Applications of Paleolimnology in Ecosystem Monitoring for Sirmilik National Park: Developing Indicators of Ecological Integrity

APPLICATIONS OF PALEOLIMNOLOGY IN ECOSYSTEM MONITORING FOR SIRMILIK NATIONAL PARK: DEVELOPING INDICATORS OF ECOLOGICAL INTEGRITY

Jane Erica Devlin

A thesis submitted in conformity with the requirements for the degree of Master of Science, Graduate Department of Geography, University of Toronto

Title Page

APPLICATIONS OF PALEOLIMNOLOGY IN ECOSYSTEM MONITORING FOR SIRMILIK NATIONAL PARK: DEVELOPING INDICATORS OF ECOLOGICAL INTEGRITY

Jane Erica Devlin

Master of Science

Graduate Department of Geography

University of Toronto

2010

Abstract

Water chemistry and bioindicators (diatoms and invertebrates) were examined for freshwater lakes, ponds and streams in two regions within Sirmilik National Park, northern

Baffin Island, Nunavut. Significant differences were recorded between the water chemistry and diatom and invertebrate assemblages of the two regions. Modern diatom assemblages were explained mainly by specific conductivity, ORP, pH, temperature, elevation and distance from the coast. Paleolimnological techniques were applied to a sediment core from

Lake BY14 on Bylot Island. Fossil diatom assemblages indicate increases in nutrients and temperature since 1935 AD. The diatom biostratigraphy does not show as large an increase in diversity and production since the middle 20th century as has been noted elsewhere, and this may be a reflection of the more nutrient-rich status of the lake relative to other Arctic lakes.

Acknowledgements

First and foremost, I want to express my great appreciation for the guidance and support of Dr. Sarah Finkelstein throughout my Masters. Her unwavering positive attitude was a constant source of inspiration for me, and I am grateful to have had the opportunity of working with her.

This research would not have been possible without funding and logistical support from a number of sources, including the Parks Canada Agency, the Northern Scientific

Training Program, the Natural Sciences and Engineering Research Council of Canada and the Polar Continental Shelf Project.

Warm thanks go to Carey Elverum and Brian Koonoo at Sirmilik National Park, for logistical support and invaluable assistance in the field. Other Park staff also made our visit to Pond Inlet very comfortable and overall a great experience – thanks to Israel Mablick,

Samson Erkloo, Andrew Arreak and Andrew Maher. Much appreciation goes to the

Nunavut Field Unit, especially Jane Chisholm, Gary Mouland, Marco Dussault and Eva

Paul for providing valuable assistance and information for this project. Additional thanks go to the Community of Pond Inlet, the staff at the Community Center, the Mittimatalik

Hunters and Trappers Organization, the Hunters and Trappers Organization of Arctic Bay and the Hamlet Councils of Pond Inlet and Arctic Bay for their support for this research. I would also like to thank Dan and Tracy Utting for assistance with permit applications and information on the Park's geomorphology.

A special mention goes to Mircea Pilaf, who provided priceless logistic and technical support during field preparations and for work in the lab. Roberto Quinlan and his students

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at York University – Andrew Medeiros, Ray Biastoch, Kristen Wazbinski and Chris

Luszczek – were of great assistance with invertebrate taxonomy and lab methods, and I thank them all for their valuable time and assistance. The sushi is on me next time!

To my lab mates – Carlos, Jen, Nicole, Krish, JP and Kailey – thanks of course for any help you may have given me on statistics, taxonomy, etc, but even more importantly, I appreciate the fun times and great memories you have given me during my time at U of

T… all the coffee runs and beef jerky! To Stephanie, Grace and Candace for the invaluable assistance in the lab – it was great to get to know you and work with each of you. Erin and

David – thanks so much for all the encouragement, moral support and for reviewing my work. I'm looking forward to all the fishing we can do together now that I'm done!

To my family and friends, including all those mentioned above, you will never know how much I appreciate your support and patience during the past couple of years – I promise to not have my nose in a book the next time we meet. Lastly, my greatest appreciation goes to Scott, for being my constant source of support and inspiration, and my number one fan. Without his support and words of encouragement (and patience, and amazing cooking!) this would not have been possible.

iv Table of Contents

TITLE PAGE ...... i

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... iii

TABLE OF CONTENTS ...... v

LIST OF TABLES ...... vii

LIST OF FIGURES ...... viii

LIST OF APPENDICES...... x

CHAPTER 1: INTRODUCTION...... 1

1.1 GENERAL INTRODUCTION AND OBJECTIVES ...... 1 1.1.1 Research objectives ...... 2 1.2 LITERATURE REVIEW ...... 3 1.2.1 Arctic limnology...... 3 Hydrological processes in high-arctic freshwater environments...... 3 Ecology of Arctic freshwater environments ...... 5 Water chemistry of high-arctic freshwater environments ...... 10 Diatoms as bioindicators...... 14 Aquatic invertebrates as a biomonitoring tool...... 18 1.2.2 Paleolimnology in Environmental Assessment and Monitoring ...... 21 Use of paleolimnology in lake ecosystem management ...... 21 Paleolimnological diatom studies from high latitudes...... 24 1.2.3 Bioassessment using aquatic benthic invertebrates...... 24 Review of literature coupling diatoms and invertebrates for biomonitoring ...... 24 1.3 STUDY SITE...... 26 1.3.1 Study Location ...... 26 1.3.2 Bedrock geology ...... 26 1.3.3 Glacial history and surficial geology ...... 26 1.3.4 Flora and fauna...... 28 1.3.5 Climate ...... 29 1.3.6 Freshwater ecosystems ...... 30 1.3.7 Humans on the landscape...... 30

CHAPTER 2: METHODS ...... 31

2.1 FIELD AND LABORATORY METHODS ...... 31 2.1.1 Site selection...... 31 2.1.2 Water chemistry...... 32 2.1.3 Lake coring...... 34 2.1.4 Modern Diatom Sampling...... 34 2.1.5 Benthic invertebrate sampling...... 35 2.1.6 Benthic invertebrate sorting and identification ...... 36 2.1.7 Diatom enumeration ...... 37 2.1.8 Radiometric dating ...... 38 2.1.9 Loss-on-ignition (LOI)...... 39

2.2 STATISTICAL ANALYSES...... 39 2.2.1 Treatment of the water chemistry and site data and preliminary analyses...... 39 2.2.2 Multivariate analysis of water chemistry data...... 41 2.2.3 Treatment of diatom data and preliminary analyses ...... 42 2.2.4 Analyses of modern diatom assemblages...... 43 2.2.5 Analyses of core BY14-2 fossil diatom assemblages...... 44 2.2.6 Reconstruction of significant variables ...... 45 2.2.7 Invertebrate assemblages ...... 45

CHAPTER 3: RESULTS ...... 47

3.1 PHYSICAL CHARACTERISTICS OF LAKE AND POND SITES ...... 47 3.2 PHYSICAL CHARACTERISTICS OF STREAM SITES...... 48 3.3 WATER CHEMISTRY...... 49 3.4 LOSS-ON-IGNITION (LOI) ...... 51 3.5 MULTIVARIATE ANALYSES OF WATER QUALITY DATA...... 51 3.6 METALS CONCENTRATIONS ...... 54 3.7 INVERTEBRATE ASSEMBLAGES ...... 54 3.8 DIATOM ASSEMBLAGES ...... 55 3.9 CHRONOLOGY OF CORE BY14-2...... 58 3.10 DOWN-CORE DIATOM ASSEMBLAGES OF CORE BY14-2...... 58 3.11 DOWN-CORE ENVIRONMENTAL RECONSTRUCTIONS OF CORE BY14-2...... 60

CHAPTER 4: DISCUSSION ...... 62

4.1 SPECIES-ENVIRONMENT RELATIONSHIPS OF DIATOMS AND BENTHIC INVERTEBRATES ...... 62 4.2 RECOMMENDATIONS FOR AQUATIC BIOMONITORING USING THE CABIN STANDARD...... 71 4.3 CORE RECONSTRUCTIONS ...... 73

CHAPTER 5: CONCLUSIONS ...... 78

REFERENCES ...... 116

APPENDIX A: WATER CHEMISTRY DATA...... 128

APPENDIX B: MODERN DIATOM SPECIES COUNTS FOR SURFACE SAMPLES ...... 130

APPENDIX C: FOSSIL DIATOM SPECIES COUNTS FOR CORE BY14-2...... 136

APPENDIX D: DIATOM ASSEMBLAGE DESCRIPTIVE METRICS FOR 25 SURFACE SAMPLES AND CORE BY14-2...... 140

List of Tables

Table 1: Water quality parameters measured ...... 80

Table 2: CABIN sampling key elements...... 81

Table 3: Summary of physical characteristics of lakes and ponds sampled...... 82

Table 4: Stream site habitat measurements ...... 83

Table 5: Summary of nutrient and physical variables for 15 sites sampled...... 84

Table 6: Summary of total metal concentrations for the 15 sites sampled...... 85

Table 7: Pearson Product Moment Correlation matrix of physical and nutrient variables. .86

Table 8: Results of PCA of water chemistry variables at 15 sites sampled...... 88

Table 9: PCA loadings for water chemistry variables at 20 lake and pond sites...... 88

Table 10: Invertebrate counts ...... 89

Table 11: Descriptive metrics for invertebrate samples...... 90

Table 12: Summary of single-variable CCAs and Monte Carlo permutation test results ....91

Table 13: Summary statistics for CCA of surface diatom assemblages...... 91

Table 14: Inter-set correlations for CCA of surface diatom assemblages...... 91

Table 15: Radiocarbon dates and calibrated calendar ages from cores BY14-2 and QB15.92

vii

List of Figures

Figure 1: Map and aerial photos of study sites...... 93

Figure 2: Maximum depths and water clarity of lakes and ponds...... 95

Figure 3: Stream cross-sectional profiles ...... 96

Figure 4: Loss-on-ignition (LOI) data for surface lake and pond sediments ...... 97

Figure 5: Principal components analysis biplot, for 15 sites...... 98

Figure 6: Screeplot for the PCA ...... 99

Figure 7: Cluster analysis of 15 sites in SNP based on 28 environmental variables...... 100

Figure 8: Cluster analysis of 15 sites in SNP based on total metals concentrations...... 101

Figure 9: Distribution of 26 most abundant diatom taxa in 21 Sirmilik study sites...... 102

Figure 10: CA plots of site scores as a function of surface diatom assemblages ...... 104

Figure 11: CCA plot of site and species scores ...... 105

Figure 12: 210Pb activity profile of core BY14-2...... 106

Figure 13: Age-depth profile of core BY14-2...... 106

Figure 14: Diatom stratigraphy of core BY14-2...... 107

Figure 15: CA plots of site scores comparing 25 surface and 20 BY14-2 core diatom

assemblages ...... 108

Figure 16: Observed versus diatom-inferred pH model performance...... 109

Figure 17: pH reconstruction for core BY14-2...... 110

Figure 18: Observed versus diatom-inferred specific conductivity model performance ...111

Figure 19: Specific conductivity reconstruction for core BY14-2 ...... 112

Figure 20: Observed versus diatom-inferred water temperature model performance...... 113

Figure 21: Water temperature reconstruction for core BY14-2...... 114

viii

Figure 22: Reconstructions of water quality variables for core BY14-2...... 115

List of Appendices

Appendix A: Water chemistry data ...... 128

Appendix B: Modern diatom species counts for surface samples...... 130

Appendix C: Fossil diatom species counts for core BY14-2...... 136

Appendix D: Diatom assemblage descriptive metrics for 25 surface samples and core

BY14-2 ...... 140

Chapter 1: Introduction

1.1 GENERAL INTRODUCTION AND OBJECTIVES

The effects of climate change on Arctic ecosystems are potentially wide ranging, yet are not fully understood. Climate warming in the Arctic over the past 150 years has been two to three times greater than that of the global average (Trenberth et al. 2007) and is projected to have greater consequences than anywhere else on Earth (ACIA 2005).

Changes in climate affect the timing and extent of major events such as sea ice breakup and freezing (Stirling and Parkinson 2006), lake ice cover (Smith 2002), spring vegetation green up (Post and Forchhammer 2008), species migration and breeding

(Dicky et al. 2008, Post and Forchhammer 2008), as well as hydrological cycles and active layer depth (Walsh et al. 2005), which controls cycling of gases from changing soil layers (Oechel et al. 1993). Arctic lakes, ponds and wetlands are already showing signs of ecological and physical changes in response to climate warming (Smith et al. 2005, Smol et al. 2005, Ellis and Rochefort 2006), including changes in species composition, increases in biological production and diversity. These changes could affect ecological as well as biogeochemical processes, including carbon cycling and storage (Oechel et al.

1993, ACIA 2005).

Biodiversity of Arctic freshwater ecosystems is considered vulnerable to a changing climate, as bottom-up food web effects have been observed in algae and invertebrates since the beginning of the Industrial Era (Quinlan et al. 2005, Smol et al. 2005, Heegaard et al. 2006). Linkages are emerging between these biotic changes and climate indicators such as decreased ice cover, increased thermal stratification, changes in water chemistry

(mainly pH and nutrient status) and shifts in species composition (Smol et al. 2005). Also

possibly affecting freshwater ecosystems in the Arctic are atmospheric contaminants,

- 2- such as NO3 and SO4 , which are transported long distances and could act to fertilize arctic lakes (Goto-Azuma and Koerner 2001, Wolfe et al. 2006).

It is important to track ecological responses of lakes to environmental changes so that we can understand system resilience, and predict future outcomes. Monitoring ecological responses to impacts is difficult, especially in the Arctic where logistical problems and expense preclude the development of extensive monitoring programs. In the absence of baseline data, the impacts of stressors such as climatic warming cannot easily be determined. Paleolimnological techniques can be used to provide quantitative reconstructions of environmental trends over past decades, centuries or millennia.

Paleolimnological records, coupled with other indicators of modern freshwater health such as bioassessment using aquatic invertebrates or algae, and water chemistry analyses, can provide a comprehensive picture of freshwater ecosystem health.

1.1.1 Research objectives

Research objectives are threefold:

a) To model modern species-environment relationships for two groups of aquatic

bio-indicators, the diatoms (unicellular algae) and benthic invertebrates, and to

measure biodiversity in freshwater lakes and streams at a series of high-arctic

study sites in Sirmilik National Park (SNP), Nunavut, Canada. This information

will provide both quantitative expressions of species-environment relations, which

can be applied to paleolimnological reconstructions, and new baseline data on

freshwater ecosystems.

b) To use lake sediment records and paleolimnological techniques to reconstruct

natural ranges of variability for the late Holocene in diatom assemblages and

biodiversity for a remote high-arctic lake in SNP.

c) To quantitatively reconstruct past limnological changes from the lake sediment

core used in objective b), using transfer functions derived from the modern

models produced in objective a).

1.2 LITERATURE REVIEW

1.2.1 Arctic limnology

Hydrological processes in high-arctic freshwater environments

Hydrological processes in the high Arctic are strongly driven by temperature and the extreme seasonal changes in weather (i.e.: solar radiation, wind and precipitation).

Areas of continuous permafrost have a brief summer during which surface water is free of ice and can flow. The timing, duration and magnitude of ice melting have important implications for chemical and biological interactions between water and the flora and fauna it supports. Ice cover on lakes and ponds is a strong determinant of available sunlight for photosynthesizing primary producers, and influences gas exchange as well as wind and solar induced mixing processes that distribute chemicals and nutrients important to biological processes. The permafrost regime controls how water flows through and interacts with the active layer soils and bedrock before reaching lakes, ponds and streams. Active layer depth, soil permeability and hydrologic conductivity affect flushing rates of dissolved organic carbon and other nutrient inputs to water bodies. The hydrologically-confining nature of permafrost contributes to a generally low level of

connectivity between ponds, lakes and wetlands (Woo and Guan 2006). Additionally, deep groundwater flow is prevented from mixing with shallow subsurface water by the essentially impermeable permafrost, so flow connectivity is restricted to between the shallow active layer, ponds, lakes, streams and rivers. With many factors predicted to change in some way, projections of future hydrological conditions are difficult to forecast. Changing permafrost will make soil permeability and moisture retention capacity increasingly difficult to estimate (MacDonald et al. 2000).

The water balance of Arctic water bodies depends largely on the input from spring snowmelt and evaporative losses throughout the summer months (Woo and Young 2006).

Annual precipitation is relatively low in the Arctic, generally <500 mm, and 100-200 mm in the area under study here (Environment Canada 1986). Climate models generally predict increases in precipitation, with estimates of +6-12% by the next century over terrestrial Arctic watersheds (Walsh et al. 2005), although uncertainties are significant.

Increased winter precipitation may result in an increased snowpack that will contribute to a larger spring freshet, and snow may last longer into the summer in isolated patches, though predictions for a longer snow-free period have also been made (Callaghan 2005,

Walsh et al. 2005).

In the Canadian Arctic, the north end of Baffin Island is well into the zone of continuous permafrost, which leads one to surmise that an increase in precipitation will result in an increased water content of active layer soils, and increased volumes in surface water and resulting discharge. The amount of precipitation in a catchment, whether direct or indirect through runoff, largely determines the volume of water feeding into a lake and will dilute solutes differently as the seasons and water levels change. Larger water bodies

tend to have lower concentrations of nutrients and ions and lower biological productivity than shallow lakes or ponds (Frey and Stahl 1958, Hamilton et al. 2001). Smaller lakes and ponds with lower volumes of water sometimes experience daily fluctuation in variables such as ions and nutrients, therefore making them chemically sensitive

(Antoniades et al. 2005).

Ecology of Arctic freshwater environments

Arctic freshwater lakes, ponds and streams have very diverse ecological characteristics, due to the wide ranges in size, physical shape, location (in terms of latitude, longitude and proximity to coastline), nutrient loading and sources of water

(Prowse et al. 2006). These ecological characteristics are determined primarily by climate, and its associated influence on hydrological processes (Prowse et al. 2006). Year round cold temperatures result in extensive ice cover, frequently >10 months per year, and consequently reduced sunlight and direct precipitation to aquatic systems through most of the year (Smol 1988). Primary productivity is linked to lake ice cover as diatom growth is limited by available light for photosynthesis. Smith (2002) found that diatom abundance may also be affected by topoclimatic differences, as a comparison of lakes at northeastern Ellesmere Island showed responses to regional cooling at different times due to proximity to glaciers and the changing regional snowline.

Productivity in such systems is generally low (compared to temperate freshwater systems) as it is limited by the extreme cold temperatures, low nutrient inputs and brief summers, which create challenging conditions for organisms (Prowse et al. 2006).

Temperature, wind, lake size and basin shape dictate thermal profiles and lake mixing. The thermal properties and mixing behaviour of a water body influences the

availability of nutrients and light for photosynthesis, the sedimentation of phytoplankton, the thermal regime and habitat structure for biota and how the lake would respond to changes in climate (Vincent and Hobbie 2000). Small shallow lakes are not thermally stratified, as they warm quickly and are well-mixed by winds (Prowse et al. 2006).

Shallow lakes may even freeze to the bottom, as ice thickness may reach up to 2.5m

(Prowse et al. 2006). Deeper lakes (>10m) can be stratified during the summer with well- developed thermoclines, as in northern Fennoscandia (Korhola et al. 2002).

Variability of Arctic lake water temperature is small and not likely an important factor for populations of important primary producers such as diatoms (Smol 1988)

(typical lake surface water temperatures of < 5°C over the year) (Schindler et al. 1974).

Ponds, however, can potentially reach warmer temperatures (up to 15°C in the summer has been recorded) (Douglas and Smol 1994), thereby influencing other important factors such as pH, ice-cover, nutrient cycling and stratification (Smol et al. 2001).

Species diversity is generally low in Arctic freshwater ecosystems relative to temperate latitudes, and increases with trophic status (Hobbie 1984). Diatoms (division

Bacillariophyta), however, remain highly diverse in the Arctic and are at the base of aquatic food webs. The great abundance of these unicellular, eukaryotic organisms makes up a high proportion of the biomass of water bodies, and is a major food source for invertebrates, young fish and protozoa. Diatoms influence water chemistry as they fix carbon, release oxygen and exchange ions (Round 1993), and in turn they are known to be strongly influenced by pH, concentrations of chemicals necessary for growth, such as silica, and the availability of nutrients and light (Smol et al. 2001). Diversity of diatoms, mosses, other primary producers and invertebrates is normally greater in stable, clear,

spring fed streams, where fish diversity increases with larger streams (Prowse et al.

2006). The suitability of water bodies as habitat for biota is dictated largely by the timing of and extent to which they freeze, as smaller streams and ponds freeze to the bottom and create severe limitations for fish and benthic organisms (Prowse et al. 2006).

Diversity of invertebrate fauna is relatively low in the Arctic due to the harsh climate, and it is thought that many invertebrate species are at their adaptational limit, making them sensitive indicators of environmental change (Danks 1992). Diptera,

Hymenoptera, Lepidoptera, mites and Collembola are the most abundant taxa in the

Arctic (Danks 1988). Insect diversity in Arctic streams is generally low, but is relatively high for Diptera (two-winged flies), especially within the Chironomidae (midge) and

Simuliidae (black fly) families, reflecting their physiological adaptations to cold environments (Hershey et al. 1995, Vincent and Hobbie 2000). Stream habitat homogeneity of Arctic freshwater systems and the short larval development period may also limit diversity (Hershey et al. 1995). Most Arctic chironomids are collectors- gatherers, and simuliids are the only common group of filter-feeders; both feed mainly on fine particulate organic matter (FPOM), and simuliids also eat dissolved organic matter

(DOM) which has been found in streams draining peat tundra (Hershey et al. 1995). The trophic levels (detritivores and herbivores) represented by these dominant aquatic insect groups indicates their role in the ecosystem; as secondary producers they are key in the cycling of algae and decaying organic material.

Other insect groups such as Ephemeroptera (mayflies), Plecoptera (stoneflies),

Trichoptera (caddisflies) – collectively known as the EPT group – and Coleoptera

(aquatic beetles) that are more abundant and diverse in temperate streams are replaced by

Diptera as the dominant insects in Arctic freshwater (Hershey et al. 1995). In a study by

Medeiros et al. (2009) on two catchments near the south end of Baffin Island, Plecoptera and Ephemeroptera together were found at relative abundances ranging from 0 to 85%, which was considered high and attributed to timing of emergence. The species richness was 12 when identified to family-level (11 families and 1 class – Oligochaeta) and increased to 26 taxa when Chironomidae were identified beyond the family into sub- family, tribe and genus. The assemblages reported by Medeiros et al. (2009) were dominated by Chironomidae (mainly families Orthocladiinae and Tanypodinae), followed by EPT (as a group) and then Simuliidae.

There has been very little investigation into the aquatic invertebrates of SNP, but in a review of terrestrial invertebrates, Gray (2006) lists the families that would likely be found in the northern Baffin region, and many of these have an aquatic life stage:

1) Class Arachnida (spiders), family Erigonidae (15 subgroups)

2) Subclass Acari (mites), families Ascidae, Rhagidiidae, Ceratozetidae (38 subgroups)

3) Class Collembola (springtails), families Hypogastruridae, Isotomidae (39 subgroups)

Class Insecta:

4) Order Mallophaga (chewing lice), family Philopteridae (28 subgroups)

5) Order Hemiptera (true bugs), family Aphididae (13 subgroups)

6) Order Diptera (flies), seven families (231 subgroups)

7) Order Lepidoptera (butterflies and moths), seven families (42 subgroups)

8) Order Hymenoptera (ants, wasps, bees, etc.), three families (92 subgroups)

Additionally, although nematodes (roundworms) are not considered invertebrates by standard protocols (Reynoldson et al. 2006), there are likely between 50 and 60 nematode

species in the general region of SNP, which live in a variety of terrestrial and aquatic habitats (Gray 2006).

Important zooplankton grazers in Arctic freshwaters include protozoa

(heterotrophic nanoflagellates and ciliates), rotifers and cladocera (Bosmina and

Daphnia). Protozoa are likely the primary grazers of bacteria in Arctic freshwater, but are not well described in the literature (Vincent and Hobbie 2000). Rotifers in the Arctic are mostly benthic and decrease in diversity with increasing latitude (Vincent and Hobbie

2000); 21 taxa have been reported for lakes on Bathurst Island (75°N) (Chengalath and

Koste 1989).

Observational reports of fish found in Sirmilik National Park include five species:

Arctic charr (Salvelinus alpinus alpinus), Fourhorn sculpin (Triglopsis quadricornis),

Cisco (Coregonus artedi), Threespine stickleback (Gasterosteus aculeatus aculeatus) and

Ninespine stickleback (Pungitius pungitius) (unpublished data, Parks Canada, Iqaluit).

The movements of these fish and their exact locations in the Park are unknown, but would be dependent on connectivity of streams or lakes to the sea (except in the possible case of land-locked populations). The fish listed above are among the higher trophic species in freshwater environments, feeding on planktonic crustaceans, aquatic invertebrates and their eggs and larvae; Arctic charr, Fourhorn sculpin and Threespine stickleback are also piscivorous (Froese and Pauly 2009).

Tens of thousands of migratory waterfowl use the freshwater habitat on the southwest plain of Bylot Island during spring and summer for nesting and brooding, and their waste is a source of nutrients (phosphorus, P and nitrogen, N) for the terrestrial and aquatic habitat there. It is likely that ice and snow conditions in the Arctic dampen the

rate of P loading to lakes (which is normally greater in temperate regions) (Michelutti et al. 2002c). This implies that a warming climate could increase nutrient loading to Arctic lakes, as melt patterns of ice and snow are changing in duration and timing, altering seasonal flushing rates (Walsh et al. 2005) and rates of erosion.

Reduced ice extent and duration have potential to affect the chemistry and trophic state of lakes, as both would lead to increased light availability in the water column, increased water temperatures, increased rates of decomposition and nutrient cycling, lengthened growing season for algae and therefore increased photosynthetic activity and productivity (Vincent and Hobbie 2000). Melting permafrost soils would lead to increased dissolved organic carbon (DOC) and particulate materials, therefore reducing water transparency (Rouse et al. 1997) and affecting light availability for primary producers.

Wrona (2005) summarizes the three major drivers that will determine the consequences of climate change for Arctic freshwater ecosystems: the timing, magnitude and duration of runoff; temperature; and changes in water chemistry variables such as nutrients, DOC and particulate organic matter. Changes in climate would alter the ice- related disturbance patterns of streams and rivers caused by damming and flooding. If these events play a role in maintaining species richness, then climate-related changes to ice damming and flooding could potentially alter the biodiversity of stream and river systems (Scrimgeour et al. 1994).

Water chemistry of high-arctic freshwater environments

Most high-arctic lakes are oligotrophic, alkaline and low in DOC (Hamilton et al. 2001).

