Mya Truncata As a Bioindicator of Chronic Municipal Wastewater Exposure and Anthropogenic Activity in Frobisher Bay, NU

Home , Monument Island, Mya (bivalve), Mya truncata, Myida, Myidae, Paquet Bay

Mya truncata as a Bioindicator of Chronic Municipal Wastewater Exposure and Anthropogenic Activity in Frobisher Bay, NU.

Christina Mae Schaefer

A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE

Department of Biological Sciences University of Manitoba Winnipeg, Manitoba December 2020

Abstract Municipal wastewater effluent is one of the largest sources of pollution to Canadian waters. Until recently, the disposal of wastewater was not of great concern to Arctic communities, but growing populations and urbanization bring more diverse contaminants that could cause deterioration of an already fragile environment. Bivalves have proven extremely useful in their ability to evaluate the importance and spatial distribution of contaminants. Thus, this thesis investigated the effects of Iqaluit’s (NU, Canada) primary treated municipal effluent in Frobisher Bay on the Arctic truncate soft-shell clam, Mya truncata. Clams were sampled from six locations along a gradient in proximity to Iqaluit’s wastewater effluent source. Four sites were chosen for their proximity and potential direct exposure to wastewater effluent and two were chosen for their distance and environmental barriers from the effluent source. Patterns of growth, stable isotopes and trace elements in the shell were linked to environmental variation on an annual scale and a parallel analysis measured the tissue-specific mRNA transcriptional response of the soft-shell clam. The results displayed slower growth and smaller shell lengths in organisms of the same age near the wastewater effluent source. Common side-effects of wastewater effluent like brackish water, increased organic input, and higher average calculated temperatures were evidenced by significantly lower ratios of carbon (1.38‰) and oxygen (1.31‰) isotopes in clam shells nearest the wastewater treatment plant (WWTP). Clams nearest the wastewater effluent source also had metals (lead and copper) characteristic of wastewater effluent accumulate in their shells over time. Given the environmental variation recorded in the shells near the WWTP, further evidence of chronic exposure impacts was supported by the cellular stress and xenobiotic response. The clams nearest the outfall exhibited lower expression of mRNA involved in the physiological response functions including antioxidants, metabolic regulators, molecular chaperones, and phase I and II detoxification response, but had heightened expression of mRNA of genes coding for enzymes that bind and remove xenobiotics. The culmination of these results demonstrates the presence of contamination and provides an early warning system into the possible adverse physiological changes that could result from chronic municipal wastewater exposure in Frobisher Bay.

Acknowledgements First, I would like to recognize my supervisor Dr. Ken Jeffries. I am extremely grateful for all the guidance and pep talks you’ve given me, for taking the time to mentor me and for helping me get through that first field season, for that, I am ever grateful. I would also like to express my gratitude to my second supervisor Dr. David Deslauriers. Thank you for all of your guidance and coding help, it absolutely would have been a mess without you. Thanks to my committee members Dr. Mark Hanson and Dr. Dirk Weihrauch you both have provided great insight and resources that made this project what it is today. A big thank you to Dr. Jason Treberg for providing equipment and facilities to age my organisms. To Jordan, my Iqaluit partner. Thank you for keeping me company, checking in, burning popcorn, learning with me and for all our northern adventures. To Dr. Chris Lewis, you sparked my passion for the Arctic. Thank-you for taking the time to bring me out on the ocean, for showing me the life changing honey Dijon mustard, and for all the laughs. I am extremely grateful to all the members of the Hunters and Trappers Association, as well as my boat captains Davidee Qaumariaq, Mathew Alainga, and Alex Flaherty. I appreciate you touring me around the bay, sharing stories and food and for showing me your culture. I would like to specifically thank Danny Gedig, who aged every single clam. I sold it as ‘easy’ when it was far from it. Dr. Alyssa Weinrauch, my first science mentor. Thanks for encouraging me when I thought R was going to defeat me, for reading over nearly everything I have written, for supporting me and coming to this new city with me. Thank-you for being the best roommate and friend I could have ever asked for. Truly, you changed my life. To my boyfriend, Bryce Dodd, you have been my biggest support. Thank- you for getting excited about the graphs I showed you with absurd yelling, for bragging about my research, and for being there for every breakdown. You cheered me on when I was discouraged, wiped my tears away when there were research catastrophes, made sure I took breaks, but most of all made me laugh away the mistakes. Lastly, to my mom, Terese and sister, Kate. Thank you for always supporting me. It has meant so much that you have answered every call and acted interested as I carefully described the innards of a clam. You both brought me back to earth and made me laugh when I most needed it. This research would not have been possible without the financial support provided through an NSERC Discovery Grant, the University Indigenous Research Program Grant and Fisheries and Oceans Canada Coastal Environmental Baseline Program.

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Dedication This thesis is dedicated to the memory of my Oma, Ruth Schaefer, who passed away during my field season in Iqaluit. She would have loved to pretend to understand what was going on in this thesis and always encouraged me to chase my dreams.

Table of Contents Abstract ...... ii Acknowledgements ...... iii Dedication ...... iv List of Tables ...... vii List of Figures ...... viii List of Abbreviations ...... ix List of Equations ...... xi Chapter 1: General Introduction ...... 1 The Canadian Arctic ...... 1 Arctic Ocean Contamination Concerns ...... 1 Local Anthropogenic Impacts ...... 2 Canadian Wastewater Management ...... 3 Biomonitors ...... 6 Biomarkers ...... 6 Sclerochronology ...... 7 Cellular Stress Response ...... 9 Study Species: Mya truncata ...... 10 Aims of this Thesis ...... 11 Figures and Tables ...... 13 Chapter 2: Shell sclerochronology, trace elements and stable isotopes of the bivalve Mya truncata from Inner Frobisher Bay: Implications of chronic exposure to primary treated wastewater ...... 15 Abstract ...... 15 Introduction ...... 15 Materials and Methods ...... 17 Sampling ...... 17 Growth Pattern Analysis ...... 18 Stable Isotopes Analysis...... 19 Trace Element Analysis ...... 21 Statistical Analysis ...... 22 Results ...... 25 Analysis of Growth Variations ...... 25 Stable Isotopes Signatures ...... 25 Trace Element Profiles ...... 26

Multivariate Analysis ...... 27 Discussion ...... 28 Growth Metrics ...... 28 Accumulation of Trace Elements ...... 30 Conclusion ...... 31 Figures and Tables ...... 32 Chapter 3: Gene expression profiling in the Arctic truncate soft-shell clam, Mya truncata, located near a municipal wastewater outfall ...... 42 Abstract ...... 42 Introduction ...... 42 Materials and Methods ...... 46 Animal collection and habitat parameters ...... 46 Sample Preparation ...... 46 RNA Extraction and cDNA Synthesis ...... 47 Primer Design and Validation ...... 48 qPCR ...... 49 Statistical Analysis ...... 50 Results ...... 50 Gill mRNA abundance ...... 50 Gill principal components analysis ...... 51 Mantle mRNA abundance ...... 51 Mantle principal components analysis ...... 52 Discussion ...... 52 Xenobiotic Response ...... 53 Cellular stress response ...... 55 Conclusion ...... 56 Figures and Tables ...... 58 Chapter 4: General Summary and Conclusions ...... 66 Future Directions ...... 69 Summary ...... 71 Literature Cited ...... 72 Appendix and Supplementary Data ...... 91

List of Tables

Table 2.1 LA-ICP-MS parameters used for trace element analysis...... 33

Table 2.2 Negative exponential detrending curve equations and significance ...... 34

Table 2.3 Negative exponential detrending curve equations and significance ...... 36

Table 2.4 Pearson cross correlation values and significance between strontium, manganese and

growth rate for each sampling location ...... 40

Table 3.1 Primer sequences used to target select genes in the transcriptome of Mya truncata 58

Table A1. Studies included in the literature review of 126 surveys conducted on Mya truncata

in the Canadian Arctic ...... 91

Table A2. Raw morphometric measurements for each sample site ...... 95

Table A3. Raw values of stable carbon (δ13C) and oxygen (δ18O) isotopes ...... 101

Table A4. Raw salinity values acquired from three separate studies in Frobisher Bay ...... 105

Table A5. Trace element concentrations back calculated and assigned calendar years ...... 112

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List of Figures

Figure 1.1 Timeline of Wastewater Events in Iqaluit, Nunavut ...... 13

Figure 1.2 Summary of published studies analysing Mya truncata in the Canadian Arctic ...... 14

Figure 2.1 Map of Mya truncata sample locations ...... 32

Figure 2.2 Morphometric measurements taken on Mya truncata shells and a cross-section of M.

truncata’s chondrophore ...... 33

Figure 2.3 Raw ontogenetic growth with detrending negative exponential curves for each sampling

location ...... 34

Figure 2.4 Standardized growth index (SGI) for Mya truncata ...... 35

Figure 2.5 Average shell δ18O versus δ13C isotope concentrations for each sampling location ...... 36

Figure 2.6 Reconstructed sea surface temperatures from shell δ18O (VPDB) and seawater δ18O

(VSMOW) ...... 37

Figure 2.7 Trace element/Ca profiles of Mya truncata shells at each sampling site ...... 38

Figure 2.8 Trace metal/Ca profiles (ppm) of Mya truncata shells at each sampling site ...... 40

Figure 2.9 Principal components analysis biplot of trace elements characterized by year and site ..... 41

Figure 3.1 Relative expression of mRNA in the gill tissue of Mya truncata for 10 genes...... 61

Figure 3.2 Principal components biplot analysis on the gill gene expression ...... 62

Figure 3.3 Relative expression of mRNA in the mantle tissue of Mya truncata for 10 genes ...... 64

Figure 3.4 Principal components biplot analysis on the mantle gene expression ...... 65

Figure A1 Age (years) distribution of Mya truncata for each sampling location ...... 108

Figure A2 Shell length (mm) distribution of Mya truncata for each sampling location ...... 109

Figure A3 Supplementary standardized growth index for Mya truncata ...... 110

Figure A4 Pearson correlation between freshwater supply and accumulated trace elements ...... 111

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List of Abbreviations µg Microgram µL Microlitre µm Micrometer µM Micromolar ‰ Per-mille Ag Silver Al Aluminum AMG Axis of Maximal Growth ANOVA Analysis of Variance As Arsenic ATP Adenosine Triphosphate Ba Barium ℃ Degrees Celsius Ca Calcium CaCO3 Calcium Carbonate CBOD Carbonaceous Biological Oxygen Demands CCF Cross Correlation Function Cd Cadmium cDNA Complementary DNA CEC Contaminants of Concern CO2 Carbon Dioxide CSR Cellular Stress Response Cu Copper CV Coefficient of Variation DDT Dichlorodiphenyltrichloroethane DEW Distant Early Warning DIC Dissolved Inorganic Carbon EPF Extrapallial Fluid Fe Iron Hg Mercury - HCO3 Bicarbonate HSD Honest Significant Difference Hz Hertz in situ Situated in the original or existing place J Joules L Length LA ICP-MS Laser Ablation Inductively Coupled Plasma Mass Spectrometry LC50 Median Lethal Concentration Li Lithium LMM Linear Mixed Models LOD Limit of Detection M3 Meters Cubed MANOVA Multivariate Analysis of Variance Mg Magnesium

ML Maximum Length mm Millimeter Mn Manganese Mo Molybdenum mRNA Messenger RNA Na Sodium NE Negative Exponential Ni Nickel PAH Polycyclic Aromatic Hydrocarbons Pb Lead PCA Principal Components Analysis PCB Polychlorinated Biphenyls POP Persistent Organic Pollutants PMGD Posterior Maximum Growth Dorsal PMGV Posterior Maximum Growth Ventral r Correlation Coefficient ROS Reaction Oxygen Species qPCR Quantitative Polymerase Chain Reaction s Second SGI Standardized Growth Index Sn Tin Sr Strontium Ti Titanium TSS Total Suspended Solids U Uranium VPDB Vienna Pee Dee Belemnite Standard VSMOW Vienna Standard Mean Ocean Water WSER Wastewater System Effluent Regulations WWTP Wastewater Treatment Plant Zn Zinc

List of Equations

Equation 2.1 Ganssens Equation ...... 20

Equation 2.2 Reconstruction of Sea surface temperature from stable oxygen isotopes ...... 20

Equation 2.3 Negative exponential detrending curve ...... 22

Chapter 1: General Introduction The Canadian Arctic The Arctic region of Canada constitutes over 40% of the country’s landmass and was transferred to Canadian sovereignty in 1880 (Government of Canada 2011; Johnson 2016). Most of the Canadian high Arctic is part of an archipelago, containing thousands of islands, six of which, are the largest islands in the world (AMAP 2012; Adams and Dunbar 2019). The islands remain relatively uninhabited except for a few small coastal communities but the coastline spans nearly 162 000 km and provides the wintering grounds for many marine mammals like narwhal, walrus, beluga and bowhead whales and is home to bearded and harp seals (Marsh 2018). The Arctic Ocean is central to life in the Canadian north. It provides an important source of nutrition, income, and a cultural sense of identity for the Arctic peoples (PAME 2013). Of the five oceans on Earth, the Arctic Ocean is the smallest and shallowest, and due to harsh environmental conditions, the least explored (Rudels 2019; Ostenso 2020). This ocean is characterized by a cold climate, low evaporation and influxes of freshwater from rivers and glaciers making it the least saline ocean (30-34 psu; AMAP 1998). Together, this creates strong stratification and allows longstanding ice cover in the winter, often remaining through the summer in northernmost regions (Rudels 2019). Presently, the ice-free period in Canada’s Arctic is approximately four months (June to September) but this period is increasing in duration as global temperatures continue to warm. Arctic temperatures are increasing at more than twice the global mean, a phenomenon deemed “Arctic amplification” (AMAP 2019). Consequently, a longer ice-free season has stimulated interest in natural resources, trade routes and economic ventures in the region due to increased accessibility (Krumhansl et al. 2015; Adams and Dunbar 2019).

Arctic Ocean Contamination Concerns The Arctic was once thought of as a pristine environment and while it remains one of the least polluted regions on earth, local and long-range contaminants are deteriorating an already fragile environment (AMAP 1998, 2015; PAME 2013; Stow et al. 2017). Contaminants of concern in the Arctic can be described in two ways. First, as substances manufactured by anthropogenic sources, whose presence would never occur naturally (e.g., polychlorinated biphenyls (PCB)) and second, as substances produced by anthropogenic sources that load onto natural cycles (e.g., mercury (Hg)) (Bidleman et al. 2003). Contamination is not always indicative of environmental or organismal adverse effects; instead, it could be used to describe

1 the introduction and detection of substances in the environment related to anthropogenic activity. When a contaminant is at a concentration high enough to cause environmental or biological damage, it is then referred to as a pollutant (Bidleman et al. 2003). The degree of differentiation between a contaminant and pollutant is not always clear but generally, pollutants differ in their definition as they are anthropogenically introduced substances that can cause any degree of harmful effects on the organism or environment (Stengel et al. 2006). Today, the Arctic Ocean carries many priority pollutants of concern including persistent organic pollutants (POPs), metals (specifically Hg, cadmium (Cd), and lead (Pb)), radionuclides, emerging chemicals of concern (CEC), acidifying substances, greenhouse gases and other climate forcing substances (PAME 2013; Stow et al. 2017; AMAP 2018; Northern Contaminants Program 2018).

Local Anthropogenic Impacts With temperatures rising and sea-ice declining, commercial activities and marine transport is escalating in the north (AMAP 2019). These new economic ventures alongside increasing temperatures are creating ties to the global economy, leading to a population expansion in the Arctic and greater human influence in the region (AMAP 1998, 2019; PAME 2013; Krumhansl et al. 2015). With this economic prosperity, physical disturbances are on the rise with commercial fisheries, oil and gas activities, increased wastewater discharge and harbour construction, all of which have not been fully assessed for their impacts on the Arctic ecosystems (PAME 2013). Evidence in bio-geophysical changes can be seen in the Arctic from increasing human activity and while Arctic species are generally not considered “sensitive” due to their environmental tolerances from extreme annual variation, the physical environment of the Arctic is considered extremely sensitive (AMAP 1998). While a broad range of contamination can be attributed to long-range transport, local contamination sources are rising with population growth and their severity is of high concern due to their close proximity to communities (Northern Contaminants Program 2013). Specific to the Canadian Arctic these local sources include the distant early warning (DEW) radar stations, abandoned mining and industrial sites, nuclear weapons accidents, wastewater outfalls, solid waste disposal sites and abandoned military stations (Bidleman et al. 2003; Stow et al. 2005). The majority of contamination in and around Iqaluit is reflective of the military presence and can be found in sewage outfalls and lagoons, the “down-the-drain” disposal method of contaminants, fuel tank pads, inadequate and abandoned solid waste landfills, and the wide use of

2 dichlorodiphenyltrichloroethane (DDT) and PCB containing devices (AMAP 1998; Northern Contaminants Program 2013).

Canadian Wastewater Management Access to effective wastewater sanitation was acknowledged as a basic human right to protect human health and the environment, by the United Nations General Assembly in 2010 (United Nations General Assembly 2015). Today, despite many wastewater treatment plants (WWTP) operating under strict governmental guidelines, harmful effluent is still discharged and operational failure, mismanagement and environmental variability bring complications of untreated sewage emptying into the environment (Daley et al. 2015; Canadian Water Network 2018). In addition, there is further bias in the level of treatment between coastal regions and freshwater inland waters. For example, in 1999, most coastal communities in Canada had primary or no wastewater treatment, whereas 84% of inland municipalities received secondary or tertiary treatment (Gunnarsdóttir et al. 2013). It stands to reason that the Government of Canada recently defined wastewater effluent to be the “largest source of pollution by volume to surface water in Canada” (Canadian Water Network 2018).

Wastewater contaminants of concern vary by region but major constituents recognized by the Canadian government include nutrients, metals, legacy contaminants, industrial and household chemicals, personal care products, pharmaceuticals and endocrine disruptors that could pose significant risks to the environment, biota and human health (Canadian Water Network 2018). These constituents can alter ecosystem health through a variety of mechanisms including oxygen depletion, pathogen introduction, and nutrient overload that can lead to acute toxicity and the loss of ecosystem services (Canadian Water Network 2018).

Wastewater Management in the Arctic Wastewater treatment in the Arctic holds a unique set of challenges due to the harsh terrain, permafrost conditions, severe weather variation, limited quantity and quality of water, high costs of electricity, transportation and limited accessibility often resulting in operation failure or mismanagement (Gunnarsdóttir et al. 2013; Johnson et al. 2014; Daley et al. 2015). In the eastern Canadian Arctic, accessibility plays a unique role in management, as much of the land is only accessible by aircraft and annual sealift – an annual summer shipment of bulk goods provided by large ships for community resupply, which makes maintenance an arduous task

(Johnson et al. 2014). Until recently, the disposal of wastewater was not of concern to Arctic regions due to the large receiving waters and relatively small communities, but recent studies have shown chronic exposure to wastewater effluent can deteriorate the quality of the marine environment (Gunnarsdóttir et al. 2013; Krumhansl et al. 2015).

Wastewater Management in Iqaluit, NU Iqaluit is located in the southeast region of Baffin Island, the largest island in Canada (63.7467° N latitude and 68.5170° W longitude) (Johnson 2016; Marsh 2018). Unlike most regions that expanded from the fur trade, Iqaluit owes its expansion to the military presence that resulted from the Second World War (Johnson 2016). At the end of the Second World War, Iqaluit had become popularized and government services drew large numbers of Inuit to permanently settle in the valley of Niaqunngut, forming the small community of Apex (Eno 2003; City of Iqaluit 2014; Johnson 2016). Prior to permanent settlement, Inuit were semi- nomadic hunters and gatherers who scattered or buried their waste (Daley et al. 2015). Now, with a permanent settlement, there had to be a new initiative to respond to larger quantities of waste. In the late 1950’s, there was minimal legislation on wastewater treatment requirements, the community had a gravity collection system which discharged raw sewage into Frobisher Bay (Prosko and Johnson 2015; Canadian Water Network 2018). The method of gravity driven discharge continued until the late 1970’s, when the construction of lift stations provided the means to pump the sewage to a macerator and lagoon system (Fig. 1.1) (Prosko and Johnson 2015). While the lagoon facility successfully operated for several decades, the macerator technology ultimately failed when most households used ‘honey bags’ – disposable plastic bags that contained waste from ‘bucket toilets’, and their thick lining clogged the macerator (Gunnarsdóttir et al. 2013; Prosko and Johnson 2015). Stricter federal wastewater management practices in the early 1990’s prompted Iqaluit to seek consultation. The design contract was awarded to a southern based company in the late 1990’s and in 1999 it became evident that the new facility did not meet code on many items and poor construction resulted in significant leaking (Johnson et al. 2014; Prosko and Johnson 2015). After construction suspension, remediation work began in 2005 and built the addition of a primary treatment building and formed plans for a future secondary treatment facility (Fig. 1.1) (Johnson et al. 2014). The primary treatment upgrade added two new screens to improve the effluent quality but remained

4 in non-compliance with the city’s water licence and in non-compliance with the Arctic pollution prevention act for disposing waste into Arctic waters (City of Iqaluit 2007).

In recent years, human activity in the Canadian Arctic has drastically increased and Iqaluit’s population has been on a steady rise with a 15.6% increase in population size between 2011 and 2016 (Statistics Canada 2017). The community continues to be a hub for the Eastern Arctic and projections show the community will grow to over 11 000 by 2022 (Statistics Canada 2017). A study conducted in 2015 showed that the daily effluent flow rate by the year 2038 could reach 16 353 m3 day-1 compared to the 2013 value of 2427 m3 day-1 (Nunami Stantec 2015). Inevitably, the wastewater treatment capacity of the community must also grow. Iqaluit has continuously had poor results in effluent toxicity largely attributed to the volume discharged and a 2015 study found anoxic impacts nearly 600 m away from the effluent source (Krumhansl et al. 2015). Since the inception of toxicity testing in 1998, Iqaluit has failed the annual ammonia- based LC50 test conducted on its effluent, labelling it ‘technically toxic’ in accordance to

Environment Canada’s test protocol EPS/1/RN/13 (Nunami Stantec 2015). These LC50 tests used 100% effluent and were considered a failure if more than 50% of the test organisms, rainbow trout (Oncorhynchus mykiss), died after 96 hours (Nunami Stantec 2015). Recently, national wastewater treatment practices have been improving with more stringent treatment standards implemented by the 2012 Wastewater Systems Effluent Regulations (WSER) (Daley et al. 2015; Canadian Water Network 2018). Northern communities face many obstacles unique to Arctic conditions with reduced administrative, financial and human resources that limit their abilities to comply with the WSER (Johnson 2008; Daley et al. 2015; Neudorf et al. 2017). As a result, the Department of Indigenous and Northern Affairs allowed five extra years for the city to upgrade its plant to secondary treatment by December 31, 2018 (Nunami Stantec 2015). The goal of the secondary treatment is to reduce the high levels of toxic waste previously being discharged into Frobisher Bay by having physical separation of solids and biological processes to remove dissolved and suspended organic compounds (Sonune and Ghate 2004; Nunami Stantec 2015). In 2015, a formal proposal of secondary treatment upgrades was accepted but with funding deficits, the construction did not begin until June of 2018, with the proposal to finish by March of 2020 (Fig. 1.1) (Government of Canada 2019).

Biomonitors Biological monitors, or biomonitors, are in situ organisms subjected to multiple simultaneous stressors (Hatje 2016). Contaminants typically become diluted in environmental matrices like air, soil and water, therefore the utility of a biomonitor can be highlighted if they are able to accumulate, and therefore quantify the biological availability of contaminants in aquatic ecosystems and act as a relative measure to represent the significance of contaminant exposure within the environment (Phillips and Segar 1986; Rainbow and Phillips 1993; Hatje 2016; Etteieb et al. 2019). Many diverse organisms continue to be used as reliable environmental biomonitors including crustaceans (e.g., amphipods), fish (e.g., chub, barbel, bream, perch), corals (e.g., red sea coral), macroalgae (e.g., red algae, green algae, yellow-green algae, diatoms), and most commonly, benthic macroinvertebrates (e.g., bivalves, arthropods) (Hatje 2016). These organisms are chosen because they possess some of the ideal characteristics to make comparisons over large spatial and temporal scales (Hatje 2016). These characteristics include, but are not limited to sedentary, abundant, fairly tolerant of environmental conditions and natural stressors, easy to identify and sample, long-lived, large enough for analysis, and resistant to handling stress (Rainbow and Phillips 1993; Boening 1999; Hatje 2016; Sleight et al. 2016). Temperate regions have highly-developed biomonitoring programs, but little is known about biomonitors in the Arctic as seasonal variation, limited food availability and cold-water adaptations could make these organisms more vulnerable to contaminants (Hatje 2016). Biomarkers Biological markers, or biomarkers are the biological responses of those organisms classified as biomonitors. These biological signatures (i.e., biomarkers) are used to evaluate the conditions, status, variation or trends in the aquatic environment that provides evidence of the exposure and further effects of contaminants (Camus 2001; Camus et al. 2003; Dimitriadis et al. 2012; Bortone 2016). Biomarkers are most commonly used to monitor water and sediment quality, identify contaminated regions, and assess organism health in aquatic environments (Camus 2001; Joyner‐Matos et al. 2009). A broad range of biomarkers can be selected from all levels of biological organization with the finer scale including genetic, physiological, biochemical and molecular features (Bortone 2016). The biomarkers can be passive (i.e. growth rate) or active (i.e. tissue contamination) and the sensitivity of these indicators will vary between direct (i.e. direct discharge of wastewater effluent) or indirect stressors (i.e. trophic interactions

6 or behavioural disruption) (Bortone 2016). Biomarkers at higher levels of organization (i.e., individual level) tend to be more ecologically relevant but have low specificity, and the opposite stands true for finer scale biological organization (i.e., cellular and tissue level; Phillips and Segar 1986). Sclerochronology Sclerochronology, the aquatic counterpart of dendrochronology, is the study of physical and chemical variations in the hard tissues of organisms and the temporal context in which they are formed (Reitz et al. 2012). Bivalves are excellent organisms for sclerochronology as they can provide precise, high-resolution biogeochemical records of environmental change and this approach has been successfully integrated into environmental biomonitoring (Schöne and Surge 2012; Reitz et al. 2012). As bivalves grow, their shell also increases in size and thickness by means of the mantle-derived calcium carbonate (CaCO3) deposition onto the organic matrix (Jones and Quitmyer 1996). This biomineralization process, takes place in the extrapallial fluid (EPF), that lies between the calcified shell and the mantle epithelium (Bourgoin 1990; Gillikin et al. 2005b, 2006a). In addition to containing the required building blocks for biomineralization 2+ - (i.e. Ca , bicarbonate (HCO3 ), organic molecules), the EPF can contain trace element ratios and stable isotopes that become incorporated into the hardened shell medium (Bourgoin 1990; Gillikin et al. 2005b). This process connects bivalve shell growth to environmental and metabolic variation, preserved in the form of growth rates, biogeochemical records and structural characteristics (Hewitt and Dale 1984; Witbaard et al. 2005; Amaro 2005; Strasser et al. 2008; Schöne and Surge 2012; Sleight et al. 2016; Román-González et al. 2017; Colonese et al. 2017). Throughout shell development, environmental properties such as temperature and salinity, food availability, water quality, productivity, ocean circulation and elemental composition can be isolated and related to spatial or temporal variations in the seawater (Strasser et al. 2008; Schöne and Surge 2012; Colonese et al. 2017).

Growth Patterns Periodic growth patterns are displayed in the calcareous shells of many bivalves and they retain an ontogenetic record of accretionary growth in the form of dark growth lines (Ridgway et al. 2011; Schöne and Surge 2012). These growth lines form in concentric rings, that apart from disturbance lines (i.e., environmental extremes), form in equal duration and the distance that

7 separates them defines growth increments (Schöne and Surge 2012; Román-González et al. 2017; Poitevin et al. 2019). Growth measurements in bivalves have evolved from being measured on the outermost layer, which captured only 50% of the actual bands, to now measuring more defined increments in the chondrophore, a cavity that supports the internal hinge in bivalves (MacDonald and Thomas 1980; Hewitt and Dale 1984). By analysing both growth increments and disturbance lines, patterns can be applied to analyse age, life history parameters (Ridgway et al. 2011), season of spawning (Jones and Quitmyer 1996; Amaro 2005), and population density (Amaro 2005; Schöne and Surge 2012). Further, growth increments can be used to evaluate environmental factors at the time of growth like temperature (Amaro et al. 2003; Witbaard et al. 2005), food quality and availability (Schöne and Surge 2012), and water quality (i.e., contaminants and primary production)(Carmichael et al. 2004; Román-González et al. 2017; Colonese et al. 2017).

Stable Isotopes Bivalve shells constant physical contact with the ocean waters allow them to register internal and environmental information characterized in the form of biogeochemical records for each defined growth increment (Schöne 2003; Carmichael et al. 2004, 2008; Schöne and Surge 2012; Colonese et al. 2017). Common biogeochemical signatures are stable isotopes, which are defined by the number of neutrons that alters the weight of the element. This weight differential allows isotopes to act as environmental signalling devices and indicate the presence and magnitude of environmental information (Siegwolf and Dawson 2007). Stable carbon isotopes (δ13C) in aquatic invertebrate shells primarily reflect ambient dissolved inorganic carbon (DIC) as less than 10% of metabolically derived carbon (CO2) exists in marine invertebrates (Gillikin et 13 al. 2006b; McConnaughey and Gillikin 2008; Poulain et al. 2015). The concentration of δ C in estuarine conditions is typically depleted compared to full-strength sea water due to the larger 13 influx of CO2 from decaying terrestrial organic matter (Colonese et al. 2017). Therefore, δ C 13 can reflect the salinity of the region having lower δ C concentrations in freshwater and estuarine regions than their marine counterparts (Gillikin et al. 2006b; Colonese et al. 2017).

Stable oxygen isotopes (δ18O) are arguably the most widely used proxy in bivalve shells because it is precipitated in equilibrium with the ambient water (Jones and Quitmyer 1996; Reitz et al. 2012). δ18O is often used to reflect water temperatures to 1℃ accuracies during the

8 animal’s growth (Dettman et al. 1999; Gillikin et al. 2006b; Colonese et al. 2017). This, in turn, is regulated by the animal’s physiological tolerance to the environmental conditions and endogenous controls (Schöne 2008). For instance, warmer ambient temperatures can elicit increased shell production, and thereby more negative oxygen isotope values (Stecher et al. 1996; Schöne 2008).

Trace Elements Trace elements are defined as chemical elements that exist in very small quantities in organisms or the environment (Mertz and Sadler 1981). They are introduced into the environment through natural (i.e., river discharge, rock erosion, hydrothermal circulation) and anthropogenic processes (i.e., fossil fuels, intentional wastewater discharges, refining and burning, accidental discharges) (Krishnakumar et al. 2018). If concentrations of elements exceed natural levels in the environment, lower trophic organisms could accumulate these elements and contribute to cycling through subsequent biomagnification across trophic levels (Szynkowska et al. 2018). The bioaccumulation capabilities of benthic organisms also means trace elements can be recorded in the shell material during biomineralization and used to monitor environmental contamination and variation (Ricardo et al. 2015; Vihtakari et al. 2017; Krishnakumar et al. 2018; Markulin et al. 2019). These elemental ratios in bivalve shells can be extremely valuable proxies, especially in the Arctic where instrumental records are short or interrupted and climate change is rapid (Vihtakari et al. 2017).

Cellular Stress Response Biomarkers can be selected from any level of biological organization but cellular level biomarkers are specific and sensitive enough to quantify the environmentally derived stress that has the potential to cause macromolecular damage (Kültz 2005; Bortone 2016). This process, called the cellular stress response (CSR), is a highly conserved defensive response that is characteristic of all cells (Kültz 2005; Li et al. 2013). These cellular stress markers are gaining popularity in ecotoxicology because their upregulation or inhibition can demonstrate not only the physiological mechanism of response to the environmental stress but also provides a relative measures of the toxic effect (Farcy et al. 2009; Flores-Nunes et al. 2015; Etteieb et al. 2019). The CSR is generally not specific to individual stressors, thereby, aspects of the CSR are categorized by defense function including oxidative stress regulation, cellular management by chaperone

9 proteins, detoxification of xenobiotics, inhibition of growth and biomineralization and have the potential to act as an early warning system against environmental stressors (Farcy et al. 2009; Falfushynska et al. 2014).

Study Species: Mya truncata Filter-feeding bivalves are ideal as monitors of environmental change as they provide an integrated response to contaminants across time (Klein et al. 1996; Strasser et al. 2008; Reitz et al. 2012; Colonese et al. 2017). Therefore, the Arctic truncate soft-shell clam, Mya truncata (Superorder = Imparidentia, Order = Myida, Superfamily = Myoidea, Family = Myidae; Linné 1758), was selected for this study as it fulfills all the requirements to be an ideal biomonitor of environmental change having features like a vast circumboreal distribution, and a long-lived, sessile, filter-feeding lifestyle (Camus et al. 2003; Fish and Fish 2011; Sleight et al. 2016, 2018). I conducted a comprehensive review, looking at 126 peer-reviewed journal articles published from 1960 to 2019 analysing various aspects of M. truncata in the Canadian Arctic (Fig. 1.2; Table A1). Results showed that 70% of studies conducted on the species were for carbon dating, with the second highest being studies on species abundance and distribution (13%) (Fig. 1.2; Table A1). This species has previously been used as a reliable biomonitor in the European and Russian Arctic for exposure to oil spills that delivered high petroleum hydrocarbon levels (Humphrey et al. 1987; Mageau et al. 1987; Neff et al. 1987; Camus et al. 2003) and climatic pressures (Hewitt and Dale 1984; Amaro 2005; Sleight et al. 2016, 2018) but has yet to be used as a biomonitor for contamination in the Canadian Arctic. M. truncata’s capacity for contaminant accumulation is of great concern as these bivalves are recreationally harvested and serve as a culturally important natural food for northern Indigenous peoples (AMAP 1998; Wakegijig et al. 2013). Likewise, these bivalves are an integral part of the food chain providing means of energy transfer from primary producers to bearded seals (Erignathus barbatus) and walruses (Odobaenus rosmarus) (Welch and Martin-Bergmann 1990; Amaro et al. 2003; Sleight et al. 2018). Furthermore, they are considered important ecological stabilizers in the processing of organic matter and nutrient cycling, for which community shifts could result in dramatic changes in ecosystem functioning (Krumhansl et al. 2015; Sleight et al. 2016).

M. truncata is characterized by a pair of white to grey aragonitic shells with a brown periostracum covering that protects the mantle and visceral mass (MacDonald and Thomas 1980;

Bourgoin and Risk 1987; Palacios et al. 1994; Sleight et al. 2016). M. truncata are classified as a soft-shelled clam and while their shells are thinner and brittle compared to their hard-shelled counterparts, their shells are still hard to the touch. Calcium carbonate can take the form of two different minerals, calcite and aragonite, and M. truncata calcifies four structurally distinct layers in the form of aragonite: (i) the outer periostracum; (ii) outer shell layer of aragonitic granular prisms; (iii) a middle layer of aragonitic crossed lamellar; and (iv) an aragonitic inner layer of complex crossed lamellar (MacDonald and Thomas 1980; Sleight et al. 2016). These clams are distinct as they cannot close their shells, resulting in a notable gape and pushing mantle (MacDonald and Thomas 1980). They are a soft-sediment burying bivalve that burrow at varying depths in both the upper sublittoral zone and the intertidal zone up to 70 m depth (Lubinsky 1980; Siferd and Welch 1992; Vinzant 2004). M. truncata have separate sexes and are broadcast spawners producing planktotrophic larvae typically soon after ice breakup, now occurring in May to early June, but have been documented to spawn as late as December, a rare occurrence during poor food conditions and low temperatures (Thorson 1950; Hewitt and Dale 1984; Vinzant 2004). A free-swimming veliger larva hatches about 12 hours after fertilization and after a two- to five-week metamorphosizing period, settles to the ocean floor where it burrows into the sediment. This is where the clam will remain for the rest of its life as M. truncata’s large shell size make it extremely hard for adults to reborrow if disturbed (Thorson 1950; Vinzant 2004; Fish and Fish 2011). M. truncata reaches maturity in one to four years (42 mm length), depending on the length of the growing season and has a typical lifespan between 10-12 years but have records of reaching up to sixty years of age (Siferd and Welch 1992; Vinzant 2004; Wood 2020). Aims of this Thesis The impacts of municipal wastewater disposal in the Canadian Arctic remains relatively understudied and the extent to which benthic communities are affected has not been fully assessed (Gunnarsdóttir et al. 2013; Krumhansl et al. 2015; Kallenborn et al. 2018). The overall objective of this thesis was to better understand the impacts of municipal wastewater on the fragile Arctic ecosystem and native clam, Mya truncata. I hypothesize that chronic municipal wastewater will be a significant source of contamination to the Arctic ecosystem and that M. truncata will demonstrate a physiological response to the effluent. More specifically, my thesis will aim to address the core objectives: (i) identify the presence of contamination near the

11 wastewater treatment plant and to provide species-specific monitoring endpoints to interpret the impact of contamination from chronic wastewater exposure in an Arctic environment, (ii) establish M. truncata’s use as a robust monitor of environmental change, and (iii) understand the integrated physiological response of molecular mechanisms in response to chronic wastewater exposure.

