lapponica 2 23 20 6 4 29 Cymbopleura lata 2 Delicata delicatula 2 Encyonema neogracile Encyonema silesiacum 10 2 1 8 Encyonopsis descripta 74 2 17 32 21 8 11 Encyonopsis falaisensis 2 2 Encyonopsis microcephala 4 4 5 6 21 8 13 25 62 1 23 5 41 68 Encyonopsis cesatii 2 Cymbopleura incerta 4 6 2 Navicymbula cf. pusilla 2 Cymbella tumida 1 Reimeria sinuata Cyclotella bodanica var. lemanica 5 5 1 2 1 Cyclotella ocellata 33 Cyclotella tripartita type Cyclotella tripartita Cyclotella cf. comensis Discostella stelligera complex 1 194 30 60 48 470 143 214 48 21 14 291 15 1 Lindavia michiganiana 18 34 22 1 25 33 47 18 8 15 14 17 2 Lindavia antiqua 2 Lindavia rossii Discostella cf. stelligera 13 288

Taxa Moose BC-02 Keg Pocket YK-40 BC-59 Octopus YK-42 Vee David BC-17 Upper MartinBC-14 BC-24 BCR-07a BC-20 BC-18 Cyclotella cf. meneghiniana 4 Caloneis silicula Caloneis bacillum Cocconeis placentula 180 43 45 2 2 Cocconeis pediculus Denticula kuetzingii 84 3 12 7 12 3 10 2 Denticula kuetzingii var. rurichae 8 Denticula valida 2 Diploneis ovalis Diploneis marginestriata 4 1 2 1 Diatoma tenuis Diatoma moniliformis Eunotia bidentula Eunotia bilunaris Eunotia faba Eunotia formica Eunotia incisa 5 Eunotia soleirolii 1 Eunotia unknown girdle YK-40 2 Eunotia unknown girdle BC-18 2 Epithemia argus Epithemia sorex 2 Epithemia adnata 27 1 Epithemia smithii Frustulia rhomboides 4 Fragilaria capucina 8 2 5 14 9 10 2 3 Fragilaria mesolepta 6 Fragilaria nanana 16 2 3 28 2 36 Fragilaria tenera 3 5 1 1 5 41 1 Fragilaria crotonensis 3 8 Fragilaria parasitica 6 1 Fragilaria constricta 2 Pseudostaurosira brevistriata 14 5 2 17 10 14 5 2 Pseudostaurosira brevistriata type 209 Staurosira construens 16 28 1 289

Taxa Moose BC-02 Keg Pocket YK-40 BC-59 Octopus YK-42 Vee David BC-17 Upper MartinBC-14 BC-24 BCR-07a BC-20 BC-18 Staurosira construens var. venter 83 172 12 16 4 165 1 172 1 Staurosirella pinnata 7 20 72 19 64 6 91 22 21 7 Synedra ulna Fragilaria vaucheriae 9 2 Pseudostaurosira cf. pseudoconstruens Synedra cyclopum 7 Gomphonema acuminatum 2 2 2 Gomphonema parvulum 17 3 Gomphonema pumilum 53 11 28 11 9 Gomphonema cf. gracile 2 Gomphonema olivaceum 3 1 Gyrosigma acuminatum 4 1 Melosira arentii Navicula angusta 1 Navicula submuralis 13 1 12 2 6 Navicula pseudoventralis 9 2 37 15 Navicula seminuloides Navicula minima 4 5 29 6 10 6 6 28 5 Navicula pupula 11 21 8 2 4 3 12 8 32 27 3 13 5 Navicula kuelbsii 11 24 1 Navicula schmassmannii 1 Navicula radiosa 27 6 6 14 4 51 15 1 37 2 10 30 Navicula cryptotenella 21 1 1 95 6 4 34 10 1 28 19 28 3 6 23 168 Navicula cryptocephala 2 2 42 10 15 3 9 8 2 12 15 3 5 30 Navicula pseudocutiformis 3 Navicula subminuscula Navicula jaagii 3 17 3 6 Navicula halophila 6 5 Navicula tusculus 1 4 Navicula libonensis 33 Navicula seminulum 6 5 31 2 55 33 Navicula lapidosa Navicula gastrum Navicula peregrina 2 1 2 Navicula cocconeiformis 1 290

Taxa Moose BC-02 Keg Pocket YK-40 BC-59 Octopus YK-42 Vee David BC-17 Upper MartinBC-14 BC-24 BCR-07a BC-20 BC-18 Navicula digitoradiata var. minima 11 1 Navicula pseudolanceolata Navicula subrotundata 1 Navicula striolata 1 3 Navicula absoluta 1 Navicula cuspidata Navicula incertata 25 Navicula rhyncocephala 3 2 2 8 Navicula pseudotuscula 1 Kobayasiella subtilissima 2 5 Navicula disjuncta 5 2 49 Navicula capitata 12 Navicula elginensis 11 Navicula gerlofii 2 Navicula stroemii 46 2 Navicula tridentula 4 2 Navicula soehrensis var. hassiaca 15 Navicula ventralis 2 Navicula jarnefeltii 1 Cavinula cf. pusio Navicula crucicula 1 1 2 Navicula vulpina 1 Navicula detenta 15 Navicula cf. bryophila 6 Neidium ampliatum 1 1 3 4 1 Hantzschia spp. BC-18 5 Nitzschia frustulum 40 6 1 1 10 2 2 4 4 1 14 Nitzschia fonticola 26 10 7 2 2 2 9 1 3 24 Nitzschia pura 1 Nitzschia palea 26 6 1 12 4 4 32 3 19 16 7 5 19 Nitzschia inconspicua 9 Nitzschia fossilis Nitzschia gracilis Nitzschia dissipata 9 2 4 1 Nitzschia linearis 2 1 2 1 291