Nutrient concentrations are low in Arctic freshwater due to many factors, including the

low biomass, limited soil development, short growing season and slow rates of erosion and nutrient transport. The nutrients N and P are commonly examined when considering the trophic state of lakes, and P is often considered the more limiting nutrient (Hamilton et al. 2001, Wetzel 2001). Seventy percent of the lakes in a 190-lake dataset in the

Canadian Arctic (Hamilton et al. 2001) have TN:TP >20:1, indicating that P is more commonly limiting. Sakamoto (1966) suggests that nitrogen limitation will not likely occur unless the N:P ratio is less than 17 (by weight). Levine and Schindler (1999) found that phytoplankton biomass and productivity increased with increasing N:P ratios, and suggest that when phytoplankton are N-limited, productivity is dominated by benthic species using N from the sediment surface. Sources of N and P inputs to Arctic lakes include atmospheric deposition, weathering of soils and rocks, nutrient fixation in the soils (Jonasson et al. 2001) and inputs from faunal waste. Generally, direct human impact from waste effluent or other point source pollution is not a significant concern in the

Arctic due to the remoteness and low human populations. There are examples such as that of Meretta Lake (Michelutti et al. 2002c), however, on Cornwallis Island (72°41.75′ N,

94° 59.58′ W), where evidence of eutrophication caused by human-effluent has caused significant changes in the diatom assemblages.

Total phosphorus (TP) concentrations of Arctic lakes are commonly <10 µg L-1

(Michelutti et al. 2002b) but can be higher depending on local geology or nutrient inputs.

In a survey of 204 lakes in the Canadian Arctic Archipelago, Hamilton et al. (2001) report a mean concentration of 12 µg L-1 for TP, with a range of 1 - 150 µg L-1. This wide range is attributed to geochemical inputs – only a small portion is soluble reactive phosphorous (SRP) and available for primary production, and the majority is biologically

unavailable precipitate from P-rich shales, or other carbonates found at some sites in the

Arctic archipelago (Hamilton et al. 2001, Wetzel 2001). In a similar study, Lim et al.

(2001) found mean TP of 12.7 µg l-1, with a range of 3.2 – 64.0 µg l-1, for 38 sites on

Bathurst Island, Nunavut. The soluble portion of this was always small (<5 µg l-1). Other studies of Arctic lakes record similar means and ranges (McNeely and Gummer 1984,

Whalen and Cornwell 1985, Douglas and Smol 1994). TP is an important variable influencing the makeup of diatom assemblages (i.e.: Bennion 1994).

Allochthonous nutrient sources (originating outside the water body) are important to productivity in oligotrophic Arctic lakes, and although P is usually limiting, inputs of

P, N and carbon (C) together are required for long term increases in productivity

(Hamilton et al. 2001). Decomposing organic matter, such as plants, release DOC that gets transported to lakes and ponds. High-arctic watersheds typically have sparse vegetation cover, and coupled with short summers and slowed rates of decay, this leads to generally low DOC concentrations. The physiological role of DOC for diatom populations is not well understood, yet significant relationships between diatoms and

DOC have been used to reconstruct vegetation and climate change (Pienitz et al. 1999).

DOC has been shown to have a negative correlation with latitude, which reflects latitudinal changes in vegetation, soils and geology (Vincent and Hobbie 2000).

Water chemistry and ion concentrations are largely controlled by bedrock type and weathering rates, as geology of the catchment affects the available nutrients and dissolved minerals, metal concentrations and the pH buffering capacity (Hamilton et al. 2001, Lim et al. 2001). For example, calcium carbonate from limestone bedrock is a source of hydrogen ion and when exchanged with water increases the pH (Hamilton et al. 2001).

Much of the Arctic is underlain by carbonate rocks of the Arctic Platform, resulting in basic, hard-water lakes with pH values around 8–8.5. Lakes in the southeastern region of the archipelago, including Baffin Island, are more often slightly acidic due to underlying

Pre-Cambrian bedrock with weak buffering capacity (Hamilton et al. 2001).

The water chemistry of lakes, ponds and streams is influenced by the volume of water diluting concentrations of various solutes, as well as the velocity of water that is causing turbulence and mixing with the substrate. Ponds tend to have higher concentrations of nutrients and solutes compared to larger lakes (Antoniades 2004), and stream velocity increases turbidity (Sheath and Müller 1997) and therefore suspended solutes. Correlations between lake surface area and nutrients showed the general pattern that larger lakes have lower concentrations of SiO2, particulate nitrogen (PN), Total

Kjeldahl Nitrogen (TKN), carbon (DIC, POC, DOC) and particulate-bound P, and lower specific conductance and pH (Hamilton et al. 2001). Lakes and streams have temporal differences in water chemistry due to the nature of how the nutrient load is transported to the water. While lakes receive the majority of water inputs and accompanying nutrients during the spring freshet, streams are more dependent on the continuous supply of nutrients from groundwater flow during the growing season (Kling 1995). Primary productivity is dependent on these nutrient inputs and is partially driven by seasonal changes in flowing water (Kling 1995), but varies according to specific physical characteristics of the system and regional environmental conditions (Wrona et al. 2005).

Stream water chemistry has been sparsely studied for the Canadian high Arctic, yet the limited investigations show that streams are also closely linked to the local geology.

Specific conductivity and ion concentrations depend on exchanges with bedrock and

surficial material, and are therefore influenced by stream velocity and erosional forces.

Antoniades et al. (2009) collected pH, specific conductivity and surface temperature data from 42 streams in the Canadian Arctic Archipelago, and found that pH reflected the overall alkaline geology (median pH of 7.9, ranging from 5.2 to 8.8) and was the variable most strongly related to diatom assemblage variance. Sheath and Müller (1997) measured pH, specific conductivity, temperature and turbidity at 83 stream reaches on Axel

Heiberg and Ellesmere Islands, and TP concentrations at 40 reaches in the Slidre river basin on Ellesmere Island. pH of these Axel Heiberg and Ellesmere Island streams was between 6.6 and 8.9 (attributed to the marine-derived sediments). Specific conductivity values measured by Antoniades et al. (2009) were between 19 and 710 μS/cm, and those measure by Sheath and Müller (1997) were higher, ranging from 25-1600 μS/cm.

Turbidity values from Sheath and Müller (1997) ranged from 0.5-1000 NTU and were well correlated with velocity (P < 0.001), illustrating the concept that faster streams carry larger sediment loads, and therefore higher dissolved ion concentrations.

Diatoms as bioindicators

Diatoms have significant potential to be used as indicators of ecological conditions, as they are the most important primary producer of Arctic lakes and individual species have known environmental optima and tolerances that can be used to assess water quality. Of particular physiological importance to diatoms are the concentrations of nutrients available for their growth (P, N and C) and silica (SiO2) that makes up their cell walls. Diatoms require nutrients in biologically available forms for basic metabolic processes such as protein synthesis. P, for example, is taken up as soluble reactive phosphorus (SRP), compared to the particulate form which is biologically unavailable to

diatoms (Hamilton et al. 2001). P is commonly an important variable explaining the variation in diatom assemblages and for measuring lake trophic state trends (i.e.: Bennion

+ - 1994, Anderson 1998, Douglas and Smol 2000). Ammonium (NH4 ) and nitrate (NO3 )

+ are two forms of nitrogen that can be used for algal production, however NH4 is preferred by phytoplankton as its uptake requires less energy (Kling 1995). This has

+ - important indications about the nitrification processes (conversion of NH4 to NO3 ) occurring in terrestrial landscapes within the catchment (Kling 1995), as changes there may affect the abundances of diatoms such as planktonic species Aulacoseira, Cyclotella and Tabellaria. Siver (1999) found that total nitrogen was important for controlling the distribution of planktonic diatoms in a suite of 50 Connecticut lakes.

Pienitz and Smol (1993) concluded that carbon as DOC and DIC were important variables in explaining diatom species composition. DOC affects light penetration through water and has been linked to effects on diatom photosynthesis (i.e.: Vincent and

Pienitz 1996). Other variables important to explaining species composition include surface water temperature (i.e.: Pienitz and Smol 1993, Joynt and Wolfe 2001) and silica

(Köster and Pienitz 2006).

Diatom studies can also indicate the water levels and hydrological conditions by the abundance of diatoms of different kinds, for example, tracking the shift in abundance from epiphytic to planktonic species (Finkelstein et al. 2005). Sub-annual variations in

- limnological variables such as NO3 , P, SiO2, and stratification regimes can be detected in seasonal patterns of diatom assemblages (i.e.: Köster and Pienitz 2006).

Nutrient concentrations vary at different sites in a lake, such as the water column, the sediment surface and on host macrophyte surfaces (Bennion 1994, Wetzel 2001,

Gislason et al. 2004). The growth habit of algae, such as diatoms, may therefore be an indication of nutrient concentration requirements of particular taxa. Periphytic diatom taxa live in benthic habitats where there are much higher concentrations of nutrients

(relative to overlying waters, where planktonic diatoms grow) and they most likely obtain organic and inorganic nutrients from the substrates such as rock, sediments, or macrophyte hosts (Siver 1999, Wetzel 2001). The substrate of the stream also plays a role in the relative abundances within diatom assemblages, as certain species have specific substrate requirements (i.e.: epilithic, growing on gravel/stone/rock and epiphytic, growing on plants) (Antoniades and Douglas 2002, Michelutti et al. 2003). Certain diatom species are more competitive than others in terms of accessing nutrients and light.

The competitive interactions between species result in changing abundances and shifts in species assemblages; theoretically reflecting the optimal environmental conditions of the most dominant species.

Antoniades (2004) found that diatom community compositions were most strongly related to specific conductivity and pH, in a study of 90 sites in the Canadian Arctic

Archipelago. Keatley (2007) found that significant portions of the variation in diatom assemblages were explained by pH, specific conductivity, surface area, elevation, and chlorophyll-a, except for sites that had considerably more vegetation, where total dissolved nitrogen (TDN) was the only variable of importance.

Stream diatom assemblages can be highly different from site to site. Antoniades et al. (2009) found highly divergent stream communities in a study region of 42 streams in the Canadian Arctic Archipelago. A canonical correspondence analysis (CCA) revealed that assemblages were significantly related to pH, temperature, latitude and longitude,

together explaining 14.7% of species variability. An analysis of similarities of these stream assemblages did not show any significant differences between epilithic and epiphytic samples; there were weak but significant differences between the geographic regions of the study. Species common to stream sites across the archipelago include

Hannaea arcus, Nitzschia perminuta, Rossithidium petersenii and Achnanthes minutissimum (Antoniades et al. 2009). Arctic stream diatom communities are susceptible to frequent disturbances due to changes in flow from snowmelt, changes in water level and dessication (Antoniades et al. 2009). There is an overall lack of information on diatoms of lotic systems and additional research here would strengthen knowledge of diatom autecology.

High-arctic lakes are often dominated by small, benthic, Fragilarioid species such as Fragilaria construens var. venter and F. pinnata (Blake et al. 1992, Smith 2002). This is common in early post-glacial sediments from Arctic sites on both carbonate (Smith

2002) and acidic terrains and likely reflects the harsh growing conditions from extensive ice cover that limits light for photosynthesis (Smith 2002).

Recent work by Antoniades (2008) on small lakes and ponds on Prince Patrick,

Ellef Ringnes and Northern Ellesmere islands describe Arctic diatom assemblages dominated by benthic and tychoplanktonic (partially benthic and partially planktonic when re-suspended in the water column) taxa, with many species from the Fragilarioids and the Achnanthaceae. Other genera, with physically larger species, included

Pinnularia, Navicula sensu stricto, Nitzschia, Eunotia, Gomphonema, Encyonema,

Cymbopleura, Cymbella, Neidium, Stauroneis, and Caloneis (in decreasing order of

abundance). In general, many species in the Arctic remain unknown and in need of better classification and description (Antoniades et al. 2008).

Aquatic invertebrates as a biomonitoring tool

In addition to using diatoms to track past environmental changes, using higher- trophic levels in biomonitoring can be a good indication of environmental change, and changes occurring in food webs of Arctic lakes (Quinlan et al. 2005) and streams. While diatoms have a short life-span and can re-establish quickly after a disturbance (within a number of hours), benthic invertebrates incorporate cumulative effects of environmental conditions because they are relatively long-lived (1 to 3 years). The Canadian Aquatic

Biomonitoring Information Network (CABIN) biomonitoring methods (Reynoldson et al.

2006, Reynoldson et al. 2007) use the presence and abundance of benthic aquatic invertebrates in conjunction with water chemistry and habitat data to assess the health of stream ecosystems compared to reference, or baseline, conditions. Freshwater ecosystems in SNP can be considered minimally disturbed sites (Stoddard et al. 2006), as they are free from human disturbance other than atmospheric or global effects such as climate change and atmospheric contaminants. Environmental and species data collected can therefore be expected to indicate the ecosystem health of this minimally disturbed environment. The health of different, yet environmentally similar streams (based on habitat and water chemistry data collected) can be assessed based on comparisons of the biota with the reference condition assemblages established during any previously studied reference sites.

Reference sites need to be established for the Canadian high Arctic so that possible future impairment or changes in freshwater ecosystem health may be detected. Aquatic

invertebrate bio-monitoring has recently commenced in northern Quebec and Labrador

(Culp 2007), in the South Nahanni watershed (Scrimgeour 2008) and in the south end of

Baffin Island (Medeiros et al. 2009), but high-arctic sites are lacking. The CABIN protocols that are used at many sites across Canada may not be appropriate at high latitude sites where organisms are often dominated by single taxa (Chironomidae), are smaller in size and sampling conditions are different from lower latitudes.

On its own, the presence of Chironomidae reveals little about the ecosystem. Slavik et al. (2004) report large increases in density of chironomids with increasing P during a long-term fertilization experiment on the North Slope of Alaska, however, greater taxonomic precision allows for more detailed environmental patterns to be detected.

Chironomid communities have been successfully used for biomonitoring of lakes, as there are characteristic species assemblages associated with particular lake environments

(Saether 1979). These characteristic communities are most closely related to levels of TP, lake depth and chlorophyll-a (Saether 1979). Though much of the literature for biomonitoring with aquatic invertebrates is on lakes, established CABIN protocols are for streams (CABIN protocols for lakes are in progress). Different invertebrate assemblages would be expected between lakes versus streams due to the differing habitat conditions, ion and nutrient concentrations and hydrological environments between the two systems.

Non-biting midges (Insecta: Diptera) include the families Chironomidae,

Chaoboridae and Ceratopogonidae and have an aquatic larval stage. Barley et al. (2006) provide quantitative models of species-environment relationships for these midge groups that included 145 lake sites from British Columbia north to Alaska and to the eastern

Canadian Arctic Islands. The authors found, using canonical correspondence analysis,

that midge distribution was most strongly controlled by mean July air temperature and lake depth. While Arctic tundra vegetation, alpine tundra vegetation, pH, dissolved organic carbon, lichen woodland vegetation and lake surface area also influenced chironomid assemblages, training sets for statistical calibration and quantitative reconstructions could only be developed for mean July air temperature and lake depth.

In oligotrophic and mesotrophic lakes such as those in the Arctic, distribution of chironomids is more strongly controlled by the availability of food than it is by oxygen conditions, which is more important in more temperate regions (Saether 1979). In the southwestern Yukon and northern British Columbia, Wilson and Gajewski (2004) found that distribution of chironomid taxa was related to organic matter in sediments (from weight lost on ignition, or LOI), TP, bottom water temperature and lake alkalinity. Recent changes occurring in chironomid assemblages have been attributed to the warming climate and associated changes to aquatic habitats (i.e.: Smol et al. 2005). This study highlights the overall usefulness of benthic invertebrates for monitoring for impacts associated with climate change.

Similar studies for invertebrate assemblages in high latitude streams have developed quantitative relationships for invertebrate-inferred pH (Larsen et al. 1996) and temperature (Barley et al. 2006). These numerical models require taxonomic resolution beyond the family level, which is specifically important for assemblages that are dominated by Chironomidae (i.e.: King and Richardson 2002, Medeiros et al. 2009).

Overall, the order Ephemeroptera is considered sensitive to disturbances, yet the

Baetidae family within Ephemeroptera is only moderately sensitive. Relative abundance of EPT in temperate streams is considered a good indication of water quality due to the

group's sensitivity to habitat disturbance (Reynoldson et al. 2006). Standard biomonitoring protocols such as the Canadian Aquatic Biomonitoring Network

(Reynoldson et al. 2006, Reynoldson et al. 2007) use the % abundance of EPT as a descriptive metric for water quality monitoring, however, EPT are neither common nor abundant in Arctic streams. For example, in the western Arctic Ephemeroptera is represented by 5 or 6 species (Hershey et al. 1995), Plecoptera by 2 species (Miller et al.

1986, Hershey et al. 1995) and Trichoptera by 4 species (Hershey et al. 1995).

Species of Collembola have been found useful for detecting metal contamination

(i.e.: Fountain and Hopkin 2004), however this may have limited practical use for remote

Arctic freshwater where metal concentrations are commonly near or below detection limits.

1.2.2 Paleolimnology in Environmental Assessment and Monitoring

Use of paleolimnology in lake ecosystem management

Diatom assemblage data and measurements of water quality variables have been used to infer past conditions of lakes. Variability in lacustrine environments can be reconstructed to identify natural ranges and trends of variables such as diatom abundance, nutrient concentrations, temperature and pH. The impact of human disturbance can also be tracked, and recovery can be tracked during management efforts for impacted lakes

(Battarbee 1999). Paleolimnology and the use of diatom-inferred transfer functions are useful tools for reconstructing past trophic conditions and identifying natural ranges of variability for setting targets and thresholds for nutrient levels when making management decisions (Brenner et al. 1993, Dixit et al. 1996, Siver 1999).

A transfer function can be constructed using a training set of diatom abundance data and measured environmental parameters from many sites. Multivariate ordination methods (i.e. Canonical Correspondence Analysis) are used to determine which variables explain significant amounts of variation in the diatom communities; the significant variables can theoretically be reconstructed based on the training set. The percent abundance of a species at all the sites is used to calculate a weighted average for determining the optima and tolerances of each species with respect to each significant variable. These optima and tolerances are the calibrated training set that is used to reconstruct past environmental variables based on the fossil assemblage data. The strength of the diatom assemblage response to a variable, among other factors, determines the robustness of a reconstruction. Shifts in diatom species composition occur rapidly when environmental conditions change, as individual taxa have specific optima and tolerance ranges of variables such as pH, specific conductivity and temperature.

Depending on sedimentation rates and integrity of the sediment deposition, changing lake conditions can be traced back over short term (<10 years), decadal, or millennial timescales by analyzing fossil diatom assemblages.

There are a number of model reconstructions of P concentrations (Anderson and

Rippey 1994, Bennion 1994, Reavie et al. 1995, Bennion et al. 1996, Hall and Smol

1996, Siver 1999), N concentrations (Christie and Smol 1993, Siver 1999) and lake-ice conditions (Smith 2002). Eutrophication of Meretta Lake on Cornwallis Island, Nunavut

(72°N) (Douglas and Smol 2000) was tracked by reconstructing TP concentrations and other trophic variables – the model was effective at detecting changes in the diatom assemblage for a period of less than 10 years, and was recommended as a potential

management tool for tracking lake recovery in restoration efforts. The naturally oligotrophic lake was dominated by small, benthic Fragilaria sensu lato diatoms (largely

F. pinnata and its varieties), and eutrophication (high TP concentrations) was evident by large relative increases in F. construens v. cf. pumila, small increases in Nitzschia and

Navicula species and relative declines in original Fragilaria assemblages. Potential future increases in P loading rates of Arctic freshwater (Wrona et al. 2005) could result in similar species trends.

The timing of sampling is an important factor in developing a calibration set for transfer functions and reducing possible error due to skewed seasonal nutrient fluxes. The accessibility of remote Arctic sites is a major consideration, and will also influence when samples can be taken. Winter TP measurements are considered the best for estimating P available for algal growth, though logistically the most difficult. Measurements of P taken in the summer include the P that is released from the sediments, and have the benefit of measuring concentrations at their maximum (Bennion 1994).

A single core is often used to reconstruct past TP concentrations (Anderson 1998).

This may be a cost-effective tool to use in lake ecosystem management, though caution should be used with this approach, and knowledge of the depositional environment should be considered when interpreting model results. Using a single sediment core requires taking it from the deepest part of the lake, where there is little chance of disturbances from the littoral zone and near shore. There is a strong rationale for taking a diatom-inferred approach for reconstructing environmental conditions in areas where historical data are lacking and where remote access does not allow for an extensive monitoring program.

Paleolimnological diatom studies from high latitudes

In recent times, diatom assemblages in representative lakes on Baffin Island have undergone a significant shift in assemblages around 1850 AD, with a trend of increasing planktonic taxa, mainly Cyclotella (Smol et al. 2005). Finkelstein and Gajewski (2008) found pronounced differences in the diatom diversity of a lake in the central Canadian

Arctic starting at about 1920, and in the 1970s saw increases in productivity unprecedented for the Holocene. Paleolimnological reconstruction of diatom assemblages by Antoniades (2005) showed relatively stable composition for centuries to millennia, and then marked changes at three sites in the mid 1800s and early 1900s where reconstructions inferred a pH increase of 0.5 to 0.8 pH units, and were strongly positively related to 30 years of recorded temperature.

Transfer functions for reconstructing lake water levels have been developed for lakes in Wood Buffalo National Park (60°N) and Fennoscandia (69°N) (Moser et al.

2000). Surface water temperature has also been reconstructed for lakes on Baffin Island

(Joynt and Wolfe 2001). DOC has been used to infer changes in vegetation over time

(Pienitz et al. 1999) and to determine the past light conditions for diatoms in a subarctic lake (Ponader et al. 2002).

1.2.3 Bioassessment using aquatic benthic invertebrates

Review of literature coupling diatoms and invertebrates for biomonitoring

Utilizing bio-indicators from multiple trophic levels can improve our knowledge of ecological effects on lakes under study. A review and analysis of Arctic paleolimnological studies using diatoms and invertebrates was done by Smol et al. (2005) to compare results of different paleoindicators. They concluded that ecological changes

driven by climate warming did in fact occur at multiple tropic levels (diatoms, chrysophytes, cladocera and chironomids). In a study using cladocera, chironomids and diatoms to detect environmental sensitivities, Heegaard et al. (2006) suggest that environmental variables affected each group differently, meaning that each group responds differently to environmental gradients. By using multiple paleoindicators,

Heegaard et al. (2006) argue that a more sensitive reception of environmental changes can be achieved.

Quinlan et al. (2005) tracked changes in chironomid (Diptera) head capsules in sediments of Cape Herschel ponds that had previously been used for diatom-inferred climate reconstruction (Douglas and Smol 1994). These two bioindicators are trophically linked, as algae are the main food source for chironomids, so changes in one will have an impact on the other. Both studies found significant changes occurring in the 19th century, with dramatic increases in abundance and diversity of chironomid populations, and shifts in community assemblages of diatoms. These changes were linked to the effects of increased warming and decreased ice cover on the lakes and show that using multiple bio-indicators such as chironomids and diatom assemblages can strengthen monitoring directly related to changing climate.

Other established protocols include multiple indicators such as periphyton (i.e.:

Jones et al. 2005) but do not always include detailed taxonomic analysis to a level that allows for quantitative assessment of habitat condition. One future direction of the

CABIN program is to incorporate other organisms such as periphyton into the standard protocols.

1.3 STUDY SITE

1.3.1 Study Location

This study is located in two physiographic regions of Sirmilik National Park (SNP), which covers an area of over 22,000 km2 on the northern tip of Baffin Island, Nunavut,

Canada, and also includes Bylot Island. The two physiographic regions are located on the

Qorbignaluk Headland (QB) and on the southwest plain of Bylot Island (BY) (Figure 1).

1.3.2 Bedrock geology

The southwest plain of Bylot Island is situated on Cretaceous sedimentary bedrock and is part of the Arctic Platform geologic province (Klassen 1993). Outcrops of sedimentary rocks such as limestone, quartz sandstone, dolomite, shale and siltstone are found amongst a thin layer of surface drift (Arsenault 2006). The Bylot sites are within the Lancaster Plateau of the Arctic Lowlands, which is characterized by high plateaus and rolling hills that are incised by stream valleys. Qorbignaluk Headland is situated within the Davis Highlands physiographic region and is characterised by crystalline bedrock and mountainous terrain with steep fiords and valleys. The Archean/Aphebian crystalline rock is part of the Canadian Shield geologic province (Klassen 1993).

1.3.3 Glacial history and surficial geology

The area under study was last glaciated by a series of local and regional ice advances (Klassen 1993). Deglaciation at the end of the last glacial age of northern Baffin

Island and the opening of Lancaster Sound took place beginning around 10,000 years ago, but ice remained on Borden and Brodeur Penninsulas (Klassen 1993), in the northwest region of Baffin Island. Between 8600 and 8400 years ago, summer

temperatures warmed in response to early Holocene insolation maxima driven by orbital parameters (Berger and Loutre 2004). The major ice sheets in the region retreated rapidly between 6,700 and 6,000 years BP. The local ice in SNP has since advanced due to

Neoglacial cooling, with most glaciers reaching their maximum extents in the past 120-

400 years (DiLabio and Shilts 1978, Klassen 1993, McCuaig 1994).

Evidence of a warming climate is found very close to the study sites, as the glaciers on Bylot Island have retreated at least several hundred meters over the past 50 years, and may still be retreating (Arsenault 2006). This is according to aerial photography analyzed from 1958, 1961 and 1982. However, while some of the smaller ice caps in 1960s topographical maps have disappeared, McCuaig (1994) has determined that some are advancing still (i.e.: Ujarasukjui glacier).

Permanent ice (including snow pack) currently covers approximately 32% of the

Park's land base, or 6983 km2 of the three sectors of the Park (Bylot Island, the Oliver

Sound area and western Borden Peninsula) (Parks Canada, unpublished). Surface water on Qorbignaluk headland is fed by largely by glacial meltwater, and a number of the study lakes are directly adjacent to glaciers (Figure 1).

The regional drift deposited by the Eclipse (late Wisconsinian) glaciation on Bylot

Island is a thin (<2m thick) deposit ranging from mud to boulders, but generally characterized by a brown-grey sandy sediment (Klassen 1993). It is found at elevations between 300 and 600 m asl on mountain slopes and elevations of 50 to 250 m asl in valleys, and is compositionally similar to the bedrock below (Klassen 1993). Erratic marine shells are found commonly in the drift material, transported from marine transgressions during previous glaciations. The southern lowlands of Bylot Island have

emerged approximately 40-45 m during the Holocene from isostatic rebound (Klassen

1993). Glacio-marine sediments can be >2 m thick and consist of sorted fine sediments, sands, gravel and often marine shells. On the southwest plain of Bylot Island, non-glacial marine sediments are found between 30 and 90 m asl, in the form of raised beaches and deltas resulting from isostatic rebound following Eclipse deglaciation (Klassen 1993).