Figures and Tables

•1964: Gravity driven discharge of raw sewage into Koojesse Inlet Raw Sewage Discharge

•1978: Construction of lagoon and macerator system. Implementation of lift stations to pump sewage to macerator system •Late 1978: Macerator system fails -- Revert back to lagoon system •Early 1990's: The city retains a consultant to complete an engineering feasibility study for improving the WWT system •1997: Regulatory pressure increases for Iqaluit to advance a system capable of Lagoon secondary treatment System •1998: Design build awarded to British Colombian based company •1999: Due to inexperience, significant problems arise resulting in facility failure and major leaking in the aeration basins •2002: Uncomissioned evaluation presents recommendations on remediation

•2005: Remedial work begins creating a primary treatment facility •2006: Construction complete for primary treatment facility and includes two new screens •2006 - 2015: Facility continually fails toxicity tests for discharged effluent •2013: WSER implemented, Iqaluit given until December 31, 2018 to upgrade Primary facility to secondary treatment Treatment •June 2015: A formal proposal of secondary treatment is presented to council but includes a $9.7 million dollar shorfall in funding •September 2016: Secondary treatment construction plans accepted •May 24, 2018: City of Iqaluit awards wastewater upgrade contract to Kudluk Construction ltd.

•June 2018: Secondary treatment construction begins Secondary •March 2020: Proposed completion date Treatment

Figure 1.1 Timeline of Wastewater Events in Iqaluit, Nunavut (Johnson 2001, 2016; Johnson and Prosko 2006; Prosko and Johnson 2015)

Figure 1.2 Summary of 126 published studies from 1960 to 2019 analysing various aspects of Mya truncata in the Canadian Arctic. Bubble size indicates year, color indicates type of data acquired (Data found in Table A1).

Chapter 2: Shell sclerochronology, trace elements and stable isotopes of the bivalve Mya truncata from Inner Frobisher Bay: Implications of chronic exposure to primary treated wastewater Abstract This study investigates the effects of year-round primary municipal wastewater effluent on ontogenetic shell growth and partitioning of trace elements and stable isotopes in the Arctic truncate soft-shell clam, Mya truncata, in Frobisher Bay (NU, Canada). The chemical composition of bivalves shells can reflect that of their environment, making them useful indicators of contamination sources and ecosystem changes. Organisms were live-collected and analyzed from six locations chosen for their proximity to the wastewater treatment plant. Organisms collected near the wastewater outflow had a slower growth rate and were significantly smaller than organisms of the same age in other locations. Furthermore, the clams near the wastewater source contained sclerochronological records of reduced stable oxygen and carbon isotopes, accompanied by the highest reconstructed temperatures of any location. These results coincide with metal accumulation of elements typically found in wastewater effluent (i.e., Cu, Pb, and Mn) indicating that the clams located near the wastewater outfall are experiencing contamination induced environmental variation like lower salinity, higher temperature, and higher input of organic matter and metals. This suggests that the wastewater treatment plant is a source of contamination to the surrounding ecosystem but also represents the utility of M. truncata as a robust analytical biomonitor.

Introduction Temperatures in the Canadian Arctic are continuing to rise at more than twice the global average, driving an economic expansion in the region (AMAP 1998, 2019; PAME 2013; Krumhansl et al. 2015). As a consequence, Nunavut’s capital city, Iqaluit, has seen a dramatic population increase, rising 15.6% between 2011 and 2016, causing the city to outgrow their municipal amenities, like their wastewater treatment plant (WWTP) (Prosko and Johnson 2015; Statistics Canada 2017). The WWTP offers primary wastewater treatment, offloading continuous year-round effluent into the Arctic ocean (Krumhansl et al. 2015; Nunami Stantec 2015). This treatment plant is not currently meeting federally enforced wastewater system effluent regulations (WSER) and far exceeds the carbonaceous biological oxygen demands (CBOD) and

15 the total suspended solids (TSS) allowance, making their undiluted effluent ‘technically toxic’ in accordance to Environment Canada’s test protocol EPS/1/RN/13 (Nunami Stantec 2015; Canadian Water Network 2018; City of Iqaluit 2019). Furthermore, with the facility over its processing capacity, the system is often overwhelmed and results in emergency lagoon decanting or the dumping of raw sewage into the relatively pristine marine ecosystem (Earth Tech (Canada) Inc. 2002; Rohner 2016; City of Iqaluit 2019). The inaccessibility and harsh environmental conditions of the Arctic have prevented the implementation of traditional monitoring protocols, but environmental proxies such as biological monitors (biomonitors) can quantify the biological availability of contaminants in aquatic ecosystems and act as a relative measure to represent the significance of contaminant exposure within the environment (Phillips and Segar 1986; Rainbow and Phillips 1993; Hatje 2016; Etteieb et al. 2019). Arctic bivalves are long-lived, sessile, filter-feeders that typically dominate northern benthic communities and are considered ideal monitors of environmental change (Rainbow and Phillips 1993; Klein et al. 1996; Boening 1999; Strasser et al. 2008; Reitz et al. 2012; Sleight et al. 2016; Colonese et al. 2017). Bivalves are of particular concern in this region as they are a popular natural food and recreationally harvested (Wakegijig et al. 2013). This chronic wastewater exposure in receiving waters could have strong influences on primary production, ecosystem structure, salinity and temperature as the physical environment of the Arctic is considered extremely sensitive (AMAP 1998). Thus, the bivalve shells in the region will likely record the effects of this wastewater exposure because temperature, food quality and availability, salinity and water quality exert the greatest control on shell growth (Amaro et al. 2003; Witbaard et al. 2005; Schöne and Surge 2012). As bivalves grow, their shell increases in size and thickness by means of calcium carbonate (CaCO3) deposition onto the organic matrix, a process termed biomineralization (Jones and Quitmyer 1996; Schöne and Surge 2012). In addition to adding the building blocks required 2+ - for biomineralization (i.e., Ca , bicarbonate (HCO3 ), organic molecules), trace elements and stable isotopes present in the ambient water at the time of growth are also incorporated into the shell (Bourgoin 1990; Gillikin et al. 2005b). Therefore, growth patterns accompanied by stable isotope profiles (δ18O and δ13C) and trace element signatures can provide biogeochemical records of internal metabolic variation and external environmental variation to interpret spatial

16 and temporal patterns at the time of growth for temperature, salinity, ocean productivity, and contamination (Schöne and Surge 2012; Reitz et al. 2012). The present study investigated the suitability of Mya truncata, the truncate soft-shell clam, as a biomonitor of continuous primary treated municipal wastewater and examined specific endpoints to determine if Iqaluit’s WWTP is a point source of contamination to the marine environment. I measured growth parameters, stable isotope profiles (δ18O and δ13C) and trace element signatures along a gradient in proximity to the WWTP in Frobisher Bay (NU, Canada). Based on the results of previous studies conducted on Iqaluit’s wastewater outfall, I hypothesized that the wastewater effluent would be a significant source of contamination to the area, eliciting different sclerochronological profiles in clams collected nearest the outfall (Krumhansl et al. 2015; Nobles and Zhang 2015; Neudorf et al. 2017; Stroski et al. 2020). Thus, I predicted that M. truncata would have impaired growth and accumulate contaminants in clams collected nearest the effluent discharge compared to other distant locations. Furthermore, based on M. truncata’s use as a biomonitor of oil spills and climatic pressure, I predicted this clam would be verified as a reliable biomonitor of wastewater contamination in the Canadian Arctic (Humphrey et al. 1987; Mageau et al. 1987; Camus et al. 2003; Amaro 2005; Sleight et al. 2018). By evaluating the annual patterns of growth, stable isotopes and trace elements, I demonstrated the use of bivalves to reconstruct contamination patterns over spatial and temporal scales to lead to a greater understanding of the effects of wastewater effluent in an Arctic ecosystem.

Materials and Methods Sampling A total of 292 M. truncata specimens were hand-collected from six sampling sites (Fig. 2.1) in August 2019, using trowels during extreme low tide (< 1 m) from inner Frobisher Bay (63° 42' 36'' N, 68° 30' 0'' W). All collections were done with the support of the Amaruq Hunters and Trappers Association and under a license approved by Fisheries and Oceans Canada (Licence No: S-19/20-1040-NU). Each sampling site was chosen for its proximity to the WWTP and harvest attendance (Fig. 2.1). Study sites include the WWTP, Tundra Ridge, Apex, and Monument Island chosen for their potential direct exposure to wastewater effluent and Aupalajat and Kituriaquannigituq, chosen for their geographical barriers and distance from the WWTP. The WWTP is located in the tidal flats receiving primary treated, year-round continuous

17 municipal wastewater discharge. Tundra Ridge is located 3 km southeast of the WWTP. Apex, just 5 km southeast of the WWTP, is the original site where the nomadic Inuit settled and is the most popular clam harvest destination due to its accessibility (i.e., road access from Iqaluit). Monument Island is an uninhabited island, 4.9 km south of the WWTP at the mouth of Koojesse Inlet marking the transition between the smaller tidal flats to the broader Frobisher Bay. Aupalajat is an uninhabited island 5.2 km west of the WWTP. This site is protected from direct exposure in the neighbouring Peterhead Inlet and is a popular clam harvest destination. Finally, Kituriaqannigituq (translated from Inuktitut as “Island of no Mosquitos”) is located at the western end of Frobisher Bay, 15 km west from the WWTP but remains a popular clam harvest destination.

After collection, the animals were transferred in coolers to 4 ℃ temperature controlled 30L holding tanks with a recirculating artificial saltwater system at 100 gallons per hour (Instant Ocean Sea Salt; AquaClear Power Filter; Blacksburg, USA). Shell morphometrics were taken upon bivalve collection including length (L), height (H), axis of maximum growth (AMG), maximum length (ML), posterior maximum growth dorsal (PMGD) and posterior maximum growth ventral (PMGV) (Fig. 2.2; Table A2) following protocols in Siferd (2005). Length was considered the maximum anterior – posterior dimension and measured with a pair of digital calipers (0.01mm; Mastercraft, Vonore, USA). After the morphological measurements were taken, clams were opened by slicing the anterior and posterior adductor muscles. Soft tissue was excised, and shells were cleaned with deionized water to remove the adhering mantle tissues, air- dried, then weighed (Table A2).

Growth Pattern Analysis In accordance with MacDonald and Thomas (1980) and Hewitt and Dale (1984), the age and internal growth bands were measured on the left shell’s chondrophore, a cavity that supports the internal hinge in bivalves (Fig. 2.2), and yields the most distinct bands in Mya shells. For reference, the growth line nearest the outer shell layer was formed during the first year of life and the line nearest the inner shell layer was formed in the most recent year (MacDonald and Thomas 1980). I initiated measurements at the most recent complete growth increments (i.e., 2018, the year prior to collection) and worked backwards in time, allowing me to associate a specific calendar year with each growth increment. I assumed that growth bands were formed annually

18 based on the periodicity verified by MacDonald and Thomas (1980). Left shells were embedded in epoxy resin (Varathane, Vernon Hills, USA) and cut in half, along the axis of maximum growth (Fig. 2.2), with a low-speed precision diamond saw (Buehler, Uzwil, Switzerland) continuously cooled in deionized water. Once in half, the side of the valve containing the nucleus of the umbo was embedded again, in a thin layer of resin. The embedded halves were then ground and polished using a standard lapidary wheel (CrystalMaster, Green Meadows, USA) with a sequence of 350-, 600-, 1200- grit silicon carbide wet-table paper and finished with a polishing pad and 0.3 µm alumina suspension (MicroPolish, Buehler, Uzwil, Switzerland). The cross sections were etched and stained using a 100:100:1 mixture of 25% glutaraldehyde, 10% acetic acid and amido black stain and left for 10 mins before extracting the shells and rinsing three times in deionized water and airdrying (Sigma Aldrich, St. Louis, USA).

To analyze the growth patterns, the chondrophores were imaged using a digital camera mounted on a dissecting microscope (Leica EZ4W, Wetzlar, Germany). The widths of the growth bands were measured digitally using the software, Image J (Abràmoff et al. 2004). Band widths were measured independently by two people. Disagreement between the two measurements was consistently due to pseudo growth bands grouped closely together, making them hard to distinguish from one another. In the rare instance that both readers could not agree, the annual readings were discarded.

Stable Isotopes Analysis Stable isotope analysis was performed on the two oldest specimens from all six sampling sites (Table A3). Sample sizes (n = 2) were chosen to display intrashell variability that is lost with averaged values and replicate samples were taken within the individuals (n = 33 – 48). The periostracum was first removed using a rotary tool (Black & Decker RTX-B, Towson, USA). Carbonate powder samples (~50 µg each) were then taken at intervals of 0.5 – 0.6 mm along the cross section between the umbo and the ventral edge, using a DremelTM drill (DremelTM, Wisconsin, USA) and later analyzed at the Manitoba Isotope Research Facility, University of Manitoba, Winnipeg, Canada. δ18O and δ13C were analyzed by a CostechTM 4101 Element Analyzer (Costech Analytical Technologies Inc., Valencia, USA) coupled to a Thermo FinniganTM Delta V plus isotope-ratio mass spectrometer via an open split interface (ThermoFinnigan, San Jose, USA). Isotopes were normalized against calibrated international

19 calcite standards, NBS-18 and -19 taken at the beginning, middle and end of each run. To check the quality of analysis performance, isotopes were calibrated against an internal calcite standard, and analysed together with unknown samples within this dataset (δ18O = ± 0.07‰; δ13C = ± 0.06‰). Shell results are reported in δ-notation for δ18O and δ13C and in per-mille (‰) versus VPDB (Vienna Pee Dee Belemnite Standard). Analytical precision (1σ) was ± 0.08‰ and ± 0.05‰ for δ13C and δ18O, respectively.

In order to reconstruct the environmental conditions experienced by M. truncata in Inner Frobisher Bay, salinity values (psu) were averaged from 2008 and 2009 (Spares et al. 2012), 2013 (Bannister et al. 2013) and 2018-2019 (Fisheries and Oceans Canada 2018, 2019; Table A4). Arctic records are scarce due to harsh environmental conditions, thus salinity was sourced from multiple datasets, across time, for a more accurate representation. Seawater δ18O results are reported in ‰ versus VSMOW (Vienna Standard Mean Ocean Water) and were estimated using Ganssens equation (Witbaard et al. 1994):

18 Equation 2.1 δ O푠푒푎푤푎푡푒푟 = 0.417 ∗ salinity − 14.555

18 18 Where δ Oseawater represents δ O (VSMOW) of the seawater, salinity is the sea surface salinity estimated from multiple surveys within Frobisher Bay and constants are based on cold water geographical regions (i.e., North Atlantic) (Witbaard et al. 1994; Witbaard 1997).

18 18 Seawater temperatures (℃) were then estimated from δ Oshell (VPDB) and δ Oseawater (VSMOW) values and correlates sea water temperature to stable oxygen isotopes. This modified equation was empirically derived for aragonitic bivalves, because aragonite and calcite fraction δ18O differently (Grossman and Ku 1986; Dettman et al. 1999):

18 18 Equation 2.2 T(℃) = 20.6 − 4.34[δ O푠ℎ푒푙푙 − (δ O푠푒푎푤푎푡푒푟 − 0.2)]

18 18 18 18 δ Oshell is the δ O (VPDB) and δ Oseawater is the δ O (VSMOW) of the seawater. Constants are defined based on all marine aragonitic molluscs. The subtraction of 0.2‰ from the 18 18 δ Oseawater adjusts the water and carbonate δ O measurements to account for the different scales on which they were measured (i.e., VPDB versus VSMOW). This modification accounts for the fractionation between water and aragonite and yields a temperature relationship precise to 1℃

20 accuracies in water greater than -10‰ (VSMOW) (Dettman et al. 1999; Gillikin et al. 2006b; Colonese et al. 2017). Trace Element Analysis Chondrophores from the right shell were mounted in 0.25-inch-thick epoxy mounts, ground and polished to obtain best analytical results. Chondrophores were cleaned to eliminate any external contamination using an ultrasonication cleaning bath (Bransonic Ultrasonic Cleaner; Brookfield, USA) for 10 min followed by a 10 min acetone bath (≥99.5% Acetone; Sigma- Aldrich, St. Louis, USA), 10 min deionized water bath and finally another 10 min acetone bath, all conducted on a platform rocker (Vari-mix, ThermoFisher, Waltham, USA). These samples were left overnight to dry with filtered air passing over the samples.

Trace elemental concentrations – 138Ba, 25Mg, 55Mn, 75As, 86Sr, 95Mo, 107Ag, 111Cd, 118Sn, 202Hg, 208Pb, 238U, 7Li, 23Na, 47Ti, 56Fe, 60Ni, 63Cu, 66Zn – in clam shell chondrophores were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA ICP-MS) using a New Wave UP-213 laser ablation system (Merchantek, Frement, California) connected to an Element 2 high resolution ICP-MS (ThermoFinnigan, San Jose, USA) (Table A5). A total of 46 chondrophores (7 – 8 chondrophores per sample site) were analyzed over 15 analytical sessions using both medium and low frequencies to analyse the suite of trace elements. Each ablation chamber held three resin-embedded chondrophores all from different sites to avoid batch effects and machine drift for each sample series. External calibration was performed using the USGS carbonate reference material MACS-3 and ablated in duplicate. MACS-3 is synthetic calcium carbonate with marine carbonate-like minor element component (elevated Mg, Sr, Mn, Fe, Ba) and measurable of almost all elements within the periodic table (Pearce et al. 1997). NIST610 silicate glass material was used as a secondary external reference, it was also ablated in duplicate and used to check quality and monitor drift (Pearce et al. 1997). MACS-3 and NIST610 was run at the beginning of each ablation chamber sample change, allowing for any correction during analysis. Ablation was conducted in line ablation mode using the following laser parameters: 55 µm beam diameter, 20 Hz frequency, approximately 6 J cm-2 laser energy density and a gas blank dwell time of 30 s prior to ablating each chondrophore to ensure the previous sample was cleared and to determine the background elemental limits of detection (LOD) (Table 2.1). The 55

µm laser beam was rastered at 10 µm s-1 at a 1 µm depth over the bivalves to produce profiles across the growth lines.

Data reduction (i.e. mapping, subtracting background, calculations of concentrations and LOD’s) used Igor Pro graphing software (Wavemetrics Incorporated) with an Iolite v3.7 package for LA ICP-MS following procedures described by Paton et al. (2011). Background signatures were subtracted, and calcium (43Ca) counts per second were used as an internal standard that normalized each element. The average LOD in ppm for each element were as follows: 138Ba = 0.0036, 25Mg = 0.074, 55Mn = 0.0243, 75As = 0.012, 86Sr = 0.16, 95Mo = 0.0021, 107Ag = 0.018, 111Cd = 0.013, 118Sn = 0.0060, 202Hg = 0.015, 208Pb = 0.0031, 238U = 0.00035. Those elements detected with medium frequency (7Li, 23Na, 47Ti, 56Fe, 60Ni, 63Cu, 66Zn) held limits of detection that were not available for recording and those values that fell below the LOD or zero were eliminated. By recording the start time, location and direction of the trace element scans, I back calculated and assigned the calendar years to each growth line using ImageJ (Abràmoff et al. 2004). Therefore, results represent average ablation samples (geometric mean of 6 to 8 shells) that reflect years 2005 to 2018.

Statistical Analysis Growth Pattern Analysis M. truncata displays an ontogenetic growth pattern, where the growth rate declines with age. More specifically, there is fast growth in the bivalves first years of life, followed by an exponential reduction in the growth rate as the species reaches its maximum size, referred to as the inflection point (Román-González et al. 2017; Kühleitner et al. 2019). To compare specific years between individuals of different ages, growth rates must be standardized by removing this growth pattern. To determine the most efficient growth model to detrend the data, I compared the following four common dendrochronology methods: smoothing spline, modified negative exponential curve (NE), simple horizontal line, or a modified Hugershoff curve (Cook et al. 1995; Bunn 2008, 2010). Ultimately, the detrending method best suited for this data was the modified negative exponential curve, which displayed the lowest coefficient of variation (CV) within the timeseries using the dendrochronology R package (dplR) (Bunn 2008). The primary ontogenetic growth signal was first removed by applying an NE detrending curve:

푏푥 Equation 2.3 푓(푥) = 푦0 + 푎 ∗ 푒

Where f(x) is shell length (mm), 푦0 is the animal length at f(∞), x represents the age (year), a is the intercept or size at first year of life, and b is the slope or rate of change in shell growth. 푦0, a and b are empirical constants which must be calculated for each species (Bunn 2008). Any years that had less than three growth measurements were eliminated from this data set. Slopes (b) from the NE curves were statistically compared between sampling sites using the R package emmeans with a one-way analysis of variance (ANOVA) and pairwise comparisons were conducted using a Tukey Honest Significant Difference (HSD) post-hoc test. All ANOVA assumptions including normal distribution, equal variances and sample independence were met. The NE detrending curve was then subtracted from the original curve to generate a unitless, standardized index of growth for each year with an average of one. Positive deviations from the mean represent better than expected years of growth, whereas negative deviations represent less than expected growth for those years. Values close to the mean of one represent a homeostatic balance in shell growth (Román-González et al. 2017). Significance of standardized growth between location and year were analyzed using linear mixed models (LMM). Significance was determined through the likelihood ratio tests of the full model with the effect in question against the model without the effect in question using the lme4 and lmerTest packages (Bates et al. 2020). Assumptions of linearity, homogeneity of variance, and normally distributed residuals were met upon log-transforming the data. The model looked at explaining the standardized index of growth in response to the fixed effects of age and location, adding random effects for each individual clam. Pairwise comparisons were completed using the Tukey HSD post-hoc test. To analyse years of extreme growth from the standardized growth index, I used the Cropper Method (Owczarek and Opała 2016). This method examines “pointer years” – years to which there is remarkable growth in response to environmental events, using a five-year moving window, with each year used as a central point. Pointer years were then identified as those with a strong relative growth increase or decrease to which at least 75% of the individuals at that sampling location were >0.75 standard deviations away from the local mean. These chronologies and extreme events were all conducted using the dendrochronology R package dplR (Bunn 2008; R Core Team 2020). Stable Isotopes Underestimates in outer shell rings and bulk sampling required stable isotope replicates across the shell to be pooled to analyse differences between locations rather than both between

23 location and years of growth (MacDonald and Thomas 1980; Hewitt and Dale 1984). A Pillai’s Trace one-way multivariate analysis of variance (MANOVA) was conducted to determine the effects of sampling location on δ18O (VPDB) and δ13C (VPDB) stable isotope concentrations. This analysis met all assumptions and preliminary tests including adequate sample size, observation independence, absence of univariate and multivariate outliers, normality (Shapiro- Wilk test), linearity (scatter plot matrix), absence of multicollinearlity (Pearson correlation coefficient), and homogeneity of variances and covariances (Levene’s test). Follow-up univariate ANOVAs were used to assess if there was a significant difference in δ18O and δ13C between sampling locations. Individual mean comparisons across all six sampling sites and between δ18O and δ13C were analysed using the Tukey HSD post-hoc test. The sea water temperature derived from δ18O (VSMOW; Equation 2.2) was analysed with a one-way ANOVA and significance between locations was determined using a Tukey HSD post-hoc test.

Trace elements Linear mixed models were used to determine the trace elemental concentration in response to fixed effects of age and site, adding random effects for each individual clam. Most elements met assumptions of linearity, homogeneity of variance and normal distribution and those elements that did not meet these assumptions (i.e., Fe, Mg, Ti, and U) were log- transformed. Significance was determined using the likelihood ratio tests and pairwise comparisons were done with Tukey HSD post-hoc tests.

Multivariate Analysis To connect growth pattern analysis to trace element signatures, a cross-correlation function (CCF) was computed to assess the relationship (r) between growth rate and specific trace elements, Mn and Sr. This function uses lag to determine the number of time periods (i.e., years) that naturally separate both time series.

A multivariate principal components analysis (PCA) was conducted on the trace element data between sample locations and years to examine overall spatial and temporal trends. Differences between PC individual scores were statistically compared between location and year by a two-way ANOVA and multiple pairwise comparisons was computed by a Tukey HSD post- hoc test. Assumptions of homogeneity of variance and normality were met. All multivariate analysis used the R package FactorMineR (Lê et al. 2008).

For all analyses, the level of significance (α) was 0.05 and all statistical analyses were run in R, v3.6.3 (R Core Team 2020).

Results Analysis of Growth Variations M. truncata specimens ranged from 4 – 32 years of age and ranged in size from 26 – 90 mm long (maximum anterior to posterior length; Table A2). Data on frequency of age and shell length distribution between sites are provided in Appendix figures A1 and A2. The ontogenetic growth trend shows a smooth decline from rapid growth in the first decade of life (Fig. 2.3). Sample size in the NE detrending curves started to decline at eight years of age and only eight specimens reached between 24 to 32 years of age (Fig. 2.3). The slopes in the NE detrending curves represent the rate of change in shell growth (Equation 2.3) and showed significant differences between sites (one-way ANOVA: F5,105 = 82.6, P < 0.001; Fig. 2.3). Comparisons between groups revealed that the growth rate at the WWTP (TukeyHSD: b = -0.01; Table 2.2) was significantly lower than Apex (TukeyHSD: b = -0.06, P < 0.001; Table 2.2), Aupalajat (TukeyHSD: b =-0.04, P < 0.01; Table 2.2), Monument Island (TukeyHSD: b = -0.03, P < 0.05; Table 2.2) and Kituriaqannigituq (TukeyHSD: b = -0.03, P < 0.05; Table 2.2). Tundra Ridge also had a significantly slower growth rate than Apex (TukeyHSD: b = -0.02, P < 0.05; Table 2.2).

The standardized growth index revealed there were no significant differences in annual growth between locations (Fig. A3) but did indicate significant differences between years (LMM: χ2 (25) = 59.355, P < 0.001; Fig. 2.4). Distinct growth variations showed an oscillating pattern having higher overall growth from 2002 to 2008, then having below average growth from 2009 to 2016, before coming back up to above average growth in 2017 and 2018 (P < 0.05; Fig. 2.4). Pointer year analysis identified individual sites experiencing remarkable growth (75% of individuals greater than 0.75 standard deviations away from the mean). Using the Cropper Method, the WWTP displayed extreme positive growth in both 1998 and 2002, and Monument Island displayed extremely positive growth in 2008.

Stable Isotopes Signatures The average stable oxygen isotope profiles (δ18O (VPDB)) from each site ranged from 1.2 to 1.8‰ and the average stable carbon isotope profiles (δ13C) from each site ranged from 1.3 to 2.1‰ (Table 2.3). Average stable isotope concentrations (δ13C and δ18O) were significantly

25 different between the six sample locations (Pillias trace MANOVA: F10,268 = 12.517, P < 0.001; Fig. 2.5). Follow up univariate analysis showed a significant difference in δ18O (univariate 13 ANOVA: F5,163 = 70.7, p < 0.001; Fig. 2.5) and δ C (univariate ANOVA: F5,163 = 70.2, p <0.001; Fig. 2.5) between sampling locations. Across sampling locations, the WWTP was separated as having the significantly lower δ13C and δ18O than any other site (TukeyHSD: P < 0.001; Fig. 2.5). Monument Island and Tundra Ridge had significantly higher δ13C than all other sites (TukeyHSD: P < 0.001; Fig. 2.5), except Kituriaqannigituq. A hierarchy of ratios formed for δ13C with Monument Island and Tundra Ridge >Apex and Aupalajat > WWTP, and Kituriaqannigituq showing no significant differences from any of the sites (Fig 2.5).

Using shell δ18O (VPDB) and estimated seawater δ18O (VSMOW) (Equation 2.2), I reconstructed average temperatures (℃) at each location: WWTP (0.62 ± 0.20) , Tundra Ridge (- 0.68 ± 0.20), Apex (-1.17 ±0.15), Monument Island (-0.39 ± 0.27), Aupalajat (-1.63 ± 0.26) and Kituriaqannigituq (-0.90 ± 0.20) (Fig. 2.6). There was a statistically significant difference in average temperature (℃) between sample locations (one-way ANOVA F5,165 =13.66, P < 0.001; Fig. 2.6). Pairwise comparisons determined that the WWTP (TukeyHSD P < 0.01; Fig. 2.6) had significantly higher temperatures than all other sites and Aupalajat was significantly lower than all other locations (TukeyHSD, P < 0.05; Fig. 2.6).

Trace Element Profiles Generally, most elements exhibited significant patterns between sampling locations and time, but Mo, Ag, Cd, Sn, Li and Zn did not differ temporally nor spatially and therefore, only those exhibiting significant patterns will be discussed herein (Table A5). Specific elements, like Mn and Sr, are sensitive to biological influences like biomineralization and displayed significant temporal (LMM: χ2 (13) = 78 and 136.2, P < 0.001; Fig. 2.7 B,D) and spatial differences (LMM: χ2 (5) = 10.8 and 15.2 , P < 0.001; Fig. 2.7 B,D). Mn and Sr followed opposite patterns over time as Sr had a marked increase in concentration from 2005 to 2018 while Mn had a decrease in concentration. Monument Island had a higher abundance of Sr than all other sites with the lowest concentrations displayed at Tundra Ridge and the WWTP (TukeyHSD, P < 0.05; Fig. 2.7 D). In contrast, Mn ratios were highest in abundance at the WWTP but displayed similar characteristics to Sr, having Mn’s lowest concentration at Tundra Ridge (TukeyHSD, P < 0.05; Fig. 2.7 B).

Other elements typically telling of environmental variation other than pollution, like Mg and Ba, displayed strong temporal differences (LMM: χ2 (13) = 163.9 and 39.6, P < 0.001; Fig. 2.7 A,C) but were indifferent between sites. From 2005 to 2018, Ba had a marked increase in concentration across all sites, while Mg had a dramatic increase in concentration only at the WWTP (Fig. 2.7 A, C).

Strong temporal differences displayed in every metal except Ti (LMM: χ2 (14) =15.2 – 227.9, P < 0.001; Fig. 2.8 A-H) but elements like Ni, Hg, Na and U remained relatively low throughout time and did not vary between locations. While trace metals fluctuated in their specific concentrations, those metals that displayed significant spatial differences (Fe, Pb, Cu, and Ti) consistently had Monument Island, Tundra Ridge or the WWTP present as the sites with the highest concentrations (LMM: χ2 (5) = 9.0 – 16.1, P <0.05; Fig. 2.8 A, E, G, I). Trace metals typically declined in concentration over time but those metals, that were highest in abundance at the WWTP (Pb and Cu) increased in concentration over time (Fig. 2.8 E, G).

Multivariate Analysis Cross correlation analysis found a significant negative relationship between Sr and growth rate at all sites (CCF: r = -0.42 to -0.81, P < 0.05; Table 2.4) except the WWTP which displayed a positive relationship (CCF: r = 0.37; Table 2.4). In contrast, Mn displayed a positive relationship with growth rate at all sites (CCF: r = 0.57 to 0.78, P < 0.05; Table 2.4) except the WWTP which displayed a negative relationship (CCF: r = -0.16; Table 2.4).

The first two dimensions of the PCA correlation matrix evaluating relationships between the entire set of trace elements explained 37% of the total variance (Fig. 2.9). The variables driving separation of sites on PC1 (19.5% variance explained) were Cu, Pb, Mg, and Fe ranked in decreasing order of effect. PC1 explained significant differences between locations but not between years (two-way ANOVA: F5, 91= 30.7, P < 0.001; Fig. 2.9). PC1 had four distinct groups, Monument Island on the negative side of PC1, Aupalajat, Apex and Kituriaqannigituq which spanned the PC2 axis division and Tundra Ridge and the WWTP on the positive end of PC1. Variables like As, Sr, Mn, Ba and Na (ranked highest to lowest in their importance) were responsible for the spatial (two-way ANOVA: F5,91 = 7.65, P < 0.001; Fig. 2.9) and temporal

(two-way ANOVA: F19,83 = 18.3, P < 0.001; Fig. 2.9) separation on PC2 (17.5% variance explained). Temporally, seven groups were formed, showing a ranked chronology with year

2000 on the positive side of PC2, to year 2019 being on the negative side of PC2 (TukeyHSD, P < 0.05; Fig. 2.9). Years that were significantly different from the rest were 2005, 2006, and 2019. Along PC2, locations were split into two groups, with Monument Island, Apex, Aupalajat, and Kituriaqannigituq on the negative end of PC2 while Tundra Ridge and the WWTP were on the positive end of PC2 (TukeyHSD, P < 0.05; Fig. 2.9).

Discussion Bivalves have proven extremely useful to evaluate the importance and spatial distribution of contaminants (Phillips and Segar 1986; Rainbow and Phillips 1993; Hatje 2016; Etteieb et al. 2019). In the coastal waters near Iqaluit, M. truncata are exposed to multiple simultaneous stressors amidst a growing community and year-round release of primary treated municipal effluent. This study provided species-specific monitoring endpoints including size and growth metrics, stable isotope profiles and trace element signatures to interpret the impacts of contamination throughout inner Frobisher Bay. In general, those clams located near the wastewater effluent exhibited a slower growth rate than all other sites and were smaller relative to clams of the same age at nearly every site except Kituriaqannigituq. Stable isotope values displayed an isotopic change between clams nearest the wastewater outflow and all other sites, having potential evidence of high input of organic matter, brackish water and elevated estimated temperatures. A wide variety of trace elements typically found in municipal wastewater discharge areas (Cu, Mg, Pb, and Mn) were also found to be accumulated in the shells of clams located nearest the wastewater outflow providing evidence of contamination to the area (Pokomeda et al. 2018).

Growth Metrics The relative growth rate of M. truncata was related to the organism’s proximity to the wastewater outflow. Those organisms located at the two sites nearest the outfall (the WWTP and Tundra Ridge, 3 kms away from the effluent source) exhibited significantly lower shell growth rates than individuals at more distant locations and contributes to the growing body of knowledge that suggests bivalves reduce growth when exposed to wastewater effluent (Horne and McIntosh 1979; Goudreau et al. 1993; Gangloff et al. 2009; Nobles and Zhang 2015). Multiple simultaneous factors have the potential to alter the growth rate of bivalves including temperature, salinity, and water quality, of which clams near the wastewater effluent experience

28 anthropogenic-driven variability of all three (Brown 1978; Gillikin et al. 2005b; Schöne and Surge 2012).

Elevated water temperatures are a common side-effect in the receiving waters of wastewater effluent (Francy et al. 1996; Tuncay 2016). Iqaluit’s effluent has previously been recorded at 15 ℃ discharging into the 1.9 ℃ receiving waters in 2013 and 2014 (Neudorf et al. 2017) which supports the higher average sea temperatures (reconstructed via δ18O VSMOW) displayed in this study and could influence the reduced growth in these organisms. These reconstructed sea temperatures only reflect ambient temperatures during active growth and therefore cannot represent temperature for the entire year.

Many studies have recorded the utility of covariant trends of δ13C and δ18O recorded in bivalve shells and the depleted ratios found in the clams nearest the WWTP could be a indicative of brackish water and increased organic matter (Jones and Quitmyer 1996; Aguirre et al. 1998; Hendry et al. 2001). The distinctive pattern of elevated organic matter near the wastewater outfall is further supported by the incorporation of Ba in the shell. Ba is often used to discern ocean productivity and nutrient dynamics and its upregulation and highly reproducible seasonality at the WWTP, Apex and Tundra Ridge provides a potential record of the WWTP discharging nutrient enriched effluent to the area (Stecher et al. 1996; Putten et al. 2000; Pearce and Mann 2006; Barats et al. 2007).

Mn and Sr are elements with a disputed history in their potential to reconstruct either environmental or biological influences but in the present study, uptake of Sr and Mn are under the strong biological influence of biomineralization rates (Dodd 1965; Palacios et al. 1994; Klein et al. 1996; Stecher et al. 1996; Dietzel et al. 2004; Carré et al. 2006). The rate of elemental incorporation is typically dictated by ontogeny related to the size of ions and the shells affinity for calcium uptake over other elements (Wheeler 1992; Ferrier-Pagès et al. 2002; Cohen and McConnaughey 2003). Sr showed strong negative correlations while Mn displayed strong positive correlations with growth rate at all sites except the WWTP. This irregular pattern from the WWTP could be explained by the overall slower growth rate or site-specific differences in sea temperature, nutrient availability, salinity, or toxicity. The abnormality displayed at the wastewater outfall is an important demonstration that bivalve physiology is strongly dependent on the environmental conditions it lives in (Gillikin et al. 2005b).