Taxa Moose BC-02 Keg Pocket YK-40 BC-59 Octopus YK-42 Vee David BC-17 Upper MartinBC-14 BC-24 BCR-07a BC-20 BC-18 Nitzschia paleaeformis Nitzschia perminuta 45 8 4 Nitzschia valdestriata 7 Nitzschia angustata 17 Nitzschia amphibia 6 4 7 3 8 3 1 1 2 Nitzschia microcephala 24 Nitzschia heufleriana 1 Rhopalodia gibba 5 2 2 Pinnularia interrupta 2 4 4 4 6 Pinnularia microstauron 2 Pinnularia gibba 7 1 4 Melosira numnuloides 6 3 Stephanodiscus spp. Vee Lake 1 Stephanodiscus parvus 13 1 Stephanodiscus parvus type Stephanodiscus minutulus type Stephanodiscus medius 4 Stephanodiscus alpinus 1 Stephanodiscus hantzschii 5 Stephanodiscus minutulus Stephanodiscus cf. medius 5 Stephanodiscus hantzschii-minutulus Stephanodiscus medius-minutulus Stephanodiscus cf. minutulus Stauroneis phoenicenteron 3 4 2 1 1 1 1 1 Stauroneis tackei 76 Stauroneis anceps 2 3 16 1 Stauroneis smithii 1 1 Stauroneis cf. recondita 1 Surirella linearis Tabellaria flocculosa str. III Tabellaria flocculosa str. IV 2 Tabellaria flocculosa str. IIIp Tabellaria fenestrata 1 Tabellaria quadriseptata 292

Appendix H Raw diatom data for Alexie Lake

Taxa 0-0.5 1-1.5 2-2.5 3-3.5 4-4.5 5-5.5 6-6.5 7-7.5 8-8.5 9-9.5 11-11.5 12-12.5 13-13.5 14-14.5 15-15.5 16-16.5 17-17.5 18-18.5 Achnanthidium minutissimum 19 16 25 21 27 25 20 32 20 32 14 26 30 22 21 19 28 35 Platessa conspicua 2 4 2 6 6 5 2 7 2 1 6 4 5 4 4 1 Achnanthes acares 4 Karayevia laterostrata 2 Karayevia cf. laterostrata 3 Rossithidium pusillum 1 3 1 2 5 2 2 5 8 3 7 2 3 5 3 4 Achnanthes ventralis 2 3 2 1 2 Psammothidium cf. kuelbsii 2 Karayevia clevei 1 4 4 3 4 2 2 3 1 1 1 Karayevia carissima 4 Psammothidium curtissimum 5 3 7 2 2 6 2 2 4 8 4 5 6 6 Planothidium lanceolatum 4 2 3 3 1 2 1 1 1 Achnanthes lacus-vulcani 3 2 Achnanthes cf. lacus-vulcani 5 Achnanthes oblongella 2 3 1 Achnanthes cf.oblongella 1 Achnanthes delicatula 1 1 Achnanthes cf. delicatula 2 2 Karayevia suchlandtii 2 3 1 8 2 7 15 7 11 3 7 10 8 11 8 7 5 Psammothidium cf. marginulatum 2 2 5 2 3 1 3 1 Psammothidium marginulatum 2 2 Achnanthes holsatica 3 Achnanthes lutheri 1 Achnanthes lavenderi 2 Achnanthes leavis 1 Planothidium cf. granum 2 1 2 1 2 Achnanthes cf. ploenensis 1 Gliwiczia calcar 1 Achnanthes cf. amoena 1 Achnanthes amoena 1 3

293

Taxa 0-0.5 1-1.5 2-2.5 3-3.5 4-4.5 5-5.5 6-6.5 7-7.5 8-8.5 9-9.5 11-11.5 12-12.5 13-13.5 14-14.5 15-15.5 16-16.5 17-17.5 18-18.5 Achnanthidium cf. rosenstockii 2 Achnanthes stolida 1 Pleurosira laevis 1 Aulacoseira subarctica 9 10 13 29 9 3 8 6 13 21 18 12 12 10 6 13 5 16 Amphora pediculus 1 1 2 4 4 1 3 2 2 1 3 2 2 5 2 4 Amphora libyca 2 Asterionella formosa 30 19 15 22 11 23 21 13 15 17 7 7 9 11 4 9 11 15 Brachysira neoexilis 8 2 4 2 2 1 2 2 2 2 1 Brachysira vitrea 2 Cymbopleura naviculiformis 2 Cymbella affinis 2 Encyonema minutum 2 1 1 4 Encyonema silesiacum 4 2 6 2 4 1 2 2 5 2 3 3 1 Encyonopsis descripta 3 2 2 7 2 2 2 1 2 2 2 Encyonopsis microcephala 5 11 11 10 8 18 12 10 12 16 4 13 13 14 11 7 13 10 Encyonopsis cesatii 1 3 Cymbopleura incerta 2 2 Cymbopleura cf. incerta 1 Cymbella tumida 1 Cymbella prostrata 2 Cymbella cuspidata 1 Reimeria sinuata 2 Cyclotella bodanica var. lemanica 2 1 2 1 2 3 5 2 3 2 2 1 6 4 Cyclotella ocellata 45 34 24 48 51 51 83 68 69 65 86 97 82 88 93 99 96 70 Cyclotella cf. tripartita 15 7 7 13 10 11 18 8 11 14 11 13 12 19 7 7 16 7 Cyclotella cf. comensis 3 7 7 6 6 7 9 9 4 4 4 5 8 14 5 9 5 1 Discostella stelligera complex 120 169 166 153 176 134 152 93 82 105 108 111 72 98 105 91 115 90 Lindavia michiganiana 10 12 11 10 9 3 14 14 12 2 11 7 5 1 3 2 Lindavia cf. rossii 1 2 1 Cyclotella cf. polymorpha 6 3 1 Cyclotella cf. kuetzingiana 1 Cyclotella radiosa 1 Cyclotella cf. radiosa 1 1 1 1 6 Cyclotella comta 1 Cyclotella cf. comta 6 4 Caloneis tenuis 2 Caloneis bacillum 2 Cocconeis placentula 1 2 5 1 1 4 8 1 1 1 1 1 3 7 Denticula kuetzingii 1 4 1 Diploneis marginestriata 1 3 1 3 1 1 2 294