Qorbignaluk Headland is generally devoid of any appreciable unconsolidated surficial deposit, but some lakes (i.e.: QB07 and QB10) have glacial lake sediments on the surrounding shorelines (Klassen 1993).

1.3.4 Flora and fauna

The lakes and ponds of Bylot are generally surrounded by prostrate shrubs and herbaceaous plants such as Arctic willow (Salix Arctica), Arctic heather (Cassiope tetragona), purple saxifrage (Saxifraga oppositifolia), Arctic poppy (Papaver lapponicum), and mountain aven (Dryas integrifolia), grasses and sedges, as well as numerous species of byrophytes and lichens. At least 105 bryophytes and 178 lichens have been identified in the Park (Zoltai et al. 1983). Bryophytes and macroscopic algae are suggested to have an important role in freshwater ecosystems, as they are a source of food and refuge from predators for aquatic invertebrates, and a substrate on which periphyton can grow (Vincent and Hobbie 2000).

Grasses and sedges in the area include graminoids such as Fisher's Tundragrass

(Dupontia fisheri), Semaphore Grass (Pleuropogon sabinei), and Polar Grass

(Arctagrostis latifolia) and sedges such as Red Cottongrass (Eriophorum russeolum var. albidum), White Cottongrass (E. scheuchzeri), Tall Cottongrass (E. angustifolium) and

Water sedge (Carex aquatilis var stans) (Zoltai et al. 1983, Gauthier et al. 1995). These

species are a major food source for Greater Snow Geese in the wetlands on the southwest plain of Bylot Island. The geese commonly feed in the wetlands where these grasses are more abundant, and then move to the lakes and ponds for refuge from predators (i.e.

Arctic Fox, Alopex lagopus, or Red Fox, Vulpes vulpes) (Hughes et al. 1994).

The south west plain of Bylot Island is the nesting ground for up to 60,000 Greater

Snow Geese (Chen caerulescens) each year (LePage et al. 1998). Breeding success of the geese depends largely on the timing of snowmelt and the availability of this nutritious vegetation for chicks (Dicky et al. 2008). Climatic variations and timing of spring green- up therefore have an important role in the success rate of the breeding. Other waterfowl commonly found in the area include the Canada Goose (Branta canadensis), Red- throated Loon (Gavia stellata), King Eider (Somateria spectabilis), Long-tailed Duck

(Clangula hyemalis) and Northern Fulmar (Fulmarus glacialis) (Gray and Gray 2006).

1.3.5 Climate

The study sites are located within the Borden Penninsula Plateau ecoregion, within the Northern Arctic ecozone (Ecological Stratification Working Group 1995). This is a high-arctic ecoclimate, where the mean summer and winter air temperatures are 1°C and -

25°C, respectively, the mean annual air temperature is approximately -13°C (Kirkwood et al. 1983). The landscape is covered by continuous permafrost and glaciers, with estimates of the permafrost thickness between 200-400 m, and an active layer of 30 to 50 cm

(Moorman 2003). Arctic lakes are generally ice-free between late-June and mid-August

(Hamilton et al. 2001), though local topoclimatic differences influence ice timing. The climate is dry polar desert, with mean annual precipitation ranges from 100-200 mm

(Environment Canada 1986). Wilmshurst et al. (2001) tracked Normalized Differential

Vegetation Index (NDVI) changes over a five year period and concluded that factors influencing NDVI, such as climate, were uniform across the park.

1.3.6 Freshwater ecosystems

Lakes, ponds and rivers cover 68.95 km2, or 0.31% of the total SNP land base, and graminoid wet meadows (wetlands) cover 961.62 km2, or 4.34% of the Park (Parks

Canada Agency, unpublished data). Most of the wetlands in the Park are located on the southwest plain of Bylot Island, where they represent 173 +/- 6 km2 or 11% of the total area of the south plain (Masse 1998). Surface water on the southwest plain of Bylot is in the form of meltwater channels, polygon lakes and shallow lakes and ponds. Qorbignaluk

Headland includes some small lakes and ponds situated on unstratified glacial drift, moraine and bedrock, including some englacial lakes. First- and second-order headwater streams feed into the lakes and ponds, and secondary or tertiary streams drain these lakes into coastal waters.

1.3.7 Humans on the landscape

The population in this ecoregion is approximately 1800 people, from Pond Inlet,

Arctic Bay and Nanisivik. Locals mainly from Pond Inlet use the south west plain of

Bylot Island for Eider egg harvesting, and as a transportation route for snow machines to popular ice fishing areas on the Borden Peninsula. The majority of Bylot Island is not only part of the National Park, but is also a Migratory Bird Sanctuary. The rugged terrain of Qorbignaluk Headland is likely rarely visited by local people, and there are no known activities occurring there.

Chapter 2: Methods

2.1 FIELD AND LABORATORY METHODS

2.1.1 Site selection

Bylot Island, the northern and eastern sections of Borden Peninsula, and the

Oliver Sound area near Pond Inlet, were examined for suitable sampling sites.

Appropriate study lakes for sediment coring were identified based on a depth requirement of 3-15 m and a surface area of 5-200 ha, using GIS, aerial photography and NTS maps

(1:50,000 for Bylot and 1:250,000 for the area near Oliver Sound). Two areas were found to have numerous lakes and ponds that fit these requirements: the south-west plain of

Bylot Island (BY) and the Qorbignaluk Headland (QB, between Oliver Sound and Tay

Sound).

Hierarchical watershed classification was used to target streams that represent multiple stream orders for these regions of the Park (i.e.: stream order was determined by the hierarchical ordering of streams based on the degree of branching). Sampling multiple stream orders and therefore a greater variety of hydrological conditions increases the variety of microhabitats that will be represented, and provides for expanded spatial extent of baseline data. Two sequences (from two different catchments) of three streams of increasing order were selected for BY (1st-, 2nd-, 3rd-order and 1st-, 2nd-, 4th-order), while for QB, a 1st order stream entering a lake (QB07), and a 2nd order stream flowing out from the same lake were selected. For sites that were sampled, lake surface area (SRFA) was measured from a 1:50,000 scale topographic map for BY sites and from aerial photographs (taken in 1958) for QB sites. Distance from the coast (DFC) and elevation

(ELEV) and stream order were determined using a 1:50,000 map for BY, and from a

1:250,000 scale map for QB sites.

2.1.2 Water chemistry

A Hydrolab datasonde-4a was used to measure temperature, pH, conductivity, redox potential and turbidity in situ. These data were collected for all stream and lake sites except BY04, QB10 and QB11 due to time constraints (therefore for 12 QB sites and 13 BY sites). The sonde was immersed in the surface water (0.5 to 1.0 m below surface), allowed to acclimate for > 2 minutes; readings taken for > 2 minutes were averaged. This was done at all sites except BYS03 for which the sonde was immersed for

30 seconds (total) and 7 readings were averaged. At the stream sites, the sonde was deployed at a riffle upstream of the sampling area.

Surface water samples were taken from 15 sites (one sample per site, except for two duplicate sites) for extensive chemical analyses at the National Lab for

Environmental Testing (NLET) in Burlington, Ontario (Table 1). Sampling was done following the Northern Environmental Monitoring and Assessment Network (EMAN-

North 2005) protocols. Grab samples (2 x 1000-mL) were taken after sample bottle conditioning. Samples were collected from the helicopter floats (for lakes) or by wading to the thalweg of the stream, and stored immediately at 4°C in a dark cooler with ice packs.

Two replicate samples (one from QB10 named QB14 and one from BY14 named

BY32) and one field blank (named QB16) containing de-ionized laboratory water were submitted to NLET for control and assurance of good quality data. Samples were prepared in the field for NLET through filtration and addition of preservatives (where

required) within 12 to 48 hours of sampling. Sample water was filtered through 0.45-μm cellulose-acetate membrane filters for dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), total dissolved nitrogen (or Total Nitrogen Filtered – TN-F) and total dissolved phosphorus (or Total Phosphorus Filtered – TP-F). For total particulate phosphorus (TP-PART) samples, 0.45-μm cellulose-acetate membrane filters which had filtered 250 mL (minimum) of sample water were transferred to separate glass bottles each containing 1.0 mL H2SO4 (30%) preservative. For unfiltered total phosphorus (TP-

U), 50 mL of unfiltered sample water was added to glass bottles containing 0.5 mL of

H2SO4 (30%) preservative.

For chlorophyll-a sample collection, a minimum of 750 mL of sample water was filtered through Whatman 4.7-cm diameter GF/C glass-fibre filters, and 0.1 to 0.2 mL of magnesium carbonate suspension (1% w/v) was added to filter paper to fully cover the paper. Filter papers were wrapped in aluminum foil and stored in the freezer until shipping to NLET for measurement of uncorrected chlorophyll (CHL-a) and corrected for phaeophytin (the detrital portion of chlorophyll cells; CHL-a-COR) (Hauer and Lamberti

2006).

Unfiltered water was used for analysis of the metals: silver (Ag), aluminum (Al), arsenic (As), boron (B), barium (Ba), beryllium (Be), bismuth (Bi), cadmium (Cd), cerium (Ce), cobalt (Co), chromium (Cr), cesium (Cs), copper (Cu), iron (Fe), gallium

(Ga), lanthanum (La), lithium (Li), manganese (Mn), molybdenum (Mo), niobium (Nb), nickel (Ni), lead (Pb), platinum (Pt), rubidium (Rb), antimony (Sb), selenium (Se), tin

(Sn), strontium (Sr), thallium (Tl), uranium (U), vanadium (V), tungsten (W), yttrium (Y) and zinc (Zn); major ions: chloride (Cl), sulphate (SO4), fluoride (F), calcium (Ca),

magnesium (Mg), sodium (Na), potassium (K) and reactive silica (SiO2); and unfiltered nutrients: soluble reactive phosphorous (SRP-U), nitrate-nitrite (NO3NO2), ammonia

(NH3) and total nitrogen (TN-U). The physical parameters total alkalinity (ALKCaCO3), specific conductivity (SPCOND), pH, true colour (COL) and turbidity (TURB) were also measured at NLET. Samples were taken between July 26th and July 31st 2008 and were submitted to NLET on August 5th 2008.

2.1.3 Lake coring

Sediment cores were collected from 16 lakes (6 for BY and 10 for QB; Appendix

A). At each lake, a sediment core up to 50 cm long, and 3.8 cm in diameter, was taken using a Glew gravity corer (Glew 1991), which was deployed from the floats of a helicopter which landed on approximately the deepest point of each lake, as determined with an electronic depth sounder. Duplicate cores were taken for two QB lakes (QB04 and QB12) and for one BY lake (BY14). Core lengths were measured immediately after collection. Sediment cores were extruded using a custom-built extruder (Glew 1988) within 48 hours of sampling. Cores were sub-sampled at 0.5-cm intervals, except for

QB03 and QB04-1, which were sub-sampled at 1.0-cm intervals. All sediments were stored in sterile, tightly sealed plastic bags at 4°C in the dark.

2.1.4 Modern Diatom Sampling

Surface samples were collected from all sites for analysis of the modern diatom assemblages. For all lakes where a sediment core was retrieved, the sediment core-top was used for analysis of modern diatom assemblages. For lakes QB08, BY04 and BY10, a core could not be retrieved so a surface grab sample was taken using an Ekman dredge.

For lake QB05 an epilithic sample was used, taken from the littoral zone near shore (no

core-top available). Epilithic samples were taken for all 8 stream sites by brushing a rock sample with a toothbrush for approximately 1 minute. All sediment samples were stored in sterile, tightly sealed plastic bags in the dark at 4°C upon collection.

2.1.5 Benthic invertebrate sampling

The sites sampled are minimally disturbed by human influence and will provide baseline data so that Reference Condition Approach (RCA) biomonitoring can be used in future ecosystem monitoring initiatives (Rosenberg et al. 1999). Benthic aquatic insects were sampled at 8 stream sites in total (6 for BY, 2 for QB). Samples were taken according to the Canadian Biomonitoring and Assessment Network (CABIN) protocols

(Rosenberg et al. 1999, Reynoldson et al. 2007), the national standard protocol used by

Environment Canada, but with specific tailoring for some procedures given Sirmilik’s high latitude. Table 2 summarizes key aspects of CABIN sampling.

The stream reaches that were identified on maps and airphotos during pre-field planning were verified upon arrival and site coordinates were taken using GPS. The length of the study reaches was set at six times the bankfull width, and always included a riffle. Photographs were taken of the field sheet (showing site number), of upstream, downstream and cross-reach views, of submerged and non-submerged substrates, and where possible, an aerial view from the helicopter. Habitat types present were categorized as: riffle, rapids, straight run or pool/back eddy. Canopy coverage was 0% in all cases given the high-arctic tundra environment. Macrophyte coverage was estimated based on a categorical scale (0%, 1-25%, 26-50%, 51-75%, or 76-100%), including emergent, submerged and floating plants. General riparian vegetation types present were identified

(grasses, shrubs, herbaceous plants, mosses and lichens) and the dominant vegetation

type was identified. Periphyton coverage on submerged rocks was categorized on a scale from 1 (< 0.5 mm thick) to 5 (> 2 mm thick).

Benthic invertebrates were then sampled with a kicknet using the kick-and-sweep method, in a downstream to upstream zig-zag pattern across the stream width for 3 minutes (except QBS02 -400µm and -200µm, sampled for 2.5 minutes and 1 minute, respectively). Kicknets with 200-µm and 400-µm mesh sizes were used, to test appropriateness for high latitude sites of the 400-μm mesh size specified by the CABIN protocol, given that individual organisms may be smaller at northern sites (Sweetman

2008). One sample for each mesh size was taken from all sites, except for BYS04 where only a 400-μm sample was taken due to time and weather constraints. Kicknet samples were transferred to 125-mL bottles and were stored at 4°C in the dark until they could be preserved in 70% ethanol within 12 hours (sites BYS04, BYS05, BYS06, QBS01 and

QBS02) or 36 hours (sites BYS01, BYS02, BYS03) of collection.

Channel and streambed measurements were taken, including substrate composition (dominant, second most dominant and surrounding material), slope of the reach, a cross-sectional profile and flow velocity (using float method).

2.1.6 Benthic invertebrate sorting and identification

Invertebrates (preserved in 70% ethanol) were picked from each sample using an

Olympus SZ61 stereomicroscope at 8x magnification. Whole sub-samples of 1-2 mL were picked through to reach 200 organisms. For samples BYS03-400, BYS04-400 and

BYS06-400, the entire sample was picked and still did not reach the 200-invertebrate minimum specified by CABIN protocols (n=31, 56 and 138, respectively). A minimum sorting efficiency of 95% was verified by re-picking the sorted material for one sample

(one in ten as specified by CABIN). Invertebrates were identified to family level or greater for insects and to sub-class or order for non-insects (as specified by CABIN), enumerated, and separated into glass scintillation vials with 70% ethanol for further taxonomic analysis. Nematodes were counted and picked but are not included in the analysis as they are not considered part of the macroinvertebrate community in the

CABIN standard (Reynoldson et al. 2006). Identifications followed Merritt and Cummins

(1996).

2.1.7 Diatom enumeration

Surface and core sediments were prepared for diatom enumeration following standard methods (Battarbee et al. 2001). 5 mL of HCl (10%) was added to 0.5-mL sediment samples to test for carbonates. No reaction was present in any samples, so 15 mL of distilled water was added to each sample and left for 24 hours. Samples were centrifuged at 2000 rpm for 5 minutes, rinsed with distilled water and aspirated, repeating this four times. To each sample 15 mL of a 50:50 molar ratio of sulphuric (H2SO4) and nitric

(HNO3) acids was added and left for 24 hours. Samples were placed in a hot water bath

(90°C) for 2 hours to complete any chemical reaction. Samples were rinsed using the above procedure until neutrality was achieved (eight or nine times for all samples). Serial diatom concentrations were dried on coverslips and mounted permanently on glass slides using Naphrax® mounting medium (refractive index 1.74). Modern (surface) diatom assemblages were enumerated for 11 QB lakes and 2 QB stream sites, 8 BY lakes and 4

BY stream sites (25 sites in total). Fossil diatom assemblages were enumerated for one core, BY14-2.

A minimum of 500 valves per sample were identified and counted at 1000x magnification using a Zeiss Axio Imager A1 light microscope with DIC optics under oil immersion. AxioVision 4.6.3 software was used to catalogue specimen photographs for most taxonomic classifications. Taxa were identified according to Krammer & Lange-

Bertalot (1991a, 1991b, 1999a, 1999b), Antoniades et al. (2008), Patrick and Reimer

(1994), Fallu et al. (2000), Cumming et al. (1995) and Lavoie et al. (2008).

2.1.8 Radiometric dating

Sediments from three cores were prepared and submitted to Flett Research Inc.

(Winnipeg, Manitoba) for dating using activity of the 210Pb radio-isotope. Bulk density was measured 67, 62, and 328 days after core collection for BY14-2, QB06, and QB15 cores, respectively. Measured volumes of sediments from the top 15 cm (QB06 and

BY14-2) or top 10 cm (QB15) were weighed at 0.5-cm increments, and dried to constant weight at 60°C. Bulk density (δ) was calculated as dry weight (g) /wet volume (mL). For each sample a minimum of 0.5 g of dry sediment was ground to a fine powder using a mortar and pestle (wiped clean between samples) and transferred into plastic vials for shipment to the lab (Schelske et al. 1994, Appleby 2001).

Cores QB15 and BY14-2 were also dated using 14C radio-isotope methods. Plant remains, moss and invertebrate exoskeletons were handpicked under a stereomicroscope after sediments were rinsed with distilled water through an 88-μm sieve. These four samples were shipped to Beta Analytic Inc. (Miami, Florida) on July 20th, 2009 for dating using Accelerator Mass Spectrometry (AMS).

2.1.9 Loss-on-ignition (LOI)

The mass lost from dried sediments of known weight after combustion at 550°C is used to assess the percent organic matter in a sample (Heiri et al. 2001). At a temperature of

950°C, the carbonate in the sample is combusted, and the change in weight can be considered a proxy for carbonate content (Heiri et al. 2001). LOI550 can be a proxy for biological production in the lake or surrounding watershed (Willemse and Törnqvist

1999). Sediments from core BY14-2 and all lake core-top samples were analysed for percentage dry weight lost at 105°C (DW105), wet density (WD), LOI550 and LOI950. A surface grab sample was also available for QB05 and was included in the analysis.

2.2 STATISTICAL ANALYSES

2.2.1 Treatment of the water chemistry and site data and preliminary analyses

For the sites where duplicate samples were taken (QB10 and BY14), averages of the duplicates were calculated for each water quality variable from the laboratory analytical results. The field blank (named QB16) was used only for the laboratory's quality control, and was removed before any statistical analyses. Values below detection limit were replaced with the median between detection limit and zero. Variables where >

45% of observations were below the detection limit were removed from the analysis

(NH3 and F, and the total metals Ag, Bi, Cs, Li, Pt, Se, Sn and W).

In situ data were used for surface water temperature, pH, specific conductivity, turbidity and ORP, except for sites QB10 and QB11 where no in situ readings were taken.

For these two sites, laboratory chemical analyses of pH and specific conductivity were used, and 'nearest neighbour' in situ values were used for ORP (according to specific

conductivity, r = 0.7331) and turbidity (according to total nitrogen-filtered, r = 0.7105).

For example, the QB10 ORP and turbidity values were determined by comparing sites with specific conductivity and total nitrogen-filtered readings closest to that of QB10, and using those sites' ORP and turbidity values. Surface water temperatures of QB10 and

QB11 were inferred based on comparison with surface area and depth of other lakes.

Differences between the BY and QB regions were assessed using two-way t-tests assuming unequal variances, with hypothesized mean differences of zero, for testing 33 water chemistry and site variables (26 nutrient, physical and ion variables in Table 1, water hardness, and for lakes: LOI550, elevation, distance from coast, Secchi depth, lake depth and surface area). Hardness of the water is determined by summing calcium and magnesium (McNeely et al. 1979).

An assumption of the multivariate statistical analyses used here is that samples are from a normally distributed population. Data were tested for normality in three ways: the

Shapiro-Wilk test, the student-t test and visual examination of histograms. Variables that were close to normal and did not require transformation were: elevation, Secchi depth,

ORP, pH, temperature, colour, SiO2-U, SRP-U, and the total metals Al, Cd, Ce, Co, Cr,

Cu, La, Rb, Sb and V. DOC was square-root-transformed and all other variables were log-transformed to make the distribution closer to normal.

The metals concentration data included 68 variables (34 metals, each having a dissolved and a total fraction). The total metal concentrations were used in the statistical analyses, which include dissolved and particulate fractions. The total metals Nb and Ni could not be normalized and were removed, therefore leaving 24 metals in the analysis:

Al, As, B, Ba, Be, Cd, Ce, Co, Cr, Cu, Fe, Ga, La, Mn, Mo, Pb, Rb, Sb, Sr, Tl, U, V, Y and Zn.

2.2.2 Multivariate analysis of water chemistry data

Multivariate statistical analyses were performed using the statistical software packages R (R Development Core Team 2008), C2 (Juggins 2005), Sigmaplot 11.0

(Systat Software Inc. 2008), Biplot for Excel (Smith and Lipkovich 2002) and PC-ORD

(McCune and Mefford 1999). Principal Components Analysis (PCA), a correlation-based method, was performed to describe relationships between variables and sites. PCAs require more observations than variables, and because this data set has more predictor variables than samples, techniques were needed to reduce the number of variables. A

Pearson Product Moment Correlation matrix was calculated for the transformed nutrient and physical variables data to identify and remove highly correlated (and therefore redundant) variables. Specific conductivity was highly correlated (P < 0.01) to 18 other variables, including, but not limited to alkalinity, colour, and the major ions Cl, SO4, Ca,

Mg, Na and K. Since these 8 variables are summarized by specific conductivity, they were not included in the PCA. Chlorophyll-a-corrected was also removed as it was highly correlated with Chlorophyll-a-uncorrected (r = 0.98, P < 0.01). The unfiltered portions of nitrogen and phosphorous (TN-U and TP-U) were retained for the analysis instead of filtered portions, as these include both the dissolved and particulate forms. Turbidity was also removed, as this was highly correlated with SRP, TN-U and TP-U (r = 0.72, 0.73 and 0.72, respectively, P < 0.01). Depth and surface area were not used in this PCA as they are only applicable to the lake and pond sites. The following 14 variables were used in the water chemistry PCA: ELEV, DFC, NO3NO2, SPCOND, ORP, pH, TEMP, CHL-a

(uncorrected), DOC, DIC, SiO2, SRP, TN-U and TP-U. Data were standardized for the

PCA by centering and scaling each variable.

To examine all the variables in the physical, nutrient and ion data set, and to further describe site differences, a cluster analysis was performed using 26 physical and nutrients variables for the 15 sites at which they were analysed (Table 5), as well as site elevation and distance from the coast (transformed data). A Q-based analysis based on dissimilarity between observations, or between differences in chemistry at each site, was used to group sites. Agglomerative hierarchical clustering methods were tested for combinations of dissimilarity measures (Euclidean, squared Euclidean, chord and Bray-

Curtis distances) and linkage methods (single, complete and average linkages).

Cophenetic correlation coefficients were calculated to indicate the strongest resulting clustering methods (Ferris 1969). A second cluster analysis was done to differentiate between sites according to total metal concentrations.

2.2.3 Treatment of diatom data and preliminary analyses

Diatom species richness was calculated for 25 surface samples using rarefaction, which standardized the richness by the minimum sampling effort of 517 valves. For core

BY14-2, rarefaction was calculated based on a minimum of 440 valves. Calculating species richness by rarefaction accounts for the different sampling efforts and allows for a more accurate comparison between sites or core increments. Rarefaction calculations were done using the program Rarefaction Calculator (Brzustowski 2009). Diatom community diversity was measured for the surface samples and core BY14-2 samples using Hill's N2 (Hill 1973), an index for the effective number of occurrences that is commonly used in paleolimnology (i.e.: Pienitz et al. 1995, Antoniades et al. 2009).

Differences in richness and diversity between the BY and QB regions were tested for significance using two-way t-tests assuming unequal variances, with hypothesized mean differences of zero.

For multivariate analyses, diatom raw counts were standardized to site totals by calculating the percent abundance of each species by site; for the core assemblages, counts were standardized as percent abundance by interval. Species with an abundance

≥1% in surface samples from any one site, or for at least one interval in the core were retained for the analysis (i.e.: Fallu et al. 2000), resulting in 88 taxa for the analysis of surface diatom assemblages. For the core BY14-2 this resulted in 35 species. For surface assemblages, percentage abundances were plotted for the most dominant species using

C2 (Juggins 2005). Dominance was calculated based on the highest relative abundance at any one site.

2.2.4 Analyses of modern diatom assemblages

A Correspondence Analyses (CA) was performed using C2 (Juggins 2005) to graphically depict the distribution of the 25 sites as a function of diatom assemblages.

Canonical correspondence analyses (CCA) were used to perform direct gradient analyses of the surface diatom dataset. The CCAs were performed using the PC-Ord and used

Hill's scaling to standardize axis scores (McCune and Mefford 1999). Axes were scaled to optimize representation of the sites, and the site scores are weighted mean scores based on species relative abundances. Twenty-four sites had associated in situ water chemistry data and surface diatom counts and were included in the CCA, with 87 diatom species.

First, to test the significance of the environmental variables for the distribution of the sites, a series of constrained CCAs were performed, using one environmental variable at a

time to constrain the first axis and then testing the significance of the first axis using a

Monte Carlo Permutation test (i.e.: Wilson and Gajewski 2002, Bunbury and Gajewski

2008, Antoniades et al. 2009). Seven in situ water chemistry variables were tested in this way, including elevation, distance from coast, specific conductivity, oxidation-reduction potential, pH, temperature and turbidity. Significant (P < 0.01, 999 permutations) variables were retained for the CCA.