Accumulation of Trace Elements The effluent released from Iqaluit’s wastewater treatment plant is consistently over its total suspended solids allowance (Nunami Stantec 2015; Canadian Water Network 2018; City of Iqaluit 2019). TSS are indissoluble suspended particles and are a conventional pollutant containing a number of trace elements and metals like Ag, Al, Ba, Cd, Cu, Fe, Hg, Mn, Sr and Zn (CCME 2006; Pokomeda et al. 2018; Canadian Water Network 2018). Evidence of contamination from the wastewater treatment plant can be seen in the metal accumulation of Cu and Pb in the shells of M. truncata nearest the outfall. Pb and Cu are two of the most common metals in municipal effluents and have been used as a reliable pollution record in bivalve shells since the first Mussel Watch contaminant monitoring program in 1986 (Goldberg 1986; Price and Pearce 1997; Lazareth et al. 2003; Gillikin et al. 2005a). In general, metals do not show any predictable variance with respect to environmental and biological controls and hence their variability can be linked to local contamination sources (Price and Pearce 1997).

The elemental composition of the shells near the WWTP evolved over time from containing higher concentrations of Mn, Na, and Ti from 2000 to 2010 to incorporating higher concentrations of Cu, Pb, and Mg in later years, 2010 to 2018. The temporal evolution of elements found in the clams near the wastewater outfall can be attributed to two things. The first is the implementation of primary treatment in 2006 which included the installation of a Salsnes filter which has been shown to remove approximately 50% of TSS in wastewater effluent (Nunami Stantec 2015). This filter would eliminate elements already low in concentrations being released into the receiving waters. The increased concentration of Cu, Pb and Mg between 2010 and 2018 may also be a result of a rising population eliciting more effluent volume and thereby causing elements most common to wastewater discharge to be in larger quantities. The connection between more effluent volume and increased elements in the receiving waters can be illustrated by the large increase in Cu and Mg in 2010. Both elements are commonly found in Iqaluit’s freshwater supply and their concentration increase strongly correlates with the surge in raw water drawn from lake Geraldine, Iqaluit’s freshwater source (r = 0.75, P < 0.01; Fig A4) (Sims and Allard 2014; City of Iqaluit 1998, 2005-2019). Further explanation for Mg’s large concentration growth could be attributed to the addition of a magnesium hydroxide coagulant, commonly used in wastewater treatment to reduce sludge and buffer pH changes (Martin Marietta Magnesia Specialties 2020).

Conclusion This study examined the effects of municipal wastewater on the Arctic truncate soft-shell clam using a combination of endpoints including growth rate, stable isotopes, and trace elements. The confined effects of reduced growth rate, metal accumulation and covariant depleted stable isotopes near the wastewater effluent source, suggest that environmental changes and contamination are anthropogenically induced. Furthermore, the detailed sclerochronological record substantiated M. truncata’s potential use as a robust Arctic biomonitor for wastewater contamination.

Figures and Tables

Figure 2.1 Map of Mya truncata sample locations in the vicinity of the wastewater effluent source in Iqaluit, Nunavut.

Figure 2.2 (Left) Morphometric measurements taken on Mya truncata shells where L = length, H = height, AMG = axis of maximum growth, ML = maximum length, PMGD = posterior maximum growth dorsal, PMGV = posterior maximum growth ventral. (Right) Polished and etched cross section of M. truncata (TR45; Table A2) showing annual growth lines in its chondrophore (i.e., 10 years old). Table 2.1 LA-ICP-MS operation and data-acquisition parameters used for the trace-element analyses. ICP-MS Model Thermo-Finnigan Element 2 Plasma Power 1265 Gas flows Plasma (Ar) Auxiliary (Ar) (L min-1) 0.78 Sample (L min-1) 0.83 Carrier (He) (L min-1) 0.86 Laser Ablation Model New Wave UP-213 Beam Diameter 55 µm Frequency 20 Hz Energy density on sample ~6 J cm-2 Data acquisition parameters Sample time 5 ms Samples per peak 5 Integration window 60% Scan method E-Scan

Figure 2.3 Raw ontogenetic growth with detrending negative exponential curves for each sampling location. NE detrending curve equations can be found in Table 2.2. Lowercase letters denote statistical significance between the slopes of sampling sites (α < 0.05) as determined by a one-way ANOVA and Tukey HSD post-hoc test. Table 2.2 Resolved negative exponential detrending curve equations and significance (Equation 2.3). Where CV is the coefficient of variation.

Site Intercept (a) Slope (b) R2 value F-statistic CV (%) Apex 118.88 -0.06 0.90 117.1 8.7 Aupalajat 32.6 -0.04 0.83 80.46 10 Monument Island 66.2 -0.03 0.70 33.37 15.6 Kituriaqannigituq 59.4 -0.03 0.69 49.97 3.2 Tundra Ridge 31.6 -0.02 0.49 15.47 6.8 Wastewater 16.6 -0.01 0.18 4.667 13.2

Figure 2.4 Standardized growth index (SGI) for Mya truncata indicating variation in annual growth unrelated to age. The y-axis is unitless and the dashed line in each plot represents an SGI of 1.0 with values above indicating better than expected years of growth and values below the line representing less than expected growth for those years. Black error bars represent standard error to the mean (SEM). Lowercase letters denote significant temporal differences using LMM and Tukey HSD post-hoc test (α < 0.05). Coral colored diamonds represent outliers.

Table 2.3 Summary of stable isotope parameters for each location where SEM is standard error to the mean Location δ13C (n) δ18O (n) δ13C mean ± SEM δ18O mean ± SEM Tundra Ridge 29 30 1.98 ± 0.06 1.65 ± 0.04 Monument island 26 22 1.92 ± 0.04 1.65 ± 0.04 Aupalajat 35 33 1.71 ± 0.05 1.81 ± 0.05 Kituriaqannigituq 24 23 1.70 ± 0.09 1.66 ± 0.04 Apex 25 25 1.63 ± 0.07 1.74 ± 0.03 Wastewater plant 33 33 1.38 ± 0.05 1.31 ± 0.04

Figure 2.5 Average shell δ18O versus δ13C isotope concentrations for each respective sampling location. Error bars shown are ± SEM for replicate samples for each site. One-way MANOVA and Tukey HSD post-hoc tests were conducted to determine effect of sampling location on δ18O and δ13C stable isotopes (α < 0.05).

Figure 2.6 Reconstructed sea surface temperatures from shell δ18O (VPDB) and seawater δ18O (VSMOW). Black error bars represent ± SEM. Lower case letters denote significant differences in temperature between locations using one-way ANOVA and Tukey HSD post-hoc test (α < 0.05).

Figure 2.7 Trace element/Ca profiles of 7-8 pooled Mya truncata shells that grew in one of six samples sites, Apex, Aupalajat, Kituriaqannigtuq, Monument Island, Tundra Ridge and the Wastewater treatment plant. Only positive error bars (SEM) shown to maintain graph clarity. The profiles are shown as a function of time ranging from 2005 to 2018. Lowercase letters denote significant temporal differences and uppercase letters denote significant spatial differences using LMM and Tukey HSD post-hoc test (α < 0.05).

Figure 2.8 Trace element/Ca profiles (ppm) of 7-8 pooled Mya truncata shells that grew in one of six samples sites, Apex, Aupalajat, Kituriaqannigtuq, Monument Island, Tundra Ridge and the Wastewater treatment plant. Only positive error bars (SEM) shown to maintain graph clarity. The profiles are shown as a function of time ranging from 2005 to 2018. Lowercase letters denote significant temporal differences and uppercase letters denote significant spatial differences using LMM and Tukey HSD post-hoc test (α < 0.05). Table 2.4 Pearson cross correlation values and significance between strontium (Sr), manganese (Mn) and growth rate (GR) for each sampling location. Where lag represents the number of years that naturally separate the two time series (α < 0.05). Site Relationship r Lag (yrs) p-value Aupalajat Sr:GR -0.69 1 <0.05 Mn:GR 0.78 0 <0.01 Kituriaqannigituq Sr:GR -0.42 0 n.s. Mn:GR 0.68 0 <0.05 Wastewater Sr:GR 0.37 -4 n.s. Mn:GR -0.16 -9 n.s. Tundra Ridge Sr:GR -0.81 0 <0.01 Mn:GR 0.57 -2 <0.05 Monument Island Sr:GR -0.65 -1 <0.05 Mn:GR 0.63 -1 <0.05 Apex Sr:GR -0.69 -1 <0.01 Mn:GR 0.7 -2 <0.05

Figure 2.9 Principal components analysis biplot of trace elements characterized by year and location. X-axis represents PC1 (19.5% variance) while the Y-axis represents PC2 (17.5% variance).

Chapter 3: Gene expression profiling in the Arctic truncate soft-shell clam, Mya truncata, located near a municipal wastewater outfall Abstract Effective wastewater treatment in the Canadian Arctic is hindered by the harsh physical environment and is often inadequate or completely lacking. Wastewater effluent contains a myriad of harmful substances that have the potential to deteriorate the quality of an already fragile coastal environment. The aim of this study was to evaluate the effects of Iqaluit’s municipal wastewater on the physiological response of the truncate soft-shell clam, Mya truncata. I investigated the tissue-specific mRNA transcript levels of ten genes from six sampling sites on a gradient from the wastewater effluent source involved in cellular stress and xenobiotic response that included molecular chaperones (heat shock proteins 60 and 90), antioxidants (manganese superoxide dismutase), metabolic regulators (lactate dehydrogenase and citrate synthase) and cellular biotransformation and detoxification (cytochrome P450, glutathione S-transferase, glutamine synthetase, multidrug resistance and ATP binding cassette). Results showed increased transcription of genes encoding the multidrug resistance protein and nitrogen specific binding enzyme glutamine synthetase, but significant reduction of the genes encoding enzymes involved in phase I and II biotransformation, metabolic function, antioxidant function and molecular chaperone activity in the clams nearest the wastewater effluent source compared with the other five sampling sites. These findings suggest that these benthic organisms are exhibiting chronic physiological responses to the wastewater exposure.

Introduction The discharge of municipal wastewater into marine and estuarine ecosystems is a global practice and despite most treatment facilities operating under strict governmental guidelines – operational failure, mismanagement and environmental variability can often cause wastewater treatment plants (WWTP) to discharge raw waste on top of the already harmful treated effluent (Daley et al. 2015; United Nations General Assembly 2015). Wastewater effluent contains a myriad of harmful substances that have the potential to deteriorate the quality of an already fragile coastal environment (Gunnarsdóttir et al. 2013; Krumhansl et al. 2015; Kallenborn et al. 2018). Wastewater effluent consists of a high input of organic matter, detergents, pharmaceuticals, personal care products, polycyclic aromatic hydrocarbons (PAHs), surfactants,

42 and trace metals, which makes wastewater effluent the largest source of pollution to surface water in Canada (McGreer 1979; Samuelson 1998; Medeiros et al. 2008; Kamel et al. 2012; Falfushynska et al. 2014; Bonnafé et al. 2015; Canadian Water Network 2018).

Wastewater management is even more challenging in the Arctic as the harsh natural environment dictates the design, accessibility, construction and operations of a treatment plant (Gunnarsdóttir et al. 2013; Johnson et al. 2014; Daley et al. 2015). Until recently, the disposal of municipal wastewater into the ocean was not of great concern to Arctic regions due to the small communities and large receiving waters. However, recent studies have shown chronic disposal in low temperature waters can reduce the breakdown of contaminants and deteriorate the quality of an already fragile environment (Gunnarsdóttir et al. 2013; Krumhansl et al. 2015; Kallenborn et al. 2018).

Bivalves are commonly used as indicator species in ecotoxicological biomonitoring programs (Choi et al. 2008; Joyner‐Matos et al. 2009; Veldhoen et al. 2009, 2011; Sifi and Soltani 2019). The soft-shell clams are located in the vicinity of Iqaluit’s municipal wastewater outfall and have the potential to make ideal biomonitors due to their long-lived, sedentary, filter feeding lifestyle (Camus et al. 2003; Fish and Fish 2011; Sleight et al. 2016, 2018). Iqaluit’s wastewater effluent has remained in non-compliance with the Arctic pollution prevention act for disposing waste into Arctic waters and was deemed toxic by annual ammonia based LC50 toxicity tests conducted with 100% effluent, considered a failure if more than 50% of the test organisms, rainbow trout (Oncorhynchus mykiss), died after 96 hours (EPS/1/RM/50 Environment Canada Test Protocol; City of Iqaluit 2007; Nunami Stantec 2015). Biweekly composite samples also exceeded federally enforced wastewater system effluent regulations (WSER) in carbonaceous biochemical oxygen demand (CBOD) and total suspended solids (TSS), thus demonstrating the need to determine the physiological impacts of this effluent source on the organisms in the ecosystem (Nunami Stantec 2015; Canadian Water Network 2018; City of Iqaluit 2019).

Molecular biomarkers (i.e., mRNA transcripts) can be used as sensitive tools to assess the physiological impacts of pollutants from these wastewater outfalls on organisms. Transcriptional changes can not only allude to the physiological mechanism of a response but can also provide a relative measure of the toxic effect (Lüdeking and Köhler 2002; Kültz 2005; Farcy et al. 2009; Li et al. 2013; Etteieb et al. 2019). Thus, this study measured M. truncata’s cellular response (i.e.

43 heat shock proteins, antioxidant enzymes and metabolic biomarkers) to environmental stress and xenobiotic response along a gradient in proximity to Iqaluit’s wastewater effluent source.

Cellular stress induces numerous anomalies in cellular function. Heat shock proteins (HSP) are a highly conserved family of molecular chaperones (Franzellitti and Fabbri 2005; Fabbri et al. 2008; Izagirre et al. 2014). They are typically recruited as first responders to cellular stress and are induced by a variety of stimuli including heat, hypoxia, non-essential and essential metals, organic compounds, and many others, rendering them indicators of the general condition of health for the organism (Fabbri et al. 2008). HSP90 was chosen as it is one of the most abundant chaperones in the cell and acts similarly to HSP70 in conformational protein regulation and cell signalling during heavy metal exposure and prolonged heat stress (Fabbri et al. 2008). The receiving waters of wastewater effluent experience a complex mixture of contaminants including metals, xenobiotics, PAH’s and many more foreign substances that could create hypoxic environmental conditions (Viarengo et al. 1997; Helene et al. 2005). Prolonged exposure could induce the production of reactive oxygen species (ROS) leading to cellular damage, oxidative stress and impaired mitochondrial adenosine triphosphate (ATP) production (Hancock et al. 2001; Veldhoen et al. 2009; Main et al. 2010; Falfushynska et al. 2014; Ransberry et al. 2015). Antioxidants can be used as ROS defense mechanisms, which can include manganese superoxide dismutase (MnSOD) recruited to inhibit the production of ROS (Meistertzheim et al. 2007; Sifi and Soltani 2019). Wastewater contaminants have also been shown to affect the metabolic rate of organisms and previous studies have observed changes in enzymes involved in ATP production under anaerobic conditions (lactate dehydrogenase (LDH)) as well as enzymes involved in the citric acid cycle (citrate synthase (CS)) that are involved in ATP production under aerobic conditions (Dunphy et al. 2006; Ransberry et al. 2015; Breitwieser et al. 2016; Sifi and Soltani 2019). Both metabolic enzymes (LDH and CS) have been used as biomarkers of general physiological condition and to evaluate the impacts of oxidative stress on organisms (Ransberry et al. 2015; Breitwieser et al. 2016; Sifi and Soltani 2019).

The xenobiotic response has previously been identified in marine invertebrates (Lüdeking and Köhler 2002; Viarengo et al. 2007a) and typically acts in a phased response pattern (Lüdeking and Köhler 2002; Bonnafé et al. 2015). The first response involves phase I

44 biotransformation enzymes like Cytochrome P450 (i.e., CYP1A1) that modify the contaminants by adding a function group ensuring that the xenobiotics are more hydrophilic and are more easily removed by phase II enzymes (Goksøyr 1995; Morel et al. 1999). The next response involves phase II biotransformation enzymes like glutathione-S-transferase (GST) and glutamine synthetase (GS) that catalyze the binding of xenobiotics with endogenous substrates. The GST enzyme is highly conserved and not specific in binding electrophilic xenobiotics and organic contaminants, while GS specifically binds to nitrogen and ammonia and is highly regulated during xenobiotic exposure (Bao et al. 2013; Bonnafé et al. 2015). The final phase III biotransformation is crucial to the excretion of xenobiotics and prevents xenobiotics from entering the cytoplasm of a cell (Lüdeking and Köhler 2002; Bonnafé et al. 2015). Therefore, phase III transporters like the ATP-binding cassette (ABC) and the multidrug resistance protein (MDRP) are often used as biomarkers of exposure to xenobiotics (Lüdeking and Köhler 2002; Bonnafé et al. 2015).

The goal of this study was to assess the effects of chronic exposure to primary treated municipal wastewater on mRNA expression in the Arctic truncate soft-shell clam, Mya truncata, in two tissues, the gill and mantle. Contaminants are typically taken up by bivalves through the gill and mantle and therefore these tissues were chosen for studying cellular responses to environmental stressors (Liu et al. 2014; Dahms et al. 2014). Iqaluit’s municipal effluent contains a variety of metals and contaminants that have been shown to result in high BOD, directly leading to hypoxia in the receiving waters (Samuelson 1998; Krumhansl et al. 2015, 2016; Nunami Stantec 2015). In previous studies, metals and hypoxic environmental conditions were found to be driving factors in the repression of mRNA expression in response to chronic xenobiotic exposure (Goksøyr 1995; Köhler and Pluta 1995; Viarengo et al. 1997; Barouki and Morel 2001). Thus, I hypothesized that chronic exposure to Iqaluit’s wastewater effluent would affect mRNA transcript levels of genes associated with contaminant exposure and general stress. I predicted that wastewater effluent would repress the bivalve’s metabolic function and antioxidant response while synchronously activating the xenobiotic response. I measured the differential mRNA expression patterns of HSP60, HSP90, MnSOD, LDH, CS, CYP1A1, GST, GS, ABCA1 and MDRP1 in bivalves collected along a gradient in proximity to Iqaluit’s wastewater effluent source. These expression patterns can be used as indicators of the physiological impact of chronic wastewater exposure and measure the anthropogenic stress in

45 animals to further understand the mechanisms responsible for coping with wastewater toxicity in an Arctic ecosystem.

Materials and Methods Animal collection and habitat parameters Seventy M. truncata specimens were hand-collected from six sampling locations (Fig. 2.1) in August 2019 using trowels during extreme low tide (< 1 m) from inner Frobisher Bay (63° 42' 36'' N, 68° 30' 0'' W). All collections were done with the support of the Amaruq Hunters and Trappers Association and under a license approved by Fisheries and Oceans Canada (Licence No: S-19/20-1040-NU). Each of the six sampling locations were chosen for the proximity to the WWTP (Fig. 2.1). Study sites include the WWTP, Tundra Ridge, Apex, and Monument Island chosen for their potential direct exposure to wastewater effluent and Aupalajat and Kituriaquannigituq, chosen for their geographical barriers and distance from the WWTP. The WWTP is located in the tidal flats receiving primary treated, year-round continuous municipal wastewater discharge. Tundra Ridge is 3 km southeast of the wastewater outflow. Monument Island is an uninhabited island 4.9 km south of the wastewater outflow, marking the transition of the smaller tidal flats to the broader bay. Apex is 5 km southeast of the WWTP and is the original site of the community where the nomadic Inuit settled. Aupalajat is an uninhabited island 5 km west of the WWTP. It is shielded from direct wastewater exposure as it is in the neighbouring bay, Peterhead Inlet. Finally, Kituriaqannigituq (translated from Inuktitut as “island of no mosquitos”) is located at the western end of Frobisher Bay, 15 km southwest from the WWTP and Iqaluit.

Sample Preparation Upon collection, clams were immediately placed in coolers containing water collected from that sample site and transported live to the remote laboratory facilities within 1-3 h. After collection, the anterior and posterior adductor muscles of the clams were sliced to open the shells. Gill and mantle tissues were excised from each individual and immediately placed in 1 mL of RNAlater (ThermoFisher, Waltham, USA). Tissues were stored and transported at -20℃ from Iqaluit, NU to the University of Manitoba, Winnipeg, MB where they were stored at -80℃.

RNA Extraction and cDNA Synthesis Total RNA was extracted from the gill and mantle tissue samples using the RNeasy Plus Mini Prep Kit (Qiagen, Hilden, Germany). Extractions were conducted in accordance with manufacturer protocols and extracted total RNA was stored at -80℃.

Total RNA quantity and sample purity was assessed using the NanoDrop One spectrophotometer (ThermoFisher, Waltham, USA) and quality was determined using a 1% w/v agarose gel with the gel stain, SYBR safe (ThermoFisher, Waltham, USA), and an ultraviolet transilluminator (Endress & Hauser, Upland, California). The 260/280 absorbance ratios were used as a first measure to assess RNA purity by comparing the ratio of nucleic acid and protein in the sample. The 260/230 ratios were used as a secondary measure by comparing the ratio of nucleic acid to impurities carried over from the extraction procedure. Gill RNA samples demonstrated a 260/230 absorbance ratio between 1.21 and 2.38 (average = 2.09) and a 260/280 absorbance ratio between 2.08 and 2.23 (average = 2.16). Mantle RNA samples demonstrated a 260/230 absorbance ratio between 0.20 and 1.66 (average = 0.85) and 260/280 ratio between 1.66 and 2.09 (average = 1.86). The low 260/230 ratios found in the mantle tissue are indicative of the presence of impurities that absorb at 230 nm like carbohydrates, phenol, guanidine and glycogen (Matlock 2015). However, upon repeating the extraction process four times with a variety of methods in initial homogenization and additional washing buffer steps, the 260/230 yield did not improve. Concerns of low 260/230 ratios were mitigated by confirming RNA quality through agarose gels, obtaining consistent amplification in the real-time quantitative Polymerase Chain Reaction (qPCR) as other samples, observing similar qualitative patterns between tissues and removing outliers in the analysis (i.e., Ct values too high [36] or final fold- change (2-∆∆Ct) values outside of 1.5 * the inter quartile range for a given site and a given single gene).

Complementary DNA (cDNA) was synthesized from 1 µg of total RNA using a QuantiTect ® Reverse Transcription Kit (Qiagen, Hilden, Germany) and was performed in accordance with manufacturer protocols. Appropriate volumes of template RNA (1µg total) were randomized on a 96-well plate and ultra pure water was added to bring the total volume to 12 µL in each well. The reaction components were then incubated at 42℃ for 2 min following the addition of 2 µL of 7x genomic DNA wipeout buffer. A subsequent mixture of 1 µL Quantiscript

Reverse Transcriptase, 4 µL 5x Quantiscript RT Buffer and 1 µL Reverse-transcription Primer Mix were added to each well, yielding a total volume of 20 µL. This mixture was incubated for 15 min at 42℃ then for 3 min at 95℃. All incubations were conducted using the SimpliAmp Thermal Cycler (ThermoFisher, Waltham, USA). For use in qPCR, cDNA was then diluted using a 1:5 dilution factor, resulting in 80 µL of ultrapure water being added to each well. The cDNA plates were stored at -20℃.

Primer Design and Validation Two reference genes and ten genes of interest were chosen for this study (Table 3.1). Target genes of interest were normalized to two reference genes, 60S ribosomal protein L18a (RPL18a) and 40S ribosomal protein S4 (RPS4). The RPL18a and RPS4 genes were evaluated as appropriate reference genes when a one-way analysis of variance (ANOVA) determined there were no significant differences in expression across sampling locations. These reference genes were further verified when the coefficient of variation of the cycle thresholds among them was less than 25% (Hellemans et al. 2007). The two reference genes were used to generate normalization factors for the remaining genes of interest (Livak and Schmittgen 2001).

Of the ten genes of interest, five were chosen for their roles in xenobiotic response: CYP1A1, GST, GS, ABCA1, MDRP1. Furthermore, three were chosen for their roles in the oxidative stress response (MnSOD) and metabolic functioning (LDH and CS). The remaining genes of interest were two molecular chaperones, HSP60 and HSP90, chosen as generalized stress markers to indicate the overall condition of the organism. The sequences of reference genes and genes of interest were downloaded from the mollusc database for M. truncata (MolluscDB: Taxonomy ID 156761; Sleight et al. 2018). Gene-specific primers were designed for unique exon regions in each candidate using NCBI Primer-BLAST (Table 3.1) (Ye et al. 2012). Primer Express v3.0.1 (ThermoFisher, Waltham, USA) was then used produce single amplicons with a size greater than 100 bp, an annealing temperature of approximately 60℃, and a GC content of approximately 56% to fit the primer criteria for SYBR Green qPCR. Forward and reverse primers were synthesized by Integrated DNA Technologies (IDT; Coralville, IA, USA). Primers were suspended in ultrapure water to a molar concentration of 100 mM and stored in 96-well plates at -20 ℃.

48 qPCR I used qPCR to measure the relative expression level of the cDNA for the 10 genes of interest. For a single reaction, primer mix was made using 0.5 µL of forward and reverse primers diluted to a concentration of 60 µM each using UltraPure water (ThermoFisher, Waltham, USA). This primer mix was further diluted with 0.9 µL of UltraPure water then combined with 6 µL of PowerUp™ SYBR™ Green (ThermoFisher, Waltham, USA) following the manufacturer’s guidelines, together making the PCR master mix. Five µL of diluted cDNA was then combined with the 7 µL of PCR master mix and these reactions were then placed in the QuantStudio 5 Real-Time PCR System (ThermoFisher, Waltham, USA) and qPCR was carried out based on the manufacturers instructions. Two-step qRT-PCR cycling conditions were used to detect fluorescence over 40 cycles: 2 min at 50℃, 2 min at 95℃, followed by 40 cycles of 15 s at 95℃, 15 s at 60℃ and 1 min at 72℃. Finally, the melt curve parameters consisted of 15 s at 95℃ and 1 min at 60℃ changing at a rate of 1.6℃ s-1, followed by a slow increase to 95℃ for 15 s rising at a rate of 0.15℃ s-1. Twelve genes were run over three plates for each tissue and each plate included no template controls using UltraPure water in place of cDNA to verify the absence of reagent contamination. qPCR data was analyzed using the QuantStudio Design and Analysis Software v1.4.2 (ThermoFisher, Waltham, USA).

Relative changes in the expression of genes of interest were analyzed based on the 2-ΔΔCt method, that normalizes gene expression to the internal housekeeping genes (RPL18a and RPS4)

(Livak and Schmittgen 2001). Normalization factors were generated by comparing the raw Ct value for the reference gene of a given sample to a calibrator sample (lowest value recorded for -∆Ct that reference gene; ∆Ct) which is subsequently used to calculate the relative quantity (2 ) then divided by the geometric mean of all the relative quantities. In the same manner, the genes of interest relative quantities (2-∆Ct) were calculated then divided by the sample specific normalization factor generated from the house-keeping genes. Normalized relative quantities -∆∆Ct were obtained by dividing by the relative quantity (∆∆Ct) and fold changes (2 ) were expressed after log2-transforming the normalized relative quantities and comparing across sample locations.

Statistical Analysis Differences in relative mRNA abundance between sample locations were evaluated using a one-way ANOVA, followed by Tukey Honest Significant Difference post-hoc test (TukeyHSD) for multiple comparisons for each tissue. Principal components analysis (PCA) was conducted to visualize the overall relationships between mRNA expression data and sampling locations for each tissue type using the R package FactorMineR (Lê et al. 2008). One-way ANOVAs were used to detect significant differences in individual PCA scores between sample locations and multiple pairwise comparisons were assessed using Tukey’s HSD post-hoc tests. All ANOVAs conducted met assumptions of independence, normality, and homogeneity of variance. All statistical analyses were assessed with the level of significance (α) of 0.05 and analyses were run using the statistical computing software, R v3.6.3 (R Core Team 2020).

Results Gill mRNA abundance In the gill tissue, all but two genes (HSP60 and CS) involved in cellular stress response showed significant differences between locations (one-way ANOVA: F5,51 = 2.7 – 3.4, P < 0.05; Fig. 3.1). Those genes that did contain significant differences shared similar abundance patterns among sites. HSP90 was low in abundance at both the WWTP and Kituriaqannigituq and had enhanced expression at Aupalajat (Tukey HSD: P < 0.05; Fig. 3.1). Similarly, LDH also had low expression at the WWTP, Kituriaqannigituq and Monument Island, with the highest significant abundance at Aupalajat (TukeyHSD: P < 0.01; Fig. 3.1). MnSOD displayed a similar arrangement to LDH with Monument Island and the WWTP having low abundance but differed with Kituriaqannigituq having the highest expression (Tukey HSD: P < 0.01; Fig. 3.1).

For the xenobiotic response, CYP1A1 showed no significant differences between sample sites (Fig. 3.1). The binding enzyme, GST exhibited significant differences between locations

(one-way ANOVA: F5,49 = 3.3, P < 0.05; Fig. 3.1) with low expression at the WWTP and Apex, but 2-fold greater abundance at Tundra Ridge (TukeyHSD: P < 0.05, Fig. 3.1). GS (one-way

ANOVA: F5,51 = 9.5, P < 0.001; Fig. 3.1) had a 2-fold higher abundance at Aupalajat compared to all other locations (TukeyHSD: P < 0.01; Fig. 3.1). Those genes involved in phase III removal and protection against xenobiotics (MDRP1 and ABCA1) had significant differences between sample locations (one-way ANOVA: F5,51 = 5.3 and 4.0, P < 0.01; Fig. 3.1, respectively) with a

3-fold increase in MDRP1 expression at the WWTP (TukeyHSD: P < 0.001; Fig. 3.1). ABCA1 expression was most abundant at Aupalajat and had reduced abundance at Kituriaqannigituq, Tundra Ridge and the WWTP (TukeyHSD: P < 0.01; Fig. 3.1).

Gill principal components analysis The first two PC dimensions explained 45.4% of total variance in the overall gill expression among sampling locations (Fig 3.2). PC1 (29.1% variance explained) variance was driven by GS, HSP90, HSP60, CS, ABCA1, GST and LDH which significantly separated sites based on their individual PC scores (one-way ANOVA: F5,51 = 4.6, P < 0.01; Fig. 3.2). PC1 displayed two distinct location groups with Kituriqannaigituq, the WWTP, Apex and Tundra Ridge on the negative side of the PC1 axis and Aupalajat on the positive side of the PC1 axis (TukeyHSD: P < 0.05; Fig. 3.2). Monument Island individuals were mixed between the two groups. PC2 (16.3% variance explained) was primarily driven by genes MDRP1 and MnSOD, which elicited significant differences in PCA scores between sample locations (one-way

ANOVA: F5,51 = 5.0, P < 0.001; Fig. 3.2). Three groups formed in pairwise analysis significantly separating the WWTP on the negative side of the PC2 axis and Kituriaqannigituq on the positive side of the PC2 axis (Tukey HSD: P < 0.001; Fig. 3.2).

Mantle mRNA abundance In the mantle tissue, 50% of genes showed no significant differences between sample sites (Fig. 3.3). Within the cellular stress response (i.e., molecular chaperones, antioxidants, metabolic regulators), only LDH showed significant differences between locations (one-way

ANOVA: F5,47 = 2.8, P <0.05; Fig. 3.3). Expression was highest at Apex and lowest at Kituriaqannigituq (Tukey HSD: P < 0.05; Fig. 3.3).

The activity of the phase I biotransformation enzyme CYP1A1 (one-way ANOVA: F5,41 = 4.1, P < 0.01; Fig. 3.3) was low at all four sites nearest the WWTP and had the highest abundance at Kituriaqannigituq (TukeyHSD: P < 0.05; Fig. 3.3.). Of the phase II biotransformation enzymes, only GS displayed significant differences between locations (one- way ANOVA: F5,49 = 5.5, P < 0.001; Fig. 3.3). The highest abundance of GS was displayed at the WWTP, Apex and Aupalajat and had the lowest expression at Kitriaqannigituq (TukeyHSD: P < 0.05; Fig. 3.3). Significant differences between sample locations were only seen in the phase

III biotransformation gene, MDRP1 (one-way ANOVA: F5,41 = 3.4, P < 0.05; Fig. 3.3). This

51 gene was highly expressed at the WWTP and exhibited a gradual decline in expression with increasing distance from the wastewater effluent source having its lowest abundance at Monument Island, Kituriaqannigituq and Aupalajat (TukeyHSD: P < 0.05; Fig. 3.3).

Mantle principal components analysis The first two PC axes explained 41.9% of the total variance in the genes expressed in the mantle among sampling locations (Fig. 3.4). The significant separation between sampling location on PC1 (24.4% variance explained) was primarily driven by GS, LDH, HSP90, CS and

MnSOD, ranked in decreasing order of contribution (one-way ANOVA: F5,50 = 6.8, P < 0.001; Fig. 3.4). Three groups emerged as Kituriaqannigituq was negatively expressed on PC1 resulting in significant differences from Apex, the WWTP and Aupalajat which were on the positive side of PC1 (TukeyHSD: P < 0.05; Fig. 3.4). Tundra Ridge was closely aligned with the three sites on the positive side of PC1 while Monument Island was found to be more closely related to Kituriaqannigituq (TukeyHSD: P < 0.05; Fig. 3.4). PC2 (17.5% variance explained) was primarily driven by the genes HSP60, GST, CYP1A1, ABCA1, and MDRP1 but showed no significant separation of sites.

Discussion Iqaluit’s wastewater treatment plant discharges approximately 1.2 x 106 m3 of municipal wastewater into Frobisher Bay each year and its raw effluent exceeds federally regulated maximums in BOD, TSS and total and fecal coliform concentrations (Nunami Stantec 2015; City of Iqaluit 2017; Canadian Water Network 2018). The presence of ammonia, phosphate, phosphorus and metals (i.e., Al, Ba, Cd, Cu, Fe, Mn, Hg, Ag, Sr, Zn) has been documented by numerous studies (Samuelson 1998; Krumhansl et al. 2015; Daley et al. 2015; City of Iqaluit 2020) and metal accumulation in M. truncata shells has been noted in the area (see Chapter 2). Many of these substances lack discharge limits and regulations in municipal wastewater. Further, as the impacts on organisms in Arctic environments is largely unknown, this study provided a more extensive investigation into the bivalve’s cellular response to chronic municipal wastewater exposure. I found significantly different responses in clams collected near the WWTP compared to those found at more distanced locations for indicators of the cellular stress— (i.e., molecular chaperones, antioxidants, metabolic regulators), and in the xenobiotic response (i.e., phase I, II and II biotransformation biomarkers). The expression of genes encoding enzymes involved in

ROS defense systems (i.e., MnSOD), molecular chaperones (i.e., HSP90), metabolic regulator (i.e., LDH) and biotransformation enzymes (i.e., CYP1A1 and GST) had significantly lower expression in the gills for clams nearest the wastewater outflow. Those genes that displayed significant differences in the mantle followed similar patterns except LDH which had the lowest expression at Kituriaqannigituq. In contrast, MDRP1 a gene heavily involved in the protection and excretion of xenobiotics and GS had the highest expression at the WWTP in both tissue types. Gene expression levels primarily depend on the functional specificity of the cells composing different organs and tissues. Thus, I found that gills typically had higher abundance and more significant differences between sites because the gills act as a front-line defense in the cellular stress response (Venier et al. 2006).

Xenobiotic Response When exposed to contamination, xenobiotics have been shown to greatly enhance the abundance of CYP1A1, but an autoregulatory negative feedback loop has been demonstrated that as CYP1A1 activity increases, so does its ROS and hydrogen peroxide by-products (Morel et al. 1999). As CYP1A1 activity increases, the resulting hydrogen peroxide acts to repress CYP1A1 at the transcriptional level to ultimately prevent ROS production and oxidative stress (Morel and Barouki 1998; Barouki and Morel 2001). This mechanism is the possible cause for the decreased CYP1A1 activity at the four sites nearest the wastewater effluent source, returning to higher abundance at the two sites shielded from direct wastewater exposure (i.e., Aupalajat and Kituriaqannigituq).

