Benthic Community and Food Web Structure in Subarctic Lakes in Relation to Mining Disturbance

Dylan R. C. Bowes

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Master of Science in Environmental Sciences and Toxicology

Guelph, Ontario, Canada

ABSTRACT

BENTHIC COMMUNITY AND FOOD WEB STRUCTURE IN SUBARCTIC LAKES IN RELATION TO MINING DISTURBANCE

Dylan R. C. Bowes Advisor: University of Guelph, 2019 Dr. P. K. Sibley

In recent years, the exceptional growth in resource extraction activities in the Canadian

Arctic has led to concerns about potential impacts on aquatic ecosystems. Gold is the most commonly extracted resource with several new mines opening in Nunavut since 2010. In this study, I evaluated food web structure in six lakes potentially affected by the Meliadine Gold mine in Nunavut, an area characterized by relatively homogeneous geological and climatic conditions. I hypothesized that the homogeneous attributes of the landscape would yield similarity among lakes with respect to water and sediment chemistry, food web structure (stable isotope analysis), community composition of benthic invertebrates, and the relative condition of fish populations. I found few differences in the above parameters across lakes and between years, supporting this hypothesis. This study provides increased understanding of regional subarctic lake food webs in the context of anthropogenic activity which will be beneficial for designing monitoring programs as industrial activity in the region increases.

ACKNOWLEDGEMENTS

I would first like to thank my advisor, Dr. Paul Sibley, for providing me with this unique research opportunity. I entered this program without prior ecology experience, but I leave with a deep appreciation for the processes occurring in our lakes and streams. Your knowledge, advice, and support have guided me through this undertaking, and for that I am extremely grateful. I would also like to thank my committee members Dr. Keith Solomon, for his guidance in all things toxicological, and Dr. Neil Rooney, for sharing his expertise in stable isotope ecology. Going forth, I hope to make you all proud.

To everyone in the field and the lab that made this project possible, thank you. Thank you to the group at Agnico Eagle for providing accommodations and transportation, and to Glenn Kadlak and our other Kivalliq monitors for field assistance. Thank you to the Arctic Raptors group, particularly Kevin Hawkshaw and Alexandre Paiement, for the experience of working in this incredible area. Special thanks to Dr. Soren Brothers, for your limnological knowledge, hard work, and suffering during field trips at Meliadine. Thank you to the undergraduate students who spent long hours in the lab processing benthic and fish samples, especially Colleen Wardlaw and Rachel Irwin. Thank you to Dr. Marc Habash for the opportunity to develop eDNA skills while completing my thesis. Thank you also to Drs. Jose Rodriguez Gil and Ryan Prosser for your support and advice.

Thank you to my lab mates, especially Jordan Musetta-Lambert and Kristin Daoust. Working with both of you on your projects in White River and Turkey Lakes was both inspiring and unforgettable. Thank you, Jordan, for being a great office mate.

Thank you to my family, especially my parents, Tanya and Wayne, for your relentless support and encouragement. You instilled in me a drive to pursue my goals and continue to provide strong examples of hard-work, determination, and courage. Your love and wisdom have made the journey to this point possible and I would not be where I am today without you. Thank you to my wife, Melissa. You encouraged me to pursue this challenge and have been a constant support every step of the way. Your love, kindness, and understanding have made it possible to achieve this goal. Thank you to Melissa’s family for their support, kindness, and wisdom.

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TABLE OF CONTENTS

ABSTRACT ...... ii ACKNOWLEDGEMENTS ...... iii LIST OF TABLES ...... vii LIST OF FIGURES ...... xi 1. INTRODUCTION AND LITERATURE REVIEW ...... 1 1.1. Mining in the Canadian Arctic ...... 1 1.1.1. History of Mining in the Canadian Arctic ...... 1 1.1.2. Current and Future Mining Operations ...... 2 1.1.3. Environmental Impacts of Mining ...... 4 1.1.3.1. Tailings ...... 4 1.1.3.2. Other considerations ...... 5 1.2. Ecology of the Canadian Arctic ...... 6 1.2.1. Benthic Algae and Phytoplankton ...... 6 1.2.2. Zooplankton ...... 8 1.2.3. Benthic Macroinvertebrate Communities ...... 8 1.2.4. Fish Community ...... 10 1.3. Isotope Analysis ...... 12 1.3.1. Background and Purpose of Isotope Analysis ...... 12 1.3.2. Existing Isotope Research ...... 13 1.4. Problem Formulation and Purpose of Study ...... 13 2. MATERIALS AND METHODS ...... 18 2.1. Site Description ...... 18 2.2. Sample Collection and Processing...... 23 2.2.1. Water Chemistry ...... 23 2.2.1.1. Chlorophyll a ...... 23 2.2.2. Sediment Sampling and Analysis ...... 25 2.2.2.1. Particle size analysis ...... 25 2.2.3. Community Assessments ...... 26 2.2.3.1. Zooplankton ...... 26

2.2.3.2. Benthic invertebrates ...... 27 2.2.3.3. Fish...... 27 2.2.4. Stable Isotope Analysis ...... 30 2.2.4.1. Seston and periphyton ...... 30 2.2.4.2. Zooplankton ...... 31 2.2.4.3. Benthic invertebrates ...... 32 2.2.4.4. Fish...... 32 2.3. Data Analysis ...... 32 2.3.1. Water and Sediment Chemistry ...... 32 2.3.2. Benthic Community Analysis ...... 34 2.3.3. Fish Metrics and Truss Analysis ...... 36 2.3.4. Isotope Analysis ...... 36 3. RESULTS ...... 38 3.1. Physicochemical Data ...... 38 3.1.1. Water ...... 38 3.1.2. Sediment ...... 45 3.1.3. Chlorophyll a ...... 51 3.2. Benthic Community Data ...... 52 3.2.1. Abundance Values and Percent Composition ...... 52 3.2.2. Diversity Metrics ...... 64 3.2.3. Canonical Correspondence Analysis ...... 66 3.3. Fish Metrics ...... 68 3.3.1. Condition, GSI, HSI, and Age ...... 68 3.3.2. Truss Analysis...... 74 3.3.3. Gut Content Analysis ...... 77 3.4. Isotope Analysis ...... 80 4. DISCUSSION AND CONCLUSION ...... 89 4.1. Water and Sediment Chemistry ...... 89 4.2. Chlorophyll a ...... 91 4.3. Community Composition of Benthic Invertebrates ...... 92 4.4. Fish Metrics ...... 96

4.5. Food Web Analysis ...... 97 4.6. Detectable Effects from Exploration Phase ...... 99 4.7. Implications and Conclusions ...... 103 5. REFERENCES ...... 105 6. APPENDICES ...... 121

LIST OF TABLES

Table 3. Water chemistry parameters (± standard deviation) measured from an elemental scan of samples collected in 2015-2016...... 44

Table 6. Mean sestonic and periphytic chlorophyll a (± standard deviation) by lake for 2015- 2016...... 52

Table 7. Summary of mean benthic invertebrate density (abundance; in number of individuals/m2) and mean percent composition data by lake (± standard deviation) for benthic grab samples collected in 2014 and 2015...... 54

Table 8. Mean abundance and mean percent composition data (± standard deviation) for Chironomidae identified to genera by lake for 2014 and 2015...... 59

Table 9. Mean (± standard deviation) lake values for total taxa and diversity indices for benthic invertebrates collected in 2014 and 2015...... 65

Table 10. Mean (± standard deviation) watershed values for total taxa and diversity indices for benthic invertebrates collected in 2014 and 2015...... 65

Table 11. Length, weight, and Fulton’s condition factor (± standard deviation) for all large species fish (dissected and non-dissected) caught from 2015-2017...... 70

Table 12. Mean gonadosomatic (GSI) and hepatosomatic (HSI) indices as well as morphometric data (± standard deviation) for large fish species...... 73

Table 13. Mean age (± standard deviation) of large fish species caught in 2015...... 74

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Table 14. Variance explained by the first three principal components of an analysis of truss data for Coregonus artedi, Thymallus arcticus, and Salvelinus namaycush caught from 2015-2017. 74

Table 17. Mean littoral proportion and mean trophic position (± standard deviation) for each type of fish caught in study lakes. Mean δ13C and δ15N values for littoral sampled invertebrates were used as surrogate data for the littoral baseline and values for phytoplankton were used as the pelagic baseline...... 87

Table 18. Correlation table for isotope and fish gut content data for 2015 samples. Correlation coefficients with an absolute value of ≥ 0.70 are indicated with an asterisk...... 87

Table A1. Summary of watershed characteristics. The number of total lakes is based on a rough count of lakes on a watershed map. N represents the number of lakes involved in calculating the total area and volume based on limited data availability...... 122

Table A2. Lake physicochemical parameters by site. Site results are depth integrated over the entire water column...... 122

Table A3. Summary of the results of one-way ANOVA tests for water and sediment physicochemical parameters, chlorophyll a, benthic invertebrate diversity metrics, and percent composition data for benthic taxa which comprised 97% of benthos as well as Chironomidae genera which comprised 84% of Chironomidae larvae...... 125

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Table A7. Method detection limits (MDLs) for elemental analyses performed on water samples...... 132

Table A9. Results of an elemental scan of as well as total nitrogen and total carbon for sediment samples. Note that results for certain sites are not available due to sample discarding...... 135

Table A10. Method detection limits (MDLs) for elemental analyses performed on sediment samples...... 137

Table A12. Summary of benthic invertebrate density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples...... 139

Table A13. Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2014...... 141

Table A14. Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2015...... 144

Table A15. Summary of benthic invertebrate diversity metrics by site and mean values (and standard deviation) by lake for benthic grab samples...... 147

Table A16. Results of the analysis of variance (ANOVA) for water and sediment chemistry data from 2015-2016 and 2014-2016, respectively...... 149

Table A17. Deployment times, catch rates, and species caught for fish sampling in 2016...... 150

Table A18. Summary of the results of one-way ANOVA tests for fish by species (for fish caught from 2015-2017)...... 151

Table A19. Summary of p-values for post-hoc Tukey’s pairwise comparisons by lake of fish morphometric data (length, weight, Fulton’s condition factor, and HSI) by species...... 152

Table A21. Truss data for fish caught from 2015-2017. Numerical intervals along the top row represent distances between key landmarks on each fish (represented by numbers 1-10)...... 155

Table A22. Number of fish caught per lake for the purposes of gut content analysis in 2015 and 2016...... 158

Table A23. Gut contents separated by prey group for fish caught in 2015 and 2016...... 161

Table A24. Year source of data in year-averaged stable isotope plots...... 165

Table A25. Stable isotope data for phytoplankton samples collected in 2014...... 166

Table A26. Isotope data and carbon and nitrogen percentages for zooplankton collected from 2014-2016...... 166

Table A27. Isotope data and carbon and nitrogen percentages for aquatic invertebrates collected from 2014-2016...... 167

Table A28. Summary of isotopic data and carbon and nitrogen percentages of fish samples collected in 2015 and 2017...... 169

Table A29. Summary of mean isotope data (and standard deviation) by lake within fish species for samples collected from 2015 to 2017...... 171

Table A30. Isotope data and carbon and nitrogen percentages for adult life stage (terrestrial) invertebrates collected from 2014-2016...... 172

Table A31. Isotope data and carbon and nitrogen percentages for spiders collected from the shorelines of study lakes in 2016...... 172

Table A32. Summary of the results of one-way ANOVA tests for δ13C and δ15N values for fish and zooplankton against year (within lake)...... 173

Table A33. Summary of p-values for post-hoc Tukey’s pairwise comparison of annual data for δ13C and δ15N values for fish (by species) and zooplankton...... 174

Table A34. Summary of p-values for post-hoc Tukey’s pairwise comparison of the proportion of littoral carbon and trophic position by lake (by fish species)...... 175

LIST OF FIGURES

Figure 1. Map of the territory of Nunavut, Canada. Mainland Nunavut is depicted as the large landmass in the lower left, and borders Northwest Territories to the west and the province of Manitoba to the south. Kivalliq comprises most of mainland Nunavut as well as Southampton Island and Coats Island...... 2

Figure 2. A generalized conceptual model showing potential aquatic ecological impacts following anthropogenic disturbance (directly via mining and indirectly via climate change). Bolded lines and shaded boxes depict the pathways and effects that are examined in this study (via stable isotope analysis for food web components, except DOC). Community composition of benthic invertebrates (bolded in the diagram) is also presented in this study...... 15

Figure 3. View of the regional landscape. Photo taken in August of 2015 between Lake A1 and Meliadine Lake looking west. Discovery mine camp is on the horizon...... 19

Figure 4. (A) Map showing the location of Meliadine Lake within Canada. The Meliadine gold mine (project area outlined) is located on the Meliadine peninsula (bordered by Meliadine Lake). (B) Expanded view of the gold mine and study area. The location of the mining camps and main access road (dashed line) from Rankin Inlet (approximately 15 km south by southeast) are indicated, as well as the study lakes...... 20

Figure 5. Study lakes showing sampling stations, zooplankton tow transects, and gill net placements...... 22

Figure 6. Diagram of landmarks used in truss analysis. Recreated from Sabadin et al. (2010). 29

Figure 7. Principal components analysis of water physicochemical data for the six lakes for 2014 (A) and 2015 (B)...... 39

Figure 8. Principal components analysis of sediment physicochemical parameters for the six lakes in 2014 (A) and 2015 (B)...... 45

Figure 9. Canonical correspondence analysis of benthic invertebrate data collected in 2014 (A) and 2015 (B) as a community data matrix with select sediment and water physicochemical data as a constraining matrix. Environmental variables with absolute factor loading values ≥ 90th percentile across selected components in a principal component analysis for sampling stations across lakes were selected for the environmental variable dataset used in the CCA. Sediment chemistry data for 2014 was supplemented for Lake A8 (average values of 2015-2016) and Lake B7 (2016 values) due to accidental sample discarding. For the same reason, sediment chemistry data for 2015 was supplemented for Lake B2 (average values of 2014 and 2016) and lakes B7 and D7 (2016 values)...... 67

Figure 10. Log-log plot of weight vs. length for whitefish (A) and grayling (B) caught from 2015-2017, and lake trout (C) caught from 2015-2016...... 71

Figure 11. Principal component analysis of truss data for Coregonus artedi, Thymallus arcticus, and Salvelinus namaycush caught from 2015-2017...... 76

Figure 12. Diet comparison of Coregonus artedi (whitefish), Thymallus arcticus (grayling), and Salvelinus namaycush (Lake trout) averaged from fish sampled from 2015 and 2016. Prey are separated into 8 groups (fish, Nematoda, Mollusca, Limnephilidae, Chironomidae, zooplankton, Amphipoda, and other) which collectively accounted for 100% of gut content material across all lakes...... 78

Figure 13. Mean diet composition by lake for whitefish (Wf), grayling (Gr), and lake trout (Lt) from 2015-2016...... 79

Figure 14. Stable isotope plots for each study lake (indicated in the upper right corner of each plot). Life stages of invertebrates are indicated in brackets (a=adult, p=pupae, l=larvae). Stable isotope compositions are expressed as delta values in permille differences from a standard (air for nitrogen and VPDB for carbon)...... 83

Figure 15. Nitrogen isotope ratio in fish tissue relative to the proportion of fish found in their respective gut contents (A) and carbon isotope ratio in fish tissue relative to the proportion of Amphipoda found in their respective gut contents (B)...... 88

Figure A1. HSI versus length for whitefish (p = 0.047)...... 158

Figure A2. Principal component analysis of truss data for Coregonus artedi and Thymallus articus (caught from 2015-2017) as well as Salvelinus namaycush (caught from 2015-2016).. 159

Figure A3. Principal component analysis of fish gut contents (by prey group) for whitefish, grayling, and lake trout caught from 2015-2016. Note that the whitefish B7-WF-7 (caught in 2016) was not included in the analysis as the high proportion of zooplankton in the gut of that fish made the scale of the plot too large to view the ordination of the remaining data...... 160

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1. INTRODUCTION AND LITERATURE REVIEW

1.1. Mining in the Canadian Arctic

1.1.1. History of Mining in the Canadian Arctic

Mining and metal utilization are deeply entrenched in the history of Canada. Radiocarbon dating has shown that the proto-Inuit Thule people used iron from Naujaat (formerly Repulse Bay) long before the arrival of European settlers (McCartney and Mack 1973). Tools were crafted from iron obtained from meteorites and copper from geological deposits (Pringle 1997). Martin Frobisher established mining operations on Baffin Island as early as 1577 (Sandlos and Keeling 2009). Iron deposits at Trois-Rivières in Quebec were exploited by early settlers from Europe as part of the first large-scale industrial operations starting in 1738. The discovery of gold deposits in Quebec and Nova Scotia were eventually followed by gold rushes to the Fraser Canyon (British Columbia 1858) and the Klondike (Yukon 1896). The latter led to a mass migration of prospectors to the region between 1896 and 1899 known as the Klondike Gold Rush (Sandlos and Keeling 2009). Gold was discovered in Yellowknife in 1935, and following surveying of the deposits, Giant Mine opened in 1948. Until its closure in 2004, the mine produced over 7,000,000 troy ounces of gold (Silke 2009). The 20th century saw the operation of several other gold mines in the Northwest Territories. The latter half of the 20th century saw large-scale gold operations expand to Nunavut (Figure 1), with the opening of the Lupin mine in 1982 (Nuna Logistics 2007).

Southampton Island Coats Island Mainland Nunavut

Figure 2. Map of the territory of Nunavut, Canada. Mainland Nunavut is depicted as the large landmass in the lower left, and borders Northwest Territories to the west and the province of Manitoba to the south. Kivalliq comprises most of mainland Nunavut as well as Southampton Island and Coats Island.

1.1.2. Current and Future Mining Operations

Mining is a significant contributor to the economy of the Canadian Arctic. In 2014, mining contributed $57 billion (or ~8%) to the GDP of Canada (Musetta-Lambert et al. 2018; MAC 2015). Of this total, $24 billion was derived from ore extraction and the remainder from subsequent processing. The mining industry in Canada employs approximately 375, 000 people and contributes to 18.2% of all exports (Musetta-Lambert et al. 2018; MAC 2015). The economies of Canada’s territories have been increasing at a higher rate than anywhere else in the country, with mining being the most important economic driver (Jeffrey 2013). Mining activities have been facilitated by increased transportation infrastructure, including improved access by air, land, and sea. Increased feasibility of commercial transit in the Northwest Passage could provide access to mining jobs and economic benefits previously unavailable to Canadians (Jeffrey 2013). Iron operations at Mary River (Baffin Island), a push for an all-weather road from Nunavut to

Manitoba (CBC News 2012) and a deep-water port at Grays Bay, Nunavut with an all-weather road servicing diamond mines in the Northwest Territories (Murray 2018) all highlight a rise in mining related activities in the region. Heightened investment from major mining corporations (Goldcorp Inc., Newmont Mining Corp., and Barrick Gold Corp.) over the last couple of years in the Yukon, and similar investments by Agnico Eagle Inc. in eastern Nunavut, suggests a potential 21st century gold rush in these Arctic territories (Freeman 2017).

Several gold deposits in the Northwest Territories and Nunavut have recently been purchased by Silver Range Resources Ltd. (Wiles 2016). Current and near-future operations in Nunavut include the Hope Bay, Back River, Meadowbank, and Meliadine mines. Commercial production began at Hope Bay in 2017. Proven and probable reserves at the property total 3.6 million ounces at an average grade of 7.7 g/t Au (TMAC Resources 2018). Federal authorities recently provided permitting for the Madrid-Boston gold project to proceed, adding three mines to the Hope Bay gold project, and substantially increasing the tailing capacity of the existing Doris Mine (Canadian Mining Journal 2018). The Back River project consists of six mineral deposits and is expected to commence commercial operations in 2019. The project will have an estimated 10-year life, with an estimated average production of 300,000 oz of gold annually (Sabina Gold and Silver Corp. 2018). The Meadowbank project commenced commercial production in 2010 and will extract a total of roughly 2.71 million ounces in proven and probable gold reserves prior to closure in 2019. However, discovery of additional large-scale deposits at Amaruq has led to approval of the satellite deposit for development, with full-scale production commencing in 2019 (Agnico Eagle 2018a). The Meliadine project will also commence commercial production in 2019, forecasted to total 5.7 million ounces of gold over a mine life of 15 years (Agnico Eagle 2018b). Proven and probable reserves at Meliadine are the highest-grade gold reserves for the company, with an average grade of 7.12 g/t between the open pit and underground mines.

The rapid increase in gold mining in the Canadian Arctic in recent years, combined with the anticipated increase in other forms of mining such as exploitation of rich deposits of rare earth elements in the eastern Canadian Arctic (Schott 2016), will significantly increase environmental pressures in the region. The nature of extracting from extensive deposits imparts

3 significant alterations on the landscape within the footprint of the mining operations. The magnitude of these alterations on a local scale is dependent upon the specific methods employed.

1.1.3. Environmental Impacts of Gold Mining

1.1.3.1. Tailings

Gold operations in Nunavut employ both open pit and underground mines (Agnico Eagle 2018b). Waste rock from open pit and underground mines is transported to a waste dump, which is located at the surface of the current pit, or in an exhausted pit. Leftover waste from ore processing is referred to as tailings. Tailings are typically a slurry which is pumped to a tailings dam and the water evaporated. Tailings can be toxic due to the presence of sulfide or other toxic gangue minerals, as well as cyanide employed in gold extraction. As such, tailings present an environmental concern. Tailings dams, unlike water dams, are designed for permanent containment. However, the failure rate of tailings dams is two orders of magnitude higher than that of conventional water dams (Azam and Li 2010). Failures of tailings dams have caused significant ecological damage, such as those at Ok Tedi (New Guinea 1984), Mount Polley (British Columbia 2014), and Bento Rodrigues (Brazil 2015). Direct leaching of contaminated water into the subsoil by tailings containment ponds can also occur (e.g. Olympic Dam mine, Australia). Tailings ponds may cause environmental damage through the release of toxic metals (e.g. arsenic, cyanide, and mercury) (Franks et al. 2011). In a study of dietary arsenic exposure in lake whitefish and lake trout, histological lesions were observed in the gallbladder, kidney, pyloric caeca, and intestine of whitefish (Pedlar et al. 2002). In addition, weight gain was lower in both species fed contaminated diets, presumably due to a reduction in feed consumption mediated by chemoreceptory detection of As (Pedlar et al. 2002). The liver and kidney are also known to be sites of accumulation of As. Inorganic mercury is methylated by bacteria into methyl mercury. This occurs predominantly in anaerobic sediments but can also occur in the gills or gut of fish (Boening 2000). Methyl mercury can penetrate the intestinal wall and lead to accumulation in fish. Sublethal mercury exposure can result in a range of physiological and reproductive effects in fish (Boening 2000). Concentrations in streams near active placer mines

4 have been found to be only slightly less than estimated no-effect concentrations in Arctic grayling, while arsenic concentrations in these streams encompass the range of no-effect concentrations (Buhl and Hamilton 1991). Acidity is generated in tailings via oxidation of sulphide minerals (mainly pyrite) and ferrous iron (Moreno and Neretnieks 2006). This can lead to acid mine drainage (AMD) which can have significant ecological impacts for centuries in areas where conditions favour acid drainage (Camden-Smith and Tutu 2014; Salonen et al. 2006). Tailings ponds may also cause environmental damage via degradation of water quality (Franks et al. 2011).

Tailings can distribute across stream basins over time. Tailings from an abandoned gold mine were found to continuously release arsenic, mercury, lead, thallium, and other metals, contaminating downstream ecosystems 50 years post-closure (Wong et al. 1999). This resulted in stream water and sediments downstream which were toxic to the benthic community, as well as loss of fish habitat. Release of cyanide-containing effluents from a Canadian tailings pond resulted in a fish kill of over 20,000 steelhead trout (Leduc 1984; cited in Eisler and Wiemeyer 2004). Despite the short half-life and rapid oxidation of cyanides in aquatic environments, acute lethality and non-lethal effects could therefore pose a threat to affected lakes and streams (Eisler and Wiemeyer 2004). Carbon-in-leach technology employs cyanide as a lixiviant to form a gold cyanide complex which is then extracted from the slurry by adsorption onto activated carbon. Oxidation of cyanide to the less toxic cyanate (OCN-) using a Cu2+ catalyst in the presence of sulphur dioxide and air is widely used for treatment of tailings and process solutions (Akcil 2003). However, cyanate is still toxic and can, in turn, generate ammonia which can also be toxic (Akcil 2003).

1.1.3.2. Other considerations

The other main component of waste generated during large-scale mining is waste rock. Waste rock is typically stored as large, partially saturated, and porous piles. Like tailings ponds, waste rock can produce AMD via bacteria-catalyzed sulfur oxidation (Lefebvre et al. 2001). Dust generated from mining, transportation and storage of waste rock, and roads can settle into surrounding lakes, affecting water chemistry and potentially aquatic ecosystems (Trombulak and Frissell 2000). 5

Mainland Kivalliq is covered with small inland lakes. In certain cases, mining in this environment necessitates large-scale dewatering and diking of lakes. Dewatering (draining) of a lake involves pumping a major volume of the lake to a discharge point (often another lake) away from the site. Diking may be used to isolate and pump water from a specific portion of the lake without draining the entire lake. Such lakes are either designated to become tailings ponds or open pit mines and are further examples of major physical alterations to the landscape. In these cases, environmental assessments are conducted to determine fish populations and habitat in the lakes for the purpose of identifying potential suitable habitat in other lakes for fish relocation.

Other factors include release of bioreactor-treated effluent (wastewater) which can be elevated in TOC, nitrogen, and phosphorus. This can result in localized eutrophication in receiving surface water. Lastly, there is potential for accidental releases of chemicals, notably spills of gasoline and diesel fuels which must be stored on-site due to a lack of long-distance transportation infrastructure during the winter months. Risk is increased in subarctic settings as the high abundance of lakes and streams results in proximity of mining operations to surface waters, increasing the probability of exposure (Lemly 1994). A thorough characterization of the ecology of lakes surrounding mining activities is therefore important in understanding how operations will impact aquatic food webs.

1.2. Ecology of the Upper Kivalliq, Nunavut Region

1.2.1. Benthic Algae and Phytoplankton

There is strong evidence, particularly in small freshwater lakes with high littoral area to volume ratios (i.e. Arctic lakes), that benthic energy pathways play a key role in lake food webs (Vadeboncoeur et al. 2002). Freshwater benthic bacterial productivity and benthic invertebrates are poorly represented in Arctic aquatic literature which has tended to focus on marine ecosystems. Benthos contributes strongly to production in freshwater lakes and energy pathways crossing “boundaries” between benthic and pelagic habitats are prevalent (Vadeboncoeur et al. 2002). In shallow oligotrophic lakes, periphyton can account for more than 95% of primary productivity; however, even deeper oligotrophic (and transparent) lakes can be dominated by

6 benthic energy (Vadeboncoeur et al. 2002). For example, benthic photosynthesis accounted for 80% of total lake photosynthesis in the ultraoligotrophic Char Lake, Northwest Territories (Welch and Kalff, 1974). In addition, Sierszen et al. (2003) determined that turbidity did not affect food web structure via suppression of benthic algae by shading, as was previously found (Hecky and Hesslein 1995). Laboratory experiments revealed that phytoplankton in oligotrophic lakes tend to be nutrient-limited relative to periphyton, which has access to nutrients in sediment (Hansson 1988, Hansson 1990). The consequence of nutrient-limitation in oligotrophic lakes is that phytoplankton may be inadequate to support planktivorous food webs, shifting consumer reliance to benthic-derived energy (Sierszen et al. 2003). These findings support the results of many other studies demonstrating the dominance of benthic primary productivity in Arctic lakes (e.g., Ask et al. 2009, Bonilla et al. 2005, Eloranta et al. 2010, Hershey et al. 2006, Karlsson and Bystrom 2005, Klemetson et al. 2003, Rautio and Vincent 2006, Rigler 1978, Vadeboncoeur and Steinman 2002, Vander Zanden et al. 2011).

Benthic periphyton consists mainly of cyanobacteria in Arctic habitats (Rautio et al. 2011). Above the tree line in the Canadian subarctic, shallow ponds typically have rocky bottoms covered with a film of cyanobacteria and, to a lesser extent, Chlorophyta and diatoms (Stanley and Daley 1976; Douglas and Smol 1995; Bonilla et al. 2009; Rautio and Vincent 2006). Diversity of species of benthic algae in the region has been shown to be inversely correlated with latitude, likely due to differing lengths of growing seasons (Rautio et al. 2011; Michelutti et al. 2003).

Phytoplankton (pelagic algae) communities of freshwater Arctic lakes generally exhibit similar composition compared to temperate regions (Rautio et al. 2011; Sheath and Steinman 1982, Sheath 1986). A review of 44 studies of phytoplankton and periphyton composition of lakes in the Northwest Territories demonstrated that major groups were Bacillariophyceae (48.3%; 761 of 1577 taxa), Chlorophyceae (30.5%; 481 taxa), and Cyanophyceae (11.0%; 173 taxa). Minor groups (< 5%) included Chrysophyceae, Dinophyceae, Cryptophyceae, Euglenophyceae, Xanthophyceae, Florideophyceae, Prasinophyceae, Prymnesiophyceae, Craspedophyceae, and Bangiophyceae (Sheath and Steinman 1982). Two glaucophytes (undetermined taxonomic position) were also cited. Green algae, chrysophytes, diatoms, and cyanobacteria contributed 79% of overall species composition (Rautio et al. 2011; Sheath 1986).

During the short summer growing season, the light phase of the photoperiod is extended. The reduced diurnal dark period inhibits growth of algae requiring day-length induction. Despite the necessity of freeze-resistant spore formation, there are few endemic algal species in the region, with species being predominantly cosmopolitan (Sheath and Steinman 1982).

1.2.2. Zooplankton

Overall, diversity of zooplankton and zoobenthos in subarctic lakes is low, due to a short- growing season, increased exposure to UV radiation, and lake freezing in winter (and potentially drying out in summer) (Rautio et al. 2011). In Canadian Arctic islands with < 350 degree-days only three species (Limnocalanus macrurus, Drepanopus bungee, and Eurytemora sp.) are found, of which only D. bungee tolerates freezing in ponds (Rautio et al. 2011; Hebert and Hann 1986). Certain Cladocera have a mechanism of protection against UV radiation, by pigmentation with photoprotective melanin, allowing survival in this environment (Rautio et al. 2011; Hebert and Emery 1990; Hessen 1996). Zooplankton in ponds which freeze are larger and 2–5 times more abundant than those in deeper lakes due to the absence of fish (Rautio et al. 2011; Rautio and Vincent 2006). Anostraca (fairy shrimp) are adapted to harsh conditions and five species are present in circumpolar ponds. Climate change will have a large impact on zooplankton diversity in the region, with documented increases from 8–35 species corresponding with a 12°C increase in mean July air temperature as a latitudinal gradient (Rautio et al. 2011). Chydoridae, Ostracoda, and insect larvae are positively correlated and Calanoida are negatively correlated with lake temperature (Rautio et al. 2011; Korhola 1999). There is evidence that phytoplankton primary production is insufficient in certain Arctic lakes to support zooplankton and, as a result, they compensate by using periphyton as a food source (Rautio et al. 2011).

1.2.3. Benthic Macroinvertebrate Communities

Chironomidae dominate the benthos in the Canadian Arctic with diversity being negatively correlated with latitude (Rautio et al. 2011; Welch 1991). There are > 30 species present at 63°N, and often abundant are Chironomus, Corynoneura, Cricotopus, Orthocladius, Procladius, Sergentia, and Tanytarsini (Walker and Mathewes 1989). Medeiros and Quinlan

(2011) found many taxa of the tribe Chironomini near Rankin Inlet, including: Microtendipes, Dicrotendipes, Cladopelma, Polypedilum, Cryptochironomus, Parachironomus, Endochironomus, and Glyptotendipes. Temperature is a significant factor controlling distribution of benthic species, with the high Arctic islands having the lowest diversity. Other important invertebrates in subarctic lakes include Ostracoda, Amphipoda, Mollusca, Turbellaria, and Oligochaeta; however, overall species diversity is often much lower than in more temperate regions (Rautio et al. 2011). In a study of 17 lakes and 3 ponds near Iqaluit and Rankin Inlet, Nunavut, Namayandeh and Quinlan (2011) determined that Gammarus lacustris lacustris was the most abundant single taxon. Amphipoda and Chironomidae were the dominant taxonomic groups. Rankin Inlet sites had lower Amphipoda and higher Plecoptera abundance compared to Iqaluit sites. Many groups were also present at Rankin Inlet which were absent at Iqaluit, including: Bivalvia, Ceratopogonidae, Collembola, Ephemeroptera, Gastropoda, Nematoda, and Plecoptera. At Rankin Inlet, Diptera (predominantly Chironomidae) constituted about 50% of overall relative abundance, Hydracarina and Trichoptera each contributed about 12%, Amphipoda, Oligochaeta, and Plecoptera each contributed about 7%, and all other groups contributed < 5% each.

Benthic invertebrates can be a useful tool for monitoring environmental change, particularly in the Canadian Arctic, where lakes and ponds are a primary landscape feature (roughly 18% of Canada’s surface waters are north of 60°N; Douglas and Smol 1999). Previous studies have investigated benthic invertebrate communities in the Northwest Territories (Welch 1973, Johnson 1975, Rosenberg and Wiens 1976, Welch 1976, Rosenberg and Wiens 1978, Andrews and Rigler 1985, Welch et al. 1987, Young and Mackie 1990, Aitken and Fournier 1993, Walker and MacDonald 1995, Currie et al. 2000, Walker et al. 2003) and Alaska (Butler 1982, Butler 1982b, Lougheed et al. 2011, Braegelman 2015, Butler and Braegelman 2018); however, only a few studies have examined the ecology of the benthic macroinvertebrate communities of freshwater subarctic lakes in Nunavut (Welch 1991, Namayendeh and Quinlan 2011, Medeiros and Quinlan 2011, Rautio et al. 2011). These studies have focused on the effect of environmental gradients acting at local and regional scales on benthic invertebrate communities. Medeiros and Quinlan (2011) examined the distribution of Chironomidae across environmental gradients in lakes and ponds in the eastern Canadian Arctic, with a large portion of the dataset being from the Kivalliq region and particularly Rankin Inlet. The researchers 9 determined that distribution of Chironomidae primarily followed a temperature gradient, with secondary relationships associated with gradients in nutrients and major ions. Namayandeh and Quinlan (2011) similarly examined benthic macroinvertebrate communities in lakes and ponds near Iqaluit and Rankin Inlet, Nunavut. The authors found that ecosystem-scale lake characteristics followed by substrate composition were most closely correlated with relative abundance.

1.2.4. Fish Community

Salvelinus alpinus (Arctic char) is present, predominantly in the upper mainland, Esox lucius (northern pike) predominantly in the lower mainland, and Thymallus arcticus (Arctic grayling), Salvelinus namaycush (lake trout), Lota lota (burbot), Cottus cognatus (slimy sculpin), and several species of Gasterosteidae (sticklebacks) present throughout most of mainland Nunavut. The subfamily Coregoninae (freshwater whitefish), consisting of several species of freshwater and anadromous whitefishes, are present throughout the region. Sander vitreus (walleye) and Salvelinus fontinalis (brook trout) are also present. Of the common Arctic fish species described above, Arctic grayling, lake trout, lake whitefish, Arctic char, and sticklebacks are the most common in the Meliadine region. Benthic and littoral invertebrates are an important food source for many of these fish species which is consistent with the findings of Sierszen et al. (2003) who found benthos to be the primary carbon source for all species of benthic and littoral fish present in Arctic lakes in the Toolik Lake region (Alaska). near Fairbanks, Alaska. However, smaller fish species can also comprise a large proportion of some fish diets. The diets of each of the fish species in the Meliadine region are briefly described below.

Arctic grayling are opportunistic feeders, with highly variable diets. They feed largely on adult stages of beetles, Chironomidae, Plecoptera, and Trichoptera, but also larval Ephemeroptera, Chironomidae, Plecoptera and Trichoptera. To a lesser extent, they also consume Amphipoda, Mollusca, and small fish and fish eggs (Stewart et al. 2007). Juvenile Arctic grayling are planktivorous, employing a stop and go feeding strategy (as opposed to ambush or continuous searching), whereas larger grayling (> 130 mm) rarely consume zooplankton because prey smaller than 1.5 mm are not retained by their gillrakers (Stewart et al. 10

2007). De Bruyn and McCart (1974) examined grayling from lakes and rivers in the Yukon, and found they relied greatly on surface insects, but also fed extensively on Trichoptera larvae, Amphipoda, and ninespine stickleback (Pungitius pungitius).

Ninespine stickleback in an Arctic lake were found to feed primarily on Chironomidae larvae and zooplankton (especially Copepoda and Daphnia) (Cameron et al. 1973). Three- spined stickleback (Gasterosteus aculeatus) have been found to feed on Oligochaeta, Sphaerium, Hydrobia, Cladocera, Copepoda, Ostracoda, Crustacea, Chironomidae larvae and pupae, diatoms, and detritus (Hynes 1950; Milinski and Heller 1978).

Lake trout in Toolik Lake in Alaska consumed Mollusca, fish (including Cottus cognatus), Trichoptera, Chironomidae, and zooplankton (Merrick et al. 1991). Large lake trout appear to restrict the foraging behavior of smaller lake trout; in the absence of larger trout, smaller trout shifted their diet to an increased reliance on offshore zooplankton (Keyse et al. 2007). Furthermore, there is evidence of resource polymorphism in lake trout, with both insect- feeding and fish-eating morphotypes co-occurring in Great Bear Lake (Blackie et al. 2003). Piscivorous lake trout (PLT) reach much larger sizes and grow at much faster rates than non- piscivorous lake trout (NPLT; Pazzia et al. 2002). NPLT consume much more food on a per weight basis; however, this difference was only slight when corrected for caloric content. NPLT also expended more energy foraging on smaller prey. Lake trout preyed primarily on littoral prey fish (Eloranta et al. 2015b).

Freshwater whitefish in Arctic lakes have been shown to consume Mollusca, Trichoptera, and Chironomidae (Merrick et al. 1991). Whitefish typically outcompete Arctic char for food sources, however, Amundsen et al. (2010) described niche partitioning of an Arctic char population in a subarctic lake in which they coexisted with grayling and whitefish. In this lake, grayling fed predominantly on surface insects and larval Trichoptera, while whitefish fed throughout the lake (mainly on crustaceans, notably zooplankton), and char fed predominantly on benthic invertebrates, consuming insects and snails (Amundsen et al. 2010).

Analysis of gut contents can provide useful dietary information for local fish populations and has long been applied in the characterization of trophic interactions. However, this approach may not accurately reflect the importance of primary energy pathways because gut content

11 analysis is reflective of short-term feeding patterns and not necessarily of time-integrated consumption of prey (Kling et al. 1992). By combining gut content analysis with stable isotope data, it is possible to distinguish more accurate trophic interactions.

1.3. Isotope Analysis

1.3.1. Background and Purpose of Isotope Analysis

Stable isotopes analyses are commonly applied to describe predator-prey interactions and energy transfer to elucidate food web structure in aquatic systems (Kling et al. 1992). The major isotopes of interest to aquatic ecologists are 13C, 15N, and 34S (Peterson and Fry 1987). Sulfur isotopes can be used to distinguish benthic and pelagic food sources. Similarly, carbon isotopes are useful in understanding the primary source of energy in an ecosystem. The slow rate of diffusion of CO2 through the unstirred boundary layer surrounding periphyton limits the rate of photosynthesis (Keeley and Sandquist 1992). This diffusive resistance is important because, within the boundary layer surrounding periphyton, there is a resulting finite carbon source. This leads to decreased biochemical discrimination by ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCo), and carbon fixation leads to an accumulation of the otherwise discriminated 13C (Keeley and Sandquist 1992; France et al. 1995b). This drives carbon enrichment of periphyton relative to phytoplankton, and as a result, globally, benthic algae is 13C-enriched by ~7‰ relative to planktonic algae in both lakes and oceans. Furthermore, since carbon is minimally fractioned with food assimilation, this difference in carbon signature is typically reflected in consumers (France et al. 1995b). Nitrogen isotopes reflect the trophic level of organisms in the food web. Analysis of organism waste shows that there is depletion of 15N relative to the diet, and thus there is a 15N enrichment in consumers relative to prey in an ecosystem. Accounting for habitat variation, average enrichment tends to range from 3–4‰ in aquatic ecosystems. In summary, typically consumers have carbon signatures similar to their food source, whereas nitrogen signature (15N content) increases consistently with increasing trophic level thus providing a basis to resolve trophic structure in food webs (Kling et al. 1992).

1.3.2. Existing Isotope Research

The use of stable isotopes to study food web structure in freshwater lakes has been steadily increasing (Beaudoin et al. 2001; Kling et al. 1992; Peterson and Fry 1987; Herwig et al. 2004). Subarctic lakes are typically characterized by simple aquatic food webs that are ideal for advanced understanding of trophic interactions (Kling et al. 1992; Sierszen et al. 2003). However, while studies of isotope analysis of marine Arctic foodwebs are abundant, there are limited data on stable isotope analysis of freshwater Arctic habitats (Gantner et al. 2009, Hecky and Hesslein 1995, Hobson and Welch 1995, Kidd et al. 1998). Select studies have used stable isotope analysis in the Canadian Arctic to provide insights into structure of food webs and energy pathways in the region. For example, δ13C measurements indicated the importance of Chironomidae in the diet of Arctic char (Gantner et al. 2009). Ranges of δ13C values have been found to be narrower in lakes of the Canadian Arctic compared to temperate Canadian lakes, indicating higher terrestrial inputs and/or macrophyte contributions to the food web relative to more temperate lakes (Hecky and Hesslein 1995). Isotope analysis of 13C was used to confirm feeding on both benthic mosses and benthic algae by primary consumers, and both sources appeared to contribute roughly equal amounts of carbon to the diet of Arctic char (Hobson and Welch 1995). The authors also determined that there was a strong positive correlation between fork length and δ15N value, confirming increasing piscivorous feeding behaviour with increasing fish length. Nitrogen isotope analysis has also been used to confirm that lake trout in Peter Lake (near Rankin Inlet, Nunavut) are mainly piscivorous, feeding on threespine stickleback (G. aculeatus) and char (Kidd et al. 1998). In addition, char were found to consume roughly equal amounts of benthic and pelagic carbon, and based on carbon and nitrogen isotope analysis, whitefish consumed mainly benthic invertebrates (Kidd et al. 1998).