2.2.5 Analyses of core BY14-2 fossil diatom assemblages

Diatom concentrations were calculated for the core as follows:

Diatom concentration (valves/cm3) = [(total valves counted x coverslip area (mm2)) / area counted (mm2)] x volume slurry (mL) volume of slurry on coverslip (mL) x actual volume sediment in slurry used (cm3)

Conversion to valves/g: (Valves/cm3) / bulk density of sediment (g / mL)

Species conc (spec/cm3) = [rarefaction x coverslip area (mm2)]/ volume of slurry (mL) Volume of slurry on coverslip (mL) x actual vol sediment in slurry used (cm3)

Conversion to species/g: (species/cm3) / bulk density of sediment (g / mL)

To examine differences in the fossil diatom assemblages through the core and to zone the stratigraphy, a cluster analysis was performed using the same methods described above (for water quality) to determine the most appropriate approach. Chord distance as the dissimilarity measure and average linkage resulted in the best cophenetic correlation

coefficient (0.856). Correspondence analysis was used to assess the similarity between fossil and modern samples.

2.2.6 Reconstruction of significant variables

The sites included in the diatom calibration sets were limited according to the availability of water chemistry data. Twenty-four sites have both in situ water chemistry data and diatom assemblage data, and 12 sites have both NLET water chemistry data and diatom assemblage data. Reconstructions were attempted using C2 (Juggins 2005) for statistically significant variables using weighted averaging partial least squares (WA-

PLS) techniques and bootstrap resampling cross-validation (i.e. Pienitz et al. 1995).

Species present at ≥1% abundance in at least one site were included in the models.

Transformed variables were used in the calibration models, and reconstructed values were reverse-transformed for plotting the reconstructions.

2.2.7 Invertebrate assemblages

Descriptive metrics were calculated for the invertebrate samples, including compositional metrics and the diversity indices according to CABIN protocols. Shannon-

Wiener diversity (H') was calculated using the following equation:

pi is the relative abundance of species, S is the species richness, and N is the total number of all individuals).

H' is larger for samples with greater species evenness or a higher number of unique species. Compositional metrics (total taxonomic richness, Ephemeroptera-

Plecoptera-Trichoptera (EPT) richness, % EPT, % Chironomidae, % non-insects and dominant taxa) were calculated, as specified in CABIN protocols (Reynoldson et al.

2006).

Chapter 3: Results

A total of 28 sites were sampled, of which 20 were lakes (8 on Bylot Island/BY sites and 12 on Qorbignaluk headland/QB sites) and 8 were streams (6 on BY; 2 on QB)

(Tables 1 and 2). The sites sampled cover a wide range of physical and chemical characteristics, and there is a clear distinction between the BY and QB regions in terms of habitat types, water chemistry, and diatom assemblages. This wide environmental gradient is ideal for capturing the species diversity of Sirmilik National Park (SNP) and for quantifying species-environment relationships. The sites used in this study represent the largest elevation gradient seen in a high-arctic limnological survey to date, ranging from 110 to 823 masl, and includes ponds, lakes and streams. The BY sites are situated between 110 and 256 masl, and are between 4.9 and 14.9 km from the coastline (Eclipse

Sound). All QB sites are at a higher elevation than BY sites, between 366 and 823 masl, and they are also generally closer to the coastline than the BY sites, ranging from 2.5 to

5.3 km to the nearest coast (Tay Sound, Paquet Bay and Stevenson Inlet) (Figure 1).

3.1 PHYSICAL CHARACTERISTICS OF LAKE AND POND SITES

Many of the sites are shallow lakes or ponds, and are likely not thermally stratified, other than perhaps only weakly and for short periods during sunny days (Table

3; Kalff 2002). These can be classified as cold polymictic lakes (multiple mixing), where the lakes are relatively shallow, very wind-exposed and are covered in ice except during the summer (Kalff 2002). The 4 deeper (>20 m) lakes on QB may stratify for periods of days to weeks during the summer.

The BY sites are generally smaller in surface area, ranging from 2.0 to 21.6 ha compared to the QB sites which range from 10.3 to 200.3 ha (t(12) = -4.72, P < 0.01).

Water clarity (as measured by Secchi disk) is generally higher at the QB sites, with an average visible depth of 5.6 m compared to 1.81 m for BY sites (t(12) = -6.73, P < 0.01;

Figure 2). Lake QB12 (depth = 20.8 m, SrfA =89.3 ha), however, has a notably shallow

Secchi depth of only 2.2 m, likely indicating the influence of the persistent ice in the catchment and associated materials transported by its meltwater. Secchi depth is indicative of suspended particles and algal growth (EMAN-North 2005).

During the processing of sediment cores and Ekman grab samples, moss was noted on the sediment surfaces from lakes QB02, QB04 QB08, QB10 BY04, BY14 and

BY16. The water sample from lake BY16 contained numerous cladocerans, and cladocera ephippia (resting eggs) were found in QB12, QB11, QB06, QB04, QB02 and

QB01.

3.2 PHYSICAL CHARACTERISTICS OF STREAM SITES

The streams sampled cover a broad range of characteristics and habitats (Table 4) and had a range of cross-sectional morphologies (Figure 3). On BY there were two 1st- order, two 2nd-order, one 3rd-order and one 4th-order stream sampled. On QB one 1st-order stream and one 2nd-order stream were sampled. BYS01, BYS04 and QBS01 (all 1st-order streams) and QBS02, a wide (wetted width = 32 m), shallow (max depth = 35 cm) 2nd- order stream flowing out of Lake QB07, were noted for the high amount of moss and lichen growing on the rocks in the stream. Four of the other QB lakes are upstream of

QB07, and therefore also of QBS02. BYS01 and BYS03 had highly embedded substrates, and the latter was also noted for having sandy and rocky banks with relatively sparse

vegetation (shrubs, moss and herbaceous) and water with a deep brownish-red hue.

BYS06 had a velocity and discharge that made sampling in the thalweg a safety concern.

This portion of the stream was therefore avoided during invertebrate and water chemistry sampling. The velocity measurements (taken using the float method) involve some error due to the float object unavoidably colliding with rocks and the stream bottom. The velocity data shown in Table 4 should therefore be interpreted with caution.

3.3 WATER CHEMISTRY

The lakes, ponds and streams selected for water sampling represent a diverse range of water chemistries (Tables 3 and 4; Appendix A). Surface water temperatures were measured in situ with the Hydrolab sonde, and although these measurements are not likely dependable for characterizing average summer water temperatures, they give a useful comparison between sites since sampling was conducted in a short time period (6 days). The BY sites were on average 2.19 °C warmer than the QB sites (t(18) = 7.26, P <

0.01; Appendix A), which reflects the significant differences in depth (t(11) = -4.20, P <

0.01) and surface area (t(12) = -4.72, P < 0.01) of the lakes of these two regions, and potentially the differences in elevations (t(12) = -8.34, P < 0.01). Pearson correlation coefficients (P < 0.01) show strong relationships as temperature decreases with increasing depth (-0.87), surface area (-0.63) and elevation (-0.84). The QB sites were slightly acidic with a mean pH of 6.2, where the BY sites were significantly (t(18) = 7.59,

P < 0.01) more alkaline with a mean pH of 7.8. The pH values reflect the carbonate nature of the underlying bedrock, where BY has limestone, siltstone, shale, dolomite and quartz sandstone, and the QB sites, on the other hand, are on crystalline shield rock with weak buffering capacity (Klassen 1993). These different geologies are not reflected

through differences in the alkalinity of the two regions (t(6) = 2.42, P > 0.05), but can be seen when comparing water hardness (t(6) = 2.59, P < 0.05). All sites sampled are considered very soft (McNeely et al. 1979) with hardness values < 30 mg/L.

Specific conductance is a good indication of the water's ionic composition and mineral concentrations, which explains the high correlation between it and the elements

Ca, Mg, Na and K (Pearson correlation coefficients of 0.98, 0.97, 0.95 and 0.87, respectively, P < 0.01; Table 7). Specific conductivity values were significantly higher at the BY sites compared to QB (means of 64.32 and 3.80 µS/cm, respectively; t(12) = 3.77,

P < 0.01), which reflects the geology and therefore the available ions of the sites. ORP was significantly lower at the QB sites compared to BY (t(15) = -4.62, P < 0.01), which is another indication of QB's reduced ion concentrations. Turbidity is a measure of the suspended particles including silt, clay, organic matter, plankton, and microscopic organisms, and is influenced by erosion, runoff, algal blooms and the flow of the water

(McNeely et al. 1979). It can affect the amount of light reaching photosynthesizing algae and plants, and therefore highly turbid waters can have a negative effect on overall biological productivity. Mean turbidity at the BY sites was approximately twice that of the mean for QB sites (t(13) = 2.58, P < 0.05). SiO2 concentrations were twice as high at

BY sites than at QB sites (2.83 and 1.16 mg/L, respectively; t(7) = 6.05, P < 0.01).

Chlorophyll-a was low at all sites compared to more southern latitudes (McNeely et al.

1979), and there was no significant difference between the BY and QB sites (t(6) = 1.55,

P > 0.05). Colour was notably higher at the BY sites than at QB, with means of 57.14 and

1.93 Pt-Co, respectively (t(6) = 7.93, P < 0.01). This reflects the more productive vegetation at the BY sites, as colour is derived from organic sources such as algae,

protozoa, and products of decaying vegetation, as well as minerals such as iron and manganese (McNeely et al. 1979). Means of all other nutrients and ions were higher at the BY sites than at QB (P < 0.01: DOC, SRP, NH3, TN-F, TN-U, TP-F, TP-U, SiO2, K;

P < 0.05: NO3NO2, Cl, Ca, Mg, Na), which may be attributed to the differences in geology and vegetation of the two regions.

3.4 LOSS-ON-IGNITION (LOI)

LOI showed that % organic matter content in lake and pond surface sediments at

QB sites was lower than that of BY sites (t(6) = 5.27, P < 0.01; Figure 4). LOI950 is negative for sites QB01, QB02, BY01, BY16, BY17 and BY18, due to measurement error associated with low values of LOI950. Absorbance of atmospheric moisture content may have caused samples to gain mass after the 950°C burn. LOI950 is considered to be zero at those sites, which is expected based on the underlying crystalline bedrock. BY15 is the lake showing the highest LOI950, a sign of the underlying carbonate geology. BY16 has the highest LOI550 value, implying the greatest production of organic matter, followed by BY18 and BY15. LOI550 values for QB were highest for QB04 and QB07 (in that order), two of the shallower lakes of that region.

3.5 MULTIVARIATE ANALYSES OF WATER QUALITY DATA

Pearson Product Moment Correlation analysis of the water chemistry data show that many variables are significantly correlated (Table 7). A principal components analysis (PCA; Figure 5) was used to visualize patterns in water chemistry and physical variables across the sites sampled. The first two axes together explain 83.3% of the variation in the dataset. Eigenvalues (λ) of axis 1 and 2 were 0.636 and 0.197, respectively. Axes 3 and 4 explained small amounts of the variation (λ = 0.054 and 0.035,

respectively) and were not considered further (Figure 6). PCA results and variable loadings onto the first two axes are shown in Tables 6 and 7.

There is a strong difference between the QB sites and the BY sites, as they are widely spread along PCA axis 1. PCA axis 2 has a much greater control on BY sites than on the QB sites, as the BY streams are distinctly separated from the lakes, and the QB sites are gathered closer to the axis mean. Axis 1 is strongly related to the nutrients TN-

U, SRP-U and DOC, as well as elevation and temperature. Axis 1 reflects the higher nutrient concentrations of BY sites in relation to QB, as well as the differences in the elevations and temperatures, as higher altitudes and the larger and colder lakes are represented as negative loadings on PCA axis 1. Surface temperature is indicative of the depths of the lake sites, as the shallower BY lakes and streams are more readily warmed by solar radiation. Other factors affecting component 1 include pH, SiO2 and conductivity, with the alkaline BY sites (also higher conductance and SiO2 concentrations) on the positive end of the axis and the acidic QB sites (lower in ion and

SiO2 concentrations) on the negative end. Lakes QB07 and QB10 are highest on axis 1 of the QB sites partially because of their shallow depths and warmer surface water temperatures. The BY streams have higher DIC than the lakes, which in turn are higher than the QB sites, which explains their axes scores in relation to this vector.

Variables important along Component 2 include DFC, Chl-a, and to a lesser extent, NO3NO2. BY16, BY15 and BY14 have the highest Chl-a concentrations of all the sites (in decreasing order), indicating higher amounts of photosynthetic plants and algae.

These higher Chl-a concentrations are also an indication of the higher productivity of

these ponds and surrounding vegetation, and correspond with the results from the LOI analysis.

The stream QBS01 has the highest NO3NO2 concentration of all the sites, and this is shown by its alignment with that vector which distinguishes it from the other QB sites.

Conversely, the sites BY14, BY15 and BY16 are plotted at the opposite end of this vector with the lowest NO3NO2 concentrations. Stream BYS06 is strongly affected along component 2 due to its high concentrations of NO3NO2, at 10 times that of the other BY sites, as well as its close proximity to the coast; the first and third most important variables for axis 2. BYS06 is also relatively high in DIC, SiO2, specific conductivity and pH, which are strong factors influencing PCA axis 1.

A cluster analysis resulted in four main groups of sites (Figure 7): Group 1)

QBS01, QBS02, QB07 and QB10; Group 2) QB06, QB11, QB01 and QB12; Group 3)

BYS03 and BYS06; Group 4) BY16, BY14, BY15, BYS04 and BYS05. The dendrogram was calculated using chord distances and average linking methods (Oksanen

2008), which had the highest cophenetic correlation coefficient of 0.871. The BY and QB sites were differentiated from each other early in the division, indicating a clear distinction due to pronounced differences with respect to most variables. The QB sites were split into two groups (named Groups 1 and 2) according to their temperatures, elevations and concentrations of SiO2. Group 1 includes the warmer, lower-altitude lakes and streams, and Group 2 the deeper, colder higher elevation lakes. Interestingly, the smaller streams BYS04 and BYS05 were grouped with the BY lakes instead of with the other, larger, BY streams that were separated out of this group.

3.6 METALS CONCENTRATIONS

Metal concentrations of all sites are within the ranges of natural fresh water

(McNeely, et al. 1979), and, more specifically, are within the ranges found in other Arctic limnological studies (i.e.: Hamilton et al. 2001, Lim et al. 2001, Keatley 2007).

Concentrations of many metals were below detection limit for > 45% of the analysed sites, including Ag, Bi, Cs, Li, Pt, Se, Sn and W. This is the case with many other Arctic freshwater studies (i.e.: Hamilton et al. 2001, Lim et al. 2001, Michelutti et al. 2002b,

Lim and Douglas 2003, Antoniades 2004), and these variables were removed from the analysis and are not discussed further. The remaining metal concentrations are summarized in Table 6.

A cluster analysis of the total metals concentrations (Figure 8), using chord distances and average linkages resulted in the highest cophenetic correlation coefficient of 0.964. There were two main groupings of the sites, and BYS05 and QB11 were outliers not belonging to either group. The groups were, 1) QBS01, QBS02, BYS03,

BYS06, BY12, BY14 and BY16, and 2) QB07, QB06, QB01, QB10, BY15 and BYS04.

Group 1 contains four out of the five streams analysed, as well as the three BY ponds.

Group 2 contains the QB lakes and BYS04, a small 1st-order stream.

3.7 INVERTEBRATE ASSEMBLAGES

Invertebrate assemblages were generally overwhelmingly dominated by

Chironomidae and Oligochaeta; other taxonomic groups present include Collembola,

Hydrachnida, Plecoptera, Simuliidae, Empididae, Tipulidae, Tabanidae and Nematoda

(Tables 10 and 11). Of the EPT ecological group (includes the orders Ephemeroptera,

Plecoptera and Trichoptera) only Plecoptera were found. Taxonomic richness (family-

level, or above family-level for non-insects) ranged from 3 to 7, and EPT richness

(family-level) was low (present at 2 of the 7 sites; 3rd-order BYS03 and 4th-order

BYS06). Shannon-Wiener diversity indices were low for all sites, but were the highest for

BYS06 and QBS01.

Although only 1 sample from QB was analyzed, it had four chironomid species not found at any of the BY sites, as well as a relatively high number of simuliids. No justification was found for sampling with a 200-μm mesh in place of the 400-μm mesh called for in standard protocols (CABIN) used in lower latitudes; comparison of % abundances of Collembola and Acari (the smallest species) and species richness of the two sizes all failed Chi-squared significance tests (P > 0.05), indicating no significant differences between invertebrate assemblages collected using mesh sizes of 200 μm vs

400 μm. Although there were higher counts for Collembola and Acari in BYS05-200 vs.

BYS05-400, the entire sample for the 400-μm net sample was not counted, resulting in a likely underestimate of diversity for that sample. Additionally, three Acari individuals were counted from BYS02-400, compared to none from the BYS02-200, which does not support the need for a finer mesh for capturing these small specimens.

3.8 DIATOM ASSEMBLAGES

Modern diatom assemblages were analysed for 25 sites in total (6 streams, 4 ponds and 15 lakes). A total of 282 diatom taxa were identified, including 29 species that were denoted with "cf", as "akin to" known species (Appendix B). Figure 9 shows the most dominant and most abundant species. Both regions were dominated by small, benthic taxa, primarily from the genera Achnanthes, Eunotia, Navicula, Nitzschia and

Pinnularia, however the assemblages show large differences between QB sites, BY

streams and BY ponds, and reflect the diversity of the freshwater habitats and chemistries of the two regions. Species richness calculated as rarefaction was not significantly different between the two regions (t(16) = -0.50, P > 0.05), despite mean diatom species richness as counts being greater for QB sites compared to BY (mean richness = 37.7 and

30.6 species, respectively; t(37) = -5.41, P <0.01). Hill's diversity (N2) was not significantly different between the two regions (t(20) = -1.58, P > 0.05; Appendix C).

Benthic Fragilarioid species Staurosirella pinnata and Staurosira venter are dominant species at the BY sites and are rare (<1% abundance) at the QB sites, where

Achnanthes species and planktonic Aulacoseira lirata and A. alpigena are abundant

(Figure 9). The QB lakes have a more diverse presence of Eunotia species compared to

BY, which is expected given their ultra-oligotrophic (Krammer and Lange-Bertalot

1991a) and acidic (Anderson et al. 1986, Antoniades 2004) status. Correspondence analysis (CA) of the sites based on diatom assemblages shows large differences between

BY sites and QB sites, and also among the BY sites (Figure 10). The first CA axis shows high correlation between species scores and site scores (λ = 0.840), with the QB sites on the negative end and BY sites on the positive end of the axis. Axis 2 (λ = 0.738) and axis

3 (λ = 0.534) are less well correlated but further show how the sites differ. The first three axes of the CA of the surface diatom assemblages explained 39.1% of the variance in the species data. Of the species with high Hill's diversity indices (N2 > 5), those with the highest axis 1 scores were Staurosirella pinnata and Achnanthidium minutissimum. Taxa with the most negative first-axis scores were Psammothidium marginulatum, Aulacoseira lirata and A. alpigena. The highest species scores on the second axis were Achnanthidium minutissimum, Nitzschia perminuta and Fragilaria capucina form 7 (Lavoie). The most

negative on the second axis were Navicula seminulum, Staurosirella pinnata, Cavinula pseudoscutiformis and Psammothidium subatomoides.

The diatom taxa present at ≥1% abundance in at least one site are shown in the

CCA plots (Figure 11). Six of the seven environmental variables tested were found to be significantly (P < 0.01; Table 12) correlated to axis 1, and were retained for the CCA, including elevation, distance from the coast, pH, ORP, temperature and specific conductivity (turbidity was not significant). CCA results confirm the relationships between sites and changes in the environmental variables that were found in the PCA of the water chemistry (Figure 5). Diatom communities of the QB sites are characterized by species of Eunotia (mainly E. meisteri, E. exigua, E. paludosa, E. muscicola var perminuta), diatoms that are typical of oligotrophic, acidic water (Antoniades et al.

2008). QB sites are also associated with the benthic diatoms Psammothidium marginulatum, Achnanthes helvetica and A. kriegeri. The QB sites are situated at the positive end of axis 1, showing little similarity with the BY sites, which are more dispersed over the length of axis 2, and on the negative side of axis 1 (Figure 11).

The total variation of species dispersion that could be explained by the constraining variables used in the CCA is 5.4797 (sum of eigenvalues). CCA axes 1 and 2 together explain 25.1% of this total variation (14.7% and 10.4%, respectively). The first

CCA axis (λ = 0.805) was most strongly correlated (interset correlations; ter Braak 1986) with specific conductivity (-0.93), pH (-0.90) and elevation (0.84). The second axis (λ =

0.57) was most strongly correlated with DFC (0.52). CCA summary results are in Tables

11 and 12.

3.9 CHRONOLOGY OF CORE BY14-2

Sediment dating with 210Pb radio-isotope methods, using a Constant Rate of

Supply model, resulted in the earliest reliable date of 1928 AD at a depth of 10.8 cm. The

CRS model predicted a reasonably constant sediment accumulation rate of 0.0205 g/cm2/yr for the upper sections of the core (0.5 - 4.25 cm) depth. Supported 210Pb activity was reached well before most depths of the section submitted for dating (~ 7 cm) at

0.0065 Bq g-1(Figure 12). This level of activity is low, but within the range found at other

Arctic lakes (i.e.: Michelutti et al. 2008; Peros and Gajewski 2009). Two samples from core BY14-2 were also dated using 14C radio-isotope methods, however, the dates are unrealistically old (Table 15) and require correction. It is likely that the error arises from re-assimilated 14C-depleted ancient carbonate from surficial or bedrock materials. To correct for this hardwater effect, the intercept of these two calibrated dates, which falls at

4070 cal yr BP for a depth of 0 cm, was subtracted from each calibrated age (Peros and

Gajewski 2009). Although errors of this kind are not uncommon in the Arctic (Peros and

Gajewski 2009) these corrected dates should be interpreted carefully. The corrected, calibrated median 14C dates were used with the 210Pb dates for the age-depth profile of

BY14-2 (Figure 13).

3.10 DOWN-CORE DIATOM ASSEMBLAGES OF CORE BY14-2

The core BY14-2 was 24.4 cm in length, and although there were no colour changes in the stratigraphy, the consistency changed at a depth of 13.0 cm from finer, less compacted material to a stiffer composition through to the core bottom. Moss and organic material were recorded in the top 0.5 cm, and again at 2-3.5 cm depth. 20 sediment samples were examined for diatoms, at 1-cm intervals for the first 14 cm, and at 2-cm

intervals from 16 to 24 cm. The diatom biostratigraphy of core BY14-2 shows some changes in the assemblages from approximately 3500 years ago through to today (Figure

14; only most abundant taxa shown).

Correspondence analysis was used to zone the stratigraphic diagram. Three zones were delineated: Zone 1 from 13 to 24 cm (~1000 – 3500 yrs BP), Zone 2 from 8 to 12 cm (~1945 – 1250 AD) and Zone 3 from 0 to 6 cm (~2008 – 1955 AD) (Figure 15). An overall trend of increasing Staurosira venter is seen from the oldest sections of the core, yet isolated periods of decreasing abundance appear at 16 cm and between 5 and 11 cm.

The planktonic species Aulacoseira alpigena, on the other hand, shows an overall decrease in abundance, with a minimum abundance in Zone 2 that coincides with the more recent drop in S. venter, at a depth of 8 cm (approx. 1945 AD; Figure 14).

Zone 2 (~1945 – 1250 AD) is characterized by small increases in Achnanthes curtissima, Aulacoseira lirata, Navicula seminulum, N. schmassmannii, and N. submolesta, while Hygroptera balfouriana, Nitzschia spp., Psammothidium marginulatum, and P. subatomoides decrease. At the beginning of Zone 3, post-1950 AD, many of the species that declined in Zone 2 regained their previous abundances (Figure

14). S. pinnata is at its greatest abundance at 3 cm (~1981 AD), in Zone 3.

The LOI analysis of BY14-2 core sediments is shown in Figure 14. Estimated percent organic matter remains relatively constant throughout the core (mean = 15.5%,

SD = 2.0). These values are similar to those recorded for small high-arctic lakes (i.e.:

Porinchu 2009). The only notable changes are the decrease at the end of Zone 1, at 13 cm, followed by a small peak (3 samples) between ~ 1930-1950 AD (11.0 - 12.5 cm) in the beginning of Zone 2. The carbonate in the core remains at <1.8% throughout the core.

3.11 DOWN-CORE ENVIRONMENTAL RECONSTRUCTIONS FOR CORE BY14-2

CCA of the modern diatom assemblages and the most extensive environmental dataset indicates that specific conductivity, ORP, pH and temperature are all significant in explaining diatom distributions (elevation and distance from the coast, while also significant, are not suitable for reconstructions). Of these variables, specific conductivity was found to explain the most variation of the first CCA axis, with λ = 0.766 (P = 0.001) in the series of single-variable CCAs. In the CCA where pH was the sole variable, λ =

0.707 (P = 0.001). These measures indicate that the influence of these two variables on axis 1 is significant, and that they account for a significant portion of the variation in the modern diatom data. A reconstruction of these variables from the fossil diatom data should therefore be possible. The proportions of the variance in the modern diatom assemblages explained by ORP and temperature are also significant (λ = 0.634 and 0.575, respectively, P = 0.003), and may also potentially be reconstructed.

The WA-PLS transfer function predicts modern pH of the study sites that closely approximates the measured values, as indicated by the relationship of observed versus inferred values (r2 = 0.866) and the low root-mean squared error (RMSE = 0.249). Thus diatom assemblages are a reasonable predictor of pH (Figure 16). Reconstruction of pH reveals little change through the core fossil record, ranging between 7.05 and 7.22 (which is within the error of 0.410 of the model – the root mean squared error of prediction or

RMSEP), with a slight trend toward increasing pH (Figure 17).

A transfer function of specific conductivity using WA-PLS performed well,

2 resulting in r = 0.938 and RMSE = 0.130 (Figure 18). The resulting reconstruction of

specific conductivity showed an overall change from 10.5 to 14 µS/cm, over the length of the core with a notable increase above 8 cm depth (~1945 AD) (Figure 19).

A transfer function of surface water temperature resulted in a good predictive

2 model using WA-PLS, resulting in r = 0.758, and RMSE = 0.839 °C (Figure 20). The reconstruction is shown in Figure 21. Temperatures reveal a gradual up-core increase of approximately 0.5°C, with a noticeable rise at 6 cm (~1955 AD).

Transfer functions and reconstructions were also developed using the water chemistry variables from NLET (Figure 22), only available for a more restricted dataset, using the WA-PLS technique for all models. The training set included 12 sites, and cross- validation could not be performed on a data set this small (Juggins 2005). Regressions of the observed versus inferred values, however, were strong (Figure 22). A noticeable trend in the reconstructions is the increasing values of temperature, Chl-a and all nutrient and ion concentrations (TN, SiO2, SRP, TP-U and DOC) since ~6 – 10 cm depth (~1935 –

1956 AD). At the same time, NO3NO2 and ORP generally decrease, and DIC shows no consistent trend. The reconstructions of pH, specific conductivity and temperature shown in Figures 17, 19 and 21 are more robust models as they include a greater number of sites in the training set (24 sites vs 12 sites for Figure 22).