A major enzyme in phase II biotransformation is GST which operates to bind xenobiotics by conjugating glutathione, acting as a first step towards their elimination from the cell (Viarengo et al. 2007b; Gagné et al. 2007; Bonnafé et al. 2015). GST has not only been shown as a response mechanism in xenobiotic metabolism but it is also an active antioxidant, binding to ROS produced by CYP1A1 (Viarengo et al. 2007b; Bonnafé et al. 2015). The reduced activity of GST near the WWTP agrees with numerous other studies demonstrating a decrease in GST expression when exposed to chronic wastewater (Gallagher and Sheehy 2000; Lüdeking and Köhler 2002; Viarengo et al. 2007b; Ballesteros et al. 2009; Gagné et al. 2009; Bonnafé et al. 2015). One hypothesis suggests that this low expression is a result of excess metabolites produced by CYP1A1 competing with GST endogenous substrates (Egaas et al. 1999; Gagné et

53 al. 2007; Tian et al. 2012; Bonnafé et al. 2015). An alternative explanation may be that the wastewater contains direct inhibitors of GST and exposes the ineffectiveness of aquatic organisms to eliminate organic compounds and metals in the effluent (Gallagher and Sheehy 2000; Ballesteros et al. 2009). Future research should consider evaluating the composition of wastewater with special consideration for GST inhibitors and isolating the gene expression and enzyme activity of GST in a controlled laboratory environment. Another enzyme in the xenobiotic response is GS, which plays an essential role in the metabolism of nitrogen (Bao et al. 2013). GS binds to ammonia to synthesize glutamine, an amino acid heavily involved in the immune system of bivalves (Hanson and Dietz 1976; Sokolowski et al. 2003). Its increased expression has been linked to increased protein catabolism, amino acid turnover, nitrogen detoxification and nitrogen pollution (Tanguy et al. 2005; Thomsen et al. 2016). I found greater expression of GS activity at the WWTP, Apex and Aupalajat and significantly lower expression at Kituriaqannigituq in the mantle tissue. The contrast of GS abundance between those sites nearest the wastewater outfall compared to Kituriaqannigituq 15 km away supports this gene’s response to nitrogen pollution in the area. The enhanced expression of GS could be related to both nitrogen pollution and increased ATP usage from active proteins (i.e., MDRP1) producing larger volume of nitrogenous waste products. Aupalajat’s high expression may indicate a separate nitrogen source to the area. Iqaluit has a long history of unauthorized landfills created without management or construction techniques (Environmental Services 1992; Johnson and Cucheran 1999). Two of these landfills (scrap metal landfill and military Upper Base) are located northeast of Aupalajat and have the potential to provide nitrogen-filled leachate to the area through the Sylvia Grinnell river and small runoff streams (Bartley et al. in prep).

P-glycoproteins (P-gp) like MDRP1 and ABCA1 are what define the xenobiotic response in bivalves (Viarengo et al. 2007b). They act as a ‘first line of defense’ by mediating the removal of xenobiotics out of the cell through ATP-dependent pumps to prevent intracellular toxicity (Smital et al. 2000; Viarengo et al. 2007b; Veldhoen et al. 2009; Navarro et al. 2012). In both tissue types, MDRP1 was significantly upregulated at the WWTP but more defined in the gill tissue with a 4-fold increase in expression, likely due to the gills being a more epithelia rich tissue type (Lüdeking and Köhler 2002). The heightened abundance of MDRP1 at the WWTP may be to increase the cellular capacity to eliminate xenobiotics from the cell (Lüdeking and

Köhler 2002). In contrast, ABCA1 was inhibited at the WWTP, Tundra Ridge and Kituriqannigituq. These transporters, although resistant to low levels of contaminants, could be sensitive to inhibitory concentrations of specific compounds deemed ‘chemosensitizers’ that reduce the functionality and defense functions allowing toxic substrates to accumulate in the cells (Cornwall et al. 1995; Kurelec 1997; Smital et al. 2000).

Cellular stress response The use of HSPs as a measure of response to a vast array of physiological stress is widely accepted in bivalves (Fabbri et al. 2008; Veldhoen et al. 2009). HSP90 has previously been shown to act similarly to HSP70 and maintain homeostasis and protect the cells against xenobiotics and other abiotic stressors (Fabbri et al. 2008; Choi et al. 2008). In this study, the similar low abundances of HSP90 in the gill at the WWTP and Kituriaqannigituq implies that expression is seemingly more related to oxidative stress than to stimuli like metals, organic compounds or xenobiotics due to Kituriaqannigituq’s distance from the wastewater effluent source. The associated decrease in expression at both sites could be in response to oxidative stress induced by estuarine hypoxia at Kituriaqannigituq and chronic contaminant exposure at the WWTP (Fabbri et al. 2008; Choi et al. 2008; Foster and Fulweiler 2019). The prevalence of hypoxic conditions at both sites is further supported by the expression pattern of MnSOD, a chief ROS scavenging enzyme (Holley et al. 2011). Excess contaminants and metal exposure have been shown to interfere with ROS defense mechanisms like MnSOD, especially in cold water environments (Canesi et al. 1998; Cossu et al. 2000; Ivanina et al. 2008; Philipp et al. 2012; Ransberry et al. 2015). Thus, the low abundance of MnSOD displayed at the WWTP is likely a response to pollution whereas Kituriaqannigituq’s high abundance demonstrates a potential antioxidant response expected during hypoxic environmental conditions. The disparity of expression profiles highlights the potential variation caused by multiple stressors acting on a single organism but also reveals MnSOD as a capable indicator of wastewater toxicity in M. truncata.

LDH can be used to evaluate the consequences of ROS production and oxidative stress, therefore a reduction in LDH expression reflects the possible decrease in biosynthetic activities ultimately resulting in the decreased capacity for ATP production under anaerobic conditions (Venkateswara Rao 2006; Sonawane 2017; Sifi and Soltani 2019). The depletion of gill LDH at

55 the WWTP and Monument Island could be explained by the prolonged stress of chronic contamination and the cellular reaction to diversify pathways during oxidative stress. Previous studies have found that LDH activity is significantly inhibited during metal exposure (Antognelli et al. 2003; Carvalho and Fernandes 2008; Sonawane 2017), and the recent discovery of metal accumulation in M. truncata shells near Iqaluit’s wastewater source (see Chapter 2: i.e., Cu and Pb) supports the notion that metals may have the capacity to reduce cellular respiration. This mechanism could be recruited to attempt to conserve energy for other physiological processes (Antognelli et al. 2003; Sonawane 2017; Sifi and Soltani 2019). However, contamination does not explain the low expression of LDH at Kituriaqannigituq. The enhanced expression of MnSOD found at Kituriaqannigituq and geographical location between two river systems could classify Kituriaqannigituq has an estuary, experiencing typical estuarine conditions of depleted dissolved oxygen levels (Foster and Fulweiler 2019). Under long-term exposure of low oxygen conditions, my results could parallel previous reports on the Manila clam by Li et al. (2019) that hypothesized, chronic hypoxia could result in the diversification of anaerobic pathways away from LDH. Further investigation on the environmental conditions in this area and mitochondrial capacity of M. truncata is necessary but this hypothesis could explain the expression patterns of Kituriaqanniituq and the diverse strategies that the truncate, soft-shell clam mobilizes when exposed to hypoxic conditions.

Interestingly, HSP60 showed no significant differences between sites in either tissue (one-way ANOVA, P = 0.2). These results contribute to the a growing body of literature that found a variety of stressors (i.e., pesticides, hypoxia, bacteria) to have no effect on HSP60 expression and sheds some doubt on its role as a biomarker of stress in bivalves (Dowling et al. 2006; Joyner‐Matos et al. 2009; Puerto et al. 2011).

Conclusion In summary, this investigation showed a decrease in transcripts of genes of molecular chaperones, antioxidants, metabolic biomarkers, and phase I and II biotransformation enzymes at sites near the wastewater effluent source. In contrast, the phase III xenobiotic defense gene and nitrogen-specific binding enzyme were significantly increased in clams near the wastewater effluent source. This suggests that Iqaluit’s primary treated wastewater effluent is causing cellular stress that could lead to physiological damage, likely related to both duration of exposure

56 and variability in contaminants. While the specific contaminants responsible for these expression patterns are not known, these cellular biomarkers provide an early warning system to the serious adverse physiological changes that may result from chronic wastewater exposure in Frobisher Bay.

Figures and Tables Table 3.1 Primer sequences used to target select genes in the transcriptome of Mya truncata. Functional Gene Full Gene Gene_ID Effcy (%) Primer Seq. Group Code Name Reference RPL18a 60S ribosomal DN129185_ 101.7 F: CAC AGC CAA Genes: protein L18a c0_g2_i1 GTC ACG TTT CTG Ribosomal R: GCC CAG TGT CCT proteins CTT CAT CTT C RPS4 40S ribosomal DN133274_ 98.1 F: CGG TAA GGG protein S4 c1_g2_i1 CGT GAA ACT GA R: TGC GAC TTG ACG GCC ATT Cellular stress HSP60 Heat shock DN157452_ 103.0 F: TGC ACT TGT GTT response: protein 60 c1_g1_i2 GCT GTG TGA Molecular R: TCA AGG GCA chaperones GGG AGT ATG GA HSP90 Heat shock DN148936_ 95.3 F: CGA CAT CAC protein 90 c2_g2_i2 CAC AGA GGA GTA TG R: GAG ATG GTC CTC CCA GTC GTT Cellular stress MnSOD Manganese DN148890_ 107.9 F: CCG GAT CTC GAC response: Superoxide c0_g1_i2 ATG TGC TA Antioxidant Dismutase R: AGT GGT ACG TGA CCG GTG GTA Cellular stress CS Citrate DN156644_ 103.4 F: GTT CAG CGC response: Synthase c3_g4_i1 AGC CAT CAC A Metabolic R: CCG TCA GCG biomarkers TAA GCT TTA GCA LDH D-Lactate DN157111_ 104.7 F: GCA GGC CTC Dehydrogenase c1_g1_i3 AAA GAG CAA AC R: TCT GAC CCC CCA TTC ATC TG Phase I CYP1A1 Cytochrome DN125127_ 105.1 F: CAG AAC GCT TCC xenobiotic P450 1A1 c1_g1_i1 TGG ACC AA response R: ATT GAG TCT GAG CGG GTG TTG Phase II GSTO Glutathione S- DN136940_ 96.2 F: CAG CAT GCC xenobiotic transferase c0_g1_i1 TTT GAC GTT GA response Omega 1 R: TGG TGT GGG TTC GGG ATA AG GS Glutamine DN157620_ 89.3 F: ACG AAG CGA Synthetase c1_g1_i3 AGA GCG AGA TC R: TGC CAG GAG GGT GTA CTC TTG Phase III MDRP1 Multidrug DN119073_ 90.5 F: CCA TCC AGA xenobiotic resistance c0_g1_i1 ACG CTG ACA AA defense protein 1 R: GAC GAC CCT GTT CTG TGA CAA C

ABCA1 ATP-binding DN161260_ 101.6 F: GTC TCC CGC cassette sub- c0_g1_i1 ATA AAC GTA ACG family A R: CCA CTG ACA member 1 ACT TCC GCT TCA

Figure 3.1 Relative expression of mRNA in the gill tissue of Mya truncata for 10 genes where black error bars represent the standard error to the mean. Lowercase letters denote statistical significance between sampling sites as determined by a one-way ANOVA and Tukey HSD post- hoc test (α < 0.05). KIT represents Kituriaqannigituq and WWTP represents the wastewater treatment plant.

Figure 3.2 Principal components biplot analysis on the gill gene expression. The X-axis represents PC1 (29.1% variance), while the Y-axis represents PC2 (16.3% variance). Larger symbols indicate the mean for that sample site.

Figure 3.3 Relative expression of mRNA in the mantle tissue of Mya truncata for 10 genes where black error bars represent the standard error to the mean. Lowercase letters denote statistical significance between sampling sites as determined by a one-way ANOVA and Tukey HSD post-hoc test (α < 0.05).

Figure 3.4 Principal components biplot analysis on the mantle gene expression. The X-axis represents PC1 (24.4% variance), while the Y-axis represents PC2 (17.5% variance). Larger symbols indicate the mean for that sample site.

Chapter 4: General Summary and Conclusions This study was the first to explicitly examine the effects of municipal wastewater on the Arctic truncate soft-shell clam, Mya truncata, using a combination of endpoints including growth metrics, biogeochemical records (i.e., trace elements and stable isotopes), and mRNA abundance profiles. Iqaluit’s wastewater effluent was qualified as toxic due to its high ammonia concentration and persistently has high BOD and TSS quantities that exceed federally mandated maximums (Nunami Stantec 2015; City of Iqaluit 2019). This thesis demonstrates a relationship between the previously recorded contamination and toxicity, possible environmental fate, and cellular response of the soft-shell clam.

Growth rate is a common metric used to assess bivalve’s physiological responses to contamination sources (Belanger 1991; Carmichael et al. 2004; Sabatini et al. 2011; Nobles and Zhang 2015). Previous studies have found a characteristic growth impairment in bivalves upon chronic exposure to wastewater effluent sources in a wide array of environments, using diverse treatment methods (i.e., primary to tertiary) (Horne and McIntosh 1979; Goudreau et al. 1993; Gangloff et al. 2009; Nobles and Zhang 2015). My study is consistent with these previous reports given the clams slower growth rate at the two sites nearest the wastewater effluent source (i.e., WWTP and Tundra Ridge). The present investigation also builds upon a previous study conducted at Iqaluit’s WWTP that recorded altered community structure in benthic fauna 580 m away from the effluent source (Krumhansl et al. 2015). My thesis shows an effect that possibly extends to nearly 3 kms away (i.e., Tundra Ridge). Numerous factors have been shown to impact the growth rate of bivalves but the isotopic reconstruction of heightened temperature, lower salinity and elevated organic matter confined to the WWTP site supports the notion that these environmental variations are likely anthropogenically-driven (Brown 1978; Gillikin et al. 2005b; Schöne and Surge 2012).

M. truncata proved to be a useful biomonitor by providing a sclerochronological record of the temporal progression of trace elements accumulated in the shells of individuals nearest the wastewater effluent source. These records display both the benefit of primary treatment and the concerns of remaining with primary treatment in a growing community. The reduction of Mn and Ti concentrations after the implementation of primary treatment in 2006, demonstrate the capabilities of a simple filtration system in reducing low concentration elements in the effluent.

In contrast, the continued accumulation of Pb from 2006 to 2018 in the shells nearest the wastewater effluent source, demonstrates the minor defense that primary treatment provides to those metals known to be abundant in effluent. Furthermore, the decline in Na concurrently appearing with the increase in Cu and Mn demonstrates the influence of wastewater encased in freshwater on the environment. The gradual decline of Na from 2005 to 2018 can be attributed to the reduction in salinity in response to growing effluent volume. Likewise, the concentration of two elements known to be in high concentration in Iqaluit’s freshwater source, Cu and Mn, drastically increased in concentration from 2010 to 2018, illustrating that regulatory concern should encompass both toxicity of the effluent released, as well as the volume of freshwater being discharged.

Given the sclerochronological evidence of contaminants presence in the shells in chapter 2, my thesis then aimed to determine if there was a relationship between contaminant exposure and induced physiological changes to M. truncata’s biology in chapter 3. Those organisms closest to the wastewater effluent source experienced significantly lower expression in the mRNA levels of molecular chaperones, antioxidants, metabolic regulators and phase I and II biotransformation genes. These results build on the existing evidence that excess contaminants accompanied by metal exposure can lead to decreased metabolic capacity and the inhibition of biosynthetic activities in bivalves (Günther and Walter 1994; Pannunzio and Storey 1998; Franzellitti and Fabbri 2005; Viarengo et al. 2007b; Choi et al. 2008; Sifi and Soltani 2019). Concurrently with the inhibition of some cellular functions, those organisms closest to the wastewater effluent source showed enhanced expression of genes involved in the binding and removal of xenobiotics (i.e., MDRP1 and GS), consistent with patterns observed in bivalves situated in other marine and freshwater systems exposed to wastewater effluent (primary to tertiary treatment) (Smital et al. 2000; Viarengo et al. 2007b; Veldhoen et al. 2009; Navarro et al. 2012). Given that the pattern of mRNA abundance was different at the wastewater treatment plant compared with the other sample sites, the expression patterns are likely a result of anthropogenic contamination. My thesis also provides new insight into the extent of hypoxic conditions from the wastewater effluent in Iqaluit previously shown by Krumhansl et al. (2015) through changes in the expression patterns of MnSOD and LDH. Therefore, my thesis demonstrates that there is a biological response of organisms near the WWTP to contaminants in the effluent.

The ecological implications of chronic wastewater exposure could disrupt the reproductive and physiological processes and influence community structure (Northington and Hershey 2006; Nobles and Zhang 2015). Reduced density, diversity, evenness and richness in the benthic community has already been documented in Frobisher Bay and with the volume of effluent increasing with time, this is subject to further deterioration (Krumhansl et al. 2015, 2016). M. truncata serve many important roles to the region. First, this species is recreationally harvested and serves as a culturally important natural food (AMAP 1998; Camus et al. 2003; Amaro et al. 2003; Wakegijig et al. 2013; Sleight et al. 2016). They are an integral part of the food chain, providing means of energy transfer from primary producers to bearded seals and walruses (Welch and Martin-Bergmann 1990). Furthermore, they are important ecological stabilizers that reduce turbidity, stabilize the shoreline, sequester carbon, process organic matter and recycle nutrients which can protect against phytoplankton blooms (National Research Council 2010; Arivalagan et al. 2016; Sleight et al. 2018; Olivier et al. 2020). Therefore, shifts in their communities could cause dramatic changes in ecosystem functioning like altering the nutrient dynamics of the region, increasing turbidity (especially in the presence of the wastewater effluent), increasing coastal erosion, and altering the ecosystem structure by limiting the transfer of energy and nutrients from primary to secondary consumers (Cranford et al. 2003; National Research Council 2010; Arivalagan et al. 2016; Sleight et al. 2018; Olivier et al. 2020). Furthermore, depressing M. truncata populations will put pressure on a community already prone to a food security crisis providing more barriers on the quality and accessibility of natural food options (Wakegijig et al. 2013). As Arctic communities continue to grow, the results from this thesis will help inform and address the often inadequate and lacking wastewater treatment in coastal communities and prioritize northern-specific community wastewater upgrades and the establishment of a shellfish sanitation program. Furthermore, a greater appreciation of the ecosystem services and cultural importance of this bivalve will help prioritize the protection and ecological restoration of this organism to enhance ecosystem functioning.

It is important to note there were several limitations to this study. The first is that these results are limited to the physiological tolerance and life span of the organism. Bivalves register internal and environmental biogeochemical records only during optimal growth conditions, this means that their records will often neglect to record variation when it is exposed to environmental conditions outside of its physiological tolerance (Schöne 2008). Alongside this

68 limitation, this study did not measure the actual toxicity and contaminant concentration of the effluent. Previous reports have shown evidence of pharmaceutical compounds in the lagoon (Stroski et al. 2020), presence of antibiotic resistant genes in the immediate receiving waters (Neudorf et al. 2017), high CBOD, TSS, Escherichia coli and total coliforms in the effluent (Neudorf et al. 2017; City of Iqaluit 2020), and have warned of the expanded survival rates of bacteria due to low temperatures and reaction rates (Samuelson 1998; Gunnarsdóttir et al. 2013). Further studies should endeavor to characterize the toxicity of the effluent and measure the conditions of the water (i.e., salinity, temperature, dissolved oxygen content, pH, etc.) to solidify the connection between the wastewater effluent and associated impact on the clams.

Regional Information In a region with little monitoring and data, certain observations were made that could improve future studies. Kituriaqannigituq, a site on the west end of Frobisher Bay (Fig. 2.1) had unique morphometric results eliciting the shortest average length (47.66 ± 1.41 mm; Table A1) but the oldest average age (15.96 ± 0.76 years; Table A1) between sites. These characteristics meant that their shell and body shape was morphologically unique including a bulging siphon accompanied by a short shell compared with all other sampling locations. Furthermore, total mercury content (mg/kg dry weight) within the soft-tissue body at each sampling location was analyzed at the University of Waterloo (Hilgendag et al. 2020). These results showed the total mercury content to be significantly higher at Kituriaqannigituq (One-way ANOVA: 0.032 mg/kg dry weight, P < 0.001) compared to all other sampling locations (Hilgendag et al. 2020). All mercury concentrations recorded were below Health Canada’s maximum allowance of mercury in bivalve edible tissue (2.5 mg/kg dry weight; Health Canada 2005). Moreover, I investigated three types of shellfish toxins for samples collected in 2018 and 2019 – paralytic shellfish poison (PSP), amnesic shellfish poison (ASP) and diarrhetic shellfish poison (DSP). PSP and ASP analyses were conducted at the Canadian Food Inspection Agency (CFIA) laboratory in Burnaby and DSP analyses were performed at the CFIA Dartmouth Laboratory. In 2018, 10 total samples from four locations (Apex, Kituriaqannigituq, Aupalajat, Peterhead Inlet) were analysed and trace concentrations of the DSP pectenotoxin-2 were found in one sample collected from Apex and two samples collected from Kituriaqannigituq, while all other samples showed concentrations below the detection limit (one sample is equivalent to 100g of tissue,

69 approximately 4-10 clams). The same analysis was conducted for 22 samples collected from six sites (Fig. 2.1) in 2019 but no traces of any toxin were found in the bivalves. All recorded concentrations were below Health Canada’s maximum allowance in bivalve edible tissues (0.2 mg/kg; Health Canada 2005). These results highlight the presence of toxin producing phytoplankton in the region and show the potential for phytoplankton blooms and the subsequent toxin accumulation in bivalves.

Future Directions This study demonstrated the utility of Mya truncata as an early warning and sensitive biomonitor for chronic municipal wastewater effluent in an Arctic ecosystem. However, a cage- transplant study and a complete array of biomarkers from multiple levels of biological organization (molecular/cellular/tissue/organism) could provide more specific insight into contaminants inducing physiological changes in the organism (Viarengo et al. 2007a; Veldhoen et al. 2009).

Traditional use of bivalves as biomonitors leaves some uncertainty with quantifying the spatial and temporal changes in contaminant exposure and associated biological effects, but the use of a caged-transplant study in the future would combine the experimental control of a laboratory environment with the realism of field monitoring (Salazar and Salazar 1997). This caged study would grant more control in the exposure gradient and duration and allow for a greater demonstration of site-specific differences, short- and long-term trends, source identification and exposure response. This methodology would result in more controlled biomonitoring allowing definitive conclusions between the contaminant exposure and biological effects in response to wastewater exposure (Salazar and Salazar 1997; Nobles and Zhang 2015).

My thesis was limited to the transcriptional level of observed changes and while these changes can act as an early warning sign of toxic effects, they cannot assess actual damage at the tissue level (Farcy et al. 2009). Future studies should examine the effect of contaminants on M. truncata at the molecular, cellular, tissue and organism level. This should include testing a larger suite of genes on a variety of tissue types involved in antioxidant defense (i.e., catalase, Cu/Zn superoxide dismutase), contaminant exposure (i.e., metallothioneins), endocrine disruption (i.e.,vitellogenin), lipid synthesis and degradation (i.e., Fatty acid binding proteins, cathepsin), and biomineralization (i.e., calponin, cartilage matrix proteins) (Piano et al. 2004; Viarengo et al.

2007a; Meistertzheim et al. 2007; Choi et al. 2008; Sleight et al. 2016). These mRNA profiles should be accompanied by enzyme assays and the assessment of lysosomal membrane stability (Viarengo et al. 2007a). Biomarkers should then be recruited to observe any morphological damage at the tissue level (i.e., microscopy and haemolymph composition) and quantify the organismal survival capacity and reproductive performance to shed light on the consequences of contamination and the possible changes that could occur at the population level (Viarengo et al. 2007b).

Summary In conclusion, this study establishes the utility of Mya truncata as a biomonitor and provides evidence that this species may be negatively impacted by primary treated wastewater in Frobisher Bay (NU, Canada). This is demonstrated through reduced growth rate, stable isotope and trace element records, elevated expression of genes involved in binding and removing xenobiotics and the inhibition of antioxidants, metabolic regulators, molecular chaperones and biotransformation genes. This thesis endeavored to bring focus to the impacts of local contamination sources in the Arctic and prioritize northern-specific wastewater upgrades to regulators and communities to improve the quality of their treatment and monitoring programs moving forward.

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Appendix and Supplementary Data Table A1. Studies included in the literature review of 126 surveys conducted on Mya truncata in the Canadian Arctic. Location Longitude Latitude Year Data Analysed Authors Pangnirtung Hamlet -65.6959 66.15113 1985 Contaminants Bourgoin (1990) Pangnirtung Dump -65.6828 66.15433 1985 Contaminants Bourgoin (1990) Pangnirtung Abundance and Aitken and Risk intertidal flats -65.7016 66.15266 1985 Distribution (1988) Ottawa Islands, Hudson Bay -80.2646 59.51211 1972 Growth Rates Andrews (1972) Abundance and Barrow straight -97.6639 74.44514 1985 Distribution Welch et al. (1992) Wellington Abundance and Channel -93.3482 75.00799 1984 Distribution Welch et al. (1992) Respiration and Resolute Bay -94.8731 74.68041 1986 Filtration Rates Welch et al. (1992) Cape Hatt -79.8646 72.4871 1984 Growth Rates Welch et al. (1992) Warwick Sound -65.3861 62.79912 1978 Radiocarbon Dates Stravers et al. (1992) Gold Cove -65.8508 62.92347 1978 Radiocarbon Dates Stravers et al. (1992) Ottawa Islands, Hudson Bay -80.3923 59.3418 1972 Stable Isotopes Andrews (1972) Home Bay- Cape Dyer -61.2665 66.66356 1972 Stable Isotopes Andrews (1972) Eastern Baffin Island -63.5284 67.36652 1972 Stable Isotopes Andrews (1972) Abundance and Assistance Bay -94.2569 74.65114 1991 Distribution Conlan et al. (1998) Abundance and Resolute Bay -94.8757 74.68513 1991 Distribution Conlan et al. (1998) Dyck and Fyles Churchill -95.05 53.18333 1963 Radiocarbon Dates (1963) Dyck and Fyles Kent Peninsula -107.017 68.65139 1963 Radiocarbon Dates (1963) Dyck and Fyles Melville Sound -107.089 68.28964 1963 Radiocarbon Dates (1963) Lang River, Dyck and Fyles Somerset Island -94.0833 72.19167 1963 Radiocarbon Dates (1963) Strathconda Fiord, Dyck and Fyles Ellesmere Island -82.85 78.7 1963 Radiocarbon Dates (1963) Hare Cape Ridge, Dyck and Fyles Ellesmere Island -86.3667 75.91667 1963 Radiocarbon Dates (1963) Swinnerton Peninsula, Dyck and Fyles Ellesmere Island -81.6667 77.3333 1964 Radiocarbon Dates (1964) Abundance and McBeth Fiord -68.0486 64.49999 1988 Distribution Dale et al. (1989)

Abundance and Iqaluit -68.4851 63.73337 1988 Distribution Dale et al. (1989) Daniel Moore Bay, NWT -109.8 67.78333 1965 Radiocarbon Dates Lowdon et al. (1967) Bathurst Inlet -106.917 67.25 1965 Radiocarbon Dates Lowdon et al. (1967) Thom Bay, Boothia Peninsula -92.2333 70.3333 1965 Radiocarbon Dates Lowdon et al. (1967) Salisbury Island, Hudson Strait -76.7167 63.41667 1966 Radiocarbon Dates Lowdon et al. (1967) Soper Lake -69.8514 62.9 1966 Radiocarbon Dates Lowdon et al. (1967) York Sound Series -66.4847 62.40139 1966 Radiocarbon Dates Lowdon et al. (1967) Apex Hill -68.3904 63.72289 1966 Radiocarbon Dates Lowdon et al. (1967) Apex near road Lowdon et al. (1967) leading to Iqaluit -68.4356 63.72723 1966 Radiocarbon Dates Frobisher, Baffin Lowdon et al. (1967) Island -68.5333 63.75 1966 Radiocarbon Dates Putnam Highland -74.0925 65.58952 1966 Radiocarbon Dates Lowdon et al. (1967) Burwash Bay, Lowdon et al. (1967) Baffin Island -71.6 66.03333 1966 Radiocarbon Dates Flint Lake, Baffin Lowdon et al. (1967) Island -73.9083 69.36667 1966 Radiocarbon Dates Piling Lake, Baffin Lowdon et al. (1967) Island -74.8 69.1 1966 Radiocarbon Dates Fellside Lake, Lowdon et al. (1967) Baffin Island -77.5 70.05 1966 Radiocarbon Dates Paquet Bay, Baffin Lowdon et al. (1967) Island -78.3333 71.88333 1966 Radiocarbon Dates Milne Inlet, Baffin Lowdon et al. (1967) Island -80.9167 71.88333 1966 Radiocarbon Dates Stanwell-Fletcher Lowdon et al. (1967) Lake -94.5 72.76667 1966 Radiocarbon Dates Peel Sound -95.3333 72.6 1966 Radiocarbon Dates Lowdon et al. (1967) Drake Bay, Prince Lowdon et al. (1967) of Wales Island -100.667 73.383 1966 Radiocarbon Dates Musox River, Lowdon et al. (1967) Bathurst Island -98.4167 75.75 1966 Radiocarbon Dates Ten Mile Lake, Newfoundland -56.7175 51.09083 1969 Radiocarbon Dates Lowdon et al. (1971) Port au Port Bay, Lowdon et al. (1971) Newfoundland -58.8342 48.71667 1969 Radiocarbon Dates New Richmond, Lowdon et al. (1971) New Brunswick -65.8675 48.16667 1969 Radiocarbon Dates Risseau-a-Rebours, Lowdon et al. (1971) Gaspe, Quebec -65.9333 49.22222 1969 Radiocarbon Dates Northern Garry Lowdon et al. (1971) Island, NWT -135.667 69.50556 1969 Radiocarbon Dates Southhampton Lowdon et al. (1971) island, NWT -84.7667 64.71667 1969 Radiocarbon Dates

Clyde Foreland, Lowdon et al. (1971) NWT -68.75 70.63333 1969 Radiocarbon Dates Cambridge Fiord -75.05 71.18333 1970 Radiocarbon Dates Lowdon et al. (1971) Kentra Bay, NWT -74.25 71.28333 1970 Radiocarbon Dates Lowdon et al. (1971) Ranoch Arm, Lowdon et al. (1971) Baffin Island -75.1333 71.45 1970 Radiocarbon Dates Pond Inlet -78 72.68333 1970 Radiocarbon Dates Lowdon et al. (1971) Lyall River, Grinell Lowdon et al. (1971) Peninsula -95.3667 76.95139 1970 Radiocarbon Dates North Kent Island -90.2167 76.81806 1970 Radiocarbon Dates Lowdon et al. (1971) Musox Fiord -87.6 76.61806 1970 Radiocarbon Dates Lowdon et al. (1971) Caledonian River, Lowdon et al. (1971) NWT -98.8 75.68472 1970 Radiocarbon Dates (Welch and Martin- Resolute Bay -94.8805 74.68953 1985 Predators Bergmann 1990) Abundance and McBeth Fiord -68 69.75 1989 Distribution Syvitski et al. (1989) Abundance and Itirbilung Fiords -69.5 69.25139 1989 Distribution Syvitski et al. (1989) Abundance and (Wacasey et al. Frobisher Bay -68.4219 63.70015 1979 Distribution 1977) Abundance and Thomson et al. Eclipse Sound -78.7646 72.76718 1986 Distribution (1986) Abundance and Thomson et al. Landcaster Sound -84.5419 74.44911 1986 Distribution (1986) Leffert Nanatak -75.5847 78.71861 1988 Radiocarbon Dates Blake (1989) Dyck and Fyles Baie Verte River -56.2833 49.90139 1961 Radiocarbon Dates (1963) Middle Arm, Green Dyck and Fyles Bay -56.1 49.7 1961 Radiocarbon Dates (1963) Anticosti Island, Dyck and Fyles Quebec -64.2694 49.83889 1960 Radiocarbon Dates (1963) Dyck and Fyles St. Epiphane -69.3172 47.93417 1962 Radiocarbon Dates (1963) Dyck and Fyles L'Isle-Verte -69.2178 47.95194 1961 Radiocarbon Dates (1963) Resolute Bay - Washburn and Bench -95.0167 74.74167 1983 Radiocarbon Dates Stuiver (1985) Resolute Bay - W Washburn and of VOR Road -95 74.73333 1984 Radiocarbon Dates Stuiver (1985) Resolute Bay - Washburn and Transmitter Station -94.9833 74.74167 1985 Radiocarbon Dates Stuiver (1985) Resolute - Washburn and McMaster River -94.9667 74.75417 1986 Radiocarbon Dates Stuiver (1985) Resolute - Mecham Washburn and Valley -94.7667 74.69583 1987 Radiocarbon Dates Stuiver (1985) Washburn and Truro Island -97.1667 73.31667 1988 Radiocarbon Dates Stuiver (1985)

Washburn and Cornwallis Island -94.7 74.73333 1989 Radiocarbon Dates Stuiver (1985) Lemmen et al. Expedition Fiord -90.6833 79.41667 1992 Radiocarbon Dates (1994) Lemmen et al. Wolfe Valley -90.9333 79.4 1993 Radiocarbon Dates (1994) Lemmen et al. Fiord Head -91.1 79.35 1994 Radiocarbon Dates (1994) Lemmen et al. Outer Fiord -91.3833 79.333 1995 Radiocarbon Dates (1994) Himmelman and Cap du Corbeau -70.459 47.42534 1988 Predators Hamel (1993) Blind Fiord -86.5824 78.13131 1998 Radiocarbon Dates Cofaigh (2002) Starfish Bay -84.7729 78.18676 1998 Radiocarbon Dates Cofaigh (2002) Irene Bay -81.9018 78.93214 1998 Radiocarbon Dates Cofaigh (2002) Baumann Fiord -86.6667 78.1333 1998 Radiocarbon Dates Cofaigh (2002) Canon Fiord -80.5167 79.58333 1998 Radiocarbon Dates Cofaigh (2002) Fosheim Peninsula -85.2167 79.8333 1998 Radiocarbon Dates Cofaigh (2002) Greely Fiord -81.5 80.4 1998 Radiocarbon Dates Cofaigh (2002) Nansen Sound -90.1667 81.1 1998 Radiocarbon Dates Cofaigh (2002) Axel Heiberg Cofaigh (2002) Island -79.1667 83.28333 1998 Radiocarbon Dates Trold Fiord -85.8667 78.1 1998 Radiocarbon Dates Cofaigh (2002) Norwegian Bay -87.7 77.11667 1998 Radiocarbon Dates Cofaigh (2002) Graham Island -89.95 77.26667 1998 Radiocarbon Dates Cofaigh (2002) Nansen Sound -88.55 80.36667 1998 Radiocarbon Dates Cofaigh (2002) Gordillo and Aitken Prince of Wales -100.809 72.97475 2000 Radiocarbon Dates (2001) Gordillo and Aitken Somerset Island -94.628 74.11727 2000 Radiocarbon Dates (2001) Prince of Wales Gordillo and Aitken (11) -102.635 72.98926 2000 Radiocarbon Dates (2001) Prince of Wales Gordillo and Aitken (13) -100.933 73.44596 2000 Radiocarbon Dates (2001) Prince of Wales Gordillo and Aitken (14,15) -100.189 73.76535 2000 Radiocarbon Dates (2001) Prince of Wales Gordillo and Aitken (21,25) -98.6238 73.62546 2000 Radiocarbon Dates (2001) Prince of Wales Gordillo and Aitken (31) -97.2676 73.85506 2000 Radiocarbon Dates (2001) Broughton Island -63.7651 67.54663 1980 Contaminants Szabo et al. (1981) Qivitu Peninsula -64.5832 67.83002 1980 Contaminants Szabo et al. (1981) Clyde Foreland, NWT -68.3557 70.46463 1980 Contaminants Szabo et al. (1981) Apex Hill -68.3904 63.72289 1965 Radiocarbon Dates Matthews (1967) Abundance and Siferd and Welch Barrow straight -97.4416 74.26989 1991 Distribution (1992) Axel Heiberg Abundance and Gordillo and Aitken Island -79.1667 82.28333 1999 Distribution (2001)

Abundance and Gordillo and Aitken Ellesmere island -69.5583 80.29119 1999 Distribution (2001) Abundance and Gordillo and Aitken Devon Island -79.8368 75.5621 1999 Distribution (2001) Abundance and Resolute Bay -94.9506 74.6603 1990 Distribution Welch et al. (1992) Respiration and Resolute Bay -94.9506 74.6603 1990 Filtration Rates Welch et al. (1992) Wagemann and Igloolik Island -81.7365 69.33637 1982 Contaminants Stewart (1994) Fisher and Stewart Igloolik Island -81.7365 69.33637 1979 Predators (1997) Fisher and Stewart Hall Beach -81.2463 68.73093 1988 Predators (1997) Lancaster Sound -84.2719 74.44561 1990 Contaminants Atwell et al. (1998) Barrow strait -97.7233 74.44618 1988 Contaminants Atwell et al. (1998) Frobisher Bay -67.2837 63.29682 1993 Stable Isotopes Hobson et al. (2002) Frobisher Bay -67.1392 63.19469 2002 Contaminants (Tomy et al. 2009) Bjorne Peninsula -85.4198 77.42639 2010 Radiocarbon Dates England et al. (2013) Fosheim Peninsula -85.4462 78.11152 2010 Radiocarbon Dates England et al. (2013) Eureka Sound -85.662 79.9368 2010 Radiocarbon Dates England et al. (2013) Hoved Island -87.0295 77.90689 2010 Radiocarbon Dates England et al. (2013) Gillis and Ballantyne Igloolik Island -69.3833 81.75 1996 Fatty Acids (1999)

Table A2. Raw morphometric measurements for each sample site. ID represents individuals at each site where MI is Monument Island, TR is Tundra Ridge, AP is Apex, WW is the wastewater treatment plant, AUP is Aupalajat and KIT is Kituriaqannigituq. Measurements taken were as follows, AMG = axis of maximal growth, ML = maximum length, PMGD = posterior maximum growth dorsal, PMGV = posterior maximum growth ventral, Wet wt = wet weight with shell on, Body wt = Body weight without shell, and Shell wt = Weight of shell. na represents missing measurements due to field conditions or broken shells.