Taxa 0-0.5 1-1.5 2-2.5 3-3.5 4-4.5 5-5.5 6-6.5 7-7.5 8-8.5 9-9.5 11-11.5 12-12.5 13-13.5 14-14.5 15-15.5 16-16.5 17-17.5 18-18.5 Diatoma tenuis 2 2 2 Eunotia arcus 2 Epithemia sorex 1 3 2 Epithemia adnata 1 Epithemia smithii 1 Epithemia girdle unknown 6 2 2 2 4 Fragilaria capucina 1 1 3 2 1 Fragilaria cf. robusta 16 Fragilaria nanana 2 1 2 1 4 Fragilaria tenera 4 1 2 1 3 4 Pseudostaurosira brevistriata 4 2 6 11 2 17 6 8 6 2 2 2 5 Staurosira construens 22 8 26 24 20 17 67 12 3 10 1 13 6 18 Staurosira construens var. venter 20 1 3 2 4 9 9 2 2 14 9 7 2 9 8 4 17 Staurosirella pinnata 3 4 6 8 12 14 9 6 3 3 5 5 4 1 6 8 1 Synedra ulna 4 Fragilaria virescens var. exigua 4 2 Gomphonema acuminatum 2 Gomphonema parvulum 1 Gomphonema pumilum 1 4 2 Gomphonema sp. Girdle 2 2 2 2 Navicula sp. 1 1 Navicula sp. 2 2 Navicula sp. Girdle 4 Navicula submuralis 3 6 2 3 2 Navicula pseudoventralis 1 2 1 1 4 2 2 Navicula stroemii 3 Navicula minima 7 1 5 4 2 5 6 Navicula pupula 2 1 3 2 1 2 1 2 2 1 Navicula kuelbsii 3 1 1 2 10 3 Navicula radiosa 1 2 1 1 1 3 1 1 1 5 Navicula cryptotenella 4 1 2 2 2 3 2 1 1 3 2 4 Navicula cryptocephala 2 2 2 2 2 2 Navicula pseudocutiformis 1 Navicula indifferens 1 2 5 3 Navicula cf. mutica 2 Navicula tusculus 1 Navicula eidrigiana 2 Navicula seminulum 6 3 6 Navicula cf. festiva 5 Navicula cf. peregrina 1 1 295

Taxa 0-0.5 1-1.5 2-2.5 3-3.5 4-4.5 5-5.5 6-6.5 7-7.5 8-8.5 9-9.5 11-11.5 12-12.5 13-13.5 14-14.5 15-15.5 16-16.5 17-17.5 18-18.5 Navicula cocconeiformis 1 2 Navicula shoenfeldii 4 Navicula jarnefeltii 1 4 1 Navicula suchlandtii 2 2 2 1 Navicula menisculus 3 Navicula cf. menisculus 1 Navicula cf. capitata 1 Navicula capitata 2 Navicula scutilloides 1 Navicula subminuscula 3 Navicula cf. bryophila 3 Navicula diluviana 1 Navicula veneta 2 Navicula cf. sublucidula 2 Navicula bacillum 2 Navicula decussis 1 Navicula porifera 1 Neidium cf. septentrionale 4 Nitzschia frustulum 2 13 2 6 5 14 10 4 6 1 14 9 7 11 6 15 12 Nitzschia fonticola 15 8 3 3 9 10 6 2 4 2 6 7 2 6 7 10 14 9 Nitzschia palea 1 2 2 6 1 6 4 2 Nitzschia gracilis 1 Nitzschia dissipata 1 2 1 Nitzschia linearis 2 2 Nitzschia perminuta 1 4 3 Nitzschia radicula 2 Nitzschia cf. radicula 2 Nitzschia sp. Alexie lake 1 Rhopalodia gibba 2 Melosira numnuloides 2 Stephanodiscus parvus 1 1 1 10 5 5 9 1 7 4 5 7 Stephanodiscus medius 7 8 7 4 5 6 9 12 9 2 7 3 4 9 3 5 9 6 Stephanodiscus alpinus 3 1 3 2 2 6 4 3 2 2 3 3 1 4 1 2 5 Stephanodiscus hantzschii 2 1 Stephanodiscus minutulus 16 15 11 3 7 7 13 14 13 7 5 10 11 5 2 7 4 6 Stauroneis phoenicenteron 1 1 Surirella angusta 3 Tabellaria flocculosa str. IV 3 Tabellaria flocculosa str. IIIp 4 7 6 1 4 3 21 6 5 6 5 13 8 2 6 8 7 8 Tabellaria quadriseptata 1 2 5 1 1 2 3 2 1 2 1 1 296

Appendix I Raw diatom data for BC-02

Taxa 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.5-7 7-7.5 7.5-8 Achnanthidium minutissimum 35 19 12 15 4 19 20 17 16 25 20 16 3 21 13 10 Achnanthidium exiguum 1 1 Achnanthidium cf. exiguum 1 Achnenthese impexa 1 1 Achnanthes cf. impexa 1 Karayevia clevei 1 Psammothidium cf. levanderi 4 Psammothidium curtissimum 3 2 2 2 3 1 1 4 3 7 8 Psammothidium subatomoides 1 1 Psammothidium lacus-vulcani 3 1 2 2 2 1 1 3 5 Psammothidium cf. lacus-vulcani 1 Rossithidium pussillum 2 1 1 1 3 2 6 1 2 1 8 7 4 1 Rossithidium cf. pussillum 1 Karayevia suchlandtii 1 Achnanthes acares 3

Achnanthes cf. acares 2 Achnanthes cf. leavis 1 Achnanthes oblongella 2 Achnanthes didyma 1 Achnanthes cf. subsala 1 Aulacoseira distans var. septentrionalis 21 17 8 5 10 11 10 6 7 23 30 20 7 15 17 19 Aulacoseira perglabra 44 20 25 6 14 15 22 14 7 29 26 14 4 23 7 5 Aulacoseira perglabra var. floriniae 31 17 17 27 24 15 13 19 24 13 32 13 14 5 1 9 Aulacoseira perglabra cf. var. floriniae 23 15 7 3 8 15 16 6 11 12 9 7 2 10 7 3 Amphora libyca 1 1 1 Asterionella formosa 1 2 Brachysira neoexilis 2 1 1 1 2 5 2 3 3 6 Brachysira procera 2 2 1 Brachysira brebissonii 1 1 Cymbella sp. Broken BC-02 7-7.5 1