Chapter 4: Discussion

4.1 SPECIES-ENVIRONMENT RELATIONSHIPS OF DIATOMS AND BENTHIC

INVERTEBRATES

This intensive study of 27 high-arctic sites for water chemistry and assemblages of modern diatoms and benthic invertebrates provides new data that will strengthen environmental modelling efforts for Arctic freshwater ecosystems. The sites cover a range of environmental characteristics and represent two of the major types of freshwater habitats in SNP. The local geology and vegetation play important roles in the characterization of water chemistry, which in turn influences the species assemblages that inhabit the lakes, ponds and streams. Differences in conductivity across the sites illustrate the role of geology, with higher values at the BY sites due to the weathering of bedrock and surficial materials. The more lush vegetation cover in the BY catchments provides a source of nutrients (phosphorous, nitrogen and carbon) for the lakes, ponds and streams, and attracts migrating waterfowl and other wildlife, which contribute faunal nutrient inputs. Thus, as expected, the BY sites with generally more abundant vegetation have higher nutrient concentrations than the QB sites.

The chemical differentiation of the sites was most strongly controlled by total nitrogen, which came out as the most significant variable according to the principal components analysis (Table 9) and underlines the important role of nutrients for these freshwater systems. Mean concentrations of TN-U were high at BY sites (0.519 mg/L) and low at QB sites (0.126 mg/L) when compared with other Arctic sites; mean TN reported by Hamilton et al. (2001) is 0.338 mg/L (TKN + PON + NO2NO3). The strong

negative correlation between total nitrogen and surface area for the present study (-0.96,

P < 0.01) shows the influence of lake size on dilution of nutrient concentrations.

Other measured variable concentrations (SiO2, SRP, DOC and DIC) were important in differentiating sites, which reflects the range of trophic levels under study.

Mean SiO2 concentrations of BY and QB (2.83 and 1.16 mg/L, respectively) were higher than values reported by Hamilton et al. (2001; 1.112 mg/L) and Lim et al. (2001; 0.79 mg/L). Mean SRP concentration of the BY sites (0.0017 mg/L) was comparable to other

Arctic studies (0.0017 mg/L (Hamilton et al. 2001); 0.0015 mg/L (Lim et al. 2001);

0.0028 mg/L (Keatley 2007)), whereas mean SRP for QB sites was lower (0.0007 mg/L).

TP-F was low (QB mean = 0.002 mg/L; BY mean = 0.006 mg/L) but within ranges of other studies of Arctic lakes and ponds (means from 0.0013 to 0.012 mg/L) (Hamilton et al. 2001, Lim et al. 2001, Michelutti et al. 2002b, Antoniades 2004). Mean DOC concentration of the QB sites (3.34 mg/L) was comparable to other Arctic studies, and the

BY sites had the second highest values reported for the Arctic islands (9.31 mg/L) compared with Melville Island (17.3 mg/L; Keatley 2007) Prince Patrick Island (6.7 mg/L; Antoniades et al. 2005), Bathurst Island (4.1 mg/L; Lim et al. 2001) and 204 other sites across the Arctic archipelago (3.93 mg/L; Hamilton et al. 2001).

The values of DOC reflect the allochthonous carbon inputs from terrestrial plants in the more vegetated catchments (all BY sites, QB07, QB10, and QB12), as runoff transports decaying plant matter into streams and lakes. DIC, on the other hand, was lower at both BY and QB (6.01 and 0.78 mg/L, respectively) compared to mean values of

21.7 and 20.1 mg/L ((Hamilton et al. 2001, Lim et al. 2001, respectively) found at other

Arctic sites. It should be noted that in the other studies cited, water samples were not

necessarily taken at the height of the growing season, as was done in the present study; thus, comparisons should not be considered absolute.

Total phosphorous-unfiltered (TP-U) of the BY sites (0.0105 mg/L) was comparable to other Arctic studies (Lim et al. 2001; 0.0127 mg/L and Hamilton et al.

2001; 0.012 mg/L) and QB was low (0.0034 mg/L). Following the classification of trophic state used by Wetzel (2001), based on unfiltered total phosphorous, BY14, BY15 and BY16 and BYS04 are mesotrophic (10-30 μg/L), sites BYS03, BYS05, BYS06 and

QB12 are oligo-mesotrophic (5-10 μg/L), and the remaining QB sites are oligotrophic (<

5 μg/L; QB01, QB06, QB07, QB10, QB11, QBS01 and QBS02). These trophic classifications are expected based on the greater vegetation cover in the catchments, shallower water and warmer water temperatures found at BY in contrast to the deeper, colder lakes and depauperate shorelines of QB. Furthermore, the concentrations of chlorophyll-a at the BY sites (0.95 μg/L) were higher than mean values found by

Hamilton et al. (0.55 μg/L; 2001), Lim et al. (0.8 μg/L; 2001) and Bouchard et al. (0.65

μg/L; 2004), and reflect the higher productivity of the relatively shallow, warm ponds with higher nutrient concentrations. The QB mean chlorophyll-a (0.20 μg/L) shows that the productivity of these sites is within the range of other Arctic sites.

The ordinations show that conductivity and pH are important environmental predictors for diatom species distributions in the sites studied here. These variables have been found elsewhere to be highly influential in explaining diatom assemblages

(i.e.:Battarbee et al. 2001). Specific conductivity is a summary of ion concentrations, and gives an indication of the interactions between the water and the watershed.

Conductivities of QB and BY sites (3.80 and 64.32 μS/cm, respectively) were low

compared with other Arctic freshwater studies (293.4 μS/cm (Hamilton et al. 2001);

160.4 μS/cm (Lim et al. 2001)), yet this variable explained the most variation in the ordinations between the sites. Concentrations of DIC, Cl, SO4, Ca, Mg, Na, K and SiO2 are summarized by conductivity, which has important implications for the flora and fauna as species may respond uniquely to varying concentrations of these major ions. Bouchard et al. (2004) report that major ions, DIC and conductivity explained the main gradient differentiating the 62 lakes studied across the whole of the Canadian Arctic archipelago.

2- The anion concentrations of the present study follow the pattern DIC > Cl- > SO4 ,

2- which is different from that of many other high-arctic sites where DIC > SO4 > Cl- has been reported (Hamilton et al. 2001, Lim et al. 2001, Michelutti et al. 2002a, Lim and

Douglas 2003). The higher relative concentration of Cl- here is attributed to the close proximity to the coast of all sites (< 15km) and the influence of sea spray (Keatley 2007,

Douglas and Smol 1994). Mean cation concentrations follow the pattern Ca2+ >Mg2+

>Na+ > K+, similar to that found in other Arctic locations including Melville (Keatley

2007), Victoria (Michelutti et al. 2002a), Devon (Lim and Douglas 2003), and Bathurst

(Lim et al. 2001) islands.

The modern diatom species assemblages reflect the chemical differences between the sites under study; the most noticeable patterns in species distribution are at the generic level. The QB sites contained larger numbers of small benthic Psammothidium and Achnanthes species such as P. helveticum, P. marginulatum, Achnanthidium minutissimum, Achnanthes curtissima and A. kriegeri. These taxa are common in waters of low nutrients and pH (Krammer and Lange-Bertalot 1991a, Krammer and Lange-

Bertalot 1991b, Antoniades et al. 2008). There were frequent occurrences and high

abundances of planktonic species Aulacoseira lirata, A. alpigena and Tabellaria flocculosa at the QB lakes, where the water is deep, clear, low in conductivity and nutrient-poor. A fourth planktonic species, Cyclotella stelligera was found in only one lake, QB15, at a relative abundance of 10.1%. This particular lake is deep (13 m), by far the largest in surface area (51% larger than the next largest lake, QB07), and was noted to be in a protected basin with a southerly exposure, meaning that thermal stratification is probable. High relative abundances of small, planktonic Cyclotella spp. have previously been linked to reduced ice cover and stronger stratification regimes (i.e.: Rühland et al.

2003). Furthermore, centric diatoms such as Cyclotella benefit from the wind-related turbulence that accompanies large surface areas, as this helps to keep them afloat (Moore

1979). The only lake dominated by planktonic species is QB04, which had the second lowest conductivity reading after QB06. QB04 is a moderately deep (10.8 m) lake open to wind that would have adequate mixing required by phytoplankton.

Species of Eunotia were high in relative abundance at the oligotrophic, acidic QB sites, including E. meisteri, E. glacialis, E. muscicola var. perminuta and E. exigua.

Eunotia are generally acidophilic (Anderson et al. 1986) and commonly associated with nutrient poor waters (Krammer and Lange-Bertalot 1991a).

The small, benthic Fragilarioid diatoms Staurosirella pinnata and Staurosira venter that dominate the BY sites are commonly the most abundant species in cold, nutrient-poor and circum-neutral to alkaline Arctic waters (Bouchard et al. 2004,

Antoniades et al. 2005, Finkelstein and Gajewski 2008). These two taxa are found in different abundances according to lake depth. In three of the four BY ponds (BY04,

BY18 and BY01) there is a marked absence of S. venter (0, 0 and 2.3%, respectively) and

dominance of S. pinnata (59, 65 and 85.5%, respectively), where in the lakes S. venter is dominant (49-80%) and S. pinnata is present at ~1-27%. The explanation for this apparent preference of the species for lakes versus ponds is most likely the differences in ion concentrations and temperatures that exist between the two, however the limited water chemistry data for BY ponds does not allow for further interpretation.

For the pond BY10, Fragilaria perminuta is the dominant Fragilarioid (12.6%), and Achnanthidium minutissimum is the dominant species overall (36%). F. perminuta is among the most negatively loaded species on the second CCA axis, which is also found in high abundance at BYS03 and BYS06. Diadesmis spp. were found in significant abundance (> 1%) in streams BYS01 and QBS02. These aerophilic taxa may indicate low or intermittent flow, or an ephemeral stream; these findings confirm these taxa as indicator species for streamflow consistency (Antoniades et al. 2009).

Mesotrophic sites (BY14, BY15 and BY16) that were on the high positive end of axis 2 in the CCA showed the highest abundances of Staurosira venter, Navicula seminulum and Hygroptera balfouriana. Moser (2004) related abundances of S. venter to the presence of calcareous bedrock and the resulting alkaline water, which holds true for the present study. Achnanthidium minutissimum was most abundant (> 2% relative abundance) at circumneutral to alkaline sites (pH = 6.93 to 8.38) with a wide range of conductivities (18.28 - 111.15 μS/cm); Antoniades (2008) found a slightly more alkaline

(pH = 7.6 - 8.6) preference with higher conductivities (60-290 μS/cm), but this might reflect the particular gradient sampled for that study. Nitzschia perminuta was most abundant in the BY streams, which had the highest conductivity measurements (64.9 to

161.9 μS/cm). This confirms the findings of Antoniades (2008), reporting a range of 47 to 386 μS/cm for N. perminuta.

Diatom assemblages are more similar between streams and lakes/ponds of the same region than they are between QB versus BY sites. This is due in part to the differences in environmental variables between the BY and QB regions, but hydrological connectivity between lakes and ponds and the streams that flow out of them is another factor. BYS01 is a 1st-order stream < 500 m downstream of lake BY01, and had a large proportion of S. pinnata (35%), where the other two BY streams had < 0.5% and 0%

(BYS03 and BYS06) of S. pinnata and were 3.5-4 km downstream of a lake or pond.

Achnanthidium minutissimum is a dominant species at BY sites (6-48% at streams, 1-36% at ponds and 0-1.4% at lakes) and is found at three QB sites (< 0.5% at QB15, 1% at

QB02 and 9.7% at QB08). QB08 has pH and specific conductivity values closest to that of the BY sites, which explains the high abundance of A. minutissimum. Antoniades et al.

(2009) found similar high abundances of A. minutissimum in a study of 42 streams from nine Arctic Islands, and suggest that its common occurrence is because it is an early colonizing species that can withstand short summer flow duration. Eunotia muscicola var. perminuta is present in QB streams in high abundances (33% and 8%), commonly in QB lakes (0.8-5% in 6 of 11 lakes) and it is absent from BY sites. This species plots among the highest on the first CCA axis, revealing its affinity for ultra-oligotrophic water.

Benthic invertebrate diversity was low across all sites sampled, which is a common finding in the high Arctic (Danks 1988). The strong dominance of

Chironomidae and Oligochaeta found here is also typical for high latitudes. Major differences between the sites are the dominant taxa, % non-insects and the presence of

individuals from the Ephemeroptera-Plecoptera-Trichoptera (EPT) ecological group.

BYS04 and BYS05 were the only sites dominated by Oligochaeta, and both were the only sites with pebble-sized substrate. All other sites had cobbles or boulders as the primary substrate and were dominated by Chironomidae (sites BYS01, BYS02, BYS03, BYS06 and QBS01). Schenková and Helešic (2006) report that aquatic oligochaete habitat is best

− explained by biochemical oxygen demand (BOD), nitrate ion (NO3 ) concentration and stream velocity. Decomposing organic matter is the food source of oligochaetes, and

− controls the BOD as well as produces NO3 , a requirement for diatom growth (Schenková and Helešic 2006). Due to the lack of reliable velocity data, a proxy for stream velocity can be used to compare stream sites. Substrate size is directly related to stream velocity, and the higher the velocity, the larger the material that can be moved by the current.

Streams with smaller particle sizes as the dominant substrate therefore indicate lower velocities.

Studies have found that freshwater midges (chironomids), identified to the species level, are good indicators of a number of environmental parameters, including pH, dissolved organic carbon, temperature, lake depth and surface area and vegetation types

(Barley et al. 2006). The taxonomic level of identification used in the present study

(generally to the Family level) does not allow for detailed interpretation, however a few chironomids were identified to sub-family and knowledge of their habitat requirements can be qualitatively discussed. Orthocladiinae (sub-family) are widespread at high latitudes, especially in oligotrophic waters (Hershey et al. 1995). They are generally burrowers (inhabiting tubes which they build), and their primary food source is either decomposing fine particulate organic matter from sediment surfaces and loose surface

films (collectors–gatherers; detritivores), or periphyton-attached algae and associated material (scrapers; herbivores) (Merritt and Cummins 1996). Corynoneura (Family

Orthocladiinae) are also collectors–gatherers, and sprawlers, meaning they inhabit substrate surfaces while keeping their respiratory organs free of silt. Diamesinae are both scrapers and collectors – gatherers (Merritt and Cummins 1996). Although Diamesinae can be burrowers or clingers (Merritt and Cummins 1996), no tube casings were found in the samples which supports the likelihood of clinger species. Tanytarsini are found in both erosional and depositional stream habitats, and either burrow into the sediments or cling to the substrate; they are generally collectors (filterers and gatherers) (Merritt and

Cummins 1996).

The chironomid dominance reveals a skewed trophic diversity toward collectors, and may include both detritivore and herbivore species. The absence of shredder-species is probably due to the lack of coarse plant detritus, as well as the life-history constraints imposed by the harsh climate (Hershey et al. 1995). The lack of predator species could be due to the fact that predators have longer life cycles than their prey and that they are limited by overwintering conditions (Hershey et al. 1995). Hershey et al. (1995) suggests that the role of simuliids in Arctic streams may be very important to the ecosystem function because other filter-feeder trophic groups are absent.

The information derived from the analysis of benthic invertebrates as specified by the national CABIN standard is useful, but is more widely applicable for numerical modelling and for monitoring when it is used in conjunction with another bioindicator such as diatoms. This is especially true for Arctic freshwater ecosystems, where invertebrate species diversity is low and diatom species diversity considerably higher.

4.2 RECOMMENDATIONS FOR AQUATIC BIOMONITORING USING THE CABIN STANDARD

A number of recommendations for optimal aquatic biomonitoring can be made resulting from this work. Of primary importance for improvement of the data is the taxonomic level to which identifications are made. The recommendations of King and

Richardson (2002) are supported here. These recommendations specify using non-

Chironomidae family-level data tiered with genus- or species-level Chironomidae data, and including individuals that are large and easily picked from whole samples (large-rare searches) that have been shown to strengthen invertebrate-inferred modelling of environmental variables (King and Richardson 2002).

A sub-sample size of 200 individuals (Rosenberg et al. 1999) was used instead of the 300 individual count recommended elsewhere (Reynoldson et al. 2006). Other studies have proven the acceptability of using a subsample size of 200 individuals (King and

Richardson 2002) for wetlands in more southerly locations, yet this remained to be tested for Arctic locations. The Marchant Box used for subsampling in CABIN protocols was not used here because of the potential for error involved – there was a high possibility that the smaller individuals (Acari and Collembola can be 0.5 mm) would get stuck in one of the many corners or creases of the device and be lost, or cause contamination of the next sample. Instead the tea-spoon method of subsampling, used in other benthic invertebrate sampling protocols (Jones et al. 2005) was used, which eliminated the potential problems with the Marchant Box.

The invertebrate sampling was not noticeably affected by the mesh size of the net

(200 vs 400 μm). Although one additional taxonomic group was found for both the

BYS05 and BYS06 200-μm net samples, this is attributed to the larger subsample sizes in

comparison with their 400-μm counterparts. The BYS02 samples resulted in three Acari individuals and one Collembola (the smallest of the fauna sampled) in the 400-μm net, where the 200-μm net had two Collembola, indicating that the mesh is retaining invertebrates of the same sizes and suggests that there is no basis for using 200-μm mesh over 400-μm.

The categorical data required by CABIN for habitat descriptions were created with more southerly locations in mind to reflect potential anthropogenic influences on a watershed. Canopy coverage will always be 0% in the high Arctic where prostrate vegetation is at most tens of centimetres tall. Collecting this data is simple and quick, yet is irrelevant for the health of high latitude streams where there is no canopy. The categories for riparian vegetation in CABIN are ferns/grasses, shrubs, deciduous trees and coniferous trees – again a reflection of latitudes below the treeline. Creating additional categories suitable for tundra (i.e.: herbaceous cover, mosses and lichens) would improve the distinction between the variety of habitats, thus improving knowledge of the site and potential relationships with species distributions.

Substrate composition measures used in the standard protocols employ three methods: visual dominant substrate size classification, substrate embeddedness, and 100 random rock measurements to determine dominant particle size. The 100 rock measurements were not done for this study owing to logistical and scheduling constraints.

The visual substrate classification gives a good estimate of the stream habitat and provides useful information for site comparisons. If future sampling efforts have the available resources, conducting the 100-rock measurements would provide additional accuracy for substrate descriptions.

Stream current velocity measurements were attempted using the float method; overall this method was not successful as the movement of the float was impeded by rocks in the shallow streams. It is recommended that future velocity measurements be taken using another approach such as a Sontek, Price meter, or velocity head rod.

The in situ water chemistry measurements were of great importance to quantifying water quality variables of the stream sites. Analysis of metal concentrations revealed that like many other Arctic sites, concentrations were generally low. Differences in total metals concentrations between sites did not result in very meaningful groupings, and were not found particularly useful. Instead the strong importance of nutrients and ions illustrated their usefulness in determining the water quality character of the sites.

4.3 CORE RECONSTRUCTIONS

Fossil diatom assemblages of core BY14-2 were differentiated into three zones, and reflect environmental changes occurring over the past ~3500 years. Zone 1 (~3500 –

1000 cal yrs BP) coincides with the stiffer sediments found in the lower sections of the core (below 13 cm), likely resulting in part from compaction by overlying deposits. Zone

1 represents the longest time period of the three zones and shows few biostratigraphic changes. It is characterized by the dominance (and increase in abundance) of Staurosira venter, and a second most dominant species Aulacoseira alpigena. The species richness is highest at the beginning of this zone, compared with the remainder of the core. S. venter is often found in records of ice-dominated systems (i.e.: Smol 1988) and is a dominant species at the modern BY lakes and ponds. The planktonic A. alpigena was not common among modern BY sites (<1.2% in BY17, BY01 and BY16), but had an abundance of 7.8% at the surface of the BY14-2 core. The presence of planktonic species

in BY14 may be due to the inflow of a small (~3ha) pond/lake ~2km upstream that provides flowing water to give planktonic diatoms buoyancy (this is similarly occurring in lake BY17).

In Zone 2 (~1945 – 1250 AD), A. alpigena declines while the other planktonic species, A. lirata and Tabellaria flocculosa, which were sparse in Zone 1, show small increases. These simultaneous changes may show a competition for resources and changes occurring in the water column where planktonic species occur. The changes in abundances may be related to climate warming beginning in the early part of the 20th century, and may also be related to the availability of nutrients required for cell growth

(Battarbee et al. 2001). Climate warming and nutrient transport are closely linked to processes such as active layer melting (Wrona et al. 2005) and increased primary production due to reduced ice-cover. The changes recorded in the 20th century in the sediments of BY14 could be caused by a combination of these factors.

The simultaneous changes in abundances of many benthic species in Zone 2 indicate changing conditions for species on the lake bottom as well. Navicula seminulum and N. schmassmannii show the most notable increases and are of similar magnitude (11 and 10.5%, respectively). Increased concentrations of DOC have been found to limit light to certain planktonic species while benefitting benthic species and increasing total diatom concentrations (Pienitz and Vincent 2000). It is possible that varying trends in the planktonic abundances in this zone are showing differences in competitive success of planktonic species under conditions of reduced light. DOC concentrations can become high enough that they are a more important limit to primary production than nutrient concentrations (Hecky and Guildford 1984). Recent massive gully erosion events

resulting in exposed scarp retreat of 15-50 m per summer have been occurring on Bylot

Island (Fortier et al. 2007) and are current evidence that climate change is causing erosion and transport of materials into streams and lakes. If this kind of mass wasting began in the early 20th century on Bylot Island, this could explain the increases of benthic diatoms that dominate over the planktonic A. alpigena in Zone 2. Small increases of A. lirata and

T. flocculosa are explained by more robust species-specific tolerances to high DOC concentrations that allow these species to exploit the available resources in the water column.

Near the latter part of Zone 1 (~1600 yr BP or 350 AD) and midway through

Zone 2 (~1935 AD), diatom concentration increases, indicating greater biological productivity, which has been linked to both more open-water conditions (i.e.: Smol 1988) and increased nutrient loading of P and N (O'Brien et al. 2005). Species richness

(rarefaction) throughout the core ranges from 33.3 to 48.4, and has a surface (modern) value (37.3) close to mean for the core (38.5). The modern sediments of BY14 have the highest diatom species richness of all the BY surface sites (min = 17.7 at BY01; mean =

29.4). The more productive BY sites have generally lower species richness compared to the QB sites (min = 14.0 at QBS01; max = 56.6 at QB15, mean = 30.6).

Zone 3 (~2008 – 1955 AD) shows that in this record, there is no significant increase in diatom species diversity or production in the most recent sediments in response to climate warming, as reported for some other Arctic lakes (i.e.: Smol et al.

2005, Finkelstein and Gajewski 2008). One possible explanation is BY14 is higher in nutrients (mesotrophic) than most Arctic lakes and may therefore not be as sensitive. The biologically available portion of phosphorous (SRP) of BY14 (1.9 μg/L) is slightly

greater than the mean value reported by Hamilton et al. (2001) of 1.7 μg/L, and total nitrogen concentration at all BY sites, including BY14, is higher than the mean found by

Hamilton et al. (2001) of 0.338 mg/L. This indicates that lake BY14 is among the more nutrient rich of Arctic lakes.

In addition to the changes inferred from the assemblage changes, quantitative reconstructions of key limnological parameters were also attempted. The reconstruction models used have good modern analogues, with 30 of the 36 fossil species present in the modern training sets. Reconstruction of variables from the NLET water chemistry data

(Figure 22) should be interpreted with caution, as the models are based on smaller training sets that cannot be cross-validated. Even with this caveat, several reconstructed variables suggest a recent change in the BY14 catchment between 1935 – 1956 AD (~6 –

10 cm depth). The pH reconstruction, for example, shows a subtle increase in pH values beginning at the depth of 10 cm (~1935 AD). Other studies have shown increases in pH with decreases in ice-cover as CO2 evasion is promoted (i.e.: Wolfe 2002). Specific conductivity also shows an increase beginning around the same time.

Nutrient reconstructions indicate increases in TN-U, TP-U, SRP and Chl-a

(Figure 22) beginning ~1935 – 1956 AD and continuing to today. Temperature also shows an increasing trend, with a change of ~0.5°C since the lowest part of the core

(Figures 21 and 22). The decrease in NO3NO2 (Figure 22) is negatively correlated with the trend of increasing total nitrogen, possibly indicating that nitrification processes have not utilized the available sources of N, and the total N has remained in particulate form or

+ as NH3 and NH4 .

The increase in Chl-a is interpreted as increases in plant biomass and productivity within the catchment, and perhaps also as an increase in diatom productivity. Diatom concentrations show a gradual increase in the same depths, but this is small compared to earlier jumps in valve concentration, which are not mirrored in the Chl-a reconstruction.

One can therefore conclude that the source of increased Chl-a is not exclusively from increased diatom production, and is explained at least in part by increased macrophyte growth or allochthonous sources. The increases in nutrients including SiO2 point to allochthonous inputs from surface water carrying eroded solutes. Together these trends support a tentative conclusion that the catchment of BY14 became more productive following increasing fluxes and increases in temperature.

Chapter 5: Conclusions

Significant differences exist between the freshwater sites studied in the BY and

QB regions of Sirmilik National Park. These differences are evident in the water chemistry and the diatom and invertebrate communities, and are in part due to the different geologies and levels of productivity of the two regions. The CABIN protocols employed are useful for detecting site differences, as was seen in the comparison of invertebrate taxa from QB and BY, however greater taxonomic resolution is required for quantitative modelling of species-environment relationships. The low species richness detected here by using the family-level identification specified in the CABIN protocols is complemented with the use of diatoms to infer quantitative species-environment relationships.

Variables that best explain variation in the diatom assemblages are specific conductivity, ORP, pH, temperature, elevation and distance from the coast. Principal components analysis of water chemistry variables concluded that site differences were strongly controlled by nutrients TN-U, SRP-U and DOC, as well as elevation and temperature along PCA axis 1 and distance from the coast and Chl-a along PCA axis 2.

Aquatic invertebrates show a response to substrate type and preferred habitat, which could be an indication of other factors such as stream velocity.

The paleolimnological reconstruction from one site on BY shows increases in nutrients TN, TP, SRP, as well as DOC, SiO2, Chl-a and temperature since ~1935 – 1956 yr AD. Changes detected in diatom communities during the 20th century indicate the impacts of climate warming and/or nutrient enhancement. Interestingly, the stratigraphy does not show an increase in diversity and production since the middle 20th century to the

same extent as has been noted elsewhere (i.e.: Smol et al. 2005), and this may be a reflection of the more nutrient-rich status of lake BY14 relative to other Arctic lakes.