ID Age Length Height AMG ML PMGD PMGV Wet Body Shell (mm) (mm) (mm) (mm) (mm) (mm) wt (g) wt (g) wt (g) MI1 9 65 40 45 64 28 44 46 na 10.46 MI2 27 71 51 52 70 35 52 75 na 19.25 MI3 6 51 31 38 50 23 32 24 na 5.35 MI4 12 69 41 50 68 33 45 52 na 13.27 MI5 9 70 40 49 67 28 42 57 na 13.16 MI6 5 37 25 27 37 21 26 11 na 2.65 MI7 na 36 23 25 35 18 24 7 na 1.79 MI8 4 26 15 17 26 14 17 2 na 0.36 MI9 14 80 46 54 76 39 53 99 na 22.77 MI10 11 72 44 52 72 30 47 61 na 16.9 MI11 11 67 41 48 65 28 43 50 na 12.67 MI12 12 64 43 48 64 27 44 52 na 12.26

MI13 9 57 36 42 53 21 37 na na 7.1 MI14 15 72 49 55 69 21 49 na na 19.85 MI15 10 57 36 42 55 13 38 na na 10.09 MI16 na 37 24 25 34 17 26 na na 1.4 MI17 6 46 28 34 42 14 29 na na 3.31 MI18 3 37 25 28 35 20 25 na na 1.13 MI19 10 57 39 44 51 21 40 na na 4.27 MI20 7 45 32 34 44 21 29 na na 2.27 MI21 10 56 38 40 52 26 38 45 21 8 MI22 17 78 44 55 70 37 51 76 36 21 MI23 6 43 28 30 40 12 30 15 8 4.08 MI24 14 66 39 45 65 25 42 58 25 15 MI25 14 55 37 40 50 23 37 40 21 8.24 MI26 14 70 47 52 64 32 50 82 35 9.12 MI27 12 62 41 45 60 32 43 48 24 10.44 MI28 6 57 38 46 55 26 38 52 27 MI29 8 57 40 44 55 20 40 42 20 5.21 MI30 9 60 41 44 56 18 38 50 22 10.44 MI31 na 55 35 37 50 24 37 38 na na MI32 na 48 31 36 45 19 33 22 na na MI33 na 42 29 30 38 21 31 20 na na MI34 6 44 30 33 43 14 30 22 9 3.56 MI35 6 40 25 29 36 15 25 14 6 3.08 MI36 6 40 24 28 36 13 25 13 6 2.32 MI37 4 43 26 30 40 15 29 15 7 3.28 MI38 10 57 35 40 53 20 36 36 17 8.39 MI39 5 41 27 26 35 14 29 14 na 2.8 MI40 5 51 34 38 50 17 34 29 15 6 MI41 8 51 37 41 52 16 37 41 18 11 MI42 8 54 35 40 51 16 37 36 17 8 MI43 6 44 30 34 43 15 31 18 8 4 MI44 5 47 31 35 44 15 33 22 12 5 MI45 6 46 29 34 43 14 30 20 10 4 MI46 5 44 30 34 42 12 30 18 9 4 MI47 5 44 28 33 40 14 29 16 8 4 MI48 8 52 34 38 50 18 35 41 21 9 MI49 5 55 33 38 49 16 33 26 14 6 TR1 20 87 55 65 65 36 54 135 44 na TR2 9 60 41 46 61 27 42 48 12 na TR3 15 76 47 52 73 35 54 87 26 na TR4 9 52 34 36 50 25 32 26 8 na TR5 8 58 41 44 57 28 44 48 14 na TR6 11 62 43 46 61 29 44 51 17 na TR7 9 48 34 36 48 23 34 27 8 na TR8 8 41 29 31 40 20 30 15 4 na TR9 5 34 23 24 34 13 23 11 3 na TR10 11 71 45 52 69 32 49 73 23 na TR11 9 55 45 49 60 19 46 na 8 na

TR12 6 44 28 35 41 15 29 na 4 na TR14 12 71 45 50 67 38 49 77 19 34 TR15 10 63 40 46 60 33 41 48 13 24 TR16 17 79 51 59 76 37 53 120 27 50 TR17 na 50 32 36 46 26 34 26 na na TR18 na 63 40 48 62 30 38 49 na na TR19 na 64 45 51 64 32 43 59 na na TR20 na 76 51 54 74 33 52 na 10 na TR21 16 90 56 65 95 37 60 194 44 90 TR22 10 70 44 53 63 24 47 82 17 34 TR23 13 78 54 59 74 32 54 126 27 54 TR24 21 77 50 60 74 25 48 99 26 51 TR25 19 80 55 64 75 30 54 122 29 45 TR26 12 74 47 56 69 32 48 87 19 38 TR27 8 56 38 43 53 23 40 41 10 21 TR28 13 63 40 48 58 26 42 58 14 23 TR29 9 55 38 40 52 27 38 36 9 16 TR30 9 56 39 42 53 28 41 45 9 19 TR31 8 43 29 33 40 19 29 20 6 9 TR32 8 49 35 39 48 24 35 27 6 12 TR33 5 48 30 33 44 22 34 20 5 10 TR34 6 53 34 39 50 28 37 31 7 15 TR35 10 50 34 30 45 17 34 25 6 12 TR36 11 57 40 43 53 20 39 44 11 21 TR37 9 58 40 44 55 18 40 50 12 22 TR38 11 51 36 39 48 18 36 36 8 17 TR39 11 57 36 42 54 17 35 34 9 16 TR40 9 53 37 40 50 13 35 40 9 20 TR41 9 64 40 50 61 17 40 57 12 28 TR42 8 51 33 38 47 17 34 33 7 15 TR43 9 55 39 42 52 17 39 49 11 20 TR44 8 60 38 43 55 18 40 45 10 23 TR45 10 60 40 45 57 21 41 58 14 25 TR46 9 63 38 43 55 20 37 59 16 26 TR47 11 55 35 41 52 20 35 39 9 19 AP1 18 71 46 55 69 26 45 72 22.36 na AP2 14 62 42 45 57 24 42 46 12.18 na AP3 9 56 39 44 52 20 37 44 11.48 na AP4 11 52 34 37 50 25 35 27 7.82 na AP5 10 59 37 43 58 27 41 40 12.97 na AP6 9 67 46 49 65 31 48 66 19.36 na AP7 11 68 44 47 65 31 47 58 16.31 na AP8 11 60 42 45 57 27 45 48 14.65 na AP9 12 58 39 42 56 26 42 44 12.03 na AP10 18 67 46 53 66 29 44 89 28.67 na AP11 8 51 37 37 48 27 37 35 7.01 na AP12 5 36 21 24 33 20 23 7 1.66 na AP13 8 64 40 48 57 20 41 55 14 20

AP14 10 43 31 32 43 14 33 24 7 11 AP24 na 44 32 35 43 10 32 33 na na AP25 na 60 43 47 54 18 40 51 na na AP26 na 55 39 43 50 14 39 47 na na AP27 11 48 37 41 47 20 36 28 5 14 AP28 10 46 30 38 44 32 14 18 6 8 AP29 12 na na na na na na 45 8 20 AP30 7 47 33 39 45 32 14 na 7.79 na AP31 7 41 31 31 38 14 28 16 5 8 AP32 12 68 43 50 59 20 43 51 13 25 AP33 7 47 30 37 45 13 33 21 4 13 AP34 11 66 44 50 61 18 46 62 18 29 AP35 6 37 24 28 33 12 25 12 3 6 AP36 13 45 30 35 43 18 34 26 8 11 AP37 12 62 42 47 61 31 42 50 16 23 AP38 15 74 48 58 70 32 48 88 25 43 AP39 11 65 43 50 60 29 43 70 16 30 AP40 14 74 49 53 69 39 52 86 23 41 AP41 6 62 43 48 60 30 43 52 14 22 AP42 20 68 48 55 66 32 47 84 28 35 AP43 10 68 32 51 69 43 50 67 16 30 AP44 9 49 25 38 48 36 39 32 12 13 AP45 12 66 41 50 63 31 44 64 16 29 AP46 13 72 47 57 72 32 45 86 24 35 AP47 11 67 45 51 66 34 47 54 16 26 AP48 12 48 34 38 46 17 33 30 8 14 AP49 9 45 32 35 42 15 28 23 5 12 AP50 6 49 35 39 45 17 35 25 6 11 AP51 8 45 33 32 41 14 32 25 6 12 AP52 7 43 32 35 41 15 29 28 6 10 AP53 10 48 38 29.5 47 16 36 35 8 16 AP54 15 50 32 35 43 18 33 24 7 11 AP55 7 39 28 32 40 15 28 18 4 8 WW1 na 59 38 41 56 26 42 34 na 14 WW2 11 55 37 40 55 27 40 35 na 9 WW3 15 54 39 41 53 24 41 33 na 9 WW4 15 75 51 53 73 34 56 82 na 23 WW5 16 60 37 40 58 27 39 37 na 12 WW6 10 61 41 42 60 27 45 55 na 8 WW7 18 60 43 41 59 26 39 42 na 12 WW8 12 56 36 38 55 26 40 37 na 11 WW9 8 49 32 35 47 24 34 21 na na WW10 16 68 44 46 65 30 49 58 na 16 WW11 11 61 40 45 60 26 42 44 na 12 WW12 8 56 34 39 54 26 38 28 na 8 WW13 13 61 42 44 59 25 43 54 na 16 WW14 10 56 38 41 55 23 40 42 na 11 WW15 8 51 34 38 50 25 36 25 na 7

WW16 18 75 50 55 76 28 52 103 na 29 WW17 17 74 47 58 69 29 47 na na na WW18 20 67 50 53 61 27 47 na na 17 WW19 20 70 55 59 66 28 54 na na na WW20 13 61 44 46 58 26 42 na na 15 WW22 25 71 51 50 60 26 52 na na 32 WW23 17 65 50 50 60 26 50 na na 19 WW24 9 50 32 34 45 15 34 na na 8 WW25 13 47 30 29 40 na na na na 4 WW26 na 74 53 58 58 24 55 na na na WW27 12 59 43 42 54 26 40 na na 11 WW28 24 na na na na na na na na 27 WW29 18 66 46 50 61 23.5 48 na na 15 WW30 9 48 32 33 44 12 33 25 10 6 WW31 na 64 39 38 59 25 45 60 23 16 WW32 na 45 32 30 41 14 32 57 22 5 WW33 12 61 44 41.5 52 20 42 21 7 14 WW34 17 59 38 44 54 23 38 40 15 9 WW35 7 51 36 41 51 22 34 34 14 7 WW36 11 62 41 44 58 24 43 48 19 15 WW37 9 54 36 41 52 14 37 35 17 9 WW38 12 57 42 45 55 17 39 48 19 13 WW39 12 56 39 42 54 20 39 43 19 11 WW40 9 53 34 39 51 19 35 39 15 8 WW41 11 63 41 45 60 23 43 54 24 13 WW42 11 52 36 42 49 20 37 33 17 8 WW43 10 47 30 34 44 18 32 22 10 5 WW44 13 60 41 47 58 15 41 46 21 14 AUP1 15 69 44 52 68 29 44 62 na 16 AUP2 10 68 42 48 66 28 46 63 na 15 AUP3 12 53 25 40 53 21 38 31 na 9 AUP4 9 55 35 40 54 22 36 30 na 8 AUP5 8 45 29 34 43 20 30 1 na 4 AUP6 17 68 45 50 67 31 46 69 na 20 AUP7 11 59 40 42 58 24 43 46 na 11 AUP8 14 65 43 49 65 26 45 77 na 20 AUP9 11 66 41 48 64 27 43 50 na 14 AUP10 16 82 52 57 81 37 57 107 na 28 AUP11 na 73 51 57 69 24 56 na na 10 AUP13 12 62 39 40 55 18 38 na na 6 AUP14 23 76 50 60 73 24 51 na na 36 AUP15 21 57 40 44 55 17 38 na na 13 AUP17 9 40 28 30 38 14 26 na na 4 AUP19 na 39 27 30 37 13 30 na na 5 AUP22 8 34 25 27 32 12 25 na na 1 AUP23 9 48 36 40 47 12 35 na na 8 AUP24 na 60 40 45 44 17 39 49 na na AUP25 na 48 35 37 48 19 34 30 na na

AUP26 na 55 37 44 53 17 35 37 na na AUP27 14 64 43 50 62 22 45 70 34 18 AUP28 16 70 50 56 70 25 50 105 48 25 AUP29 12 58 39 43 54 20 41 55 22 11 AUP30 11 52 35 37 50 17 35 36 15 7 AUP31 8 54 35 37 51 17 34 38 18 8 AUP32 7 50 32 38 46 21 30 28 13 6 AUP33 8 50 35 38 47 18 35 38 18 8 AUP34 7 52 33 39 47 18 33 30 12 6 AUP35 11 57 38 41 55 19 38 42 17 9 AUP36 13 60 40 48 55 20 39 51 22 13 AUP37 6 55 36 40 53 27 36 37 16 7 AUP38 10 50 37 41 50 22 36 35 15 7 AUP39 11 59 40 45 56 29 44 48 23 11 AUP40 11 43 32 35 42 22 32 25 11 6 AUP41 9 63 45 53 62 32 44 62 27 14 AUP42 10 63 40 45 59 30 43 48 22 13 AUP43 13 61 39 43 59 26 44 47 24 9 AUP44 5 30 21 22 27 14 21 5 3 1 AUP45 5 35 23 25 33 16 25 8 3 2 AUP46 3 29 18 20 28 14 20 5 2 1 AUP47 9 48 33 33 45 15 32 26 12 8 AUP48 8 60 40 45 55 19 37 44 21 7 AUP49 6 52 35 40 50 21 34 33 16 na AUP50 10 56 39 45 55 17 35 40 19 11 AUP51 9 61 40 40 57 20 42 44 20 10 AUP52 9 45 30 32 42 20 32 18 10 5 AUP53 9 50 32 34 46 19 33 30 13 6 AUP54 6 48 30 33 44 16 33 24 11 6 AUP55 9 39 27 30 39 15 28 28 13 5 AUP56 8 49 34 38 44 13 32 31 14 6 KIT1 16 43 33 34 44 19 34 27 na 8 KIT2 14 46 34 33 48 21 34 33 na 9 KIT3 4 38 26 29 88 17 28 18 na 4 KIT4 11 42 33 34 44 28 34 27 na 7 KIT5 11 40 29 32 41 11 31 24 na 7 KIT6 23 52 39 41 53 23 42 55 na 15 KIT7 16 60 47 48 63 26 50 86 na 22 KIT8 7 35 22 24 35 14 23 12 na 3 KIT9 15 71 47 48 68 33 54 77 na 24 KIT10 11 41 34 35 43 20 35 31 na 9 KIT11 13 70 44 50 66 30 55 31 10 KIT12 17 65 44 50 63 23 48 61 26 19 KIT13 12 50 38 40 50 22 36 34 14 10 KIT14 na 40 27 27 35 24 30 13 7 2 KIT15 11 40 32 34 43 20 31 25 13 5 KIT16 17 45 40 45 52 25 35 40 20 12 KIT17 19 54 37 42 56 23 39 70 26 13

100

KIT18 15 57 41 44 61 28 45 59 26 15 KIT19 17 51 40 40 49 20 40 51 19 11 KIT20 13 44 31 33 46 21 32 29 15 8 KIT21 12 48 35 32 49 21 36 33 14 7 KIT22 17 56 41 42 52 24 38 40 17 10 KIT23 13 40 32 35 44 20 31 26 15 6 KIT24 18 62 48 50 66 31 45 79 36 16 KIT25 25 58 45 51 64 27 45 74 33 24 KIT26 na 59 44 47 56 30 43 60 na na KIT27 na 47 35 38 51 22 35 40 na na KIT28 na 36 28 29 38 21 28 19 na na KIT29 17 60 52 54 54 24 50 91 26 21 KIT30 11 45 34 37 46 17 35 54 24 9 KIT31 16 55 41 45 58 21 40 78 39 18 KIT32 16 50 39 37 50 19 38 66 31 15 KIT33 16 47 40 43 50 20 41 61 31 12 KIT34 19 54 43 50 57 24 41 76 32 20 KIT35 19 50 42 40 49 20 41 52 17 14 KIT36 5 30 24 24 30 13 23 12 6 2 KIT37 10 33 25 25 34 14 23 17 9 3 KIT38 8 34 26 27 36 12 22 19 10 3 KIT39 na 34 26 30 37 14 25 17 11 4 KIT40 9 32 23 26 32 13 23 13 8 2 KIT41 11 41 32 35 45 15 30 34 16 6 KIT42 17 60 40 43 55 20 40 60 23 17 KIT43 11 45 34 35 42 15 35 27 13 6 KIT44 13 45 33 37 45 15 30 42 19 8 KIT45 16 47 40 40 50 15 34 52 20 11 KIT46 16 57 38 43 52 20 38 55 20 15 KIT47 10 40 27 31 39 13 27 28 14 5 KIT48 17 52 37 44 54 15 35 59 25 15 KIT49 12 47 31 35 44 15 33 30 13 6 KIT50 11 35 27 31 40 10 26 31 13 5

Table A3. Raw values of stable carbon (δ13C) and oxygen (δ18O) isotopes. ID represents individuals at each site where MI is Monument Island, TR is Tundra Ridge, AP is Apex, WW is the wastewater treatment plant, AUP is Aupalajat and KIT is Kituriaqannigituq

ID δ13C (‰ VPDB) δ18O (‰ VPDB) AP10 1.042078 1.431014 AP10 1.007042 1.978335 AP10 1.143017 1.772947 AP10 1.75038 1.844579 AP10 1.292291 1.559362 AP10 2.052362 2.08898 AP10 2.189368 1.77543 AP10 1.877115 1.798354 AP10 1.784005 1.902114

101

AP10 1.926654 1.752949 AP10 1.832517 1.606078 AP10 1.85196 1.639518 AP10 1.662629 1.588867 AP10 1.505253 1.491828 AP10 1.568941 1.54297 AP10 1.507018 1.771799 AP38 1.856982 1.756191 AP38 1.725539 1.744911 AP38 1.751441 1.816235 AP38 1.481477 1.591114 AP38 1.304503 1.803773 AP38 1.572698 1.761504 AP38 2.028002 1.679162 AP38 1.082965 2.098906 AP38 1.927932 1.803282 MI14 2.0056141 1.3041418 MI14 1.9125685 0.7350195 MI14 1.9687167 1.2688995 MI14 1.7396319 0.8491062 MI14 1.9810693 1.2931593 MI14 1.9823753 1.4196088 MI14 1.7864974 1.8600159 MI14 1.772503 1.7208111 MI14 1.8051566 1.801317 MI14 1.6135779 1.6514546 MI14 1.573525 1.939866 MI14 1.783795 1.7004796 MI22 1.58575 1.455846 MI22 1.86596 1.714843 MI22 1.866282 1.614727 MI22 1.819312 1.405182 MI22 2.067351 1.642928 MI22 1.822207 1.46798 MI22 2.049206 1.667195 MI22 1.887354 1.456174 MI22 1.996574 1.803285 MI22 2.184132 1.540615 MI22 2.386395 2.208955 MI22 2.074428 1.593576 MI22 2.140057 1.845095 MI22 2.124052 1.521268 AUP14 1.40068 1.813611 AUP14 1.348747 1.830335 AUP14 1.294689 1.762127 AUP14 1.273131 1.848207 AUP14 1.293 1.909365 AUP14 1.430991 2.06677

102

AUP14 1.201215 1.73868 AUP14 1.470376 2.18597 AUP14 1.477133 1.974623 AUP14 1.50577 1.656863 AUP14 1.996791 1.455845 AUP14 1.993734 1.839845 AUP14 1.890285 1.660962 AUP14 1.960945 1.34353 AUP14 1.740179 1.569635 AUP14 1.808394 1.404032 AUP14 1.814669 1.431086 AUP14 1.581869 1.255646 AUP14 1.930249 2.0623426 AUP28 1.624114 1.650635 AUP28 1.562828 1.677361 AUP28 1.610538 1.713922 AUP28 1.537976 1.785413 AUP28 1.483446 1.738847 AUP28 1.788267 1.727206 AUP28 1.685963 1.789676 AUP28 2.019094 1.731961 AUP28 1.748199 2.8835603 AUP28 1.476158 2.842133 AUP28 2.370595 1.77869 AUP28 2.250726 2.230573 AUP28 1.976467 1.953639 AUP28 2.33836 2.35994 AUP28 2.009282 2.055788 AUP28 1.786337 2.035785 WW18 1.252458 1.255483 WW18 1.363287 1.195145 WW18 1.282892 1.431088 WW18 0.951015 1.37862 WW18 1.020022 1.404526 WW18 1.495221 1.481589 WW18 1.344226 1.296638 WW18 1.333271 1.338121 WW18 1.261305 0.861971 WW18 1.559691 0.905094 WW18 1.72296 0.952643 WW18 1.776277 1.772373 WW18 1.310108 1.806564 WW18 1.475725 1.630303 WW18 1.26494 1.030034 WW18 1.423769 1.354025 WW18 1.303449 1.343859 WW18 0.940582 1.315984 WW28 2.021374 1.356155

103

WW28 1.404413 1.23056 WW28 1.044835 1.106932 WW28 0.735776 1.103653 WW28 1.004856 0.882303 WW28 1.439647 1.300244 WW28 1.831562 1.545368 WW28 1.339737 1.563076 WW28 1.491773 0.97609 WW28 1.557092 1.273846 WW28 1.25109 1.367305 WW28 1.504322 1.459124 WW28 0.884112 1.758028 WW28 2.014005 1.345826 WW28 1.840411 1.193012 KIT25 1.9990429 1.4751922 KIT25 1.8934221 1.5040496 KIT25 1.728306 1.6378432 KIT25 1.7594855 1.3941946 KIT25 1.7876403 1.3113935 KIT25 1.8212653 1.5955408 KIT25 1.7912442 1.3012278 KIT25 1.6362478 1.5186423 KIT25 1.7553025 1.5925895 KIT25 2.1716723 1.6939184 KIT25 2.2316823 1.5761932 KIT34 1.0745269 1.7672429 KIT34 0.9825864 1.6643595 KIT34 0.9944933 1.8458799 KIT34 1.1397895 1.6578064 KIT34 1.0910357 1.6750083 KIT34 1.115493 1.4199296 KIT34 1.441967 1.7947003 KIT34 1.7343294 1.9517121 KIT34 2.2044583 2.0254343 KIT34 2.0963309 2.0849035 KIT34 2.1940317 1.7236649 KIT34 1.7948292 1.9209127 KIT34 2.3851855 1.8291695 TR1 1.3870373 1.3630417 TR1 1.3937945 1.5384818 TR1 1.5318336 1.2908981 TR1 1.9359761 1.6470251 TR1 1.5719773 1.5713058 TR1 2.2096394 1.8463714 TR1 2.30232 2.0975182 TR1 2.1532426 1.501024 TR1 2.4458141 1.816391 TR1 2.3943571 2.0953885

104

TR1 2.4231588 1.9053491 TR1 1.9505842 2.1307751 TR1 2.2125357 1.9246807 TR24 1.595952 1.6846741 TR24 1.8690703 1.5595102 TR24 1.7074584 1.3329047 TR24 1.8122068 1.6325771 TR24 1.9908101 1.8386715 TR24 1.8167926 1.3373608 TR24 1.7097111 1.707446 TR24 1.7912893 1.2561026 TR24 2.1525185 1.5706505 TR24 1.9932237 1.429923 TR24 2.0872559 1.7954212 TR24 2.0571025 1.7357881 TR24 1.9758461 1.7528261 TR24 2.356609 1.4251721 TR24 2.4434327 1.4530226 TR24 2.2297524 1.5241236 TR24 2.146726 1.1876228

Table A4. Raw and averaged salinity values acquired from three separate studies conducted in Frobisher Bay from 2008/2009 from Spares et al. (2012), 2013 from Bannister et al. (2013) and Fisheries and Oceans Canada (2020) from 2018 and 2019.

Data Set Collection Date Depth Salinity (m) Values (psu) Spares et al. (2012) July – September 2008/2009 1m 12.1 (average) Spares et al. (2012) July – September 2008/2009 1.5m 15.5 (average) Spares et al. (2012) July – September 2008/2009 6.5m 31.9 (average) Spares et al. (2012) July – September 2008/2009 0.5m 28.7 (average) Spares et al. (2012) July – September 2008/2009 1.5m 30.1 (average) Spares et al. (2012) July – September 2008/2009 6.5m 32.1 (average) Spares et al. (2012) July – September 2008/2009 20.0m 32.2 (average) Bannister et al. 2013 June 2013 0m 32.556 Bannister et al. 2013 June 2013 0.5m 32.53 Bannister et al. 2013 June 2013 1m 32.478 Bannister et al. 2013 June 2013 1.5m 32.426 Bannister et al. 2013 June 2013 2m 32.405 Bannister et al. 2013 June 2013 2.5m 32.41 Bannister et al. 2013 June 2013 3m 32.416 Bannister et al. 2013 June 2013 3.5m 32.41 Bannister et al. 2013 July 2013 0m 32.459 Bannister et al. 2013 July 2013 0.5m 32.426 Bannister et al. 2013 July 2013 1m 32.424 Bannister et al. 2013 July 2013 1.5m 32.429 Bannister et al. 2013 July 2013 2m 32.438 Bannister et al. 2013 July 2013 2.5m 32.441

105

Bannister et al. 2013 July 2013 3m 32.439 Bannister et al. 2013 July 2013 3.5m 32.438 Bannister et al. 2013 September 2013 0m 32.771 Bannister et al. 2013 September 2013 0.5m 32.611 Bannister et al. 2013 September 2013 1m 32.452 Bannister et al. 2013 September 2013 1.5m 32.392 Bannister et al. 2013 September 2013 2m 32.354 Bannister et al. 2013 September 2013 2.5m 32.319 Bannister et al. 2013 September 2013 3m 32.301 Bannister et al. 2013 September 2013 3.5m 32.29 Bannister et al. 2013 November 2013 0m 32.715 Bannister et al. 2013 November 2013 0.5m 32.514 Bannister et al. 2013 November 2013 1m 32.455 Bannister et al. 2013 November 2013 1.5m 32.394 Bannister et al. 2013 November 2013 2m 32.361 Bannister et al. 2013 November 2013 2.5m 32.337 Bannister et al. 2013 November 2013 3m 32.312 Bannister et al. 2013 November 2013 3.5m 32.32 Fisheries and Oceans Canada August 3, 2018 1m 29.6908 Fisheries and Oceans Canada August 3, 2018 1m 30.6092 Fisheries and Oceans Canada August 3, 2018 1m 31.1887 Fisheries and Oceans Canada August 14, 2018 1m 30.7216 Fisheries and Oceans Canada August 14, 2018 1m 29.0919 Fisheries and Oceans Canada August 14, 2018 1m 30.5155 Fisheries and Oceans Canada August 17, 2018 1m 29.6586 Fisheries and Oceans Canada August 17, 2018 1m 29.9986 Fisheries and Oceans Canada August 17, 2018 1m 30.9904 Fisheries and Oceans Canada August 22, 2018 1m 25.7011 Fisheries and Oceans Canada August 22, 2018 1m 31.5870 Fisheries and Oceans Canada August 22, 2018 1m 31.0744 Fisheries and Oceans Canada August 31, 2018 1m 30.8989 Fisheries and Oceans Canada August 31, 2018 1m 31.5836 Fisheries and Oceans Canada August 31, 2018 1m 31.3659 Fisheries and Oceans Canada September 6, 2018 1m 31.3700 Fisheries and Oceans Canada September 6, 2018 1m 30.4450 Fisheries and Oceans Canada September 6, 2018 1m 31.2534 Fisheries and Oceans Canada September 15, 2018 1m 30.3414 Fisheries and Oceans Canada September 16, 2018 1m 31.5767 Fisheries and Oceans Canada September 16, 2018 1m 31.8694 Fisheries and Oceans Canada September 17, 2018 1m 31.7344 Fisheries and Oceans Canada September 17, 2018 1m 31.2730 Fisheries and Oceans Canada September 17, 2018 1m 32.0461 Fisheries and Oceans Canada September 25, 2018 1m 32.1634 Fisheries and Oceans Canada September 25, 2018 1m 32.0037 Fisheries and Oceans Canada September 25, 2018 1m 32.0834 Fisheries and Oceans Canada October 2, 2018 1m 32.2043 Fisheries and Oceans Canada October 2, 2018 1m 32.1320 Fisheries and Oceans Canada October 2, 2018 1m 32.1844

106

Fisheries and Oceans Canada October 2, 2018 1m 32.2398 Fisheries and Oceans Canada July 19, 2019 1m 31.2784 Fisheries and Oceans Canada July 19, 2019 1m 31.1405 Fisheries and Oceans Canada July 30, 2019 1m 30.6953 Fisheries and Oceans Canada July 30, 2019 1m 30.5608 Fisheries and Oceans Canada July 30, 2019 1m 30.9143 Fisheries and Oceans Canada August 6, 2019 1m 31.3291 Fisheries and Oceans Canada August 6, 2019 1m 31.4742 Fisheries and Oceans Canada August 6, 2019 1m 31.2092 Fisheries and Oceans Canada August 12, 2019 1m 31.5882 Fisheries and Oceans Canada August 12, 2019 1m 31.1103 Fisheries and Oceans Canada August 12, 2019 1m 31.0679 Fisheries and Oceans Canada August 19, 2019 1m 31.4768 Fisheries and Oceans Canada August 19, 2019 1m 31.1464 Fisheries and Oceans Canada August 19, 2019 1m 31.2788 Fisheries and Oceans Canada August 26, 2019 1m 31.7488 Fisheries and Oceans Canada August 26, 2019 1m 31.7502 Fisheries and Oceans Canada August 26, 2019 1m 31.8020 Fisheries and Oceans Canada September 2, 2019 1m 30.5557 Fisheries and Oceans Canada September 2, 2019 1m 31.6892 Fisheries and Oceans Canada September 2, 2019 1m 31.5706 Fisheries and Oceans Canada September 9, 2019 1m 32.1259 Fisheries and Oceans Canada September 10, 2019 1m 31.1015 Fisheries and Oceans Canada September 10, 2019 1m 32.1029 Fisheries and Oceans Canada September 12, 2019 1m 32.1449 Fisheries and Oceans Canada September 12, 2019 1m 31.9427 Fisheries and Oceans Canada September 12, 2019 1m 31.9947 Fisheries and Oceans Canada September 16, 2019 1m 31.6366 Fisheries and Oceans Canada September 16, 2019 1m 28.9074 Fisheries and Oceans Canada September 16, 2019 1m 30.8813 Fisheries and Oceans Canada September 23, 2019 1m 32.0937 Fisheries and Oceans Canada September 23, 2019 1m 31.9992 Fisheries and Oceans Canada September 23, 2019 1m 29.8791

107

Figure A1. Age (years) distribution of Mya truncata for each sampling location. The median was chosen to measure central tendency due to the skewed data set.

108

Figure A2. Shell length (mm) distribution of Mya truncata for each sampling location. The median was chosen to measure central tendency due to the skewed data set.

109

Figure A3. Supplementary standardized growth index for Mya truncata indicating variation in annual growth unrelated to ontogenetic age for each location from Inner Frobisher Bay. Y-axis unitless and the dashed line in each plot represents an SGI of 1.0 with values above indicating better than expected years of growth and values below the line representing less than expected growth for those years. Lowercase letters denote significant temporal differences using LMM and Tukey post-hoc test (α < 0.05).

110

r = 0.75, P < 0.01 r = 0.71, P < 0.01

Figure A4. Pearson correlation coefficient between the raw water drawn from Iqaluit’ freshwater supply, Lake Geraldine, and trace elements accumulating in bivalve shells over time. Correlation coefficients (r) correspond to shells from the wastewater treatment plant and the p-value denotes the significance of the relationship (α < 0.05).

111

Table A5. Trace element concentrations back calculated and assigned calendar years. Averaged ablation samples represent the geometric mean of 6 to 8 shells. Yr represents year and ID represents individuals at each site where MI is Monument Island, TR is Tundra Ridge, AP is Apex, WW is the wastewater treatment plant, AUP is Aupalajat and KIT is Kituriaqannigituq. Blanks are values below LOD and were eliminated from the dataset.