1.4. Problem Formulation and Purpose of Study

Mining of minerals such as gold has increased significantly in the Arctic over the past two decades, and this trend is expected to continue in the future. The ecology of small Arctic lakes in Nunavut has been poorly documented and it is unknown if and to what extent they may respond to the perturbations expected from mining activities. This study focused on a gold mine operated by Agnico Eagle near Rankin Inlet, Nunavut which is currently in exploration phase and is scheduled to commence full operation in 2019. Operations do and will include open pit mines, water retention dikes, lake dewatering, blasting, ore processing, and tailings ponds occupying approximately 2400 hectares (Agnico Eagle 2018b). The landscape within and surrounding the mining area contains many small (< 20 ha) lakes which could be impacted by increased dust deposition from road building and mining operations, increased sediment loading due to run-off from newly exposed surfaces (such as roads), leaching from waste rock piles, and increased water/sediment metal inputs via the above sources (Figure 2).

The purpose of the present study was twofold: 1) to comprehensively characterize the ecology of six lakes lying within the projected area of the mine footprint by assessing water and sediment chemistry, food web structure (stable isotopes analysis), community composition of benthic invertebrates, phytoplankton, zooplankton, and the relative health and condition of fish populations to establish a baseline to which post-opening mining effects can be compared; and 2) to determine if detectable effects resulting from the exploration and pre-operational phases are occurring.

Anthropogenic Receiving Affected Aquatic Receptors Disturbance Environmental Matrix Toxic metals (tailings e.g., As, - Hg, CN ) Surface water Piscivorous Planktivorous CO2 (Water chemistry) fish fish

Dust deposition (waste rock/road construction)

and phases,

ydrodynamic factors ydrodynamic

Bioturbation, binding binding Bioturbation, h Acid-mine drainage Pelagic Sediment Benthic (Tailings/Waste Phytoplankton invertebrates invertebrates Rock) (zooplankton)

Lake Bacterial

diking/dewatering plankton

Shallow esorption Chemical spills groundwater d esuspension, (e.g. Fuel) r Detritus

orption/ Periphyton/microbial s Burial, Burial, (organic DOC biofilm/macrophytes matter) Climate change Deep sediment

To address both goals, I had two objectives. The first objective was to determine whether the lakes, which are situated in a landscape characterized by relatively homogeneous geological characteristics, would similarly exhibit limnological homogeneity. All the lakes are oligotrophic, small (< 100 ha), relatively shallow (max depth < 6 m), and seasonally connected via their respective watersheds which drain to Meliadine Lake. Considering these characteristics, I tested the null hypothesis that food web structure, based on community composition of benthic invertebrates and fish, and stable isotope analysis would be similar across lakes (null hypothesis).

The second objective was to determine if there were differences in food web structure, and community composition in the lakes across study years, particularly in relation to pre- operational mining activities which increased over the study period. Considering the early phase of mining operations, I tested the null hypothesis that food web structure, based on community composition of benthic invertebrates and fish, and stable isotope analysis would be similar across years and unaffected by the mining activities.

A third objective, linked to the first two, was to determine the relative importance of benthic versus pelagic energy pathways in the lakes. Due to the shallow, rocky nature of the lakes, the photic zone extends to the bottom and there is an extensive littoral zone. I predicted that the food webs in these lakes will thus be driven by benthic productivity and the benthic zone will constitute the dominant energy pathway. In contrast, I predicted that nutrients derived from phytoplankton and consumed by zooplankton will contribute to a lesser extent as the pelagic energy pathway.

While previous studies have used isotope analysis to examine food webs in marine ecosystems in Nunavut, there are few isotopic studies regarding food webs in freshwater ecosystems in the study region. This study is unique in that it is one of the first to assess food web structure in small freshwater Arctic lakes using an integrative approach based on stable isotope analysis, community composition, and water chemistry. This research is also significant because it represents one of the few studies to examine these data in the context of mining disturbance - an industry which is expected to increase significantly in this region in the future.

This study provides an extensive dataset related to benthic community composition, fish gut content and condition, and food web isotope analysis, with the purpose of forming a baseline dataset prior to full-scale operation of mining activity. The aim is for similar data to be collected in the years subsequent to commencement of operations in 2019, to determine the degree to which the lakes may be impacted by the mining operations. In addition to generating potentially new insights into the ecology of these poorly studied lakes, the data collected in this study will also serve as baseline data against which subsequent data collections can be compared. Finally, although not specifically considered in this thesis, the data-set may also be used to evaluate climate-driven changes in lake ecology in the region (Brothers et al. 2019).

2. MATERIALS AND METHODS

2.1. Site Description

Gold reserves at the Meliadine gold project are in the Tiriganiaq and Wesmeg deposits along with potential gold resources in another five nearby deposits (Agnico Eagle 2018b). The deposits are all located on an 80-km long greenstone belt composed of Archean volcanic and sedimentary rocks. These rock layers follow northwest across the Meliadine peninsula. The Tiriganiaq zone is located within an iron oxide formation within sedimentary rock. The Wesmeg deposit is in mafic rock (igneous or volcanic rock rich in magnesium and iron). The deposits themselves are mesothermal quartz veins as well as quartz lodes or sulphide replacements associated with the regional fault system (Agnico Eagle 2018b).

Vegetation in the tundra consists of low-lying shrubs, mosses, and lichens. Flowering Eudicotidae such as Empetrum nigrum (crowberry) and Rubus chamaemorus (cloudberry) are also present. There is no tree growth due to the short growing season and low temperatures. A photo depicting the landscape is presented in Figure 3. Tundra biodiversity is generally low, with 1,700 species of vascular plants, 48 species of land mammals, and few fish species, although there is extensive bird migration to the region annually. The upper Kivalliq region (including the hamlet of Rankin Inlet) is classified as tundra Köppen climate. Winters are long and dark, with temperatures staying below freezing from late September to early June. Mean temperature in January (coldest month) is -30.8°C and in July (warmest month) 10.5°C (Government of Canada 2018). The winter months are also windy, with average wind speeds in excess of 19 km/h; however, precipitation is low (310 mm annually with maximum precipitation in the summer months). Tundra permafrost typically extends 25–90 cm down, however the top layer melts in the warm summer months resulting in damp terrain. There is also low evaporation, and since water cannot penetrate the underlying permafrost or bedrock, extensive lakes and marshes/bogs are found in the summer months. The Kivalliq region contains a high density of small (< 100 ha), shallow (zmax < 5 m), oligotrophic lakes with light penetrating to the lake bed; Figure 4. The general chemistry of lakes in this region is like those studied at Bathurst

Island in the high Arctic (latitude 75°–76° N; Lim et al. 2001), indicating relative homogeneity of inland lakes in the territory despite large geographical distances.

Figure 3. View of the regional landscape. Photo taken in August of 2015 between Lake A1 and Meliadine Lake looking west. Discovery mine camp is on the horizon.

Meliadine gold mine and study lakes

Six lakes were investigated during the ice-free season (late June-late September) once per year for four years (2014-2017; Figure 5). Study lakes in 2014 (July 17th – July 22nd) included: A1, A2, A6, A8, B2, and B7. Study lakes in 2015-2017 included: A1, A6, A8, B2, B7, and D7 which were sampled between August 25th and September 5th, July 31st and August 5th, and July 25th and July 30th, respectively. Lake A2 was eliminated after 2014 due to its proximity and hydrological connectivity to A1; Lake D7 was added in 2015 to provide another watershed proximal to proposed mining activities. The six lakes had surface areas ranging from 4.6 to 88.5 ha (mean = 48.5 ha), and mean volume ranging from 777-1419 x 103 m3 (volume not determined for lakes A1 or A2). The letter designation for each lake reflects the watershed in which the lake resides (see Table A1 for a summary of watershed characteristics). They were also shallow with an overall mean depth of 1.56 m (mean depth not determined for A1) and max depths ranging from < 2.0 m (A2) to 5.1 m (B7). The lakes were also relatively clear, with zsecchi > zmax for all lakes. Lake morphometry data are summarized in Table 1.

2.2. Sample Collection and Processing

2.2.1. Water Chemistry

Water was sampled directly from the sub-surface of each lake (n = 3 sites) into 250 mL Nalgene containers by submerging the containers and sealing them underwater after allowing all air to escape. Samples were then frozen for analytical purposes and submitted to the Agriculture and Food Laboratory (Laboratory Services Division) at the University of Guelph (Guelph, Ontario, Canada) for elemental analysis, including: boron, calcium, copper, iron, magnesium, manganese, molybdenum, nitrate nitrogen (2016 only), phosphorous, potassium, sodium, sulfur (2016 only), total Kjeldahl nitrogen (TKN; 2015 only), and zinc. Analysis was not completed for 2014 as these samples were accidentally discarded by a University of Guelph employee. A vertical profile of each site was taken using a YSI EXO2® sonde fitted with probes to measure conductivity, dissolved oxygen, dissolved organic matter, turbidity, depth, temperature, and pH at 0.5 m intervals. Salinity was calculated using measured conductivity values. Hardness and alkalinity of water were measured on-site (2015 and 2016 only) for both sub-surface water (sampled into 1L Nalgene containers) and water sampled from the bottom of the lake (obtained via Kemmerer sampler) using a Water Ecology Test Kit® (model AL-36DT; Hach Company).

Lake A1 Lake A2

Lake A6 Lake A8

Lake B2

Lake B7

Lake D7

Figure 5. Study lakes showing sampling stations, zooplankton tow transects, and gill net placements.

Table 1. Summary of lake morphometry and GPS data for the study lakes. Maximum depth for Lake A2 is estimated based on size relative to Lake A1. Summarized from Golder Associates Limited (2009). Site Depth Max Depth Mean Depth Volume Area Latitude Longitude Lake Site (m) (m) (m) (x103 m3) (ha) (N) (W) A1A 1.80 63°00.003' 92°07.703' A1 A1B 2.07 2.0 17.8 62°59.957' 92°07.508' A1C 1.13 63°00.093' 92°08.100' A2A 1.00 63°00.130' 92°08.713' A2 A2B 0.50 < 2.0* 4.6 63°00.154' 92°08.669' A2C 0.50 63°00.247' 92°08.725' A6A 2.43 63°00.082' 92°10.245 A6 A6B 2.67 4.3 1.5 840 55.6 63°00.022' 92°10.517' A6C 2.90 62°59.870' 92°10.282' A8A 3.27 63°00.845' 92°12.496' A8 A8B 2.53 4.2 1.6 1419 88.5 63°00.797' 92°12.206' A8C 2.27 63°00.908' 92°12.527' B2A 2.71 63°00.695' 92°25.535' B2 B2B 3.07 3.4 1.6 777 48.4 63°00.477' 92°15.849' B2C 1.93 63°00.409' 92°15.664' B7A 3.67 63°02.038' 92°15.176' B7 B7B 1.00 5.1 1.5 853 56.5 63°01.860' 92°14.385' B7C 2.37 63°02.194' 92°15.548' D7A 1.90 63°01.581' 92°16.317' D7 D7B 1.96 2.8 1.6 1183 68.3 63°01.748' 92°16.782' D7C 1.88 63°01.889' 92°16.935'

2.2.1.1. Chlorophyll a

Chlorophyll a was determined from 500 mL water samples collected directly from the sub-surface of the lake at each site (n = 3) into 1-L Nalgene containers. An aliquot (500-750 mL) of each sample was filtered through a 1 µm glass micro-fiber filter on-site and the filtered volume recorded. The filter was wrapped in aluminum foil and frozen until analysis.

Chlorophyll processing and measurements were performed in the dark (Bruinsma 1963). Filters were thawed for 30 minutes, then added to a 50 mL polypropylene conical centrifuge tube with 3 mL of 90% acetone. A tissue miser (Thermo Fisher Scientific) was used to macerate each filter (not exceeding 4 minutes to prevent overheating; Arar and Collins 1997). The tubes were covered with aluminum foil and steeped for 45 minutes. Following the extraction period, the tube contents were filtered individually through 5 mL of glass wool packed in a 10-mL syringe and rinsed with 2 mL of acetone into a 10-mL glass collecting tube. An additional 3 mL of 23 acetone was passed through the syringe into the collecting tube and the final extract volume recorded. The extraction process was repeated using a new glass fiber filter as a blank at the start and end of each sample run (approximately 8 samples). A TD-700 laboratory fluorometer (Turner Designs, Sunnyvale, CA) equipped with narrow-band excitation (436 nm) and emission (680 nm) filters was used to measure chlorophyll. The narrow-band wavelength filters eliminated the need for sample acidification to correct for phaeophytin (Arar and Collins 1997). Prior to sample measurement, the background fluorescence detected by the instrument was recorded. The fluorescence of the extract was measured (as a direct concentration in µg/L; the blank was measured prior to samples) and recorded after allowing the reading to stabilize for 1 minute.

A chlorophyll stock prepared by dissolving chlorophyll a powder (Sigma-Aldrich) in 90% acetone was quantified (at 9,273 µg/L) using a Genesys 10S UV-Vis spectrophotometer (Thermo Fisher Scientific). The absorbance of the stock was measured (n=3) at 665 and 750 nm using a 90% acetone solution as a blank. Following chlorophyll measurement, the sample was acidified, and absorbance again measured at 665 and 750 nm to correct for phaeophytin in the sample, as the Genesys spectrophotometer was not equipped with narrow-band filters. Using mean values, the concentration of chlorophyll in the stock was calculated using the following equation (Strickland and Parsons 1972; Parsons et al. 1984):

Chl-a (µg/L) = (26.7 푥 ((퐴퐵푆665표 − 퐴퐵푆665푎) − (퐴퐵푆750표 − 퐴퐵푆750푎)) 푋 1000)

푙 where ABS665o is the absorbance at 665 nm without acid, ABS665a is the absorbance at 665 nm following acidification, ABS750o is the absorbance at 750 nm without acid, ABS750a is the absorbance at 750 nm following acidification, and l is the path length.

The TD-700 fluorometer was calibrated using the direct concentration approach according the instruments operating manual (Turner Designs 2002). This is a multi-point calibration including five standards (prepared using the above described stock solution) and a blank which are used to create a six-point standard curve (for the range of 0-125 µg/L; expected range of 0-3 µg/L in water samples) from which the concentration of the chlorophyll in each sample could be interpolated. The concentration of chlorophyll at the site was calculated as:

푉푒푥푡푟푎푐푡 퐶ℎ푙-푎푠𝑖푡푒(μg/L) = ((퐶ℎ푙-훼푒푥푡푟푎푐푡 − 푏푒푥푡푟푎푐푡) 푥 ( )) − ((퐶ℎ푙-훼푏푙푎푛푘 − 푏푏푙푎푛푘) 푥 푉푏푙푎푛푘) 푉푓𝑖푙푡푒푟푒푑

where b is the background reading of the instrument prior to measuring each respective sample or blank and Vfiltered is the sample volume (L) filtered on-site.

2.2.2. Sediment Sampling and Analysis

Sediment was sampled using a Ponar grab. Single samples were collected at each site and the top 10 cm of the grab sub-sampled using a stainless steel spoon. The sediment was transferred to Whirl-Pak® bags (100 g per site) and refrigerated (4°C) for later physical and chemical analyses. A subsample of sediment (50 g) from each site was submitted to the Agriculture and Food Laboratory (Laboratory Services Division) at the University of Guelph for elemental analysis (metals) and pH measurements. Elemental analysis included: arsenic, cadmium, chromium, cobalt, copper, lead, mercury, molybdenum, nickel, selenium, and zinc. Method detection limits for elemental scans are listed in Table A7 in the Appendix. To measure total carbon, 0.6 g subsamples were dried overnight (60°C) in aluminum weigh dishes and then gently ground into a powder using a mortar and pestle. Sub-samples (0.2–0.3 g) from each sediment were then subjected to total carbon and organic carbon analyses. For total carbon analysis, the subsamples were placed in clay crucibles and total carbon measured via off-gassing at 1300°C using a LECO RC-412 carbon analyzer. For organic carbon analyses, subsamples were combusted at 500°C to remove organic material. Total organic carbon (TOC) was calculated as the difference between total carbon and total inorganic carbon (residue remaining after combustion). Finally, a subsample (0.2 g) of dried sediment was used to measure total nitrogen via off-gassing at 900°C using a LECO FP-428 nitrogen analyzer.

2.2.2.1. Particle size analysis

Particle size composition of subsamples (150–200 mg) of oven-dried sediment (60°C overnight) from each site were pre-treated with successive additions of ~30% H2O2 to react organic material until additional H2O2 didn’t react. Analysis of particle size was conducted using a Beckman-Coulter LS 13 320 laser diffraction particle size analyzer (range 0.017-2000

µm). Each sample was analyzed in triplicate. Data yielded included summary statistics (mean, median, mode, etc.), size centiles, size fractionations, as well as a cumulative frequency distribution of percent volume of the sample by particle size in µm. The median (or D50- meaning a cumulative 50% point of diameter of the particle size distribution) was used instead of the mean, as the distribution was not normal with varying kurtosis, skew, and modality.

Particle size distributions were described using D-values, particularly D50, which is the x-intercept for 50% of the cumulative mass (i.e. D50 is the diameter at which 50% of the sample's mass is comprised of particles with a diameter less than this value). The mass of an ellipsoid is given by

훱 푚 = 퐴퐵퐶휌 6 where A, B, and C are the diameters of the 3 axes of the particle, and Π and ρ are constants. Using A, B, and C, the relative mass of each particle can be calculated and summed to obtain the total sample mass. The particles are then arranged in ascending order and the D-values obtained from the distribution.

2.2.3. Community Assessments

2.2.3.1. Zooplankton

Zooplankton were sampled at each site using a vertical tow with a Wisconsin sampling net (sample transect of ~2 x zmax at each site). The samples were washed into 250 mL Nalgene containers and preserved using Khale’s solution. These samples were used for community assessment and form the basis for another thesis and are thus not presented here. Zooplankton were also collected for stable isotopes purposes using a cross-lake surface plankton tow net (250 µm; 50 cm opening). The net was towed across an approximately 100 m transect to collect adequate biomass for stable isotope analysis. The sample was rinsed and swirled in the collecting bucket to concentrate the plankton and then rinsed into a 500 mL Mason jar and frozen at -20°C.

2.2.3.2. Benthic invertebrates

Benthic invertebrate data included in the study is for years 2014 and 2015. Sediment was sampled using a Ponar grab sampler (Wildco E-411-1728-G30; sample volume: 2.4 L) at each lake station. The entire contents of each grab were passed through a 300 µm brass sieve on-site and the retained sample transferred to a 500 mL glass mason jar and preserved with ~100 mL of Kahle’s preservative. In the laboratory, the sample was rinsed on a 300 µm sieve to remove the Kahle’s solution and transferred to a series of petri dishes. All invertebrates were identified (without subsampling) to the greatest taxonomic resolution possible (genera in most cases) and stored in glass vials containing 80% ethanol solution. Midges and oligochaetes were mounted on slides using CMC-9 mounting media (Masters Company) and allowed to dry prior to identification using a compound microscope. Due to very high abundances at each site, midges were subsampled for identification to genus (250 midges/site). Midges were identified using Merritt and Cummins (2008) and Wiederholm (1983) and oligochaetes were identified using Brinkhurst (1986). All other benthic invertebrates were identified using Merritt and Cummins (2008), or Pennak (1978) for Amphipoda and Clarke (1981) for Mollusca.

Benthic invertebrates for stable isotope analysis were sampled by both D-frame kick sampling and picked directly from rocks using forceps from the nearshore littoral zone in each lake. Invertebrates were sorted on-site, placed into Whirl-Pak® bags and stored at -20°C.

2.2.3.3. Fish

Fish sampling and processing were conducted under University of Guelph Animal Utilization protocol #3308. Targeted fish species included arctic char (Salvelinus alpinus), arctic grayling (Thymallus arcticus), cisco (Coregonus artedi), and lake trout (Salvelinus namaycush); however, no arctic char were caught during the study. A total of 96 fish were permitted to be sampled per field campaign (4 fish per species per lake per year). Large fish were caught using a 45 m gill net with six 7.5 m long panels containing different mesh sizes (mesh side measurements ranging from 1.3–4.5 cm). The nets were typically installed at locations corresponding to maximum lake depth. Nets were hand-deployed and checked every 2 hours until the catch limit (4 fish per species per lake) was reached (any fish present at check times 27 were removed and processed; fish past the catch limit were measured for length and weight and released). Sticklebacks were collected mainly using dip nets, and occasionally via seining.

Fish were sampled in years 2015-2017. Large fish were measured, weighed, and truss analysis performed prior to dissection on site. Traditional morphometric descriptions of fish are centered on length-weight measurements, and derivations there-of (e.g. Fulton’s condition factor) and disregard allometry (Strauss and Bookstein 1982). Strauss and Bookstein (1982) provide a geometric protocol for selection of characters or landmarks which circumvents the weaknesses of more traditional protocols and provides even coverage of landmarks on the fish. This protocol forms the basis of truss analysis. Heads were removed and frozen in Whirl-Pak® bags for later otolith extraction and determination of age. Liver and gonads were removed and transferred to Whirl-Pak® bags and frozen; stomachs were transferred to Whirl-pack bags and preserved with Kahle’s solution. Finally, a section of muscle tissue (~25 g) was extracted and frozen in a Whirl-Pak® bag. Sticklebacks were placed whole in Whirl-Pak® bags and frozen for later weight and length measurements, and organ dissection. These data form the basis for a separate Master’s thesis and, apart from the SI analysis, are not reported further here.

Truss analysis was performed for all large fish that were sampled (up to n=4 fish per species per lake) prior to dissection. This consisted of measuring the distances between 10 morphological “landmarks” on the fish based on a truss network (Strauss and Bookstein 1982) as shown in Figure 6. These landmarks included: the tip of the lower lip (1), the snout (2), the base of the front of the pelvic fin (3), the base of the front (4) and rear (6) of the dorsal fin, the base of the front (5) and rear (7) of the anal fin, the base of the rear of the adipose fin (8), and the base of the caudal fin on the bottom (9) and top (10) of the fish.

Box Truss 1 Box Truss 2 Box Truss 3 Box Truss 4

4 2 6 1 8 10

9 5 7 3

Figure 6. Diagram of landmarks used in truss analysis. Recreated from Sabadin et al. (2010).

Liver and gonad tissues were thawed at the University of Guelph and wet mass was recorded using a microbalance. The hepatosomatic index (HSI) and gonadosomatic index (GSI) were calculated as:

푚 퐻푆퐼 = 푙𝑖푣푒푟 푥 100 푚푓𝑖푠ℎ

푚푔표푛푎푑푠 퐺푆퐼 = 푥 100 푚푓𝑖푠ℎ where m is the mass in grams and the indices are expressed as a percentage.

For all fish caught, weight and total length were measured. Fulton’s condition factor (K) was calculated as:

푚 퐾 = 푥 100 푙3 where m is the mass of the fish (in grams) and l is the total length of the fish (in cm).

Otoliths were dissected from thawed fish heads at the University of Guelph. They were mounted in EnviroTex Lite® Pour-On High Gloss Finish and allowed to dry before cross- sectioning with a jeweller’s saw. Cross sections were sanded and immersed in glycerin. The number of annuli on each half of the cross-section was determined under a dissecting microscope. Counts were conducted by myself and a technician; in cases where there was a discrepancy between counted annuli (this was never > 1), recounts were conducted.

Data on gut contents are presented for years 2015 and 2016. In the laboratory, formalin was removed from the bags containing the fish stomachs, replaced with EtOH (80% solution) and allowed to offgas for seven days to minimize exposure to formalin vapours during dissection. The stomachs were then rinsed with deionized water over a 250 µm sieve to remove ethanol and transferred to a petri dish. The stomach was opened using a scalpel by a shallow incision, and all contents transferred to another petri dish for identification under a microscope. Invertebrates present were identified to the highest taxonomic resolution possible using Merritt and Cummins (2008). Prey were then sorted into 8 groups (Amphipoda, Chironomidae, fish, Limnephilidae, Mollusca, Nematoda, zooplankton, and other). The “other” group consisted of Arachnida, Hirudinea, Hydrachnidia, Oligochaeta, Ostracoda, and other aquatic and terrestrial insects (e.g. Ceratopogonidae, Coleoptera, etc.).

2.2.4. Stable Isotope Analysis

2.2.4.1. Seston and periphyton

Water was collected directly from the sub-surface of the lake into Nalgene containers (1- L sample). A 500-mL subsample was filtered through a glass fiber-filter (1 µm pore size) on-site for subsequent stable isotope analysis of seston. The filter was wrapped in aluminum foil and stored at -20°C. Small periphyton-covered rocks were collected from the littoral zone of each lake. Periphyton was scraped from the exposed surface of the rocks using a wire brush. The material was rinsed into a small ceramic tray using filtered lake water which was then hand- pumped through a glass fiber filter (1 µm pore size). The filter was wrapped in aluminum foil and stored at -20°C.

Frozen filters containing seston (18 samples) were submitted to Isotope Tracer Technologies Inc in Waterloo, Ontario, Canada in 2014 for pre-processing and analysis. Filters were analyzed on a Finnigan Mat DeltaPlus XL IRMS® with a ConFlo III Interface® coupled with a CE Instruments EA 1110 CHN®. Data were corrected and normalized using international standards and four calibrated internal standards. The analytical precision for samples was ± 0.3‰. For seston samples collected in 2015-2016 (34 samples), material was scraped from the glass micro-fibre filters into vials and lyophilized using a Labconco Lyph-Lock 4.5® freeze

30 dryer. Samples were then immediately transferred to a desiccator for storage prior to grinding. Freeze-dried samples were ground using a mortar and pestle. The mortar and pestle were cleaned in between samples using pure sand (cat no. AC370940010; Thermo Fisher Scientific), followed by rinsing with 95% EtOH and wiping with a Kimwipe (Mr. Richard Heemskerk-UW- EIL, personal communication, March 6, 2017). Ground samples were transferred back into scintillation vials and returned to the desiccator for storage prior to weighing. Using an analytical microbalance, 0.4 mg of tissue was weighed into 5 x 3.5 mm tin capsules (Elemental Microanalysis; catalogue number D1002) and the capsules sealed. The capsules were placed in sealed 96-well microplates and sent to the Laboratory for Stable Isotope Science (LSIS) at The University of Western Ontario in London, Ontario, Canada. Periphyton samples (collected in 2015 and 2016 only) were also processed (as outlined for seston) for submission to LSIS in 2015-2016 (21 samples). For all stable isotope analyses every 20th sample was weighed and submitted in duplicate for analysis.

Carbon and nitrogen isotopic signatures for 2015-2016 samples were determined at LSIS using a Thermo Scientific Delta V isotope ratio mass spectrometer coupled to a Costech Elemental Analyzer and operated in continuous-flow mode. The stable carbon and nitrogen isotope compositions were calibrated to VPDB and air using USGS40 (L-glutamic acid, accepted δ13C = −26.39 ‰, δ15N = −4.52 ‰) and USGS41a (L-glutamic acid, accepted δ13C = +36.55 ‰, δ15N = +47.57 ‰). Analytical accuracy and precision were monitored using a suite of internal and international standards interspersed among the samples (F. Longstaffe, personal communication, Nov. 12, 2018).

2.2.4.2. Zooplankton

Zooplankton samples derived from the bulk plankton tows were thawed and poured into several petri dishes. Contents were sorted under a dissecting microscope to remove debris. The samples were not organized into taxonomic groups; each SI estimate therefore represents the whole zooplankton community. The samples were then filtered onto a glass fiber-filter (1 µm pore size) and scraped into vials. The samples were processed (as above) for submission to LSIS in 2014-2016 (48 samples; 3 analytical replicates per lake per year when amount of sample permitted). 31

2.2.4.3. Benthic invertebrates

Benthic macroinvertebrate samples were thawed, identified, and transferred to glass vials. Samples were processed (as above) for submission to LSIS in 2014-2016 (147 samples). Due to the small size of many invertebrates, multiple individuals were pooled (within lakes) and ground together for drying, weighing and analysis (at least 0.4 mg of tissue). For Hydrachnidia (and select individual samples), inadequate tissue mass was available even when using pooled samples; these samples were not submitted for analysis. Invertebrates with a shell (gastropods) or a thick exoskeleton (amphipods) were acid-digested in 1.0 M HCl to remove calcium carbonate prior to preparation for isotopic analysis as described above (43 samples).

2.2.4.4. Fish

Fish tissue stable isotope data are from 2015 and 2017 (large fish tissue from 2016 was accidently discarded by a University of Guelph employee). Fish tissue samples were thawed, and a small section of muscle tissue was dissected. Sticklebacks (2015-2017) were thawed and processed similarly. For all fish, muscle tissues were transferred to glass vials and pre-processed (as above) for submission to LSIS in 2015 and 2017 for large fish (44 samples) and 2015-2017 for sticklebacks (26 samples).

2.3. Data Analysis

2.3.1. Water and Sediment Chemistry

Due to the lack of water chemistry data for 2014, the possibility of using site data from 2015 and 2016 as surrogate data for 2014 for use in subsequent analyses (e.g. CCA) was investigated. A one-way analysis of variance (ANOVA) was conducted on each water chemistry parameter from 2015 and 2016 as a linear mixed-effects model with year as a fixed effect and site as a random effect. There was no significant difference across years for potassium, calcium, oxygen percentage, chlorophyll a, sodium, and magnesium. Thus, site data averaged from 2015 and 2016 was used as surrogate site data for 2014 in the CCA. Similarly, for sediment chemistry

32 data, due to a lack of consistent data across sites for some parameters in 2014 and 2015, I used site data from 2016 as surrogate data in these years. As above, a one-way ANOVA was conducted on each sediment chemistry parameter (on data available across 2014-2016) as a linear mixed-effects model with year as a fixed effect and site as a random effect. There were no significant differences across years for all parameters except for zinc, selenium, lead, and copper. While the four metals were significantly different, the differences across years were small (mean CV across years of 22%, 29%, 20%, and 36%, respectively). Thus, site data from 2016 was used as surrogate site data for 2014 and 2015 in the CCA.

Mixed model ANOVAs were used to determine if there were differences among lakes and across years for hardness, alkalinity, conductivity, oxygen concentration and saturation, dissolved organic matter, turbidity, pH, potassium, and sodium for water chemistry data and for total nitrogen, total inorganic carbon, total organic carbon, pH, copper, zinc, D50, and percent silt for sediment chemistry data. Mixed models were used because random effects aid in controlling for heterogeneity when it is constant over time and not correlated with independent variables. Specifically, they were used to compare lakes as a fixed effect with year as a random effect (in order to determine the extent of limnological heterogeneity - my first hypothesis), and to compare years as a fixed effect with lake as a random effect (in order to determine if detectable effects resulting from the exploration and pre-operational phases are occurring – my second hypothesis). They were also employed for seston and periphyton chlorophyll a. Mixed models for certain variables vs. watershed, site depth, lake area, or lake volume as a fixed effect with year as a random effect were also constructed to determine potential correlations between these independent variables and water and sediment chemistry parameters where relevant. Models were constructed using the ‘nlme’ package in R (Pinheiro 2017). Specifically, linear mixed-effects models were fit using the ‘lme’ function and analyzed using a one-way ANOVA. The package ‘lsmeans’ (Lenth 2018) was used for multiple comparisons via Tukey’s test by fitting a mixed model in the ‘lme4’ package (Bates et al. 2018). Normality of dependent variables was assessed using a Shapiro-Wilk test (‘shapiro.test’ function). Density and Q-Q plots were created using the ‘ggpubr’ package (Kassambara 2018). Residual plots for each dependent-independent variable combination were constructed using the ‘lm’ function. Bartlett’s test was used to assess homoscedasticity (‘bartlett.test’ function). In cases where there was found to be heteroscedasticity, dependent variables were transformed using a series of 33 transformations (log10, reciprocal, natural exponential, square root, arcsine square root) and retested using Bartlett’s until equal variance was achieved (p ≥ 0.05). Transformed dependent variables were then used in the mixed-models. In certain cases where p < 0.05 for all attempted transformations (particularly where the normality assumption was violated), the Brown-Forsythe test (median based test) was used (‘hov’ function; ‘HH’ package (Heiberger 2018)) as it is more robust to non-normally distributed data.

2.3.2. Benthic Community Analysis

Total abundance (number/m2), Shannon’s diversity index, equitability (evenness), and the total number of species in the community (richness) were used as comparative indicators of benthic community composition. Shannon’s diversity index (H) was calculated as: 푅

퐻 = − ∑ 푝𝑖 ln 푝𝑖 𝑖=1 where pi is the proportion of invertebrates belonging to the ith identification level (typically genus).

Shannon’s equitability (or evenness; EH) was calculated as:

퐻 퐸 = 퐻 ln 푆 where S is the total number of genera (or subfamily, etc.) in the sample (richness).

Mixed-effects models were employed as outlined above using percent composition of the five most abundant taxa groups which across lakes comprised 97% of all benthic invertebrates in 2014 and 2015 (Chironomidae, Ostracoda, Sphaeriidae, Valvata, and Oligochaeta). These five taxa groups were assessed for differences among lakes and across years. This was also conducted for the five most abundant Chironomidae genera which across lakes comprised 84% of all benthic invertebrates in 2014 and 2015 (Corynocera, Pagasitella, Tanytarsus, Cladotanytarsus, and Procladius). Lastly, this was conducted for benthic invertebrate diversity metrics (diversity, richness, and equitability). Explanatory relationships between benthic

34 community composition and the suite of environmental variables was assessed using CCA using the ‘vegan’ package in R (Oksanen et al. 2018). For the benthic invertebrate community data matrix, all taxa which occurred in < 5% of samples and had < 5% abundance in each sample were removed. Rare taxa provide limited interpretive value (Poos and Jackson 2012) and reflect random sampling effects and may exert disproportionate influence in statistical models (Gauch 1982). Abundance data was square-root transformed to avoid biasing the analysis based on high density taxa. Canonical Correspondence Analysis (CCA) was used to relate benthic community structure to the water and sediment variables presented in sections 2.2.1 and 2.2.2, respectively. For water data, boron, copper, iron, molybdenum, phosphorus, and zinc were below the method detection limits across most sites and not included in the CCA. In addition, nitrate nitrogen, sulfur, and TKN data were only available for a single year and thus were also omitted. For sediment data, cadmium, and mercury were below their respective method detection limits (see Table A10 in the Appendix) across most sites and thus were omitted. In CCA, if there are at least as many environmental variables as samples (in this case sites), then the ordination is no longer “constrained” and all the variation in species composition has been explained due to overfitting (correlation coefficients will become one regardless of whether they are correlated or not). Thus, to reduce the number of environmental variables, a principle components analysis (PCA) was performed on the water and sediment chemistry dataset separately for 2014 and 2015 (conducted using the ‘vegan’ package in R (Oksanen et al. 2018)). The input environmental dataset consisted of all parameters listed in sections 2.2.1 and 2.2.2 except those mentioned above. Despite data being available for 2015 only, hardness, alkalinity, and chlorophyll a were included due to their presumed importance as environmental drivers of benthic community composition. Principle components axes which explained ≥ 5% of the variance in the model were considered. Absolute values for all variable (factor) loadings for these axes were sorted and percent rank calculated across all axes. Variables for which loadings were ≥ 95th centile in either year were included. Then, the centile cutoff was reduced until the number of variables was one less than the number of observations (sites) in the dataset (which was the 92.5 centile) in order to meet the requirement of the CCA and to include the maximum number of environmental parameters in the model.

2.3.3. Fish Metrics and Truss Analysis

Mixed-effects models were employed as outlined above for fish length, weight, and condition factor (by species and subsequently by lake; also, by age for condition factor) with year as a random effect, and for GSI and HSI (by species and subsequently by fish length or age) with year as a random effect. PCA was used to compare truss measurements. A PCA plot was constructed for all fish on a single plot, as well as for individual species using the ‘ggplot2’ and ‘ggfortify’ packages in R (Wickham 2009; Tang et al. 2016). SigmaPlot® (Version 11.0; SYSTAT Software) was used to prepare log-log regression plots of weight versus length as well as percent diet composition bar charts by species.

2.3.4. Isotope Analysis

Mixed-effects models were employed as outlined above to determine significant differences for carbon and nitrogen isotopic data between years (within lake). Significant differences between δ13C values for benthic invertebrates and values for phytoplankton (within each lake for data averaged across 2014-2016) were also investigated (comparisons with periphyton data were unavailable due to machine detection issues). To test for dominance of benthic algal pathways in the study lakes, the proportion of littoral carbon in upper-trophic levels (i.e. fish) was investigated (Vander Zanden and Rasmussen 1999). Littoral-sampled invertebrates were used as a surrogate for the benthic/littoral baseline of periphyton, and values for phytoplankton were used as the pelagic baseline. The trophic level of fish was also calculated. The proportion of littoral carbon and trophic position were calculated according to Johnson et al. (2018) as:

푃푟표푝. 퐿𝑖푡푡표푟푎푙 퐶푎푟푏표푛 13 13 13 = (훿 퐶푝푟푒푑푎푡표푟 − 훿 퐶푝푒푙푎푔𝑖푐 푏푎푠푒푙𝑖푛푒)/(푚푒푎푛 훿 퐶푙𝑖푡푡표푟푎푙 푏푎푠푒푙𝑖푛푒 13 − 훿 퐶푝푒푙푎푔𝑖푐 푏푎푠푒푙𝑖푛푒)

푇푟표푝ℎ𝑖푐 푃표푠𝑖푡𝑖표푛 (푃푟푒푑푎푡표푟) 15 15 = 2 + (훿 푁푝푟푒푑푎푡표푟 − (훿 푁푝푒푙푎푔𝑖푐 푏푎푠푒푙𝑖푛푒 푥 (1 − 훼) 15 + 훿 푁푙𝑖푡푡표푟푎푙 푏푎푠푒푙𝑖푛푒 푥 훼))/3.4

where α is the proportion of littoral carbon. Correlation analysis was conducted for isotope data relative to proportions of food groups in the gut contents of large fish. A one-way ANOVA was conducted on correlations with an absolute value ≥ 0.50 to determine if they were significant (p < 0.05). These correlations were also plotted as linear regressions using Sigmaplot® (SYSTAT Software).

3. RESULTS

3.1. Physicochemical Data

3.1.1. Water

Principal components analysis of water physicochemical data for 2014 and 2015 is presented in Figure 7. Conductivity, turbidity, and oxygen saturation drive site ordination in both years; hardness and alkalinity also drive site ordination in 2015. In both years, sites are clustered by lake and watershed.

Total hardness and alkalinity at the surface of the lakes was nearly identical to values measured at the bottom of the lake (data not shown); therefore, only surface alkalinity and hardness is presented. Hardness ranged from 39–88 mg/L in 2015 and from 53–112 mg/L in 2016 (Table 2; see Table A2 for site data). Hardness differed significantly by lake (p < 0.001; see Table A3 for a summary of p-values) and all lake combinations differed significantly (p < 0.001), except for lakes A6 and B7 (p = 0.264), and lakes B2 and D7 (p = 0.774; see Table A4 for a summary of p-values for pairwise comparisons of lakes). Watershed A was significantly harder than both B and D (p < 0.001; log transformed data; see Table A5 for a summary of p- values for pairwise comparisons of watersheds). Watershed B did not differ significantly from watershed D. Hardness was significantly higher in 2016 (p < 0.001; see Table A6 for a summary of p-values for pairwise comparisons of annual data). In 2015, mean hardness was 42 ± SD of 2 mg/L in watershed D, 47 ± 9 mg/L in watershed B, and 69 ± 14 mg/L in watershed A. In 2016, mean hardness was 54 ± 1 mg/L in watershed D, 61 ± 8 mg/L in watershed B, and 91 ± 17 mg/L in watershed A.

Alkalinity ranged from 28–53 mg/L across lakes in 2015 and from 42–59 mg/L in 2016 (Table 2). Alkalinity was found to differ significantly by lake (p < 0.001). Specifically, all lake combinations differed significantly (Table A4), except for lakes A6 and A8 (p = 0.970), lakes A6 and B2 (p = 0.260), and lakes B2 and B7 (p = 0.745). Watershed B was significantly lower than both A and D (p = 0.001). Watershed A did not differ significantly from watershed D (p = 0.567). Alkalinity was significantly higher in 2016 (p < 0.001). In 2015, mean alkalinity was

28 ± 2 mg/L in watershed B, 36 ± 5 mg/L in watershed A, and 40 ± 1 mg/L in watershed D. In 2016, mean alkalinity was 43 ± 2 mg/L in watershed B, 50 ± 7 mg/L in watershed A, and 50 ± 3 mg/L in watershed D.

A6A A6B A6C A8B A8C

Figure 7. Principal components analysis of water physicochemical data for the six lakes for 2014 (A) and 2015 (B).

Conductivity ranged from 64–203 mg/L across lakes in 2014, from 89–195 mg/L in 2015, and from 108–229 mg/L in 2016 (Table 2). Conductivity did not differ significantly by lake (p = 0.145; log-transformed data). Conductivity differed significantly by watershed (p = 0.008; log transformed data). Conductivity in watershed A was significantly higher than watershed B (p=0.016). Watershed D did not differ significantly from watersheds A (p = 0.079) or B (p = 0.959). Conductivity increased significantly over the periods 2014-2015 and 2015- 2016 (p < 0.001 for both). In 2014, mean conductivity was 87 ± SD of 26 mg/L in watershed B, and 131 ± 45 mg/L in watershed A. In 2015, mean conductivity was 96 ± 1 mg/L in watershed D, 104 ± 17 mg/L in watershed B, and 146 ± 37 mg/L in watershed A. In 2016, mean conductivity was 118 ± 1 mg/L in watershed D, 120 ± 14 mg/L in watershed B, and 180 ± 38 mg/L in watershed A.

Dissolved oxygen ranged from 9.88–10.92 mg/L across lakes in 2014, from 10.80–11.61 mg/L in 2015, and from 9.91–10.36 mg/L in 2016 (Table 2). Dissolved oxygen did not differ significantly among lakes (p = 0.330). Dissolved oxygen did not differ significantly among watersheds (p = 0.277), however there was a negative correlation with site depth (p = 0.002) and lake area (p < 0.001). Dissolved oxygen increased significantly over the period 2014-2015 and decreased significantly over the period 2015-2016 (p < 0.001 for both). In 2014, mean dissolved oxygen was 10.06 ± 0.25 mg/L in watershed B and 10.38 ± 0.46 mg/L in watershed A. In 2015, mean dissolved oxygen was 10.80 ± 0.96 mg/L in watershed D, 11.36 ± 0.09 mg/L in watershed B, and 11.42 ± 0.35 mg/L in watershed A. In 2016, mean dissolved oxygen was 9.92 ± 0.06 mg/L in watershed B, 10.12 ± 0.23 mg/L in watershed A, 10.20 ± 0.25 mg/L in watershed D. Oxygen saturation did not differ significantly among lakes (p = 0.778) nor among watersheds (p = 0.640), however there was a negative correlation with the site depth (p = 0.002). Oxygen saturation was negatively correlated with lake area (p = 0.043; log transformed data). There were no significant differences between years.