Growing populations of Greater Snow Geese on Bylot Island may have altered various nutrient concentrations (P, N and C) in lakes and ponds by disrupting important nutrient cycling processes. Further research into the influence of the goose colony on the nutrient cycling of lakes and ponds on Bylot Island may elucidate how these lakes would respond ecologically to further population increases. This study has shown that paleolimnological evidence has detected environmental change in this important high-arctic region, and can be used in further study to track ecological trends.

Table 1: Water quality parameters measured Water Quality Parameter Abbreviation Units In situ (Hydrolab) temperature TEMP °C pH pH - specific conductivity SPCOND µS/cm redox potential ORP mV Turbidity TURB NTU Filtered nutrients and dissolved inorganic carbon DIC mg/L chlorophyll-a (NLET) dissolved organic carbon DOC mg/L total dissolved nitrogen (or Total Nitrogen TN-F mg/L Filtered) total dissolved phosphorous (or Total TP-F mg/L Phosphorous Filtered) total particulate phosphorous TP-P mg/L unfiltered total phosphorous TP-U mg/L chlorophyll-a CHL-a μg/L Unfiltered nutrients soluble reactive phosphorous SRP-U mg/L (NLET) nitrate-nitrite NO3NO2 mg/L Ammonia NH3 mg/L total nitrogen TN-U mg/L Major ions (NLET) Chloride Cl mg/L Sulphate SO4 mg/L Fluoride F mg/L calcium Ca mg/L Magnesium Mg mg/L Sodium Na mg/L Potassium K mg/L reactive silica SiO2 mg/L Physical parameters total alkalinity ALKCaCO3 mg/L (NLET) specific conductivity SPCOND µS/cm pH pH - true colour COL Pt-Co Turbidity TURB NTU Metals (NLET) Silver Ag μg/L Aluminum Al μg/L Arsenic As μg/L Boron B μg/L barium Ba μg/L beryllium Be μg/L bismuth Bi μg/L cadmium Cd μg/L cerium Ce μg/L cobalt Co μg/L chromium Cr μg/L copper Cu μg/L iron Fe μg/L gallium Ga μg/L lanthanum La μg/L lithium Li μg/L manganese Mn μg/L molybdenum Mo μg/L

niobium Nb μg/L nickel Ni μg/L lead Pb μg/L platinum Pt μg/L rubidium Rb μg/L antimony Sb μg/L selenium Se μg/L tin Sn μg/L strontium Sr μg/L thallium Tl μg/L uranium U μg/L vanadium V μg/L tungsten W μg/L yttrium Y μg/L zinc Zn μg/L

Table 2: CABIN sampling key elements. Source: (Rosenberg et al. 1999, Reynoldson et al. 2007). Categorical data is collected for habitat description, according to a nationalized standard (i.e.: including land use and canopy coverage)

In situ water quality data is collected (pH, conductivity, temperature, dissolved oxygen)

Water samples are taken and sent for laboratory analyses for a suite of water quality variables (i.e.: nutrients, major ions, alkalinity, etc.)

Invertebrates are sampled using a 400-µm sieve for 3- min intervals

Substrate characteristics are determined visually and with a 100-pebble count

Channel and streambed measurements are taken, including channel profile and stream velocity

Table 3: Summary of physical characteristics of lakes and ponds sampled. Distance Depth Secchi Surface Elevation From Lake Latitude Longitude (m) depth (m) Area (ha) (masl) Coastline (km) BY01 72°53'25" 79°15'24" 1.85 (bottom) 4.8 256 14.9 BY04 72°50'01" 79°18'22" 1.20 (bottom) 13.4 179 10.1 BY10 72°50'03" 79°30'11" < 2.0 (bottom) 21.6 163 7.9 BY14 72°55'55" 79°42'05" 4.30 1.55 7.3 198 11.2 BY15 72°55'41" 79°42'55" 3.80 2.50 5.1 195 10.5 BY16 72°56'29" 79°35'59" 3.70 1.85 2.0 255 13.8 BY17 72°55'05" 79°38'47" 3.05 1.85 2.9 198 10.7 BY18 72°51'32" 79°41'06" 1.90 (bottom) 3.8 110 4.9 QB01 72°10'32" 78°30'31" 17.65 7.25 39.0 533 2.9 QB02 72°10'53" 78°35'37" 7.60 5.13 45.5 579 3.0 QB03 72°11'29" 78°34'07" 22.10 6.25 132.1 488 3.8 QB04 72°12'07" 78°35'53" 10.80 8.38 114.8 602 4.1 QB05 72°12'01" 78°39'12" 13.10 6.00 31.9 823 3.8 QB06 72°12'50" 78°38'40" 20.60 3.95 50.9 686 5.0 QB07 72°14'37" 78°42'50" 4.40 (bottom) 113.7 427 3.1 QB08 72°11'09" 78°35'49" 4.95 (bottom) 10.3 671 3.3 QB10 72°13'56" 78°41'59" 3.80 (bottom) 52.0 442 5.3 QB11 72°13'13" 78°41'14" 37.90 6.30 97.4 518 4.0 QB12 72°12'40" 78°41'54" 20.80 2.20 89.3 579 3.5 QB15 72°09'34" 78°29'28" 13.00 8.63 200.3 366 2.5

Mean 9.89 4.06 51.9 404 6.7 Min 72°09'34" 78°29'28" 1.20 1.20 2.0 110 2.5 Max 72°56'29" 79°42'55" 37.90 8.63 200.3 823 14.9

Table 4: Stream site habitat measurements. Data collected according to CABIN protocols (Reynoldson, et al. 2006). Substrate: BF- Site Macrophyte WW / Max Velocity Reach Habitat Riparian Periphyton A) Dominant Wetted Reach (Stream Location Coverage BFW Depth (m/s) Habitat sampled Veg* Coverage B) 2nd Dominant Depth Order) (%) (m) (cm) slope C) Surrounding (m) (n) moss, A) COB BYS01 72°53.337' N riffle riffle shrub, 1-25 2 B) PEB 7.6 / 30.2 No data 19 1.9° 0.59 (3) (1st-order) 79°15.605' W grass C) CRS sand grass, A) COB BYS02 72°51.445' N moss, riffle riffle 1-25 2 B) COB 9.3 / 21.1 0.45 20 1.2° 0.47 (1) (2nd-order) 79°15.532' W herb, C) GRVL shrub shrub, A) BLDR BYS03 72°47.824' N riffle, 10.3 / riffle moss, 0 2 B) COB 1.6 43 0.8° no data (3rd-order) 79°12.564' W run 43.6 herb C) CRS sand riffle, A) PEB BYS04 72°49.322' N moss, run, riffle** 1-25 2 B) COB 2.62 / 5.9 0.35 7 1.9° 0.29 (1) (1st-order) 79°05.125' W shrubs pool C) GRVL moss, A) PEB BYS05 72°48.605' N grass, 13.3 / riffle riffle 1-25 2 B) COB 0.75 10 2.0° 0.57 (1) (2nd-order) 79°04.44' W shrub, 27.5 C) GRVL herb moss, A) BLDR BYS06 72°47.409' N riffle, side of herb, 0 2 B) COB 7 / 17.8 2.0*** 55 no data no data (4th-order) 79°00.362' W run riffle shrub C) CRS sand grass, A) BLDR QBS01 72°14.834' N moss, 20.4 / riffle riffle 1-25 3 B) COB 0.5 18 1.9° 1.21 (3) (1st-order) 78°43.039' W shrub, 31.2 C) CRS sand herb herb, A) BLDR QBS02 72°14.886' N grass, riffle riffle 0 3 B) COB 32 / 37 1.73 35 1.5° no data (2nd-order) 78°42.751' W moss, C) CRS sand shrub *Dominant riparian vegetation in bold; **200μm sample not taken; ***visual estimate. Periphyton categories: 2= Rocks slightly slippery, yellow-brown to light green colour (0.5-1 mm thick) and 3= Rocks have a noticeable slippery feel, footing is slippery, with patches of thicker green to brown algae (1-5 mm thick) (Reynoldson, et al. 2006). COB = cobble; PEB = pebble; BLDR = boulder; CRS sand = coarse sand. WW = wetted width; BFW = bankfull width; BF-Wetted Depth = bankfull depth. Velocities are the mean of n measurements.

Table 5: Summary of nutrient and physical variables for the 15 sites sampled for detailed water chemistry (includes 6 streams and 9 lakes, where lakes are >2 m depth). Sites sampled: BY14, BY15, BY16, BYS03, BYS04, BYS05, BYS06, QB01, QB06, QB07, QB10, QB11, QB12, QBS01 and QBS02. Data for each site are included in Appendix A. Variable Abbr. Mean Median Min. Max. SD Detection limit & units BY mean QB mean Temperature † Temp 7.51 8.28 4.93 9.65 1.54 °C 8.68 6.49 pH‡ pH 7 6.7 6 8.5 0.9 7.8 6.2 Specific Conductivity‡ SpCond 32 4.7 2.6 161.9 48.4 0.1 µS/cm 64.32 3.80 Redox Potential† ORP 478.69 487.26 409 514.5 34.8 0.01 mV 455.26 499.20 Total Alkalinity as CaCO3 ALKCaCO3 12.55 1.6 0.7 75.6 21.8 0.1 mg/L 25.73 1.02 Turbidity† Turb 16.66 14.37 7.6 48.21 9.55 0.01 NTU 21.07 12.23 Dissolved Organic Carbon DOC 6.1 6.3 0.8 13 3.8 0.1 mg/L 9.31 3.34 Dissolved Inorganic Carbon DIC 3.2 1 0.6 17 4.7 0.2 mg/L 6.01 0.78 Nitrate-Nitrite NO3-NO2 0.048 0.037 DL 0.139 0.044 0.005 mg/L 0.02 0.07 Ammonia NH3 0.012 0.005 DL 0.049 0.014 0.005 mg/L 0.02 0.00 Total Nitrogen Filtered* TNF 0.328 0.222 0.101 0.937 0.226 0.014 mg/L 0.507 0.172 Total Nitrogen TN-U 0.309 0.19 0.09 0.937 0.246 0.014 mg/L 0.519 0.126 Soluble Reactive Phosphorous SRP-U 0.0012 0.0011 0.0005 0.0024 0.0006 0.0002 mg/L 0.0017 0.0007 Total Phosphorous Filtered** TPF 0.0038 0.0024 0.0015 0.0102 0.0025 0.0005 mg/L 0.006 0.002 Unfiltered Total Phosphorous TPU 0.0067 0.0047 0.0024 0.0164 0.0046 0.0005 mg/L 0.0105 0.0034 Total Particulate Phosphorous TPP 0.0121 0.0095 DL 0.0520 0.0123 0.0005 mg/L 0.0179 0.0071 Chlorophyll-a CHLA 0.55 0.2 DL 3.7 0.93 0.01 mg/L 0.95 0.20 Chlorophyll-a (corrected) CHLA-COR 0.55 0.2 DL 4.3 1.07 0.01 mg/L 0.95 0.20 True Colour Col 27.7 3.6 DL 78.5 31 0.5 Pt-Co 57.14 1.93 Chloride Cl 1.8 0.35 0.31 8.07 2.49 0.02 mg/L 3.46 0.34 Sulphate SO4 1.06 0.47 0.3 6.02 1.44 0.02 mg/L 1.83 0.38 Calcium Ca 3.49 0.6 0.27 19.8 5.66 0.05 mg/L 7.04 0.38 Magnesium Mg 1.44 0.2 0.12 8.25 2.32 0.01 mg/L 2.93 0.14 Sodium Na 1.36 0.45 0.31 5.84 1.57 0.01 mg/L 2.48 0.38 Potassium K 0.24 0.2 0.11 0.58 0.14 0.01 mg/L 0.35 0.15 Reactive Silica SiO2 1.94 1.62 0.87 3.64 0.99 0.02 mg/L 2.83 1.16 DL: detection limit; *: or Total dissolved nitrogen; **: or Total dissolved phosphorous; †:In situ data; ‡:In situ data for 13 sites and NLET values for QB10 and QB11; All other variables measured at NLET (Burlington, Ontario).

Table 6: Summary of total metal concentrations for the 15 sites sampled for water chemistry (includes 6 streams and 9 lakes, where lakes are >2 m depth). Sites sampled: BY14, BY15, BY16, BYS03, BYS04, BYS05, BYS06, QB01, QB06, QB07, QB10, QB11, QB12, QBS01, QBS02. All units are in µg/L. All measurements were made at NLET. Data for each site are included in Appendix A. detection BY QB Variable mean median min max stdev limit mean mean Al 45.6 31.2 7.7 90.2 26.8 0.2 50.2 41.5 As 0.06 0.02 DL 0.17 0.06 0.01 0.11 0.01 B 1.2 0.5 0.3 4.7 1.4 0.5 2.2 0.4 Ba 4.57 2.75 1.42 12.80 3.50 0.05 7.45 2.06 Be 0.004 0.003 0.002 0.009 0.002 0.001 0.006 0.003 Cd 0.004 0.003 0.001 0.008 0.002 0.001 0.005 0.003 Ce 0.602 0.464 0.133 1.210 0.350 0.002 0.531 0.665 Co 0.047 0.053 0.017 0.091 0.023 0.002 0.062 0.035 Cr 0.166 0.166 0.057 0.300 0.089 0.005 0.227 0.113 Cu 0.87 0.77 0.32 1.49 0.36 0.02 1.17 0.62 Fe 99.1 60.1 16.6 380.0 108.1 0.5 158.8 46.9 Ga 0.015 0.011 0.003 0.038 0.011 0.001 0.011 0.019 La 0.332 0.285 0.096 0.608 0.170 0.001 0.295 0.365 Mn 1.47 1.24 0.44 3.48 0.93 0.05 1.10 1.81 Mo 0.04 0.02 DL 0.23 0.06 0.01 0.04 0.04 Nb 0.003 0.002 0.001 0.009 0.002 0.001 0.002 0.004 Ni 0.61 0.25 0.09 1.63 0.54 0.02 1.11 0.17 Pb 0.034 0.022 0.007 0.171 0.040 0.005 0.046 0.024 Rb 0.37 0.39 0.17 0.62 0.14 0.01 0.26 0.47 Sb 0.004 0.004 0.001 0.010 0.003 0.001 0.007 0.002 Sr 7.93 1.93 1.25 31.30 9.87 0.05 15.23 1.56 Tl 0.005 0.002 DL 0.022 0.007 0.001 0.009 0.001 U 0.0285 0.0181 0.0031 0.1060 0.0279 0.0005 0.0351 0.0227 V 0.162 0.189 0.064 0.287 0.077 0.005 0.215 0.116 Y 0.091 0.069 0.029 0.201 0.055 0.001 0.127 0.060 Zn 0.40 0.31 0.09 1.22 0.32 0.05 0.63 0.21 DL: detection limit

Table 7: Pearson Product Moment Correlation matrix of physical and nutrient variables. Bold and bold italic denote significant correlation at P<0.05 and P<0.01, respectively. SRFA, Depth, and Secchi for lakes only (n=20); Elev and DFC for all sites (n=28); SPCOND, ORP, pH, Temp and Turb in situ data (n=27); all other variables n=15. Depth 0.69 Elev 0.60 0.74 DFC -0.79 -0.66 -0.41 Secchi 0.73 0.71 0.65 -0.77 SPCOND -0.70 -0.79 -0.83 0.24 -0.65 ORP 0.47 0.71 0.69 -0.14 0.53 -0.89 pH -0.75 -0.79 -0.77 0.24 -0.65 0.96 -0.83 TEMP -0.63 -0.87 -0.84 0.56 -0.70 0.70 -0.58 0.66 TURB -0.44 -0.67 -0.46 0.35 -0.62 0.54 -0.75 0.56 0.53 NO3NO2 0.82 0.78 0.42 -0.71 0.40 -0.38 0.15 -0.45 -0.58 -0.22 NH3 -0.90 -0.65 -0.60 0.61 -0.66 0.51 -0.48 0.58 0.60 0.69 -0.66 ALKCaCO3 -0.96 -0.65 -0.85 -0.07 -0.52 0.99 -0.84 0.95 0.61 0.44 -0.34 0.40 Chl-a -0.85 -0.60 -0.29 0.74 -0.60 0.10 -0.01 0.24 0.36 0.28 -0.55 0.57 0.02 CHL-a-COR -0.81 -0.58 -0.24 0.71 -0.59 0.05 0.06 0.19 0.33 0.18 -0.49 0.45 -0.01 COL -0.94 -0.63 -0.86 0.33 -0.68 0.91 -0.74 0.88 0.75 0.55 -0.60 0.73 0.87 DOC -0.56 -0.69 -0.65 0.34 -0.88 0.73 -0.59 0.84 0.72 0.62 -0.62 0.66 0.72 DIC -0.82 -0.74 -0.79 -0.18 -0.55 0.96 -0.83 0.90 0.56 0.38 -0.25 0.27 0.98 CL -0.86 -0.62 -0.78 -0.06 -0.62 0.94 -0.92 0.90 0.60 0.64 -0.34 0.58 0.93 SO4 -0.96 -0.62 -0.77 -0.31 -0.56 0.84 -0.77 0.82 0.45 0.38 0.02 0.26 0.84 Ca -0.95 -0.63 -0.81 -0.13 -0.54 0.98 -0.86 0.93 0.58 0.46 -0.31 0.38 1.00 Mg -0.97 -0.63 -0.79 -0.13 -0.61 0.97 -0.87 0.92 0.57 0.47 -0.30 0.37 0.99 Na -0.95 -0.68 -0.86 -0.14 -0.63 0.95 -0.89 0.94 0.59 0.55 -0.19 0.47 0.95 K -0.80 -0.45 -0.76 -0.13 -0.23 0.87 -0.74 0.91 0.44 0.37 -0.27 0.28 0.90 SiO2 -0.87 -0.65 -0.91 0.07 -0.61 0.94 -0.78 0.89 0.69 0.43 -0.35 0.47 0.93 SRP-U -0.78 -0.47 -0.79 0.31 -0.78 0.77 -0.71 0.77 0.59 0.72 -0.39 0.83 0.69 TN-F -0.86 -0.63 -0.75 0.18 -0.88 0.87 -0.85 0.86 0.65 0.77 -0.44 0.75 0.82 TN-U -0.96 -0.60 -0.81 0.25 -0.73 0.89 -0.84 0.87 0.67 0.73 -0.47 0.79 0.83 TP-F -0.95 -0.61 -0.72 0.48 -0.65 0.72 -0.70 0.75 0.64 0.76 -0.63 0.92 0.65 TP-U -0.94 -0.57 -0.61 0.67 -0.73 0.56 -0.50 0.64 0.56 0.72 -0.65 0.95 0.46 TP-PART -0.84 -0.45 -0.28 0.62 -0.55 0.13 -0.06 0.24 0.26 0.30 -0.33 0.49 0.06 SRFA Depth Elev DFC Secchi SPCOND ORP pH TEMP TURB NO3NO2 NH3 ALKCaCO3

Table 7 (continued):

CHL-a-Cor 0.98 Col 0.33 0.28 DOC 0.33 0.27 0.81 DIC -0.09 -0.10 0.80 0.66 CL- 0.00 -0.07 0.87 0.75 0.90

SO4 -0.01 -0.03 0.64 0.52 0.82 0.76 Ca -0.04 -0.07 0.85 0.72 0.99 0.94 0.83 Mg -0.05 -0.08 0.85 0.70 0.99 0.95 0.80 1.00 Na 0.06 0.01 0.82 0.69 0.91 0.92 0.94 0.94 0.93 K 0.13 0.11 0.69 0.62 0.87 0.75 0.88 0.88 0.85 0.89

SiO2 0.12 0.10 0.91 0.68 0.90 0.85 0.80 0.92 0.91 0.90 0.81 SRP-U 0.30 0.20 0.86 0.72 0.60 0.79 0.60 0.68 0.68 0.75 0.54 0.77 TN-F 0.24 0.16 0.92 0.83 0.77 0.94 0.64 0.83 0.84 0.84 0.63 0.79 0.88 TN-U 0.31 0.22 0.95 0.78 0.76 0.93 0.65 0.83 0.83 0.85 0.65 0.84 0.91 0.98 TP-F 0.50 0.41 0.90 0.75 0.54 0.77 0.45 0.63 0.63 0.67 0.49 0.70 0.90 0.90 0.94 TP-U 0.70 0.59 0.75 0.68 0.34 0.57 0.28 0.43 0.43 0.49 0.37 0.52 0.84 0.76 0.81 0.93 TP-PART 0.93 0.92 0.33 0.25 -0.05 0.03 0.09 0.00 -0.01 0.12 0.19 0.16 0.33 0.26 0.34 0.47 0.65 Chl-a Chl- Col DOC DIC CL SO4 Ca Mg Na K SiO2 SRP TN- F TN-U TP- F TP-U a-Cor

Table 8: Results of Principal Components Analysis (PCA) of water chemistry variables at 15 sites in SNP (see Figure 5 for sites). Variables used in the analysis are listed in Table 9. Standard deviations indicate the lengths of gradients represented by the axes. Eigenvalues (λ) indicate the proportion of variance explained by each axis.

PCA Axis 1 PCA Axis 2 PCA Axis 3 PCA Axis 4 Standard deviation 2.984 1.660 0.871 0.697 Proportion of variance (λ) 0.636 0.197 0.054 0.035 Cumulative proportion 0.636 0.833 0.887 0.922

Table 9: PCA loadings of variables along axes 1 and 2, for water chemistry variables at 20 lake and pond sites in SNP (see Figure 5 for sites). Loadings indicate the importance of the variable in determining the position of a site along an environmental gradient. Loadings Axis 1 Axis 2 Elev -0.306 0.062 DFC 0.098 0.546 NO3NO2 -0.190 -0.367 SPCOND 0.313 -0.184 ORP -0.267 0.266 PH 0.318 -0.108 Temp 0.262 0.111 Chl-a 0.119 0.472 DOC 0.286 0.077 DIC 0.277 -0.294 SIO2 0.303 -0.163 SRP 0.294 0.030 TN-U 0.319 -0.019 TP-U 0.261 0.302

Table 10: Invertebrate counts for 7 stream sites in SNP. Nematode counts are shown but are not included in assemblage analysis, according to CABIN protocol (Reynoldson, et al. 2007).

Site BYS01 BYS02 BYS02 BYS03 BYS04 BYS05 BYS05 BYS06 BYS06 QBS01 Sample mesh size (um)μ 400 200 400 400 400 200 400-1 200 400 400 Non-insects Collembola (order) 4 2 1 7 313 17 5 1 Hydrachnida, Acari (class, subclass) 1 3 3 50 5 1 Oligochaeta (subclass) 95 41 60 14 42 955 450 68 16 2 Nematode (phylum) 64 20 21 1 34 140 29 4 2 4 TOTAL non-insect count (not including nematodes) 100 43 64 14 52 1318 472 74 17 2 Class Insecta Plecoptera Nemouridae (order and family) 1 10 4 Diptera (order): Chironomidae (family) (TOTAL) 343 409 195 16 4 492 111 150 107 192 Orthocladiinae (sub-family) 325 402 192 12 4 492 111 114 79 37 Orthocladiinae Corynoneura (genus) 14 1 10 5 Tanytarsini (tribe) 4 4 2 2 unk sp1 3 1 26 21 Diamesinae (sub-family) 1 2 hairy sp1 25 hairy sp2 65 hairy sp3 5 Orthocladiinae sp1 60 Simuliidae (family) 110 Empididae (family) 1 1 1 Tipulidae (family) 1 4 4 9 Tabanidae (family) 1 1 2 TOTAL INSECTA count 346 414 196 17 4 494 111 165 121 202

TOTAL COUNTS 446 457 260 31 56 1812 583 239 138 204 (total counts do not include nematodes)

Table 11: Descriptive metrics for invertebrate samples from 7 stream sites in SNP. Site: BYS01 BYS02 BYS02 BYS03 BYS04 BYS05 BYS05 BYS06 BYS06 QBS01 Sample mesh size (μm) 400 200 400 400 400 200 400 200 400 400 Total taxonomic richness (family level or above for non-insects) 7 5 5 3 4 5 4 7 6 4 EPT richness (family level or genus level) 0 0 0 1 0 0 0 1 1 0 % EPT 0.0 0.0 0.0 3.2 0.0 0.0 0.0 4.2 2.9 0.0 % Chironomids 76.9 89.5 75.0 51.6 7.1 27.2 19.0 62.8 77.5 94.1 % Non-insects 22.4 9.4 24.6 45.2 92.9 72.7 81.0 31.0 12.3 1.0 Shannon-Weiner diversity index (H') 0.7990 0.4757 0.7038 1.1549 0.7942 1.1003 0.6570 1.3961 1.3603 1.5606 Dominant taxa Chiron. Chiron. Chiron. Oligo. Oligo. Oligo. Oligo. Chiron. Chiron. Chiron. Higher values of the Shannon-Wiener index (H'), indicate higher diversity. Abbreviation Chiron. = Chironomid; Oligo. = Oligochaete.

Table 12: Summary of single-variable CCAs and Monte Carlo permutation test results – species environment correlations. Eigenvalues (λ) indicate the amount of variance extracted by the first axis. CCAs included diatom assemblages from 24 sites in SNP, shown in Figure 11.

Monte Carlo test, 999 runs Real data λ (randomized data) Spp-Envt Corr. Mean Minimum Maximum P Elevation 0.637 0.906 0.719 0.529 0.892 0.001 Distance From Coast 0.532 0.858 0.716 0.479 0.889 0.008 Specific Conductivity 0.766 0.969 0.719 0.522 0.891 0.001 ORP 0.634 0.912 0.721 0.469 0.919 0.003 pH 0.707 0.931 0.719 0.530 0.905 0.001 Temperature 0.575 0.871 0.718 0.521 0.881 0.003 Turbidity 0.399 0.826 0.711 0.446 0.923 0.111

Table 13: Summary statistics for Canonical Correspondence Analysis (CCA) of surface diatom assemblages at 24 sites in SNP (Figure 11). Environmental variables used in the analysis are listed in Table 14. Eigenvalues (λ) indicate the amount of variance extracted by each axis. Total variance ("inertia") in the species data = 5.4797 Axis 1 Axis 2 Axis 3 Eigenvalue (λ) 0.805 0.571 0.319 Variance in species data (% of variance 14.7 10.4 5.8 explained) Cumulative % explained 14.7 25.1 30.9 Pearson Correlation, Spp-Envt* 0.987 0.900 0.878 * Correlation between sample scores for an axis derived from the species data and the sample scores that are linear combinations of the environmental variables.