ID Yr Ba Mg Mn As Sr Mo Ag Cd Sn Hg Pb U Li Na Ti Fe Ni Cu Zn AP1 2019 20.17 152.02 0.73 0.06 1891.50 0.01 0.02 0.02 0.11 0.09 0.04 0.00 0.72 5340.90 0.40 1.48 0.24 0.91 0.69 AP1 2018 16.14 119.15 0.74 0.04 1932.06 0.01 0.02 0.04 0.12 0.07 0.04 0.00 0.67 5223.16 0.92 0.47 0.07 0.72 0.42 AP1 2017 38.03 126.81 0.65 0.05 1927.96 0.01 0.01 0.02 0.11 0.05 0.04 0.00 0.60 5276.98 0.29 0.17 0.11 0.84 0.31 AP1 2016 16.15 118.38 0.77 0.06 1898.05 0.01 0.03 0.02 0.12 0.06 0.04 0.00 0.73 5204.45 0.25 0.31 0.10 0.89 0.42 AP1 2015 6.49 99.13 1.10 0.06 1876.54 0.01 0.02 0.03 0.12 0.06 0.06 0.00 0.64 5238.96 0.24 0.35 0.10 0.86 0.45 AP1 2014 17.81 92.81 1.04 0.06 1938.04 0.01 0.02 0.02 0.12 0.06 0.07 0.00 0.63 4838.26 0.21 0.38 0.20 0.88 0.32 AP1 2013 9.98 81.94 1.16 0.06 1859.50 0.01 0.02 0.03 0.11 0.05 0.07 0.00 0.71 5017.79 0.31 0.23 0.20 0.96 0.61 AP1 2012 4.50 82.81 1.14 0.08 1818.54 0.01 0.02 0.02 0.12 0.06 0.08 0.00 0.54 4854.58 0.25 0.29 0.18 1.04 0.45 AP1 2011 29.85 74.19 1.16 0.08 1892.39 0.02 0.02 0.01 0.10 0.05 0.07 0.00 0.60 5043.56 0.19 0.39 0.10 0.86 0.45 AP1 2010 43.23 69.26 1.18 0.09 2176.01 0.01 0.02 0.02 0.13 0.06 0.04 0.00 0.57 5188.70 0.24 1.36 0.11 0.84 0.43 AP1 2009 49.90 72.81 1.14 0.11 2070.15 0.03 0.01 0.03 0.12 0.07 0.04 0.00 0.54 4621.90 0.27 2.83 0.16 0.70 0.57 AP1 2008 21.90 76.50 0.99 0.09 1787.28 0.05 0.05 0.04 0.13 0.05 0.04 0.00 0.41 5397.38 0.88 AP2 2019 25.97 167.59 0.23 0.07 1902.04 0.01 0.02 0.16 0.08 0.06 0.00 0.72 4291.67 0.82 0.37 0.11 1.66 1.00 AP2 2018 13.85 129.63 0.29 0.06 1583.55 0.01 0.02 0.03 0.10 0.05 0.05 0.00 0.55 4621.72 0.26 0.72 0.18 0.75 0.42 AP2 2017 28.39 100.02 0.30 0.08 1343.13 0.01 0.01 0.04 0.07 0.05 0.05 0.00 0.56 4530.85 0.25 0.18 0.75 0.66 AP2 2016 14.36 86.99 0.39 0.06 1565.35 0.02 0.01 0.08 0.05 0.05 0.00 0.62 4566.84 0.36 0.28 0.15 0.95 0.45 AP2 2015 3.42 91.94 0.39 0.07 1523.70 0.01 0.02 0.02 0.07 0.05 0.05 0.00 0.50 4450.47 0.33 0.61 0.17 1.09 0.49 AP2 2014 19.29 73.93 0.60 0.07 1358.51 0.01 0.01 0.02 0.08 0.06 0.07 0.00 0.66 4318.95 0.28 0.33 0.12 1.12 0.50 AP2 2013 6.65 66.21 0.69 0.08 1645.72 0.01 0.01 0.03 0.12 0.05 0.09 0.00 0.52 4287.59 0.37 1.02 0.13 0.62 0.51 AP2 2012 4.05 65.79 0.51 0.07 2123.06 0.02 0.02 0.02 0.09 0.06 0.07 0.00 0.57 4010.71 0.27 3.83 0.12 0.78 0.44 AP2 2011 21.67 63.50 0.68 0.06 2248.12 0.02 0.01 0.08 0.07 0.07 0.00 0.54 4371.98 0.37 5.49 0.09 0.66 0.57 AP2 2010 19.23 69.08 0.80 0.06 1836.90 0.02 0.01 0.02 0.10 0.05 0.04 0.00 0.47 4704.69 0.43 0.10 0.75 0.41 AP2 2009 29.60 81.35 1.15 0.06 1586.07 0.04 0.01 0.19 0.04 0.08 0.55 5089.40 0.93 1.31 1.00 AP10 2019 48.96 170.05 0.46 0.09 2809.35 0.02 0.06 0.05 0.19 0.13 0.11 0.00 0.50 4223.67 0.51 1.37 0.70 2.73 1.85 AP10 2018 67.06 156.85 0.54 0.09 2898.35 0.01 0.04 0.04 0.14 0.06 0.08 0.00 0.72 4119.66 0.27 0.75 0.26 2.09 0.66 AP10 2017 44.40 147.52 0.51 0.09 2535.48 0.01 0.03 0.03 0.13 0.06 0.08 0.00 0.60 4341.89 0.26 0.65 0.21 1.68 0.53 AP10 2016 28.98 135.34 0.47 0.09 2714.18 0.02 0.02 0.02 0.15 0.07 0.11 0.00 0.59 4084.59 0.28 0.46 0.19 2.00 1.13 AP10 2015 23.26 141.83 0.60 0.11 2515.33 0.02 0.04 0.03 0.14 0.08 0.08 0.00 0.50 4529.20 0.30 0.43 0.19 1.58 0.44 AP10 2014 18.02 123.32 0.96 0.11 1917.62 0.02 0.03 0.05 0.13 0.08 0.08 0.00 0.63 4565.96 0.36 0.53 0.28 1.63 0.84 AP10 2013 6.63 121.32 0.70 0.11 1974.19 0.01 0.06 0.08 0.15 0.10 0.07 0.00 0.48 4323.83 3.33 0.96 0.26 1.55 0.49 AP10 2012 40.81 111.65 0.63 0.08 2013.76 0.01 0.03 0.04 0.14 0.09 0.06 0.00 0.60 4660.15 0.52 0.80 0.28 1.50 0.98 AP10 2011 37.96 108.00 0.58 0.09 2086.12 0.01 0.04 0.04 0.14 0.07 0.06 0.00 0.53 4662.23 0.34 0.44 0.23 1.67 0.57 AP10 2010 41.79 99.86 0.56 0.11 2004.68 0.01 0.03 0.02 0.14 0.07 0.06 0.00 0.52 4437.13 0.26 0.41 0.25 1.75 0.65

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AP10 2009 39.68 96.59 0.80 0.12 1898.43 0.02 0.04 0.02 0.15 0.07 0.06 0.00 0.53 4346.15 0.33 0.40 0.28 2.38 0.60 AP10 2008 18.23 93.03 0.84 0.10 1744.33 0.02 0.04 0.03 0.18 0.08 0.06 0.00 0.66 4464.47 0.39 0.43 0.31 2.10 0.68 AP10 2007 22.42 91.13 0.61 0.10 1726.20 0.01 0.03 0.02 0.18 0.06 0.06 0.00 0.63 4598.28 0.88 0.90 0.24 2.40 0.88 AP10 2006 12.73 74.50 0.67 0.10 1742.27 0.01 0.04 0.02 0.17 0.05 0.05 0.00 0.57 4398.40 0.29 2.44 0.21 2.13 0.85 AP10 2005 9.42 82.85 0.67 0.10 1634.68 0.01 0.03 0.03 0.15 0.06 0.06 0.00 0.55 4283.93 0.21 2.74 0.22 1.45 0.59 AP10 2004 6.92 98.07 0.66 0.10 1639.35 0.02 0.02 0.03 0.11 0.06 0.04 0.00 0.56 4902.66 0.37 5.89 0.12 1.07 0.43 AP10 2003 38.56 107.33 0.87 0.10 1484.66 0.01 0.03 0.02 0.10 0.05 0.05 0.49 5396.60 0.56 0.30 1.12 0.98 AP26 2019 24.63 126.71 1.19 0.06 2455.80 0.01 0.03 0.02 0.10 0.11 0.00 0.75 4712.80 0.74 2.18 0.14 3.24 0.61 AP26 2018 14.13 108.00 1.05 0.07 2648.79 0.02 0.02 0.01 0.16 0.08 0.11 0.00 0.63 4619.91 0.61 0.84 0.16 1.59 0.44 AP26 2017 36.37 100.13 1.39 0.06 2796.61 0.01 0.02 0.01 0.11 0.06 0.11 0.00 0.70 4562.08 0.36 0.55 0.09 1.55 0.65 AP26 2016 20.84 84.65 1.37 0.04 2882.03 0.02 0.02 0.02 0.13 0.06 0.14 0.00 0.55 4350.37 0.75 0.59 0.21 1.52 0.50 AP26 2015 8.46 85.76 1.30 0.05 2515.53 0.01 0.02 0.03 0.12 0.06 0.09 0.00 0.77 4222.02 0.28 0.80 0.08 1.76 0.47 AP26 2014 17.89 75.89 1.31 0.05 2451.16 0.02 0.02 0.03 0.12 0.07 0.07 0.00 0.56 4422.53 0.40 0.75 0.12 1.58 0.42 AP26 2013 6.06 81.02 1.04 0.07 2381.98 0.03 0.02 0.02 0.10 0.06 0.07 0.00 0.48 4738.25 0.28 0.79 0.10 1.56 0.36 AP26 2012 3.81 78.46 1.08 0.06 2247.09 0.03 0.01 0.01 0.14 0.06 0.06 0.00 0.49 4423.81 0.44 2.12 0.13 6.08 0.32 AP26 2011 19.23 99.86 1.22 0.10 2413.53 0.02 0.02 0.01 0.17 0.06 0.06 0.00 0.59 4826.44 0.67 0.22 6.63 0.46 AP26 2010 17.05 138.98 1.32 2159.71 0.05 0.02 0.03 0.04 AP38 2019 25.67 146.11 0.37 0.07 2087.31 0.01 0.02 0.01 0.18 0.06 0.05 0.00 0.63 4747.30 0.82 1.39 0.41 2.00 1.07 AP38 2018 36.74 140.17 0.41 0.08 2283.56 0.02 0.02 0.02 0.07 0.05 0.04 0.00 0.61 4635.54 0.22 0.48 0.20 1.17 0.54 AP38 2017 49.59 145.96 0.43 0.06 2356.26 0.02 0.02 0.02 0.09 0.05 0.04 0.00 0.59 4869.31 0.48 0.29 0.17 1.27 0.40 AP38 2016 33.64 124.27 0.50 0.06 2259.84 0.01 0.01 0.02 0.11 0.06 0.04 0.00 0.61 4401.68 0.33 0.44 0.48 1.50 0.42 AP38 2015 10.72 105.08 0.56 0.09 2232.73 0.02 0.02 0.02 0.10 0.06 0.04 0.00 0.57 4637.51 0.27 0.56 0.34 1.48 0.49 AP38 2014 29.46 88.61 0.67 0.09 1970.22 0.01 0.03 0.03 0.13 0.07 0.05 0.00 0.59 4666.42 0.26 0.47 0.20 2.26 0.58 AP38 2013 11.97 99.55 0.67 0.06 2013.40 0.02 0.02 0.02 0.12 0.06 0.05 0.00 0.70 4407.43 0.25 0.45 0.20 2.00 0.78 AP38 2012 4.20 90.47 0.45 0.08 1926.61 0.01 0.02 0.01 0.15 0.08 0.04 0.00 0.61 4370.45 0.49 0.41 0.14 1.92 0.53 AP38 2011 29.09 78.39 0.53 0.08 1624.65 0.01 0.02 0.02 0.12 0.07 0.04 0.00 0.61 4499.82 0.38 0.32 0.18 2.30 0.55 AP38 2010 25.56 79.82 0.54 0.09 1714.84 0.01 0.02 0.02 0.12 0.07 0.03 0.00 0.51 4479.03 0.31 0.32 0.18 1.56 0.55 AP38 2009 35.48 80.75 0.55 0.09 1403.19 0.02 0.02 0.02 0.15 0.07 0.03 0.00 0.54 4332.11 0.33 0.26 0.18 1.55 0.47 AP38 2008 40.16 89.36 0.72 0.09 1793.70 0.01 0.02 0.03 0.12 0.08 0.03 0.00 0.61 4296.98 0.29 1.63 0.17 1.64 0.55 AP38 2007 53.77 95.99 0.98 0.10 2153.92 0.03 0.02 0.02 0.18 0.09 0.04 0.00 0.64 4471.05 0.71 4.46 0.19 1.44 0.72 AP40 2019 71.51 1.02 0.04 3815.32 0.02 0.02 0.04 0.13 0.16 0.04 0.00 0.47 5519.27 0.19 0.92 0.12 1.34 0.50 AP40 2018 21.95 160.21 0.72 0.07 2984.62 0.01 0.03 0.14 0.13 0.05 0.00 0.50 5618.71 0.16 0.38 0.06 0.67 0.14 AP40 2017 100.1 119.00 0.79 0.06 3209.67 0.03 0.02 0.03 0.12 0.11 0.05 0.00 0.39 5223.89 0.19 0.08 0.05 0.72 0.18 AP40 2016 34.39 124.58 1.04 0.06 3610.39 0.02 0.03 0.13 0.09 0.07 0.00 0.43 4504.98 0.37 0.59 0.12 1.55 0.12 AP40 2015 25.70 110.51 1.28 0.06 3409.56 0.04 0.03 0.02 0.35 0.09 0.13 0.39 4422.54 0.79 0.76 0.16 2.53 0.18 AP40 2014 21.76 104.98 1.55 0.06 3266.13 0.03 0.02 0.05 0.19 0.12 0.13 0.00 0.44 4990.64 0.19 0.16 0.08 1.65 0.22 AP40 2013 8.60 92.14 1.32 0.07 3166.56 0.03 0.04 0.20 0.09 0.10 0.00 0.42 5185.59 0.20 0.40 0.08 1.74 0.23 AP40 2012 55.76 79.29 1.19 0.07 2874.41 0.02 0.02 0.04 0.15 0.09 0.12 0.00 0.42 5517.59 0.13 0.11 0.11 1.29 0.17 AP40 2011 84.18 80.70 1.35 0.08 3117.08 0.03 0.03 0.13 0.11 0.08 0.00 0.45 5440.41 0.13 0.10 0.14 0.73 0.26

113

AP40 2010 95.46 70.48 1.07 0.08 2793.35 0.03 0.03 0.04 0.10 0.10 0.06 0.00 0.34 5404.19 0.12 0.08 0.06 0.59 0.22 AP40 2009 52.71 72.41 1.24 0.13 2662.50 0.03 0.02 0.15 0.10 0.04 0.00 0.43 5450.43 0.16 0.49 0.09 0.79 0.18 AP40 2008 12.86 105.29 1.49 0.03 0.03 0.05 0.24 0.07 0.05 0.27 6514.17 0.37 0.19 1.11 0.38 AP42 2019 61.97 132.10 1.08 0.05 2607.17 0.02 0.05 0.03 0.11 0.08 0.00 0.50 4308.04 0.45 0.93 0.44 2.29 1.19 AP42 2018 23.93 113.86 0.86 0.05 2405.97 0.02 0.03 0.04 0.13 0.11 0.08 0.00 0.44 4126.66 0.21 1.13 0.29 2.65 0.48 AP42 2017 13.85 101.28 0.82 0.05 2430.04 0.02 0.03 0.03 0.11 0.09 0.11 0.00 0.49 4031.16 0.24 0.63 0.25 1.49 0.41 AP42 2016 20.42 95.99 1.33 0.06 2216.80 0.02 0.03 0.03 0.12 0.06 0.09 0.00 0.48 4242.89 0.32 0.68 0.20 2.04 0.85 AP42 2015 36.14 94.40 0.97 0.09 1752.92 0.02 0.02 0.03 0.10 0.06 0.09 0.00 0.54 4400.84 0.81 0.77 0.19 1.32 0.77 AP42 2014 43.82 82.05 1.15 0.07 2182.58 0.02 0.04 0.03 0.10 0.07 0.07 0.00 0.43 4599.45 0.38 0.62 0.25 0.96 0.58 AP42 2013 53.44 80.55 1.01 0.05 2039.45 0.02 0.05 0.03 0.14 0.07 0.07 0.00 0.38 4465.47 0.22 0.51 0.38 1.38 0.73 AP42 2012 47.71 94.13 1.04 0.10 1890.89 0.03 0.08 0.10 0.14 0.11 0.07 0.00 0.41 4286.00 0.56 0.57 0.45 1.15 0.58 AP42 2011 29.30 77.87 1.15 0.07 1874.65 0.02 0.03 0.03 0.11 0.07 0.06 0.00 0.56 4547.64 0.47 0.56 0.26 0.78 0.37 AP42 2010 38.60 80.41 1.06 0.07 1839.78 0.02 0.03 0.04 0.10 0.08 0.05 0.00 0.65 4769.66 0.25 0.51 0.16 0.67 0.67 AP42 2009 16.63 76.10 1.06 0.08 1782.77 0.02 0.03 0.03 0.11 0.07 0.05 0.00 0.55 4687.51 0.25 0.67 0.21 0.73 0.56 AP42 2008 15.97 77.73 1.23 0.08 1813.21 0.02 0.02 0.04 0.11 0.07 0.06 0.00 0.55 4637.34 0.46 1.98 0.21 0.75 0.45 AP42 2007 7.18 77.81 1.11 0.09 1868.91 0.02 0.03 0.05 0.10 0.06 0.08 0.00 0.53 4522.81 0.37 3.08 0.24 0.83 0.55 AP42 2006 17.89 80.07 1.34 0.07 2011.46 0.03 0.04 0.03 0.10 0.07 0.09 0.00 0.51 4364.47 0.29 3.86 0.21 0.87 0.69 AP42 2005 28.95 75.59 1.47 0.07 2104.32 0.04 0.04 0.04 0.11 0.06 0.06 0.00 0.65 4289.45 0.33 3.29 0.20 0.90 0.65 AP42 2004 16.70 82.28 1.35 0.06 1853.84 0.03 0.03 0.04 0.11 0.06 0.06 0.00 0.50 4209.91 0.23 3.97 0.18 0.93 0.74 AP42 2003 25.27 102.23 1.47 0.06 1894.93 0.03 0.03 0.03 0.12 0.06 0.06 0.53 4307.62 0.32 6.04 0.18 0.86 0.85 AP46 2019 8.96 1143.60 1.61 0.17 761.00 0.03 0.15 0.32 0.41 0.28 0.51 0.01 2.50 3915.09 12.25 11.04 1.71 6.99 13.77 AP46 2018 10.21 975.70 1.73 0.13 792.18 0.04 0.28 0.25 0.17 0.38 0.01 1.98 3152.12 7.22 7.43 0.69 4.14 6.46 AP46 2017 17.59 978.96 1.58 0.15 732.30 0.03 0.11 0.25 0.29 0.17 0.42 0.01 2.16 3019.92 6.39 4.93 0.71 4.17 5.69 AP46 2016 11.73 855.69 1.84 0.11 758.10 0.03 0.14 0.12 0.25 0.13 0.12 0.01 1.53 2698.75 2.79 3.06 0.46 1.98 3.14 AP46 2015 4.32 512.29 1.56 0.10 1052.96 0.02 0.07 0.13 0.19 0.11 0.16 0.01 1.32 3547.63 3.35 1.78 0.41 1.99 4.48 AP46 2014 19.51 58.42 1.13 0.08 1935.78 0.01 0.04 0.06 0.12 0.10 0.11 0.00 0.77 4718.04 1.11 1.44 0.28 1.63 1.43 AP46 2013 10.32 51.44 1.39 0.08 1911.67 0.01 0.03 0.06 0.10 0.10 0.08 0.00 0.71 4575.32 0.64 0.94 0.28 2.10 1.67 AP46 2012 4.14 59.05 1.23 0.07 1813.26 0.02 0.05 0.06 0.13 0.10 0.09 0.00 0.77 4599.94 1.46 1.71 0.16 3.21 1.31 AP46 2011 35.54 80.65 1.32 0.09 1896.58 0.02 0.05 0.05 0.17 0.07 0.09 0.00 0.60 5021.96 1.26 1.40 0.17 2.06 1.93 AP46 2010 37.42 215.16 2.30 0.24 1565.37 0.04 0.12 0.10 0.03 0.72 5519.44 AUP1 2019 72.58 146.45 0.93 0.07 3126.14 0.01 0.03 0.04 0.13 0.21 0.11 0.00 0.46 4593.79 0.56 1.45 0.19 1.83 0.71 AUP1 2018 46.39 132.20 0.70 0.07 2596.23 0.02 0.05 0.10 0.13 0.10 0.00 0.63 4223.28 0.49 0.69 0.17 1.88 0.81 AUP1 2017 18.86 119.00 0.84 0.09 2475.82 0.02 0.06 0.03 0.10 0.11 0.12 0.00 0.47 4030.85 0.46 1.02 0.13 2.07 0.51 AUP1 2016 17.71 140.77 0.73 0.07 2150.20 0.02 0.07 0.06 0.12 0.07 0.11 0.00 0.47 4002.55 0.25 0.80 0.12 2.63 1.01 AUP1 2015 11.79 113.51 1.03 0.08 2150.85 0.02 0.05 0.10 0.07 0.13 0.00 0.56 4102.58 1.21 0.96 0.14 2.36 0.68 AUP1 2014 39.07 94.59 0.86 0.10 1947.02 0.01 0.03 0.03 0.10 0.11 0.10 0.00 0.57 3993.16 0.46 0.48 0.14 2.57 0.80 AUP1 2013 29.57 88.92 0.87 0.09 1812.37 0.01 0.04 0.04 0.10 0.11 0.08 0.00 0.51 3939.20 0.75 0.51 0.23 1.80 0.74 AUP1 2012 51.90 70.65 1.05 0.10 1694.69 0.02 0.03 0.10 0.10 0.10 0.08 0.00 0.58 3913.57 0.42 0.57 0.26 1.75 0.86 AUP1 2011 32.50 87.24 1.08 0.09 1566.99 0.02 0.02 0.07 0.12 0.11 0.07 0.00 0.54 3973.77 0.67 1.13 0.20 1.43 0.70

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AUP1 2010 23.06 94.35 1.17 0.13 1445.68 0.02 0.06 0.11 0.17 0.07 0.12 0.01 0.42 4674.09 1.06 2.79 0.28 2.08 3.70 AUP6 2019 95.76 1.59 0.07 3046.41 0.02 0.02 0.03 0.13 0.08 0.10 0.00 0.60 4197.24 0.37 1.01 0.25 5.41 0.85 AUP6 2018 62.31 167.82 1.33 0.10 2599.07 0.01 0.01 0.08 0.06 0.07 0.00 0.63 4345.01 0.57 0.58 0.17 3.91 0.47 AUP6 2017 37.08 149.25 1.20 0.11 2830.78 0.02 0.03 0.03 0.08 0.07 0.10 0.61 3690.37 3.30 0.51 0.24 3.95 0.50 AUP8 2019 30.91 183.52 1.54 0.05 2016.30 0.02 0.03 0.05 0.11 0.09 0.08 0.00 0.57 4837.39 0.43 0.84 0.11 0.83 0.81 AUP8 2018 14.42 159.71 1.01 0.07 1697.24 0.01 0.03 0.08 0.09 0.10 0.07 0.00 0.68 4229.28 0.47 0.12 0.43 0.75 AUP8 2017 18.43 153.27 1.29 0.03 2192.27 0.01 0.04 0.08 0.10 0.11 0.00 0.60 4160.61 0.34 0.29 0.09 0.46 0.87 AUP8 2016 10.98 141.26 1.39 0.04 2313.97 0.01 0.04 0.04 0.08 0.06 0.10 0.00 0.69 4176.48 0.43 0.36 0.15 0.56 0.66 AUP8 2015 9.26 116.54 1.61 0.05 2352.03 0.01 0.04 0.03 0.08 0.06 0.09 0.00 0.49 4110.79 1.00 0.48 0.11 0.40 0.39 AUP8 2014 32.43 89.98 1.40 0.04 2159.54 0.02 0.04 0.05 0.08 0.05 0.10 0.00 0.54 4375.00 1.33 0.44 0.08 0.47 0.53 AUP8 2013 35.51 73.78 1.61 0.03 2159.32 0.02 0.03 0.04 0.08 0.06 0.08 0.00 0.46 4404.23 0.46 0.31 0.09 0.42 0.54 AUP8 2012 52.91 80.75 1.83 0.03 2103.84 0.03 0.03 0.07 0.13 0.05 0.09 0.00 0.52 4264.13 0.50 0.69 0.13 0.38 0.55 AUP8 2011 41.58 78.97 2.52 0.04 2285.89 0.02 0.06 0.14 0.04 0.08 0.00 0.51 4322.57 0.46 3.80 0.10 0.45 0.54 AUP8 2010 21.83 78.41 2.43 0.08 2187.07 0.01 0.05 0.14 0.04 0.10 0.00 0.51 4234.43 0.30 16.36 0.11 0.50 0.53 AUP8 2009 22.56 82.53 1.90 0.11 2035.35 0.02 0.03 0.11 0.04 0.06 0.00 0.45 4560.72 0.28 20.59 0.09 0.47 0.63 AUP8 2008 9.97 94.80 1.24 0.06 1835.31 0.02 0.03 0.03 0.15 0.04 0.04 0.00 0.42 4703.16 0.42 15.34 0.10 0.79 0.64 AUP8 2007 29.21 106.04 2.27 0.07 1681.17 0.02 0.04 0.17 0.05 0.09 0.48 4968.52 1.52 0.16 0.85 0.34 AUP10 2019 45.51 0.17 0.05 3224.38 0.02 0.06 0.03 0.19 0.09 0.06 0.00 0.47 4027.22 0.41 0.66 0.41 2.34 1.41 AUP10 2018 27.12 141.88 0.42 0.07 2927.15 0.02 0.06 0.17 0.08 0.07 0.00 0.43 4097.00 0.31 0.55 0.27 2.01 1.07 AUP10 2017 15.27 121.24 0.61 0.08 2529.68 0.02 0.05 0.08 0.16 0.08 0.07 0.00 0.40 4112.79 0.30 0.37 0.30 1.95 0.65 AUP10 2016 37.23 113.48 0.54 0.07 2314.67 0.02 0.03 0.05 0.17 0.09 0.09 0.00 0.55 4049.33 0.33 0.35 0.24 1.93 0.71 AUP10 2015 52.43 97.74 0.65 0.08 2403.31 0.02 0.04 0.05 0.18 0.08 0.06 0.00 0.51 4257.89 0.36 0.31 0.18 1.68 0.96 AUP10 2014 49.54 83.30 0.65 0.06 2266.92 0.04 0.06 0.03 0.17 0.08 0.08 0.00 0.65 4276.71 0.30 0.32 0.19 2.19 0.93 AUP10 2013 42.81 89.41 0.98 0.07 2308.95 0.02 0.05 0.04 0.15 0.11 0.06 0.00 0.52 4189.15 0.29 1.81 0.14 1.52 0.43 AUP10 2012 26.72 86.30 0.91 0.06 2101.19 0.02 0.07 0.03 0.15 0.10 0.05 0.00 0.53 4203.92 0.49 15.10 0.16 1.43 0.74 AUP10 2011 29.69 88.83 0.71 0.10 2195.79 0.01 0.05 0.04 0.14 0.10 0.05 0.00 0.60 4548.05 0.44 32.36 0.15 1.29 0.58 AUP10 2010 13.51 81.42 0.73 0.12 1993.58 0.02 0.04 0.02 0.15 0.07 0.05 0.00 0.54 4539.28 0.27 27.63 0.13 1.18 0.61 AUP10 2009 11.78 84.66 0.73 0.08 2103.76 0.01 0.05 0.02 0.16 0.07 0.04 0.01 0.73 4656.87 0.34 16.85 0.16 1.22 0.67 AUP10 2008 7.66 76.93 0.75 0.08 1800.32 0.01 0.05 0.03 0.13 0.06 0.03 0.58 4729.05 0.30 17.35 0.21 1.14 0.61 AUP10 2007 3.54 68.70 0.72 0.10 1394.05 0.02 0.04 0.05 0.15 0.05 0.02 0.39 4700.89 0.74 0.33 1.32 1.18 AUP12 2019 41.23 67.92 0.57 0.07 2113.13 0.03 0.03 0.03 0.24 0.04 0.07 0.00 0.43 3324.98 0.59 5.58 0.11 3.46 0.57 AUP12 2018 39.94 68.98 0.47 0.03 1987.52 0.03 0.01 0.02 0.26 0.04 0.05 0.00 0.25 3196.59 1.99 1.23 0.06 4.13 0.44 AUP12 2017 28.80 72.66 0.52 0.04 2135.92 0.04 0.01 0.02 0.34 0.04 0.08 0.00 0.27 3463.13 1.00 0.91 0.17 3.27 0.44 AUP12 2016 21.48 71.03 0.73 0.04 2059.35 0.03 0.01 0.02 0.49 0.03 0.06 0.00 0.41 3616.07 0.58 1.53 0.38 5.19 0.46 AUP12 2015 14.19 61.36 0.73 0.05 1876.61 0.07 0.01 0.02 0.71 0.03 0.08 0.00 0.52 3693.37 0.47 1.97 0.22 2.86 0.51 AUP12 2014 20.86 63.81 0.51 0.07 2025.15 0.05 0.01 0.01 0.51 0.04 0.06 0.00 0.44 4263.35 0.25 0.25 0.02 1.02 0.67 AUP12 2013 11.67 67.37 0.80 0.06 2105.97 0.03 0.02 0.01 0.26 0.04 0.06 0.00 0.51 4045.91 0.25 0.60 0.02 1.64 1.00 AUP12 2012 11.24 71.64 0.90 0.07 1997.71 0.03 0.02 0.01 0.22 0.04 0.06 0.01 0.53 4392.61 0.37 3.88 0.05 2.09 1.22 AUP12 2011 13.10 80.43 0.93 0.11 1952.22 0.04 0.02 0.03 0.23 0.03 0.08 0.00 0.50 4527.34 1.01 6.55 0.14 1.26 1.12

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AUP12 2010 17.40 86.40 1.21 0.22 1937.88 0.10 0.05 0.05 0.30 0.04 0.22 0.01 0.72 4292.93 0.21 15.39 0.16 1.16 0.71 AUP12 2009 36.30 79.67 0.80 0.19 2030.57 0.01 0.01 0.01 0.24 0.03 0.05 0.01 0.66 4480.69 0.51 116.07 0.12 1.81 1.13 AUP12 2008 14.78 114.16 0.76 0.27 1887.08 0.01 0.01 0.02 0.25 0.03 0.04 0.49 4667.28 0.43 5.20 3.82 AUP14 2019 71.64 250.81 1.00 0.07 3798.61 0.03 0.05 0.07 0.14 0.13 0.11 0.00 0.46 4490.02 0.28 0.68 0.15 1.32 0.85 AUP14 2018 29.02 232.45 0.94 0.04 2984.90 0.03 0.04 0.03 0.11 0.08 0.08 0.00 0.61 4399.35 0.24 0.46 0.27 1.35 0.96 AUP14 2017 18.39 223.18 1.09 0.02 2966.11 0.02 0.05 0.03 0.09 0.07 0.09 0.00 0.52 4678.44 0.23 0.46 0.26 1.24 0.66 AUP14 2016 55.03 204.59 0.92 0.03 2803.47 0.02 0.04 0.03 0.09 0.07 0.07 0.00 0.48 4751.28 0.27 0.38 0.27 1.55 0.58 AUP14 2015 55.76 124.62 1.34 0.04 2669.33 0.02 0.04 0.06 0.09 0.08 0.06 0.00 0.51 4790.74 0.17 0.38 0.30 1.39 0.60 AUP14 2014 28.63 103.32 0.82 0.04 2498.66 0.01 0.04 0.03 0.08 0.08 0.06 0.00 0.63 5019.52 0.21 0.25 0.15 1.25 0.54 AUP14 2013 18.05 86.60 1.08 0.03 2263.14 0.01 0.04 0.08 0.07 0.06 0.00 0.48 4966.38 1.10 0.38 0.14 1.29 0.35 AUP14 2012 16.10 83.57 1.15 0.04 2259.50 0.01 0.04 0.03 0.06 0.08 0.05 0.00 0.67 4578.66 0.20 0.35 0.17 1.09 0.76 AUP14 2011 11.65 74.56 1.27 0.04 2302.33 0.01 0.05 0.04 0.07 0.09 0.06 0.00 0.77 4647.09 3.30 0.93 0.20 1.15 0.48 AUP14 2010 17.96 76.15 1.43 0.05 2505.86 0.02 0.03 0.03 0.11 0.08 0.06 0.00 0.67 4414.19 0.84 2.13 0.32 1.77 0.47 AUP14 2009 19.84 69.77 1.13 0.05 2471.67 0.02 0.05 0.02 0.14 0.09 0.07 0.00 0.77 4437.98 0.22 1.78 0.23 1.87 0.90 AUP14 2008 26.95 67.33 1.40 0.05 2332.90 0.03 0.08 0.04 0.18 0.08 0.07 0.00 0.61 4157.66 2.10 8.18 0.29 2.74 1.38 AUP14 2007 39.67 63.10 1.72 0.12 2283.19 0.04 0.06 0.06 0.19 0.08 0.07 0.00 0.60 4116.10 3.62 3.81 0.39 1.85 0.90 AUP14 2006 19.33 67.34 1.12 0.18 2044.62 0.10 0.06 0.05 0.14 0.08 0.06 0.01 0.48 4162.00 0.69 3.60 0.21 1.37 0.67 AUP14 2005 12.49 94.98 1.21 0.14 1616.20 0.15 0.07 0.05 0.16 0.08 0.05 0.02 0.61 4944.43 1.60 5.06 0.34 1.92 1.71 AUP15 2019 47.07 188.49 0.25 0.04 4299.87 0.04 0.02 0.19 0.11 0.16 0.00 0.35 5457.26 0.11 0.26 0.05 2.93 0.37 AUP15 2018 37.63 170.21 0.39 0.03 3754.99 0.03 0.01 0.18 0.10 0.19 0.00 0.40 6012.99 0.20 0.20 0.07 1.73 0.13 AUP15 2017 9.46 157.21 0.10 0.04 3696.53 0.02 0.03 0.11 0.11 0.18 0.00 0.37 4619.84 0.08 0.20 0.06 1.96 0.13 AUP15 2016 67.75 127.07 0.54 0.05 3658.71 0.02 0.02 0.03 0.10 0.09 0.19 0.00 0.39 5661.51 0.16 0.08 0.08 1.35 0.11 AUP15 2015 91.33 115.00 1.08 0.05 4017.75 0.02 0.02 0.02 0.12 0.07 0.24 0.00 0.26 5113.04 0.16 0.16 0.05 1.42 0.13 AUP15 2014 48.21 94.20 0.40 0.06 3370.58 0.01 0.03 0.17 0.09 0.15 0.00 0.40 5144.28 0.09 0.15 0.08 2.07 0.15 AUP15 2013 45.80 109.01 0.65 0.05 3385.55 0.03 0.02 0.04 0.25 0.07 0.19 0.00 0.32 5333.74 0.09 0.20 0.04 1.46 0.18 AUP15 2012 25.37 95.87 0.10 0.06 3296.50 0.03 0.03 0.12 0.11 0.15 0.00 0.27 5531.64 0.08 0.10 0.05 1.13 0.22 AUP15 2011 21.63 82.80 0.96 0.08 2933.53 0.03 0.02 0.04 0.14 0.06 0.19 0.00 0.36 5938.41 0.12 0.14 0.04 1.13 0.09 AUP15 2010 13.40 69.93 0.63 0.10 2438.96 0.04 0.02 0.02 0.18 0.09 0.17 0.00 0.33 5460.97 0.15 0.16 0.08 1.26 0.14 AUP15 2009 11.75 68.60 1.47 0.10 2776.60 0.04 0.01 0.03 0.13 0.08 0.15 0.00 0.40 5628.69 0.15 0.20 0.06 0.80 0.11 AUP15 2008 28.54 78.85 2.74 0.10 3103.21 0.03 0.02 0.02 0.10 0.08 0.19 0.00 0.36 5938.77 0.11 0.66 0.05 0.67 0.09 AUP15 2007 32.24 89.92 3.04 0.10 3279.45 0.03 0.02 0.11 0.09 0.15 0.00 0.40 5772.63 0.12 4.11 0.04 0.70 0.11 AUP15 2006 20.49 87.01 2.41 0.18 2938.10 0.02 0.01 0.14 0.08 0.14 0.00 0.33 5787.95 0.17 13.39 0.04 0.81 0.10 AUP15 2005 24.08 88.55 2.56 2425.30 0.04 0.02 0.02 0.17 0.07 0.19 0.02 0.45 7339.65 0.52 0.06 0.84 0.16 AUP15 2004 16.22 46.86 0.43 1446.51 0.03 0.02 0.07 0.10 0.00 0.32 9066.38 0.26 TR1 2019 42.55 169.20 0.31 0.07 2906.83 0.01 0.03 0.02 0.15 0.15 0.13 0.00 0.49 4213.42 0.79 0.84 0.14 2.48 0.85 TR1 2018 28.76 152.16 0.57 0.06 3028.60 0.01 0.03 0.02 0.11 0.13 0.11 0.00 0.52 4261.01 0.41 0.32 0.09 1.29 0.42 TR1 2017 24.42 138.67 0.49 0.07 2530.56 0.01 0.03 0.08 0.12 0.09 0.08 0.00 0.49 4517.07 1.58 0.25 0.11 0.95 0.53 TR1 2016 9.22 123.98 0.49 0.08 2268.43 0.01 0.02 0.04 0.10 0.10 0.06 0.00 0.47 4443.31 0.87 0.21 0.13 1.06 0.32 TR1 2015 22.41 110.72 0.42 0.06 1970.01 0.01 0.03 0.03 0.10 0.10 0.07 0.00 0.52 4765.86 0.47 0.17 0.09 1.08 0.30