297

Taxa 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.5-7 7-7.5 7.5-8 Cymbopleura amphicephala 1 Cymbopleura cf. amphicephala 1 Encyonema silesiacum 2 1 2 4 1 Encyonopsis descripta 1 Encyonopsis microcephala 3 3 2 2 2 4 5 2 4 5 3 Encyonopsis cesatii 3 2 2 Cymbella gracilis 1 Cymbella cf. subaequalis 1 Cyclotella bodanica var. lemanica 14 6 2 4 6 4 3 1 4 2 3 3 10 4 2 Cyclotella ocellata 31 19 19 18 33 26 41 28 25 38 48 45 33 40 29 34 Cyclotella tripartita 5 5 5 7 6 6 6 7 7 10 10 13 13 7 4 8 Cyclotella comensis 2 2 1 2 2 3 5 1 Cyclotella cf. comensis 3 3 2 1 Discostella stelligera complex 241 121 183 106 120 97 102 62 48 64 63 67 48 48 55 71 Lindavia michiganiana 18 8 7 7 7 4 4 7 4 2 7 1 2 1 4 4 Lindavia antiqua 1 Lindavia rossii 5 2 2 4 2 2 6 5 3 3 4 7 11 4 7 Caloneis bacillum 1 Denticula kuetzingii 1 1 Diploneis marginestriata 3 1 Epithemia sorex 2 Fragilaria virecens var. exigua 2 1 1 1 Fragilaria capucina 1 1 1 Fragilaria arcus 1 2 1 2 Fragilaria nanana 4 4 5 6 3 2 3 4 3 2 2 2 5 1 2 Fragilaria tenera 2 7 3 8 1 3 3 4 3 1 5 4 1 2 Pseudostaurosira brevistriata 27 14 19 13 34 14 22 21 26 30 25 29 18 30 33 39 Staurosira construens 8 1 2 5 1 1 Staurosira construens var. venter 107 61 86 40 63 49 82 81 102 96 92 89 60 82 90 83 Staurosirella pinnata 24 17 12 12 10 11 14 15 5 2 8 7 16 10 5 Gomphonema sp. Girdle BC-02 5-5.5 2 Gomphonema sp. Girdle BC-02 1.5-2.0 2 Gomphonema cf. subtile 1 Navicula cf. impexa 1 Navicula sp. BC-02 5.5-6.0 2 Navicula sp. BC-02 7.5-8.0 1 298

Taxa 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.5-7 7-7.5 7.5-8 Navicula sp. Girdle BC-02 6-6.5 4 Navicula submuralis 2 7 5 7 3 6 6 1 13 5 3 8 10 4 6 Navicula pseudoventralis 1 Navicula minima 3 2 1 4 5 2 6 4 7 4 5 Navicula pupula 1 4 1 1 1 3 1 Navicula kuelbsii 24 14 16 9 2 15 25 22 8 20 14 20 15 7 15 14 Navicula schmassmannii 2 Navicula cf. schmassmannii 1 Navicula cryptotenella 1 1 Navicula cryptocephala 2 1 Navicula pseudocutiformis 2 1 2 Navicula jaagii 2 Navicula seminulum 8 3 3 6 1 4 8 12 5 16 7 13 7 5 12 12 Navicula medocris 4 2 2 Navicula cf. medocris 2 Navicula cocconeiformis 1 Navicula cf. cocconeiformis 2 Navicula schoenfeldii 2 Navicula disjuncta 2 2 2 2 1 1 3 1 1 Navicula elginensis 2 Navicula indifferens 2 2 2 4 Navicula sp. Broken BC-02 2.5-3.0 1 Navicula sp. Girdle BC-02 4.5-5.0 2 Nitzschia frustulum 1 1 1 2 2 1 Nitzschia fonticola 5 2 2 3 1 1 3 3 1 3 9 1 Nitzschia palea 1 1 Nitzschia dissipata 2 Nitzschia amphibia 1 2 Pinnularia interrupta 2 1 1 Pinnularia microstauron 2 1 1 1 Stephanodiscus sp. Broken BC-02 6.5-7 1 Stephanodiscs cf. niagarae 1 Stauroneis anceps 1 1 1 1 Tabellaria flocculosa str. III 1 2 2 1 2 Tabellaria flocculosa str. IV 1 Tabellaria flocculosa str. IIIp 1 1 1 299

Appendix J Raw diatom data for BC-17 Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 12.25 14.25 16.25 18.25 Achnanthidium minutissimum 137 52 72 72 95 99 101 102 78 66 77 43 62 48 65 Psammothidium curtissimum 2 2 2 Rossithidium pussillum 2 Achnanthes cf. leavis 2 Aulacoseira cf. carrispunctata 3 Asterionella formosa 1 2 1 Brachysira neoexilis 63 69 60 64 51 29 43 31 69 78 70 58 77 80 62 Brachysira procera 1 2 Brachysira cf. porcera 2 1 Brachysira brebissonii 2 Cymbopleura lapponica 20 1 Encyonema silesiacum 1 6 4 2 1 2 3 2 10 2 4 1 Encyonopsis descripta 32 10 16 28 16 9 19 4 5 8 5 2 Encyonopsis falaisensis 2 1 5 Encyonopsis microcephala 62 34 35 59 36 31 24 32 57 67 62 50 67 67 71 Encyonopsis cesatii 8 17 17 18 12 19 9 22 24 14 29 14 18 24 Cymbopleura incerta 6 10 3 4 1 3 6 8 5 4 2 5 Cymbella cf. ehrenbergii 2 Cyclotella cf. comensis 1

Discostella stelligera complex 48 31 50 61 49 29 22 31 47 37 36 48 54 60 39 Lindavia michiganiana 18 9 7 10 14 18 14 17 10 13 7 14 9 21 13 Caloneis cf. alpestris 2 Denticula kuetzingii 7 3 6 3 4 5 2 Frustulia rhomboides 4 3 8 4 1 1 2 1 1 Fragilaria sp. girdle BC17 5.25 4 Fragilaria capucina 8 1 1 1 Fragilaria nanana 1 1 1 1 Fragilaria tenera 1 2 Synedra ulna 1 Navicula erifuga 9 6 8 3 4 5 3 3 3 4 2

300

Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 12.25 14.25 16.25 18.25 Navicula minima 1

Navicula pupula 8 2 1 1 4 4 5 7 4 1 2 4 4 2 3 Navicula radiosa 15 5 2 5 4 3 5 4 2 3 2 3 Navicula cryptotenella 19 20 13 14 28 24 21 29 21 23 19 24 16 18 12 Navicula cryptocephala 12 4 1 5 2 1 8 6 3 5 1 1 Navicula jaagii 17 18 12 2 8 9 3 2 6 6 5 9 1 5 1 Navicula cf. seminulum 1 Navicula peregrina 1 1 Navicula cf. peregrina 1 Navicula cuspidata 1 Navicula rhyncocephala 3 2 5 1 4 4 2 2 Navicula cf. rhyncocephala 2 1 Kobayasiella subtilissima 5 3 7 12 6 1 3 11 6 11 4 6 3 Navicula disjuncta 8 Navicula cf. bryophila 6 Navicula cf. striolata 1 Navicula cf. veneta 2 2 Neidium ampliatum 4 4 2 2 2 1 1 4 2 3 Nitzschia frustulum 2 2 6 2 1 2 4 3 2 2 Nitzschia fonticola 9 2 2 1 3 4 7 2 18 27 13 6 Nitzschia palea 3 2 4 2 3 1 1 3 1 3 Nitzschia perminuta 2 2 Nitzschia angustata 3 3 1 1 1 2 Nitzschia amphibia 1 3 Rhopalodia gibba 2 Pinnularia interrupta 2 Pinnularia microstauron 1 1 3 1 1 2 1 Pinnularia maior 2 Stauroneis phoenicenteron 1 1 1 2 3 3 1 Tabellaria flocculosa 1