Dissolved organic matter (DOM) ranged from 0.20–4.99 RFU across lakes in 2014, from 0.98–6.53 RFU in 2015, and from 0.11–3.99 RFU in 2016 (Table 2). DOM did not differ significantly among lakes (p = 0.122) but was negatively correlated with area of the lake (p < 0.001; square-root transformed DOM data for both). DOM did not differ significantly among watersheds (p = 0.124; exponential transformed data). DOM was negatively correlated with site

40 depth and lake volume (p < 0.001). DOM increased significantly over the period 2014-2015 and decreased significantly over the period 2015-2016 (p < 0.001 for both). In 2014, mean DOM was 1.4 ± 0.1 RFU in watershed B, and 2.4 ± 2.0 RFU in watershed A. In 2015, mean DOM was 3.2 ± 0.0 RFU in watershed D, 3.3 ± 2.5 RFU in watershed A, and 3.5 ± 0.1 RFU in watershed B. In 2016, mean DOM was 1.6 ± 1.8 RFU in watershed A, 1.8 ± 0.0 RFU in watershed D, and 1.9 ± 0.1 RFU in watershed B.

Turbidity ranged from 75–166 NTU (Nephelometric Turbidity Units) across lakes in 2014, from 80–176 NTU in 2015, and from 88–188 NTU in 2016 (Table 2). Turbidity differed significantly among lakes (p < 0.001). All lake combinations were significantly different (p < 0.001). Turbidity did not differ significantly by watershed (p = 0.184; exponential transformed data), site depth (p = 0.145) or lake area (p = 0.714). Turbidity increased significantly over the periods 2014-2015 and 2015-2016 (p < 0.001 for both). In 2014, mean turbidity was 71 ± 20 NTU in watershed B and 108 ± 36 NTU in watershed A. In 2015, mean turbidity was 87 ± 0 NTU in watershed D, 93 ± 14 NTU in watershed B, and 136 ± 30 NTU in watershed A. In 2016, mean turbidity was 94 ± 0 NTU in watershed D, 97 ± 10 NTU in watershed B, and 148 ± 30 NTU in watershed A.

Across lakes, pH ranged from 7.83–8.12 in 2014 and from 7.79–8.21 in 2016 (Table 2). Due to equipment issues, pH data from 2015 were inaccurate. pH was found to differ significantly by lake (p < 0.001; exponential transformed data): Lake A2 was significantly different from all lakes except Lake D7, and Lake D7 was significantly different from all lakes except Lake A2 (Table A4). Watershed D was significantly higher than both A and B (p < 0.001). Watershed A did not differ significantly from watershed B (p = 0.948). There was no significant difference across years (p = 0.127). In 2014, mean pH was 7.9 ± 0.2 in watershed B, and 8.0 ± 0.1 in watershed A. In 2016, mean pH was 7.8 ± 0.1 in watershed A, 7.9 ± 0.0 in watershed B, and 8.2 ± 0.0 in watershed D. All lakes were measured over the span of 14 days, and thus temperature data are for reference and not comparison among lakes.

Phosphorous was below the limit of detection (< 0.070 mg/L; see Table A7 for method detection limits) for all sites in both 2015 and 2016, except for site D7B in 2016 (0.076 mg/L; Table 3; see Table A8 in the Appendix for site data). Total Kjeldahl nitrogen was below the

41 limit of detection (< 1.0 mg/L) for all sites in both 2015 and 2016 except for site B2A (1.24 mg/L). Nitrate nitrogen ranged from 0.046–0.132 mg/L across lakes in 2016. Due to a lack of consistent data for these parameters statistical analyses were not performed.

Potassium ranged from 1.10–1.90 mg/L across lakes in 2015 and from 1.07–1.90 mg/L in 2016 (Table 3). Potassium differed significantly by lake (p < 0.001; p-values for pairwise comparison listed in Table A4 in the Appendix). Watershed A was significantly higher than watershed B (p = 0.007). Watershed D did not differ from watersheds A (p = 0.068) or B (p = 0.962). There was no significant difference across years (p = 0.147). In 2015, mean potassium was 1.17 ± 0.06 mg/L in watershed D, 1.20 ± 0.11 mg/L in watershed B, and 1.42 ± 0.36 mg/L in watershed A. In 2016, mean potassium was 1.23 ± 0.06 mg/L in watershed D, 1.13 ± 0.10 mg/L in watershed B, and 1.52 ± 0.32 mg/L in watershed A.

Sodium ranged from 3.67–7.37 mg/L across lakes in 2015 and from 3.50–7.50 mg/L in 2016 (Table 3). Sodium differed significantly by lake (p < 0.001). Sodium differed significantly by watershed (p < 0.001). All lake and watershed combinations were found to be significantly different (p-values summarized in Table A4). There was no significant difference across years (p = 0.827). In 2015, mean sodium was 4.70 ± 1.14 mg/L in watershed B, 6.13 ± 0.94 mg/L in watershed A, and 7.37 ± 0.15 mg/L in watershed D. In 2016, mean sodium was 4.47 ± 1.06 mg/L in watershed B, 6.22 ± 0.77 mg/L in watershed A, and 7.50 ± 0.10 mg/L in watershed D.

Table 2. Summary of water hardness, alkalinity, and depth-integrated mean lake physicochemical parameters for 2014-2016. Parameters were measured using a Hach Water Ecology Test Kit® and a YSI EXO2 Sonde® probe. Standard deviation is not shown due to extremely low site and depth variability (mean within-lake coefficient of variation of 0.0 (within year) for all measured parameters). Temperature Conductivity O2 O2 fDOM* fDOM* Turbidity Year Lake Hardness Alkalinity pH (°C) (µS) (mg/L) (%) (RFU) (QSU) (mg/L) A1 12.95 106 10.39 98.49 3.32 10.14 90 7.95 A6 13.71 90 10.25 98.86 0.94 3.01 75 7.91 A8 13.42 125 9.97 95.49 0.20 0.78 105 7.83 2014 B2 13.33 64 10.25 97.96 1.47 4.61 53 7.88 B7 14.86 111 9.88 97.79 1.35 4.22 89 7.93 A2 14.37 203 10.92 108.53 4.99 15.32 166 8.12 A1 63 43 7.87 128 11.61 98.00 6.53 19.76 123 11.37 A6 57 32 8.86 116 11.34 97.60 2.37 7.31 109 10.59 A8 88 32 10.49 195 11.29 101.27 0.98 3.12 176 9.41 2015 B2 39 29 10.42 89 11.35 101.56 3.41 10.44 80 8.53 B7 56 28 10.70 119 11.37 102.40 3.55 10.85 106 9.51 D7 42 40 10.40 96 10.80 96.52 3.17 9.71 87 9.29 A1 85 59 13.37 162 10.36 99.08 3.99 14.68 136 7.90 A6 75 45 14.36 148 9.95 97.31 0.85 5.31 121 7.79 A8 112 47 14.20 229 10.04 97.94 0.11 3.07 188 7.82 2016 B2 53 44 14.10 108 9.91 96.43 1.83 8.23 88 7.85 B7 68 42 15.03 132 9.93 98.60 2.02 8.81 106 7.88 D7 54 50 15.47 118 10.20 102.18 1.75 8.00 94 8.21

Note: due to equipment issues, pH data from 2015 is unavailable. Hardness and alkalinity were not measured in 2014. *fDOM refers to fluorescent dissolved organic matter and is measured in relative fluorescent units (RFU) or quinine sulfate units (QSU)

Table 3. Water chemistry parameters (± standard deviation) measured from an elemental scan of samples collected in 2015-2016. Year Analysis (mg/L) A1 A6 A8 B2 B7 D7 Calcium 23.00 (1.00) 20.67 (0.58) 31.67 (0.58) 14.00 (1.00) 21.33 (0.58) 12.67 (0.58) Iron 0.008 0.006 0.006 0.020 Magnesium 3.03 (0.06) 2.63 (0.06) 4.17 (0.06) 1.93 (0.06) 2.40 (0.00) 2.73 (0.12) 2015 Manganese 0.0025 (0.0004) 0.0034 (0.003) 0.0028 (0.0007) 0.0010 (0.0003) 0.0029 (0.0005) 0.0019 (0.0002) Potassium 1.23 (0.06) 1.13 (0.06) 1.90 (0.00) 1.10 (0.00) 1.30 (0.00) 1.17 (0.06) Sodium 6.33 (0.06) 4.97 (0.06) 7.10 (0.10) 5.73 (0.15) 3.67 (0.06) 7.37 (0.15) Total Kjeldahl Nitrogen 1.24 Calcium 23.00 (1.00) 20.00 (0.00) 31.00 (0.00) 13.67 (2.31) 18.33 (1.53) 14.00 (1.00) Copper 0.003 0.004 0.004 0.004 0.005 (0.002) Iron 0.007 (0.000) 0.007 (0.001) 0.007 Magnesium 3.17 (0.06) 2.70 (0.00) 4.17 (0.06) 1.93 (0.06) 2.20 (0.00) 2.77 (0.06) Manganese 0.0010 0.0015 (0.0007) 0.0010 0.0010 2016 Molybdenum 0.014 (0.001) 0.012 NO3-N 0.132 (0.104) 0.051 (0.003) 0.065 (0.009) 0.052 (0.003) 0.054 (0.003) 0.046 (0.007) Phosphorus 0.076 Potassium 1.50 (0.00) 1.17 (0.06) 1.90 (0.00) 1.07 (0.12) 1.20 (0.00) 1.23 (0.06) Sodium 6.17 (0.06) 5.37 (0.06) 7.13 (0.06) 5.43 (0.06) 3.50 (0.00) 7.50 (0.10) Sulfur 2.33 (0.06) 2.00 (0.01) 2.13 (0.06) 1.70 (0.00) 1.40 (0.10) 1.47 (0.06)

3.1.2. Sediment

Principle components analysis of sediment physicochemical data for 2014 and 2015 is presented in Figure 8. Arsenic, copper, chromium, nickel, zinc, and D50 drive site ordination in both years. In both years, sites are clustered by lake.

Figure 8. Principal components analysis of sediment physicochemical parameters for the six lakes in 2014 (A) and 2015 (B).

Total sediment nitrogen ranged from 0.16–0.64% across lakes in 2014, from 0.37–1.01 in 2015, and from 0.50–1.14% across lakes in 2016 (Table 4; see Table A9 in the Appendix for site data). Nitrogen differed significantly by lake (p < 0.001): Lake A8 differed from lakes A1 (p < 0.001), A2 (p = 0.003), and A6 (p = 0.013), and Lake B7 differed from lakes A1 (p = 0.007) and A2 (p = 0.009). Nitrogen did not differ by watershed (p-values summarized in Table A3). Nitrogen was positively correlated with site depth (p = 0.025) and lake area (p < 0.001). There was no correlation with lake volume (p = 0.109). There were no significant differences across years (p = 0.074). In 2014, mean total sediment nitrogen was 0.27 ± SD of 0.12% in watershed A and 0.64 ± 0.20% in watershed B. In 2015, mean total sediment nitrogen in watershed A was 0.67 ± 0.41%. In 2016, mean total sediment nitrogen was 0.50 ± 0.20% in watershed D, 0.68 ± 0.42% in watershed A, and 0.99 ± 0.30% in watershed B. Sediment chemistry data were unavailable for lakes A8 and B7 in 2014, and lakes B2, B7, and D7 in 2015 as these samples were accidentally discarded.

Total inorganic carbon (TIC) ranged from 0.13–0.39% across lakes in 2014, from 0.08– 0.41% in 2015, and from 0.05–0.34% in 2016 (Table 4). TIC did not differ significantly by lake (p = 0.555; reciprocal transformed data), watershed (p = 0.678), site depth (p = 0.273), lake area (p = 0.710), or volume (p = 0.903; square-root transformed data). There were no significant differences across years (p = 0.782). In 2014, mean TIC was 0.25 ± 0.21% in watershed A and 0.26 ± 0.10% in watershed B. In 2015, mean TIC in watershed A was 0.25 ± 0.17%. In 2016, mean TIC was 0.15 ± 0.17% in watershed B, 0.24 ± 0.16% in watershed A, and 0.26 ± 0.14% in watershed D.

Total organic carbon (TOC) ranged from 6.50–9.02% across lakes in 2014, from 6.41– 11.40% in 2015, and from 7.03–11.75% in 2016 (Table 4). Although TOC differed significantly by lake (p = 0.034), there were no significant differences among lakes (p-values summarized in Table A4 in the Appendix). TOC did not differ by watershed (p = 0.270). TOC was positively correlated with site depth (p = 0.021) and lake area (p = 0.002). There was no correlation with lake volume (p = 0.536). There were no significant differences across years (p = 0.262). In 2014, mean TOC was 6.84 ± 1.21% in watershed A and 9.02 ± 0.78% in watershed B. In 2015, mean TOC in watershed A was 9.33 ± 3.35%. In 2016, mean TOC was 7.78 ± 1.56% in watershed D, 9.07 ± 3.99% in watershed A, and 10.64 ± 1.97% in watershed B.

The pH of sediment was not measured in 2014 but ranged from 3.70–4.13 across lakes in 2015 and from 3.83–4.85 in 2016 (Table 4). Sediment pH did not differ significantly by lake (p = 0.645), watershed (p = 0.860), site depth (p = 0.442), lake area (p = 0.673), or lake volume (p = 0.497). There were no significant difference across years (p = 0.357). In 2015, mean pH in watershed A was 3.89 ± 0.81. In 2016, mean pH was 4.15 ± 0.49 in watershed D, 4.17 ± 0.85 in watershed A, and 4.24 ± 0.69 in watershed B.

Copper ranged from 34–83 µg/g (dry weight) across lakes in 2014, from 56–100 µg/g in 2015, and from 40–78 µg/g in 2016 (Table 4; see Table A10 in the Appendix for method detection limits). Copper did not differ significantly by lake (p = 0.833) or by watershed (p = 0.870; exponential transformed copper data for both). Copper decreased significantly over the period 2015-2016 (p = 0.028). In 2014, mean copper was 57 ± 25 µg/g in watershed A and 80 ± 11 µg/g in watershed B. In 2015, mean copper in watershed A was 79 ± 27 µg/g. In 2016, mean copper was 46 ± 0 µg/g in watershed D, 59 ± 24 µg/g in watershed A, and 61 ± 14 µg/g in watershed B.

Zinc ranged from 59–96 µg/g (dry weight) across lakes in 2014 and from 71–77 µg/g across lakes in 2016 (Table 4). In 2016, zinc ranged from 47–65 µg/g across lakes, with Lake B2 being the lowest and lakes A1 and A6 being the highest. Zinc differed significantly by lake (p = 0.006): Lake A2 differed from lakes A6 (p = 0.031), B2 (p = 0.004), and D7 (p = 0.027). Zinc did not differ by watershed (p = 0.841; exponential transformed data). There were no significant differences across years (p = 0.060). In 2014, mean zinc was 59 ± 7 µg/g in watershed B and 79 ± 21 µg/g in watershed A. In 2015, mean zinc in watershed A was 73 ± 13 µg/g. In 2016, mean zinc was 50 ± 1 µg/g in watershed D, 53 ± 7 µg/g in watershed B, and 64 ± 13 µg/g in watershed A.

All samples contained primarily silt (Table 5; i.e., highest percentage of particles were in the 3.9–63 µm particle size range; mean silt composition of 61 ± 10% across all lakes for 2014- 2016) except for A6A and A6C in 2014 (sand and silt co-dominant), A1C in 2015 (clay dominant), and A8B in 2016 (sand dominant; see Table A11 in the Appendix for site data). Percent sediment silt composition ranged from 48–74% across lakes in 2014, from 53–67% across lakes in 2015, and from 53–71% across lakes in 2016. Sand was less prevalent (Table 4;

47 percent composition 24 ± 14% across all lakes for 2014-2016), although certain lakes had greater amounts, notably lakes A6 (39 ± 9% across years) and A8 (32 ± 14% across years). Clay was the lowest constituent (15 ± 10% across all lakes for 2014-2016), although certain lakes had greater amounts, notably lakes A1 (28 ± 13% across years), A2 (18 ± 4%), and D7 (20 ± 2%). D50 (particle diameter at 50% cumulative sample mass) ranged from 13–52 µm across lakes in 2014, with Lake A2 being the lowest and Lake A6 being the highest. In 2015, D50 ranged from 12–43 µm across lakes, with Lake A1 being the lowest and Lake A6 being the highest. In 2016, D50 ranged from 9–53 µm across lakes, with Lake A1 being the lowest and Lake A8 being the highest. In 2014, mean D50 was 22 ± 5 µm in watershed B and 26 ± 20 µm in watershed A. In 2015, mean D50 in watershed A was 27 ± 14 µm. In 2016, mean D50 was 16 ± 2 µm in watershed D, 20 ± 4 µm in watershed B, and 32 ± 28 µm in watershed A.

Table 4. Mean lake values (± standard deviation) for sediment chemistry for 2014-2016. Results of an elemental scan as well as combustion analysis of nitrogen and carbon are included. Note that pH was not measured in sediments in 2014. 2014 2015 Analysis Units A1 A2 A6 B2 A1 A6 A8 Arsenic 31 (4) 25 (1) 95 (19) 51 (31) 28 (9) 67 (55) 91 (4) Cadmium 0.27 0.21 0.45 (0.14) 0.29 (0.06) 0.51 (0.06) 0.34 (0.03) Chromium 72 (19) 70 (14) 40 (7) 49 (6) 68 (16) 50 (10) 50 (10) Cobalt 15 (3) 17 (3) 19 (5) 17 (5) 14 (3) 20 (10) 15 (3) Copper 53 (11) 34 (8) 83 (25) 80 (11) 56 (7) 100 (27) 81 (26) µg/g Lead 14 (3) 13 (3) 10 (3) 11 (1) 15 (4) 15 (3) 18 (1) dry Mercury 0.037 0.046 (0.005) 0.041 0.056 (0.022) 0.050 (0.009) Molybdenum 2.3 (0.7) 2.4 (0.7) 4.2 (1.1) 6.6 (2.5) 2.5 (0.5) 4.6 (1.8) 4.7 (1.2) Nickel 42 (9) 40 (7) 47 (15) 46 (8) 41 (5) 56 (15) 51 (7) Selenium 0.57 (0.13) 0.37 (0.09) 0.74 (0.13) 0.84 (0.23) 0.63 (0.12) 0.89 (0.25) 0.74 (0.13) Zinc 82 (19) 96 (14) 59 (15) 59 (7) 77 (14) 71 (14) 73 (15) pH n/a 3.70 (0.95) 4.13 (1.21) 3.83 (0.32) N 0.34 (0.15) 0.16 (0.08) 0.29 (0.04) 0.64 (0.20) 0.37 (0.20) 0.64 (0.51) 1.01 (0.25) TC 6.85 (1.32) 6.74 (0.83) 7.71 (1.94) 9.28 (0.85) 6.67 (1.81) 10.27 (4.28) 11.81 (1.57) % TIC 0.13 (0.12) 0.23 (0.03) 0.39 (0.33) 0.26 (0.10) 0.26 (0.09) 0.08 (0.09) 0.41 (0.10) TOC 6.71 (1.37) 6.50 (0.86) 7.32 (1.65) 9.02 (0.78) 6.41 (1.81) 10.18 (4.31) 11.40 (1.66) TC/TIC n/a 169 (223) 30 (7) 27 (12) 39 (15) 27 (10) 343 (434) 30 (12) 2016 Analysis Units A1 A6 A8 B2 B7 D7 Arsenic 32 (10) 68 (33) 96 (4) 24 (11) 31 (7) 26 (4) Cadmium 0.47 (0.04) 0.29 (0.02) 0.2 Chromium 54 (7) 50 (15) 42 (8) 41 (4) 39 (9) 42 (4) Cobalt 12 (2) 19 (8) 14 (7) 10 (3) 11 (2) 14 (1) Copper 40 (4) 78 (29) 59 (21) 58 (17) 63 (15) 46 (0) µg/g Lead 13 (2) 14 (5) 15 (2) 12 (2) 14 (1) 12 (0) dry Mercury 0.054 (0.022) 0.048 0.049 (0.016) 0.055 (0.009) 0.044 (0.010) Molybdenum 1.9 (0.1) 3.7 (1.2) 3.5 (1.9) 3.7 (1.1) 3.8 (1.4) 3.2 (0.1) Nickel 34 (4) 47 (15) 46 (15) 35 (6) 43 (1) 40 (2) Selenium 0.44 (0.05) 0.64 (0.22) 0.59 (0.08) 0.65 (0.25) 0.74 (0.11) 0.62 (0.07) Zinc 65.00 (7.55) 64.67 (20.60) 62.33 (13.50) 46.50 (6.36) 57.00 (2.65) 49.50 (0.71) pH n/a 3.90 (0.53) 4.40 (1.21) 4.20 (0.98) 4.85 (0.64) 3.83 (0.35) 4.15 (0.49) N 0.51 (0.28) 0.73 (0.19) 0.80 (0.71) 0.75 (0.37) 1.14 (0.13) 0.50 (0.20) TC 7.30 (2.71) 10.50 (2.78) 10.13 (6.33) 9.28 (2.10) 11.80 (1.14) 8.04 (1.41) % TIC 0.27 (0.09) 0.34 (0.14) 0.11 (0.17) 0.30 (0.18) 0.05 (0.01) 0.26 (0.14) TOC 7.03 (2.72) 10.16 (2.72) 10.01 (6.29) 8.98 (1.92) 11.75 (1.15) 7.78 (1.56) TC/TIC n/a 29 (13) 34 (15) 436 (519) 34 (13) 268 (76) 38 (27)

Table 5. Particle size analysis indicating D50 (particle size at the 50th percentile for cumulative sample mass) and clastic classification of sediments retrieved via Ponar grab sampler in 2014-2016 (± standard deviation). Sediment chemistry data for lakes A8 and B7 in 2014, and lakes B2, B7, and D7 in 2015 is unavailable as these samples were accidentally discarded. D10 D50 D90 Sand Silt Clay Year Lake µm 63-2000 µm 3.9-63 µm < 3.9 µm A1 1.9 (0.5) 14 (7) 92 (56) 16 (12) 64 (7) 20 (5) A2 1.9 (0.4) 13 (3) 55 (12) 8 (3) 74 (2) 18 (4) 2014 A6 5.0 (0.7) 52 (12) 193 (37) 44 (7) 48 (6) 8 (1) B2 2.8 (0.4) 22 (5) 108 (16) 22 (6) 65 (4) 14 (2) A1 1.6 (0.7) 12 (8) 79 (49) 14 (10) 54 (14) 32 (24) 2015 A6 5.9 (1.9) 43 (3) 225 (44) 40 (2) 53 (4) 7 (2) A8 4.3 (0.6) 26 (2) 141 (31) 24 (5) 67 (4) 9 (1) A1 0.9 (0.0) 9 (1) 75 (23) 12 (4) 56 (3) 32 (2) A6 4.6 (0.2) 34 (11) 165 (52) 31 (13) 60 (13) 9 (0) A8 7.1 (3.9) 53 (38) 205 (86) 41 (16) 53 (13) 7 (3) 2016 B2 3.2 (0.2) 22 (4) 116 (37) 22 (5) 67 (4) 12 (1) B7 2.9 (0.6) 18 (3) 95 (21) 16 (4) 71 (4) 13 (3) D7 1.4 (0.3) 16 (2) 81 (3) 15 (1) 65 (1) 20 (2)

3.1.3. Chlorophyll a

Sestonic chlorophyll a ranged from 0.79–1.75 µg/L in 2015 and from 0.83–2.12 µg/L in 2016 (Table 6). Sestonic chlorophyll a differed significantly by lake (p = 0.0003; reciprocal transformed data): Lake A6 differed from Lake A1 (p = 0.024), and Lake B7 differed from lakes A1 (p = 0.001), A8 (p = 0.019), B2 (p = 0.004), and D7 (p = 0.005). Sestonic chlorophyll a did not differ significantly by watershed (p = 0.236) or by site depth (p = 0.370). Sestonic chlorophyll a was significantly higher in 2016 (p = 0.023). In 2015, mean sestonic chlorophyll a was 1.05 ± SD of 0.21 µg/L in watershed A, 1.07 ± 0.16 µg/L in watershed B, and 1.75 ± 0.12 µg/L in watershed D. In 2016, mean sestonic chlorophyll a was 1.15 ± 0.18 µg/L in watershed D, 1.23 ± 0.45 µg/L in watershed B, and 1.60 ± 0.40 µg/L in watershed A.

Periphytic chlorophyll a was measured at lakes A1 (22.20 ± 2.35 µg/L) and A6 (28.51 ± 7.55) in 2015 (the remaining samples were accidentally discarded by a University of Guelph employee). In 2016, periphytic chlorophyll a ranged from 3.86–44.07 µg/L across lakes (Table 6). Periphytic chlorophyll a did not differ by lake (p = 0.370), by watershed (p = 0.375), or by

50 maximum lake depth (p = 0.973). There were no significant differences across years (p = 0.931). In 2015, mean periphytic chlorophyll a was 25.36 ± 6.27 µg/L in watershed A. In 2016, mean periphytic chlorophyll a was 8.20 ± 5.29 µg/L in watershed D, 8.73 ± 5.86 µg/L in watershed B, and 23.02 ± 19.55 µg/L in watershed A.

Table 6. Mean sestonic and periphytic chlorophyll a (± standard deviation) by lake for 2015-2016. Year Lake Sestonic chlorophyll a (µg/L) Periphytic chlorophyll a (µg/L) A1 1.17 (0.15) 22.20 (2.35) A6 0.79 (0.04) 28.51 (7.55) A8 1.17 (0.01) 2015 B2 1.20 (0.08) B7 0.94 (0.09) D7 1.75 (0.12) A1 2.12 (0.07) 21.14 (8.86) A6 1.30 (0.13) 44.07 (14.21) A8 1.38 (0.06) 3.86 (1.83) 2016 B2 1.63 (0.06) 11.19 (3.83) B7 0.83 (0.14) 7.09 (7.16) D7 1.15 (0.18) 8.20 (5.29)

3.2. Benthic Community Data

3.2.1. Abundance Values and Percent Composition

The five most abundant taxa groups which across lakes comprised 97% of all benthic invertebrates in both 2014 and 2015 were Chironomidae, Ostracoda, Sphaeriidae, Valvata, and Oligochaeta. These five taxa groups were assessed for differences among lakes and across years. The benthic invertebrate community in 2014 and 2015 was dominated by Chironomidae (Table 7; see Table A12 for site data). In 2014, Chironomidae comprised 65 (Lake A1) – 95% (Lake A8) with a mean of 78% ± SD of 12%. Overall mean lake numbers reached as high as 19,809 larvae/m2 in Lake B2, and the overall mean density across lakes was 8584 ± 6680 larvae/m2. In 2015, lakes were again dominated by Chironomidae larvae, which comprised 27% (Lake D7) - 89% (Lake B7) with a mean of 65% ± 26%. In Lake A1, Chironomidae was co-dominant with

Ostracoda (both 41%) and in D7, Chironomidae (27%) was co-dominant with Sphaeriidae (36%) and Ostracoda (26%). Overall mean lake numbers reached as high as 67,456 larvae/m2 in Lake B7 in 2015, and the overall mean density for all lakes was 20,657 ± 26,994 larvae/m2.

Valvata were present in all lakes, but not abundant (highest proportion of 7% in Lake A1 in 2014, and 9% in Lake A6 in 2015). Sphaeriidae were present in all lakes in 2014 and were especially abundant in lakes A1 (11%), A6 (19%), and B2 (22%). In 2015, they were abundant in all lakes (9–36%) and were highest in Lake D7 (36%) and Lake A6 (24%). It should be noted that while Physella were collected only in lakes A6 and D7 in 2015 via sediment grab, the genus was also observed in the rocky shoreline of all study lakes. Lake A1 was the only lake sampled in 2014 to have G. lacustris present (< 1%), however in 2015, they were present in all lakes except for Lake A6 (≤ 1% in all other lakes except Lake D7 (7%)). Oligochaeta were also present in all lakes in 2014 (ranging from < 1% in Lake A8 to 15% in Lake A1) and in 2015 (< 1–4%), with Spirosperma ferox and Tubifex tubifex being particularly abundant. Ostracoda were present in lakes A1 (< 1%), A2 (10%), and B2 (< 1%) in 2014, and all lakes except for Lake A8 in 2015 (< 1% in all lakes except Lake A1 (41%) and Lake D7 (27%)). Ceratopogonidae larvae were only present in lakes A1 (< 1%) and A2 (12%) in 2014, and Lake A1 in 2015 (1%). Hydrachnidia were present in all lakes (< 1%) except A6 and A8 in 2014; Hydrachnidia were present in all lakes in 2015 (< 1%).

Table 7. Summary of mean benthic invertebrate density (abundance; in number of individuals/m2) and mean percent

composition data by lake (± standard deviation) for benthic grab samples collected in 2014 and 2015.

eta

Cladocera Copepoda Ostracoda Hydrachnidia Hirudinea Nematoda Oligocha heringianus S.

Lake Measure Year lacustris G.

Abundance 173 (263) 14 (25) 115 (200) 29 (50) 101 (109)

2014

% composition 0.81 (1.25) 0.07 (0.12) 0.55 (0.94) 0.14 (0.24) 2.07 (3.25) A1 Abundance 317 (477) 17039 (13882) 202 (246) 29 (25) 14 (25) 173 (263) 2015 % composition 0.74 (0.69) 41.05 (7.87) 0.36 (0.38) 0.06 (0.05) 0.14 (0.24) 1.48 (0.40) Abundance 14 (25) 404 (353) 14 (25) 72 (90) A2 % composition 0.29 (0.51) 9.87 (8.71) 0.29 (0.51) 1.53 (1.68) 2014 Abundance % composition A6 Abundance 14 (25) 14 (25) 14 (25) 260 (225) 2015 % composition 0.29 (0.51) 0.21 (0.36) 0.24 (0.42) 4.11 (1.48) Abundance 2014 % composition A8 Abundance 14 (25) 14 (25) 130 (115) 58 (66) 2015 % composition 0.14 (0.25) 0.14 (0.24) 0.87 (1.05) 0.28 (0.11) Abundance 29 (50) 29 (50) 14 (25) 14 (25) 2014 % composition 0.07 (0.11) 0.07 (0.11) 0.03 (0.06) 0.06 (0.10) B2 Abundance 346 (229) 87 (115) 58 (66) 130 (130) 14 (25) 159 (175) 2015 % composition 1.29 (0.94) 0.48 (0.49) 0.53 (0.76) 0.58 (0.73) 0.07 (0.11) 2.34 (0.54) Abundance 108 (153) 65 (92) 43 (61) 2014 % composition 0.82 (1.16) 0.49 (0.69) 0.33 (0.46) B7 Abundance 22 (31) 87 (122) 22 (31) 65 (92) 130 (184) % composition 0.05 (0.07) 0.08 (0.12) 0.05 (0.07) 0.06 (0.09) 1.56 (0.38) 2015 Abundance 390 (245) 1580 (275) 22 (31) 22 (31) D7 % composition 7.39 (6.41) 26.39 (4.19) 0.29 (0.40) 1.83 (0.64) Note: (a) and (l) indicate adult and larval forms, respectively.

Table 7 (cont’d). Summary of mean benthic invertebrate density (abundance; in number of individuals/m2) and mean

percent composition data by lake (± standard deviation) for benthic grab samples collected in 2014 and 2015.

Chaetogaster Vedjovskyella Nais Tubificinae ferox S. T. tubifex arctica S. Physella

Lake Measure Year Naididae

Abundance 130 (130) 188 (164) 274 (438)

2014

% composition 2.53 (3.86) 3.12 (4.39) 7.03 (12.04) A1 Abundance 260 (270) 14 (25) 14 (15) 2015 % composition 0.89 (0.79) 0.04 (0.06) 0.14 (0.24) Abundance 58 (100) 289 (427) 14 (25) A2 % composition 1.17 (2.03) 5.58 (8.20) 0.28 (0.48) 2014 Abundance 29 (25) % composition 1.87 (2.54) A6 Abundance 14 (25) 2015 % composition 0.29 (0.51) Abundance 14 (25) 2014 % composition 0.12 (0.20) A8 Abundance 2015 % composition Abundance 173 (300) 72 (90) 43 (75) 115 (200) 2014 % composition 0.40 (0.69) 0.42 (0.37) 0.10 (0.17) 0.27 (0.46) B2 Abundance 130 (225) 72 (125) 43 (43) 144 (250) 14 (25) 2015 % composition 0.59 (1.02) 0.33 (0.57) 0.15 (0.21) 0.65 (1.13) 0.02 (0.03) Abundance 108 (153) 195 (275) 2014 % composition 0.82 (1.16) 1.47 (2.08) B7 Abundance 411 (398) 281 (153) 22 (31) % composition 0.85 (1.03) 0.38 (0.02) 0.02 (0.03) 2015 Abundance 65 (92) 43 (61) D7 % composition 1.38 (1.95) 0.57 (0.81) Note: (a) and (l) indicate adult and larval forms, respectively.

Table 7 (cont’d). Summary of mean benthic invertebrate density (abundance; in number of individuals/m2) and mean

percent composition data by lake (± standard deviation) for benthic grab samples collected in 2014 and 2015.

Sphaeriidae (l) Elateridae Leptocerida (a) Diptera (l) Ceratopogonidae (a) Chironomidae (l) Chironomidae taxa Total

Lake Measure Year Valvata

Abundance 981 (1178) 2265 (3267) 14 (25) 87 (87) 29 (25) 12451 (11563) 16851 (11591)

2014

% composition 7.45 (5.73) 10.82 (15.27) 0.39 (0.67) 1.05 (1.18) 0.44 (0.63) 63.54 (4.76) A1 Abundance 938 (1039) 2756 (1833) 14 (25) 14 (25) 115 (200) 14 (25) 16274 (12004) 38190 (26589) 2015 % composition 3.96 (3.47) 9.62 (5.70) 0.14 (0.24) 0.04 (0.06) 1.13 (1.95) 0.02 (0.04) 41.28 (3.73) Abundance 58 (100) 115 (200) 14 (25) 43 (43) 505 (214) 2914 (1171) 4516 (958) A2 % composition 1.69 (2.92) 3.38 (5.85) 0.42 (0.73) 0.85 (0.83) 12.09 (7.00) 62.56 (3.60) 2014 Abundance 144 (250) 750 (1075) 58 (100) 1861 (976) 2842 (2119) % composition 2.82 (4.89) 19.48 (17.49) 1.13 (1.96) 74.70 (7.03) A6 Abundance 563 (312) 1457 (745) 3607 (218) 5944 (976) 2015 % composition 9.16 (4.65) 23.67 (8.47) 62.02 (4.17) Abundance 58 (100) 274 (295) 14 (25) 6464 (5067) 6824 (5182) 2014 % composition 0.47 (0.81) 4.53 (4.87) 0.68 (1.18) 94.21 (9.16) A8 Abundance 260 (229) 1601 (606) 14 (25) 14918 (10976) 17010 (11662) 2015 % composition 1.40 (0.46) 11.31 (6.46) 0.05 (0.08) 85.81 (9.88) Abundance 664 (486) 4429 (4527) 29 (50) 19809 (12545) 25241 (17943) 2014 % composition 2.43 (0.71) 14.55 (6.40) 0.12 (0.20) 81.51 (7.93) B2 Abundance 909 (338) 3491 (1037) 202 (238) 29273 (33047) 35073 (34175) 2015 % composition 4.05 (2.25) 16.26 (9.79) 0.50 (0.13) 73.88 (7.59) Abundance 43 (61) 390 (245) 22 (31) 8007 (4958) 8981 (6029) 2014 % composition 0.33 (0.46) 4.40 (0.21) 0.16 (0.23) 91.19 (10.84) B7 Abundance 368 (153) 5562 (704) 108 (153) 67456 (43184) 74533 (43643) % composition 0.67 (0.60) 8.67 (4.13) 0.10 (0.15) 88.76 (11.66) 2015 Abundance 87 (122) 2251 (1102) 1688 (796) 6146 (2020) D7 % composition 1.14 (1.62) 35.60 (6.23) 26.78 (2.24) Note: (a) and (l) indicate adult and larval forms, respectively.

Chironomidae larvae in all lakes in 2014 were dominated by Chironominae (Table 8; see Table A13 for site data; 54–100%; mean 87 ± SD of 12%; percentage data not shown). Within this sub-family, lakes were most strongly represented by the Tanytarsini (14–94%; mean 59 ± 28%), except for Lake A2 and sites A1C and A6C, which were dominated by Chironomini (42%, 67%, and 47%, respectively). Lake A1 was most strongly represented by Pagastiella (25%), Tanytarsus (23%), and Corynocera (20%). Lake A2 was mainly represented by Dicrotendipes (23%), Tanytarsus (18%), Procladius (17%), and Einfeldia (12%). Lakes A6 and A8 were represented by Corynocera (46% and 70%) and Cladopelma (17% and 7%). Lake B2 was represented by Corynocera (56%) and Tanytarsus (20%), and Lake B7 by Corynocera (78%) and Phaenopsectra (9%).

Chironomidae larvae were again dominated by Chironominae in 2015 (Table 8; see Table A14 for site data; 37–98%; mean 89 ± 15%; percentage data not shown). Specifically, most sites were strongly represented by Tanytarsini (20–91%; mean 58 ± 26%), except for Lake A6 and sites A1A, A1B, which were dominated by Chironomini (62%, 51%, and 54%, respectively), and site D7B which was dominated by Tanypodinae (63%). Lake A1 was most strongly represented by Pagastiella (31%), Cladotanytarsus (30%), Tanytarsus (16%), and Polypedilum (15%); Lake A6 by Pagastiella (31%), Cladopelma (21%), and Corynocera (21%); lake A8 by Corynocera (78%); Lake B2 by Corynocera (67%) and Pagastiella (14%); Lake B7 by Corynocera (85%); and Lake D7 by Procladius (47%) and Pagastiella (15%).

The percentage of Chironomidae in the benthos differed significantly by lake (p < 0.001; p-values summarized in Table A3 in the Appendix): Lake A1 differed from A8 (p = 0.001) and B7 (p = 0.004), Lake A2 differed from A8 (p = 0.042), and Lake D7 differed from A6 (p = 0.039), A8 (p < 0.001), B2 (p = 0.005), and B7 (p < 0.001). Percent Chironomidae differed by watershed (p = 0.002): watershed D was significantly lower than both A (p = 0.018) and B (p = 0.002) (mean percent Chironomidae values of 18 ± 16%, 69 ± 21%, and 69 ± 34% across years, respectively). Percent Chironomidae was positively correlated with station depth (p < 0.001), area (p = 0.003), and temperature (p = 0.034) but was not correlated with volume (p = 0.525) or dissolved oxygen (p = 0.124). Percent Chironomidae was higher in 2014 than 2015 (p = 0.026). Percent Chironomidae (being the dominant taxonomic group) was compared to select sediment parameters (nitrogen, TIC, TOC, pH, copper and zinc), however, there were no significant

56 correlations (Table A3). G. lacustris were also compared to copper and zinc however there was no significant correlation with either (p-values of 0.317 and 0.439, respectively).

Percent Ostracoda did not differ significantly by lake (p = 0.644) or by year (p = 0.312; both exponential transformed). Percent Sphaeriidae differed significantly by lake (p = 0.002): Lake D7 differed from A1 (p = 0.020), A2 (p = 0.006), A8 (p = 0.009), and B7 (p = 0.010). Percent Sphaeriidae did not differ by year (p = 0.162). Percent Valvata did not differ by lake (p = 0.643) or by year (p = 0.383). Percent Oligochaeta did not differ by lake (p = 0.572) or by year (p = 0.273).

The 5 most abundant Chironomidae genera which comprised 84% of the Chironomidae larvae across lakes for 2014-2015 were: Corynocera (56%), Pagastiella (11%), Tanytarsus (10%), Cladotanytarsus (4%), and Procladius (3%). These genera were compared to select water physico-chemical parameters (site depth, lake area and volume, temperature, and DO). Percent composition of Chironomidae larvae by Corynocera differed significantly by lake (p < 0.001; see Table A4 in the Appendix for p-values for pairwise comparisons), watershed (p = 0.001), and was positively correlated with site depth (p < 0.001), area (p < 0.001), and temperature (p = 0.031), but did not differ by volume (p = 0.682), DO (p = 0.195), or across years (p = 0.472). Percent Pagastiella was negatively correlated with temperature (p < 0.001) and increased from 2014-2015 (p < 0.001) but did not differ by lake (p = 0.754), watershed (p = 0.711), site depth (p = 0.862), lake area (p = 0.169), lake volume (p = 0.081), or DO (p = 0.876). Percent Tanytarsus differed by lake (p = 0.013), was negatively correlated with site depth (p = 0.003) and lake area (p < 0.001) but did not differ by watershed (p = 0.637), lake volume (p = 0.142), temperature (p = 0.939), DO (p = 0.193), or across years (p = 0.513). Percent Cladotanytarsus was negatively correlated with lake area (p = 0.032) and temperature (p = 0.006) but did not differ by lake (p = 0.643), watershed (p = 0.367), site depth (p = 0.277), lake volume (p = 0.684), DO (p = 0.727), or across years (p = 0.312). Percent Procladius differed by lake (p < 0.001), watershed (p < 0.001), and was negatively correlated with site depth (p = 0.019) but did not differ by lake area (p = 0.148), lake volume (p = 0.665), temperature (p = 0.423), DO (p = 0.554), or across years (p = 0.786).