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TR1 2014 22.01 100.67 0.41 0.05 1972.03 0.01 0.03 0.04 0.09 0.10 0.05 0.00 0.54 4632.41 0.29 0.13 0.09 0.81 0.40 TR1 2013 20.98 87.47 0.31 0.06 1876.68 0.01 0.02 0.02 0.11 0.09 0.05 0.00 0.53 4410.48 0.18 0.16 0.07 0.97 0.38 TR1 2012 29.95 92.33 0.56 0.05 1761.82 0.01 0.03 0.04 0.12 0.09 0.05 0.00 0.69 4804.49 0.22 0.31 0.08 0.92 0.37 TR1 2011 9.04 81.70 0.39 0.05 1594.31 0.01 0.03 0.03 0.14 0.09 0.05 0.00 0.57 4495.80 0.21 0.13 0.10 0.96 0.35 TR1 2010 15.78 85.39 0.36 0.07 1585.90 0.01 0.04 0.04 0.14 0.08 0.04 0.00 0.63 4731.52 0.31 0.16 0.09 1.30 0.35 TR1 2009 5.31 84.84 0.35 0.07 1600.90 0.02 0.02 0.03 0.14 0.08 0.04 0.00 0.54 4829.41 0.19 0.14 0.08 1.32 0.34 TR1 2008 7.75 79.40 0.48 0.08 1529.76 0.02 0.03 0.02 0.12 0.06 0.04 0.00 0.67 4866.13 0.25 0.29 0.06 1.42 0.43 TR1 2007 3.87 85.53 0.37 0.07 1551.02 0.02 0.02 0.02 0.12 0.07 0.03 0.00 0.65 4705.09 0.17 1.40 0.09 1.40 0.24 TR1 2006 9.12 88.82 0.50 0.06 1476.75 0.02 0.02 0.03 0.17 0.06 0.04 0.00 0.58 5321.00 0.39 1.81 0.10 1.35 0.29 TR3 2019 30.57 269.93 0.78 0.07 2384.02 0.04 0.06 0.06 0.38 0.13 0.07 0.00 0.54 4808.23 2.35 2.50 0.59 1.69 0.72 TR3 2018 33.67 244.73 0.80 0.04 2002.16 0.04 0.03 0.03 0.26 0.12 0.06 0.00 0.54 4709.24 0.60 0.57 0.14 1.06 1.00 TR3 2017 13.23 208.21 1.00 0.11 2410.39 0.02 0.09 0.07 0.32 0.08 0.08 0.00 0.43 4195.77 0.26 0.48 0.18 1.24 0.98 TR3 2016 25.16 200.85 1.00 0.09 2054.23 0.02 0.05 0.05 0.21 0.07 0.06 0.00 0.55 4459.65 0.32 0.47 0.14 1.39 0.87 TR3 2015 28.48 164.72 0.82 0.08 1693.22 0.01 0.05 0.05 0.22 0.07 0.05 0.00 0.64 4709.51 0.43 0.58 0.17 1.62 0.90 TR3 2014 35.72 155.68 0.75 0.11 1525.84 0.02 0.06 0.04 0.25 0.05 0.05 0.00 0.57 4801.92 0.37 0.47 0.22 1.60 0.84 TR3 2013 27.78 142.74 0.74 0.10 1702.80 0.02 0.04 0.06 0.16 0.06 0.05 0.00 0.72 4632.91 0.64 0.56 0.30 1.62 1.08 TR3 2012 10.41 129.37 0.89 0.10 1305.22 0.02 0.04 0.05 0.15 0.07 0.04 0.00 0.70 4778.71 0.49 0.41 0.19 1.56 0.67 TR3 2011 15.41 110.92 0.67 0.09 1289.99 0.02 0.06 0.07 0.13 0.07 0.04 0.00 0.80 4796.29 0.53 0.33 0.15 0.89 0.66 TR3 2010 10.64 108.79 0.68 0.10 1261.88 0.04 0.06 0.05 0.14 0.08 0.04 0.00 0.64 4685.08 0.33 0.78 0.12 0.78 0.79 TR3 2009 9.33 101.60 0.68 0.11 1314.87 0.03 0.06 0.04 0.12 0.07 0.04 0.00 0.81 4673.89 0.39 4.02 0.13 0.79 0.64 TR3 2008 8.61 102.02 0.75 0.12 1327.11 0.04 0.04 0.03 0.15 0.06 0.03 0.00 0.70 4914.96 0.49 4.72 0.17 0.82 1.23 TR3 2007 0.66 5369.76 0.24 1.59 2.90 TR10 2019 23.82 90.12 1.03 0.06 2003.30 0.01 0.04 0.32 0.05 0.07 0.00 0.66 5324.47 0.55 1.10 0.13 4.91 2.79 TR10 2018 7.39 84.51 0.84 0.08 1655.13 0.01 0.02 0.06 0.17 0.05 0.07 0.00 0.54 4875.46 4.51 0.71 0.16 4.75 2.89 TR10 2017 19.59 74.26 0.82 0.06 1826.09 0.01 0.05 0.03 0.16 0.06 0.06 0.00 0.65 4718.75 2.96 1.00 0.12 4.20 1.17 TR10 2016 6.28 65.63 0.95 0.07 1942.64 0.01 0.04 0.15 0.06 0.06 0.00 0.60 4446.01 1.41 0.61 0.10 1.36 0.47 TR10 2015 20.10 65.91 1.01 0.07 2152.16 0.01 0.13 0.05 0.06 0.00 0.70 4532.11 4.24 0.72 0.11 2.43 0.71 TR10 2014 27.10 94.54 1.29 0.10 2266.58 0.03 0.05 0.48 0.05 0.09 0.06 0.58 4593.00 1.82 0.53 0.11 1.94 0.80 TR10 2013 16.96 109.27 1.02 0.14 1730.44 0.07 0.09 0.05 0.61 0.05 0.07 0.06 0.60 4704.94 0.89 0.72 0.11 1.44 0.73 TR10 2012 13.95 99.61 0.54 0.09 1457.56 0.10 0.03 0.04 0.38 0.03 0.04 0.00 0.39 5377.56 0.19 3.36 0.25 2.30 2.75 TR10 2011 20.67 122.63 1.00 0.24 1971.58 0.03 0.03 0.37 0.06 0.05 0.01 0.72 5926.86 TR14 2019 34.11 0.50 0.08 1612.26 0.03 0.08 0.07 0.59 0.59 0.07 0.00 0.90 5178.45 3.66 4.06 0.63 5.37 1.28 TR14 2018 7.93 128.46 0.41 0.09 1372.65 0.01 0.03 0.05 0.49 0.30 0.05 0.00 0.97 4711.06 0.97 0.61 0.59 3.89 0.64 TR14 2017 17.92 104.01 0.42 0.08 1324.62 0.01 0.02 0.04 0.39 0.21 0.05 0.00 0.60 4920.45 0.78 0.36 0.42 5.46 0.86 TR14 2016 10.12 95.53 0.62 0.08 1243.40 0.02 0.04 0.03 0.30 0.18 0.07 0.00 0.78 4692.66 14.18 0.76 0.28 5.11 0.76 TR14 2015 4.55 79.27 0.65 0.06 1171.25 0.02 0.02 0.03 0.27 0.20 0.05 0.00 0.63 4580.29 6.10 0.49 0.13 3.61 0.90 TR14 2014 12.82 75.43 0.72 0.09 1322.20 0.01 0.03 0.05 0.15 0.19 0.06 0.00 0.68 4657.30 0.41 0.35 0.21 3.02 1.25 TR14 2013 6.16 75.69 0.74 0.08 1181.74 0.02 0.03 0.06 0.15 0.17 0.05 0.00 0.58 4622.96 0.70 0.35 0.20 2.85 0.63 TR14 2012 2.11 75.56 0.63 0.09 1250.45 0.01 0.03 0.06 0.22 0.16 0.07 0.00 0.64 4524.03 0.54 0.36 0.24 2.80 0.53

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TR14 2011 16.42 71.42 0.75 0.08 1373.89 0.02 0.04 0.04 0.20 0.14 0.07 0.00 0.69 4699.10 4.95 0.44 0.13 2.80 0.49 TR14 2010 17.18 70.85 0.62 0.09 1391.80 0.02 0.03 0.03 0.18 0.12 0.04 0.00 0.68 4783.29 1.29 1.43 0.18 5.62 0.53 TR14 2009 7.04 81.46 0.60 0.08 1266.34 0.03 0.04 0.02 0.54 0.10 0.06 0.00 0.52 5130.72 13.11 5.50 0.39 6.30 1.81 TR16 2019 48.91 179.51 0.66 0.11 2931.66 0.02 0.02 0.04 0.38 0.09 0.09 0.00 0.43 4087.66 3.03 1.38 0.25 8.99 0.86 TR16 2018 55.17 112.41 0.64 0.11 2552.60 0.02 0.03 0.03 0.43 0.06 0.07 0.00 0.41 3392.13 0.90 0.95 0.25 9.45 0.78 TR16 2017 72.31 95.13 0.86 0.10 2616.67 0.02 0.05 0.04 0.44 0.08 0.04 0.00 0.51 4079.49 10.19 1.26 0.14 5.01 0.75 TR16 2016 94.48 89.70 0.99 0.10 2131.11 0.01 0.03 0.16 0.53 0.05 0.04 0.00 0.66 3928.74 0.42 1.14 0.22 6.32 0.67 TR16 2015 75.18 79.70 1.05 0.07 2324.94 0.02 0.03 0.03 0.54 0.04 0.04 0.00 0.46 3992.65 0.39 0.72 0.25 4.58 0.63 TR16 2014 44.66 90.16 0.83 0.11 2109.43 0.02 0.03 0.07 0.47 0.06 0.04 0.00 0.51 4067.04 2.36 0.85 0.13 4.38 0.82 TR16 2013 43.03 83.09 0.75 0.10 1902.45 0.02 0.05 0.09 0.45 0.07 0.04 0.00 0.56 3989.40 0.76 0.79 0.15 3.77 1.09 TR16 2012 15.08 78.94 0.80 0.12 1635.74 0.02 0.03 0.07 0.53 0.05 0.05 0.00 0.71 4503.57 0.77 0.86 0.23 3.17 0.97 TR16 2011 13.01 70.49 0.78 0.09 1431.17 0.03 0.04 0.05 0.43 0.06 0.03 0.00 0.52 4441.68 0.43 1.41 0.17 3.25 0.64 TR16 2010 8.09 75.80 0.75 0.10 1565.48 0.02 0.03 0.08 0.45 0.05 0.04 0.00 0.67 4636.51 0.51 2.73 0.14 2.96 0.70 TR16 2009 16.97 85.76 0.90 0.07 1503.39 0.02 0.03 0.05 0.41 0.06 0.04 0.00 0.66 4707.17 0.58 6.19 0.15 3.47 0.56 TR16 2008 21.59 101.49 0.81 0.08 1750.28 0.07 0.02 0.05 0.35 0.04 0.04 0.00 0.54 4816.08 0.71 21.31 0.12 3.29 0.57 TR16 2007 10.74 118.30 0.78 0.29 1905.28 0.36 0.03 0.03 0.41 0.04 0.04 0.01 0.66 4687.98 1.93 82.65 0.21 4.05 0.78 TR16 2006 29.29 135.54 0.96 0.46 2144.96 0.56 0.03 0.05 0.33 0.03 0.04 0.58 4898.19 3.10 92.18 0.21 2.84 1.03 TR16 2005 21.81 143.16 1.02 0.17 1961.22 0.22 0.03 0.08 0.44 0.04 0.07 0.39 5444.49 0.88 42.93 0.48 5.34 2.24 TR23 2019 27.40 118.37 0.63 0.06 2552.59 0.02 0.02 0.03 0.30 0.20 0.11 0.00 0.68 4569.47 1.69 1.15 0.08 13.27 0.75 TR23 2018 37.88 111.67 0.62 0.05 2571.16 0.01 0.02 0.03 0.23 0.10 0.10 0.00 0.50 4326.30 0.28 0.62 0.07 6.55 1.04 TR23 2017 17.67 90.79 0.68 0.06 1877.51 0.01 0.03 0.02 0.17 0.06 0.08 0.00 0.52 4715.07 1.31 0.43 0.10 2.41 0.90 TR23 2016 8.26 83.21 0.63 0.06 1712.37 0.01 0.03 0.02 0.18 0.07 0.06 0.00 0.60 4887.95 0.20 0.38 0.17 1.34 0.63 TR23 2015 14.69 72.49 0.56 0.06 1877.16 0.01 0.02 0.04 0.22 0.05 0.07 0.00 0.66 4943.52 0.44 1.72 0.09 1.29 0.71 TR23 2014 20.20 68.95 0.55 0.06 1639.01 0.01 0.01 0.03 0.16 0.06 0.05 0.00 0.54 4941.28 0.25 0.21 0.06 0.84 0.83 TR23 2013 23.39 67.00 0.55 0.06 1622.73 0.02 0.02 0.03 0.18 0.05 0.05 0.00 0.67 4828.16 0.20 0.34 0.14 1.42 0.76 TR23 2012 29.76 73.33 0.96 0.06 2003.73 0.02 0.02 0.02 0.21 0.06 0.07 0.00 0.67 4965.05 0.80 0.50 0.15 1.45 0.73 TR23 2011 11.66 75.37 1.04 0.06 1785.93 0.02 0.04 0.02 0.20 0.05 0.08 0.00 0.67 4701.94 0.22 0.59 0.39 2.66 1.31 TR24 2019 50.39 1.10 0.08 2526.85 0.01 0.12 0.21 0.17 0.14 0.00 0.66 4123.72 1.86 0.78 0.32 2.82 0.43 TR24 2018 41.77 147.03 0.96 0.07 2221.08 0.01 0.04 0.18 0.10 0.14 0.00 0.48 4194.83 0.38 0.70 0.24 2.32 1.22 TR24 2017 37.69 129.20 1.17 0.05 2343.80 0.01 0.05 0.02 0.15 0.09 0.20 0.00 0.55 3851.86 0.53 1.22 0.40 3.20 1.59 TR24 2016 29.42 95.86 1.33 0.08 2198.33 0.02 0.03 0.03 0.16 0.07 0.20 0.00 0.38 4087.17 0.37 1.38 0.27 3.00 1.16 TR24 2015 12.80 94.31 1.95 0.08 2165.85 0.02 0.04 0.07 0.19 0.06 0.14 0.00 0.66 4216.63 5.82 0.89 0.30 2.95 1.19 TR24 2014 47.37 108.48 1.65 0.05 2166.30 0.03 0.04 0.04 0.30 0.07 0.18 0.00 0.62 4173.22 0.80 1.91 0.16 2.56 1.09 TR24 2013 47.89 114.96 1.74 0.05 2393.26 0.02 0.04 0.05 0.25 0.06 0.16 0.00 0.58 4395.66 0.38 0.73 0.21 1.93 1.31 TR24 2012 34.96 91.54 1.46 0.05 2165.62 0.02 0.07 0.04 0.29 0.05 0.14 0.00 0.43 4440.81 2.94 1.10 0.36 1.65 0.90 TR24 2011 33.34 78.20 2.16 0.05 2171.75 0.02 0.04 0.04 0.17 0.05 0.13 0.00 0.54 4202.71 4.39 0.87 0.72 1.09 0.72 TR24 2010 22.80 82.06 1.67 0.05 2038.78 0.03 0.06 0.05 0.16 0.04 0.11 0.00 0.48 4521.91 0.97 0.40 0.22 1.10 0.64 TR24 2009 31.12 96.06 1.60 0.04 2366.00 0.02 0.05 0.05 0.14 0.06 0.11 0.00 0.58 4425.87 0.26 1.15 0.12 1.08 0.76 TR24 2008 9.52 78.12 1.36 0.05 1996.69 0.01 0.07 0.06 0.13 0.05 0.09 0.00 0.56 4583.41 1.98 8.52 0.23 0.99 0.76

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TR24 2007 8.59 107.74 1.22 0.11 1978.01 0.01 0.03 0.03 0.14 0.04 0.06 0.00 0.51 4630.69 0.24 16.21 0.12 1.67 0.71 TR24 2006 13.78 149.22 1.15 0.15 1937.05 0.03 0.05 0.06 0.22 0.05 0.06 0.00 0.50 4789.59 0.24 25.15 0.19 2.01 1.46 TR24 2005 4.60 136.79 0.98 0.13 1416.31 0.03 0.12 0.42 0.05 0.08 0.39 5185.04 1.64 17.81 0.14 3.41 2.73 KIT6 2019 68.33 163.06 0.89 3320.36 0.04 0.02 0.08 0.05 0.14 0.00 0.45 3702.82 0.27 0.28 0.09 1.02 0.54 KIT6 2018 26.05 140.97 1.34 3034.69 0.06 0.04 0.08 0.05 0.11 0.00 0.51 4149.13 0.51 0.18 0.08 1.00 0.55 KIT6 2017 21.61 112.11 1.67 2663.05 0.04 0.10 0.06 0.08 0.00 0.52 4283.60 0.22 0.20 0.10 1.23 0.65 KIT6 2016 31.67 97.21 1.80 2621.81 0.03 0.08 0.10 0.05 0.07 0.00 0.50 4326.40 0.27 0.16 0.05 1.43 0.57 KIT6 2015 25.68 98.40 1.67 2553.01 0.06 0.05 0.11 0.05 0.07 0.00 0.39 4424.15 0.36 0.13 0.10 1.44 0.60 KIT6 2014 22.23 92.26 1.32 2319.52 0.04 0.02 0.09 0.04 0.05 0.00 0.63 4434.87 0.46 0.34 0.10 1.19 0.52 KIT6 2013 15.37 96.16 1.17 2284.22 0.03 0.04 0.10 0.05 0.06 0.00 0.37 4665.01 0.24 0.28 0.08 1.24 0.79 KIT6 2012 18.80 100.01 0.97 1696.13 0.04 0.05 0.12 0.05 0.05 0.00 0.63 4463.80 0.21 0.24 0.05 1.36 0.39 KIT6 2011 4.60 86.55 0.65 1081.46 0.05 0.05 0.04 0.10 0.05 0.03 0.00 0.53 4692.55 0.23 0.40 0.10 1.63 0.42 KIT6 2010 10.32 112.35 0.79 1048.67 0.18 0.03 0.03 0.16 0.06 0.03 0.01 0.65 4639.75 0.25 2.90 0.13 2.09 0.76 KIT6 2009 26.89 276.87 0.53 2655.10 0.41 0.01 0.18 0.05 0.08 0.15 0.52 4734.86 0.33 26.11 0.16 2.52 0.61 KIT6 2008 14.54 122.32 1.47 2015.34 0.20 0.05 0.04 0.25 0.04 0.07 0.02 0.53 4938.56 0.35 13.00 0.06 1.26 0.58 KIT6 2007 17.13 125.47 1.62 1912.91 0.17 0.06 0.03 0.39 0.06 0.08 0.02 0.60 4552.33 0.21 9.57 0.18 1.54 0.49 KIT6 2006 12.49 135.87 1.40 2019.37 0.37 0.04 0.04 0.47 0.06 0.11 0.41 4524.16 1.86 31.38 0.19 1.67 1.26 KIT6 2005 0.60 5107.18 1.41 12.27 0.15 1.04 KIT17 2019 26.32 168.56 1.07 2373.26 0.02 0.01 0.02 0.23 0.11 0.07 0.00 0.55 4533.88 0.37 2.20 0.15 2.77 0.80 KIT17 2018 25.73 95.66 1.02 2269.88 0.02 0.02 0.01 0.17 0.08 0.06 0.00 0.58 4733.43 0.20 1.10 0.12 2.23 0.34 KIT17 2017 31.20 101.92 0.87 2134.40 0.01 0.02 0.02 0.22 0.06 0.07 0.00 0.45 4423.84 0.39 3.27 0.28 1.94 0.39 KIT17 2016 19.06 98.74 0.97 2217.56 0.02 0.01 0.03 0.16 0.09 0.06 0.00 0.54 4748.24 0.77 0.14 1.65 0.35 KIT17 2015 12.47 100.01 0.79 2447.16 0.02 0.01 0.02 0.14 0.07 0.05 0.00 0.53 4199.65 0.42 3.09 0.08 2.04 0.27 KIT17 2014 11.61 90.17 0.97 2244.05 0.02 0.01 0.01 0.14 0.05 0.05 0.00 0.40 4477.52 0.21 1.22 0.12 1.52 0.40 KIT17 2013 28.95 109.27 0.69 2446.16 0.02 0.01 0.12 0.07 0.06 0.00 0.51 4301.35 0.25 1.77 0.12 1.83 0.29 KIT17 2012 27.49 96.50 0.80 2198.11 0.03 0.01 0.16 0.07 0.08 0.00 0.62 4572.53 0.24 1.69 0.18 1.54 0.52 KIT17 2011 30.05 85.30 0.92 1993.05 0.04 0.04 0.19 0.07 0.08 0.00 0.56 4331.18 0.20 3.31 0.13 1.80 0.37 KIT17 2010 41.67 70.52 1.29 1835.21 0.03 0.02 0.02 0.13 0.07 0.06 0.00 0.51 4667.73 0.20 1.62 0.12 1.77 0.45 KIT17 2009 15.35 72.59 1.42 1664.48 0.02 0.01 0.01 0.13 0.07 0.07 0.00 0.43 4761.67 0.29 2.96 0.16 2.07 0.35 KIT17 2008 30.16 66.88 1.47 1662.50 0.02 0.02 0.03 0.10 0.07 0.07 0.00 0.57 4679.29 0.19 6.44 0.12 1.72 0.31 KIT17 2007 9.99 86.40 1.49 1659.71 0.01 0.01 0.02 0.10 0.07 0.06 0.00 0.52 4789.75 0.29 7.77 0.28 2.00 0.33 KIT17 2006 12.85 131.87 1.90 1938.52 0.01 0.03 0.11 0.07 0.06 0.00 0.56 4972.78 0.18 7.73 0.41 1.71 0.34 KIT17 2005 8.94 179.65 1.81 0.09 1824.18 0.01 0.02 0.01 0.12 0.08 0.05 0.00 0.45 4758.46 0.23 10.69 0.53 1.24 0.44 KIT17 2004 12.61 170.58 1.58 0.12 1958.39 0.02 0.01 0.05 0.09 0.06 0.06 0.53 4914.78 1.04 0.40 1.42 KIT19 2019 22.36 343.72 0.75 0.07 2456.34 0.02 0.02 0.26 0.19 0.18 0.00 0.64 4371.98 1.81 8.70 0.37 3.11 KIT19 2018 12.92 263.69 0.79 0.05 2318.17 0.01 0.02 0.03 0.22 0.12 0.10 0.00 0.55 4275.56 1.88 1.75 0.30 1.61 1.51 KIT19 2017 29.22 221.48 0.85 0.06 1957.28 0.01 0.02 0.18 0.07 0.09 0.00 0.42 4582.62 0.81 2.21 0.08 1.15 1.35 KIT19 2016 32.67 170.07 1.07 0.06 2288.12 0.02 0.03 0.05 0.21 0.08 0.09 0.00 0.42 4711.00 0.94 0.83 0.14 1.10 1.10 KIT19 2015 43.92 146.90 1.08 0.08 2008.19 0.02 0.04 0.02 0.22 0.07 0.08 0.00 0.41 4460.85 1.19 0.63 0.18 1.28 1.35

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KIT19 2014 33.11 163.86 0.95 0.06 2052.92 0.01 0.02 0.02 0.16 0.07 0.07 0.00 0.46 4571.51 0.64 0.94 0.16 1.17 1.79 KIT19 2013 16.93 124.96 1.12 0.08 1856.02 0.01 0.03 0.02 0.18 0.06 0.09 0.00 0.41 4644.56 1.30 0.89 0.15 1.75 2.03 KIT19 2012 10.42 93.74 1.42 0.09 1891.16 0.02 0.04 0.03 0.19 0.05 0.10 0.00 0.43 4676.99 0.72 0.81 0.15 1.83 1.63 KIT19 2011 7.30 94.59 1.45 0.11 1837.76 0.01 0.05 0.02 0.17 0.06 0.10 0.00 0.48 4467.82 0.57 0.80 0.21 1.42 1.54 KIT19 2010 3.98 97.97 1.04 0.08 1706.71 0.01 0.03 0.02 0.16 0.04 0.13 0.00 0.58 4243.69 0.29 1.53 0.19 1.23 1.31 KIT19 2009 10.07 127.89 1.26 0.09 1831.85 0.01 0.02 0.02 0.14 0.05 0.10 0.00 0.51 4405.05 0.49 5.53 0.27 1.41 1.72 KIT19 2008 11.89 160.24 1.17 0.13 1847.58 0.01 0.04 0.04 0.15 0.04 0.11 0.00 0.46 4401.62 0.30 13.93 0.09 1.09 1.20 KIT19 2007 9.73 162.53 1.24 0.20 1778.38 0.03 0.07 0.02 0.21 0.04 0.21 0.00 0.48 5019.30 0.18 0.14 1.98 2.61 KIT25 2019 90.30 276.24 1.43 0.09 2962.96 0.03 0.06 0.11 0.17 0.18 0.15 0.00 0.46 4464.84 0.66 1.33 0.11 0.56 0.58 KIT25 2018 23.36 196.99 0.67 2440.64 0.02 0.04 0.10 0.11 0.12 0.00 0.61 4205.23 0.29 0.96 0.26 0.19 KIT25 2017 27.20 200.46 0.87 2307.01 0.02 0.14 0.08 0.10 0.00 0.37 3801.32 0.25 0.35 0.08 0.41 0.32 KIT25 2016 13.69 188.93 0.62 2607.60 0.03 0.06 0.12 0.06 0.08 0.00 0.45 3614.98 0.29 0.34 0.09 0.29 0.58 KIT25 2015 10.22 177.28 0.53 2637.19 0.03 0.02 0.12 0.07 0.07 0.00 0.47 3897.30 0.27 0.17 0.05 0.38 0.38 KIT25 2014 34.23 194.19 0.61 2293.62 0.05 0.03 0.13 0.07 0.09 0.00 0.55 4165.56 0.25 0.20 0.05 0.33 0.35 KIT25 2013 24.99 150.46 0.65 2111.03 0.04 0.04 0.11 0.06 0.07 0.00 0.49 4321.78 0.50 0.22 0.38 0.41 KIT25 2012 46.32 150.75 0.54 2135.53 0.04 0.02 0.11 0.05 0.07 0.00 0.62 4021.12 0.18 0.26 0.09 0.24 0.46 KIT25 2011 45.27 116.43 0.78 2126.25 0.03 0.02 0.12 0.07 0.05 0.00 0.69 4228.05 0.20 0.22 0.07 0.43 0.27 KIT25 2010 19.01 117.18 1.03 1861.61 0.03 0.04 0.12 0.07 0.06 0.00 0.53 4264.69 0.29 0.50 0.14 0.37 0.45 KIT25 2009 18.83 108.73 0.68 1768.68 0.05 0.04 0.13 0.09 0.06 0.00 0.38 4040.23 0.19 0.20 0.12 0.38 0.38 KIT25 2008 9.06 81.99 0.71 1617.06 0.03 0.06 0.14 0.09 0.05 0.00 0.61 4253.90 0.37 0.70 0.10 0.48 0.53 KIT25 2007 6.87 73.66 0.79 1513.96 0.03 0.04 0.12 0.07 0.05 0.00 0.69 4163.95 0.20 0.20 0.08 0.44 0.40 KIT25 2006 3.80 73.73 0.78 0.09 1512.90 0.02 0.03 0.13 0.07 0.06 0.00 0.72 4286.90 0.88 0.42 0.12 0.68 0.64 KIT25 2005 10.96 55.27 1.33 0.09 1470.92 0.05 0.03 0.13 0.08 0.06 0.00 0.65 4257.24 0.25 0.28 0.08 0.74 0.34 KIT25 2004 15.40 53.17 1.55 0.09 1392.98 0.02 0.02 0.02 0.14 0.09 0.05 0.00 0.64 4565.12 0.36 0.24 0.13 0.75 0.43 KIT25 2003 9.25 54.41 1.48 0.08 1417.38 0.02 0.03 0.04 0.14 0.08 0.05 0.00 0.70 4397.09 0.26 0.27 0.14 1.01 0.61 KIT25 2002 10.31 58.31 1.46 1344.35 0.02 0.04 0.03 0.12 0.08 0.05 0.00 0.73 4428.08 0.26 0.69 0.12 0.97 0.58 KIT25 2001 16.39 65.05 1.83 0.09 1621.40 0.03 0.05 0.05 0.13 0.08 0.06 0.00 0.69 4339.03 0.30 1.74 0.11 0.98 0.55 KIT25 2000 15.30 78.24 1.85 0.08 1761.72 0.07 0.05 0.06 0.14 0.07 0.09 0.01 0.52 4672.58 1.25 4.29 0.18 1.32 0.72 KIT29 2019 39.71 159.42 0.48 3567.62 0.04 0.04 0.02 0.21 0.11 0.14 0.00 0.35 2953.12 1.47 2.38 0.46 3.38 1.02 KIT29 2018 41.92 137.20 0.57 3127.15 0.04 0.02 0.03 0.16 0.09 0.13 0.00 0.54 3500.17 5.04 1.21 0.24 3.62 1.26 KIT29 2017 42.86 140.24 0.80 3115.16 0.03 0.02 0.03 0.13 0.06 0.13 0.00 0.35 3373.32 0.35 0.99 0.14 2.80 3.55 KIT29 2016 61.23 124.84 0.79 2808.07 0.04 0.03 0.08 0.07 0.12 0.00 0.61 3838.69 0.35 1.09 0.26 2.86 4.41 KIT29 2015 27.31 119.01 0.90 2550.18 0.03 0.03 0.02 0.09 0.09 0.10 0.00 0.58 4259.10 1.46 0.66 0.19 1.95 1.77 KIT29 2014 17.58 102.77 1.03 2332.53 0.02 0.03 0.02 0.07 0.08 0.08 0.00 0.53 4268.15 0.48 0.49 0.15 2.13 0.68 KIT29 2013 9.30 99.64 0.95 2107.84 0.03 0.02 0.05 0.07 0.09 0.08 0.00 0.60 4297.26 0.39 0.57 0.14 2.17 0.62 KIT29 2012 18.49 100.64 1.05 2092.84 0.03 0.05 0.09 0.09 0.10 0.00 0.51 4164.24 0.37 0.61 0.14 2.19 0.73 KIT29 2011 16.23 89.03 1.38 1999.17 0.03 0.02 0.08 0.09 0.10 0.00 0.57 4245.01 1.30 1.62 0.15 1.87 0.99 KIT29 2010 18.44 86.01 1.57 1993.02 0.03 0.02 0.03 0.08 0.09 0.09 0.00 0.55 4101.77 0.42 3.10 0.14 2.02 0.44 KIT29 2009 18.26 90.35 1.43 2078.32 0.02 0.02 0.07 0.07 0.09 0.11 0.00 0.54 4226.73 0.35 4.33 0.15 2.06 0.44

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KIT29 2008 23.77 101.06 1.48 2020.19 0.02 0.02 0.02 0.07 0.10 0.14 0.00 0.43 4334.01 0.44 5.52 0.14 1.78 0.55 KIT29 2007 11.70 108.55 1.34 1947.65 0.02 0.02 0.03 0.08 0.10 0.09 0.00 0.57 4293.69 0.70 4.94 0.20 1.60 0.45 KIT29 2006 14.76 122.24 1.36 1969.32 0.02 0.02 0.02 0.10 0.09 0.08 0.01 0.44 4346.28 0.39 6.55 0.23 1.70 0.80 KIT29 2005 14.22 131.10 1.50 2075.43 0.01 0.03 0.02 0.11 0.09 0.08 0.01 0.44 4186.20 1.27 9.59 0.36 1.31 1.30 KIT29 2004 20.62 98.76 1.35 2037.54 0.02 0.03 0.03 0.16 0.07 0.06 KIT34 2019 58.30 177.23 0.42 2812.42 0.03 0.07 0.07 1.65 0.09 0.20 0.00 0.74 4831.76 3.79 6.34 0.93 8.32 2.05 KIT34 2018 20.92 168.16 0.39 2647.75 0.03 0.01 0.03 0.92 0.08 0.25 0.00 0.40 4173.39 0.73 1.99 0.38 10.73 1.06 KIT34 2017 37.68 113.85 0.58 2740.94 0.02 0.04 0.04 0.57 0.09 0.14 0.00 0.56 4127.39 2.43 2.45 0.10 2.48 1.03 KIT34 2016 78.59 91.96 0.73 2634.06 0.03 0.05 0.02 0.73 0.06 0.11 0.00 0.38 4270.48 0.50 1.65 0.19 3.08 1.37 KIT34 2015 48.90 100.72 0.87 2713.42 0.02 0.02 0.04 0.67 0.06 0.10 0.00 0.72 4465.86 9.24 2.31 0.35 2.88 1.17 KIT34 2014 24.00 132.91 0.90 2532.86 0.03 0.04 0.56 0.08 0.10 0.00 0.43 4369.78 2.78 1.23 0.25 3.92 1.29 KIT34 2013 24.52 86.39 0.97 2356.13 0.02 0.03 0.04 0.65 0.08 0.12 0.00 0.50 4626.74 0.91 4.18 0.31 3.63 1.00 KIT34 2012 12.26 75.69 1.10 2470.87 0.02 0.01 0.11 0.70 0.07 0.10 0.00 0.69 4365.91 1.58 3.36 0.24 3.70 0.98 KIT34 2011 13.42 66.98 1.71 2498.49 0.02 0.02 0.03 0.58 0.07 0.10 0.00 0.55 4406.47 0.48 0.92 0.21 2.63 0.81 KIT34 2010 8.08 71.90 1.57 2380.98 0.02 0.04 0.06 0.73 0.08 0.10 0.00 0.76 4719.05 0.47 2.75 0.16 2.89 0.71 KIT34 2009 21.72 73.92 2.16 2512.99 0.02 0.04 0.04 0.53 0.07 0.08 0.00 0.54 4873.67 2.17 5.45 0.17 2.78 0.84 KIT34 2008 33.63 103.56 2.85 2838.71 0.04 0.04 0.67 0.08 0.07 0.00 0.52 4678.14 1.61 2.94 0.38 2.69 1.04 KIT34 2007 16.89 112.16 2.20 0.09 2731.10 0.03 0.05 0.04 0.52 0.10 0.08 0.00 0.58 4989.16 0.70 10.02 0.18 1.89 0.57 KIT34 2006 23.06 133.85 2.83 0.11 1689.15 0.04 0.11 0.08 1.21 0.10 0.10 0.00 0.55 4760.20 0.99 15.94 0.44 2.65 0.55 KIT35 2019 20.51 148.40 1.01 2341.68 0.05 0.02 0.07 0.10 0.00 0.48 4625.18 0.29 3.84 0.14 0.86 KIT35 2018 18.57 127.14 1.16 2416.97 0.08 0.03 0.51 0.05 0.06 0.00 0.43 4442.31 2.67 0.12 10.45 0.63 KIT35 2017 22.14 117.54 1.02 2463.21 0.05 0.03 0.46 0.04 0.09 0.62 4735.93 0.20 1.30 0.10 9.09 0.74 KIT35 2016 33.97 113.83 1.13 2394.88 0.04 0.02 0.25 0.03 0.06 0.00 0.54 4750.04 0.28 1.71 0.11 5.96 0.47 KIT35 2015 19.82 103.69 1.13 2630.28 0.04 0.04 0.26 0.04 0.07 0.00 0.51 4420.11 0.37 0.70 0.12 4.95 0.76 KIT35 2014 19.55 121.84 1.09 2591.48 0.03 0.21 0.06 0.06 0.00 0.67 4262.24 0.41 1.22 0.16 3.49 0.74 KIT35 2013 15.59 122.10 1.14 2753.79 0.04 0.20 0.03 0.09 0.00 0.74 4302.97 0.39 1.42 0.10 3.50 0.64 KIT35 2012 37.49 117.91 1.03 2465.03 0.03 0.03 0.13 0.03 0.08 0.00 0.72 4415.93 0.28 0.54 0.29 2.63 0.29 KIT35 2011 25.55 113.63 0.89 2362.40 0.05 0.02 0.14 0.04 0.06 0.66 4451.52 0.29 1.26 0.19 2.40 0.44 KIT35 2010 28.30 82.27 0.94 1880.78 0.08 0.04 0.19 0.04 0.08 0.00 0.67 4760.59 0.32 0.80 0.16 2.78 0.54 KIT35 2009 50.03 96.10 1.19 2166.48 0.06 0.02 0.20 0.04 0.07 0.00 0.62 4673.98 0.36 0.70 0.16 3.02 0.56 KIT35 2008 18.60 76.16 1.15 1611.31 0.03 0.04 0.12 0.04 0.07 0.00 0.61 4928.43 0.26 0.57 0.12 2.39 0.43 KIT35 2007 8.53 66.03 1.43 1402.34 0.03 0.03 0.14 0.04 0.09 0.00 0.77 5069.76 0.31 0.54 0.11 1.98 0.54 KIT35 2006 5.72 66.48 1.59 1395.60 0.04 0.04 0.14 0.05 0.08 0.00 0.69 4889.39 0.50 2.92 0.12 1.95 0.66 KIT35 2005 7.06 72.18 1.94 1507.59 0.02 0.15 0.04 0.07 0.00 0.65 4428.90 0.23 11.96 0.17 2.00 0.67 KIT35 2004 3.79 69.68 1.77 1623.11 0.06 0.04 0.13 0.04 0.08 0.00 0.61 4801.97 1.77 0.26 1.79 0.93 MI2 2019 24.69 0.10 0.13 0.05 0.03 0.12 0.13 0.05 0.00 0.53 4906.65 0.64 2.64 0.21 0.74 MI2 2018 12.36 0.11 0.13 2748.27 0.05 0.12 0.03 0.12 0.12 0.05 0.00 0.60 4267.27 0.39 1.15 0.22 0.32 MI2 2017 13.34 172.96 0.19 0.08 3223.41 0.03 0.03 0.03 0.13 0.08 0.08 0.00 0.41 3786.19 1.18 1.40 0.15 1.40 0.51 MI2 2016 42.62 172.20 0.20 0.08 2958.35 0.03 0.03 0.02 0.11 0.08 0.06 0.00 0.30 4279.60 0.28 1.00 0.12 0.63 1.04