301

Appendix K Raw diatom data for BC-43 Taxa 0-0.5 1-1.5 2-2.5 3-3.5 4-4.5 5-5.5 6-6.5 7-7.5 8-8.5 9-9.5 10-10.5 12-12.5 14-14.5 16-16.5 Achnanthidium minutissimum 10 18 6 9 14 38 80 56 79 44 53 50 50 49 Achnanthidium exiguum 1 Karayevia clevei 3 2 4 2 5 3 3 Platessa conspicua 2 10 4 11 10 14 27 14 Planothidium lanceolatum 2 Psammothidium curtissimum 33 38 35 20 16 71 53 34 37 48 42 45 36 27 Psammothidium subatomoides 2 Rossithidium pussillum 5 4 2 Aachnanthes pericava 2 Amphora pediculus 114 118 74 41 27 48 111 230 192 242 188 156 162 235 Amphora libyca Amphora ovalis 2 1 5 2 1 2 1 2 1 Asterionella formosa 5 Brachysira neoexilis 2 1 3 3 Delicata delicatula 1 1 Encyonema silesiacum 6 1 1 18 49 23 1 4 3 2 2 6 Encyonopsis microcephala 2 2 1 2 5 4 Cyclotella bodanica var. lemanica 1 2 Discostella stelligera complex 1 1 2 2 Lindavia michiganiana 1 4 1 1 2 Caloneis bacillum 2 2 2 Caloneis schumanniana 2 Cocconeis placentula 1 4 10 40 22 11 29 36 29 23 Denticula kuetzingii 1 5 3 1 Diploneis ovalis 1 3 Diploneis marginestriata 2 3 1 2 4 1 4 Diatoma tenuis 2 Epithemia sorex 1 Epithemia adnata 2 6 14 1 3 1 1 3 Fragilaria capucina 2 Fragilaria nanana 2 2 Fragilaria parasitica 1 2 1 2 302

Taxa 0-0.5 1-1.5 2-2.5 3-3.5 4-4.5 5-5.5 6-6.5 7-7.5 8-8.5 9-9.5 10-10.5 12-12.5 14-14.5 16-16.5 Pseudostaurosira brevistriata 250 136 167 284 262 146 77 45 54 25 46 51 58 73 Staurosira construens 2 1 Staurosira construens var. venter 50 100 47 34 53 13 13 8 5 8 12 12 4 Staurosirella pinnata 25 35 40 18 3 2 12 4 4 4 3 5 6 Gomphonema pumilum 4 2 2 Gomphonema cf. pumilum girdle 2 Gomphonema bohemicum 2 Gyrosigma acuminatum 1 1 2 3 1 1 6 1 Navicula submuralis 2 Navicula pseudoventralis 2 Navicula minima 18 6 2 2 2 2 Navicula pupula 6 3 2 5 8 3 3 3 6 2 4 6 Navicula kuelbsii 5 8 Navicula radiosa 2 1 3 Navicula cryptotenella 3 3 4 7 7 6 1 10 7 3 6 1 Navicula cryptocephala 7 4 6 1 3 4 1 2 1 Navicula seminulum 1 Navicula lapidosa 1 Navicula peregrina 1 Navicula cocconeiformis 1 1 3 8 7 Navicula cf. suchlandtii 12 Navicula digitoradiata 3 Navicula suchlandtii 3 6 5 12 Navicula cf. bryophila 2 10 2

Nitzschia frustulum 4 7 4 6 19 22 18 3 8 9 2 Nitzschia fonticola 14 6 8 14 28 45 30 8 21 3 1 6 Nitzschia dissipata 2 7 1 2 2 2 1

Nitzschia amphibia 3 3 13 2 5 3 1 2 5 Hantzschia spp. 1 3 Pinnularia interrupta 2 2 2 2 1

Pinnularia gibba 1 1 Stauroneis phoenicenteron 1 Stauroneis anceps 2 1 1 1 303

Appendix L Raw diatom data for Jackfish Lake

Taxa 0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 11.25 12.25 13.25 14.25 15.25 16.25 17.25 18.25 19.25 20.25 22.25 24.25 26.25 28.25 Achnanthidium minutissimum 19 6 4 4 3 7 3 6 4 6 9 5 5 2 17 10 14 14 15 4 8 7 5 8 Achnanthidium exiguum 2 1 4 3 3 2 3 7 6 6 4 1 2 2 1 1 2 1 5 4 Karayevia clevei 2 6 3 4 1 3 5 1 6 2 2 1 7 1 10 3 6 14 4 2 5 6 4 Platessa conspicua 2 3 2 4 4 6 10 Planothidium lanceolatum 14 2 1 3 5 3 3 1 3 3 2 8 11 4 5 16 8 15 10 2 7 5 10 10 Planothidium delicatulum 2 2 Psammothidium daonense 1 Psammothidium marginulatum 1 1 4 6 1 1 5 1 1 Psammothidium curtissimum 2 4 2 2 4 8 2 10 6 2 8 8 5 7 6 2 Achnanthes cf. bioretii 1 Gliwiczia calcar 1 1 1 1 1 1 1 1 Planothidium cf. joursacense 1 Achnanthes ploenensis 2 Achnanthes lacus-vulcani 1 1 Karayevia carissima 1 Psammothidium subatomoides 1 Karayevia suchlandtii 2 3 Aulacoseira lirata 2 Aulacoseira subarctica 4 2 4 1 2 1 1 2 2 2 4 Aulacoseira perglabra var. floriniae 3 6 4 4 3 5 3 8 14 24 11 16 28 16 23 12 23 17 14 7 13 17 26 Aulacoseira ambigua 2 1 1 4 2 6 7 5 16 17 14 22 21 11 16 10 9 12 5 6 10 4 Aulacoseira crassipunctata 6 Amphora pediculus 12 4 10 16 13 2 1 8 2 2 1 3 4 1 4 4 5 4 6 12 8 4 12 3 2 Amphora libyca 1 1 1 3 1