Table 8. Mean abundance and mean percent composition data (± standard deviation) for Chironomidae identified to genera by lake for 2014 and 2015. A1 A2 % % % Abundance composition Abundance composition Abundance composition Taxa 2014 2014 2015 2015 2014 2014 Chironomidae sp. 739 (842) 3.92 (3.50) 245 (238) 7.07 (7.24) Chironominae 11368 (10811) 87.70 (6.33) 15551 (11464) 94.62 (3.36) 1876 (677) 66.58 (12.24) Chironominae sp. 43 (75) 1.12 (1.95) Chironomini 4000 (3795) 41.42 (22.39) 7905 (7129) 37.21 (26.53) 1183 (450) 42.25 (9.98) Chironomini sp. 71 (24) 1.30 (1.30) 72 (90) 2.66 (2.63) Chironomus 14 (25) 0.05 (0.09) 14 (25) 0.90 (1.56) Cladopelma 71 (24) 2.09 (3.00) Cryptochironomus 157 (165) 2.07 (1.51) 77 (30) 0.67 (0.40) Cryptotendipes Dicrotendipes 43 (43) 1.18 (1.43) 248 (276) 1.87 (1.51) 678 (214) 24.02 (2.85) Einfeldia 29 (25) 1.05 (1.50) 346 (312) 12.28 (8.40) Glyptotendipes 14 (25) 0.93 (1.60) Microtendipes 29 (50) 0.10 (0.18) Pagastiella 3054 (4216) 14.52 (16.19) 5052 (5042) 22.00 (19.31) Phaenopsectra 404 (360) 16.36 (24.43) 43 (43) 1.65 (1.45) Polypedilum 156 (109) 1.92 (1.11) 2486 (2086) 12.51 (10.29) 29 (50) 0.75 (1.30) Sergentia Tanytarsini 7368 (7135) 46.28 (28.27) 7646 (4373) 57.42 (23.56) 649 (189) 23.21 (3.48) Tanytarsini sp. 1115 (1316) 5.80 (5.31) 58 (100) 1.75 (3.04) Cladotanytarsus 416 (578) 1.97 (2.22) 4899 (4770) 23.27 (14.11) 29 (50) 0.75 (1.30) Corynocera 2458 (4109) 10.28 (16.48) 58 (100) 0.21 (0.36) C. Oliveri 388 (602) 3.33 (5.48) Micropsectra 110 (191) 0.45 (0.78) 29 (50) 0.75 (1.30) Paratanytarsus 14 (25) 0.13 (0.22) 14 (25) 0.37 (0.63) Stempellinella Tanytarsus 2865 (2487) 24.32 (18.73) 2675 (518) 33.57 (36.93) 534 (90) 19.95 (6.16) Orthocladiinae 43 (75) 0.39 (0.67) 317 (477) 1.77 (1.59) 274 (264) 8.48 (7.61) Orthocladiinae sp. 14 (25) 0.13 (0.22) 43 (43) 1.19 (1.13) Cricotopus 173 (263) 0.91 (0.86) 58 (100) 1.75 (3.04) Eukiefferiella 14 (25) 0.05 (0.09) Hydrobaenus Orthocladius 28 (48) 0.11 (0.19) Parakiefferiella Paraphaenocladius Psectrocladius 29 (50) 0.26 (0.45) 130 (189) 0.78 (0.68) 173 (156) 5.53 (4.34) Tanypodinae 300 (134) 7.99 (10.04) 391 (155) 3.56 (2.63) 519 (241) 17.81 (3.01) Tanypodinae sp. 14 (25) 0.37 (0.65) Ablabesmyia Larsia 29 (25) 0.42 (0.59) Monopelopia 14 (25) 0.05 (0.09) Procladius 300 (134) 7.99 (10.04) 348 (130) 3.09 (2.09) 505 (218) 17.50 (2.62) Diamesinae 14 (25) 0.05 (0.09) Diamesinae sp. Monodiamesa Pagastia Potthastia Pseudodiamesa 14 (25) 0.05 (0.09) Total Chironomid Larvae 12451 (11563) 16274 (12004) 2914 (1171)

Table 8 (cont’d). Mean abundance and mean percent composition data (± standard deviation) for Chironomidae identified to genera by lake for 2014 and 2015. A6 % % Abundance composition Abundance composition Taxa 2014 2014 2015 2015 Chironomidae sp. 130 (43) 9.48 (7.32) Chironominae 1587 (889) 83.28 (7.25) 3304 (264) 91.54 (2.56) Chironominae sp. 14 (25) 0.58 (1.01) Chironomini 505 (393) 31.58 (19.93) 2251 (488) 62.14 (10.51) Chironomini sp. 14 (25) 1.96 (3.40) 29 (25) 0.78 (0.67) Chironomus 144 (132) 3.98 (3.55) Cladopelma 317 (222) 21.19 (14.99) 765 (152) 21.20 (3.95) Cryptochironomus 43 (75) 1.75 (3.04) 43 (75) 1.28 (2.22) Cryptotendipes Dicrotendipes 14 (25) 0.61 (1.05) 43 (0) 1.20 (0.07) Einfeldia 43 (43) 1.22 (1.19) Glyptotendipes Microtendipes 14 (25) 0.40 (0.69) Pagastiella 101 (100) 5.49 (3.49) 1111 (304) 30.54 (6.61) Phaenopsectra 14 (25) 0.58 (1.01) 14 (25) 0.40 (0.69) Polypedilum 29 (50) 0.76 (1.31) Sergentia 14 (25) 0.38 (0.66) Tanytarsini 1068 (866) 51.11 (27.34) 1053 (261) 29.41 (7.97) Tanytarsini sp. 58 (66) 2.36 (2.67) Cladotanytarsus 14 (25) 0.58 (1.01) 245 (66) 6.76 (1.60) Corynocera 851 (798) 40.86 (27.64) 765 (250) 21.47 (7.83) C. Oliveri Micropsectra 14 (25) 0.58 (1.01) Paratanytarsus 14 (25) 0.40 (0.69) Stempellinella Tanytarsus 130 (75) 6.72 (0.74) 29 (25) 0.78 (0.67) Orthocladiinae 14 (25) 0.58 (1.01) 115 (25) 3.23 (0.84) Orthocladiinae sp. Cricotopus Eukiefferiella Hydrobaenus Orthocladius 14 (25) 0.40 (0.69) Parakiefferiella Paraphaenocladius Psectrocladius 14 (25) 0.58 (1.01) 101 (25) 2.83 (0.88) Tanypodinae 130 (150) 6.66 (5.27) 173 (87) 4.85 (2.45) Tanypodinae sp. 29 (25) 1.19 (1.03) Ablabesmyia Larsia 58 (50) 1.65 (1.43) Monopelopia 14 (25) 0.40 (0.69) Procladius 101 (139) 5.47 (5.28) 101 (25) 2.80 (0.68) Diamesinae 14 (25) 0.38 (0.66) Diamesinae sp. Monodiamesa Pagastia 14 (25) 0.38 (0.66) Potthastia Pseudodiamesa Total Chironomid Larvae 1861 (976) 3607 (218)

Table 8 (cont’d). Mean abundance and mean percent composition data (± standard deviation) for Chironomidae identified to genera by lake for 2014 and 2015. A8 Abundance % composition Abundance % composition Taxa 2014 2014 2015 2015 Chironomidae sp. Chironominae 6218 (4952) 96.47 (4.12) 14254 (10809) 94.49 (2.87) Chironominae sp. 14 (25) 0.18 (0.31) Chironomini 1053 (785) 25.48 (18.62) 1529 (600) 13.02 (8.19) Chironomini sp. 14 (25) 0.71 (1.23) 29 (25) 0.21 (0.24) Chironomus 14 (25) 0.05 (0.09) Cladopelma 462 (507) 10.95 (9.45) 14 (25) 0.16 (0.27) Cryptochironomus 159 (175) 4.28 (3.71) 101 (50) 0.86 (0.67) Cryptotendipes Dicrotendipes 101 (66) 1.73 (0.36) 188 (109) 1.35 (0.45) Einfeldia 43 (43) 1.69 (2.26) Glyptotendipes 14 (25) 0.27 (0.47) 29 (50) 0.31 (0.54) Microtendipes 29 (50) 0.10 (0.18) Pagastiella 101 (109) 1.58 (2.15) 952 (568) 8.49 (7.37) Phaenopsectra 87 (87) 3.37 (4.52) 29 (50) 0.10 (0.18) Polypedilum 72 (66) 0.90 (0.82) 130 (75) 1.32 (1.25) Sergentia 14 (25) 0.05 (0.09) Tanytarsini 5165 (5303) 70.99 (19.92) 12711 (10660) 81.29 (8.83) Tanytarsini sp. 260 (115) 6.45 (4.65) 58 (66) 0.31 (0.27) Cladotanytarsus 29 (50) 0.54 (0.93) 317 (66) 2.99 (1.79) Corynocera 4501 (4888) 59.40 (21.48) 11614 (10258) 72.80 (12.04) C. Oliveri Micropsectra Paratanytarsus 289 (388) 3.73 (2.70) 462 (363) 3.37 (3.33) Stempellinella Tanytarsus 87 (115) 0.87 (0.90) 260 (216) 1.82 (1.40) Orthocladiinae 72 (90) 1.20 (1.77) 130 (43) 1.19 (0.88) Orthocladiinae sp. 14 (25) 0.18 (0.31) Cricotopus Eukiefferiella Hydrobaenus 29 (25) 0.23 (0.28) Orthocladius 29 (25) 0.23 (0.28) Parakiefferiella Paraphaenocladius 14 (25) 0.05 (0.09) Psectrocladius 72 (90) 1.20 (1.77) 43 (43) 0.49 (0.47) Tanypodinae 173 (150) 2.33 (2.42) 519 (216) 4.16 (2.07) Tanypodinae sp. 29 (50) 0.54 (0.93) Ablabesmyia 14 (25) 0.18 (0.31) Larsia 43 (75) 0.36 (0.63) 188 (66) 1.51 (0.62) Monopelopia Procladius 101 (90) 1.44 (1.64) 317 (152) 2.47 (1.16) Diamesinae 14 (25) 0.16 (0.27) Diamesinae sp. Monodiamesa Pagastia Potthastia 14 (25) 0.16 (0.27) Pseudodiamesa Total Chironomid Larvae 6464 (5067) 14918 (10976)

Table 8 (cont’d). Mean abundance and mean percent composition data (± standard deviation) for Chironomidae identified to genera by lake for 2014 and 2015. B2 Abundance % composition Abundance % composition Taxa 2014 2014 2015 2015 Chironomidae sp. 558 (728) 1.91 (2.22) Chironominae 18389 (11080) 94.60 (4.21) 27187 (30718) 92.92 (0.52) Chironominae sp. 106 (184) 0.33 (0.58) 17 (30) 0.12 (0.20) Chironomini 1700 (1493) 8.30 (2.80) 5098 (2713) 28.86 (16.57) Chironomini sp. 35 (61) 0.11 (0.19) 60 (65) 0.85 (1.18) Chironomus Cladopelma 720 (1034) 2.98 (2.86) 556 (311) 3.19 (1.88) Cryptochironomus 110 (90) 0.55 (0.18) 165 (249) 0.47 (0.41) Cryptotendipes 35 (61) 0.11 (0.19) Dicrotendipes 63 (75) 0.45 (0.39) Einfeldia 29 (50) 0.43 (0.74) Glyptotendipes Microtendipes 109 (95) 1.32 (1.48) Pagastiella 679 (457) 3.25 (0.64) 4179 (2532) 22.55 (14.87) Phaenopsectra 14 (25) 0.21 (0.37) 14 (25) 0.24 (0.42) Polypedilum 14 (25) 0.21 (0.37) 14 (25) 0.24 (0.42) Sergentia Tanytarsini 16583 (9634) 85.96 (6.38) 22072 (28874) 63.94 (16.88) Tanytarsini sp. 1118 (709) 5.49 (1.31) 92 (118) 0.23 (0.20) Cladotanytarsus 263 (230) 1.03 (0.91) 1762 (2074) 4.92 (3.75) Corynocera 10995 (6541) 59.44 (13.06) 19559 (26128) 55.86 (17.69) C. Oliveri 97 (168) 0.46 (0.80) Micropsectra 192 (183) 0.80 (0.88) Paratanytarsus Stempellinella 29 (50) 0.49 (0.84) Tanytarsus 3917 (2808) 18.74 (9.59) 629 (636) 2.45 (0.45) Orthocladiinae 219 (378) 1.05 (1.81) 619 (466) 2.93 (1.08) Orthocladiinae sp. 404 (459) 1.14 (1.05) Cricotopus 24 (42) 0.12 (0.20) Eukiefferiella Hydrobaenus Orthocladius Parakiefferiella 75 (130) 0.11 (0.19) Paraphaenocladius Psectrocladius 194 (336) 0.93 (1.61) 140 (122) 1.68 (1.84) Tanypodinae 644 (915) 2.44 (2.54) 1467 (1872) 4.15 (1.23) Tanypodinae sp. Ablabesmyia Larsia 84 (75) 0.34 (0.35) 619 (1029) 1.01 (1.47) Monopelopia 151 (261) 0.22 (0.39) Procladius 560 (896) 2.10 (2.61) 697 (602) 2.92 (0.89) Diamesinae Diamesinae sp. Monodiamesa Pagastia Potthastia Pseudodiamesa Total Chironomid Larvae 19809 (12545) 29273 (33047)

Table 8 (cont’d). Mean abundance and mean percent composition data (± standard deviation) for Chironomidae identified to genera by lake for 2014 and 2015. B7 D7 Abundance % composition Abundance % composition Abundance % composition Taxa 2014 2014 2015 2015 2015 2015 Chironomidae sp. 43 (61) 0.96 (1.36) Chironominae 7683 (4805) 95.72 (0.74) 65744 (42879) 96.99 (1.48) 822 54.81 (25.84) Chironominae sp. 43 (61) 0.38 (0.53) Chironomini 1082 (673) 13.50 (0.05) 6700 (5065) 9.47 (1.45) 454 (92) 28.85 (8.16) Chironomini sp. 22 (31) 0.19 (0.27) 22 (31) 1.92 (2.72) Chironomus Cladopelma 130 (122) 2.59 (3.13) 161 (227) 0.16 (0.23) 22 (31) 0.96 (1.36) Cryptochironomus 65 (31) 0.86 (0.15) 1240 (519) 2.00 (0.51) 108 (31) 6.73 (1.36) Cryptotendipes Dicrotendipes 161 (227) 0.16 (0.23) 22 (31) 0.96 (1.36) Einfeldia 43 0.67 (0.41) Glyptotendipes Microtendipes 161 (227) 0.16 (0.23) Pagastiella 130 (184) 1.13 (1.59) 4817 (3638) 6.81 (1.03) 260 17.31 (8.16) Phaenopsectra 693 (551) 8.06 (1.89) Polypedilum 161 (227) 0.16 (0.23) 22 (31) 0.96 (1.36) Sergentia Tanytarsini 6557 (4071) 81.84 (0.16) 59044 (37815) 87.52 (0.03) 368 (92) 25.96 (17.68) Tanytarsini sp. 173 (122) 2.09 (0.24) 22 (31) 1.92 (2.72) Cladotanytarsus 22 (31) 0.19 (0.27) 223 (139) 0.33 (0.01) 87 (122) 7.69 (10.88) Corynocera 6254 (3764) 78.63 (1.67) 57233 (37194) 84.51 (1.03) 130 8.65 (4.08) C. Oliveri Micropsectra 22 (31) 0.19 (0.27) Paratanytarsus 62 (88) 0.17 (0.24) Stempellinella Tanytarsus 87 (122) 0.75 (1.06) 1525 (569) 2.50 (0.76) 130 (61) 7.69 (0.00) Orthocladiinae 108 (31) 1.53 (0.56) 125 (176) 0.34 (0.48) 43 (61) 3.85 (5.44) Orthocladiinae sp. 43 0.67 (0.41) 43 (61) 3.85 (5.44) Cricotopus 22 (31) 0.19 (0.27) Eukiefferiella Hydrobaenus Orthocladius 22 (31) 0.48 (0.68) 125 (176) 0.34 (0.48) Parakiefferiella Paraphaenocladius Psectrocladius 22 (31) 0.19 (0.27) Tanypodinae 173 (184) 1.80 (1.18) 1588 (481) 2.67 (1.00) 822 (857) 41.35 (31.28) Tanypodinae sp. 22 (31) 0.96 (1.36) Ablabesmyia 22 (31) 0.19 (0.27) Larsia 285 (51) 0.50 (0.25) Monopelopia Procladius 151 (153) 1.61 (0.92) 1302 (430) 2.17 (0.75) 801 (826) 40.38 (29.92) Diamesinae Diamesinae sp. Monodiamesa 22 (31) 0.48 (0.68) Pagastia Potthastia Pseudodiamesa Total Chironomid Larvae 8007 (4958) 67456 (43184) 1688 (796)

3.2.2. Diversity Metrics

Shannon’s diversity index ranged from 1.28–2.40 in 2014 and from 1.05–2.20 in 2015 (Table 9; see Table A15 for site values). Diversity differed significantly by lake (p = 0.001): Lake B7 differed significantly from lakes A1 (p = 0.010), A2 (p = 0.001), and A6 (p = 0.014), and Lake A2 differed from Lake A8 (p = 0.016). Diversity did not differ by watershed (p = 0.089), however it was negatively correlated with site depth (p < 0.001) and lake area (p = 0.003). There was no correlation with lake volume (p = 0.418). There was no significant difference across years (p = 0.716). In 2014, mean diversity was 1.50 ± SD of 0.35 in watershed B and 1.99 ± 0.50 in watershed A (Table 10). In 2015, mean diversity was 1.46 ± 0.54 in watershed B, 1.82 ± 0.37 in watershed A, and 1.85 ± 0.14 in watershed D.

Taxa richness ranged from 12–22 in 2014 and from 15–22 in 2015 (Table 9). Richness did not differ significantly by lake (p = 0.153), watershed (p = 0.254), site depth (p = 0.100), lake area (p = 0.212), or lake volume (p = 0.697). There were no significant differences across years (p = 0.107). In 2014, mean taxa richness was 16 ± 5 in watershed A and 18 ± 5 in watershed B (Table 10). In 2015, mean taxa richness was 15 ± 1 in watershed D and 20 ± 3 in watersheds A and B.

Equitability (evenness) ranged from 0.45–0.86 in 2014 and from 0.37–0.77 in 2015 (Table 9). Equitability differed significantly by lake (p < 0.001; p-values summarized in Table A4 in the Appendix) and by watershed (p = 0.021). Watershed B was lower than watershed A (p = 0.021). Watershed D did not differ significantly from watershed A (p = 0.943) or B (p = 0.217). Equitability was negatively correlated with site depth (p = 0.007) and lake area (p = 0.014). There was no correlation with lake volume (p = 0.523). There was no significant difference across years (p = 0.201). In 2014, mean equitability was 0.52 ± 0.09 in watershed B and 0.73 ± 0.16 in watershed A (Table 10). In 2015, mean equitability was 0.49 ± 0.16 in watershed B, 0.62 ± 0.14 in watershed A, and 0.69 ± 0.07 in watershed D.

Table 9. Mean (± standard deviation) lake values for total taxa and diversity indices for benthic invertebrates collected in 2014 and 2015. Shannon's Diversity Richness Shannon's Year Lake Total Taxa Index (H) (S) Equitability (EH) A1 16879 (11622) 2.22 (0.28) 22 (5) 0.72 (0.09) A2 4516 (958) 2.40 (0.12) 16 (2) 0.86 (0.01) A6 2842 (2119) 1.77 (0.49) 12 (5) 0.73 (0.17) 2014 A8 6824 (5182) 1.57 (0.60) 14 (3) 0.60 (0.23) B2 25421 (17943) 1.71 (0.31) 18 (4) 0.59 (0.06) B7 9003 (5999) 1.28 (0.30) 19 (9) 0.45 (0.02) A1 38190 (26589) 1.80 (0.11) 20 (5) 0.61 (0.08) A6 5944 (976) 2.20 (0.12) 18 (3) 0.77 (0.00) A8 17010 (11662) 1.47 (0.33) 21 (3) 0.48 (0.12) 2015 B2 35073 (34175) 1.86 (0.40) 22 (2) 0.61 (0.12) B7 74533 (43643) 1.05 (0.18) 18 (2) 0.37 (0.08) D7 6146 (2020) 1.85 (0.14) 15 (1) 0.69 (0.07)

Table 10. Mean (± standard deviation) watershed values for total taxa and diversity indices for benthic invertebrates collected in 2014 and 2015. Shannon's Richness Shannon's Year Watershed Total Taxa Diversity Index (H) (S) Equitability (EH) A 7765 (7925) 1.99 (0.50) 16 (5) 0.73 (0.16) 2014 B 17212 (15838) 1.50 (0.35) 18 (5) 0.52 (0.09) A 20381 (20306) 1.82 (0.37) 20 (3) 0.62 (0.14) 2015 B 54803 (39080) 1.46 (0.54) 20 (3) 0.49 (0.16) D 6146 (2020) 1.85 (0.14) 15 (1) 0.69 (0.07)

Diversity metrics were compared to select sediment parameters (nitrogen, TIC, TOC, and pH) with year as a random effect, however, only diversity and equitability were negatively correlated with sediment nitrogen (p-values of 0.033 and 0.010, respectively). All other tests were non-significant (see Table A3).

3.2.3. Canonical Correspondence Analysis

For results of the one-way ANOVA assessments of interannual differences for available water and sediment chemistry data, see Table A16. The CCA ordination for the 2014 benthic invertebrate dataset showed that sediment nitrogen, D50, % sand composition, arsenic, and TIC were the variables most strongly correlated with benthic community composition (especially Paratanytarsus and Tanypodinae; Figure 9). Sediment chromium and zinc were strongly correlated with benthic invertebrates in A1C (T. tubifex, S. heringianus, and Ceratopogonidae larvae) and turbidity and conductivity were strongly correlated with invertebrate composition in B7C (Glyptotendipes and Einfeldia). Sediment cobalt, % silt composition, dissolved oxygen, oxygen saturation were strongly correlated with benthic invertebrate composition (G. lacustris, Ostracoda, Cladocera, and other Chironomidae genera) in the remaining lakes which are clustered together in ordination space.

The CCA ordination for the 2015 benthic invertebrate dataset showed that sediment nitrogen, percent silt composition, oxygen saturation, alkalinity, and chromium were the variables most strongly correlated with benthic community composition (Chironomidae genera; Figure 9). Sediment arsenic, cobalt, D50, and % sand composition were strongly correlated with benthic invertebrates in Lake A6 and site B2B (Einfeldia, Chironomus, Cladopelma, and Sergentia). TIC was strongly correlated with benthic invertebrates in Lake A1 (Cricotopus, Hirudinea, and Ostracoda). Chlorophyll a was strongly correlated with site D7C (Naididae). Conductivity, turbidity, dissolved oxygen, and zinc were strongly correlated with benthic invertebrate composition (Physella, Psectrocladius, Oligochaeta, Valvata, and Vedjovskyella) in the remaining lakes which are clustered together in ordination space.

3.3. Fish Metrics

3.3.1. Condition, GSI, HSI, and Age

Lake A1 was dominated by whitefish (n = 2; 100%) in 2015 and consisted of whitefish (n = 4; 80%) and lake trout (n = 1; 20%) in 2016 (Lake A1 was not sampled for fish in 2017 as weather conditions prevented access to the lake; see Table A17 for catch rates by lake). Lake A6 was represented by grayling (n = 4; 80%) and lake trout (n = 1; 20%) in 2015 and solely by grayling (n = 4; 100%) in 2016 and 2017. Lake A8 consisted of whitefish (n = 1; 20%) and grayling (n = 4; 80%) in 2015 and was represented solely by grayling in 2016 and 2017 (n = 8 and 6, respectively; 100%). Lake B2 consisted of whitefish (n = 8; 57%) and lake trout (n = 6; 43%) in 2015, grayling (n = 9; 90%) and lake trout (n = 1; 10%) in 2016, and grayling (n = 4; 100%) in 2017. Lake B7 consisted of whitefish (n = 80; 98%) and grayling (n = 2; 2%) in 2015, whitefish (n = 10; 71%) and grayling (n = 4; 29%) in 2016, and grayling (n = 1; 100%) in 2017. Only one whitefish was caught in Lake D7 in 2016 (no fish were caught in 2015 or 2017).

Length for whitefish ranged from 18.3–23.2 cm across lakes in 2015 and from 19.1–32.7 cm in 2016 (see Table 11). In 2017, whitefish were only caught in Lake B7, where the mean length was 38.7 cm. Mean length for grayling ranged from 36.5–39.6 cm across lakes in 2015, from 26.5–41.2 in 2016, and from 31.8–40.5 cm in 2017. Lake trout were caught in lakes B2 and A6 in 2015, where mean lengths were 23.1 and 38.5 cm, respectively. In 2016, one lake trout was caught in each of lakes A1 (19.1 cm) and B2 (29.7 cm). Length did not differ significantly by lake for whitefish (p = 0.283; see Table A18 for results of one-way ANOVA tests), however it did for grayling (ANOVA; p < 0.0001; although there were no significant differences among lakes; see Table A19 for results of pairwise comparisons), and for lake trout (p = 0.019). Specifically, lake trout length was significantly lower in Lake A1 compared to Lake A6 (p = 0.017).

Weight for whitefish ranged from 35–155 g across lakes in 2015 and from 48–299 g in 2016 (Table 11). In 2017, whitefish were only caught in Lake B7, where the mean weight was 463 g. Mean weight for grayling ranged from 375–667 g across lakes in 2015, from 145–581 g in 2016, and from 268–575 g in 2017. Mean weight for lake trout was 117 g in Lake B2 and 626

67 g in Lake A6 in 2015. In 2016, one lake trout was caught in each of lakes A1 (40 g) and B2 (227 g). Weight did not differ significantly by lake for whitefish (p = 0.123), however it did for grayling (p < 0.001): grayling length was higher in Lake A6 than in lakes B2 and B7, and higher in Lake A8 than in lakes B2 and B7 (p < 0.001). Weight differed significantly by lake for lake trout (p = 0.001), with Lake A6 being higher than lakes A1 (p = 0.002) and B2 (p = 0.001).

Fulton’s condition factor (K) for whitefish ranged from 0.57–1.06 across lakes in 2015 and from 0.66–1.13 in 2016 (Table 11). In 2017, mean whitefish K was 0.79 in Lake B7. Mean K for grayling ranged from 0.77–1.20 across lakes in 2015, from 0.71–0.88 in 2016, and from 0.78–0.93 in 2017. Mean K for lake trout was 0.88 in Lake B2 and 1.10 in Lake A6 in 2015. In 2016, mean lake trout K was 0.57 in Lake A1 and 0.87 in Lake B2. K differed significantly by lake for whitefish (p = 0.002), with Lake D7 being higher than lakes A8 (p = 0.004) and B7 (p = 0.024). Exponential transformed K did not differ significantly by lake for grayling (p = 0.316). However, K differed significantly by lake for lake trout (p = 0.004), with Lake A1 being lower than lakes A6 (p = 0.002) and B2 (p = 0.006), and Lake A6 being higher than Lake B2 (p = 0.030). K did not differ significantly with fish age for whitefish (p = 0.948), grayling (p = 0.313), or lake trout (p = 0.106).

Weight was positively correlated with length (p < 0.001) when run as log-transformed data in a linear model for whitefish, grayling, and lake trout with year as a random effect. Log- log plots were created for each species (Figure 10).

Table 11. Length, weight, and Fulton’s condition factor (± standard deviation) for all large species fish (dissected and non-dissected) caught from 2015-2017.

Species Year Lake N Length (cm) Weight (g) Fulton's condition factor (K) A1 2 23.0 (3.5) 130 (42) 1.06 (0.14) A8 1 18.3 35 0.57 2015 B2 8 20.3 (4.8) 91 (66) 0.91 (0.11) B7 80 23.2 (9.9) 155 (158) 0.81 (0.11) Whitefish A1 4 19.1 (1.2) 48 (17) 0.66 (0.13) 2016 B7 10 32.7 (1.9) 277 (79) 0.78 (0.14) D7 1 29.8 299 1.13 2017 B7 3 38.7 (4.5) 463 (136) 0.79 (0.11) A6 4 39.6 (1.9) 585 (98) 0.93 (0.07) 2015 A8 4 39.0 (4.7) 667 (46) 1.20 (0.46) B7 2 36.5 (1.8) 375 (49) 0.77 (0.01) A6 4 38.1 (2.0) 489 (78) 0.88 (0.03) A8 8 41.2 (3.2) 581 (118) 0.82 (0.06) 2016 Grayling B2 9 26.5 (4.2) 145 (79) 0.71 (0.23) B7 4 32.0 (3.6) 262 (78) 0.79 (0.07) A6 4 39.0 (5.1) 553 (169) 0.93 (0.14) A8 6 40.5 (1.6) 575 (48) 0.87 (0.08) 2017 B2 4 31.8 (4.6) 268 (123) 0.81 (0.28) B7 1 35.6 350 0.78 A6 1 38.5 626 1.10 2015 Lake B2 6 23.1 (3.6) 117 (60) 0.88 (0.06) Trout A1 1 19.1 40 0.57 2016 B2 1 29.7 227 0.87

log(weight)=2.9611*log(length)-2.0454 A R2=0.95

log(weight)=3.3964*log(length)-2.6910 B R2=0.99

Log(Weight)

log(weight)=3.4917*log(length)-2.7744 C R2=0.92

Figure 10. Log-log plot of weight vs. length for whitefish (A) and grayling (B) caught from 2015-2017, and lake trout (C) caught from 2015-2016.

Gonadosomatic indices (GSIs) for whitefish were 0.12% in Lake A1 and 2.67% in Lake B7 (Table 12; see Table A20 for data for individual fish). In 2016, mean whitefish GSIs were 2.24% in Lake B7 and 0.25% in Lake D7. In 2017, mean whitefish GSI was 1.76% in Lake B7. Mean GSIs for grayling ranged from 0.92–1.52% across lakes in 2015. In 2017, mean grayling GSIs ranged from 0.22–0.64%. Mean GSI for lake trout was 0.05% in Lake A6 in 2015. GSIs did not differ significantly by lake for whitefish (p = 0.190) or grayling (p = 0.832). GSIs did not differ significantly by length for whitefish (p = 0.105) or grayling (p = 0.255). A GSI value was only available for one lake trout, and thus statistical analyses were not conducted for lake trout GSIs. GSI did not differ significantly with fish age for whitefish (p = 0.148) or grayling (p = 0.292).

Hepatosomatic indices (HSIs) for whitefish ranged from 0.40–0.81% across lakes in 2015 (Table 12). In 2016, mean whitefish HSIs were 1.00% in Lake D7 and 1.92% in Lake B7. In 2017, mean whitefish GSI in Lake B7 was 0.54%. Mean HSIs for grayling ranged from 0.47– 0.74% across lakes in 2015 and from 0.38–0.52% in 2017. Mean HSIs for lake trout were 0.89% in Lake B2 and 1.02% in Lake A6 in 2015. HSIs differed significantly by lake for whitefish (p = 0.044), however there were no significant differences among lakes. HSIs did not differ significantly by lake for grayling (p = 0.663) or lake trout (p = 0.517). However, HSI was positively correlated with length for whitefish (p = 0.047; Figure A1), but there was no correlation for grayling (p = 0.220) or lake trout (p = 0.200). HSI did not differ significantly with age for whitefish (p = 0.063), grayling (p = 0.143), or lake trout (p = 0.216).

Table 12. Mean gonadosomatic (GSI) and hepatosomatic (HSI) indices as well as morphometric data (± standard deviation) for large fish species. Length Weight Fulton's condition factor Species Year Lake N GSI (%) HSI (%) (cm) (g) (K) A1 2 23.0 (3.5) 130 (42) 1.06 (0.14) 0.12 (0.03) 0.40 (0.05) 2015 B2 4 24.2 (2.4) 141 (53) 0.95 (0.14) 0.47 (0.16) B7 4 37.2 (2.0) 498 (91) 0.96 (0.03) 2.67 (2.30) 0.81 (0.38) Whitefish B7 1 32.7 312 0.89 2.24 1.92 2016 D7 1 29.8 299 1.13 0.25 1.00 2017 B7 4 38.7 (4.5) 463 (136) 0.79 (0.11) 1.76 (0.39) 0.54 (0.03) A6 4 39.6 (1.9) 585 (98) 0.93 (0.07) 1.52 (1.05) 0.74 (0.27) 2015 A8 4 39.0 (4.7) 667 (46) 1.20 (0.46) 1.22 (0.51) 0.66 (0.24) B7 2 36.5 (1.8) 375 (49) 0.77 (0.01) 0.92 (0.29) 0.47 (0.00) Grayling A6 1 31.7 350 1.10 0.22 0.38 2017 A8 5 40.3 (1.7) 574 (53) 0.88 (0.08) 0.64 (0.25) 0.52 (0.23) B2 3 32.9 (4.9) 280 (147) 0.74 (0.30) 0.57 (0.36) 0.38 Lake A6 1 38.5 626 1.10 0.05 1.02 2015 Trout B2 4 24.5 (3.1) 138 (63) 0.88 (0.08) 0.89 (0.16)

Fish age estimates based on otoliths were available for fish that were caught and dissected in 2015 (Table 13). The mean age of grayling ranged from 5.5 years in Lake A6 and 7.0 years in Lake A8. The mean age of whitefish ranged from 4.3 in Lake B2 to 7.5 years in Lake B7. The mean age of lake trout ranged from 5.3 in Lake B2 and 7.0 in Lake A6.

Table 13. Mean age (± standard deviation) of large fish species caught in 2015. Species Lake N Age (years) A6 4 5.5 (1.3) Grayling A8 1 7.0 A1 2 4.5 (0.7) Whitefish B2 4 4.3 (0.5) B7 4 7.5 (0.6) A6 1 7.0 Lake trout B2 4 5.3 (0.5) Note: age data are unavailable for 2016-2017 as samples were accidentally discarded

3.3.2. Truss Analysis

PCA was performed on truss data (presented in Table A21) for large fish caught from 2015-2017, with all species combined into a single analysis (Table 14), and a separate analysis performed on each species. For the combined data analysis, the first three principal components were found to explain 88% of the variance in the analysis, with a 68%, 14%, and 6% proportion of the variance being explained by PC1, PC2, and PC3, respectively.

Table 14. Variance explained by the first three principal components of an analysis of truss data for Coregonus artedi, Thymallus arcticus, and Salvelinus namaycush caught from 2015-2017. PC1 PC2 PC3 Variance 14.30 2.85 1.32 Standard deviation 3.78 1.69 1.15 Proportion of variance 0.68 0.14 0.06 Cumulative proportion 0.68 0.82 0.88

Absolute values of the factor loadings for the first three components were low (all ≤ 0.520; Table 15). Results of the combined and individual analyses yielded a similar pattern of factor loadings, and so only the results of the combined analysis are presented in the table below.

Table 15. Factor loadings for each principal component that explained ≥5% of the variance of a PCA of truss data for all large fish caught. Absolute values for factor loadings were typically <0.300, however, certain factor loadings were higher in magnitude and are bolded. Measure PC1 PC2 PC3 1-2 0.216 -0.024 0.214 1-3 0.186 -0.413 -0.063 1-4 0.194 -0.150 0.494 2-3 0.183 -0.419 -0.072 2-4 0.189 -0.167 0.469 3-4 0.244 -0.061 -0.120 3-5 0.114 0.518 0.062 3-6 0.143 0.469 0.211 4-5 0.228 0.028 -0.230 4-6 0.169 0.042 -0.520 5-6 0.251 -0.018 -0.016 5-7 0.248 -0.051 -0.078 5-8 0.257 -0.035 0.101 6-7 0.253 -0.037 -0.072 6-8 0.246 -0.104 -0.033 7-8 0.234 0.163 -0.002 7-9 0.210 0.180 0.049 7-10 0.254 0.037 0.002 8-9 0.247 0.126 0.031 8-10 0.202 0.085 -0.255 9-10 0.240 0.094 -0.067

Based on the PCA ordination plot (Figure 11), there is no clear separation of fish species, however measures 3–5 and 3–6 drive separation (on the secondary axis) of individual fish into two “lines” of data which otherwise ordinate based on measures 1-3, 2-3, and 4-5 (on the primary axis). Measures 1-3 and 2-3 also had absolute factor loading values on the second axis that were higher than for most other measures.

Figure 11. Principal component analysis of truss data for Coregonus artedi, Thymallus arcticus, and Salvelinus namaycush caught from 2015-2017.

PCA plots for individual fish species are presented in the Appendix (Figure A2). The PCA plot for C. artedi indicates clear separation by lake, with measures 1-3, 2-3, 1-4, and 2-4 driving separation of individual fish on the primary axis, and measures 3-5 and 3-6 driving separation on the secondary axis. The plot for T. arcticus indicates separation by watershed along the secondary axis with overlap of fish within watershed. Measures 1-3, 2-3, and 1-4 drive separation on the primary axis, and measures 3-5, 3-6, and 4-5 drive separation on the secondary axis. The plot for S. namaycush has too few data points to infer lake- or watershed-based separations. Measures 1-3, 2-3, 1-4, and 2-4 drive separation on the primary axis, and measures 3-4, 3-5, 3-6, 4-5, and 4-6 drive separation on the secondary axis.

Based on visual inspection and ranking of measures (by absolute values of factor loadings) for combined and individual species PCA plots, measures associated with the anterior and head region (1-3, 2-3, and 4-5) were expressed most strongly across species. These measures were compared across lake for each species. Measures 1-3, 2-3, and 4-5 for whitefish were significantly different across all lake comparisons (except comparing B7 and D7).

Measures 1-3, 2-3, and 4-5 for grayling differed across all lake comparisons (except comparing A6 and A8, and B2 and B7). Measures 1-3, 2-3, and 4-5 differed for lake trout across all lake comparisons.

3.3.3. Gut Content Analysis

Across both years (2015 and 2016) whitefish consumed Mollusca (25%), Chironomidae (18%), Nematoda (11%), Amphipoda (11%), fish (5%), zooplankton (4%), Limnephilidae (< 1%), and other prey (25%; Figure 12; see Table A23 for a complete list of taxa identified in gut contents). Across both years, grayling consumed Chironomidae (32%), Limnephilidae (29%), Mollusca (7%), Nematoda (7%), Amphipoda (4%), fish (4%), zooplankton (< 1%), and other prey (17%). Lake trout were more specialist and piscivorous, with diet across years consisting of fish (59%), Limnephilidae (23%), Chironomidae (9%), and other prey (8%). A PCA plot of fish gut contents (by prey group) for whitefish, grayling, and lake trout is presented in the Appendix (Figure A3). See Table A22 for a summary of the number of fish analyzed for gut content analysis by lake and year.

Whitefish diets in Lake A1 consisted primarily of Hirudinea (39%), ninespine stickleback (29%), and Gammarus lacustris (20%; Figure 13). In Lake B2 they consumed primarily Nematoda (53%) and Chironomidae pupae (35%). In Lake B7 they consumed primarily Sphaeriidae (55%), Hirudinea (16%), and Chironomidae (14%, with 6% each for larvae and pupae and 2% for adults). In Lake D7, they consumed primarily Gammarus lacustris (91%).

Grayling diets in Lake A6 consisted primarily of Limnephilidae pupae (37%) and larvae (16%) as well as Nematoda (20%). In Lake A8 they consumed primarily Limnephilidae pupae (35%) and Chironomidae larvae (33%) as well as Mollusca (10%; 8% Physidae). In Lake B2 they consumed primarily Hydrachnidia (42%), Gammarus lacustris (20%), Chironomidae larvae (17%) and pupae (11%). In Lake B7 they consumed primarily Chironomidae pupae (50%) and larvae (14%), as well as Valvata (12%).

Lake trout diets in Lake A1 consisted almost entirely of larval (78%) and adult (21%) Limnephilidae. In Lake A6, they consumed primarily ninespine stickleback (50%) and

76 unidentifiable minnows (42%). In Lake B2, they consumed primarily ninespine stickleback (67%), Chironomidae larvae (14%), and Hirudinea (11%).

Other Amphipoda Zooplankton Chironomidae Limnephilidae Mollusca Nematoda Fish

Other Amphipoda Zooplankton Chironomidae Limnephilidae Mollusca Nematoda

Fish Composition of diet (%) diet of Composition

Lake

Figure 13. Mean diet composition by lake for whitefish (Wf), grayling (Gr), and lake trout (Lt) from 2015-2016.

3.4. Isotope Analysis

Phytoplankton from Lake A1 had a mean δ13C value of -27.36‰ and a mean δ15N value of 1.883‰ in 2014 (Figure 14; see Table A24 for year source of year-averaged data; isotope data for phytoplankton is summarized in Table A25). Zooplankton from this lake had a similar mean δ13C value of -27.43‰ and a mean δ15N value of 3.16‰ (see Table A26 for zooplankton data). Benthic invertebrate δ13C values ranged from -27.02‰ to -21.18‰, with Limnephilidae adults being the lowest and Hydrachnidia being the highest (see Table A27 for aquatic invertebrate data). Benthic invertebrate δ15N values ranged from 4.49‰ to 6.62‰, with Chironomidae larvae being the lowest and Limnephilidae (Trichoptera) adults being the highest. Fish δ13C values were -24.93‰ for whitefish and -23.29‰ for stickleback (see Table A28 for individual fish data and Table A29 for mean fish values by lake and year). Fish δ15N values were 10.25‰ for whitefish and 10.48‰ for stickleback. Unfortunately, data were not available for phytoplankton samples from 2015 and 2016, Amphipoda, and Gastropoda due to sample quality or instrumentation issues. Isotope ratio data for shoreline-collected adult life stage (terrestrial) invertebrates and spiders is presented in Tables A30 and A31, respectively.

Phytoplankton from Lake A6 had a mean δ13C value of -27.23‰ and a mean δ15N value of 1.98‰ in 2014 (Figure 14). Zooplankton in Lake A6 had a similar mean δ13C value of - 27.71‰ and a mean δ15N value of 4.66‰. Benthic invertebrate δ13C values ranged from - 29.67‰ to -21.20‰, with Culicidae (Diptera) adults being the lowest, and Laccophilus (Coleoptera) adults being the highest. Benthic invertebrate δ15N values ranged from 2.38‰ to 6.34‰, with Carabidae (Coleoptera) adults being the lowest and Perlodidae (Plecoptera) adults being the highest. Fish δ13C values ranged from -23.35‰ to -21.54‰, with stickleback being the lowest and grayling being the highest. Fish δ15N values ranged from 9.48‰ to 12.05‰, with stickleback being the lowest and lake trout being the highest.

Phytoplankton from Lake A8 had a mean δ13C value of -25.95‰ and a mean δ15N value of 3.57‰ in 2014 (Figure 14). Zooplankton in Lake A8 had a mean δ13C value of -26.90‰ and a mean δ15N value of 6.77‰. Benthic invertebrate δ13C values ranged from -23.94‰ to - 19.29‰, with Chironomidae larvae being the lowest and Physidae being the highest. Benthic invertebrate δ15N values ranged from 3.58‰ to 7.80‰, with Tipulidae larvae being the lowest

79 and Perlodidae adults being the highest. Fish δ13C values were -21.86‰ for stickleback and - 20.99‰ for grayling. Fish δ15N values were 11.02‰ for grayling and 11.04‰ for stickleback.

Phytoplankton from Lake B2 had a mean δ13C value of -27.40‰ and a mean δ15N value of 2.52‰ in 2014 (Figure 14). Zooplankton had a mean δ13C value of -28.76‰ and a mean δ15N value of 4.30‰. Benthic invertebrate δ13C values ranged from -25.18‰ to -18.68‰, with Tipulidae adults being the lowest and Dytiscidae larvae being the highest. Benthic invertebrate δ15N values ranged from 1.06‰ to 7.74‰, with Tipulidae adults being the lowest and Limnephilidae adults being the highest. Fish δ13C values ranged from -22.67‰ to -18.81‰, with sticklebacks being the lowest and burbot being the highest. Fish δ15N values ranged from 9.03‰ to 11.15‰, with grayling being the lowest and lake trout being the highest.

Phytoplankton from Lake B7 had a mean δ13C value of -24.97‰ and a mean δ15N value of 3.36‰ in 2014 (Figure 14). Zooplankton had a mean δ13C value of -28.48‰ and a mean δ15N value of 5.67‰. Benthic invertebrate δ13C values ranged from -21.23‰ to -18.56‰, with Isoperla (Plecoptera) adults being the lowest and Physidae being the highest. Benthic invertebrate δ15N values ranged from 2.94‰ to 8.82‰, with Tipulidae larvae being the lowest and Chloroperlidae (Plecoptera) adults being the highest. Fish δ13C values ranged from -22.94‰ to -19.36‰, with stickleback being the lowest and grayling being the highest. Fish δ15N values ranged from 9.13‰ to 10.20‰, with grayling being the lowest and stickleback being the highest.