Table 14: Inter-set correlations (correlations of environmental variables with the species- weighted-average site scores; ter Braak 1986) for 6 variables used in CCA of surface diatom assemblages at 24 sites in SNP (Figure 11). Axis 1 Axis 2 Axis 3 ELEV 0.840 -0.039 -0.067 DFC -0.581 0.516 0.045 SPCOND -0.930 -0.248 0.011 ORP 0.748 0.392 -0.353 PH -0.901 -0.124 -0.091 TEMP -0.775 -0.005 -0.188

Table 15: Radiocarbon dates and calibrated calendar ages from cores BY14-2 and QB15. Dates were calibrated using CALIB v5.0.1 (Stuiver, et al. 1993) and the INTCAL04 calibration curve (Reimer, et al. 2004). *Require correction because they are unrealistically old for these depths (Figure 13). ** Not used for age modelling. Conventional δ13C/12C Calibrated age, Median Depth Material radiocarbon ratio (o/oo) 2-sigma range calibrated age Lab code (cm) dated age (yr BP) (cal yr BP) (cal yr BP) BY14-2 15.5 - moss, 5620 ± 40 NA 6310-6479 *6400 Beta - 16.0 chironomids 262063

22.0 - moss, 6430 ± 40 -25.9 7278-7424 *7360 Beta - 22.5 chironomids 262064 QB15 12.5 - moss, 880 ± 40 -25.7 699-915 **790 Beta - 13.0 chironomids 262065

15.0 - wood 110 ± 40 -27.5 -4-273 (modern) **120 Beta - 16.0 262066

BY sites QB sites N

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500 km

QBS02 01 BS + Q

t e

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+ t + + + +

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P aq ue t B ay N

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Figure 1: Map of Baffin Island showing general location of study sites (top; http://maps.google.ca) and aerial photographs (National Air Photo Library, Natural Resources Canada) showing QB study sites (bottom; airphotos taken in August 1958).

BYS06 d

BYS05 s

i l

BYS04 c E

BYS03

o f

BY04

u D BYS02 BYS01 BY01 BY18 BY17 BY16

BY14 BY15 N 1000m

Figure 1 (continued): Aerial photographs showing BY study sites (taken in July 1982). Reproduced with the permission of Natural Resources Canada 2010, courtesy of the National Air Photo Library.

0 4 5 1 2 3 4 8 0 1 2 5 1 1 1 05 06 07 BY01BY04BY BY BY BY16BY17BY18QB0 QB0 QB0 QB0 QB QB QB QB0 QB1 QB1 QB1 QB1 0

Depth (m) Depth 25

40 Visible Depth (m) Total Lake Depth (m)

Figure 2: Maximum depths and water clarity of lakes and ponds, as measured by hand-held depth sonde and Secchi disk.

Distance along cross section (m) 0 5 10 15 20 25 30 0

Depth (cm) Depth 40

60 QBS01 QBS02 BYS01 BYS02 BYS03 BYS04 BYS05 BYS06

Figure 3: Stream cross-sectional profiles, looking downstream. Vertical exaggeration is approximately x10.

% Weight Loss -10 0 10 20 30 40 50

BY01

BY14

BY15

BY16

BY17

BY18

QB01

QB02

Site QB03

QB04

QB05

QB06

QB07

QB10

QB12

QB15

% Organic Matter (LOI 550) % Carbonate (LOI 950)

Figure 4: Loss-on-ignition (LOI) data for surface lake and pond sediments. Negative values indicate measurement error associated with low values for LOI950. Values are considered zero for sites with negative LOI950.

0.8

DFC BY16 CHL-a 0.6

BY15 0.4 TP-U BY14 ORP

0.2 TEMP DOC BYS04 ELEV QB10 QB07 SRP-U QB06 QB12 0 TN-U

λ -0.8 -0.6 0 QB11 -0.4 -0.2 0.2 0.4 0.6 0.8 QBS02 QB01 pH BYS03 -0.2 SiO2 Axis 2 = 0.197 SPCOND QBS01

BYS05 -0.4 DIC

NO32 NO

-0.6

BYS06 -0.8 Axis 1 λ = 0.636

Figure 5: Principal components analysis biplot, for 15 sites (those with complete water chemistry data) and 14 variables: Elev, DFC, NO3NO2, SpCond, ORP, pH, Temp, Chl-a- U, DOC, DIC, SiO2, SRP, TN-U and TP-U. QB sites are squares and BY sites are circles. Arrows are vectors indicating importance of variables along PCA axis 1 and 2. The lengths of the vectors indicate the relative importance of different variables in explaining site distributions. The angle between a vector and the axes (and other vectors) indicates the strength of the correlations. Acute angles are highly correlated, and right angles indicate no relationship exists between variables (Jongman, et al. 1987).

Inertia 02468

Comp.1 Comp.3 Comp.5 Comp.7 Comp.9

Component

Figure 6: Screeplot for the PCA shown in Fig 5. The dashed line indicates the change in slope of the line. Components 1 and 2 explain a large amount of inertia, or variation, in the sites based on the variables used in the analysis. Component 3 (where slope changes drastically) explains little of the variation in the sites and does not warrant further consideration.

Chord Distance Chord 0.0 0.1 0.2 0.3 0.4

1 2

3 6 4 5

0 0

7 0 6 1 2 0 0 0 0

1 6 4 5

1 S S 0 1 0 0 1

S S 1 1 1 S S

B B B B B B B B

Y Y Y Y Y Y Y

Q Q Q Q Q Q B B B Q B B B B

Sites

Figure 7: Cluster analysis of 15 sites in SNP based on 28 environmental variables: all variables in Table 7 except depth and secchi. The dashed line indicates the cut-off for site groupings.

100

0.2 0.3 0.4 0.5 Chord Distance Chord 0.0 0.1

1 2

6 3 5 4

0 0

0 1 6 7 0 0 0 0

4 6 2 5

1 S

1 0 0 0 S

1 1 1 S S S 1 S

B B B B B

B B

Y Y Y Y Y Y Y Y

Q Q Q Q B B B Q B B B Q Q B B Sites

Figure 8: Cluster analysis of 15 sites in SNP based on total metals concentrations. The dashed line indicates the cut-off for site groupings. Metals analyzed: Al, As, B, Ba, Be, Cd, Ce, Co, Cr, Cu, Fe, Ga, La, Mn, Mo, Pb, Rb, Sb, Sr, Tl, U, V, Y and Zn.

101

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o t a a a p e l v l a a l f s c c s p r r i i e a a r r a a a a a e e l i i i i i i i l r r r r r s s s s e a a a a a o o t o o l l l l l l r r i i i i c c o l e u u g g g g a a l l c b a a a a a a y r r r r a

St St F F F F Au Au T C

QBS01 QB streams QBS02 QB15 QB01 QB02 QB08 QB03 QB lakes QB05 QB04 QB12 QB06 QB10 QB07 BYS01 BYS02 BY BYS03 streams BYS06 *BY04 *BY10 *BY18 *BY01 BY ponds BY17 and lakes BY15 BY14 BY16

0 20 40 60 80 0 20 40 60 80 0 20 0 0 0 20 0 20 40 60 0 20 0 20 0 20 Relative Abundance (%) Figure 9 (continued on next page): Distribution of 26 most abundant diatom taxa in 21 Sirmilik study sites. Plus signs indicate presence of <0.5%. Sites are arranged by study region, water body type and order of increasing latitude. Species are grouped by ecological type and by decreasing abundance. Asterisks (*) denote ponds.

102

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u x e s d i d y d m m p e s i s s i i m s r a d u u u s p e h i e e h h p i i c s t t t a h h h m h i d d a a t o t t t o o i i a i i a r a l i l n n n a n l h h h h a i m t m t u m l u t a a a a i i c c u c t u n n m n n o n s m s i s m s i s n s s v v h a h h n h a z a z t t u n o o a i i a c s c c u c s s r i

A P A A E A R P R N N P N F P C

QBS01 QB streams QBS02 QB15 QB01 QB02 QB08 QB03 QB05 QB lakes QB04 QB12 QB06 QB10 QB07 BYS01 BYS02 BY BYS03 streams BYS06 *BY04 *BY10 *BY18 *BY01 BY ponds BY17 and lakes BY15 BY14 BY16

0 20 40 60 0 20 40 60 0 20 40 0 20 40 0 20 400 20 0 20 0 200 20 0 20 0 20 0 0 0 0 0 Relative Abundance (%) Figure 9 (continued)

103

b) QBS01 4.0

3.0 3.0 a) BY10

2.0 BYS06 2.0

BYS03

BYS02

1.0 1.0 λ QBS02

λ QBS02 BYS01 QBS01 QB08 Axis 3 = 0.534 QB05 BYS02 BYS01 BY18 BY01 QB05 QB03 BY04 QB03 QB10 BY15 QB15 BY17 BY16 0.0 QB02 BY04 0.0 QB06 Axis 2 0.738 = QB06 QB12 QB10 BY14 BYS06 QB01 BYS03 QB07 BY18 QB04 QB12 QB08 BY10 BY01 QB15 QB01 -1.0 -1.0 QB07 BY14 QB04 QB02 BY15 BY17

BY16 -2.0 -2.0 -2.0 -1.0 0.0 1.0 2.0 -2.0 -1.0 0.0 1.0 2.0 Axis 1 λ = 0.840 Axis 1 λ = 0.840 Figure 10: CA plots of site scores as a function of surface diatom assemblages, using 25 sites and 88 diatom species and showing a) CA axis 1 vs CA axis 2 and b) CA axis 1 vs CA axis 3. Eigenvalues are interpreted as the correlation between species scores and site scores (Pielou 1984).

104

4 a)

BY16

BY17 BY15 2 BY14

BY01

BY18 DFC QB04 ORP QB12 BYS01 TEMP QB07 QB01 0 PH QB15 QB06

λ QB03 ELEV QB02 SPCOND QB05 QBS02 BYS02 QB10 QBS01 Axis 2 = 0.571

QB08 BYS03 -2 BYS06

BY10

-4 -5 -3 -1 13 Axis 1 λ = 0.805

4 b) ACHNOD ACH01Q

NAVS1Q NAVSP1 STVENT

NAVSEM 2 HYGBAL FRAVRE

PINMIC

STPINN STAS1A CAVPSE EUNMNR AULALP DIAING CMPAMP ACHDAO EUNNYM AULLIR ACHCAR NAVSUB FRACAV ACHSAC PINMM3 FRUSAX CAVCOC ROSPET EUNBMC ACHCHI ACHCUR

λ EUNARC PSAMAR ACHACR 0 EUNBIL CRACUS NITSPP PSASUB EUNGLA TABFLO STANEO EUNMEI FRACV2 NITPER FRACP7 FRUCRA ACHHLV NAVDIG EUNSEP EUNEXG EUNMSP ENCFOG ACHKRI PSALEV EUNPRS PINUND1 ACHUND

Axis 2 Axis = 0.571 FRAUND PINBIP EUNPAL NAVLIB ENCSIL EUNTEN DIACON BRACPR EUNPRA EUNUND

EUNUND1 ENCLAN MERCIR ENCMIN NAVSCH -2 ROSPUS ACHMIN ACHLAE CYMNEO HANARC DIATEN GEIBOR NAVPTS

NAVCRP AMPLBC NAVVIR CRAACC SELPU3 FRAPER PINUND -4 PSABIO 13 Axis 1 λ = 0.805 Figure 11: CCA plot of (a) site scores (triangles) as weighted means of species scores (circles), showing 24 sites from SNP (sites with in situ water chemistry data were used) and the six variables used in the CCA. These six environmental variables explain the majority of the variation in the diatom data (Table 14). (b) CCA plot of species scores (87 species). See Appendix B for taxon abbreviations shown in (b).

105

210Pb Activity (Bq g-1) 0 0.05 0.1 0.15 0.2 0.25 0.0

2.0

4.0

6.0

8.0 Depth (cm) Depth 10.0

12.0

14.0

16.0

Figure 12: 210Pb activity profile of core BY14-2. Background level of activity (0.0065 Bq g-1) is reached at 7 cm

Cal yr BP

8000 6000 4000 2000 0 0

4070 cal yr BP

5 )

m 10 th (c th p De 15

210Pb dates Median 14C dates Corrected Median 14 C dates (- intercept of 4070) Regression line Corrected age profile

Figure 13: Age-depth profile of core BY14-2.

106

Fragilarioids Planktonics Benthics

llu) a F ( e 3 p ) ty ie) m u o gua tu v xi a oides all ta e l r. nii u m rmis a in o C (F va g s mis r. cur a an a bat ima ta morphoa s na s m st ar u a s BE do for p ion r e e m s s n l 46 "?" (La s ulum l s nut i a rat s te cen pig ulo ass lfourian m rt il ole doscutifo s c in mi u h s u nt e en pinnata al m p itulu ium iu c QUE c e nd P e n ) v la oc m h p bmo id er s 1 u e c h e c s u h p e p es ic m rel s s t h s h ps on R D (c i ria fl la tera ba ia t mis ingeaeria m la c sira oseira lirata u hia mo mothidh n a ia praerupta v s ro ros coseira lla ula c ula dig c ant ula t ie 0 0 ar A pth u ic ic m s o nularia inu c 5 e agilariala vire lac be v grop a am tz ades n v e e ta tau r u a avic itzs y av avic i innul un i a iatom p 2 OI55 OI9 Y D S S F A Au Ta N N N H NaviculaN s Ps Ps Ni Achn N AchnaD P E P C D S N L L 0 2006 1 1997 3 Zone 1987 2 1979 3 1969 4 1960 5 1953 6 1949 7 oe2 Zone 1942 8 9 1937 10 1928 11 12 13 14 15 *2330 16 17 1 Zone 18 19 20 21 *3290 22 23 24

02040600 0 0200 0 0200200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0102030 40 50 2 3 4 5 6 0200 x10e9 valves/g % % Relative Abundance (%) Figure 14: Diatom stratigraphy of core BY14-2 (23 most abundant taxa shown). Plus signs indicate presence of <0.5%. Species are grouped by ecological type and by decreasing abundance. Ages based on activity of 210Pb are shown; * denotes ages from corrected 14C radioisotope dating (tentative data; cal yr BP). Zones are based on cluster analysis of diatom assemblages (Figure 15). Species Richness is calculated by rarefaction and N2 is Hills's diversity index (the effective richness).

107

3.0 a) QBS01

2.0 2.0 QBS02 b) 2 BY10 1 QB05 3 QB03 4 QB10 BYS02 BYS06 QB01 QB15 0 1.0 QB06 QB12 1.0 Zone 3 QB04 BYS03 QB07 QB02 5 QB08 6 λ BYS01 11 0.0 0.0

λ 14

Axis 2 = 0.694 12 7 8 BY14-2 core BY04 22 BY18 20 assemblages BY14 18 BY01 Axis 2 Axis = 0.039 13 2 BY17 9 Zone 1 Zone 2 -1.0 BY15 -1.0 BY16 16 24 10

-2.0 -2.0 -2.0 -1.0 0.0 1.0 2.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 Axis 1 λ = 0.103 Axis 1 λ = 0.733

Figure 15: CA plots of site scores showing sites (includes core depths) as a function of diatom assemblages: a) CA of 25 surface and 20 BY14-2 core diatom assemblages, using 93 diatom species, and b) CA of 20 BY14-2 core diatom assemblages, using 35 diatom species.

108

9.0 1.0 a) b)

8.0 0.0

7.0

-1.0 6.0

2 pH Bootstrap Estimate (WA- PLS) pH Bootstrap(WA- Estimate rboot = 0.687 RMSEP = 0.41 pH Bootstrap Residuals (WA-PLS) 5.0 -2.0 5.0 6.0 7.0 8.0 9.0 5.06.07.08.09.0

9.0 0.75 c) d)

0.50

8.0 0.25

7.0 0.00

-0.25

6.0 (WA-PLS) pH residuals pH Estimates (WA-PLS) -0.50 2 r = 0.866 RMSE = 0.249 5.0 -0.75 5.0 6.0 7.0 8.0 9.0 5.0 6.0 7.0 8.0 9.0

Observed pH

Figure 16: Observed versus diatom-inferred pH using weighted averaging partial least squares (WA-PLS) regression technique and a) bootstrap cross-validation. c) Model without cross-validation. Residual errors (observed minus diatom-inferred pH) are shown in b) and d). RMSEP is root mean squared error of prediction. RMSE is root mean squared error. The model is based on a training set of 24 modern sites: QBS01, QBS02, QB01, QB02, QB03, QB04, QB05, QB08, QB06, QB07, QB10, QB12, QB15, BYS01, BYS02, BYS03, BYS06, BY01, BY10, BY14, BY15, BY16, BY17 and BY18.

109

pH reconstruction (WA-PLS)

6.45 6.60 6.75 6.90 7.05 7.20 7.35 7.50

0.0

2.5

5.0

7.5

10.0

12.5 Depth (cm) Depth

15.0

17.5

20.0

22.5

25.0

Figure 17: pH reconstruction for core BY14-2, using WA-PLS regression and bootstrap cross-validation. Error bars indicate the bootstrap estimated standard error of prediction for each depth.

110

3.0 0.4 µS/cm) a) µS/cm) b)

0.2

2.0 0.0

-0.2

1.0 -0.4

-0.6 2 rboot = 0.831 RMSEP = 2.48

0.0 -0.8 0.0 1.0 2.0 3.0 0.0 1.0 2.0 3.0 Specific Conductivity Bootstrap Residuals (log Residuals Bootstrap Conductivity Specific Specific Conductivity Bootstrap Estimates (log (log Estimates Bootstrap Conductivity Specific

3.0 0.3 c) d) µS/cm) µS/cm) 0.2

0.1 2.0 0.0

-0.1

-0.2 1.0

-0.3

-0.4 r=2 0.938 RMSE = 0.130 Specific Conductivity Estimates (log Specific Conductivity Residuals (log 0.0 -0.5 0.0 1.0 2.0 3.0 0.0 1.0 2.0 3.0 Observed Specific Conductivity (log µS/cm)

Figure 18: Observed versus diatom-inferred specific conductivity for core BY14-2 using log-transformed data, weighted averaging partial least squares (WA-PLS) regression technique and a) bootstrap cross-validation. c) Model without cross-validation. Residual errors (observed minus diatom-inferred specific conductivity) are shown in b) and d). RMSEP is root mean squared error of prediction. RMSE is root mean squared error. The model is based on a training set of 24 modern sites: QBS01, QBS02, QB01, QB02, QB03, QB04, QB05, QB08, QB06, QB07, QB10, QB12, QB15, BYS01, BYS02, BYS03, BYS06, BY01, BY10, BY14, BY15, BY16, BY17 and BY18.

111

specific conductivity reconstruction (µS/cm ; WA-PLS)

8 9 10111213141516

0.0

2.5

5.0

7.5

10.0 Depth (cm)

12.5

15.0

17.5

20.0

22.5

25.0

Figure 19: Specific conductivity reconstruction for core BY14-2, using WA-PLS regression and bootstrap cross-validation. Error bars indicate the bootstrap estimated standard error of prediction for each depth.

112

10.8 3.0 a) b)

9.6 2.0

8.4 1.0

7.2 0.0

6.0 -1.0

4.8 -2.0 r2 = 0.495 RMSEP = 1.279 Temperature BootstrapEstimates (°C) Temperature boot Residuals Bootstrap (°C) Temperature 3.6 -3.0 3.6 4.8 6.0 7.2 8.4 9.6 10.8 3.6 4.8 6.0 7.2 8.4 9.6 10.8

10.8 2.0 c) d)

9.6 1.0

8.4 0.0

7.2

-1.0 6.0

-2.0

Temperature Estimates (°C) Temperature 4.8 Residuals (°C) Temperature 2 r = 0.758 RMSE = 0.839

3.6 -3.0 3.6 4.8 6.0 7.2 8.4 9.6 10.8 3.6 4.8 6.0 7.2 8.4 9.6 10.8

Observed Water Temperature (°C)

Figure 20: Observed versus diatom-inferred water temperature using weighted averaging partial least squares (WA-PLS) regression technique and a) bootstrap cross-validation. c) Model without cross-validation. Residual errors (observed minus diatom-inferred temperature) are shown in b) and d). RMSEP is root mean squared error of prediction. RMSE is root mean squared error. The model is based on a training set of 24 modern sites: QBS01, QBS02, QB01, QB02, QB03, QB04, QB05, QB08, QB06, QB07, QB10, QB12, QB15, BYS01, BYS02, BYS03, BYS06, BY01, BY10, BY14, BY15, BY16, BY17 and BY18.

113

water temperature reconstruction (ºC; WA-PLS) 1 6.0 7.0 8.0 9.0 0 . 0 . . . . 001. 501. 002. 25.0 22.5 20.0 17.5 15.0 12.5 10.0 7.5 5.0 2.5 0.0 Depth ( cm )

Figure 21: Water temperature reconstruction for core BY14-2, using WA-PLS regression and bootstrap cross-validation. Error bars indicate the bootstrap estimated standard error of prediction for each depth.

114

490 r2 = 0.98 r2 = 0.77 0.36 RMSE = 0.021 489 RMSE = 11.161 0.35 0.34 488

0.33 (mV) ORP

TN-U (mg/L) 487 0.32

0.31 486 0510 15 20 25 0510 15 20 25

2.3 8.52 r2 = 0.92 r2 = 0.79 RMSE = 0.251 8.40 RMSE = 0.677 2.2 8.28

2 8.16 2.1 SiO (mg/L) 8.04 temperature (°C) temperature

2.0 7.92 0510 15 20 25 0510 15 20 25

0.0016 r2 = 0.84 r2 = 0.98 RMSE = <0.001 RMSE = <0.001 0.0015 0.011 g/L) m ( P

R 0.0014 0.010 S TP-U (mg/L)

0.0013 0.009 0510 15 20 25 0510 15 20 25

7.7 0.03 2 2 7.6 r = 0.83 r = 0.78 7.5 RMSE = 1.169 RMSE = 0.021 0.02 7.4 7.3

7.2 32 0.01 DOC (mg/L) DOC 7.1 NO NO (mg/L) 7.0 6.9 0.00 0510 15 20 25 0510 15 20 25

r2 = 0.78 1.92 1.92 RMSE = 0.466 1.80

1.80 a 1.68 1.68 1.56 DIC (mg/L) DIC 2 1.56 r = 0.96 1.44 RMSE = 0.600 chlorophyll- (mg/L) 1.44 1.32 0510 15 20 25 0510 15 20 25

Depths (cm)

Figure 22: Reconstructions of water quality variables for core BY14-2, based on weighted averaging partial least squares (WA-PLS) models. RMSE is root mean squared error. The training set for these reconstructions includes the 12 sites with the full set of water chemistry measurements: QBS01, QBS02, QB01, QB06, QB07, QB10, QB12, BYS03, BYS06, BY14, BY15 and BY16.