304

Taxa 0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 11.25 12.25 13.25 14.25 15.25 16.25 17.25 18.25 19.25 20.25 22.25 24.25 26.25 28.25 Amphora ovalis 11 8 4 6 2 1 3 1 1 4 Amphora inariensis 1 Asterionella formosa 5 3 2 2 1 2 2 6 1 22 11 5 7 8 4 3 5 6 1 3 4 3 Brachysira neoexilis 2 Cymbella cymbiformis 1 Cymbella cistula 2 Cymbella proxima 2 Cymbella affinis 2 Encyonema silesiacum 2 2 2 4 2 1 Encyonopsis microcephala 1 2 4 2 1 1 Encyonopsis cesatii 2 Encyonopsis descripta 1 Reimeria sinuata 1 Cymbella aspera 1 Cyclostephanos cf. tholiformis 1 1 Cyclotella bodanica var. lemanica 1 2 1 2 1 3 1 6 1 2 1 4 5 Cyclotella ocellata 1 2 2 3 1 2 1 Cyclotella tripartita 1 5 2 2 4 2 Discostella stelligera complex 1 1 1 2 1 3 2 2 43 22 17 13 22 34 21 30 24 12 11 3 4 4 Lindavia michiganiana 8 5 5 3 4 3 5 4 1 6 7 5 8 6 3 5 2 6 1 Cyclotella comensis 1 Caloneis silicula 2 Caloneis cf. silicula 3 Caloneis bacillum 2 3 3 1 2 Cyclotella menghiniana 2 Cocconeis placentula 3 2 4 3 1 1 2 4 2 1 4 2 1 2 3 2 Cocconeis pseudothumensis 3 2 Cocconeis neothumensis 3 2 3 Denticula kuetzingii 2 2 1 Diploneis ovalis 3 3 2 2 2 1 1

305

Taxa 0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 11.25 12.25 13.25 14.25 15.25 16.25 17.25 18.25 19.25 20.25 22.25 24.25 26.25 28.25 Diploneis marginestriata 1 1 1 2 2 1 1 3 1 1 2 2 2 Diatoma tenuis 13 6 1 1 Epithemia adnata 2 1 2 6 Epithemia sorex 1 Frustulia rhomboides 1 Fragilaria capucina 22 11 3 20 6 6 6 1 9 7 3 4 1 5 2 2 4 1 6 2 Fragilaria nanana 10 2 3 1 6 4 1 Fragilaria tenera 2 2 1 Fragilaria constricta 1 9 1 Fragilaria parasitica 6 4 4 4 5 4 2 1 5 2 9 2 2 2 13 4 7 8 1 2 11 6 Pseudostaurosira brevistriata 72 32 69 67 99 58 47 50 60 68 29 68 42 34 30 29 57 48 45 32 38 44 42 40 50 Staurosira construens 2 13 1 17 44 27 26 21 19 25 8 35 10 59 29 19 40 19 18 25 23 10 19 21 Staurosira construens var. venter 46 24 106 92 101 104 55 83 34 47 65 66 90 125 77 107 97 106 100 90 108 108 99 75 134 Staurosirella pinnata 81 53 49 53 68 90 126 115 111 111 55 55 71 69 43 70 60 104 58 78 73 107 106 89 93 Staurosira construens var. binodis 1 25 Synedra ulna 2 Pseudostaurosira pseudoconstruens 1 Gomphonema acuminatum 3 2 Gomphonema parvulum 2 Gomphonema pumilum 2 2 2 2 1 2 2 2 2 Gyrosigma acuminatum 1 3 1 2 1 Melosira arentii 1 1 1 1 1 Navicula angusta 2 Geissleria decussis 2 3 Navicula minuscula 1 Navicula submuralis 4 2 8 4 7 2 8 6 14 3 26 26 26 17 24 16 23 21 12 31 29 25 27 20 Navicula jaernefeltii 2 1 4 7 5 5 6 2 1 5 1 10 4 6 3 4 Navicula pseudoventralis 1 1 1 3 Navicula minima 2 3 4 2 6 8 2 5 6 11 10 8 7 8 8 Navicula pupula 2 1 5 4 3 2 2 5 3 5 3 6 2 6 4 1 4 1 1 3 1

306

Taxa 0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 11.25 12.25 13.25 14.25 15.25 16.25 17.25 18.25 19.25 20.25 22.25 24.25 26.25 28.25 Navicula kuelbsii 2 2 3 3 1 5 8 4 9 15 10 4 15 1 9 16 1 Kobayasiella jaagii 1 Navicula subplacentula 2 Navicula mutica 3 8 6 Cavinula cocconeiformis 1 1 1 3 3 1 1 Navicula vitabunda 2 2 Navicula jentzschii 1 Navicula menisculus 13 36 20 3 4 8 5 4 Navicula radiosa 2 1 2 Navicula cryptotenella 2 1 1 1 2 1 Navicula cryptocephala 13 7 12 5 7 4 6 7 2 2 3 2 5 2 Navicula pseudocutiformis 1 1 1 4 5 2 1 4 6 3 Navicula subminuscula 2 2 4 2 Navicula seminulum 2 2 8 9 2 4 8 2 4 2 6 4 Navicula gastrum 1 1 2 2 2 2 2 Navicula peregrina 4 2 Navicula subrotundata 1 1 2 5 Navicula schoenfeldii 2 1 3 4 4 1 3 Cavinula scutelloides 2 2 5 1 2 1 1 1 1 2 Navicula capitata 2 3 1 3 Navicula elginensis 2 4 4 3 1 1 Navicula porifera 1 Navicula bryophila 2 Navicula porifera var. opportuna 4 Navicula unknown girdle 9-9.5 2 Aneumastus tusculus 2 Navicula cf. saxofila 1 5 Neidium ampliatum 1 Hantzschia spp. 1 Jackfish Lake 2 Nitzschia cf. radicula 4