Phytoplankton data is not available for Lake D7 as only phytoplankton samples from 2014 were viable and Lake D7 was not sampled in 2014. Zooplankton in Lake D7 had a mean δ13C value of -28.30‰ and a mean δ15N value of 3.93‰ (Figure 14). Benthic invertebrate δ13C values ranged from -26.81‰ to -20.00‰, with Chironomidae larvae being the lowest and Perlodidae adults being the highest. Benthic invertebrate δ15N values ranged from -0.36‰ to 6.30‰, with Tipulidae adults being the lowest and Colymbetes (Coleoptera) adults being the highest. Stickleback δ13C was -23.47‰ and δ15N was 10.50‰.

Results of one-way ANOVA tests for carbon and isotope data for fish and zooplankton against year and post-hoc pairwise annual comparisons are presented in Tables A32 and A33, respectively. Values of δ13C for zooplankton differed significantly by year for lakes A8 and B2 (for 2014-2015 and 2015-2016) and B7 (2014-2015 only). Whitefish values for δ13C in lake B7

80 did not differ by year. Values of δ13C for sticklebacks for δ13C differed in lake D7 only (although this was across a two-year period 2015-2017). Grayling values for δ13C differed across years for lakes A6 and A8 (for 2015-2017). Values of δ15N for zooplankton differed significantly by year in Lake B7 (for 2014-2015). Grayling values for δ15N differed significantly by year for lake A8 (for 2015-2017).

A1 A6

A8 B2

15 δ

B7 D7

13 δ CVPDB

Figure 14. Stable isotope plots for each study lake (indicated in the upper right corner of each plot). Data labels are associated with the point immediately above and to the left. Life stages of invertebrates are indicated in brackets (a=adult, p=pupae, l=larvae). Stable isotope compositions are expressed as delta values in permille differences from a standard (air for nitrogen and VPDB for carbon). See Table A24 for year source of year-averaged data.

In support of determining the relative contribution of benthic energy pathways to food web structure in the study lakes, δ13C values of collected samples were fit by lake as a linear model with organism as the dependent variable. A post-hoc Tukey’s pairwise comparison, specifically for comparisons with phytoplankton was conducted (Table 16). In all lakes (except for Lake D7), δ13C values of most organisms (invertebrates and fish) were significantly higher than those of plankton. In fact, only one organism (adult Culicidae) was 13C depleted relative to tested phytoplankton in Lake A6. All fish species across all lakes (except for sticklebacks in Lake B7) were significantly different from zooplankton. This strongly suggests that energy in the study lakes, especially at higher trophic levels, is primarily derived through benthic pathways. The notable exception was Lake D7, with δ13C values of most organisms not being significantly different from those of zooplankton. This is especially surprising considering Lake D7 consistently exhibited low turbidity. This lack of significant differences is unlikely to be a result of comparing invertebrate and fish values to zooplankton as opposed to phytoplankton, as mean values of δ13C for zooplankton were lower than those for phytoplankton across all other study lakes. Notably, Lake D7 had a phosphorous concentration of 0.076 mg/L, the only lake for which the concentration was above the method detection limit (0.07 mg/L). In addition, Lake D7 had the highest mean chlorophyll a concentration among lakes in 2015 (1.75 µg/L) and a moderate concentration (1.15 µg/L) in 2016. Thus, increased pelagic primary productivity facilitated by a higher concentration of phosphorous in the water column (relative to other study lakes) could explain increased reliance on pelagic energy pathways in this lake.

Table 16. Summary of p-values for post-hoc Tukey’s pairwise comparison of δ13C values for invertebrates and fish to values for phytoplankton. δ13C values were fit by lake as a linear model with organism as the dependent variable. Values indicating non-significance (p ≥ 0.05) are bolded. Note that for Lake D7, organisms were compared to zooplankton as a surrogate for phytoplankton data which were not collected in 2014 for this lake. Group ID A1 A6 A8 B2 B7 D7 Agabus (a) 0.0006 Carabidae (a) 0.0023 Chironomidae (a) 0.0032 0.0011 0.0096 Chironomidae (l) 0.9933 0.0674 0.5326 0.5646 0.0008 0.9649 Chironomidae (l) Chloroperlidae (a) 0.0015 Colymbetes (a) 0.1535 Culicidae (a) 0.5098 0.0015 Dytiscidae (a) 0.0021 0.0029 0.0034 Dytiscidae (l) <0.0001 Empididae (l) 0.0009 Grensia (a) 0.7984 0.0732 Grensia (l) 0.1351 0.1675 Hydrachnidia <0.0001 0.0015 0.0027 0.0738 0.0001 Hydrophilidae (l) 0.0649 Isoperla (a) 0.0208 Invertebrates Laccophilus (a) 0.0003 Limnephilidae (a) 1.0000 0.8197 Limnephilidae (l) 0.3473 0.0001 0.0021 Limnephilus (a) 0.0059 0.0114 Limnephilus (l) 0.9714 Nemoura (l) 0.7107 Perlodidae (a) <0.0001 <0.0001 0.0027 Physidae 0.0001 0.0008 Plecoptera (a) 0.0019 Plecoptera (l) 0.0353 Simuliidae (a) 0.9057 Tipulidae (a) 0.7093 0.4645 Tipulidae (l) 0.0005 0.9944 0.0002 <0.0001 0.5457 Trichoptera (l) 0.1797 Trichoptera (p) 0.0099 Zooplankton 1.0000 1.0000 0.9506 0.9173 0.0017 Whitefish 0.0363 <0.0001 0.0176 Grayling <0.0001 <0.0001 <0.0001 0.0002 Fish Lake Trout 0.0008 <0.0001 Burbot <0.0001 Stickleback <0.0001 0.0001 0.0001 0.0018 0.9072 0.0026

Mean lake littoral carbon (calculated based on littoral and pelagic baseline carbon signatures and then averaged across fish species) ranged from 0.59–1.40, with Lake B7 being the lowest and Lake D7 being the highest (Table 17). Averaged across lakes, mean littoral carbon for fish species ranged from 0.80–1.76, with whitefish being the lowest and burbot being the highest. The proportion of carbon in whitefish derived from littoral sources (hereafter referred to as “littoral carbon”) ranged from 0.52–1.33 across years and differed significantly by lake (p < 0.001): Lake B2 differed from B7 (p < 0.001; see Table A34 for summary of pairwise lake comparisons); but not by year (p = 0.380). Grayling littoral carbon ranged from 1.00–1.54 and differed significantly by lake (p < 0.001): Lake A6 differed from lakes A8 and B7 (p < 0.001), and Lake B2 differed from lakes A8 and B7 (p < 0.001); and by year (p = 0.003 across 2015- 2017; see Table A35). Lake trout littoral carbon was 1.37 in Lake B2 and 1.51 in Lake A6 and did not differ significantly by lake (p = 0.505; lake trout data only available for 2015). Stickleback littoral carbon ranged from 0.36–1.45 and differed significantly by lake (p = 0.017): Lake A1 differed from B7 (p = 0.046); but not by year (p = 0.198). Averaged over lakes and fish species for 2015 and 2017, the mean proportion of littoral carbon was 1.16 ± SD of 0.14

Mean lake trophic position (averaged across fish species) ranged from 3.39–3.68, with Lake B2 being the lowest and Lake A1 being the highest. Averaged across species, mean trophic position for fish species ranged from 2.64–3.57, with burbot being the lowest and whitefish being the highest. Whitefish trophic position ranged from 3.18–3.82 and differed significantly by lake (p = 0.002): Lake B2 differed from A1 and B7 (p = 0.007 and 0.002, respectively); but not by year (p = 0.596). Grayling trophic position ranged from 2.83–3.47 and differed significantly by lake (p < 0.001): Lake B2 differed from lakes A6, A8, and B7 (p < 0.001); and by year (p < 0.001 across 2015-2017). Lake trout trophic position was 3.50 in Lake B2 and 3.75 in Lake A6 and differed significantly between the two lakes (p = 0.048; lake trout data only available for 2015). Stickleback trophic position ranged from 3.36–3.86 with no significant differences across lakes (p = 0.270) or across years (p = 0.422). Averaged over lakes and fish species for 2015 and 2017, the mean trophic position was 3.41 ± SD of 0.17.

Table 17. Mean proportion of littoral carbon and mean trophic position (± standard deviation) for each type of fish caught in study lakes. Mean δ13C and δ15N values for littoral sampled invertebrates were used as surrogate data for the littoral baseline and values for phytoplankton were used as the pelagic baseline. Mean Proportion of Lake Fish Mean Trophic Position Littoral Carbon Whitefish 0.86 (0.11) 3.82 (0.16) A1 Stickleback 1.45 (0.07) 3.45 (0.25) Grayling 1.54 (0.19) 3.23 (0.37) A6 Lake Trout 1.51 3.75 Stickleback 1.05 (0.34) 3.36 (0.24) Grayling 1.09 (0.09) 3.47 (0.27) A8 Stickleback 0.90 (0.12) 3.60 (0.17) Whitefish 1.33 (0.09) 3.18 (0.16) Grayling 1.43 (0.03) 2.83 (0.02) B2 Lake Trout 1.37 (0.17) 3.50 (0.07) Burbot 1.76 (0.02) 2.64 (0.06) Stickleback 0.97 (0.10) 3.43 (0.15) Whitefish 0.52 (0.24) 3.70 (0.20) B7 Grayling 1.00 (0.01) 3.30 (0.06) Stickleback 0.36 3.86 D7 Stickleback 1.40 (0.44) 3.48 (0.23)

Correlation analysis conducted for isotope data and fish gut content (Table 18) indicated a strong (r > 0.70) correlation between fish in the gut and δ15N (r = 0.74) and between Amphipoda in the gut and δ13C (r = -0.72). These correlated variables were plotted below with a linear regression (Figure 15).

Table 18. Correlation table for isotope and fish gut content data for 2015 samples. Correlation coefficients with an absolute value of ≥ 0.70 are indicated with an asterisk.

13 15 δ CVPDB δ Nair Fish Nematoda Snails and Clams Limnephilidae Chironomidae Amphipoda Other 13 δ CVPDB 1.00 15 δ Nair -0.10 1.00 Fish 0.13 0.74* 1.00 Nematoda 0.13 -0.11 -0.20 1.00 Snails and Clams -0.08 -0.40 -0.26 -0.27 1.00 Limnephilidae 0.28 0.01 -0.19 -0.26 -0.30 1.00 Chironomidae 0.34 -0.36 -0.23 0.56 -0.11 -0.27 1.00 Amphipoda -0.72* 0.00 -0.14 -0.13 -0.22 -0.15 -0.18 1.00 Other -0.30 -0.04 -0.13 -0.21 -0.02 -0.40 -0.23 0.19 1.00

15 A δ Nair=1.8711*pfish+9.9274 R2=0.55

13 B δ CPDB= -5.8942*pAmphipoda -20.9917 R2=0.52

4. DISCUSSION AND CONCLUSION

Resource extraction in the Arctic has been steadily intensifying in recent decades but the potential impacts of these activities on lake ecosystems is poorly understood, in part, due to a paucity of knowledge about the basic ecology and food web structure of Arctic lakes. The purpose of my study was: 1) to comprehensively characterize the ecology of six lakes within the projected areal footprint of a gold mine by assessing water and sediment chemistry, food web structure (stable isotopes analysis), community composition of benthic invertebrates, and the relative health and condition of fish populations to establish a baseline to which post-opening mining effects can be compared; and 2) to determine if detectable effects resulting from the exploration and pre-operational phases are occurring. I hypothesized that the relative homogeneity of the landscape (geology and vegetation) would yield lakes with similar physicochemical characteristics and therefore comparable food web structure based on community composition of benthic invertebrates and fish, and stable isotope analysis (proportion of littoral carbon and trophic position of fish). I further hypothesized that food web structure and community composition of benthic invertebrates and fish would be similar across years and unaffected by pre-operational mining activities.

4.1. Water and Sediment Chemistry

In total, 44% (80 of 180) of post-hoc pairwise lake comparisons for physicochemical water parameters were significantly different. Hardness, alkalinity, turbidity, potassium, and sodium were significantly different among lakes (p < 0.001), whereas conductivity, DOM, dissolved oxygen, and pH did not differ significantly. While significant between-lake differences were observed, in most cases, the ranges of values were relatively narrow, and, with a few exceptions, may be more indicative of low variability than biologically or ecologically significant trends. An exception was hardness and conductivity, which were both greater in Lake A8 compared to the other lakes. This lake is bordered by road construction on either side, as well as an adjacent quarry. Deposition of dust from road traffic and construction, coupled with higher than background water losses due to requirements of the camp (this lake serves as the source of water for the mining camp) could explain the higher conductivity in this lake.

The range of values for most parameters is like those found in the region. For example, across 38 lakes and ponds on Bathurst Island, Nunavut, Lim et al. (2001) showed that pH varied between 8.0-8.6 compared to 7.8-8.2 across lakes at Meliadine. At Meliadine, conductivity ranged from 64-229 µS/cm which is like the ranges determined by Lim et al (2001; 69–282 µS/cm) and Namayandeh and Quinlan (2011; 20–481 µS/cm) in a study of five Iqaluit and 15 Rankin Inlet lakes and ponds. Namayandeh and Quinlan (2011) also found dissolved oxygen ranged from 7.3–15.7 mg/L, which overlapped those found in Meliadine lakes (9.9–11.6 mg/L).

Dissolved oxygen/oxygen saturation and DOM were negatively correlated with depth of the lakes. The decline in both parameters may reflect the influence of wind-driven mixing. Shallow lakes (A1, D7) would likely be fully mixed and thus have greater dissolved oxygen compared to deeper lakes in which wind would have less influence at depth and decomposition of organic matter (e.g. respiration) would consume oxygen. Wind-driven currents in shallow lakes might also disturb bottom sediments to a greater degree compared to deep lakes, leading to greater DOM in the water column. Exposure to UV radiation results in the photochemical conversion of DOM to CO2 in surface waters (Corey et al. 2015), an effect which would be expected to be reduced in deeper lakes. However, since DOM was negatively correlated with depth, factors that lead to increased DOM (such as wind-driven sediment disturbance and precipitation-driven organic inputs from the surrounding landscape) likely outweigh the impact of UV degradation of DOM in lakes in the region. Dissolved oxygen and oxygen saturation were also negatively correlated with lake area. The negative correlation between DO and depth in the present study contradicts the results of Leppi et al. (2016), suggesting that within the relatively shallow (< 5 m) study lakes at Meliadine, area may be more important than depth as a predictor of water column DO. DOM was also negatively correlated with volume, but this may reflect collinearity between volume and site depth. Oxygen saturation in the study lakes was high (99.2 ± 3.0% across lakes for 2014-2016) compared to a mean of 49% in 20 lakes near the Ikpikpuk River (Alaska; Leppi et al. 2016).

For sediment physicochemical parameters, 7% (12 of 162) of pairwise lake comparisons were significantly different. Concentration of copper, TIC, and pH in sediment did not differ significantly by lake. Although nitrogen, TOC, D50, percent silt composition, and zinc differed by lake, values were comparable. Few studies have evaluated sediment physicochemical

89 properties in Arctic lakes. Sediment copper and zinc (mean values of 65 and 67 μg/g dry weight across lakes for 2014-2016, respectively) were lower than values for sediment collected by sediment traps in Arctic lakes near Baker Lake, Nunavut (mean values of 136 and 218 μg/g dry weight; VanEngen 2012). Canadian environmental quality guidelines mandate interim sediment concentrations of 35.7 and 123 mg/kg for copper and zinc, respectively (CCME 1999; CCME 2018). At Meliadine, 95% of measured sediment samples exceeded the copper guideline whereas none of the samples exceeded the zinc guideline. Similarly, after applying a correction of half the limit of detection for data below the method detection limit, 16% of water samples exceeded the Canadian guideline for the protection of aquatic life for copper (2 µg/L), whereas none of the samples exceeded the long-term exposure guideline for zinc (7 µg/L) (CCME 2008; CCME 1999). Copper is commonly associated with iron deposits and is most common in volcanic basalt rocks, often near contact with sedimentary rock layers (LeBlanc and Billaud 1978; Ferrario and Garuti 1980). A fault line present at Meliadine which separates sedimentary and volcanic rocks (both of which contain a series of iron oxide formations) could thus explain copper concentrations in the region. Proximity to preliminary mining operations is unlikely to be responsible for copper concentrations in these lakes, as copper did not differ significantly by lake or by watershed. Reported values for sediment pH in the present study are low, ranging from 3.7–4.9 across lakes for 2015-2016 compared to other studies. For example, a mean pH value of 7.1 was reported in sediment from an Arctic lake in the Svalbard archipelago and a range of 6.0– 7.3 was reported for core samples in Toolik Lake, Alaska (Wang et al. 2016, Klingensmith and Alexander 1983). These differences may partly reflect the extended storage of sediment samples (3 months refrigerated) prior to taking pH measurements. Sediment total nitrogen and TOC were positively correlated with site depth and lake area. Variation in sediment organic matter between Arctic lakes has been shown to be greater than that between shallow and deep locations within a lake, indicating landscape-scale control of organic matter variation in these lakes (Fortino et al. 2016). The authors suggest that the variation in percent organic matter is affected by benthic primary production. Lake bathymetry could therefore control percent organic matter in the study lakes indirectly by influencing rates of benthic primary production (i.e. different depths throughout the lake allowing varying degrees of light penetration).

4.2. Chlorophyll a

Sestonic chlorophyll a was similar among lakes in most comparisons, with significant differences observed in only 17% of comparisons (5 out of 30). Further, no significant correlation between productivity (as chlorophyll a) and depth in the studied lakes was found, likely owing to the photic zone extending to the bottom of all study lakes. Lim et al. (2001) reported that chlorophyll a ranged from below the limit of detection (< 0.001 µg/L) to 3.4 µg/L (mean = 0.8 µg/L) across 38 lakes and ponds on Bathurst Island, Hamilton et al. (2001) reported chlorophyll a concentrations of < 1.9 μg/L across 204 lakes in the Canadian Arctic Archipelago, and Namayandeh and Quinlan (2011) reported a range of 0.4–3.3 µg/L across five Iqaluit and 15 Rankin Inlet region lakes and ponds. Sestonic chlorophyll a in the Meliadine region exhibited similar though less variable values (0.79–1.75 µg/L). Periphytic chlorophyll a was not significantly different across lakes. Rautio et al. (2011) reported a range of benthic chlorophyll a density in cyanobacterial mats of 85 ± 46 mg/m2 for sites at Resolute, Nunavut, and concentrations up to 105.6 mg/m2 were reported for Ward Hunt Lake in the Canadian High Arctic (Bonilla et al. 2005). Björk-Ramberg and Ånell (1985) reported concentrations of chlorophyll a for epipelic and epilithic algae of 100 and 20 mg/m2, respectively, for two lakes in northern Sweden. The small proportion of significant differences in chlorophyll a concentrations among lakes in the present study is consistent with the findings of similar benthic community composition among lakes (discussed below).

4.3. Community Composition of Benthic Invertebrates

Diversity and equitability were relatively homogeneous, with significant differences among lakes observed in only 16% (10 of 63) of pairwise comparisons. Based on the five major taxonomic groups (Chironomidae, Ostracoda, Sphaeriidae, Valvata, and Oligochaeta), which comprised 97% of the benthos in 2014 and 2015, 11% (12 of 105) pairwise comparisons for percent composition were significantly different. Finally, based on Chironomidae genera, the dominant benthic group in each year, 23% (24 of 105) of pairwise lake comparisons for percent composition were significantly different. Collectively, and despite a greater proportion of significant differences in water and sediment chemistry characteristics among lakes, these data

91 provide support for the hypothesis that benthic community structure across lakes is similar. Little information on benthic community composition exists for Arctic lakes in the Nunavut region. Moore (1979) found that diversity indices and indicator taxa for benthic invertebrates near an operating metal mine in the Canadian subarctic were ineffective in monitoring metal contamination compared to benthic abundance. In the present study, however, there was no correlation between concentrations of copper and zinc in sediment and overall abundance of benthos (data not shown).

The relative dominance of Chironomidae, Oligochaeta, Sphaeriidae, etc. in the present study are corroborated by existing studies of benthic communities in Arctic lakes (Hershey 1985, Hershey et al. 2006; Gajewski et al. 2005, Merrick et al. 1992; Sierszen et al. 2003; Namayandeh and Quinlan 2011, Giberson et al. 2007). Other less abundant taxa found in the present study (e.g. Gammarus lacustris, Ceratopogonidae, Dytiscidae, Ephemeroptera, Hydracarina, Plecoptera, Tipula spp., and Trichoptera) have also been previously reported near Rankin Inlet and Iqaluit (Namayandeh and Quinlan 2011). The latter authors found that Diptera (predominantly Chironomidae) constituted about 50% of overall relative abundance, Hydrachnidia and Trichoptera each contributed about 12%, Amphipoda, Oligochaeta, and Plecoptera each contributed about 7%, and all other groups contributed < 5% each at sites near Rankin Inlet. In the Meliadine area, Chironomidae dominated (88% on average across years), Hydrachnidia and G. lacustris each contributed < 1%, and Oligochaeta contributed 3%. Trichoptera and Plecoptera, while not present in benthic grab samples, were sampled along the rocky shoreline for isotope analysis and Trichoptera were also prevalent in the gut contents of some fish species. Differences between my findings and those of Namayandeh and Quinlan (2011) may reflect differences in sampling methods. Namayandeh and Quinlan (2011) employed a kick-and-sweep method along transects in shoreline areas, which would collect more Trichoptera (and less Chironomidae) compared to the sediment grab I used in offshore areas. Moore (1978) sampled 20 lakes in the Canadian Arctic and subarctic in the Northwest Territories and found larval Chironomidae to be the dominant taxonomic group, followed by Mollusca, and Amphipoda. Mollusca consisted of Pisidium (Sphaeriidae) and Valvata. Another study of a subarctic lake in the Northwest Territories with sediments contaminated with heavy metals from mine effluent determined that Chironomidae, followed by Mollusca, Oligochaetes, and

Amphipoda were the most abundant taxonomic groups (14, 5, 3, and 3 species of 25 total species, respectively; Moore 1979), which is similar to the present study.

Diversity (taxa richness) and equitability of benthic invertebrate were negatively correlated with site depth but this is a common relationship reported in the literature (Tarrats et al. 2017, Lindegaard 1992, Korhola et al. 2000, Chen et al. 2014, Burlakova et al. 2018, Poznanska et al. 2008, Golder Associates 2015; Jones 2005). Diversity and equitability were also negatively correlated with areas of the lakes. A modest negative correlation between diversity and equitability and areas of the lakes (r = -0.50) has previously been reported in lakes and ponds near Iqaluit and Rankin Inlet (Namayandeh and Quinlan 2011). The authors also found correlations between species diversity and concentrations of certain major ion (e.g. calcium, magnesium, and conductivity); however, these measured parameters were not compared to diversity in the present thesis.

The percent contribution of Chironomidae to the benthos was low in Lake D7 (27%) compared to the rest of lakes (mean 65% in 2015). This was influenced by high percent composition of Sphaeriidae in Lake D7 (36%). Sphaeriidae has been documented to be prevalent in shallower lakes, being negatively correlated with depth (Dermott 1978), with spatial distribution being influenced by substrate type, abundance of aquatic mosses, and water chemistry (Bespalaya 2015). Lake D7 has a lower maximum depth than all other lakes except A1 and A2. Percent Chironomidae was also relatively low in the shallow Lake A1 in 2014 (64% compared to 81% across other lakes) and 2015 (41% compared to 80% across all other lakes except Lake D7). Percent Chironomidae was found to be positively correlated with site depth (r = 0.66) and lake area (r = 0.58). In the case of depth, this trend contrasts with the more typical declines in Chironomidae (and other benthos) with lake depth shown in other studies. Temperature and dissolved oxygen are two key factors controlling the distribution of Chironomidae in relation to depth (Tarrats et al. 2017, Medeiros and Quinlan 2011). While DO was weakly negatively correlated with depth (r = -0.32) in the present study, it was not correlated with percent Chironomidae. Temperature was positively correlated with percent Chironomidae (r = 0.97), however this is likely spurious as there was no correlation between temperature and depth. Thus, the increase in percent composition of Chironomidae with depth is likely confounded by other factors such as dissolved oxygen, temperature, light intensity, the presence

93 of macrophytes as habitat, and temporal differences in sampling. For example, different emergence patterns (controlled by temperature and light intensity) are known to drive temporal variability in Chironomidae communities, while other environmental factors (which may be depth-dependent), such as the presence of macrophytes, are the cause of spatial variability (Tarrats et al. 2017).

Pagastiella and Cladotanytarsus did not differ significantly across lakes, while Tanytarsus differed significantly in 14% and Corynocera and Procladius differed in 57% and 43% of comparisons, respectively. Notably, Lake D7 had a higher percent composition of Procladius than any other lake (40% compared to a mean of 7 ± 15% across lakes in 2015). Growth of Procladius has been closely linked to suitable water temperatures in the spring (Dermott et al. 1977), and the genus is highly sensitive to minor climate change conditions and is valuable in reconstructing past temperatures (Millet et al. 2009). The relative warm temperature of Lake D7 compared to other study lakes (mean temperature of 13.4 ± 2.6°C compared to a mean of 12.4 ± 2.3°C across all other lakes for 2015-2017) could partly explain the prevalence of Procladius in this lake (although no significant correlation with water temperature was found).

In a study of 50 lakes across the Canadian Arctic Islands, Gajewski et al. (2005) found that several of the more abundant Chironomidae taxa in the high Arctic were Micropsectra, Paracladius, Heterotrissocladius, Tanytarsus lugens/Corynocera oliveri, Psectrocladius, Eukiefferiella, Procladius, Chironomus, and Abiskomyia, several of which (Micropsectra, Tanytarsus, C. oliveri, and Psectrocladius) were found in the present study. Medeiros and Quinlan (2011) examined the distribution of Chironomidae across environmental gradients in lakes and ponds in the eastern Canadian Arctic, with a large portion of the dataset being from the Kivalliq region and particularly Rankin Inlet. They determined that distribution of Chironomidae followed primarily a temperature gradient, with secondary relationships along a nutrient gradient being significant as well (Medeiros and Quinlan 2011). Their study was the first to show evidence of warm-water adapted taxa in lakes of the colder region of the eastern Canadian subarctic north of the tree line. Warmer and more productive lakes surrounding Rankin Inlet (relative to more northern sites) contained warm-adapted genera of the Chironomini tribe, such as Microtendipes, Dicrotendipes, Cladopelma, Polypedilum, Cryptochironomus, Parachironomus, Endochironomus, and Glyptotendipes, all of which (except for

Parachironomus and Endochironomus) were found in the Meliadine region in the present study. Lakes in southern Kivalliq have been shown to be dominated by Tanytarsini (e.g. Cladotanytarsus, Tanytarsus, and Corynocera), with Corynocera ambigua being the dominant species in the region (Medeiros and Quinlan 2011). These genera also occurred commonly in the present study. Conversely, deep lakes in colder regions at higher latitudes (Iqaluit) contain primarily cold-adapted subfamilies Diamesinae and Orthocladinae and genera (Abiskomyia, Pseudodiamesa, Heterotrissocladius, and Hydrobaenus/Oliveridia; Medeiros and Quinlan 2011), none of which were found in the shallow lakes at Meliadine. Namayandeh and Quinlan (2011) also examined benthic macroinvertebrate communities in lakes and ponds near Iqaluit and Rankin Inlet, Nunavut. The authors found that ecosystem-scale lake characteristics (e.g. nutrients, major ion chemistry, lake morphometry) followed by substrate type explained most of the variation in the relative abundance of Chironomidae.

To distinguish between natural and anthropogenic effects on benthic community composition in the region, sensitive taxa were compared to concentrations of potentially toxic metals in sediments. As Chironomidae are typically more sensitive to copper and zinc than other metals (except for mercury and cadmium, for which data were below the LOD for most sites across 2014-2016; Khangarot and Ray 1989, Postma et al. 1995), percent Chironomidae was compared to these two metals in sediment. No significant correlations (p-values of 0.130 and 0.089, respectively) were observed. Similarly, G. lacustris, also known to be sensitive to metals (Von der Ohe 2005), showed no significant correlation with copper (p = 0.317) or zinc (p = 0.439).

4.4. Fish Metrics

I found little evidence of differences among lakes for the four fish species based on HSI, GSI, condition factor, and truss analysis. Overall, only 18% (13 of 73) pairwise comparisons for HSI, GSI, and condition factor were significantly different. Collectively, the HSI, GSI, and condition factor data indicate similarity in fish health across lakes, supporting my hypothesis of homogeneity.

GSI for whitefish and grayling, and HSI for grayling and lake trout did not differ by lake (there was insufficient lake trout data for analysis of GSI). Whitefish HSI differed significantly in some lake comparisons and was positively correlated with fish length. Although data for the studied fish species is scarce in the literature, a correlation between HSI and length has been previously been reported in the literature for other fish (Oddone and Velasco 2006). Condition factor (K) for grayling did not differ by lake; however, it did for whitefish (D7 was higher than both A8 and B7) and for lake trout (Lake A1 was lower than B2 and A6, and Lake B2 was lower than A6). A limitation in the application of Fulton’s condition factor as a measure of fish health is that the exponent b in the equation (W = aLb) should be empirically determined by measuring a large number of fish. Since the length-weight dataset was limited in the present study, I conducted truss analysis to provide additional insight into relative fish condition.

PCA of truss data across species yielded absolute factor loading values which were ≤ 0.520. Unfortunately, studies employing truss analysis with freshwater Arctic fish populations are uncommon and do not always report factor loadings (Forseth et al. 2003; Wilson and Hebert 1993). However, these values appear to be typical for factor loadings for truss data. For example, Fitzgerald et al. (2002) found factor loadings on the first and second axes for yellow perch were all ≤ 0.464. Based on an ordination plot for the current study, measures associated with the anterior and head region (1-3, 2-3, and 4-5) were expressed most strongly across all fish species and differences in these measures among lakes (within species) were detectable, with 80% of comparisons being significant. While truss data for the fish species examined in this study is lacking in the literature, truss measures related to head morphometry have previously been used to distinguish between whitefish populations (Ward 2001), suggesting that the measures most strongly expressed across species in this study may be useful in monitoring Arctic Salmonidae populations.

4.5. Food Web Analysis

The results of this study, based on the analyses of stable isotopes, indicate that the food web structure was similar across lakes and between years and did not falsify the hypothesis that relative food web structure in the lakes in this region is similar. Fish and invertebrate values for δ13C were found to differ significantly from planktonic δ13C values for most organisms (and all 96 fish species) across all lakes (except for Lake D7). This provides strong evidence that higher trophic levels derive energy primarily from benthic as opposed to pelagic sources. The notable exception was Lake D7, with δ13C values of most organisms not being significantly different from those of zooplankton. Lake D7 had the highest mean pelagic chlorophyll a concentration among lakes in 2015 (1.75 µg/L) and a moderate concentration in 2016 (1.15 µg/L), which might explain increased reliance on pelagic energy pathways in this lake. Despite the lack of significant differences from zooplankton, mean values for all organisms in Lake D7 still indicate enrichment of 13C relative to zooplankton, supporting the importance of energy derived from benthic sources.

Isotopic signatures from invertebrates in the present study are based on samples collected from rocky shoreline areas; analyses were not performed on invertebrates collected from the benthic grabs. Gut content analysis showed that many invertebrates in the fish guts (notably Trichoptera larvae and pupae, Amphipoda, and snails) were uncommon in benthic grab samples suggesting that fish were foraging in nearshore areas. Thus, in this study, “littoral” refers to energy derived from these nearshore invertebrates. The proportion of carbon derived from littoral areas differed significantly by lake for whitefish and grayling but not for lake trout. Overall, 24% (6 of 25) of pairwise lake comparisons for the proportion of littoral carbon in fish were significantly different. These results indicate a strong reliance on benthic-derived carbon by fish; most were > 1 indicating a carbon source that is 13C-enriched relative to all sampled “baseline” organisms. This source is likely periphyton (and grazers such as Gastropoda which comprised a high proportion of the diet in certain whitefish and grayling). Measured values of δ15N in fish tissue were positively correlated with the proportion of fish in the gut contents (R2 = 0.55; p < 0.001; Figure 15). Chironomidae comprised a significant proportion of the diet in whitefish (18%) and lake trout (9%). Kidd et al. (1998) reported percent Chironomidae in the diet of whitefish and lake trout of 63% and 92%, respectively (for Peter Lake, a large lake adjacent to Meliadine Lake). Mollusca comprised 25% of whitefish diet in the Meliadine lakes, compared to 16% at Peter Lake. While Amphipoda were important for whitefish (11%) at Meliadine, they were consumed exclusively by lake trout in Peter Lake (17%). Fish comprised 60% of lake trout diet at Meliadine, and 75% of lake trout diet at Peter Lake. Limnephilidae were important prey for grayling and lake trout at Meliadine but were not found in gut contents at Peter Lake. This is likely explained by differences in lake morphometry. Peter Lake is a large lake with a maximum 97 depth of 23.5 m and surface area of 154 km2, compared to a maximum depth of 5 m and a mean surface area of 0.56 km2 for the peninsular lakes at Meliadine. Thus, the ratio of pelagic to littoral habitat and food resources in Peter Lake is substantially higher than in the present study, such that fish would not rely on Limnephilidae (a predominantly littoral invertebrate). Measured δ13C values in fish tissue were negatively correlated with the proportion of Amphipoda in the gut contents (R2 = 0.52; p < 0.001). The species of Amphipoda collected for isotope analysis in the study included predominantly Gammarus lacustris and to a lesser extent Gammarus fasciatus. Although isotope analyses on collected Gammarus samples were unsuccessful, based on the negative correlation with δ13C, Gammarus would be expected to be 13C depleted relative to certain other analyzed prey. Most Gammaridae are epibenthic; Gammarus spp. exhibit plasticity as herbivores/predators (MacNeil et al. 1997), and G. lacustris is a known detritivore which may also consume algae, mainly diatoms (Wilhelm and Schindler 2000).

The results of the present study agree with the existing evidence for the reliance of arctic lake food webs on energy derived via benthic pathways. Eloranta et al. (2015a) demonstrated that the relative proportion of benthic macroinvertebrates in the gut contents of Arctic char, as well as calculated littoral reliance values for the species were inversely correlated with surface area of the lakes. Based on a linear regression, for the mean surface area of Meliadine study lakes (0.56 km2), the authors estimate a littoral reliance of approximately 0.65 for Arctic char. The mean proportion of littoral carbon across lakes and fish species at Meliadine was 1.13 (although the inclusion of periphyton and grazer isotope data would decrease this value). Sierszen et al. (2003) found that benthos was the primary source of carbon for all fish present in two Arctic lakes in the Toolik region (Alaska). Finally, terrestrial invertebrates, notably Carabidae (Coleoptera) adults, comprised a minor component of fish diets. These were likely foraged from the water surface where they may have been caught in the surface tension.

Trophic position differed significantly by lake for whitefish, grayling, and lake trout, but not for stickleback. Overall, 24% (6 of 25) of post-hoc pairwise lake comparisons for the trophic position of fish were significantly different. The trophic values derived in the present study (3.18–3.82) are like those reported elsewhere. For example, Hecky and Hesslein (1995) reported trophic positions for whitefish of 3–4. General trophic structure at Meliadine was like Peter Lake (Kidd et al. 1998), with lake trout being tertiary consumers, whitefish and stickleback being

98 secondary consumers, and invertebrates mainly primary consumers. As in the results of Kidd et al. (1998), the trophic position of sticklebacks (3.49 ± 0.22) in the present study was like that of whitefish (3.57 ± 0.31). The high calculated trophic values for sticklebacks in the present study could be due to their feeding primarily on Chironomidae (64%) and Limnephilidae (8%), prey items which tended to be 15N-enriched relative to other invertebrates. Parasitic infections of sticklebacks by Schistocephalus solidus noted at Meliadine and described in the literature (Giles 1983; Barber and Scharsack 2010; Scharsack et al. 2007) are unlikely to have inflated measured 15N values for sticklebacks as Cestoda parasites of G. aculeatus tend to be 15N-depleted relative to their host (Pinnegar et al. 2001). Mean δ15N values at Peter Lake reported by Kidd et al. (1998) were similar to those at Meliadine in the present study for lake trout (11.6 vs 11.3‰), whitefish (10.2 vs 9.9‰), sticklebacks (8.2‰ vs 10.3‰), with invertebrate values at Peter Lake ranging from 1.5‰ (Tipulidae) to 6.5‰ (adult Chironomidae) compared to values at Meliadine which ranged from 2.2‰ (Limnephilus larvae) to 6.2‰ (Nemouridae larvae).

4.6. Detectable Effects from Exploration Phase

The second objective of my study was to determine if detectable effects resulting from the exploration and pre-operational phases are occurring which would be evident from changes in water quality characteristics and/or food web structure over time. It is important to note that the strength of these comparisons is limited by the number of years sampled (2-3), depending on parameter. Overall, 80% of post-hoc pairwise annual comparisons across water and sediment parameters were significantly different (12 of 15). However, except for conductivity and turbidity, which increased over the period 2014-2016, all other parameters either did not differ across years, or increased and decreased, indicating no significant temporal trends (such as for dissolved oxygen and DOM).

Conductivity increased over the 2014-2016 sampling period. Increases in conductivity were likely driven by changes in hardness and alkalinity, which were higher in 2016 than in 2015, as conductivity typically increases with alkalinity (Sechriest 1960). Since these parameters typically peak in early spring, decrease to a minimum on ice melt, then gradually rise to maximum again in the following spring (Howard and Prescott 1973), it is unlikely these differences were a result of seasonality based on different sampling periods. For example, 99 samples in 2016 were collected in early August compared to late August/early September for 2015; if seasonality played a role in hardness/alkalinity trends, the opposite trend would be expected. That there were increases in hardness, alkalinity, and conductivity in all lakes may indicate that intensifying use of roads and mining operations (e.g. transport of waste rock) are affecting the lakes, likely via deposition of dust. While I did not measure deposition of dust in the lakes, observation of mining activities (notably road traffic) confirmed the aerial transport of dust over many of the lakes. Conductivity in Lake A8 increased at a relatively high rate (mean increase of 104 μS over 2014-2016 compared to 45 ± 17 μS across all other lakes). This may reflect its closer proximity to the mining activities compared to the other lakes. Among other water quality indicators, dissolved oxygen and DOM increased from 2014-2015 and decreased from 2015-2016 although these differences may reflect low sample size (and low variability for DO: mean CV of 0.067 for 2014-2016 across lakes). Sediment parameters were more consistent over time compared to water parameters with only 7% (1 of 15) of pairwise annual comparisons being significantly different. Among metals in sediment, concentrations of copper decreased from 2015-2016 in lakes in watershed A (the only available data). However, this decrease was small (mean decrease of 20 μg/g dry weight), and the detected difference may reflect low variability (mean CV of 0.188 across lakes for 2014-2016). The decrease in concentration of Cu is, nonetheless, interesting in that the lakes in watershed A lie to the east of much of the mining activity and are subject, as noted above, to increased dust deposition from the prevailing northwesterly and westerly winds. That there was either no change or a small decrease in sediment parameters indicates that the mining activities have, as yet, not influenced properties of sediment. Regardless, apparent changes in water chemistry do not appear to be reflected in biological impacts since there was a low frequency of significant differences among lakes and over time for biotic indicators.

Based on biotic metrics, I found little evidence of temporal changes across lakes. One exception was sestonic chlorophyll a, which increased significantly between 2015 and 2016. This difference; however, may reflect a seasonal influence because sampling in 2016 was conducted in early August (when photosynthesis and algal growth would be expected to be higher due to warmer temperatures) compared to late August in 2015. In contrast, periphytic chlorophyll a did not differ by year. Biotic metrics (diversity, richness, and equitability) and percent composition of most major benthic invertebrate taxa (Ostracoda, Oligochaeta, Valvata,

100 and Sphaeriidae) showed no significant differences between years, indicating no apparent effects associated with pre-operational activities. Percent composition of Chironomidae decreased significantly between 2014 and 2015 overall but this trend was influenced by the inclusion of Lake D7 in 2015 (it was not sampled in 2014), which had a much lower proportion of Chironomidae than any other lake. Analysis of percent Chironomidae without D7 showed no significant differences between years, across lakes.

Based on stable isotopes analysis, I found weak evidence for trends over time implicating effects resulting from the mining operations. Values of δ13C for zooplankton, grayling, and stickleback differed significantly across years (values for whitefish did not). Despite interannual differences (44%; 8 of 18 comparisons), overall δ13C values for zooplankton and fish were similar across years by lake. Values of δ15N for zooplankton and grayling differed significantly across years while values for whitefish and stickleback did not. Overall, there were fewer significant differences among δ15N values for zooplankton and fish (11%; 2 of 18) than for δ13C values. The proportion of littoral carbon and trophic position differed significantly by year for grayling (comparisons across only two years: 2015-2017; isotope data unavailable for large fish in 2016), but not for whitefish and stickleback (lake trout data were only available for 2015). Overall, 25% of post-hoc pairwise annual comparisons for each of the proportion of littoral carbon and trophic position by species were significantly different (1 of 4). While select significant differences in isotopic ratios of carbon and nitrogen were noted for zooplankton and fish, these trends were likely influenced by spatial and temporal differences in isotopic signatures. While samples for an additional year for large fish species would be beneficial, the results suggest that the proportion of energy in upper trophic levels (i.e. fish) derived from littoral sources (i.e. benthic algae) as well as the trophic positions of fish are similar across years.

Syvaranta et al. (2006) assessed spatial and temporal variation of isotope signatures in an Arctic lake in Finland. Consumers in pelagic environments are subject to temporal variation of isotopic signatures due to seasonal changes in food resources and lipid content of primary consumers (Syvaranta et al. 2006; Grey et al. 2011; Matthews and Mazumder 2005). Zooplankton community composition is also subject to rapid changes and different taxonomic groups can have variable isotope ratios due to different food sources (Syvaranta et al. 2006; Matthews and Mazumder 2003). This could explain noted significant interannual increases in

101 zooplankton δ13C values in lakes A8, B2, and B7. The authors found pelagic δ15N did increase steadily during the ice-free period (mean increase for seston and zooplankton of 6‰; Syvaranta et al. 2006). This was hypothesized to be the result of gradual 15N-enrichment of the inorganic nitrogen pool due to a reduction in nitrogen pool size caused by a decrease in concentrations of nitrate in the epilimnion. This may explain the significant increases in zooplankton δ15N values in lake B7 from 2014-2015 (sampling conducted in mid-late July and late August, respectively) and 2014-2016 (sampling in mid-late July and early August respectively). The comparison between 2015 and 2016 was not significant (sampling in late August and early August, respectively), suggesting that the rate of increase of δ15N in zooplankton is higher in early-mid- summer than later in the season. Grayling δ15N values increased over the 2015-2017 period (sampling in late August and late July, respectively) although grayling in Lake A8 consumed mainly Limnephilidae and Chironomidae (benthic/littoral resources).