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MI2 2015 35.64 142.93 0.23 0.08 2793.48 0.02 0.02 0.02 0.11 0.10 0.06 0.00 0.51 4368.31 2.71 0.10 0.70 0.76 MI2 2014 64.34 116.69 0.25 0.07 2313.40 0.05 0.04 0.04 0.12 0.11 0.06 0.00 0.46 4326.34 0.34 5.90 0.16 0.78 0.40 MI2 2013 53.03 130.20 0.30 0.09 2252.39 0.02 0.03 0.08 0.10 0.13 0.05 0.00 0.51 4709.05 0.50 5.84 0.14 0.77 1.08 MI2 2012 33.24 118.44 0.41 0.14 2175.07 0.02 0.04 0.07 0.10 0.11 0.08 0.00 0.56 4617.15 0.46 9.72 0.15 0.52 0.50 MI2 2011 28.56 111.93 0.26 0.11 2180.28 0.03 0.04 0.07 0.12 0.14 0.09 0.00 0.61 4893.23 0.85 21.65 0.16 0.47 1.49 MI2 2010 10.97 90.35 0.41 0.09 2060.47 0.04 0.05 0.06 0.13 0.11 0.09 0.00 0.30 4420.73 0.55 14.69 0.16 0.61 0.86 MI2 2009 13.55 81.99 0.58 0.08 2190.91 0.03 0.05 0.04 0.11 0.13 0.08 0.00 0.78 4728.42 1.73 10.83 0.65 0.94 MI2 2008 10.57 75.09 0.80 0.07 2094.71 0.03 0.04 0.06 0.11 0.10 0.08 0.00 0.68 4981.00 0.50 18.10 0.15 0.43 0.66 MI2 2007 16.25 79.74 0.66 0.09 2122.28 0.03 0.04 0.03 0.10 0.13 0.04 0.02 0.68 4615.07 0.97 22.51 0.27 0.45 0.89 MI2 2006 18.88 97.39 0.77 0.19 2425.75 0.04 0.09 0.03 0.11 0.10 0.04 0.66 5212.66 0.71 22.33 0.62 0.57 1.15 MI2 2005 24.94 102.04 0.79 0.25 2128.51 0.07 0.07 0.03 0.16 0.05 0.63 4831.30 0.76 0.41 0.98 2.30 MI4 2019 28.82 189.26 2.19 0.06 2894.89 0.01 0.07 0.26 0.10 0.07 0.00 0.67 4081.58 1.37 0.70 0.07 1.35 0.86 MI4 2018 19.05 180.75 1.20 0.03 2694.55 0.02 0.02 0.24 0.10 0.06 0.00 0.39 4211.63 0.19 0.69 0.16 2.20 1.43 MI4 2017 35.55 152.37 1.56 0.06 2648.70 0.03 0.04 0.02 0.23 0.09 0.10 0.00 0.65 4669.71 0.20 0.75 0.07 2.15 0.72 MI4 2016 15.82 105.16 2.60 0.08 2623.27 0.01 0.04 0.04 0.23 0.07 0.08 0.00 0.47 4257.67 0.25 0.89 0.07 2.15 0.59 MI4 2015 18.85 99.73 2.08 0.07 2451.78 0.02 0.03 0.06 0.19 0.07 0.07 0.00 0.58 4529.64 0.23 0.50 0.14 1.67 0.57 MI4 2014 22.47 87.09 1.28 0.06 1916.58 0.02 0.03 0.03 0.17 0.05 0.05 0.00 0.52 4379.23 0.21 0.54 0.11 1.52 0.59 MI4 2013 25.58 86.13 1.70 0.07 1782.46 0.03 0.04 0.04 0.19 0.07 0.05 0.00 0.50 4492.62 0.38 3.62 0.23 1.43 0.74 MI4 2012 15.93 72.99 2.06 0.09 1867.72 0.01 0.04 0.02 0.18 0.08 0.05 0.00 0.51 4376.81 0.38 18.60 0.11 0.78 0.45 MI4 2011 31.31 80.51 1.86 0.32 1904.27 0.01 0.03 0.04 0.15 0.07 0.05 0.00 0.49 4667.97 0.42 56.99 0.20 0.84 0.48 MI4 2010 23.81 87.88 2.46 0.35 2085.12 0.01 0.05 0.08 0.19 0.09 0.05 0.03 0.53 5015.32 0.99 62.27 0.14 1.73 1.25 MI4 2009 18.98 113.10 3.01 0.41 2089.57 0.03 0.04 0.04 0.27 0.05 0.08 0.43 4877.10 0.43 2.87 MI9 2019 44.18 202.73 0.83 0.08 2784.53 0.03 0.07 0.25 0.52 0.13 0.10 0.00 0.51 4628.58 2.60 3.31 0.18 1.83 1.62 MI9 2018 28.95 175.52 0.45 0.03 2484.39 0.01 0.04 0.34 0.06 0.09 0.00 0.47 3761.98 0.49 1.40 0.18 1.32 0.68 MI9 2017 28.48 157.83 0.81 0.05 3157.92 0.01 0.04 0.02 0.22 0.07 0.08 0.00 0.57 4197.27 0.32 0.66 0.12 1.10 0.96 MI9 2016 24.02 148.22 1.19 0.06 2806.53 0.01 0.03 0.02 0.22 0.05 0.08 0.00 0.50 4771.65 0.20 0.51 0.11 1.08 0.94 MI9 2015 9.12 106.72 0.84 0.07 2414.13 0.02 0.03 0.03 0.14 0.04 0.05 0.00 0.54 4510.53 0.28 0.47 0.13 0.89 0.51 MI9 2014 24.64 92.96 0.91 0.06 1968.99 0.01 0.02 0.04 0.15 0.05 0.05 0.00 0.72 4886.03 0.78 0.57 0.16 0.74 0.80 MI9 2013 32.01 84.17 0.92 0.07 1958.26 0.02 0.05 0.05 0.12 0.04 0.05 0.00 0.61 4911.82 0.24 0.41 0.09 0.69 0.84 MI9 2012 33.61 78.26 0.92 0.07 1783.60 0.01 0.04 0.02 0.17 0.03 0.06 0.00 0.50 4639.77 0.19 0.40 0.09 0.79 0.95 MI9 2011 41.79 80.77 1.11 0.06 1474.71 0.02 0.04 0.04 0.12 0.05 0.08 0.00 0.48 4501.41 0.24 0.46 0.11 0.83 0.82 MI9 2010 12.58 86.14 0.65 0.06 1587.30 0.02 0.04 0.04 0.15 0.04 0.08 0.00 0.45 4365.07 0.25 0.30 0.10 0.78 0.68 MI9 2009 48.44 73.15 1.13 0.10 2037.57 0.03 0.04 0.05 0.18 0.05 0.09 0.00 0.53 4455.01 0.30 0.34 0.12 0.88 0.69 MI9 2008 9.49 80.54 0.64 0.07 2227.66 0.02 0.02 0.03 0.24 0.05 0.08 0.00 0.52 4498.93 0.94 2.88 0.11 1.25 1.65 MI9 2007 5.72 63.92 0.56 0.06 2050.14 0.01 0.03 0.03 0.11 0.05 0.06 0.00 0.42 4038.45 0.31 1.72 0.13 1.10 1.09 MI9 2006 9.43 69.70 0.90 0.09 2039.14 0.01 0.02 0.03 0.11 0.05 0.05 0.00 0.69 4463.19 0.31 8.83 0.16 0.81 0.82 MI9 2005 20.68 75.93 1.03 0.18 1797.75 0.01 0.04 0.03 0.11 0.05 0.06 0.00 0.53 4681.37 0.82 23.25 0.13 0.89 0.95 MI9 2004 0.61 4851.91 0.82 0.35 1.25 1.57 MI14 2019 53.80 208.60 1.25 0.07 3779.26 0.02 0.05 0.04 0.15 0.05 0.06 0.00 0.57 3845.94 0.52 1.77 0.50 2.07 1.07

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MI14 2018 18.31 172.97 1.17 0.06 3477.30 0.01 0.04 0.14 0.05 0.06 0.00 0.47 3563.79 0.31 0.55 0.22 1.18 0.69 MI14 2017 38.73 172.35 0.96 0.06 2903.89 0.01 0.06 0.05 0.16 0.05 0.06 0.00 0.59 4157.01 0.92 0.36 0.22 1.64 0.64 MI14 2016 65.62 149.30 1.29 0.06 2846.74 0.02 0.06 0.05 0.14 0.06 0.04 0.00 0.45 4560.05 0.29 0.13 1.19 0.89 MI14 2015 55.81 142.75 1.15 0.07 2863.89 0.02 0.08 0.03 0.16 0.06 0.06 0.00 0.61 4688.16 0.33 0.35 0.43 1.25 1.34 MI14 2014 58.66 133.06 1.44 0.07 3116.55 0.02 0.04 0.18 0.07 0.06 0.00 0.98 4267.03 0.34 0.32 0.22 1.28 0.45 MI14 2013 34.85 117.99 1.21 0.08 2742.30 0.02 0.05 0.09 0.16 0.09 0.06 0.00 0.58 4409.89 0.39 0.30 0.27 1.47 0.97 MI14 2012 66.33 111.72 1.44 0.08 2423.00 0.02 0.08 0.05 0.15 0.06 0.05 0.00 0.79 4327.78 0.31 0.20 1.09 1.04 MI14 2011 21.30 107.40 1.35 0.08 2450.82 0.02 0.06 0.05 0.18 0.05 0.07 0.00 0.76 4465.39 0.37 0.47 0.20 0.92 0.55 MI14 2010 13.92 96.93 1.10 0.07 2471.23 0.02 0.05 0.03 0.18 0.07 0.06 0.00 0.75 4430.69 0.41 0.25 0.16 1.12 1.01 MI14 2009 8.32 97.42 1.01 0.09 2409.49 0.03 0.06 0.06 0.17 0.08 0.07 0.00 0.56 4347.06 1.59 0.34 0.15 1.32 0.77 MI14 2008 23.14 101.31 1.63 0.09 2381.71 0.04 0.06 0.08 0.16 0.06 0.06 0.00 0.82 4430.25 0.39 0.27 0.23 1.52 1.25 MI14 2007 19.48 94.91 1.55 0.10 2277.42 0.02 0.07 0.07 0.18 0.07 0.05 0.00 0.62 4457.90 0.45 3.99 0.22 1.45 0.88 MI14 2006 23.90 84.26 1.51 0.12 2301.18 0.02 0.05 0.05 0.17 0.07 0.05 0.00 0.68 4375.81 0.45 17.68 0.17 1.28 0.90 MI14 2005 38.51 104.14 1.74 0.22 2583.29 0.01 0.07 0.04 0.17 0.08 0.08 0.01 0.66 4418.35 0.32 32.10 0.18 1.77 1.13 MI14 2004 19.82 145.20 1.22 0.09 2109.84 0.02 0.10 0.05 0.20 0.06 0.08 0.03 0.64 4637.96 0.28 42.26 0.19 1.72 2.69 MI22 2019 32.93 199.75 0.86 0.04 2728.63 0.03 0.07 0.16 0.57 0.12 0.01 0.66 5506.65 0.85 1.30 0.16 0.96 0.43 MI22 2018 20.49 182.99 0.66 0.08 3515.34 0.03 0.10 0.12 0.52 0.07 0.00 0.62 5209.03 0.22 0.55 0.18 0.83 0.43 MI22 2017 49.50 146.04 0.83 0.07 3075.70 0.04 0.13 0.26 0.08 0.00 0.46 5285.15 0.23 0.48 0.11 0.94 0.32 MI22 2016 23.44 128.31 0.81 0.06 3108.82 0.03 0.02 0.02 0.13 0.18 0.07 0.01 0.47 4965.45 0.22 0.92 0.09 0.90 0.22 MI22 2015 7.68 136.70 0.86 0.13 3008.13 0.02 0.13 0.19 0.06 0.00 0.43 5012.78 0.22 29.83 0.08 0.76 0.22 MI22 2014 26.02 98.40 1.05 0.17 3374.73 0.03 0.03 0.13 0.14 0.10 0.01 0.52 5172.19 0.26 22.60 0.08 0.78 0.15 MI22 2013 7.56 86.09 0.74 0.12 3179.20 0.02 0.02 0.03 0.10 0.12 0.09 0.06 0.45 4739.63 0.22 14.00 0.09 0.73 0.18 MI22 2012 4.43 85.97 0.74 0.09 3075.61 0.03 0.02 0.03 0.11 0.11 0.09 0.12 0.45 4565.80 3.07 21.05 0.11 0.92 0.19 MI22 2011 34.58 84.19 0.99 0.09 3277.36 0.03 0.02 0.02 0.11 0.10 0.08 0.13 0.44 5163.00 0.23 10.23 0.10 0.75 0.21 MI22 2010 22.73 82.96 0.68 0.11 3117.67 0.03 0.02 0.11 0.12 0.07 0.24 0.40 4874.25 0.48 11.33 0.06 0.77 0.20 MI22 2009 66.10 95.77 1.16 0.16 3129.35 0.04 0.02 0.03 0.12 0.11 0.06 0.38 0.47 5233.52 0.19 9.76 0.07 0.85 0.19 MI22 2008 38.74 97.32 1.19 0.12 2779.06 0.03 0.03 0.11 0.10 0.06 0.33 0.40 5021.68 0.20 6.71 0.07 0.97 0.18 MI22 2007 23.74 122.80 1.81 2950.68 0.06 0.05 0.10 0.08 0.09 0.44 5674.55 0.37 12.40 0.27 1.60 0.25 MI24 2019 19.40 136.69 0.22 0.04 2771.17 0.02 0.02 0.19 0.06 0.07 0.00 0.46 4529.69 0.25 1.39 0.56 2.93 0.83 MI24 2018 50.34 97.46 0.28 0.06 2841.84 0.01 0.02 0.17 0.04 0.08 0.00 0.61 4427.71 0.25 0.51 0.17 1.50 0.47 MI24 2017 16.71 80.26 0.35 0.07 2474.28 0.01 0.02 0.02 0.15 0.06 0.08 0.00 0.51 4356.78 0.23 0.39 0.32 1.21 0.38 MI24 2016 14.72 80.96 0.64 0.05 2408.35 0.02 0.01 0.04 0.17 0.06 0.07 0.00 0.57 4738.20 0.64 0.59 0.24 2.01 0.48 MI24 2015 13.75 94.14 0.85 0.10 2376.31 0.04 0.03 0.03 0.76 0.06 0.13 0.00 0.53 4475.88 0.39 0.60 0.28 2.15 0.49 MI24 2014 14.30 90.08 1.57 0.05 2286.12 0.05 0.03 0.02 0.93 0.06 0.10 0.00 0.44 4672.91 0.34 0.48 0.15 1.20 0.52 MI24 2013 4.07 72.62 0.76 0.11 2176.27 0.02 0.02 0.03 0.17 0.09 0.07 0.00 0.61 4605.96 0.24 11.20 0.27 1.09 0.51 MI24 2012 34.10 77.39 1.03 0.08 2363.66 0.01 0.02 0.02 0.15 0.09 0.07 0.00 0.58 4692.58 0.28 18.13 0.23 1.19 0.44 MI24 2011 43.24 104.32 1.13 0.08 2689.49 0.02 0.02 0.13 0.08 0.08 0.00 0.38 4509.33 0.29 9.71 0.25 1.38 0.40 MI24 2010 51.96 116.66 1.19 0.08 2228.11 0.01 0.02 0.02 0.18 0.10 0.04 0.00 0.50 4655.80 9.04 0.26 1.86 0.43 MI24 2009 47.16 141.46 0.94 0.11 2078.74 0.02 0.02 0.04 0.21 0.09 0.04 0.01 0.55 4807.42 0.32 11.41 0.26 1.82 0.95

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MI24 2008 40.83 162.55 1.24 0.20 2070.91 0.04 0.04 0.03 0.43 0.13 0.04 0.52 4568.05 0.34 0.57 MI25 2019 42.80 146.51 1.34 0.07 2614.18 0.02 0.05 0.05 0.43 0.09 0.06 0.00 0.76 5291.60 1.88 1.06 0.41 1.20 2.75 MI25 2018 19.36 87.43 1.41 0.06 2383.74 0.01 0.02 0.02 0.18 0.05 0.04 0.00 0.51 4959.31 0.40 0.66 0.19 1.01 0.58 MI25 2017 31.40 64.69 0.96 0.04 2280.52 0.02 0.02 0.06 0.18 0.06 0.05 0.00 0.36 4512.05 2.01 0.38 0.11 1.77 0.65 MI25 2016 30.66 54.12 1.15 0.04 2622.53 0.03 0.02 0.03 0.19 0.05 0.05 0.00 0.51 5033.99 0.52 0.45 0.15 1.54 0.90 MI25 2015 13.67 60.37 0.88 0.04 2838.44 0.04 0.02 0.02 0.17 0.08 0.06 0.00 0.54 4516.53 0.58 0.54 0.17 1.33 0.81 MI25 2014 4.74 58.96 0.74 0.05 2536.36 0.02 0.03 0.03 0.15 0.07 0.05 0.00 0.55 4320.46 0.44 4.32 0.19 0.97 0.90 MI25 2013 38.88 72.46 1.18 0.05 2043.50 0.02 0.06 0.02 0.21 0.07 0.04 0.00 0.45 4613.01 1.91 6.93 0.24 0.99 1.22 MI25 2012 33.27 92.76 1.86 0.10 1980.94 0.05 0.08 0.03 0.34 0.06 0.07 0.54 4929.24 0.95 8.02 0.29 1.65 2.71 WW6 2019 39.34 92.91 0.85 0.05 4986.47 0.05 0.03 0.02 0.40 0.06 0.41 0.00 0.18 3876.91 0.81 1.36 0.07 2.60 0.45 WW6 2018 53.82 153.66 1.22 0.05 3187.26 0.04 0.02 0.13 0.07 0.23 0.00 0.16 5721.14 0.24 0.36 0.04 1.31 0.23 WW6 2017 67.66 124.29 1.00 0.07 3339.30 0.03 0.02 0.03 0.17 0.05 0.23 0.00 0.23 5313.40 0.10 0.31 0.09 0.90 0.12 WW6 2016 63.30 87.06 1.22 0.07 3013.03 0.01 0.02 0.21 0.05 0.19 0.00 0.25 4498.40 0.05 0.19 0.04 1.00 0.14 WW6 2015 49.92 94.98 1.30 0.09 3053.59 0.02 0.02 0.06 0.19 0.06 0.21 0.00 0.27 5987.45 0.83 0.42 0.20 1.12 0.51 WW6 2014 97.72 100.25 1.47 0.09 3139.66 0.03 0.02 0.03 0.18 0.06 0.20 0.00 0.23 6680.67 0.85 0.52 0.08 1.14 0.30 WW6 2013 28.31 79.22 1.28 0.10 3007.75 0.03 0.01 0.02 0.16 0.07 0.25 0.00 0.26 5202.95 0.46 0.57 0.04 0.98 0.26 WW6 2012 20.11 67.88 1.27 0.10 2664.98 0.03 0.02 0.18 0.09 0.21 0.00 0.21 5375.75 0.14 0.26 0.02 1.19 0.32 WW6 2011 11.22 66.79 1.23 0.11 2195.59 0.03 0.04 0.16 0.07 0.19 0.00 0.26 4727.70 0.14 0.31 0.05 1.75 0.31 WW6 2010 14.34 65.86 1.33 0.11 2409.29 0.04 0.02 0.24 0.07 0.24 0.00 0.24 5347.29 0.07 0.34 0.05 2.00 0.25 WW6 2009 22.66 61.81 1.43 0.11 2222.28 0.03 0.02 0.02 0.21 0.09 0.19 0.00 0.26 5341.49 0.09 0.19 0.06 2.15 0.23 WW6 2008 28.92 65.87 1.73 0.12 2195.85 0.06 0.02 0.05 0.23 0.09 0.23 0.00 0.30 5783.40 0.09 0.50 0.11 2.27 0.60 WW6 2007 43.48 58.78 1.77 0.12 2150.49 0.06 0.02 0.04 0.20 0.09 0.14 0.00 0.23 5291.20 0.10 0.61 0.10 1.79 0.33 WW6 2006 24.28 68.74 1.68 0.10 1768.58 0.06 0.02 0.04 0.24 0.11 0.13 0.00 0.30 5026.47 0.25 0.78 0.08 1.47 0.40 WW6 2005 21.77 83.78 2.58 0.09 1865.05 0.06 0.04 0.05 0.22 0.09 0.10 0.01 0.24 6543.70 0.38 1.74 0.12 1.22 0.54 WW7 2019 39.29 1.28 0.09 2138.24 0.02 0.02 0.03 0.20 0.05 0.08 0.00 0.56 5526.11 0.69 2.87 0.14 2.57 0.17 WW7 2018 13.15 114.73 0.70 0.08 1691.13 0.01 0.01 0.02 0.11 0.02 0.06 0.00 0.52 5920.01 0.27 0.89 0.14 2.51 0.36 WW7 2017 23.02 102.69 0.71 0.08 1503.92 0.01 0.01 0.02 0.10 0.03 0.06 0.00 0.57 6093.33 0.22 0.26 0.12 3.93 0.28 WW7 2016 14.09 100.28 0.85 0.10 1612.44 0.01 0.01 0.02 0.16 0.03 0.08 0.00 0.58 5651.37 0.34 0.22 0.18 3.51 0.33 WW7 2015 6.58 98.01 0.85 0.10 1591.23 0.01 0.01 0.03 0.14 0.02 0.08 0.00 0.62 5435.18 0.18 0.21 0.08 2.35 0.31 WW7 2014 13.49 92.36 0.90 0.09 1557.11 0.01 0.01 0.02 0.10 0.02 0.07 0.00 0.60 5937.27 0.20 0.18 0.07 1.99 0.22 WW7 2013 6.40 94.87 1.15 0.10 1761.21 0.02 0.01 0.04 0.10 0.02 0.09 0.00 0.63 5770.12 0.16 0.20 0.07 2.07 0.26 WW7 2012 3.71 90.01 1.05 0.08 1811.15 0.03 0.01 0.02 0.12 0.02 0.07 0.00 0.59 5741.85 0.19 0.43 0.07 2.08 0.25 WW7 2011 32.41 94.50 1.62 0.08 1990.89 0.04 0.01 0.01 0.26 0.02 0.07 0.00 0.59 5546.04 0.25 2.51 0.07 1.57 0.30 WW7 2010 34.74 87.34 2.07 0.09 1907.33 0.04 0.03 0.02 0.37 0.02 0.09 0.54 6054.86 0.60 0.11 1.62 0.60 WW7 2009 76.66 3.55 1698.13 0.04 0.03 0.69 6530.01 WW18 2019 62.64 0.39 2127.28 0.05 0.79 0.11 0.69 0.09 0.12 0.01 0.90 5559.48 0.19 7.15 0.23 WW18 2018 33.16 279.47 0.18 2149.85 0.03 0.50 0.06 0.41 0.06 0.09 0.00 0.54 4600.24 0.95 27.67 0.14 5.06 0.46 WW18 2017 26.80 194.67 2.64 0.19 2130.59 0.03 0.51 0.06 0.32 0.06 0.09 0.00 0.47 4492.89 0.58 22.75 0.10 3.83 0.70 WW18 2016 9.26 139.64 1.31 0.18 1950.06 0.01 0.52 0.04 0.27 0.07 0.06 0.00 0.44 4520.32 0.33 9.12 0.13 4.20 0.36

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WW18 2015 20.28 108.21 0.70 0.16 1907.09 0.01 0.57 0.03 0.18 0.09 0.07 0.00 0.53 4467.71 0.27 2.17 0.14 5.23 0.44 WW18 2014 9.36 100.37 0.40 0.20 1811.74 0.02 0.59 0.05 0.16 0.07 0.06 0.00 0.63 4511.37 0.21 0.91 0.13 3.50 0.48 WW18 2013 4.33 99.08 0.60 0.21 1661.75 0.02 0.70 0.05 0.20 0.09 0.06 0.00 0.65 4560.90 0.25 0.57 0.20 3.05 0.52 WW18 2012 26.02 95.38 0.48 0.23 1491.93 0.02 0.88 0.05 0.20 0.10 0.05 0.00 0.81 4386.94 0.33 0.54 0.18 3.57 0.75 WW18 2011 19.92 89.98 0.41 0.41 1347.33 0.03 1.59 0.09 0.26 0.16 0.06 0.00 WW18 2010 27.51 94.79 0.65 1826.88 0.06 3.11 0.22 0.47 0.29 0.10 0.00 WW19 2019 34.49 247.64 0.41 0.07 2723.80 0.03 0.05 0.07 0.25 0.08 0.28 0.00 0.74 3708.48 0.53 2.72 0.34 6.95 0.97 WW19 2018 25.66 181.06 0.53 0.05 2955.67 0.03 0.06 0.03 0.16 0.07 0.22 0.00 0.57 3368.65 1.60 1.77 0.44 6.51 0.71 WW19 2017 54.49 786.90 1.02 0.08 2581.80 0.02 0.05 0.02 0.21 0.09 0.14 0.00 0.92 4068.85 1.81 1.15 0.32 3.52 0.82 WW19 2016 84.86 1433.84 1.40 0.08 2649.93 0.02 0.08 0.03 0.15 0.09 0.11 0.00 0.78 4378.33 0.99 0.75 0.16 1.67 0.64 WW19 2015 82.18 2211.38 1.71 0.09 1881.93 0.02 0.05 0.03 0.11 0.08 0.14 0.00 1.32 2903.89 0.50 0.96 0.18 2.21 0.42 WW19 2014 33.44 2306.88 2.45 0.12 727.39 0.02 0.04 0.05 0.10 0.08 0.11 0.00 1.70 2466.75 0.27 0.68 0.24 2.51 0.82 WW19 2013 38.29 1314.78 2.20 0.08 1040.22 0.02 0.06 0.03 0.11 0.09 0.14 0.00 1.03 3731.77 1.23 0.73 0.11 2.04 0.61 WW19 2012 18.56 165.42 0.85 0.09 2108.23 0.03 0.04 0.04 0.10 0.08 0.13 0.00 0.63 4844.97 1.64 0.97 0.19 2.08 0.58 WW19 2011 11.82 127.48 0.76 0.10 1887.32 0.03 0.05 0.03 0.17 0.08 0.10 0.00 0.62 4736.58 0.29 0.67 0.18 2.24 0.50 WW19 2010 7.27 121.17 0.66 0.10 1731.14 0.03 0.03 0.04 0.12 0.08 0.07 0.00 0.53 4762.27 0.53 0.64 0.24 2.44 0.54 WW19 2009 18.91 97.82 1.15 0.10 1736.77 0.03 0.05 0.04 0.12 0.06 0.10 0.00 0.69 4788.76 0.29 0.43 0.26 2.44 0.48 WW19 2008 20.52 86.75 1.01 0.10 1869.59 0.04 0.04 0.04 0.13 0.07 0.10 0.00 0.62 4750.37 0.53 0.54 0.19 2.56 0.50 WW19 2007 19.05 82.62 1.18 0.11 1810.75 0.06 0.03 0.04 0.15 0.06 0.14 0.00 0.52 4800.00 0.29 1.01 0.21 2.41 0.85 WW19 2006 27.94 93.73 1.31 0.14 1874.77 0.04 0.05 0.03 0.17 0.05 0.09 0.00 0.56 5014.75 0.31 3.15 0.17 2.00 1.10 WW22 2019 10.24 1484.71 1.87 0.07 1780.19 0.01 0.04 0.04 0.18 0.05 0.12 0.00 1.03 3422.32 0.28 0.66 0.36 4.43 0.90 WW22 2018 3.80 2819.95 2.78 0.07 687.36 0.01 0.04 0.07 0.13 0.09 0.11 0.00 1.63 2497.33 0.51 0.47 0.18 3.95 1.35 WW22 2017 10.98 2509.08 2.67 0.05 742.32 0.02 0.04 0.03 0.13 0.09 0.14 0.00 1.35 2350.41 0.63 0.47 0.22 2.92 1.80 WW22 2016 18.31 1987.14 2.58 0.05 697.02 0.01 0.04 0.05 0.15 0.10 0.13 0.00 1.59 2286.47 0.37 0.51 0.32 2.43 1.54 WW22 2015 16.27 1522.11 2.00 0.05 607.48 0.02 0.03 0.03 0.12 0.08 0.14 0.00 1.43 2168.69 0.34 0.52 0.25 2.30 1.05 WW22 2014 3.34 1334.93 0.89 0.05 607.35 0.01 0.04 0.05 0.12 0.10 0.10 0.00 1.12 2208.81 0.43 0.28 0.16 1.88 0.72 WW22 2013 16.10 2185.40 2.39 0.07 665.61 0.02 0.06 0.07 0.14 0.14 0.17 0.00 1.49 2234.99 0.35 0.39 0.26 3.18 0.85 WW22 2012 12.32 1954.04 4.15 0.07 638.77 0.02 0.05 0.08 0.14 0.12 0.21 0.00 1.33 2258.41 0.83 0.68 0.13 2.88 0.83 WW22 2011 47.91 686.07 2.38 0.05 2927.63 0.02 0.03 0.04 0.16 0.08 0.25 0.00 0.77 3798.28 0.27 0.47 0.13 2.79 0.52 WW22 2010 21.65 70.91 1.66 0.06 2586.28 0.02 0.03 0.03 0.17 0.07 0.18 0.00 0.49 4151.03 0.54 0.76 0.17 2.18 0.56 WW22 2009 16.85 67.72 1.97 0.05 1781.10 0.03 0.05 0.03 0.16 0.06 0.11 0.00 0.47 3979.96 0.38 0.89 0.24 1.30 0.42 WW22 2008 23.56 74.80 1.83 0.06 1787.40 0.04 0.03 0.04 0.15 0.05 0.09 0.00 0.60 4193.67 0.38 1.28 0.29 1.12 0.68 WW22 2007 14.75 140.66 1.42 0.07 1586.83 0.04 0.04 0.03 0.16 0.06 0.07 0.00 0.44 4410.73 0.23 1.32 0.25 1.31 1.18 WW28 2019 72.53 302.01 0.09 2618.87 0.05 0.07 0.04 0.53 0.08 0.15 0.00 0.62 4458.01 0.60 1.52 0.23 1.47 WW28 2018 39.03 233.51 1.52 0.08 2812.40 0.01 0.05 0.05 0.25 0.07 0.08 0.00 0.58 4338.91 0.54 0.65 0.09 4.53 0.66 WW28 2017 14.23 173.63 0.69 0.11 2366.48 0.02 0.05 0.07 0.22 0.05 0.12 0.00 0.53 4484.75 0.57 0.35 0.15 6.76 0.96 WW28 2016 8.84 115.68 0.74 0.09 2145.79 0.02 0.06 0.07 0.19 0.06 0.09 0.00 0.77 4217.80 0.68 0.33 0.23 8.89 0.55 WW28 2015 16.12 112.47 0.88 0.09 1418.31 0.02 0.09 0.08 0.21 0.07 0.07 0.00 0.59 4474.92 0.78 0.35 0.15 6.88 0.80 WW28 2014 26.00 93.05 0.68 0.08 1477.65 0.02 0.08 0.08 0.17 0.05 0.07 0.00 0.62 4484.52 0.49 0.35 0.14 6.03 0.78

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WW28 2013 31.19 90.41 0.59 0.09 1555.27 0.02 0.04 0.03 0.19 0.04 0.08 0.00 0.61 4397.17 0.47 0.31 0.13 5.52 0.64 WW28 2012 27.88 94.23 0.66 0.08 1500.04 0.02 0.09 0.06 0.19 0.05 0.07 0.00 0.60 4586.02 0.00 0.38 0.19 4.91 0.94 WW28 2011 16.11 85.93 0.69 0.09 1311.14 0.03 0.08 0.10 0.17 0.08 0.06 0.00 0.48 4342.70 0.41 0.38 0.23 3.04 0.48 WW28 2010 24.16 84.78 0.68 0.09 1504.89 0.03 0.11 0.07 0.16 0.10 0.09 0.00 0.47 4465.63 0.64 0.52 0.14 2.26 0.53 WW28 2009 7.92 78.38 0.82 0.12 1422.57 0.03 0.08 0.09 0.18 0.09 0.09 0.00 0.60 4337.63 0.97 0.48 0.27 2.87 1.11 WW28 2008 9.49 77.12 0.94 0.11 1449.58 0.04 0.08 0.10 0.21 0.09 0.12 0.00 0.62 4561.40 0.54 0.40 0.19 2.55 0.66 WW28 2007 11.05 74.43 1.68 0.14 1758.49 0.04 0.09 0.05 0.22 0.07 0.16 0.00 0.62 4098.13 0.35 0.47 0.33 2.48 0.78 WW28 2006 6.54 74.67 0.66 0.09 1727.98 0.04 0.10 0.06 0.19 0.09 0.12 0.00 0.52 4253.18 0.33 1.29 0.18 2.38 0.93 WW28 2005 34.28 71.75 1.53 0.12 1768.94 0.03 0.09 0.06 0.18 0.08 0.10 0.00 0.78 4376.48 0.43 2.34 0.19 2.39 1.05 WW28 2004 17.59 62.02 1.20 0.10 1515.43 0.04 0.09 0.11 0.20 0.08 0.09 0.00 0.63 4435.40 0.47 2.45 0.17 1.74 0.82 WW28 2003 33.59 74.30 1.56 0.09 1816.14 0.03 0.11 0.11 0.24 0.10 0.08 0.00 0.79 4607.56 0.40 3.22 0.19 1.95 1.36 WW28 2002 61.38 106.80 1.51 0.06 1560.35 0.03 0.07 0.03 0.38 0.03 0.10 0.47 4907.42 0.21 2.43 2.55 WW28 2001 62.22 149.94 2.46 0.22 1610.14 0.13 0.08 0.70 0.05 0.22 0.74 4855.91 0.45 4.11 WW29 2019 44.50 113.62 1.39 0.09 1464.56 0.01 0.03 0.02 0.20 0.03 0.14 0.00 0.58 4844.19 0.72 0.81 0.33 6.08 0.57 WW29 2018 16.17 95.25 1.11 0.09 1460.34 0.01 0.04 0.14 0.06 0.15 0.00 0.73 4830.45 0.35 0.40 0.25 4.98 0.90 WW29 2017 34.27 90.80 0.70 0.08 1570.23 0.02 0.04 0.14 0.06 0.11 0.00 0.61 4381.86 0.30 0.63 0.26 5.78 0.36 WW29 2016 17.01 81.77 0.89 0.09 1452.51 0.02 0.02 0.17 0.04 0.15 0.00 0.67 4459.61 0.70 0.48 0.27 8.19 0.51 WW29 2015 6.03 78.70 0.76 0.08 1344.26 0.02 0.04 0.26 0.06 0.11 0.00 0.54 4439.91 0.55 0.32 0.30 8.49 0.54 WW29 2014 19.04 79.37 1.10 0.10 1450.55 0.01 0.02 0.17 0.05 0.13 0.00 0.43 4410.70 0.34 0.48 0.29 6.53 0.67 WW29 2013 7.40 72.88 1.07 0.07 1414.33 0.01 0.03 0.03 0.13 0.07 0.12 0.00 0.55 4327.98 0.40 0.60 0.18 2.36 0.53 WW29 2012 3.31 68.40 1.00 0.08 1520.52 0.01 0.03 0.11 0.05 0.14 0.00 0.53 4115.23 0.36 0.41 0.19 2.28 0.42 WW29 2011 23.57 72.94 1.29 0.08 1626.91 0.01 0.02 0.12 0.04 0.10 0.00 0.67 4399.41 0.37 0.32 0.23 1.86 0.65 WW29 2010 26.30 76.21 1.37 0.11 1678.79 0.03 0.02 0.12 0.06 0.07 0.00 0.51 4490.07 0.30 0.72 0.28 1.45 0.53 WW29 2009 61.11 73.09 1.47 0.09 1197.29 0.04 0.04 0.04 0.15 0.06 0.07 0.00 0.51 4773.18 0.10 1.47 1.10 WW34 2019 33.49 119.39 1.93 0.07 2720.34 0.01 0.02 0.02 0.14 0.06 0.23 0.00 0.44 4445.52 0.55 2.15 0.14 2.74 0.72 WW34 2018 34.28 100.59 2.53 0.05 2788.27 0.02 0.01 0.03 0.10 0.05 0.24 0.00 0.39 4446.14 0.22 0.72 0.22 1.90 0.49 WW34 2017 16.39 97.71 2.27 0.05 2899.60 0.01 0.01 0.11 0.07 0.20 0.00 0.45 4614.78 0.19 0.39 0.08 1.70 0.54 WW34 2016 32.26 103.49 2.17 0.10 2850.64 0.01 0.02 0.02 0.12 0.07 0.18 0.00 0.63 4957.43 0.26 0.49 0.08 1.74 0.35 WW34 2015 20.84 110.27 2.97 0.08 2912.59 0.01 0.01 0.12 0.05 0.19 0.00 0.49 4795.63 0.20 0.79 0.08 1.84 0.67 WW34 2014 7.14 94.67 1.97 0.09 2489.45 0.01 0.02 0.02 0.11 0.06 0.13 0.00 0.62 4615.66 0.53 10.21 0.17 1.91 0.53 WW34 2013 32.35 84.65 1.72 0.11 2187.16 0.01 0.01 0.02 0.28 0.06 0.09 0.00 0.66 4565.31 0.75 8.91 0.13 2.93 0.64 WW34 2012 44.31 102.04 2.72 0.09 2081.49 0.01 0.02 0.01 0.19 0.06 0.06 0.00 0.60 4872.75 0.47 0.21 1.59 1.88

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