307

Taxa 0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25 10.25 11.25 12.25 13.25 14.25 15.25 16.25 17.25 18.25 19.25 20.25 22.25 24.25 26.25 28.25 Nitzschia radicula 2 Nitzschia heufleriana 1 Nitzschia frustulum 4 3 1 2 2 6 2 2 2 2 5 Nitzschia fonticola 7 1 2 2 1 1 2 2 1 Nitzschia palea 6 2 Nitzschia dissipata 1 2 Nitzschia amphibia 1 4 1 Nitzschia valdestriata 1 Rhopalodia gibba 1 2 1 1 Pinnularia interrupta 2 6 8 2 2 2 2 1 5 2 Pinnularia microstauron 4 4 2 2 4 2 Pinnularia gibba 2 2 1 Pinnularia viridis 2 Stephanodiscus parvus 5 5 3 2 3 1 4 2 3 5 1 2 Stephanodiscus medius 28 52 18 10 1 3 9 17 14 9 11 7 7 7 2 2 3 4 1 3 1 3 4 Stephanodiscus alpinus 1 2 1 Stephanodiscus niagarae 1 1 2 3 1 1 2 3 1 4 2 3 5 5 2 2 1 1 7 Stephanodiscus hantzschii 8 3 9 10 1 1 3 2 1 1 1 1 4 3 1 1 3 3 6 1 Stephanodiscus minutulus 22 24 9 5 4 3 2 8 6 5 5 18 20 5 8 3 6 4 3 4 2 1 3 Stephanodiscus hantzschii-minutulus 3 8 10 4 5 2 10 1 3 4 1 2 2 1 Stephanodiscus medius-minutulus 132 180 129 76 31 34 69 61 79 69 143 25 16 1 8 2 Stauroneis phoenicenteron 1 2 Stauroneis tackei 2 2 2 Stauroneis anceps 1 2 2 1 2 1 4 1 1 Stauroneis prominula 2 Surirella linearis 1 1 Rhoicosphenia abbreviata 2 Tabellaria flocculosa str. IV 1 1 1 2 1 Tabellaria flocculosa Tabellaria flocculosa str. IIIp 1 1 1 1 1 2 2 1 1 1 1 1

308

Appendix M Raw diatom data for Vee Lake Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 8.25 9.25 10.3 11.3 12.3 13.3 14.3 16.3 18.3 20.3 22.3 24.3 27.3 Achnanthes sp. Vee 5-5.5 1 Achnanthidium minutissimum 71 21 28 43 28 34 22 18 10 11 25 18 15 19 17 9 10 13 11 5 Achnanthidium exiguum 2 1 4 1 1 6 14 9 6 7 4 6 2 5 6 11 7 3 5 Karayevia clevei 1 1 1 3 2 1 5 3 1 2 1 Platessa conspicua 4 1 6 11 11 17 11 29 33 51 22 24 23 19 21 6 21 25 27 Planothidium lanceolatum 6 1 3 5 1 4 2 3 6 7 3 8 9 7 13 6 11 7 7 5 Planothidium delicatulum 1 Psammothidium marginulatum 1 Psammothidium cf. marginulatum 2 Psammothidium curtissimum 4 4 1 1 2 4 1 5 11 2 2 6 7 Psammothidium subatomoides 1 2 2 Rossithidium pussillum 2 1 2 3 1 1 2 1 Nupela impexiformis 1 Achnanthes sp. Vee 11-11.5 1 Achnanthes cf. kryophila 1 Achnanthes leavis 4 2 Achnanthes childanos 2 1 1 Achnanthes ventralis 2 1 Achnanthes cf. ventralis 3 Achnanthes oblongella 2 Achnanthes ziegleri 1 Aulacoseira ambigua 1 1 6 17 14 14 5 7 6 Amphora pediculus 11 3 7 2 15 12 7 19 30 31 23 20 10 17 18 13 12 25 20 13 Amphora libyca 5 2 3 2 2 3 3 4 11 4 6 8 6 3 5 9 3 5 Asterionella formosa 33 11 24 22 16 11 33 9 2 9 16 46 46 61 29 4 4 1 Amphipleura pellucida 1 Brachysira neoexilis 2 4 2 Brachysira cf. porcera 4 Cymbella sp. girdle Vee 2-2.5 2 Cymbella sp. Vee 14-14.5 2 Cymbella cistula 2 Cymbella cf. gaeumannii 1 Delicata delicatula 2 Cymbella lanceolata 2 Encyonema silesiacum 1 1 1 1 1 4 309

Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 8.25 9.25 10.3 11.3 12.3 13.3 14.3 16.3 18.3 20.3 22.3 24.3 27.3 Encyonopsis descripta 2 Encyonopsis falaisensis 2 3 Encyonopsis microcephala 13 5 2 2 5 2 1 4 2 2 6 Encyonopsis cesatii 2 2 2 2 Navicymbula cf. pusilla 2 Cymbella tumida 2 3 Cymatopleura elliptica 1 Cymatopleura sp. Vee 24-24.5 2 Cyclotella bodanica var. lemanica 1 1 4 9 8 4 4 1 3 4 2 12 17 10 11 9 10 10 Discostella stelligera complex 143 108 142 91 64 58 56 4 6 4 8 5 3 2 2 1 1 Lindavia michiganiana 33 24 25 20 10 7 3 9 10 17 42 10 10 5 8 7 12 1 1 5 Caloneis alpestris 2 Caloneis bacillum 1 Caloneis sp. girdle (Vee 27-27.5) 2 Caloneis sp. Vee 11-11.5 1 Cocconeis placentula 2 2 1 6 4 2 1 3 3 4 11 1 3 5 2 3 2 2 1 2 Cocconeis neothumensis 1 2 3 3 2 1 Denticula kuetzingii 1 2 2 1 2 Diploneis marginestriata 2 1 1 2 1 4 4 2 1 1 1 4 1 1 Eunotia arcus 1 Eunotia bilunaris 2 1 2 3 1 Eunotia faba 1 Eunotia praerupta 2 1 1 1 1 Eunotia incisa 2 2 Eunotia soleirolii 1 Eunotia paludosa 2 Eunotia pectinalis 1 1 Eunotia sp. girdle 1 Epethemia sp. girdle 2-2.5 1 Epithemia argus 1 Epithemia sorex 2 Epithemia sp. Vee (27-27.5) 1 Epithemia sp. broken 20-20.5 2 Epithemia sp. girdle Vee 12-12.5 1 Fragilaria capucina 10 5 8 5 2 7 4 8 3 2 4 68 58 54 7 3 1