Conversely, littoral invertebrate communities can exhibit significant spatial variation in isotopic ratios (Syvaranta et al. 2006) which, in turn, can influence isotope ratios in fish. In that study, seasonal (spring to fall) differences in isotope ratios in fish were explained by immigration of a downstream spawning population. In the Meliadine region, whitefish, cisco, char and lake trout are known to move between the peninsular lakes and Meliadine Lake. Foraging in these different areas could affect isotope ratios, especially when considering the potential for distinct “profundal” baseline isotope values in the deeper, light-limited basins of Meliadine (depths up to 14.7 m recorded in the east basin; Golder Associates Ltd. 2009). VanderZanden and Rasmussen (1999) report consistent differences in isotope ratios between habitat types (i.e., littoral, pelagic, and profundal). Similarly, Syvaranta et al. (2006) reported that invertebrates from profundal habitats were significantly 15N-enriched and 13C-depleted relative to littoral invertebrates and that differences between habitats were greater than those between taxa. Denitrification and ammonification were suggested as possible reasons for 15N-enrichment of profundal relative to littoral invertebrates, and pelagic phytoplankton are typically 13C-depleted relative to periphyton due to boundary layer effects which limit CO2 diffusion to benthic algal cells (Syvaranta et al. 2006; Hecky and Hesslein 1995). Thus, the influence of profundal zone foraging in Meliadine Lake could explain significant decreases in grayling δ13C values (in lakes A6 and A8) and the proportion of littoral carbon for grayling from 2015-2017, as well as a significant increase in trophic level of grayling over the same period. Although data are not available for grayling,

102 seasonal changes in δ13C isotopic signature have been previously reported for whitefish (Visconti et al. 2014). In summary, spatial and seasonal (temporal) variability of isotope ratios could partially explain detected annual differences in the δ13C/δ15N values as well as proportions of littoral carbon and trophic positions for fish (and δ15N values for plankton).

4.7. Implications and Conclusions

Considering the relatively homogeneous landscape characteristics (geology, vegetation) of the Meliadine region, I predicted that the small, shallow tundra lakes typical of the area would exhibit similar limnological and ecological characteristics. For both spatial (among lakes within years) and temporal (across years within lakes) comparisons, fewer than 25% of sediment quality parameters, invertebrate and fish metrics, and food web structure based on stable isotope signatures and trophic position, were significantly different. Spatial and temporal differences in water chemistry (49% and 80%, respectively) were largely driven by specific variables (hardness, conductivity, and turbidity). Annual differences in isotope ratios of carbon (44%) could be a result of variability due to fish migration patterns and shifting composition of the zooplankton community over time, although this was not assessed in the present study. The weight of evidence, therefore, does not falsify my hypothesis of physicochemical and biotic homogeneity among these peninsular lakes over time.

Testing the hypothesis of limnological and biotic homogeneity was motivated, in part, from an industrial monitoring standpoint. Due to the high density of small inland lakes surrounding, and potentially affected, by mining activities, monitoring impacts in all of them would incur significant costs. However, if physicochemical properties and biotic composition of the lakes were relatively invariant in space and time, then it might be possible to monitor only a subset of easily accessible lakes in proximity to mining with the understanding that they are representative of the overall population of peninsular lakes. The results of the study do not falsify the hypothesis that inland lakes in the study area are similar and that monitoring a subset of peninsular lakes would be ecologically representative of the landscape. It should be noted that occasional differences among watersheds were observed for certain parameters. In developing monitoring programs, therefore, it may be necessary to adopt a stratified-random sampling

103 design in which lakes within a given watershed are sub-sampled in proportion to the size of the watershed.

The secondary purpose of this study was to provide a baseline dataset prior to full-scale operation of mining activity, with the aim of similar data being collected in the years subsequent to commencement of operations, to determine the degree to which the lakes may be impacted by mining operations. While consultant groups have collected similar baseline datasets (e.g. for water physicochemical parameters and benthic invertebrate communities pertaining to environmental risk assessments), the present study is unique in its incorporation of food web structure using isotope analysis supplemented by gut content analysis. Integrated studies such as this are uncommon in the region, particularly in the context of ongoing industrial operations and will help in better understanding spatiotemporal variation in support of monitoring and environmental impact assessments at proposed mining sites. Future research in the Meliadine area should include collection of similar data subsequent to commencement of full-scale operations in 2019 to assess potential post-opening impacts associated with the significant increases in mining operations. A similar approach in the territory of Nunavut during and following full-scale operations of the mines would provide useful insight into mining effects on subarctic lakes. In addition, collection of data in subsequent years could provide a more robust assessment of temporality (interannual variability), as comparisons in the current study were only conducted over a 2- or 3-year period. Finally, continued monitoring of lakes in the region could be used to evaluate climate-driven changes in lake ecology in the region.

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6. APPENDICES

6.1 Appendix A

Table A2. Lake physicochemical parameters by site. Site results are depth integrated over the entire water column. Max Temperature Conductivity O O fDOM* fDOM* Turbidity Site 2 2 pH depth (m) (°C) (µS) (mg/L) (%) (RFU) (QSU) (mg/L) 2014 A1A 1.40 12.7 105.9 10.3 96.8 3.3 10.1 90.0 7.9 A1B 2.00 13.2 106.5 10.3 98.2 3.3 10.2 89.0 7.9 A1C 1.00 13.0 106.7 10.6 100.4 3.3 10.2 90.0 8.0 A6A 1.50 13.8 90.3 10.2 99.0 1.0 3.1 75.0 7.9 A6B 3.50 13.7 89.7 10.3 98.7 0.9 3.0 74.0 7.9 A6C 3.00 13.7 89.9 10.3 98.9 0.9 3.0 75.0 7.9 A8A 4.00 13.4 125.5 9.3 89.1 0.2 0.8 105.4 7.8 A8B 3.00 13.3 125.0 10.3 98.7 0.2 0.8 104.9 7.8 A8C 2.00 13.6 125.6 10.3 98.7 0.2 0.8 104.0 7.9 B2A 2.80 13.2 64.5 10.2 97.1 1.6 5.1 54.0 7.9 B2B 3.00 13.3 63.3 10.2 97.7 1.4 4.3 53.0 7.8 B2C 2.00 13.5 63.6 10.3 99.0 1.4 4.4 53.0 7.9 B7A 3.50 14.8 110.5 9.6 95.2 1.4 4.3 89.0 8.2 B7B† 1.00 15.0 111.2 10.1 100.2 1.3 4.2 90.0 7.7 B7C 2.50 14.9 110.7 9.9 98.0 1.3 4.2 89.0 7.9 A2A 1.00 13.9 205.4 10.8 104.6 5.1 15.5 169.0 8.1 A2B 0.50 14.5 201.6 11.3 110.8 5.2 15.9 164.0 8.1 A2C 0.50 14.6 202.3 10.7 110.2 4.6 14.5 164.0 8.1 *fDOM refers to fluorescent dissolved organic matter and is measured in relative fluorescent units (RFUs) or quinine sulfate units (QSUs) †2014 measurements only available at a depth of 1.00 m at site B7B

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Max Temperature Conductivity O O fDOM* fDOM* Turbidity Site 2 2 pH depth (m) (°C) (µS) (mg/L) (%) (RFU) (QSU) (mg/L) 2015 A1A 2.00 8.1 128.5 11.6 98.1 6.6 20.1 123.0 11.4 A1B 2.20 7.9 128.0 11.5 97.1 6.5 19.6 123.2 11.5 A1C 1.20 7.6 127.0 11.7 98.8 6.5 19.6 124.3 11.3 A6A 2.80 8.5 114.1 11.4 97.3 2.4 7.4 108.2 10.7 A6B 2.00 8.6 115.2 11.4 97.5 2.4 7.3 109.0 10.8 A6C 2.70 9.5 117.9 11.3 98.1 2.3 7.2 109.0 10.2 A8A 2.80 10.7 196.2 11.6 104.9 1.0 3.2 176.0 9.6 A8B 2.40 10.6 195.4 11.7 104.7 1.0 3.0 175.0 9.4 A8C 2.60 10.2 194.1 10.6 94.3 1.0 3.1 176.0 9.2 B2A 2.64 10.7 91.4 11.3 101.9 3.5 10.8 82.0 7.9 B2B 3.20 10.2 87.4 11.4 101.6 3.3 10.2 79.0 8.8 B2C 2.30 10.3 87.4 11.3 101.1 3.4 10.4 79.0 8.9 B7A 4.00 10.8 118.9 11.4 102.8 3.6 10.9 106.0 10.0 B7B† 1.00 11.1 120.9 11.5 104.4 3.4 10.5 107.0 10.2 B7C 2.30 10.2 117.5 11.2 100.0 3.6 11.1 106.0 8.3 D7A 1.90 10.2 95.0 11.3 100.9 3.2 9.7 87.0 9.1 D7B 2.10 10.4 96.3 11.4 101.8 3.2 9.7 87.0 9.4 D7C 2.10 10.6 96.7 9.7 86.9 3.1 9.6 87.0 9.4 2016 A1A 2.00 13.5 163.1 10.3 98.9 4.1 15.2 136.0 7.8 A1B 2.00 13.3 162.1 10.6 100.9 4.1 15.0 136.0 8.0 A1C 1.20 13.3 162.2 10.2 97.5 3.8 13.8 136.0 7.9 A6A 3.00 14.4 148.1 10.0 97.7 0.9 5.3 121.0 7.8 A6B 2.50 14.4 148.0 10.1 98.6 0.8 5.3 121.0 7.8 A6C 3.00 14.3 147.9 9.8 95.7 0.9 5.3 121.0 7.8 A8A 3.00 14.3 229.8 10.2 99.8 0.1 3.1 188.0 7.9 A8B 2.20 14.2 229.4 9.9 96.3 0.1 3.0 188.0 7.8 A8C 2.20 14.1 229.2 10.0 97.7 0.1 3.1 188.0 7.8 B2A 2.70 14.1 108.6 9.9 96.0 1.9 8.4 89.0 7.9 B2B 3.00 14.1 107.4 10.0 97.1 1.8 8.2 88.0 7.9 B2C 1.50 14.1 107.5 9.9 96.2 1.8 8.1 88.0 7.8 B7A 3.50 15.1 132.8 9.9 98.5 2.0 8.8 106.0 7.8 B7B† 1.00 14.8 131.8 10.0 98.9 2.0 8.8 106.0 7.9 B7C 2.30 15.2 132.7 9.9 98.4 2.0 8.9 106.0 7.9 D7A 1.90 15.6 118.2 10.5 105.5 1.7 7.9 94.0 8.2 D7B 1.82 15.7 118.6 10.0 100.9 1.8 8.0 94.0 8.2 D7C 1.65 15.1 116.8 10.1 100.2 1.8 8.1 94.0 8.2 *fDOM refers to fluorescent dissolved organic matter and is measured in relative fluorescent units (RFUs) or quinine sulfate units (QSUs)

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Independent Shapiro-Wilk Equal Variance Equal Variance ANOVA Dependent Variable Variable (p-value) Test (p-value) Transformation (p-value) Lake 0.0136* Bartlett 0.5269 n/a <0.0001* Hardness Watershed 0.0136* Bartlett 0.4302 log10 <0.0001* Year 0.0136* Bartlett 0.3363 n/a <0.0001* Lake 0.0861 Bartlett 0.9542 n/a <0.0001* Alkalinity Watershed 0.0861 Bartlett 0.4660 n/a 0.0001* Year 0.0861 Bartlett 0.9558 n/a <0.0001* Lake 0.0003* Brown-Forsythe 0.0760 log10 0.1451 Conductivity Watershed 0.0003* Bartlett 0.0780 log10 0.0080* Year 0.0003* Bartlett 0.6700 n/a <0.0001* Lake 0.0003* Bartlett 0.9136 n/a 0.0036* Watershed 0.0003* Bartlett 0.9624 n/a 0.2768 Depth 0.0003* n/a 0.0018* Dissolved O2 Area 0.0003* Bartlett 0.9136 n/a 0.0004* Volume 0.0003* Bartlett 0.9816 n/a 0.5754 Year 0.0003* Brown-Forsythe 0.5569 n/a <0.0001* Lake 0.0007* Brown-Forsythe 0.3048 n/a 0.0019* Watershed 0.0007* Brown-Forsythe 0.6305 n/a 0.6404 Depth 0.0007* n/a 0.0016* O2 saturation Area 0.0007* Brown-Forsythe 0.3103 log10 0.0433* Volume 0.0007* Both <0.0500* n/a 0.8396 Year 0.0007* Brown-Forsythe 0.3031 n/a 0.5469 Lake 0.0029* Bartlett 0.6672 sqrt 0.1221 Watershed 0.0029* Brown-Forsythe 0.1603 exponential 0.1236 Depth 0.0029* n/a <0.0001* fDOM Area 0.0029* Bartlett 0.6672 sqrt <0.0001* Volume 0.0029* Bartlett 0.2440 n/a <0.0001* Year 0.0029* Bartlett 0.3535 n/a <0.0001* Lake 0.0004* Brown-Forsythe 0.0819 n/a <0.0001* Watershed 0.0004* Brown-Forsythe 0.2925 exponential 0.1844 Depth 0.0004* n/a 0.1454 Turbidity Area 0.0004* Both <0.0500* 0.7144 Volume 0.0004* Both <0.0500* <0.0001* Year 0.0004* Bartlett 0.9050 n/a <0.0001* Lake 0.0005* Brown-Forsythe 0.3058 exponential <0.0001* Watershed 0.0005* Bartlett 0.1601 n/a <0.0001* Depth 0.0005* n/a 0.0478* pH Area 0.0005* Both <0.0500* 0.0753 Volume 0.0005* Both <0.0500* 0.7217 Year 0.0005* Bartlett 0.7010 0.1270 Lake 0.0000* Both <0.0500* <0.0001* Potassium Watershed 0.0000* Both <0.0500* 0.0050 Year 0.0000* Bartlett 0.8196 0.1473

Note: n/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p<0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

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Table A3 (cont’d). Summary of the results of one-way ANOVA tests for water and sediment physicochemical parameters, chlorophyll a, benthic invertebrate diversity metrics, and percent composition data for benthic taxa which comprised 97% of benthos as well as Chironomidae genera which comprised 84% of Chironomidae larvae. Independent Shapiro-Wilk Equal Variance Equal Variance ANOVA Dependent Variable Variable (p-value) Test (p-value) Transformation (p-value) Lake 0.0057* Bartlett 0.1562 n/a <0.0001* Sodium Watershed 0.0057* Both <0.0500* <0.0001* Year 0.0057* Bartlett 0.8853 0.8274 Lake 0.1069 Bartlett 0.1539 reciprocal 0.0003* Watershed 0.1069 Bartlett 0.6868 n/a 0.2362 Seston chlorophyll a Station depth 0.1069 n/a 0.3698 Year 0.1069 Bartlett 0.2498 0.0232 Lake 0.3584 n/a 0.3700 Watershed 0.3584 n/a 0.3750 Periphyton chlorophyll a Max Depth 0.3584 n/a 0.9729 Year 0.3584 Bartlett 0.3014 n/a 0.9306 Lake 0.0676 Bartlett 0.1428 n/a 0.0002* Watershed 0.0676 Bartlett 0.6611 n/a 0.0451* Station depth 0.0676 n/a 0.0246* Sediment Nitrogen Area 0.0676 Bartlett 0.1428 n/a 0.0002* Volume 0.0676 Bartlett 0.3782 n/a 0.1086 Year 0.0676 Bartlett 0.1064 n/a 0.0735 Lake 0.0320* Brown-Forsythe 0.5527 reciprocal 0.5554 Watershed 0.0320* Bartlett 0.9098 n/a 0.6780 Station depth 0.0320* n/a 0.2730 TIC Area 0.0320* Both <0.0500* 0.7095 Volume 0.0320* Bartlett 0.0592 sqrt 0.9029 Year 0.0320* Bartlett 0.8640 n/a 0.7821 Lake 0.5428 Bartlett 0.0655 n/a 0.0345* Watershed 0.5428 Bartlett 0.2254 n/a 0.2695 Station depth 0.5428 n/a 0.0210* TOC Area 0.5428 Bartlett 0.0655 n/a 0.0017* Volume 0.5428 Bartlett 0.1182 n/a 0.5356 Year 0.5428 Brown-Forsythe 0.0765 n/a 0.2619 Lake 0.0454* Bartlett 0.6893 n/a 0.6453 Watershed 0.0454* Bartlett 0.8091 n/a 0.8602 Station depth 0.0454* n/a 0.4420 Sediment pH Area 0.0454* Bartlett 0.6893 n/a 0.6726 Volume 0.0454* Bartlett 0.5814 n/a 0.4969 Year 0.0454* Bartlett 0.7381 n/a 0.3571 Lake 0.0001* Brown-Forsythe 0.6164 n/a 0.0003* Watershed 0.0001* Brown-Forsythe 0.8388 exponential 0.8206 Station depth 0.0001* n/a 0.0409* D50 Area 0.0001* Bartlett 0.0662 log10 <0.0001* Volume 0.0001* Bartlett 0.8207 reciprocal 0.8138 Year 0.0001* Bartlett 0.4736 n/a 0.5671 Note: n/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p<0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

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Table A3 (cont’d). Summary of the results of one-way ANOVA tests for water and sediment physicochemical parameters, chlorophyll a, benthic invertebrate diversity metrics, and percent composition data for benthic taxa which comprised 97% of benthos as well as Chironomidae genera which comprised 84% of Chironomidae larvae. Independent Shapiro-Wilk Equal Variance Equal Variance ANOVA Dependent Variable Variable (p-value) Test (p-value) Transformation (p-value) Lake 0.0992 Bartlett 0.0953 n/a 0.0106* Watershed 0.0992 Brown-Forsythe 0.9095 exponential 0.9094 Station depth 0.0992 n/a 0.4470 % Silt Area 0.0992 Bartlett 0.0953 n/a 0.5037 Volume 0.0992 Bartlett 0.1002 n/a 0.9427 Year 0.0992 Bartlett 0.9350 n/a 0.9729 Lake 0.0377* Brown-Forsythe 0.8228 exponential 0.8329 Sediment Copper Watershed 0.0377* Brown-Forsythe 0.8387 exponential 0.8704 Year 0.0377* Bartlett 0.5651 n/a 0.0363* Lake 0.0941 Bartlett 0.1514 n/a 0.0058* Sediment Zinc Watershed 0.0941 Brown-Forsythe 0.8388 exponential 0.8412 Year 0.0941 Bartlett 0.1235 n/a 0.0600 Lake 0.1352 Bartlett 0.6284 n/a 0.0012* Watershed 0.1352 Bartlett 0.5647 n/a 0.0888 Station depth 0.1352 n/a 0.0003* Area 0.1352 Bartlett 0.6284 n/a 0.0026* Volume 0.1352 Bartlett 0.7393 n/a 0.4179 Diversity Year 0.1352 Bartlett 0.4705 n/a 0.7162 Nitrogen 0.3065 n/a 0.0327* TIC 0.3065 n/a 0.0861 TOC 0.3065 n/a 0.1381 Sediment pH 0.3065 n/a 0.9945 Lake 0.8324 Bartlett 0.6035 n/a 0.1534 Watershed 0.8324 Bartlett 0.2833 n/a 0.2537 Station depth 0.8324 n/a 0.1003 Area 0.8324 Bartlett 0.6035 n/a 0.2121 Volume 0.8324 Bartlett 0.5187 n/a 0.6969 Richness Year 0.8324 Bartlett 0.1104 n/a 0.1067 Nitrogen 0.5966 n/a 0.2787 TIC 0.5966 n/a 0.1674 TOC 0.5966 n/a 0.8755 Sediment pH 0.5966 n/a 0.3261 Lake 0.3466 Bartlett 0.1133 n/a 0.0002* Watershed 0.3466 Bartlett 0.5135 n/a 0.0208* Station depth 0.3466 n/a 0.0068* Area 0.3466 Bartlett 0.1133 n/a 0.0140* Volume 0.3466 Bartlett 0.3874 n/a 0.5230 Equitability Year 0.3466 Bartlett 0.6305 n/a 0.2014 Nitrogen 0.4857 n/a 0.0099* TIC 0.4857 n/a 0.5465 TOC 0.4857 n/a 0.2517 Sediment pH 0.4857 n/a 0.7602 Note: n/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p<0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

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Independent Shapiro-Wilk Equal Variance Equal Variance ANOVA Dependent Variable Variable (p-value) Test (p-value) Transformation (p-value) Lake 0.0082* Bartlett 0.0764 n/a <0.0001* Watershed 0.0082* Bartlett 0.0653 n/a 0.0025* Station depth 0.0082* n/a 0.0001* Area 0.0082* Bartlett 0.0764 n/a 0.0027* Volume 0.0082* Bartlett 0.0887 n/a 0.5254 Temperature 0.0082* n/a 0.0342* DO 0.0082* n/a 0.1235 % Chironomidae Year 0.0082* Bartlett 0.4504 n/a 0.0259* Nitrogen 0.0623 n/a 0.1881 TIC 0.0623 n/a 0.1150 TOC 0.0623 n/a 0.1356 Sediment pH 0.0623 n/a 0.6452 Copper 0.0623 n/a 0.1305 Zinc 0.0623 n/a 0.0887 Lake 2.28E-03* Brown-Forsythe 0.4609 n/a <0.0001* Watershed 2.28E-03* Bartlett 0.0589 n/a 0.0012* Station depth 2.28E-03* n/a <0.0001* Area 2.28E-03* Both <0.0500* n/a 0.0001* % Corynocera Volume 2.28E-03* Bartlett 0.0977 n/a 0.6822 Temperature 2.28E-03* n/a 0.0314* DO 2.28E-03* n/a 0.1948 Year 2.28E-03* Bartlett 0.7744 n/a 0.4722 Lake 6.14E-05* Brown-Forsythe 0.7861 exponential 0.7540 Watershed 6.14E-05* Bartlett 0.7638 n/a 0.7113 Station depth 6.14E-05* n/a 0.8619 Area 6.14E-05* Both <0.0500* n/a 0.1693 % Pagastiella Volume 6.14E-05* Bartlett 0.1210 n/a 0.0812 Temperature 6.14E-05* n/a 0.0001* DO 6.14E-05* n/a 0.8760 Year 6.14E-05* Bartlett 0.0517 n/a 0.0005* Lake 1.68E-07* Brown-Forsythe 0.1611 n/a 0.0126* Watershed 1.68E-07* Brown-Forsythe 0.4140 n/a 0.6372 Station depth 1.68E-07* n/a 0.0032* Area 1.68E-07* Both <0.0500* n/a 0.0005* % Tanytarsus Volume 1.68E-07* Both <0.0500* n/a 0.1421 Temperature 1.68E-07* n/a 0.9386 DO 1.68E-07* n/a 0.1933 Year 1.68E-07* Bartlett 0.1243 n/a 0.5127 Note: n/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p<0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

127

Independent Shapiro-Wilk Equal Variance Equal Variance ANOVA Dependent Variable Variable (p-value) Test (p-value) Transformation (p-value) Lake 2.68E-08* Brown-Forsythe 0.6449 exponential 0.6433 Watershed 2.68E-08* Brown-Forsythe 0.3969 n/a 0.3666 Station depth 2.68E-08* n/a 0.2768 Area 2.68E-08* Bartlett 0.0502 square-root 0.0317* % Cladotanytarsus Volume 2.68E-08* Bartlett 0.0956 square-root 0.6837 Temperature 2.68E-08* n/a 0.0058* DO 2.68E-08* n/a 0.7268 Year 2.68E-08* Brown-Forsythe 0.3100 exponential 0.3119 Lake 5.78E-09* Brown-Forsythe 0.1219 square-root <0.0001* Watershed 5.78E-09* Brown-Forsythe 0.0591 square-root 0.0001* Station depth 5.78E-09* n/a 0.0194* Area 5.78E-09* Bartlett 0.0798 square-root 0.1485 % Procladius Volume 5.78E-09* Bartlett 0.0862 square-root 0.6649 Temperature 5.78E-09* n/a 0.4233 DO 5.78E-09* n/a 0.5543 Year 5.78E-09* Brown-Forsythe 0.9798 n/a 0.7856 Copper 2.05E-07* n/a 0.3170 % Amphipoda Zinc 2.05E-07* n/a 0.4392 Lake 6.74E-09* Both 0.6452 exponential 0.6435 % Ostracoda Year 6.74E-09* Both 0.3101 exponential 0.3120 Lake 5.14E-10* Brown-Forsythe 0.6832 n/a 0.5716 % Oligochaeta Year 5.14E-10* Brown-Forsythe 0.2164 n/a 0.2727 Lake 0.0001* Brown-Forsythe 0.6397 exponential 0.6434 % Valvata Year 0.0001* Bartlett 0.9841 n/a 0.3826 Lake 0.0059* Bartlett 0.4303 n/a 0.0022* % Sphaeriidae Year 0.0059* Bartlett 0.9911 n/a 0.1617 Note: n/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p<0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

128

Table A4. Summary of p-values for post-hoc Tukey’s pairwise comparisons of lakes for water and sediment physicochemical parameters, chlorophyll a, and benthic invertebrate percent composition and diversity metrics (only dependent variables for which the one-way ANOVA was significant are shown). Water Sediment Dissolved O2 pH Lake Hardness Alkalinity O2 saturation Turbidity e Potassium Sodium N TOC D50 % silt Zinc A1 A2 0.3228 0.0012* <.0001* 0.0623 0.9518 1.0000 1.0000 0.1083 0.2721 A1 A6 0.0001* <.0001* 0.6135 0.9998 0.0308* 0.7131 0.0006* <.0001* 0.8319 0.3970 0.0004* 0.9251 0.7144 A1 A8 <.0001* <.0001* 0.2954 1.0000 <.0001* 0.4227 <.0001* <.0001* 0.0007* 0.0801 0.0071* 0.9986 0.9626 A1 B2 <.0001* <.0001* 0.5558 1.0000 <.0001* 0.8698 <.0001* <.0001* 0.3344 0.6857 0.7956 0.6792 0.1371 A1 B7 <.0001* <.0001* 0.1875 0.9937 0.0181* 1.0000 0.1350 <.0001* 0.0067* 0.0809 0.9851 0.2937 0.6510 A1 D7 <.0001* <.0001* 0.1377 0.9993 <.0001* 0.0001* 0.0109* <.0001* 1.0000 0.9982 0.9992 0.9052 0.3537 A2 A6 0.0261* 0.0005* <.0001* 0.0030* 0.5075 0.6919 0.0295* 0.0185* 0.0309* A2 A8 0.0098* 0.0008* <.0001* 0.0011* 0.0033* 0.2714 0.1138 0.2931 0.1226 A2 B2 0.0223* 0.0014* <.0001* 0.0056* 0.1609 0.8312 0.9682 0.8323 0.0036* A2 B7 0.0060* 0.0048* <.0001* 0.0414* 0.0090* 0.1944 0.9988 0.9994 0.0561 A2 D7 0.0071* 0.0091* <.0001* 0.4464 0.9883 0.9979 1.0000 0.9356 0.0268* A6 A8 <.0001* 0.9705 0.9982 1.0000 <.0001* 0.9990 <.0001* <.0001* 0.0127* 0.9244 0.9977 0.7523 0.9995 A6 B2 <.0001* 0.2605 1.0000 0.9993 <.0001* 0.9999 0.6830 0.0005* 0.9269 1.0000 0.0973 0.1843 0.7990 A6 B7 0.2642 0.0057* 0.9858 0.9415 1.0000 0.8349 0.2637 <.0001* 0.0583 0.7547 0.1131 0.0654 0.9966 A6 D7 <.0001* <.0001* 0.9113 0.9857 0.0004* <.0001* 0.8752 <.0001* 0.9896 0.9903 0.1670 0.5491 0.8752 A8 B2 <.0001* 0.0391* 0.9994 1.0000 <.0001* 0.9853 <.0001* <.0001* 0.3051 0.9255 0.3528 0.9400 0.6758 A8 B7 <.0001* 0.0002* 1.0000 0.9785 <.0001* 0.5601 <.0001* <.0001* 1.0000 0.9970 0.3203 0.5845 0.9731 A8 D7 <.0001* <.0001* 0.9933 0.9963 <.0001* <.0001* <.0001* 0.0107* 0.0521 0.7998 0.3690 0.9852 0.7636 B2 B7 <.0001* 0.7453 0.9925 0.9969 <.0001* 0.9450 0.0109* <.0001* 0.4229 0.7625 0.9998 0.9785 0.9985 B2 D7 0.7741 <.0001* 0.9353 0.9998 0.9966 <.0001* 0.1350 <.0001* 0.7990 0.9972 0.9985 1.0000 1.0000 B7 D7 <.0001* <.0001* 0.9992 1.0000 0.0008* 0.0001* 0.8752 <.0001* 0.0709 0.6241 1.0000 0.9936 0.9950

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Table A4 (cont’d). Summary of p-values for post-hoc Tukey’s pairwise comparisons of lakes for water and sediment physicochemical parameters, chlorophyll a, and benthic invertebrate percent composition and diversity metrics (only dependent variables for which the one-way ANOVA was significant are shown). Chlorophyll Invertebrates % % % % % Lake (Seston chlorophyll a)-1 Diversity Equitability Chironomidae Corynocera Tanytarsus Procladius Sphaeriidae A1 A2 0.6576 0.2305 0.9969 0.9929 0.9515 0.0734 0.9157 A1 A6 0.0236* 1.0000 0.8432 0.4083 0.0913 0.0311* 0.9991 0.2907 A1 A8 0.8563 0.2038 0.4797 0.0010* <.0001* 0.0145* 0.8016 0.9991 A1 B2 0.9931 0.9107 0.9263 0.0441* <.0001* 0.2086 0.9202 0.9393 A1 B7 0.0010* 0.0104* 0.0190* 0.0035* <.0001* 0.0393* 0.9287 0.9937 A1 D7 0.9917 0.9966 0.9998 0.5053 1.0000 0.4219 0.0006* 0.0195* A2 A6 0.5947 0.8150 0.9361 0.0677 0.5686 0.0316* 0.0781 A2 A8 0.0164* 0.0078* 0.0421* <.0001* 0.4092 0.0052* 0.9881 A2 B2 0.1764 0.0394* 0.4338 0.0001* 0.9418 0.0092* 0.4551 A2 B7 0.0010* 0.0003* 0.0705 <.0001* 0.5165 0.0154* 0.9989 A2 D7 0.5604 0.7212 0.4095 0.9939 0.9378 0.3668 0.0060* A6 A8 0.2594 0.2525 0.0462* 0.1164 0.0047* 0.9999 0.9652 0.1276 A6 B2 0.0834 0.9465 0.2433 0.8863 0.0487* 0.9652 0.9947 0.8723 A6 B7 0.8183 0.0136* 0.0011* 0.2045 0.0002* 1.0000 0.9937 0.1380 A6 D7 0.0874 0.9986 0.9974 0.0387* 0.4931 0.9997 0.0003* 0.4443 A8 B2 0.9906 0.8203 0.9774 0.6892 0.9528 0.8684 0.9999 0.7412 A8 B7 0.0194* 0.6614 0.4980 1.0000 0.6479 1.0000 1.0000 1.0000 A8 D7 0.9921 0.8983 0.6139 0.0003* 0.0008* 0.9961 <.0001* 0.0089* B2 B7 0.0043* 0.1033 0.1516 0.7883 0.1865 0.9277 1.0000 0.6881 B2 D7 1.0000 1.0000 0.9162 0.0054* 0.0049* 1.0000 0.0001* 0.0995 B7 D7 0.0046* 0.2704 0.0716 0.0007* 0.0001* 0.9978 0.0002* 0.0098*

Table A5. Summary of p-values for post-hoc Tukey’s pairwise comparisons of watersheds for water and sediment physicochemical parameters and benthic invertebrate percent composition and diversity metrics (only dependent variables for which the one-way ANOVA was significant are shown). Water Sediment Invertebrates % % % Watershed log10(Hardness) Alkalinity log10(Conductivity) pH Potassium Sodium N Equitability Chironomidae Corynocera Procladius A B <.0001* 0.0001* 0.0159* 0.9478 0.0068* 0.0001* 0.0877 0.0206* 0.1387 0.0022* 0.3438 A D <.0001* 0.5669 0.079 <.0001* 0.0684 0.0103* 0.6083 0.9432 0.0176* 0.5268 0.0002* B D 0.4768 0.0001* 0.9588 <.0001* 0.9616 <.0001* 0.1081 0.2174 0.0020* 0.0185* 0.0001*

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Table A6. Summary of p-values for post-hoc Tukey’s pairwise comparisons of annual data for water and sediment physicochemical parameters, chlorophyll a, and benthic invertebrate abundance and diversity metrics. 2014-2015 2014-2016 2015-2016 ANOVA Data Type Parameter (p-value) Estimate p-value Estimate p-value Estimate p-value Hardness <0.0001* -17.2556 <.0001* Alkalinity <0.0001* -13.9778 <0.0001* Conductivity <0.0001* -30.0756 <.0001* -55.9715 <.0001* -25.8959 <.0001*

Dissolved O2 <0.0001* -1.1199 <.0001* 0.1094 0.6274 1.2293 <.0001*

Water O2 saturation 0.5469 -1.2486 0.5610 -0.2826 0.9704 0.9661 0.6801 fDOM <0.0001* -1.8885 <.0001* -0.3120 0.0475* 1.5765 <.0001* Turbidity <0.0001* -36.3381 <.0001* -45.0260 <.0001* -8.6880 0.026* Potassium 0.1473 -0.0389 0.1473 Sodium 0.8274 0.0111 0.8274 N 0.0735 -0.2712 0.1745 -0.2963 0.0711 -0.0251 0.9808 TIC 0.7821 0.0015 0.9998 0.0394 0.8093 0.0379 0.8479 TOC 0.2619 -1.7585 0.3398 -1.6099 0.3029 0.1485 0.9905 pH 0.3571 -0.2986 0.3545 Sediment D50 0.5671 7.0809 0.5411 2.8016 0.8827 -4.2793 0.7534 % Silt 0.9729 0.8831 0.9766 0.7645 0.9772 -0.1186 0.9995 Copper 0.0363* -11.1674 0.4035 9.7039 0.4158 20.8713 0.028* Zinc 0.0600 -0.1500 0.9997 11.8250 0.1092 11.9750 0.1174 Chlorophyll Seston 0.0232* -0.2319 0.0232* a Periphyton 0.9306 -0.8834 0.9286 Diversity 0.7162 0.0467 0.7160 Richness 0.1067 -2.4775 0.1051 Equitability 0.2014 0.0534 0.2007 % Chironomidae 0.0259* 11.9023 0.0256* Benthos % Ostracoda 0.3120 -1.75E20 0.3021 % Oligochaeta 0.2727 3.0195 0.2707 % Valvata 0.3826 -1.0783 0.3815 % Sphaeriidae 0.1617 -4.6640 0.1612

Table A7. Method detection limits (MDLs) for elemental analyses performed on water samples. Analysis MDL (mg/L) Boron 0.006 Calcium 0.009 Copper 0.003 Iron 0.006 Magnesium 0.03 Manganese 0.0006 Molybdenum 0.01 Sodium 0.04 Phosphorus 0.07 Zinc 0.006

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Table A8. Water chemistry parameters by site for samples collected in 2015 and 2016. Units are in mg/L unless otherwise indicated. Results of an elemental scan as well as measured chlorophyll a are included. Note that data are unavailable for 2014 due to accidental sample discarding. Chlorophyll NO - Year Site Calcium Magnesium Manganese 3 a (µg/L) N A1A 23 1.32 3.0 0.0029 n/a A1B 22 1.18 3.0 0.0021 n/a A1C 24 1.02 3.1 0.0026 n/a A6A 21 0.76 2.7 0.0036 n/a A6B 21 0.78 2.6 0.0035 n/a A6C 20 0.83 2.6 0.0030 n/a A8A 32 1.18 4.1 0.0035 n/a A8B 31 1.16 4.2 0.0022 n/a A8C 32 1.19 4.2 0.0028 n/a B2A 15 1.28 2.0 0.0007 n/a 2015 B2B 14 1.19 1.9 0.0012 n/a B2C 13 1.12 1.9 0.0011 n/a B7A 21 0.90 2.4 0.0024 n/a B7B 21 0.87 2.4 0.0033 n/a B7C 22 1.05 2.4 0.0029 n/a D7A 13 1.88 2.8 0.0021 n/a D7B 13 1.75 2.8 0.0018 n/a D7C 12 1.63 2.6 0.0019 n/a Mean 21 1.17 2.8 0.0024 n/a s.d. 6 0.32 0.7 0.0009 n/a A1A 23 2.07 3.2 < 0.00060 0.251 A1B 24 2.20 3.2 < 0.00060 0.081 A1C 22 2.10 3.1 < 0.00060 0.063 A6A 20 1.23 2.7 < 0.00060 0.053 A6B 20 1.45 2.7 0.0010 0.053 A6C 20 1.22 2.7 < 0.00060 0.047 A8A 31 1.37 4.2 0.0010 0.071 A8B 31 1.33 4.2 0.0020 0.069 A8C 31 1.45 4.1 < 0.00060 0.055 B2A 15 1.57 1.9 < 0.00060 0.049 2016 B2B 11 1.64 1.9 < 0.00060 0.054 B2C 15 1.68 2.0 < 0.00060 0.053 B7A 20 0.90 2.2 < 0.00060 0.056 B7B 17 0.67 2.2 0.0010 0.056 B7C 18 0.93 2.2 < 0.00060 0.050 D7A 15 1.33 2.8 < 0.00060 0.054 D7B 14 0.97 2.7 < 0.00060 0.043 D7C 13 1.14 2.8 0.0010 0.040 Mean 20 1.40 2.8 0.0012 0.067 s.d. 6 0.42 0.7 0.0004 0.047 Note: Results of the elemental analysis are not shown if the majority of the results were below the MDL. Results measured but not shown include: boron, copper, iron, molybdenum, phosphorous, and zinc.

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Table A8 (cont’d). Water chemistry parameters by site for samples collected in 2015 and 2016. Units are in mg/L unless otherwise indicated. Results of an elemental scan as well as measured chlorophyll a are included. Note that data are unavailable for 2014 due to accidental sample discarding. Year Site Potassium Sodium Sulfur Total Kjeldahl Nitrogen 2015 A1A 1.2 6.3 n/a < 1.0 A1B 1.2 6.3 n/a < 1.0 A1C 1.3 6.4 n/a < 1.0 A6A 1.2 5.0 n/a < 1.0 A6B 1.1 5.0 n/a < 1.0 A6C 1.1 4.9 n/a < 1.0 A8A 1.9 7.0 n/a < 1.0 A8B 1.9 7.1 n/a < 1.0 A8C 1.9 7.2 n/a < 1.0 B2A 1.1 5.9 n/a 1.24 B2B 1.1 5.6 n/a < 1.0 B2C 1.1 5.7 n/a < 1.0 B7A 1.3 3.7 n/a < 1.0 B7B 1.3 3.7 n/a < 1.0 B7C 1.3 3.6 n/a < 1.0 D7A 1.2 7.4 n/a < 1.0 D7B 1.2 7.5 n/a < 1.0 D7C 1.1 7.2 n/a < 1.0 Mean 1.3 5.9 n/a 1.24 s.d. 0.3 1.3 n/a n/a 2016 A1A 1.5 6.2 2.4 n/a A1B 1.5 6.2 2.3 n/a A1C 1.5 6.1 2.3 n/a A6A 1.2 5.4 2.1 n/a A6B 1.1 5.3 1.9 n/a A6C 1.2 5.4 2.0 n/a A8A 1.9 7.1 2.2 n/a A8B 1.9 7.1 2.1 n/a A8C 1.9 7.2 2.1 n/a B2A 1.0 5.4 1.7 n/a B2B 1.2 5.4 1.7 n/a B2C 1.0 5.5 1.7 n/a B7A 1.2 3.5 1.4 n/a B7B 1.2 3.5 1.5 n/a B7C 1.2 3.5 1.3 n/a D7A 1.2 7.6 1.5 n/a D7B 1.3 7.5 1.5 n/a D7C 1.2 7.4 1.4 n/a Mean 1.3 5.9 1.8 n/a s.d. 0.3 1.4 0.4 n/a Note: Results of the elemental analysis are not shown if the majority of the results were below the MDL. Results measured but not shown include: boron, copper, iron, molybdenum, phosphorous, and zinc.

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Table A9. Results of an elemental scan of as well as total nitrogen and total carbon for sediment samples. Note that results for certain sites are not available due to sample discarding. Arsenic Cadmium Chromium Cobalt Copper Lead Mercury Molybdenum Nickel pH Selenium Zinc TN TC TIC TOC TC/TIC Year Site µg/g dry % n/a A1A 35 <0.20 79 16 57 15 <0.035 2.9 46 n/a 0.65 89 0.329 6.63 0.02 6.62 425 A1B 28 <0.20 51 11 41 11 <0.035 1.6 31 n/a 0.42 60 0.202 5.64 0.25 5.39 23 A1C 29 0.27 87 17 62 17 0.037 2.5 48 n/a 0.64 96 0.500 8.26 0.14 8.12 60 A2A 26 <0.20 84 20 42 16 <0.035 3.2 47 n/a 0.46 110 0.214 6.93 0.21 6.72 33 A2B 26 0.21 68 16 33 13 <0.035 2.2 39 n/a 0.35 95 0.206 7.45 0.22 7.23 34 A2C 24 <0.20 57 14 27 11 <0.035 1.9 34 n/a 0.29 83 0.065 5.83 0.27 5.56 21 A6A 74 0.29 35 17 62 7.3 <0.035 2.9 36 n/a 0.62 50 0.284 5.78 0.17 5.61 34 2014 A6B 110 0.56 36 15 76 8.8 <0.035 4.8 41 n/a 0.73 51 0.334 7.68 0.23 7.45 33 A6C 100 0.51 48 24 110 13 <0.035 4.9 64 n/a 0.87 77 0.264 9.66 0.76 8.90 13 B2A 86 0.35 55 23 92 12 <0.035 9.0 55 n/a 1.10 67 0.470 8.55 0.15 8.40 57 B2B 37 0.23 49 16 77 10 0.042 6.9 44 n/a 0.75 58 0.584 9.08 0.31 8.77 30 B2C 29 0.28 44 13 70 12 0.049 4.0 39 n/a 0.66 53 0.859 10.22 0.33 9.89 31 Mean 50 0.34 58 17 62 12 0.043 3.9 44 n/a 0.63 74 0.359 7.64 0.25 7.39 66 s.d. 32 0.13 18 4 25 3 0.006 2.2 9 n/a 0.23 20 0.214 1.55 0.18 1.46 114 A1A 25 <0.20 55 11 48 11 <0.035 2.1 36 3.2 0.49 66 0.417 6.16 0.36 5.80 17 A1B 38 <0.20 62 14 60 15 <0.035 2.3 41 3.1 0.68 71 0.147 5.17 0.18 4.99 28 A1C 21 <0.20 86 16 59 18 0.041 3.1 45 4.8 0.71 93 0.538 8.69 0.24 8.45 36 A6A 38 0.55 61 20 120 18 0.071 5.7 60 3.7 1.10 83 1.162 14.69 0.02 14.67 843 A6B 32 <0.20 41 11 69 12 0.040 2.6 39 5.5 0.61 56 0.613 9.97 0.18 9.79 54 2015 A6C 130 0.47 47 30 110 14 <0.035 5.6 69 3.2 0.97 74 0.151 6.14 0.05 6.09 133 A8A 88 0.38 42 12 61 18 0.052 4.0 45 4.2 0.66 60 1.208 13.11 0.30 12.81 44 A8B 90 0.32 61 18 110 19 0.040 6.1 59 3.6 0.89 89 0.723 10.06 0.49 9.57 21 A8C 96 0.32 47 14 73 18 0.057 4.1 50 3.7 0.67 69 1.101 12.26 0.46 11.80 27 Mean 62 0.41 56 16 79 16 0.050 4.0 49 3.9 0.75 73 0.673 9.58 0.25 9.33 134 s.d. 39 0.10 14 6 27 3 0.012 1.5 11 0.8 0.19 13 0.410 3.35 0.17 3.35 268

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Table A9 (cont’d). Results of an elemental scan of as well as total nitrogen and total carbon for sediment samples. Note that results for certain sites are not available due to sample discarding. Arsenic Cadmium Chromium Cobalt Copper Lead Mercury Molybdenum Nickel pH Selenium Zinc TN TC TIC TOC TC/TIC Year Site µg/g dry % n/a A1A 28 <0.20 47 10 36 10 <0.035 1.9 30 3.7 0.39 57 0.349 5.40 0.19 5.21 28 A1B 25 <0.20 55 12 40 14 <0.035 1.9 35 3.5 0.45 66 0.346 6.09 0.37 5.72 16 A1C 44 <0.20 61 13 44 14 <0.035 1.8 37 4.5 0.49 72 0.835 10.40 0.24 10.16 43 A6A 78 0.50 65 22 100 19 0.069 4.8 61 4.6 0.84 84 0.943 12.85 0.49 12.36 26 A6B 31 <0.20 35 9.1 46 9.8 0.038 2.4 32 5.5 0.40 43 0.592 11.22 0.22 11.00 50 A6C 95 0.44 51 25 89 13 <0.035 4.0 49 3.1 0.69 67 0.642 7.43 0.31 7.12 24 A8A 100 0.30 42 11 57 16 0.048 3.2 44 3.9 0.59 62 1.356 15.49 0.02 15.47 1023 A8B 92 0.29 50 21 80 15 <0.035 5.5 61 3.4 0.67 76 0.001 3.15 0.01 3.14 247 A8C 95 0.27 34 8.7 39 13 <0.035 1.8 32 5.3 0.51 49 1.051 11.75 0.32 11.43 37 2016 B2B 31 <0.20 44 12 70 13 0.060 4.5 39 4.4 0.83 51 1.011 10.77 0.43 10.34 25 B2C 16 <0.20 38 8.4 46 10 0.037 2.9 30 5.3 0.47 42 0.489 7.80 0.18 7.62 44 B7A 27 <0.20 49 13 80 13 0.064 5.2 44 4.2 0.86 60 1.297 12.86 0.04 12.82 356 B7B 39 <0.20 35 12 57 14 0.047 3.7 44 3.5 0.70 56 1.074 11.95 0.05 11.90 231 B7C 28 <0.20 33 9.1 53 15 0.054 2.4 42 3.8 0.65 55 1.056 10.59 0.05 10.54 218 D7B 23 <0.20 45 15 46 12 0.037 3.1 41 3.8 0.57 50 0.352 7.04 0.36 6.67 19 D7C 28 0.20 39 13 46 12 0.051 3.2 38 4.5 0.67 49 0.641 9.04 0.16 8.88 58 Mean 49 0.33 45 13 58 13 0.051 3.3 41 4.2 0.61 59 0.752 9.61 0.21 9.40 153 s.d. 31 0.11 10 5 20 2 0.011 1.2 10 0.7 0.15 12 0.385 3.25 0.15 3.26 255

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Table A10. Method detection limits (MDLs) for elemental analyses performed on sediment samples. Analysis Result (µg/g dry) Arsenic 0.35 Cadmium 0.2 Chromium 1.1 Cobalt 0.4 Copper 0.6 Lead 2.5 Molybdenum 0.7 Nickel 1 Selenium 0.1 Zinc 2.3 Mercury 0.035

136

Table A11. Particle size analysis by site for samples collected from 2014-2016. D50 (cumulative 50% point of diameter of the particle size distribution) as well as percent sand, silt, and clay are included. Sand (63-2000 Silt (3.9-63 Clay (< 3.9 Sediment D10 D50 D90 Year Site µm) µm) µm) Name µm % A1A Sandy Silt 1.8 11.9 70.6 11.2 69.1 19.7 A1B Sandy Silt 2.4 22.3 156.0 29.2 55.5 15.3 A1C Silt 1.4 8.7 49.7 7.5 66.5 25.9 A2A Silt 1.4 9.8 41.3 5.4 72.0 22.6 A2B Sandy Silt 1.9 13.6 65.4 10.5 72.4 17.1 A2C Silt 2.3 16.0 58.8 9.1 76.3 14.6 2014 A6A Sandy Silt 5.3 60.0 192.5 48.9 43.4 7.6 A6B Sandy Silt 4.2 38.3 156.8 36.0 54.5 9.5 A6C Sandy Silt 5.4 56.6 230.8 47.7 44.9 7.4 B2A Sandy Silt 2.4 17.3 91.4 16.0 68.6 15.4 B2B Sandy Silt 3.2 27.1 123.1 27.4 60.8 11.8 B2C Sandy Silt 2.8 20.3 108.3 21.6 64.5 14.0 A1A Sandy Silt 2.0 15.1 98.1 17.5 64.3 18.3 A1B Sandy Silt 2.0 17.5 115.2 22.7 59.6 17.7 A1C Mud 0.7 2.7 22.5 2.9 38.0 59.1 A6A Sandy Silt 6.0 41.8 262.5 37.8 55.5 6.7 2015 A6B Sandy Silt 3.9 41.5 236.6 41.8 48.1 10.1 A6C Sandy Silt 7.8 46.6 176.6 40.2 54.6 5.3 A8A Sandy Silt 4.8 27.9 150.5 29.5 62.4 8.0 A8B Sandy Silt 3.7 23.8 106.6 19.6 69.9 10.4 A8C Sandy Silt 4.5 25.4 166.8 21.8 69.6 8.7 A1A Sandy Silt 0.9 9.8 101.8 17.5 52.5 30.0 A1B Silt 0.8 7.5 61.6 9.8 56.1 34.1 A1C Sandy Silt 0.9 8.3 62.8 10.1 58.7 31.2 A6A Sandy Silt 4.6 24.7 114.5 18.1 73.4 8.5 A6B Sandy Silt 4.8 46.2 217.8 44.4 47.4 8.2 A6C Sandy Silt 4.4 32.1 162.5 31.9 59.3 8.9 A8A Sandy Silt 4.3 26.4 142.1 28.8 62.2 9.0 A8B Silty Sand 11.6 95.8 302.6 58.4 38.1 3.5 2016 A8C Sandy Silt 5.5 36.1 169.8 34.9 58.1 7.0 B2B Sandy Silt 3.1 20.0 89.7 18.0 69.7 12.4 B2C Sandy Silt 3.3 25.0 142.5 25.1 63.4 11.5 B7A Sandy Silt 2.2 14.9 102.3 15.2 68.3 16.5 B7B Sandy Silt 3.3 21.6 111.1 20.6 67.8 11.7 B7C Sandy Silt 3.2 18.8 71.4 12.3 75.6 12.1 D7B Sandy Silt 1.6 17.4 83.7 15.9 66.2 17.9 D7C Sandy Silt 1.2 15.1 78.9 14.0 64.6 21.3

137

Table A12. Summary of benthic invertebrate density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples.

2014

heringianus

Cladocera Copepoda Ostracoda Hydrachnidae S. Naididae Vedjovskyella Tubificinae ferox S. T. tubifex Valvata Sphaeriidae (l) Elateridae (a) Diptera (l) Ceratopogonidae (l) Chironomidae taxa Total Lake Site lacustris G. A1A 476 43 346 87 87 130 260 2337 6016 173 11210 21165 A1B 43 43 216 736 43 24584 25667 A1 A1C 216 260 303 779 390 43 43 87 43 1558 3722 Mean 173 14 115 29 101 130 188 274 981 2265 14 87 29 12451 16851 s.d. 263 25 200 50 109 130 164 438 1178 3267 25 87 25 11563 11591 A2A 563 43 173 346 43 649 1601 3419 A2B 173 779 43 87 260 3852 5194 A2 A2C 43 649 43 173 87 43 606 3289 4934 Mean 14 404 14 72 58 289 14 58 115 14 43 505 2914 4516 s.d. 25 353 25 90 100 427 25 100 200 25 43 214 1171 958 A6A 43 433 1991 173 2467 5107 A6B 130 2381 2510 A6 A6C 43 130 736 909 Mean 29 144 750 58 1861 2842 s.d. 25 250 1075 100 976 2119 A8A 43 43 2034 2121 A8B 606 5367 5973 A8 A8C 43 173 173 11989 12379 Mean 14 58 274 14 6464 6824 s.d. 25 100 295 25 5067 5182 B2A 43 173 779 3073 87 20905 25061 B2B 43 130 736 6752 7661 B2 B2C 87 87 43 519 130 346 1082 9479 31769 43542 Mean 29 29 14 14 173 72 43 115 664 4429 29 19809 25421 s.d. 50 50 25 25 300 90 75 200 486 4527 50 12545 17943 B7A 216 4501 4718 B7C 216 130 87 216 390 87 563 43 11513 13244 B7 Mean 108 65 43 108 195 43 390 22 8007 8981 s.d. 153 92 61 153 275 61 245 31 4958 6029

138

Table A12 (cont’d). Summary of benthic invertebrate density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples.

2015

Cladocera Ostracoda Hydrachnidae Hirudinea Nematoda Oligochaeta Naididae Chaetogaster Na Vedjovskyella Tubificinae arctica S. Physella Valvata Sphaeriidae Leptoceridae (a) Diptera (l) Ceratopogonidae (a) Chironomidae (l) Chironomidae taxa Total Lake Site lacustris G. A1A 866 31207 476 43 43 563 43 1991 43 27917 63193 A1B 16447 130 43 476 43 43 2078 4848 43 16967 41118 A1 A1C 87 3463 43 173 43 693 1428 43 346 3939 10258 Mean 317 17039 202 29 14 173 260 14 14 938 2756 14 14 115 14 16274 38190 s.d. 477 13882 246 25 25 263 270 25 25 1039 1833 25 25 200 25 12004 26589 A6A 43 130 822 1212 3809 6016 A6B 43 519 649 2294 3376 6882 A6 A6C 43 130 43 216 866 3636 4934 Mean 14 14 14 260 14 563 1457 3607 5944 s.d. 25 25 25 225 25 312 745 218 976 A8A 173 130 519 2034 43 27571 30471 A8B 43 216 43 173 909 9219 10604 A8 A8C 43 87 1861 7964 9955 Mean 14 14 130 58 260 1601 14 14918 17010 s.d. 25 25 115 66 229 606 25 10976 11662 B2A 433 260 346 43 43 1082 4069 476 67088 73840 B2B 87 43 130 130 130 519 2294 43 5930 9306 B2 B2C 519 216 43 43 390 216 433 87 1125 4112 87 14803 22074 Mean 346 87 58 130 159 130 72 144 43 14 909 3491 202 29273 35073 s.d. 229 115 66 130 25 175 225 125 250 43 25 338 1037 238 33047 34175 B7A 173 130 130 43 390 260 6060 216 97992 105393 B7C 43 43 260 693 173 476 5064 36920 43672 B7 Mean 22 87 22 65 130 411 22 281 368 5562 108 67456 74533 s.d. 31 122 31 92 184 398 31 153 153 704 153 43184 43643 D7B 216 1775 43 87 173 3030 2251 7574 D7C 563 1385 43 130 1472 1125 4718 D7 Mean 390 1580 22 22 65 43 87 2251 1688 6146 s.d. 245 275 31 31 92 61 122 1102 796 2020

139

Table A13. Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2014. A1 A2 Taxa A1A A1B A1C Mean s.d. A2A A2B A2C Mean s.d. Chironomidae sp. 563 1656 739 842 260 476 245 238 Chironominae 10085 22763 1255 11368 10811 1255 2597 1775 1876 677 Chironominae sp. 130 43 75 Chironomini 2684 8278 1039 4000 3795 822 1688 1039 1183 450 Chironomini sp. 87 83 43 71 24 43 173 72 90 Chironomus 43 14 25 Cladopelma 43 83 87 71 24 Cryptochironomus 346 83 43 157 165 Cryptotendipes Dicrotendipes 87 43 43 43 433 822 779 678 214 Einfeldia 43 43 29 25 260 693 87 346 312 Glyptotendipes 43 14 25 Microtendipes Pagastiella 1298 7864 3054 4216 Phaenopsectra 519 693 404 360 43 87 43 43 Polypedilum 260 166 43 156 109 87 29 50 Sergentia Tanytarsini 7401 14486 216 7368 7135 433 779 736 649 189 Tanytarsini sp. 779 2566 1115 1316 173 58 100 Cladotanytarsus 173 1076 416 578 87 29 50 Corynocera* 173 7202 2458 4109 C. oliveri 1082 83 388 602 Micropsectra 331 110 191 87 29 50 Paratanytarsus 43 14 25 Stempellinella Tanytarsus 5151 3228 216 2865 2487 433 606 563 534 90 Orthocladiinae 130 43 75 43 216 563 274 264 Orthocladiinae sp. 43 14 25 87 43 43 43 Cricotopus 173 58 100 Eukiefferiella Hydrobaenus Orthocladius 83 28 48 Parakiefferiella Paraphaenocladius Psectrocladius 87 29 50 43 130 346 173 156 Tanypodinae 433 166 303 300 134 303 779 476 519 241 Tanypodinae sp. 43 14 25 Ablabesmyia Larsia Monopelopia Procladius 433 166 303 300 134 303 736 476 505 218 Diamesinae Diamesinae sp. Monodiamesa Pagastia Potthastia Pseudodiamesa Total Chironomid Larvae 11210 24584 1558 12451 11563 1601 3852 3289 2914 1171

140

Table A13 (cont’d). Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2014. A6 A8 Taxa A6A A6B A6C Mean s.d. A8A A8B A8C Mean s.d. Chironomidae sp. 87 173 130 130 43 Chironominae 2034 2164 563 1587 889 2034 4934 11686 6218 4952 Chironominae sp. 43 14 25 Chironomini 952 216 346 505 393 736 1948 476 1053 785 Chironomini sp. 43 14 25 43 14 25 Chironomus Cladopelma 563 130 260 317 222 260 1039 87 462 507 Cryptochironomus 130 43 75 130 346 159 175 Cryptotendipes Dicrotendipes 43 14 25 43 87 173 101 66 Einfeldia 87 43 43 43 Glyptotendipes 43 14 25 Microtendipes Pagastiella 216 43 43 101 100 216 87 101 109 Phaenopsectra 43 14 25 173 87 87 87 Polypedilum 87 130 72 66 Sergentia Tanytarsini 1039 1948 216 1068 866 1298 2986 11210 5165 5303 Tanytarsini sp. 130 43 58 66 216 390 173 260 115 Cladotanytarsus 43 14 25 87 29 50 Corynocera* 649 1731 173 851 798 995 2424 10085 4501 4888 C. oliveri Micropsectra 43 14 25 Paratanytarsus 87 43 736 289 388 Stempellinella Tanytarsus 173 173 43 130 75 43 216 87 115 Orthocladiinae 43 14 25 173 43 72 90 Orthocladiinae sp. Cricotopus Eukiefferiella Hydrobaenus Orthocladius Parakiefferiella Paraphaenocladius Psectrocladius 43 14 25 173 43 72 90 Tanypodinae 303 43 43 130 150 260 260 173 150 Tanypodinae sp. 43 43 29 25 87 29 50 Ablabesmyia Larsia 130 43 75 Monopelopia Procladius 260 43 101 139 173 130 101 90 Diamesinae Diamesinae sp. Monodiamesa Pagastia Potthastia Pseudodiamesa Total Chironomid Larvae 2467 2381 736 1861 976 2034 5367 11989 6464 5067

141

Table A13 (cont’d). Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2014. B2 B7 Taxa B2A B2B B2C Mean s.d. B7A B7C Mean s.d. Chironomidae sp. 291 1381 558 728 87 43 61 Chironominae 19813 6666 28688 18389 11080 4285 11080 7683 4805 Chironominae sp. 319 106 184 87 43 61 Chironomini 1093 606 3400 1700 1493 606 1558 1082 673 Chironomini sp. 106 35 61 43 22 31 Chironomus Cladopelma 73 173 1913 720 1034 216 43 130 122 Cryptochironomus 73 43 213 110 90 43 87 65 31 Cryptotendipes 106 35 61 Dicrotendipes 146 43 63 75 Einfeldia 87 29 50 43 43 43 Glyptotendipes Microtendipes Pagastiella 801 173 1063 679 457 260 130 184 Phaenopsectra 43 14 25 303 1082 693 551 Polypedilum 43 14 25 Sergentia Tanytarsini 18720 6060 24969 16583 9634 3679 9436 6557 4071 Tanytarsini sp. 1457 303 1594 1118 709 87 260 173 122 Cladotanytarsus 364 425 263 230 43 22 31 Corynocera* 10052 4977 17957 10995 6541 3592 8916 6254 3764 C. oliveri 291 97 168 Micropsectra 364 213 192 183 43 22 31 Paratanytarsus Stempellinella Tanytarsus 6192 779 4781 3917 2808 173 87 122 Orthocladiinae 656 219 378 87 130 108 31 Orthocladiinae sp. 43 43 43 Cricotopus 73 24 42 43 22 31 Eukiefferiella Hydrobaenus Orthocladius 43 22 31 Parakiefferiella Paraphaenocladius Psectrocladius 583 194 336 43 22 31 Tanypodinae 146 87 1700 644 915 43 303 173 184 Tanypodinae sp. Ablabesmyia 43 22 31 Larsia 146 106 84 75 Monopelopia Procladius 87 1594 560 896 43 260 151 153 Diamesinae Diamesinae sp. Monodiamesa 43 22 31 Pagastia Potthastia Pseudodiamesa Total Chironomid Larvae 20905 6752 31769 19809 12545 4501 11513 8007 4958

142

Table A14. Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2015. A1 A6 Taxa A1A A1B A1C Mean s.d. A6A A6B A6C Mean s.d. Chironomidae sp. Chironominae 26446 16615 3592 15551 11464 3592 3073 3246 3304 264 Chironominae sp. Chironomini 14370 9086 260 7905 7129 2813 1991 1948 2251 488 Chironomini sp. 43 43 29 25 Chironomus 43 14 25 260 173 144 132 Cladopelma 909 779 606 765 152 Cryptochironomus 87 100 43 77 30 130 43 75 Cryptotendipes Dicrotendipes 563 50 130 248 276 43 43 43 43 0 Einfeldia 43 87 43 43 Glyptotendipes Microtendipes 87 29 50 43 14 25 Pagastiella 10085 5070 5052 5042 1428 822 1082 1111 304 Phaenopsectra 43 14 25 Polypedilum 3506 3865 87 2486 2086 87 29 50 Sergentia 43 14 25 Tanytarsini 12076 7530 3333 7646 4373 779 1082 1298 1053 261 Tanytarsini sp. Cladotanytarsus 9825 4568 303 4899 4770 260 173 303 245 66 Corynocera 173 58 100 476 909 909 765 250 C. Oliveri Micropsectra Paratanytarsus 43 14 25 43 14 25 Stempellinella Tanytarsus 2078 2962 2986 2675 518 43 43 29 25 Orthocladiinae 866 87 317 477 87 130 130 115 25 Orthocladiinae sp. Cricotopus 476 43 173 263 Eukiefferiella 43 14 25 Hydrobaenus Orthocladius 43 14 25 Parakiefferiella Paraphaenocladius Psectrocladius 346 43 130 189 87 130 87 101 25 Tanypodinae 563 351 260 391 155 87 173 260 173 87 Tanypodinae sp. Ablabesmyia Larsia 43 43 29 25 87 87 58 50 Monopelopia 43 14 25 43 14 25 Procladius 476 351 216 348 130 87 87 130 101 25 Diamesinae 43 14 25 43 14 25 Diamesinae sp. Monodiamesa Pagastia 43 14 25 Potthastia Pseudodiamesa 43 14 25 Total Chironomid Larvae 27917 16967 3939 16274 12004 3809 3376 3636 3607 218

143

Table A14 (cont’d). Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2015. A8 B2 Taxa A8A A8B A8C Mean s.d. B2A B2B B2C Mean s.d. Chironomidae sp. Chironominae 26705 8786 7271 14254 10809 62344 5540 13676 27187 30718 Chironominae sp. 43 14 25 51 17 30 Chironomini 1688 2034 866 1529 600 7002 1991 6300 5098 2713 Chironomini sp. 43 43 29 25 130 51 60 65 Chironomus 43 14 25 Cladopelma 43 14 25 904 303 461 556 311 Cryptochironomus 130 43 130 101 50 452 43 165 249 Cryptotendipes Dicrotendipes 303 173 87 188 109 Einfeldia Glyptotendipes 87 29 50 Microtendipes 87 29 50 173 154 109 95 Pagastiella 866 1558 433 952 568 5647 1255 5634 4179 2532 Phaenopsectra 87 29 50 43 14 25 Polypedilum 87 87 216 130 75 43 14 25 Sergentia 43 14 25 Tanytarsini 25017 6752 6363 12711 10660 55342 3549 7324 22072 28874 Tanytarsini sp. 130 43 58 66 226 51 92 118 Cladotanytarsus 260 390 303 317 66 4066 43 1178 1762 2074 Corynocera 23459 5627 5757 11614 10258 49695 3246 5737 19559 26128 C. Oliveri Micropsectra Paratanytarsus 693 649 43 462 363 Stempellinella 87 29 50 Tanytarsus 476 43 260 260 216 1355 173 359 629 636 Orthocladiinae 130 87 173 130 43 1129 216 512 619 466 Orthocladiinae sp. 43 14 25 904 307 404 459 Cricotopus Eukiefferiella Hydrobaenus 43 43 29 25 Orthocladius 43 43 29 25 Parakiefferiella 226 75 130 Paraphaenocladius 43 14 25 Psectrocladius 87 43 43 43 216 205 140 122 Tanypodinae 736 303 519 519 216 3614 173 615 1467 1872 Tanypodinae sp. Ablabesmyia 43 14 25 Larsia 260 130 173 188 66 1807 51 619 1029 Monopelopia 452 151 261 Procladius 476 173 303 317 152 1355 173 563 697 602 Diamesinae 43 14 25 Diamesinae sp. Monodiamesa Pagastia Potthastia 43 14 25 Pseudodiamesa Total Chironomid Larvae 27571 9219 7964 14918 10976 67088 5930 14803 29273 33047

144

Table A14 (cont’d). Summary of Chironomidae genera density (abundance; in number of individuals/m2) by site and mean abundance (and standard deviation) by lake for benthic grab samples for 2015. B7 D7 Taxa B7A B7C Mean s.d. D7B D7C Mean s.d. Chironomidae sp. Chironominae 96064 35423 65744 42879 822 822 822 Chironominae sp. Chironomini 10281 3118 6700 5065 519 390 454 92 Chironomini sp. 43 22 31 Chironomus Cladopelma 321 161 227 43 22 31 Cryptochironomus 1606 873 1240 519 130 87 108 31 Cryptotendipes Dicrotendipes 321 161 227 43 22 31 Einfeldia Glyptotendipes Microtendipes 321 161 227 Pagastiella 7390 2245 4817 3638 260 260 260 Phaenopsectra Polypedilum 321 161 227 43 22 31 Sergentia Tanytarsini 85783 32305 59044 37815 303 433 368 92 Tanytarsini sp. 43 22 31 Cladotanytarsus 321 125 223 139 173 87 122 Corynocera 83534 30933 57233 37194 130 130 130 C. Oliveri Micropsectra Paratanytarsus 125 62 88 Stempellinella Tanytarsus 1928 1123 1525 569 173 87 130 61 Orthocladiinae 249 125 176 87 43 61 Orthocladiinae sp. 87 43 61 Cricotopus Eukiefferiella Hydrobaenus Orthocladius 249 125 176 Parakiefferiella Paraphaenocladius Psectrocladius Tanypodinae 1928 1247 1588 481 1428 216 822 857 Tanypodinae sp. 43 22 31 Ablabesmyia Larsia 321 249 285 51 Monopelopia Procladius 1606 998 1302 430 1385 216 801 826 Diamesinae Diamesinae sp. Monodiamesa Pagastia Potthastia Pseudodiamesa Total Chironomid Larvae 97992 36920 67456 43184 2251 1125 1688 796

145

Table A15. Summary of benthic invertebrate diversity metrics by site and mean values (and standard deviation) by lake for benthic grab samples.

Year Site Total taxa Shannon's Diversity Index (H) Richness (S) Shannon's Equitability (EH) A1A 21165 2.31 28 0.69 A1B 25749 1.90 19 0.65 A1C 3722 2.44 19 0.83 Mean 16879 2.22 22 0.72 s.d. 11622 0.28 5 0.09 A2A 3419 2.26 14 0.86 A2B 5194 2.46 18 0.85 A2C 4934 2.48 17 0.88 Mean 4516 2.40 16 0.86 s.d. 958 0.12 2 0.01 A6A 5107 2.14 18 0.74 A6B 2510 1.21 9 0.55 A6C 909 1.95 9 0.89 Mean 2842 1.77 12 0.73 2014 s.d. 2119 0.49 5 0.17 A8A 2121 1.80 11 0.75 A8B 5973 2.02 17 0.71 A8C 12379 0.90 14 0.34 Mean 6824 1.57 14 0.60 s.d. 5182 0.60 3 0.23 B2A 25061 1.84 19 0.63 B2B 7661 1.35 14 0.51 B2C 43542 1.93 22 0.62 Mean 25421 1.71 18 0.59 s.d. 17943 0.31 4 0.06 B7A 4761 1.07 12 0.43 B7C 13244 1.50 25 0.46 Mean 9003 1.28 19 0.45 s.d. 5999 0.30 9 0.02

146

Table A15 (cont’d). Summary of benthic invertebrate diversity metrics by site and mean values (and standard deviation) by lake for benthic grab samples.

Year Site Total taxa Shannon's Diversity Index (H) Richness (S) Shannon's Equitability (EH) A1A 63193 1.67 25 0.52 A1B 41118 1.85 16 0.67 A1C 10258 1.87 19 0.64 Mean 38190 1.80 20 0.61 s.d. 26589 0.11 5 0.08 A6A 6016 2.17 17 0.77 A6B 6882 2.09 15 0.77 A6C 4934 2.34 21 0.77 Mean 5944 2.20 18 0.77 s.d. 976 0.12 3 0.00 A8A 30471 1.11 24 0.35 A8B 10604 1.75 21 0.58 A8C 9955 1.56 19 0.53 Mean 17010 1.47 21 0.48 2015 s.d. 11662 0.33 3 0.12 B2A 73840 1.40 20 0.47 B2B 9306 2.04 21 0.67 B2C 22074 2.15 24 0.68 Mean 35073 1.86 22 0.61 s.d. 34175 0.40 2 0.12 B7A 105393 0.92 19 0.31 B7C 43672 1.18 16 0.43 Mean 74533 1.05 18 0.37 s.d. 43643 0.18 2 0.08 D7B 7574 1.75 15 0.64 D7C 4718 1.95 14 0.74 Mean 6146 1.85 15 0.69 s.d. 2020 0.14 1 0.07

147

Table A16. Results of the one-way analysis of variance (ANOVA) for water and sediment chemistry data from 2015-2016 and 2014- 2016, respectively. Water Sediment parameter p-value parameter p-value K 0.2180 TIC 0.7835 Ca 0.2208 TC/TIC 0.5946

O2% 0.4152 Arsenic 0.5774 Chl-a 0.7868 TC 0.1850 Na 0.8563 TOC 0.1744 Mg 0.8634 Nickel 0.0804 Alkalinity <0.0001* Cobalt 0.0682 Conductivity <0.0001* Chromium 0.0646 fDOM (RFU) <0.0001* Molybdenum 0.0545 Hardness <0.0001* Zinc 0.0262*

O2 (mg/L) <0.0001* Selenium 0.0206* Turbidity <0.0001* Lead 0.0199* Salinity <0.0001* Copper 0.0116*

148

Table A17. Deployment times, catch rates, and species caught for fish sampling in 2016. Total deployment Total catch Catch rate Catch rate Lake Species caught (hr) # (fish/hr) (hr/fish) A1 5.00 5 1.00 1.00 C. artedi (4), S. namaycush (1) A6 5.33 4 0.75 1.33 T. articus (4) A8 6.62 8 1.21 0.83 T. arcticus (8) Mean 5.65 6 0.99 1.05 s.d. 0.85 2 0.23 0.26 B2 5.25 10 1.90 0.53 T. arcticus (9), S. namaycush (1) B7 4.63 14 3.02 0.33 C. artedi (10), T. arcticus (4) Mean 4.94 12 2.46 0.43 s.d. 0.44 3 0.79 0.14 D7 22.17 1 0.05 22.17 C. artedi (1)

149

Table A18. Summary of the results of one-way ANOVA tests for fish by species (for fish caught from 2015-2017).

Dependent Independent Shapiro-Wilk Equal Variance Equal Variance ANOVA Species Variable Variable (p-value) Test (p-value) Transformation (p-value) Length Lake 0.0037* Bartlett 0.3343 n/a <0.0001* Weight Lake 0.0464* Bartlett 0.6996 n/a <0.0001* Fulton Lake 3.08E-07* Brown-Forsythe 0.5183 exponential 0.3158 GSI Lake 0.0053* Bartlett 0.1706 n/a 0.8315 GSI Length 0.0053* n/a 0.2550 HSI Length 0.0225* n/a 0.2201 HSI Lake 0.0225* n/a 0.6634 Grayling Fulton Age 3.08E-07* Brown-Forsythe 0.7128 n/a 0.3132 GSI Age 0.0053* n/a 0.2925 HSI Age 0.0225* n/a 0.1430 α Lake 0.0133* Bartlett 0.0584 n/a <0.0001* TP Lake 0.2310* Bartlett 0.1300 n/a 0.0001* α Year 0.0133* Bartlett 0.4799 n/a 0.0029* TP Year 0.2310* Bartlett 0.0522 n/a <0.0001*

log10weight log10length 4.92E-06* n/a <0.0001* Length Lake 5.41E-10* n/a 0.2830 Weight Lake 1.01E-09* n/a 0.1232 Fulton Lake 0.0574* n/a 0.0019* HSI Lake 0.0013* n/a 0.0440* GSI Lake 7.55E-06* n/a 0.1895 GSI Length 7.55E-06* n/a 0.1054 HSI Length 0.0013* n/a 0.0466* Whitefish Fulton Age 0.0574 Bartlett 0.4093 n/a 0.9482 HSI Age 0.0013* Bartlett 0.1224 n/a 0.0631 GSI Age 7.55E-06* n/a 0.1485 α Lake 1.66E-02* Bartlett 0.2382 n/a 0.0002* TP Lake 9.18E-02* Bartlett 0.8796 n/a 0.0022* α Year 1.66E-02* Bartlett 0.6840 n/a 0.3800 TP Year 9.18E-02* Bartlett 0.8760 n/a 0.5964

log10weight log10length 2.56E-10* n/a <0.0001* Length Lake 0.3060 n/a 0.0187* Weight Lake 0.0025* n/a 0.0013* Fulton Lake 0.4355 n/a 0.0037* HSI Lake 0.1362 n/a 0.5173 HSI Length 0.1362 n/a 0.2000 Lake Trout Fulton Age 0.4355 n/a 0.1065 HSI Age 0.1362 n/a 0.2157 α Lake 0.8846 n/a 0.5046 TP Lake 0.5362 n/a 0.0478* log10weight log10length 0.8075 n/a <0.0001* α Lake 0.1286 n/a 0.0172* TP Lake 0.4648 n/a 0.2696 Stickleback α Year 0.1286 Bartlett 0.3407 0.1976 TP Year 0.4648 Bartlett 0.7438 0.4219 Note: α and TP represent the proportion of littoral carbon and trophic position, respectively. N/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p < 0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

150

Table A19. Summary of p-values for post-hoc Tukey’s pairwise comparisons by lake of fish morphometric data (length, weight, Fulton’s condition factor, and HSI) by species. Whitefish Grayling Lake trout Lake Fulton HSI Length Weight Length Weight Fulton A1 A6 0.0166* 0.0015* 0.0016* A1 A8 0.2560 A1 B2 0.7813 0.9820 0.2287 0.2028 0.0059* A1 B7 0.9976 0.2392 A1 D7 0.0521 0.7239 A6 A8 0.9901 0.2738 A6 B2 0.3428 <.0001* 0.0240* 0.0013* 0.0298* A6 B7 0.5888 0.0001* A8 B2 0.0588 0.1417 <.0001* A8 B7 0.2400 0.3841 <.0001* A8 D7 0.0040* B2 B7 0.2095 0.2346 0.9975 0.0839 B2 D7 0.2093 0.5772 B7 D7 0.0244* 0.1207

151

Table A20. Fish data by species for individuals caught from 2015-2017. Only specimen data which were dissected and for which a gonad and/or liver weight were measured are shown (full length and weight dataset not shown). Fulton's Gonad Liver Age Length Weight Species Year Lake Fish ID Sex* condition weight weight GSI (%) HSI (%) (years)† (cm) (g) factor (K) (g)†† (g) A6-GR-1 M 4 37.1 480 0.94 2.9883 2.1971 0.62 0.46 A6-GR-2 F 6 39.6 610 0.98 12.4146 4.4335 2.04 0.73 A6 A6-GR-3 F 7 41.6 708 0.98 19.4653 7.8115 2.75 1.10 A6-GR-4 M 5 40.2 540 0.83 3.5803 3.5543 0.66 0.66

A8-GR-1 F 39.5 640 1.04 12.4918 6.2014 1.95 0.97 2015 A8-GR-2 M 41.7 670 0.92 6.5466 2.5708 0.98 0.38 A8 A8-GR-3 M 42.6 730 0.94 8.5944 4.3764 1.18 0.60 A8-GR-4 M 7 32.2 626 1.88 4.8862 4.1969 0.78 0.67

B7-GR-1 M 37.8 410 0.76 4.6162 1.8472 1.13 0.45 B7 Grayling B7-GR-2 M 35.2 340 0.78 2.4176 1.6784 0.71 0.49

A6-GR-04 M 31.7 350 1.10 0.7544** 1.3396 0.22 0.38

A8-GR-01 M 40.4 630 0.96 4.2317** 2.6757 0.67 0.42

A8-GR-02 F 41.6 570 0.79 4.9000 1.9956 0.86 0.35

A8 A8-GR-03 F 40.9 550 0.80 5.0000 5.0000 0.91 0.91

2017 A8-GR-04 F 41.2 620 0.89 2.5680** 3.4885 0.41 0.56

A8-GR-06 F 37.3 500 0.96 1.7922** 1.7569 0.36 0.35

B2-GR-01 M 38.1 370 0.67 0.6991 0.19

B2 B2-GR-02 F 32.3 360 1.07 2.2389** 1.3707 0.62 0.38

B2-GR-04 P 28.3 110 0.49 1.0026 0.91 *M, F, and P denote male, female, and premature (indeterminate), respectively. A question mark indicates uncertainty in the sex of the fish. **Indicates tissue which was liquefied prior to weighing due to decomposition. These weights may be unreliable. †Age data unavailable for 2016 fish as otoliths were discarded. ††Gonad and liver weights unavailable for 2016 fish.

152

Table A20 (cont’d). Fish data by species for individuals caught from 2015-2017. Only specimen data which were dissected and for which a gonad and/or liver weight were measured are shown (full length and weight dataset not shown). Fulton's Gonad Liver Age Length Weight Species Year Lake Fish ID Sex* condition weight weight GSI (%) HSI (%) (years)† (cm) (g) factor (K) (g)†† (g) A1-WF-1 F 4 25.5 160 0.96 0.2201 0.5745 0.14 0.36 A1 A1-WF-2 M? 5 20.5 100 1.16 0.0999 0.4351 0.10 0.44

B2-WF-2 ? 4 25.4 144 0.88 0.9654 0.67

B2-WF-3 ? 4 26.2 200 1.11 1.0855 0.54 B2 B2-WF-4 ? 4 20.8 71 0.79 0.2500 0.35 2015 B2-WF-5 ? 5 24.5 150 1.02 0.4890 0.33 B7-WF-43 M 8 38.8 560 0.96 6.6608 2.7005 1.19 0.48 B7-WF-44 F 8 39 590 0.99 35.8935 7.9613 6.08 1.35 Whitefish B7 B7-WF-45 M 7 35.4 410 0.92 6.1215 2.7496 1.49 0.67 B7-WF-46 M 7 35.4 430 0.97 8.1547 3.1919 1.90 0.74

B7 B7-WF-7 M 32.7 312 0.89 7.0000 6.0000 2.24 1.92 2016 D7 D7-WF-1 M 29.8 299 1.13 0.7500 3.0000 0.25 1.00

B7-WF-01 F 43.4 570 0.70 8.0431 3.2461 1.41 0.57

B7-WF-02 F 38.2 510 0.91 8.6336 2.6601 1.69 0.52 2017 B7 B7-WF-03 F 34.4 310 0.76 6.7822 1.6444 2.19 0.53

B7-WF-04 3.4616 0.8094 2015 A6 A6-LT-1 M 7 38.5 626 1.10 0.2976 6.3709 0.05 1.02 B2-LT-1 ? 5 21.2 80 0.84 0.6061 0.76 Lake B2-LT-2 M 6 26.4 170 0.92 1.8087 1.06 Trout B2 B2-LT-3 ? 5 27.8 210 0.98 2.0584 0.98 B2-LT-4 ? 5 22.5 91 0.80 0.6819 0.75 *M, F, and P denote male, female, and premature (indeterminate), respectively. A question mark indicates uncertainty in the sex of the fish. **Indicates tissue which was liquefied prior to weighing due to decomposition. These weights may be unreliable. †Age data unavailable for 2016 fish as otoliths were discarded. ††Gonad and liver weights unavailable for 2016 fish.

153

Table A21. Truss data for fish caught from 2015-2017. Numerical intervals along the top row represent distances between

key landmarks on each fish (represented by numbers 1-10).

2 3 4 3 4 4 5 6 5 6 6 7 8 7 8 8 9 9

10 10 10

------

- -

Fish Species Year ID -

1 1 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 8

7 8 9

A6-GR-1 1.4 16.3 15.7 16 10 9 9 6.7 15.6 8 8.4 3.5 5.3 10.4 8.8 3.1 3.1 4 4.3 2.5 2.9 A6-GR-2 1.4 17.4 12.3 16.8 10.8 10 9.7 7.5 16.8 8.5 9.3 3.6 5.8 11.4 9.8 3.4 3.1 4.2 4.7 2.8 2.9 2015 A6-GR-3 1.6 18.7 13 17.7 11.7 10.2 9.8 8 17.7 8.2 9.5 3.4 6 11.3 9.8 3.7 3.5 4.5 4.7 2.7 3 A6-GR-4 1.7 17.6 12 16.6 10.2 9.5 8.8 7 16.5 8.6 8.3 3.5 5.5 10.5 9.4 3.3 3.3 4.5 4.5 3 2.7 A6-GR-1 1.3 16.3 11.1 16.3 10.1 9.0 9.1 6.7 16.9 8.3 8.7 3.4 5.6 10.4 9.2 3.4 4.2 4.2 4.4 2.8 3.0 A6-GR-2 1.4 16.8 11.8 16.6 10.5 9.8 8.5 6.8 17.1 9.3 8.6 3.8 5.2 11.6 9.5 3.2 3.5 4.1 4.6 3.2 3.2 2016 A6-GR-3 1.3 16.4 11.3 15.9 10.6 9.3 8.8 6.3 16.7 7.8 8.3 3.1 5.2 10.2 9.3 3.0 3.4 4.2 4.4 3.0 2.8 A6-GR-4 1.2 14.7 10.3 14.5 9.1 8.8 8.0 6.3 15.1 7.4 8.3 2.9 4.8 10.3 8.7 3.2 3.0 3.2 4.3 2.8 2.7 A6-GR-01 1.7 6.6 12.4 6.2 n/a 7.5 22.4 16 19.1 9.8 9.3 4.2 5.2 9.6 8.5 15 4.2 4.9 6 3.1 3.2 A6-GR-02 1.1 5.9 10.5 5.4 9 6.6 20.2 14 16.4 7.8 9.3 3.3 5.4 11.3 7.6 4 3.2 4.4 5.1 2.9 3.1 2017 A6-GR-03 1 6 10.8 5.7 9.9 6.8 20.8 14 16.8 8.5 9.1 3.4 5.5 10.7 6.3 3.4 3.9 2.9 n/a 3.5 2.8 Grayling A6-GR-04 0.7 5 9.6 4.8 8.7 6.1 17.3 12 13.17 6.1 7.6 2.7 4.3 9.2 8 2 3.2 3.6 n/a 2.8 2.4 A8-GR-1 1.1 17.2 n/a 16.8 10.5 9.5 9.1 7.3 16.5 8 8.7 3.1 5.5 10.3 9.2 3.4 4.2 4.2 4.7 2.9 2.9 A8-GR-2 1.4 18.1 n/a 18.7 10 10.4 10.3 7.4 9.3* 8.8 8.9 3.5 5.8 10.6 9.1 3.7 4.2 4.3 4.8 3.1 3.1 2015 A8-GR-3 1.6 18.4 n/a 17.1 11.3 9.7 9.9 7.4 16.8 9.5 9.3 3.4 5.7 11.3 9.5 3.6 4 4.7 5.5 3.7 3 A8-GR-1 1.7 18.4 12.3 17.5 11.1 9.8 9.6 7.5 17.7 9.5 8.7 3.9 5.9 10.7 10 3.5 3.2 4.5 4.7 3.4 3 A8-GR-1 1.4 15.3 11.8 14.2 9.6 7.6 8.4 5.8 14.2 7.2 7.8 2.4 4.5 10.2 8.4 2.9 3.4 4.0 4.1 2.7 2.5 A8-GR-2 1.2 16.1 11.2 15.0 9.8 8.7 8.7 6.8 15.1 7.8 8.4 3.2 5.1 10.5 9.2 3.2 3.8 4.4 4.1 3.5 2.5 2016 A8-GR-3 1.6 18.8 12.6 18.4 11.5 10.6 9.6 7.3 18.3 10.1 9.1 4.0 6.6 11.4 10.2 3.6 4.3 5.4 5.4 4.2 3.3 A8-GR-4 1.6 19.2 12.6 18.6 10.9 10.6 10.2 7.0 19.0 9.8 9.7 3.8 5.7 11.6 10.8 3.6 4.1 5.0 5.2 3.2 3.1 A8-GR-5 1.3 17.3 11.6 16.3 10.4 9.0 9.3 6.1 15.2 7.8 6.3 3.4 5.4 10.9 9.4 3.2 3.4 4.2 4.7 3.4 3.0 2017 A8-GR-01 1.8 5.8 11.5 4.8 9.6 8 20.9 15 16.3 8.7 8.7 3.5 5.6 11.2 8.6 3.7 3.6 4.6 5.5 3.9 3.3

154

Table A21 (cont’d). Truss data for fish caught from 2015-2017. Numerical intervals along the top row represent

distances between key landmarks on each fish (represented by numbers 1-10).