310

Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 8.25 9.25 10.3 11.3 12.3 13.3 14.3 16.3 18.3 20.3 22.3 24.3 27.3 Fragilaria arcus 2 Fragilaria nanana 28 34 25 15 15 8 11 3 1 3 18 5 7 1 Fragilaria tenera 5 7 6 2 2 4 1 1 5 4 2 6 1 Fragilaria parasitica 1 3 2 1 6 2 5 4 1 1 1 14 Fragilaria constricta 1 Pseudostaurosira brevistriata 17 10 33 23 30 45 29 40 48 37 13 20 12 21 16 19 21 26 29 Pseudostaurosira brevistriata type 26 Staurosira construens 3 9 3 6 1 2 5 2 1 Staurosira construens var. venter 4 14 17 10 31 23 15 26 13 13 12 7 7 5 6 17 3 16 7 27 Staurosirella pinnata 64 34 32 30 34 73 106 114 95 67 44 50 44 53 47 74 76 65 89 78 Staurosirella leptostauron 2 2 2 2 2 2 2 4 4 4 Synedra ulna 2 1 2 2 Fragilaria cf. robusta 1 Synedra cyclopum 7 Gomphonema acuminatum 2 2 Gomphonema angustum 3 7 6 Gomphonema angustatum 3 Gomphonema clavatum 3 Gomphonema pumilum 11 7 4 10 13 10 2 3 3 Gomphonema cf. gracile 2 Gomphonema sp. broken Vee 20-20.5 2 Gomphonema sp. girdle Vee 14-14.5 2 Gomphonema sp. girdle Vee 11-11.5 2 Gomphonema sp. girdle Vee 9-9.5 4 Gomphonema sp. girdle Vee 8-8.5 1 Gomphonema sp. girdle Vee 4-4.5 4 Gomphonema cf. subtile 3 Gyrosigma acuminatum 1 1 1 6 3 1 5 11 5 23 13 13 Mastoglia smithii 2 Melosira arentii 1 1 Melosira numnuloides 2 5 Navicula sp. Vee 3-3.5 2 Navicula sp. 1 girdle Vee 10-10.5 2 Navicula sp. 2 teratology Vee 10-10.5 2 Navicula cf. geoppertiana 1

311

Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 8.25 9.25 10.3 11.3 12.3 13.3 14.3 16.3 18.3 20.3 22.3 24.3 27.3 Navicula sp. 1 broken Vee 14-14.5 1 Navicula sp. 2 broken Vee 14-14.5 2 Navicula cf. hambergii 1 Navicula erifuga 2 1 1 1 3 2 Navicula submuralis 1 3 1 2 1 7 3 7 10 10 Navicula pseudoventralis 2 1 2 2 4 1 2 5 6 3 4 Navicula minima 6 2 2 1 6 1 2 1 1 4 1 3 3 3 2 2 5 Navicula pupula 3 5 2 1 3 2 3 5 4 4 6 5 9 7 1 6 1 1 Navicula radiosa 4 2 2 3 2 Navicula cryptotenella 1 5 6 1 1 2 5 1 1 2 Navicula cryptocephala 8 1 1 4 Navicula pseudocutiformis 2 4 Navicula jaagii 1 Navicula seminulum 2 2 2 1 1 Navicula cf. seminulum 4 1 Navicula veneta 2 Navicula gastrum 1 Navicula peregrina 1 Navicula cocconeiformis 1 1 1 2 1 7 Navicula cf. digitoradiata 2 Navicula subrotundata 2 Navicula schoenfeldii 2 1 Navicula absoluta 1 3 2 8 Navicula explanata 1 1 Navicula viridula 3 1 Navicula cuspidata 2 1 Navicula rhyncocephala 2 3 2 Navicula elginensis 2 1 3 1 4 2 2 Navicula stroemii 2 Navicula soehrensis 1 Navicula ventralis 2 Navicula jarnefeltii 2 1 2 3 2 1 Navicula cf. jarnefeltii 4 Navicula mutica Navicula porifera 1 1

312

Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 8.25 9.25 10.3 11.3 12.3 13.3 14.3 16.3 18.3 20.3 22.3 24.3 27.3 Navicula crucicula 1 Navicula splendicula 2 Navicula cf. capitata 4 Navicula cf. veneta 2 Navicula sp. Vee 27-27.5 2 Navicula cf. leavissima 9 Navicula cf. detenta 6 Navicula cf. eidrigiana 1 Navicula lacunolaciniata 3 Navicula cf. soodensis 1 Navicula cf. reinhardtii 1 Navicula cf. similis 2 Navicula sp. broken Vee 13-13.5 5 Navicula sp. Vee 11-11.5 1 Navicula sp. Vee 8-8.5 1 Neidium ampliatum 1 2 2 2 1 1 Neidium sp. Vee 11-11.5 3 Neidium cf. dubium 2 Nitzschia frustulum 10 1 2 2 Nitzschia fonticola 2 2 1 1 2 1 3 2 1 4 2 Nitzschia palea 4 1 2 3 Nitzschia gracilis 2 2 Nitzschia dissipata 1 4 Nitzschia linearis 1 Nitzschia angustata 1 1 Nitzschia amphibia 3 2 1 1 1 1 1 3 4 5 2 1 2 Nitzschia heufleriana 1 Actinella punctata 1 Rhopalodia gibba 1 1 Rhoicosphenia abbreviata 1 Pinnularia interrupta 4 2 1 1 5 1 6 8 3 5 3 2 8 3 Pinnularia microstauron 3 4 1 2 4 6 2 1 Pinnularia cf. microstauron 3 Pinnularia sp. girdle Vee 3-3.5 2 Pinnularia viridis 1

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Taxa 0.75 1.25 2.25 3.25 4.25 5.25 6.25 8.25 9.25 10.3 11.3 12.3 13.3 14.3 16.3 18.3 20.3 22.3 24.3 27.3 Pinnularia gibba 1 5 1 Pinnularia sp. Vee 11-11.5 3 Pinnularia sp. Vee 27-27.5 2 Pinnularia obscura 4 Stephanodiscus spp. Vee Lake 1 Stephanodiscus parvus 1 Stephanodiscus alpinus 1 Stephanodiscus minutulus 4 Stauroneis phoenicenteron 1 1 2 1 3 4 4 5 8 4 4 Stauroneis lauenburgiana 2 Stauroneis anceps 3 5 5 1 1 3 2 2 7 3 12 4 37 24 34 27 9 7 Stauroneis smithii 1 Tabellaria flocculosa str. III 1 Tabellaria flocculosa str. IIIp 1 Tabellaria quadriseptata 1 1

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