3 4 3 4 4 5 6 5 6 6 7 8 7 8 8 9 9

Fish 2

10 10 10

------

- -

Year ID -

1 1 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 8

8 9 Species 7 B2-GR-1 0.8 12.0 9.1 11.8 7.7 6.7 6.2 5.2 12.2 5.9 6.8 2.2 3.8 8.2 7.2 2.6 2.7 3.2 3.4 2.4 2.2 2016 B2-GR-2 1.0 12.2 8.7 11.6 7.1 6.8 6.4 4.6 11.6 5.2 6.1 2.1 3.6 8.3 6.7 2.6 2.4 2.9 3.1 2.3 2.1 B2-GR-3 0.8 11.9 8.3 11.2 7.1 6.7 6.3 4.7 11.5 5.6 5.9 2.0 3.8 7.4 6.8 2.5 2.4 2.8 3.3 2.2 2.1 B2-GR-01 1 5.1 10.1 5.1 9 5.4 n/a n/a 13.2 7.2 7.1 3.2 4.9 8.9 6.9 3.4 2.3 4 n/a 3.1 2.3 B2-GR-02 1 4.9 9.7 4.9 8.9 6.3 n/a n/a 13.2 6.2 7.9 2.3 4.7 9.2 6.2 3.3 2.9 4.3 n/a 4.1 2.3 2017 B2-GR-03 0.4 4.7 8.9 3.2 8.1 5.3 14.8 10 13 5.3 5.1 2.1 4 5.8 5.2 4 3.3 3.1 4.1 3.1 3.1 B2-GR-04 0.4 5 8.6 5 8 5.4 14.4 10 11.6 5.4 6.4 2.2 4.6 7.6 7.2 3 4 3.2 3.2 2.8 2 Grayling B7-GR-1 0.8 15.6 10.5 15.4 9.4 7.9 8.5 6 14.7 7.4 8.1 3.2 4.7 9.8 9.1 3 3.2 3.6 4 2.7 2.6 2015 B7-GR-2 0.9 15.1 9.9 14.5 8.9 7.8 8.5 5.6 14.8 7.6 7.5 3 4.7 7.8 8.3 2.8 2.6 3.8 4 3.1 2.3 B7-GR-1 1.1 14.6 10.5 14.2 9.9 7.6 7.5 5.7 14.1 7.2 7.3 3.6 4.7 9.4 7.7 3.0 3.4 3.8 3.9 3.3 2.3 B7-GR-2 1.1 14.7 10.2 14.3 9.3 8.0 7.8 5.5 7.8 6.8 7.2 3.0 5.1 9.7 9.2 2.9 2.8 3.6 3.7 2.3 2.3 2016 B7-GR-3 0.7 11.9 8.1 11.4 7.4 5.9 6.6 4.3 11.2 5.4 6.1 2.0 3.7 7.5 6.4 2.6 2.5 3.1 3.1 2.3 2.0 B7-GR-4 0.8 13.0 9.4 12.4 8.2 6.3 6.2 4.7 12.3 5.5 6.8 2.2 4.1 8.2 6.7 2.5 2.8 3.2 3.4 2.4 2.2 2017 B7-GR-01 1.3 6 10.5 5.8 9.2 6.2 17.8 12 14 6.8 8.2 2.7 4.9 9.7 8.2 3.4 3.8 3.8 4.9 2.1 2.5

155

Table A21 (cont’d). Truss data for fish caught from 2015-2017. Numerical intervals along the top row represent distances

between key landmarks on each fish (represented by numbers 1-10).

2 3 4 3 4 4 5 6 5 6 6 7 8 7 8 8 9 9

10 10 10

------

- -

Fish species Year ID -

1 1 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 8

7 8 9

A1-WF-1 1.2 11.5 10.8 10.9 10 5.1 5.5 4.7 8.1 2.6 5.6 2.2 4 7 0.8* 2.2 2.7 3 2.9 2.1 1.6 2015 A1-WF-2 0.7 9.3 8.8 8.8 8 3.8 4.5 3.8 6.1 2 4.5 1.6 3 5.7 5.5 1.8 1.8 2.4 2.2 1.7 1.2 A1-WF-1 0.4 7.7 7.3 7.6 6.7 3.3 3.9 1.3 3.4 5.4 3.6 1.3 2.5 4.7 4.3 1.5 1.7 1.8 1.2 1.9 1.2 A1-WF-2 0.4 7.9 6.8 7.3 6.4 3.2 3.7 1.7 3.2 5.5 4.6 1.5 2.6 4.9 4.4 1.1 1.8 2.1 1.5 1.9 1.2 2016 A1-WF-3 0.4 8.3 7.9 8.3 7.3 3.7 4 1.8 3.6 5.9 4.4 1.4 2.7 5 4.2 1.7 1.2 1.9 1.7 2 1.2 A1-WF-4 0.6 9.2 8.6 8.7 7.7 3.8 3 1.9 3.8 6.4 4.7 1.7 2.9 5.9 5.3 1.8 1.8 2.3 1.6 2.3 1.4 B2-WF-4 0.7 3.6 6.1 2.4 5.6 3.6 9.9 7.1 7.8 3.9 4.4 1.6 2.5 5.3 5.2 1.6 2.1 2.2 2.5 1.3 1.4 B2-WF-5 0.8 4.2 7.3 4 6.7 4.3 12.7 8.4 10.5 5.1 5.7 2 3 6.8 6.1 2.1 3 2.6 3.2 2.4 1.9 2015 B7-WF-43 1.3 16.9 15.1 16.3 13.8 7.2 9.2 7.2 12.5 4.2 9 3.4 5.7 10.1 10.6 3.1 2.8 3.5 4.4 3.2 2.7 Whitefish B7-WF-44 1.3 17.3 16.4 16.7 15.4 7.2 9.3 7.4 12.4 4.1 8.1 2.9 5.8 10.4 9.4 3.3 2.6 4.4 4.3 2.4 2.3 B7-WF-46 0.9 15.9 14.7 15.8 13.3 6.3 8.3 6.2 11.4 3.6 8.3 2.8 5.3 9.5 9.1 2.8 3.2 3.8 3.9 2.8 2.3 B7-WF-8 1.3 6.1* 14.2 5.5* 13.3 5.8 7.5 5.4 10.5 3.6 10.1 2.8 4.6 12.1 11.3 2.5 2.6 3.6 3.6 2.2 2.1 2016 B7-WF-9 1 14.7 13.3 13.2 11.9 5.7 6.8 5.7 9.8 8.7 6.7 2.9 4.5 8.5 7.3 2.2 2.8 3.3 3.6 1.8 2.4 B7-WF-10 1.2 16.2 15.5 15.7 14.1 6.8 6.3 6.7 11.4 4.2 8.1 3.6 5.6 9.6 8.7 2.9 2.5 3.3 3.6 2 2.5 B7-WF-01 1.7 6.8 16.4 5.9 n/a† 11.5 20.6 15 12.3 3.4 9.3 3.2 5.1 10.8 8.5 4 3.9 4.4 4.5 2.9 2.5 2017 B7-WF-02 1.9 6.9 12.7 5.9 n/a 10.6 19.1 11 11.9 3.8 8.4 3 5 9.8 7.1 3.1 3.2 4 3.1 2.4 2.3 B7-WF-03 1.3 6.1 14.2 5.1 12.9 9.2 17 12 3.3 10.2 7.6 2.6 4.7 9.2 7.4 3.4 3.7 4.1 3.4 3.9 2.2 2016 D7-WF-1 1.1 13.1 9.9 13.2 8.9 7.4 6.8 5.8 12.6 6.3 7 2.3 4.3 7.8 5.8 3.1 2.6 3.2 3.2 2.4 2.3 A1-LT-1 0.6 7.7 7.3 7.5 6.6 2.8 4.8 1.8 2.8 6.1 4.2 1.3 2.2 5.3 5.2 2.5 2.1 2.6 1.4 2.3 2.2 A6-LT-1 2 18.6 16.5 18.1 14.7 8.5 7 7.2 11.8 3.8 8.3 2.9 6 9.8 8 3.7 4.1 4.5 4.7 3.1 3 B2-LT-1 0.8 4.3 9.1 4.2 7.5 4.4 9.1 7.5 5.8 2.7 4.1 1.6 3.1 5.2 4.3 2 2.7 2.7 3.1 1.6 1.5 2015 Lake Trout B2-LT-2 1.4 4.8 10.4 4.2 9.8 6.8 7.8 8.3 7.2 2.5 5.1 1.3 3.4 5.2 5.8 2.2 3.5 3.3 3 1.2 2.2 B2-LT-3 1.5 5.7 12.1 5.2 10.7 7.4 12.2 9.8 7.7 2.8 5.2 6.5* 4 6.7 6.1 2.3 3.4 3.2 3.9 1.6 2.1 B2-LT-6 1.1 4.4 9.7 3.9 4.2 5.2 10.2 6 6.2 2.1 4.1 2.1 3 4.2 5.3 2.1 2.2 2.3 3.2 1.5 2 2016 B2-LT-1 1.3 14.6 13.5 13.2 12 5.6 4.7 4.6 7.9 2.8 5.7 2.3 4.1 7.4 6.3 2.4 3.2 3.1 3.3 2 2

156

HSI = 0.0184*length + 0.1205 R2 = 0.09

Figure A1. HSI versus length for whitefish (p=0.047).

Table A22. Number of fish caught per lake for the purposes of gut content analysis in 2015 and 2016. Species Year A1 A6 A8 B2 B7 D7 Total 2015 4 4 2 10 Grayling 2016 4 4 4 4 16 2015 2 4 4 10 Whitefish 2016 4 4 1 9 2015 1 2 3 Lake Trout 2016 1 1 2 Total 7 9 8 11 14 1

157

Figure A2. Principal component analysis of truss data for Coregonus artedi and Thymallus articus (caught from 2015-2017) as well as Salvelinus namaycush (caught from 2015-2016).

158

Figure A3. Principal component analysis of fish gut contents (by prey group) for whitefish, grayling, and lake trout caught from 2015- 2016. Note that the whitefish B7-WF-7 (caught in 2016) was not included in the analysis as the high proportion of zooplankton in the gut of that fish made the scale of the plot too large to view the ordination of the remaining data.

159

Table A23. Gut contents separated by prey group for fish caught in 2015 and 2016. 2015 Prey Grayling

A6 A8 B7

01 02 03 04 01 02 03 04

01 02

------

- -

GR GR GR GR GR GR GR

Clusters ID GR

GR GR

------

- -

B7 B7

A6 A6 A6 A6 A8 A8 A8 A8

Cottus cognatus 1 Gasterosteidae 1 1 6 Fish - Gasterosteus aculeatus (3) - Pungitius pungitius (9) 2 3 3 Minnow (Unidentifiable)* 2 2 2 Leech Hirudinea 2 1 2 Nematodes Nematoda 6 1 1 2 1 Amphipoda 3 1 Amphipods - Gammarus lacustrus 2 3 9 Hydrachnidia 1 1 Water - Lebertiidae 2 2 mites - Arrenurus 1 Physidae 82 Snails and Sphaeridae Clams Valvata 13 1 5 4 Chironomidae larvae 1 6 1 Chironomidae pupae 1 1 1 5 Ceratopigonidae Larvae 1 Dytiscidae Aquatic Hydrophilidae 1 Insects Limnephilidae larvae 5 6 3 2 1 Limnephilidae Pupae 29 86 58 38 40 97 45 43 1 Limniphilidae Adult 1 Simulidae Adult 1 2 1 Carabidae 1

Terrestrial -Ichneumonidae 3 Insects -Proctotrupidae 1 Staphylinidae 1 Spiders Araneae *One of each minnow (unidentifiable) refers to an indeterminate amount of unidentifiable tissue. Anything greater than 1 refers to a whole fish **Only in in B7-WF-7 †A8-GR-4 only; vial was missing and contents could not be ID'd further

160

Table A23 (cont’d). Gut contents separated by prey group for fish caught in 2015 and 2016. 2015 Prey Whitefish Lake Trout

A1 B2 B7 A6 B2

01 02

02 03 04 05 43 44 45 46

02 03

- -

------

- -

LT LT

Clusters ID LT

WF WF

WF WF WF WF WF WF WF WF

- -

------

B2 B2

B2 B2 B2 B2 B7 B7 B7 B7

A1 A1 Cottus cognatus Gasterosteidae 1 1 Fish - Gasterosteus aculeatus (3) 1 - Pungitius pungitius (9) 6 12 6 Minnow (Unidentifiable)* 5 Leech Hirudinea 15 3 1 10 7 1 4 Nematodes Nematoda 42 138 45 42 Amphipoda 1 3 3 Amphipods - Gammarus lacustrus 12 9 Hydrachnidia 1 1 Water - Lebertiidae 3 8 3 7 mites - Arrenurus 1 2 Physidae Snails and Sphaeridae 1 33 1 10 3 Clams Valvata 3 Chironomidae larvae 1 3 Chironomidae pupae 66 22 37 25 Ceratopigonidae Larvae 1 Dytiscidae 1 Aquatic Hydrophilidae Insects Limnephilidae larvae 5 1 1 5 1 2 Limnephilidae Pupae Limniphilidae Adult 1 1 Simulidae Adult 2 1 Carabidae

Terrestrial -Ichneumonidae Insects -Proctotrupidae Staphylinidae Spiders Araneae 1 *One of each minnow (unidentifiable) refers to an indeterminate amount of unidentifiable tissue. Anything greater than 1 refers to a whole fish **Only in in B7-WF-7 †A8-GR-4 only; vial was missing and contents could not be ID'd further

161

Table A23 (cont’d). Gut contents separated by prey group for fish caught in 2015 and 2016. 2016 Prey Grayling

A6 A8 B2 B7

1 2 3 4 2 3 4 5

1 2 3 4 1 2 3 4

------

GR GR GR GR GR GR GR GR

GR GR GR GR GR GR GR

Clusters ID GR

------

B2 B2 B2 B2 B7 B7 B7 B7

A6 A6 A6 A6 A8 A8 A8 A8 Pungitius pungitius 3 1 4 2 3 1 1 1 (9) Fish Lota lota 1 Minnow 1 1 1 1 2 2 3 1 1 (Unidentifiable)* Leech Hirudinea 2 1 10 Nematodes Nematoda 8 24 3 6 7 9 Oligochaetes Oligochaeta 3 2 Amphipods Gammarus lacustrus 3 21 16 2 Water mites Hydrachnidia 4 16 3 1 9 9 2 19 28 19 20 11 9 5 6 11 Physidae 1 Snails and Sphaeridae 4 4 3 2 1 Clams Valvata 3 1 1 Cladocera** Zooplankton Copepoda Ostracods Ostracoda 3 Diptera† 16 Chironomidae larvae 6 3 1 12 72 30 32 86 2 7 84 43 23 9 18 11 Chironomidae pupae 1 2 7 1 1 2 65 80 72 29 8 Chironomidae adult 1 1 12 2 1 3 Ceratopogonidae 1 Adult Culicidae Adult Aquatic

Insects Coleoptera Dytiscidae 1 Gyrinidae 1 Limnephilidae larvae 49 8 1 25 3 1 1 2 2 8 6 3 3 6 Limniphilidae Adult Tabanidae Adult 1 Tipulidae Adult 1 4 4 Terrestrial Hymenoptera 1 Insects *One of each minnow (unidentifiable) refers to an indeterminate amount of unidentifiable tissue. Anything greater than 1 refers to a whole fish **Only in in B7-WF-7 †A8-GR-4 only; vial was missing and contents could not be ID'd further

162

Table A23 (cont’d). Gut contents separated by prey group for fish caught in 2015 and 2016. 2016 Lake Prey Whitefish Trout

A1 B7 D7 A1 B2

1 2 3 4 1

7 8 9

- - - - -

- - -

WF WF WF WF WF

WF WF

Clusters ID WF

- - - - -

- - -

B7 B7 B7

A1 A1 A1 A1 D7 B7 Pungitius pungitius (9) ≥15 ≥17 3 11 Fish Lota lota Minnow (Unidentifiable)* 1 1 1 1 1 Leech Hirudinea 1 5 13 4 Nematodes Nematoda Oligochaetes Oligochaeta 1 Amphipods Gammarus lacustrus 122 Water mites Hydrachnidia 2 3 3 Physidae Snails and Clams Sphaeridae 1 207 21 156 Valvata 6 5 Cladocera** 466 Zooplankton Copepoda 141 Ostracods Ostracoda Diptera† Chironomidae larvae 4 29 2 2 10 1 9 Chironomidae pupae 1 7 4 1 Chironomidae adult 1 1 3 1 Ceratopogonidae Adult Culicidae Adult 1 Aquatic Insects Coleoptera 1 Dytiscidae 1 Gyrinidae Limnephilidae larvae 45 Limniphilidae Adult 12 Tabanidae Adult Tipulidae Adult 1 Terrestrial Hymenoptera 7 Insects *One of each minnow (unidentifiable) refers to an indeterminate amount of unidentifiable tissue. Anything greater than 1 refers to a whole fish **Only in in B7-WF-7 †A8-GR-4 only; vial was missing and contents could not be ID'd further

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Table A24. Year source of data in year-averaged stable isotope plots. Species A1 A6 A8 B2 B7 D7 Agabus (a) 15 Burbot 15 Carabidae (a) 14 Chironomidae (a) 16 16,17 16 14 Chironomidae (l) 14,15 14 14,15 14,15 14,16 15 Choroperlidae (a) 14 Colymbetes (a) 15,16 Culicidae (a) 14 14 Dytiscidae (a) 14,15,16 14,15 14,16 Dytiscidae (l) 14 Grayling 15,17 15,17 17 15 Grensia (l) 16 16 Grensia (a) 14 16 Hydrachnidia 14 14 15 14,15 14,15 Hydrophilidae (l) 14 Isoperla (a) 14 Laccophilus (a) 14 Lake Trout 15 15 Limnephilidae (a) 14 14 Limnephilidae (l) 14 14,16 14,16 Limnephilus (a) 14 14 Limnephilus (l) 16 Nemoura (l) 15 Perlodidae (a) 14 14 16 Physidae 14 14 Plankton 14,15,16 14,15,16 14,15,16 14,15,16 14,15,16 15,16 Plecoptera (a) 14 Plecoptera (l) 14,15 Simuliidae 16 Stickleback 15 15,16,17 15,16 17 16 15,17 Tipulidae (a) 16 16 Tipulidae (l) 14,16 14 15,16 14,15,16 15 Trichoptera (l) 14,15 Trichoptera (p) 15 Whitefish 15 15 15,17

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Table A25. Stable isotope data for phytoplankton samples collected in 2014. 13 15 Site Filtered volume (mL) δ CVPDB δ Nair A1A 500 -27.24 1.63 A1B 500 -27.51 2.31 A1C 500 -27.33 1.71 A2A 450 -27.65 0.10 A2B 450 -26.96 0.43 A2C 500 -27.19 0.66 A6A 700 -26.97 2.25 A6B 700 -27.34 1.76 A6C 700 -27.37 1.93 A8A 700 -25.73 3.09 A8B 500 -26.09 4.29 A8C 600 -26.03 3.33 B2A 700 -27.15 3.11 B2B 700 -27.77 1.72 B2C 700 -27.28 2.73 B7A 300 -25.04 3.34 B7B 500 -25.15 3.76 B7C 500 -24.72 2.99

Table A26. Isotope data and carbon and nitrogen percentages for zooplankton collected from 2014-2016. 13 15 Lake Year δ CVPDB % Carbon δ Nair % Nitrogen 2014 -27.63 (0.45) 42.96 (2.04) 3.59 (0.71) 6.78 (2.50) A1 2015 -27.06 (0.43) 27.90 (7.92) 2.72 (0.59) 4.03 (2.08) 2016 -27.29 (0.11) 37.56 (0.08) 2.52 (0.22) 5.21 (0.12) A2 2014 -25.54 (0.09) 19.39 (0.21) 4.76 (1.50) 8.48 (6.26) 2014 -27.95 (0.38) 43.84 (3.33) 3.80 (0.37) 6.87 (0.73) A6 2015 -27.97 (0.21) 38.12 (0.73) 6.18 (1.45) 6.59 (0.49) 2016 -26.95 (0.60) 47.54 (0.58) 3.65 (0.02) 7.29 (0.15) 2014 -28.42 (0.11) 50.75 (2.28) 2.42 (5.99) 7.93 (1.42) A8 2015 -26.61 (0.34) 44.65 (16.32) 11.85 (6.31) 14.32 (9.81) 2016 -25.59 (0.25) 44.11 (0.83) 7.74 (0.08) 8.13 (0.31) 2014 -29.53 (0.05) 49.54 (1.49) 4.55 (0.50) 7.60 (0.41) B2 2015 -29.07 46.84 4.30 8.45 2016 -27.89 (0.05) 46.47 (1.88) 4.06 (0.01) 8.83 (0.28) 2014 -28.89 (0.12) 53.60 (0.45) 5.02 (0.21) 6.42 (0.26) B7 2015 -28.06 (0.11) 50.54 (2.72) 5.94 (0.12) 9.27 (0.37) 2016 -28.48 (0.24) 49.73 (0.34) 6.05 (0.12) 7.98 (0.09) 2015 -28.20 42.45 4.60 6.69 D7 2016 -28.33 (0.08) 39.55 (1.02) 3.70 (0.24) 5.93 (0.68)

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Table A27. Isotope data and carbon and nitrogen percentages for aquatic invertebrates collected from 2014-2016. Sample Life 13 15 Lake Year ID Taxonomic ID Stage* δ CVPDB % Carbon δ Nair % Nitrogen A1-14-8 Chironomidae l -26.35 54.32 3.96 9.37 A1-14-3 Colymbetes a -26.74 47.04 5.70 8.65 2014 A1-14-4 Dytiscidae a -23.29 50.63 5.17 9.04 A1-14-2 Hydrachnidia n/a -21.18 48.32 5.16 10.20 A1-14-10 Tipulidae l -23.57 49.67 7.98 9.46 A1 A1-15-8 Chironomidae l -27.12 48.46 5.02 8.05 2015 A1-15-5 Colymbetes a -24.25 50.40 5.94 8.79 A1-16-4 Agabus a -24.05 49.21 5.71 8.67 2016 A1-16-1 Limnephilus l -26.38 61.17 -4.49 14.24 A1-16-5 Tipula l -23.58 41.17 3.47 7.73 A2-14-6 Agabus a -25.64 46.42 5.13 8.24 A2-14-7 Dytiscidae a -25.95 46.65 3.47 10.68 A2 2014 A2-14-1 Trichoptera l -25.97 24.82 5.06 3.74 A2-14-5 Trichoptera l -25.82 51.75 5.89 8.80 A6-14-12 Carabidae a -22.12 35.02 2.38 6.34 A6-14-1 Chironomidae l -23.62 44.68 4.96 9.49 A6-14-5 Hydrachnidia n/a -21.92 47.48 5.55 10.49 2014 A6-14-4 Hydrophilidae l -23.60 47.77 6.06 9.74 A6 A6-14-3 Laccophilus a -21.20 45.69 4.83 10.21 A6-14-9 Limnephilidae l -24.51 47.81 4.61 6.07 A6-14-11 Tipulidae l -25.97 36.05 2.56 7.81 2015 A6-15-6 Agabus a -21.53 50.69 5.55 9.45 2016 A6-16-3 Grensia l -23.96 41.43 3.88 8.72 A8-14-10 Chironomidae l -22.04 44.98 5.07 8.81 A8-14-14 Dytiscidae a -22.00 48.16 5.00 8.21 2014 A8-14-2 Grensia l -24.32 50.24 5.74 6.82 A8-14-3 Physidae n/a -19.29 28.36 4.63 4.83 A8-15-5 Chironomidae l -25.83 47.77 6.72 8.72 A8 A8-15-3 Hydrachnidia n/a -20.61 48.23 6.88 10.63 2015 A8-15-2 Hygrotus a -21.52 49.86 4.79 8.38 A8-15-6 Tipulidae l -21.21 41.95 3.62 7.69 A8-15-4 Trichoptera l -22.33 41.80 6.20 8.13 2016 A8-16-4 Tipula l -20.62 41.27 3.54 7.05 *larvae (l), pupae (p), adult (a)

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Table A27 (cont’d). Isotope data and carbon and nitrogen percentages for aquatic invertebrates collected from 2014-2016. Sample Life 13 15 Lake Year ID Taxonomic ID Stage* δ CVPDB % Carbon δ Nair % Nitrogen B2-14-1 Chironomidae l -22.81 43.33 4.26 9.20 B2-14-2 Dytiscidae l -18.68 49.56 3.97 9.67 2014 B2-14-6 Hydrachnidia n/a -26.10 54.32 6.53 10.18 B2-14-8 Limnephilidae l -22.05 48.17 5.47 6.99 B2-14-16 Nemouridae l -23.60 40.90 6.16 10.27 B2 B2-15-10 Chironomidae l -27.08 45.62 5.42 8.13 B2-15-2 Hydrachnidia n/a -21.36 58.58 5.79 14.53 2015 B2-15-5 Plecoptera l -23.18 46.79 6.01 11.48 B2-15-4 Trichoptera p -21.63 48.62 5.63 7.19 B2-16-1 Grensia l -21.24 42.62 4.34 7.24 2016 B2-16-3 Limnephilus l -21.64 47.87 5.46 6.52 B7-14-15 Chironomidae l -20.02 46.95 4.01 9.59 B7-14-11 Dytiscidae a -20.23 50.24 5.06 8.66 B7-14-6 Hydrachnidia n/a -20.14 48.02 6.14 9.45 2014 B7-14-13 Limnephilidae l -20.49 46.72 5.70 7.24 B7-14-16 Limnephilidae l -21.72 48.37 5.91 6.93 B7-14-9 Physidae n/a -18.56 27.85 5.84 4.72 B7 B7-14-14 Tipulidae l -17.94 42.01 2.59 7.31 B7-15-5 Hydrachnidia n/a -18.25 41.44 5.70 8.97 2015 B7-15-7 Tipulidae l -17.71 33.95 2.76 6.41 B7-16-2 Chironomidae l -19.77 43.70 2.32 9.82 B7-16-5 Hygrotus a -20.62 49.31 4.70 8.38 2016 B7-16-3 Limnephilus l -20.03 49.72 5.58 6.70 B7-16-1 Tipula l -20.02 38.13 3.49 6.30 D7-15-10 Chironomidae l -26.81 45.52 5.75 8.10 D7-15-5 Colymbetes a -24.57 49.47 6.34 10.67 2015 D7-15-1 Nemoura l -25.93 45.45 4.28 9.61 D7 D7-15-11 Tipulidae l -25.52 39.15 4.50 7.09 D7-16-5 Colymbetes a -25.55 52.23 6.26 9.44 2016 D7-16-1 Grensia l -24.21 48.56 5.04 7.62 *larvae (l), pupae (p), adult (a)

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Table A28. Summary of isotopic data and carbon and nitrogen percentages of fish samples collected in 2015 and 2017. 13 15 Taxonomic ID Lake Year Sample ID δ CPDB % Carbon δ Nair % Nitrogen B2-BT-1 -18.89 47.25 9.35 14.09 Burbot B2 2015 B2-BT-1-2015 -18.74 47.16 9.12 13.88 A6-GR-1 -21.14 47.06 9.31 14.57 A6-GR-2 -21.28 47.39 9.76 14.06 2015 A6-GR-3 -21.23 46.96 10.10 14.09 A6 A6-GR-4 -21.11 47.70 10.30 13.98 A6-GR-17-3 -21.54 49.87 11.50 13.14 2017 A6-GR-17-4 -22.96 52.85 11.30 11.26 A8-GR-1 -20.72 47.40 10.07 13.99 A8-GR-2 -20.59 47.51 10.53 14.08 2015 A8-GR-3 -20.81 48.14 11.20 13.82 Grayling A8-GR-4 -20.59 46.18 9.72 14.05 A8 A8-GR-17-1 -21.72 38.78 10.58 10.47 A8-GR-17-3 -20.87 39.64 11.79 10.59 2017 A8-GR-17-4 -20.94 48.16 11.77 12.74 A8-GR-17-5 -21.25 49.52 12.03 12.26 A8-GR-17-6 -21.44 48.11 11.49 12.44 B2-GR-17-2 -20.56 47.38 8.92 13.65 B2 2017 B2-GR-17-4 -20.33 50.24 9.15 13.69 B7-GR-1 -19.31 47.68 9.30 14.52 B7 2015 B7-GR-2 -19.42 45.95 8.97 14.32 A6 2015 A6-LT-1 -21.66 49.27 12.05 13.48 B2-LT-1 -21.69 46.86 10.82 14.39 Lake Trout B2-LT-2 -19.71 46.84 11.35 13.86 B2 2015 B2-LT-3 -20.88 47.23 11.06 13.69 B2-LT-4 -20.62 45.44 11.38 13.78

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Table A28 (cont’d). Summary of isotopic data and carbon and nitrogen percentages of fish samples collected in 2015 and 2017. 13 15 Taxonomic ID Lake Year Sample ID δ CPDB % Carbon δ Nair % Nitrogen A1-15-ST-1 -23.07 46.02 11.49 10.54 A1 2015 A1-15-ST-2 -23.37 52.08 9.54 9.80 A1-15-ST-3 -23.41 44.06 10.41 12.23 A6-15-ST-1 -23.62 45.46 8.71 10.61 2015 A6-15-ST-2 -24.42 47.73 8.79 11.37 A6-15-ST-3 -20.98 42.77 10.16 11.05 A6 A6-16-ST-2 -23.42 47.91 9.37 11.86 2016 A6-16-ST-3 -23.36 46.57 9.76 10.78 2017 A6-17-ST-1 -24.31 47.73 10.11 9.95 A8-15-ST-1 -22.27 48.58 11.25 10.77 2015 A8-15-ST-2 -22.01 45.93 11.27 10.00 A8-15-ST-3 -20.89 44.53 10.53 11.99 A8 Stickleback A8-16-ST-1 -22.00 44.00 11.37 11.35 2016 A8-16-ST-2 -22.32 45.62 11.13 10.74 A8-16-ST-3 -21.71 44.67 10.67 11.41 B2-17-ST-1 -22.54 44.29 9.39 11.53 B2 2017 B2-17-ST-2 -22.28 41.91 10.56 11.65 B2-17-ST-3 -23.19 48.26 9.73 10.58 B7 2016 B7-16-ST-1 -22.94 47.93 10.20 11.35 D7-15-ST-1 -24.60 45.55 10.77 11.10 2015 D7-15-ST-2 -23.89 45.07 10.44 11.57 D7-15-ST-3 -25.58 50.46 10.96 10.66 D7 D7-17-ST-1 -22.81 41.34 10.42 11.92 2017 D7-17-ST-2 -21.36 39.46 10.16 11.60 D7-17-ST-3 -22.56 38.98 10.27 11.47 A1-WF-1 -25.15 46.61 10.45 14.48 A1 2015 A1-WF-2 -24.72 45.37 10.05 14.04 B2-WF-2 -20.64 46.28 9.96 13.79 B2-WF-3 -21.50 47.26 10.40 13.80 B2 2015 B2-WF-4 -20.56 46.32 9.64 13.86 B2-WF-5 -20.93 47.32 9.89 13.53 B7-WF-33 -22.38 46.28 9.94 14.08 Whitefish B7-WF-44 -22.60 46.73 10.08 14.08 2015 B7-WF-45 -22.39 47.13 9.90 13.99 B7-WF-46 -22.55 47.56 9.63 14.45 B7 B7-WF-17-? -18.85 42.97 9.11 12.60 B7-WF-17-? -22.58 46.16 9.45 11.19 2017 B7-WF-17-? -23.08 44.24 10.45 13.75 B7-WF-17-1 -22.12 43.33 10.16 13.89

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Table A29. Summary of mean isotope data (and standard deviation) by lake within fish species for samples collected from 2015 to 2017. 13 15 ID Lake Year δ CVPDB s.d. δ Nair s.d. 2015 -21.19 0.08 9.87 0.43 A6 2017 -22.37 0.74 10.76 1.10 2015 -20.68 0.11 10.38 0.64 Grayling A8 2017 -21.24 0.35 11.53 0.57 B2 2017 -20.48 0.11 9.40 0.45 B7 2015 -19.36 0.08 9.13 0.24 A1 2015 -23.29 0.19 10.48 0.98 2015 -23.01 1.80 9.22 0.81

A6 2016 -23.09 0.51 9.63 0.23 2017 -24.31 0.00 10.11 0.00 2015 -21.72 0.73 11.02 0.42 Stickleback A8 2016 -22.01 0.31 11.06 0.36 B2 2017 -22.67 0.47 9.89 0.60 B7 2016 -22.94 0.00 10.20 0.00 2015 -24.69 0.85 10.72 0.26 D7 2017 -22.25 0.78 10.28 0.13 A1 2015 -24.93 0.30 10.25 0.28 B2 2015 -20.91 0.43 9.97 0.32 Whitefish 2015 -22.48 0.11 9.89 0.19 B7 2017 -21.66 1.91 9.79 0.62

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Table A30. Isotope data and carbon and nitrogen percentages for adult life stage (terrestrial) invertebrates collected from 2014-2016.

13 15 Lake Year Sample ID Taxonomic ID δ CVPDB % Carbon δ Nair % Nitrogen 2014 A1-14-6 Limnephilidae -27.02 48.57 6.62 7.65 A1 2016 A1-MI-1 Chironomidae -23.33 44.61 6.55 9.62 A6-14-14 Culicidae -29.67 47.50 4.42 10.96 A6-14-7 Grensia -25.24 47.66 4.59 9.36 2014 A6-14-6 Isoperla -20.45 42.64 6.55 12.81 A6 A6-14-2 Perlodidae -22.06 43.07 6.14 10.63 2016 A6-MI-1 Chironomidae -22.52 47.00 6.22 10.74 2017 A6-17-1 Chironomidae -23.32 41.79 5.88 8.11 A8-14-1 Isoperla -19.75 42.56 7.94 11.48 2014 A8-14-11 Limnephilus -20.96 48.92 7.02 10.37 A8 A8-14-12 Perlodidae -20.12 44.74 7.67 11.20 2016 A8-16-2 Grensia -22.14 49.35 6.83 6.54 B2-14-9 Culicidae -20.63 53.08 7.58 15.83 2014 B2-14-12 Limnephilidae -24.84 40.94 7.74 9.17 B2-14-10 Plecoptera -20.75 23.92 5.84 5.37 B2 B2-MI-1 Chironomidae -21.61 47.40 5.57 10.12 2016 B2-TI-1 Tipulidae -25.48 48.11 0.68 11.03 B2-TI-2 Tipulidae -24.87 47.71 1.45 10.20 B7-14-3 Chloroperlidae -18.82 45.23 8.82 13.73 B7-14-5 Isoperla -22.97 47.65 7.14 11.77 B7 2014 B7-14-4 Isoperla -19.28 47.28 8.41 12.09 B7-14-2 Limnephilus -19.81 48.77 7.92 11.15 D7-16-9 Perlodidae -20.00 43.99 6.19 12.72 D7 2016 D7-16-8 Simuliidae -26.50 45.84 5.53 10.61 D7-TI-1 Tipulidae -25.31 47.04 -0.36 13.24

Table A31. Isotope data and carbon and nitrogen percentages for spiders collected from the shorelines of study lakes in 2016. 13 15 Lake Sample ID δ CVPDB % Carbon δ Nair % Nitrogen A1-SP-A -24.95 42.08 7.36 8.87 A1 A1-SP-B -24.09 44.19 7.75 10.25 A6-SP-A -22.58 46.99 7.28 9.82 A6 A6-SP-B -22.53 45.46 7.20 10.46 A8-SP-A -22.57 47.93 8.44 11.25 A8 A8-SP-B -22.84 47.28 8.10 10.90 B2-SP-A -23.71 46.22 7.25 10.14 B2 B2-SP-B -23.22 48.90 7.37 10.73 B7-SP-A -20.11 46.33 8.07 10.65 B7 B7-SP-B -19.55 46.46 7.66 10.37 D7-SP-A -23.86 45.70 7.21 10.93 D7 D7-SP-B -24.09 47.82 8.25 11.04

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Table A32. Summary of the results of one-way ANOVA tests for δ13C and δ15N values for fish and zooplankton against year (within lake).

Dependent Shapiro-Wilk Equal Variance Equal Variance ANOVA Lake Taxa Variable (p-value) Test (p-value) Transformation (p-value) Zooplankton δ13C 0.4254 Bartlett 0.5211 n/a 0.2916 A1 Zooplankton δ15N 0.1848 Bartlett 0.6087 n/a 0.1514 Zooplankton δ13C 0.1655 Bartlett 0.5457 n/a 0.0604 Grayling δ13C 0.0199* Brown-Forsythe 0.1843 n/a 0.0222* Stickleback δ13C 0.2675 Bartlett n/a n/a 0.6997 A6 Zooplankton δ15N 0.0241* Brown-Forsythe 0.2909 n/a 0.0435* Grayling δ15N 0.3410 Bartlett 0.1927 n/a 0.1907 Stickleback δ15N 0.2536 Bartlett n/a n/a 0.4710 Zooplankton δ13C 0.0958 Bartlett 0.4708 n/a 0.0001* Grayling δ13C 0.2566 Bartlett 0.0761 n/a 0.0182* Stickleback δ13C 0.1334 Bartlett 0.2988 n/a 0.5634 A8 Zooplankton δ15N 0.0547 Brown-Forsythe 0.4089 n/a 0.1775 Grayling δ15N 0.4659 Bartlett 0.8247 n/a 0.0240* Stickleback δ15N 0.1535 Bartlett 0.8363 n/a 0.9044 Zooplankton δ13C 0.0256* Bartlett n/a n/a <0.0001* B2 Zooplankton δ15N 0.0139* Bartlett n/a n/a 0.3396 Zooplankton δ13C 0.3803 Bartlett 0.4779 n/a 0.0122* Whitefish δ13C 0.0002* Brown-Forsythe 0.2178 n/a 0.4222 B7 Zooplankton δ15N 0.0325* Bartlett 0.4910 n/a 0.0027* Whitefish δ15N 0.9506 Bartlett 0.0831 n/a 0.7746 Zooplankton δ13C 0.9523 Bartlett n/a n/a 0.2736 Stickleback δ13C 0.9696 Bartlett 0.9117 n/a 0.0213* D7 Zooplankton δ15N 0.4339 Bartlett n/a n/a 0.0816 Stickleback δ15N 0.5760 Bartlett 0.3936 n/a 0.0613 Note: n/a for equal variance test indicates the equal variance test could not be performed due to the presence of less than 2 observations in one or more group. A significant (p < 0.0500) value for the equal variance test indicates all tests were significant and the assumption of homoscedasticity was violated.

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Table A33. Summary of p-values for post-hoc Tukey’s pairwise comparison of annual data for δ13C 15 and δ N values for fish (by species) and zooplankton. 2014-2015 2014-2016 2015-2016 2015-2017 Dependent Independent ANOVA Lake Variable Variable (p-value) Estimate p-value Estimate p-value Estimate p-value Estimate p-value Grayling δ13C 0.0029* 0.1791 0.0028* A6 Zooplankton δ15N 0.0435* -2.3771 0.0604 0.1561 0.9822 2.5332 0.0708 Zooplankton δ13C 0.0001* -1.8124 0.0008* -2.8315 0.0001* -1.0191 0.0103* A8 Grayling δ13C 0.0181* 0.5670 0.0181* Grayling δ15N 0.0240* -1.1537 0.0240* B2 Zooplankton δ13C <0.0001* -0.4549 0.0034* -1.6338 <.0001* -1.1789 0.0001* Zooplankton δ13C 0.0112* -0.7757 0.0100* -0.3520 0.1572 0.4236 0.0652 B7 Zooplankton δ15N 0.0027* -0.8937 0.0050* -1.0093 0.0029* -0.1156 0.6987 D7 Sitickleback δ13C 0.0213* -2.4460 0.0213*

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Table A34. Summary of p-values for post-hoc Tukey’s pairwise comparison of the proportion of littoral carbon and trophic position by lake (by fish species). Stickleback Grayling Whitefish Lake Trout Lake α TP α TP α TP TP A1 A6 0.3515 0.9925 A1 A8 0.1222 0.9128 A1 B2 0.2731 1.0000 0.0500 0.0066* A1 B7 0.0465* 0.5588 0.1105 0.7123 A1 D7 0.9949 0.9999 A6 A8 0.9576 0.4273 <.0001* 0.4860 A6 B2 0.9703 0.9976 1.0000 0.0004* 0.0478* A6 B7 0.2925 0.2984 <.0001* 0.3089 A6 D7 0.4481 0.9233 A8 B2 1.0000 0.8683 0.0005* <.0001* A8 B7 0.5163 0.8567 0.1329 0.8207 A8 D7 0.1592 0.9301 B2 B7 0.6769 0.5153 0.0003* 0.0003* 0.0005* 0.0025* B2 D7 0.2394 0.9992 B7 D7 0.0594 0.5791 Note: α and TP represent the proportion of littoral carbon and trophic position, respectively.

Table A35. Summary of p-values for post-hoc Tukey’s pairwise comparison of annual data for the proportion of littoral carbon and trophic position of fish for grayling (one-way ANOVAs for other fish species were non-significant). Dependent ANOVA 2015-2017 Fish Variable p-value Estimate p-value α 0.0029* 0.1791 0.0028* Grayling TP <0.0001* -0.5015 <.0001*

174