115

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Appendix A: Water chemistry data

Variable BY01 BY10 BY14 BY15 BY16 BY17 BY18 BYS01 BYS02 BYS03 BYS04 BYS05 BYS06 Temp 8.06 9.23 8.46 8.56 8.48 8.95 8.79 8.89 10.70 9.65 9.06 8.49 8.05 pH 7.05 7.93 7.05 7.56 7.35 7.09 8.32 7.31 7.29 7.68 7.99 8.46 8.38 SpCond 19.55 104.79 11.51 18.24 14.25 15.83 102.95 19.71 18.28 68.30 64.89 161.90 111.15 ORP 466.80 376.08 487.26 491.02 483.63 466.16 424.60 477.36 457.20 478.00 409.00 415.41 422.51 Turb 15.26 50.68 17.04 16.51 16.74 16.90 16.67 13.50 60.25 8.90 48.21 22.45 17.64

ALKCACO3 - - 3.30 4.90 4.72 - - - - 29.30 16.40 75.60 45.90 DOC - - 7.3 7.8 7.7 - - - - 10.4 13 11.6 7.4 DIC - - 1.3 1.65 1.5 - - - - 6.6 3.9 17 10.1

NO3-NO2 - - 0.005 0.007 0.0025 - - - - 0.006 0.009 0.012 0.114 NH3 - - 0.029 0.029 0.023 - - - - 0.015 0.049 0.011 0.006 TN-F - - 0.316 0.327 0.394 - - - - 0.488 0.937 0.627 0.459 TN-U - - 0.35 0.346 0.43 - - - - 0.491 0.937 0.636 0.445 SRP-U - - 0.0019 0.0016 0.0013 - - - - 0.0016 0.0024 0.0016 0.0016 TP-F - - 0.0061 0.0047 0.0064 - - - - 0.0051 0.0102 0.006 0.0036 TP-U - - 0.0114 0.0127 0.0131 - - - - 0.0061 0.0164 0.0093 0.0047 TP-PART - - 0.0117 0.0232 0.052 - - - - 0.0057 0.0173 0.0057 0.0095 CHLA - - 0.80 1.10 3.70 - - - - 0.05 0.70 0.20 0.10 CHLA-COR - - 0.80 0.80 4.30 - - - - 0.05 0.40 0.20 0.10 Col - - 46.1 31.4 53.2 - - - - 76.6 78.5 71.1 43.1 Cl- - - 0.8 1.12 0.72 - - - - 3.13 8.07 6.39 4.02

SO4 - - 0.69 0.75 0.9 - - - - 1.51 1.49 1.46 6.02 Ca - - 1.15 1.38 1.39 - - - - 8.05 6 19.8 11.5 Mg - - 0.52 0.63 0.67 - - - - 3.3 2.89 8.25 4.25 Na - - 1 1.14 1.09 - - - - 2.07 3.11 3.09 5.84 K - - 0.2 0.3 0.31 - - - - 0.33 0.26 0.47 0.58 SiO2 - - 2.68 1.7 2.35 - - - - 3.43 2.66 3.64 3.38 # Cores extracted 1 0 2 1 1 1 1 n/a n/a n/a n/a n/a n/a

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Appendix A (continued)

Variable QB01 QB02 QB03 QB04 QB05 QB06 QB07 QB08 QB10 QB11 QB12 QB15 QBS01 QBS02 Temp 4.93 5.53 5.36 5.50 4.12 6.02 8.28 7.20 8.56 5.54 5.16 6.75 6.07 7.34 pH 6.28 6.46 6.31 6.40 6.75 6.41 6.66 6.93 6.2 6.1 6.26 6.46 6.10 5.95 SpCond 4.34 3.39 3.70 2.89 4.80 2.63 3.45 7.85 4.7 4.6 3.30 5.01 3.69 3.68 ORP 486.71 504.75 512.29 507.57 502.67 512.33 495.50 486.75 502.67 502.67 506.00 500.14 473.19 514.50 Turb 12.04 12.68 11.74 11.74 12.00 14.37 11.85 13.08 11.85 7.60 13.80 11.57 18.74 7.60

ALKCACO3 1.60 - - - - 0.86 1.09 - 1.18 1.00 0.86 - 0.70 0.85 DOC 1.6 - - - - 6.1 6.3 - 4 0.8 5.5 - 1.3 1.1 DIC 0.7 - - - - 0.6 1 - 0.9 0.9 0.7 - 0.7 0.7

NO3-NO2 0.037 - - - - 0.074 0.029 - 0.0525 0.066 0.078 - 0.139 0.088

NH3 0.003 - - - - 0.0025 0.005 - 0.0025 0.0025 0.0025 - 0.0025 0.0025 TN-F 0.101 - - - - 0.204 0.181 - 0.178 0.139 0.222 - 0.211 0.142 TN-U 0.09 - - - - 0.128 0.101 - 0.1055 0.104 0.133 - 0.19 0.154 SRP-U 0.0006 - - - - 0.0007 0.0005 - 0.00055 0.0005 0.0011 - 0.0011 0.0009 TP-F 0.0023 - - - - 0.0017 0.0018 - 0.00165 0.0015 0.0019 - 0.0024 0.0018 TP-U 0.0029 - - - - 0.0034 0.0029 - 0.00235 0.0029 0.0051 - 0.0039 0.0034 TP-PART 0.0052 - - - - 0.0057 0.0046 - 0.00395 0.0047 0.0112 - 0.0113 0.0101 CHLA 0.10 - - - - 0.05 0.50 - 0.18 0.20 0.30 - 0.05 0.20 CHLA-COR 0.05 - - - - 0.05 0.50 - 0.23 0.20 0.30 - 0.05 0.20 Col 1.6 - - - - 0.0 3.3 - 1.8 0.8 1.2 - 3.1 3.6 Cl- 0.31 - - - - 0.32 0.35 - 0.35 0.35 0.34 - 0.33 0.34

SO4 0.47 - - - - 0.3 0.37 - 0.355 0.33 0.38 - 0.45 0.4 Ca 0.6 - - - - 0.42 0.49 - 0.32 0.31 0.32 - 0.27 0.28 Mg 0.2 - - - - 0.12 0.15 - 0.14 0.13 0.14 - 0.13 0.14 Na 0.39 - - - - 0.38 0.42 - 0.365 0.33 0.31 - 0.45 0.42 K 0.26 - - - - 0.15 0.18 - 0.115 0.12 0.15 - 0.11 0.11 SiO2 1.16 - - - - 0.97 1.16 - 1.16 0.95 0.87 - 1.62 1.41 # Cores extracted 1 1 1 2 0 1 1 0 1 1 2 1 n/a n/a

129

Appendix B: Modern diatom species counts for surface samples

Sample label BY01-B BY04-EM-Y BY10-EM-Y BY14-1-B BY15-B BY16-B BY17-B BY18-B BYS01-RS-Y BYS02-RS-X BYS03-RS-Y BYS06-RS-X QB01-B QB02-B QB 03-B QB04-1-B QB05-RS-y QB06-0-0.5-B QB07-B QB08-EkM-y QB10-B QB12-2-B QB15-B QBS01-RS-x QBS02-RS-Y Depthdowncore(cm) 0000000000000000000000000 Total valves counted 697 576 571 590 893 982 1209 537 663 566 548 672 617 613 569 603 591 641 556 537 567 606 555 517 559 Diatom concentration (valves/cm3) n/ a n/a n/a n/a n/ a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/an/an/an/an/an/an/an/an/an/a Rarefaction 17.66 31.91 31.21 25.10 29.25 22.38 29.01 29.66 35.80 29.45 33.80 29.39 27.40 43.95 32.57 19.55 24.45 21.35 25.76 33.91 47.12 26.87 56.58 14.00 23.68 Species conc (spec/cm3) n/ a n/a n/a n/a n/ a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/an/an/an/an/an/an/an/an/an/a Abbrev. Taxon name No. of lakes ACHUND Achn_unknown 5 6 7 2195 ACHACR Achnanthes acares 5 1 39 6 18 2 ACHBSB Achnanthes biasolettiana var. subatomoides 1 2 ACHBIC Achnanthes bicapitata 2 22 ACHCAR Achnanthes carissima 1 33 ACHCHI Achnanthes chilandos 2 110 ACHCUR Achnanthes curtissima 14 4 2 2 46 234 3 31 2 28 205 10 64 1 19 ACHDAO Achnanthes daonensis 2 15 2 ACHHLV Achnanthes helvetica 18 5 1 5 6 7 14 15 8 64 9 14 70 4 48 33 17 314 10 ACHHOL Achnanthes holsti 1 1 ACHIMP Achnanthes impexiformis 0 ACHKRZ Achnanthes kranzii 1 1 ACHKRI Achnanthes kriegeri 16 2 3 34 2 12 12 11 5121775241318160 ACHLAE Achnanthes laevis var. laevis 2 18 10 ACHLAT Achnanthes laterostrata 3 4 1 4 PSALEV Psammothidium levanderi 6 14 3 69 583 9 ACHLCV Achnanthes locus-vulcani 0 PSAMAR Psammothidium marginulatum 18 3 9 5 2 7 5 194 33 255 56 333 203 102 3 54 227 90 177 ACHNOD Achnanthes nodosa (or N. seminulum?) 1 48 ACHRUP Achnanthes rupestoides 0 ACHSAC Achnanthes saccula 3 16 24 ACHSCO Achnanthes scotia 0 ACH01Q Achnanthes sp1QUEBEC 1 12 ACH11Q Achnanthes sp11QUEBEC 1 2 ACHSUB Achnanthes subcapitata 1 1 ACHTHM Achnanthes thermalis 1 1 ACHVNT Achnanthes ventralis 2 2 2 ACHMIN Achnanthidium minutissimum 15 8 31 208 4 13 6 5 32 42 103 239 320 7 52 2 ADLBRY Adalfia bryophila 1 2 ADLMIN Adalfia minuscula 1 2 AMPCOP Amphora copulata 2 4 2 AMPLBC Amphora libyca 1 9 AMPVEN Amphora sp. [cf. A. veneta] 2 2 4 AULALP Aulacoseira alpigena 15 1 46 3 15 1 29 6 27 79 156 73 2 9 124 22 AULLIR Aulacoseira lirata 13 42 40 216 20 50 344 4 22 52 14 16 136 130 AULUND Aulacoseira sp und 1 2 BRAMIC Brachysira microcephala 1 1 BRACPR Brachysirasp.[cf.B.procera] 7 11 22 231 3 CALSIL Caloneis silicula 5 2 1 1 1 2 CALBAC Caloneis bacillum 5 2 2 2 25 CALCAI Caloneis sp. [cf. C. aemula var. inflata] 1 3 CALFAS Caloneis fasciata 1 3 CALFUS Caloneis fusus 1 2 CALUND Caloneis sp und 2 1 1 CAVCOC Cavinula cocconeiformis 2 9 19 CAVJAE Cavinula jaernefeltii 1 4 CAVPSE Cavinula pseudoscutiformis 13 131967627 7 68 1 CAVVIN Cavinula vincentii 0

130

Sample label BY01-B BY04-EM-Y BY10-EM-Y BY14-1-B BY15-B BY16-B BY17-B BY18-B BYS01-RS-Y BYS02-RS-X BYS03-RS-Y BYS06-RS-X QB01-B QB02-B QB03-B QB04-1-B QB05-RS-y QB06-0-0.5-B QB07-B QB08-EkM-y QB10-B QB12-2-B QB15-B QBS01-RS-x QBS02-RS-Y Abbrev. Taxon name No. of lakes CENUND Centric sp und 3 241 CHABER Chamaepinnularia begerii 1 4 CRAACC Craticula accomoda 1 13 CRACUS Craticula cuspidata 1 7 CRAHAL Craticula halophila 2 4 5 CRAMOL Craticula molesta 5 121 22 CRABUD Craticula sp. [cf. C. buderi] 1 1 CYCOCE Cyclotella ocellata 1 1 CYCMNG Cyclotella sp. [cf. C. meneghiniana] 1 1 CYCSTE Cyclotella stelligera 1 56 CYMDEL Cymbella sp. [cf. C. delicatulata] 1 2 CYMMNL Cymbella minuta sensu lato 1 1 CYMNAV Cymbopleura naviculiformis 1 1 CYMNEO Cymbella neocistula 2 66 CYMAFF Cymbellasp.[cf.C.affinis] 1 1 CYMCM1 Cymbella sp. [cf. C. spp. in fig 7-9, pl 45 in Cummings] 1 1 CMBUND Cymbella sp und 2 21 CMPAMP Cymbopleura amphicephala 5 3 5 1 11 5 CYMANS Cymbopleura angusta var. spitsbergensis 1 2 CYMLAP Cymbopleura lapponica 2 52 CYMTYN Cymbopl eura t ynnii 0 CMPSP1 Cymbopleura sp1 1 4 CYMUND Cymbella sp und (broken) 0 DENTEN Denticula tenuis 1 3 DIAS1A Diadesmis "sp1- Antoniades" 1 1 DIACON Diadesmis contenta 2 110 DIAING Diadesmis ingeaeformis 6 1 3 7 2 11 DIAPAR Diadesmis paracontenta 1 1 DIALAE Diadesmis laevissima 2 1 2 DIAUND Diatoma sp und 1 1 DIATEN Diatoma tenuis 4 18 30 41 53 DIPMAR Diploneis sp. [cf. D. marginstriata] 2 1 4 DIPOVA Diploneis ovalis 0 DIPPAR Diploneis sp. [cf. D. parma] 1 1 ENCELG Encyonema elginense 1 3 ENCFOG Encyonema fogedii 8 10 3 3 2 15 29 5 1 ENCGAE Encyonema gaeumannii 1 5 ENCLAN Encyonema lange-bertalotti 7 4 4 16 1 2246 ENCMIN Encyonema minutum 3 4 25 2 ENCPAU Encyonema paucistriatum 1 1 ENCSIL Encyonema silesiacum 3 1 7 2 ENCUN2 Encyonema sp 1 (pic 36, QB10) 1 4 ENCUND Encyonema sp und (pic 31 BY17) 1 2 ENCVEN Encyonema ventricosum 3 2 3 2 ENCCES Encyonopsis sp. [cf. E. cesatiformis] 1 1 EOLSP4 Eolimna Sp.4, Lavoie et al 1 1 EPISOR Epithemia sorex 0 EUNARL Eunotia arculus 1 1 EUNARC Eunotia arcus 4 1 46 2 1 EUNBIL Eunotia bilunaris 4 7322 EUNBMC Eunotia bilunaris var mucophila 4 51 81 EUNBOR Eunotia boreotenuis 5 11 122 EUNDEN Eunotia denticula 2 2 2 EUNDIO Eunotia diodon 3 1 1 1

131

Sample label BY01-B BY04-EM-Y BY10-EM-Y BY14-1-B BY15-B BY16-B BY17-B BY18-B BYS01-RS-Y BYS02-RS-X BYS03-RS-Y BYS06-RS-X QB01-B QB02-B QB03-B QB04-1-B QB05-RS-y QB06-0-0.5-B QB07-B QB08-EkM-y QB10-B QB12-2-B QB15-B QBS01-RS-x QBS02-RS-Y Abbrev. Taxon name No. of lakes EUNELE Eu notia elegans 1 2 EUNEXG Eunotia exigua 3 236 EUNFLE Eunotia flexulosa 2 1 2 EUNUND Eunotia girdle view (s=12-14, l=13) 1 6 EUNGLA Eunotia glacialis 4 86 119 EUNGRO Eunotia groenlandica 1 2 EUNIMP Eunotia implicata 5 2 1 2 1 1 EUNINC Eunotia incisa 1 1 EUNINT Eunotia intermedia 2 2 1 EUNMEI Eunotia meisteri 4 45 6 10 2 EUNMNR Eunotia minor 4 3 14 6 7 EUNMON Eunotia monodon 2 11 EUNMSP Eunotia muscicola var. perminuta 10 592995 74316945 EUNNYM Eunotia nymanniana 5 92 1 15 3 EUNPAL Eunotia paludosa 5 12 2412 EUNPEC Eunotia pectinalis 1 1 EUNPCM Eunotia pectinalis var. minor 1 2 EUNPRA Eunotia praerupta 4 8 17 3 EUNPRB Eunotia praerupta [cf. E. p. var. biggiba] 3 31 3 EUNPRB Eunotia praerupta var. bidens 1 2 EUNPRC Eunotia praerupta var.curta 4 1 1 22 EUNRHO Eunotia rhomboidea 2 3 2 EUNRHY Eunotia rhynchocephala var satelles 1 4 EUNROS Eunotia rostellata 1 3 EUNSEP Eunotia septentrionalis 3 2 8 11 EUNSDI Eunotia serra var. di adema 0 EUNSOL Eunotia soleirolii 1 2 EUNBRZ Eunotia sp. [cf E. "Brazilian species" (Lange-Bertalot)]* 1 1 EUNPRS Eunotia sp. [cf. E.praerupta var. suecica] 1 7 EUNSUB Eunotia subarcuatoides 1 4 EUNSUD Eunotia sudetica 1 3 EUNSUE Eunotia suecica 1 4 EUNTEN Eunotia tenella 5 3 429 1 EUNUND Eunotia sp und 8 1 1 3 7261519 EUNVAL Eunotia valida 1 4 FRACAP Fragilaria capucina 2 12 FRACP3 Fragilaria capucina forme 3 0 FRACP5 Fragilaria capucina forme 5 1 1 FRACP7 Fragilaria capucina forme 7 9 15 11 4 20 2 10 2 9 4 FRACAV Fragilaria capucina var. vaucheriae 12 1 4 2 5 66 98 14 22 3 1 16 3 FRACV2 Fragilaria capucina var. vaucheriae- form 2 4 1 1 46 12 FRAEXI Fragilaria exigua 0 FRAPER Fragilaria perminuta 2 6 72 FRAUND Fragilaria sp und 9 2 1 3 3 6 4 56 1 FRAVRE Fragilaria virescens var. exigua 4 9 27 1 5 FRUCRA Frustulia crassinervia 6 14 2911 FRUSAX Frustulia saxonica 9 716228 2 32 18 3 1 SPEUND Eunotia or fragilaria sp und (girdle) 2 2 2 GEIBOR Geissleria boreosiberica 1 7 GEISCH Geissleria schoenfeldii 0 GOMCAN Gomphonema sp. [cf. G. anglicum] 0 GOMCLA Gomphonema clavatulum 1 3 GOMGRA Gomphonema gracile 1 5

132

Sample label BY01-B BY04-EM-Y BY10-EM-Y BY14-1-B BY15-B BY16-B BY17-B BY18-B BYS01-RS-Y BYS02-RS-X BYS03-RS-Y BYS06-RS-X QB01-B QB02-B QB03-B QB04-1-B QB05-RS-y QB06-0-0.5-B QB07-B QB08-EkM-y QB10-B QB12-2-B QB15-B QBS01-RS-x QBS02-RS-Y Abb rev. Taxon name No. of lakes GOMMIC Gomphonema micropus 2 1 4 GOMPAR Gomphonema parvulum 2 1 5 COMPPR Gomphonema parvulum var. parvulus 0 COMPRO Gomphonema sp. [cf. G. productum] 1 6 GOMUND Gomphonema sp und 4 1 1 1 1 HANARC Hannaea arcus 2 15 35 HYGBAL Hygroptera balfouriana 7 5 1 16 3 13 11 4 KARLAT Karayevia laterostrata 1 1 MERCIR Meridion circulare 3 1 17 2 NAVS1Q Navicula "sp1 Quebec" (Fallu) 3 6 28 16 NAV40Q Navicula "sp40 Quebec" (Fallu) 0 NAVAUT Navicula aff. Utermoehlii 1 2 NAVAGR Navicula agrestis 1 3 NAVCHI Navicula sp. [cf. N. chiarae] 1 2 NAVCIN Navicula cincta 2 5 4 NAVCON Navicula contenta 0 NAVCRP Navicula cryptocephala 5 4 77 9 6 6 NAVDIA Navicula diadesmis 1 2 NAVDIG Navicula digitulus 4 12 6 24 NAVEGR Navicula egregia 1 2 NAVELG Navicula elginensis 1 2 NAVEXP Navicula explanata 0 NAVFES Navicula festiva 1 2 NAVGER Navicula gerloffii 1 3 NAVKRA Navicula krasskei 1 1 NAVLIB Navicula libonensis 1 20 NAVMIC Navicula micropuncta 1 2 NAVMIN Navicula minima 0 NAVCMN Navicula sp. [cf. N. miniscula] 0 NAVMUR Navicula muraloides 0 NAVPUM Navicula pupula var. mutata 1 3 NAVRAD Navicula radiosa 1 2 NAVRHY Navicula rhynchocephala 0 NAVSAL Navicula salinarum 1 1 NAVSCH Navicula schmassmannii 5 6 14 630 1 NAVSEM Navicula seminulum 7 12 13 29 48 28 6 5 NAVCSE Navicula sp. [cf. N. seminuloides] 0 NAVPTS Navicula phylleptosoma 3 5 4 41 NAVPSE Navicula pseudotenelloides 1 4 NAVMEG Naviculoid sp. [cf. Microstatus egregius] 1 1 NAVRHE Navicula sp. [cf. N. rhynchocephala f. elegans] 2 1 1 NAVSCH Navicula sp. [cf. N. schadei] 1 1 NAVSMI Navicula sp. [cf. N. seminulum var. intermedia] 1 1 NAVSUB Navicula submolesta 1 3 NAVSUB Navicula submuralis 2 32 19 NAVUND Navicula sp und 8 2 11 4 1 14 23 82 2 NAVVEN Navicula ventralis 1 2 NAVVIR Navicula viridulacalcis 1 16 NEIWUL Neidiopis wulffii 0 NEIAFF Neidium affine 5 1 5 223 NEIALP Neidium alpinum 0 NEIAMP Neidium ampliatum 1 2

133

Sample label BY01-B BY04-EM-Y BY10-EM-Y BY14-1-B BY15-B BY16-B BY17-B BY18-B BYS01-RS-Y BYS02-RS-X BYS03-RS-Y BYS06-RS-X QB01-B QB02-B QB03-B QB04-1-B QB05-RS-y QB06-0-0.5-B QB07-B QB08-EkM-y QB10-B QB12-2-B QB15-B QBS01-RS-x QBS02-RS-Y Abbrev. Taxon name No. of lakes NEIBIS Neidium bisulcatum 9 1 1 2 1 1 22 1 1 NEIDEC Neidium decoratum 1 5 NEIIRI Neidium iridis 1 1 NEIUND Neidium sp und (broken) 1 1 NITSPP Nitzschia spp 21 14 64 21 22 12 11 12 10 56 11 28 32 4 5 1646318 NITPER Nitzschia perminuta 9 5 1 7 49 23 20 35 28 7 PININT Pinnularia sp. [cf. P. intermedia] 1 1 PINBAL Pinnularia balfouriana 1 3 PINBIC Pinnularia biceps var. biceps 2 4 4 PINBIP Pinnularia biceps var. petersenni 4 3 5181 PINBBA Pinnularia brebissonii var. acuta 0 PINDIS Pinnularia divergens var. sublinearis 1 1 PINDIV Pinnularia divergentissima 2 12 PINGRU Pinnularia grunowii 1 2 PININM Pinnularia intermedia 1 1 PINOBS Pinnularia obscura 0 PINRAB Pinnularia rabenhorstii 2 2 1 PINSCH Pinnularia schroeterae 2 51 PIN182 Pinnularia Pl 182 Ettl - unidentified sp 1 2 PINUND Pinnularia Sp1 1 54 PINMES Pinnularia sp [cf. P. mesolepta] 1 2 PINMIC Pinnularia sp [cf. P.microstauron] 5 2 7 5 21 PINMM3 Pinnularia mesolepta morphotype 3 12 5 2 6 1 20 4 2013217312 PINUND Pinnularia sp und 5 1 1 5 17 1 PIN65A Pinnularia Pl 65 Ant on - unidentified sp 0 PIN46L Pinnularia und-Lavoie Pl46 "?" 0 PLAEXP Placoneis explanata 1 4 PLALAN Plantothidium lanceolatum 1 5 PLNOES Planothidium oestrupii 2 5 2 PLNPER Planothidium sp. [cf. P. peragalloi] 2 1 3 PSABRO Psammnothidium broenlundense 1 1 PSABIO Psammothidium bioretii 1 7 PSACHI Psammothidium chlidanos 1 2 PSASUBPsammothidiumsubatomoides 11 192 5112521293 930 40 PSAVEN Psammot hidium ventrale 2 2 1 PSEBRE Pseudostaurosira brevistriata 0 PSEPSE Pseudostaurosira pseudoconstruens 0 PUNLAN Punctastriata sp. [cf. P. lancettula] 1 1 ROSPET Rossithidium petersenii 10 1 57 81 29 12 1 1 2 21 ROSPUS Rossithidium pussilla 9 3 36 11 13 15 10 15 142 2 SELBLA Sellaphora blackfordensis 1 1 SELPU3 Sellaphora pupula (complex) forme 3 (Lavoie) 1 8 SELPSE Sellaphora pseudonana 0 SELR/B Sellaphora rectangularis? or blackfordensis? 2 1 1 SELUND Sellaphora sp und 2 1 1 STAS1A Stauroneis sp1 (Antoniades) 2 22 9 STAS2A Stauroneis sp2 (Antoniades) 0 STAAGR Stauroneis agrestis 1 2 STAAMP Stauroneis amphicephala 5 2 2 2 2 1 STAGRA Stauroneis gracilis 2 1 1 STAJAR Stauroneis jarensis / S. reichardtii 2 2 1 STAKRI Stauroneis krigerii 1 2 STANEO Stauroneis neohyalina 7 2 5 1 25819 STASMI Stauroneis smithii 2 2 1

134

Sample label BY01-B BY04-EM-Y BY10-EM-Y BY14-1-B BY15-B BY16-B BY17-B BY18-B BYS01-RS-Y BYS02-RS-X BYS03-RS-Y BYS06-RS-X QB01-B QB02-B QB03-B QB04-1-B QB05-RS-y QB06-0-0.5-B QB07-B QB08-EkM-y QB10-B QB12-2-B QB15-B QBS01-RS-x QBS02-RS-Y Abbrev. Taxon name No. of lakes STATHE Stauroneis thermicoloides 1 2 STAUND Stauroneis sp und 1 3 STCONC Staurosira construens 1 1 STVENT Staurosira venter 7 16 289 456 752 675 1 5 STPINN Staurosirella pinnata 11 596 340 9 88 243 13 222 351 232 2 4 STPINL Staurosirella pinnata - long form 1 2 STPINI Staurosirella pinnata var intercedens 0 SURBOH Surirella bohemica 1 2 SURROB Surirella roba 1 4 SURAMO Surirella sp. [cf. S. amoena] 1 5 AURANG Surirella sp. [cf. S. angusta] 2 11 SURUND Surirella sp und (broken) 0 SURMIN Surirella sp. [cf. S. minuta] 1 2 TABFLO Tabellaria flocculosa 21 4 14 3 5 16 1 38 47 1 1 7 6 87311416610889 TETGLA Tetracyclus glans 0 TRYLEV Tryblionella levindensis 1 4 TRYSP1 Tryblionella sp1 Antoniades 1 1 ULNUND Ulnaria sp und (broken) 1 1 UNDUND Und species 13 4 7 3 23 22 1 2 12 17 5 2 2 3 NAVSP1 Navicula sp1 2 12 7 UROERI Urosolenia eriensis 2 2 2

* or Peronia fibula (Antoniades)

135

Appendix C: Fossil diatom species counts for core BY14-2

Sample label BY14-2-0-0.5-B BY14-2-1-1.5-B BY14-2-2-2.5B BY14-2-3-3.5-B BY14-2-4-4.5B BY14-2-5-5.5-B BY14-2-6-6.5B BY14-2-7-7.5-B BY14-2-8-8.5-B-2 BY14-2-9-9.5-B BY14-2-10-10.5-B-3 BY14-2-11-11.5-B-2 BY14-2-12-12.5-B BY14-2-13-13.5-B-3 BY14-2-14-14.5-B-3 BY14-2-16-16.5-B-4 BY14-2-18-18.5-B-4 BY14-2-20-20.5-B-4 BY14-2-22-22.5-B-3 BY14-2-24-24.5-B-4 Depth down core (cm) 0 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 16.5 18.5 20.5 22.5 24.5 Number of transects 21111211123112232272 Total valves counted 00000000000000000000 Diatom concentration (valves/cm 3) 2.13E+08 8.40E+08 1.06E+09 6.24E+08 6.20E+08 4.51E+08 4.85E+08 5.80E+08 4.48E+08 4.82E+08 4.15E+09 5.72E+08 5.91E+08 5.52E+09 4.53E+09 1.35E+09 1.78E+09 1.72E+09 1.64E+09 1.77E+09 Rarefaction 37.28 33.28 35.04 38.09 33.26 41.95 35.80 37.60 42.29 36.91 33.61 33.64 40.58 39.37 37.00 39.82 34.84 42.66 48.44 47.57 Species conc (spec/cm3) 154,657 138,033 145,337 157,997 137,952 174,034 148,497 155,969 175,443 153,104 10,572,560 139,548 168,348 12,384,733 11,640,200 1,466,458 1,283,351 1,571,172 15,239,947 1,751,938 Abb rev. Taxon name No. of lakes ACHUND Achn_unknown 2 1 5 ACHACR Achnanthes acares 4 2 1 63 ACHBSB Achnanthes biasoletti ana var. subatomoides 0 ACHBIC Achnanthes bicapitata 0 ACHCAR Achnanthes carissima 12 1 1 1 3 2212101011 1 ACHCHI Achnanthes chilandos 12 1 2 3 1 154562911 ACHCUR Achnanthes curtissima 20 3 10 12 10 7 17 9 14 13 25 18 20 5 1 1 2 4 2 3 3 ACHDAO Achnanthes daonensis 2 13 ACHHLV Achnanthes helvetica 17 4 1 2 7 10 3 5151112 3117 ACHHOL Achnanthes holsti 0 ACHIMP Achnanthes impexiformis 1 1 ACHKRZ Achnanthes kranzii 0 ACHKRI Achnanthes kriegeri 7 2 3412 21 ACHLAE Achnanthes laevis var. laevis 0 ACHLAT Achnanthes laterostrata 0 PSALEV Psammothidium levanderi 8 2 771 1114 ACHLCV Achnanthes locus-vulcani 5 5 6 116 PSAMAR Psammothidium marginulatum 20 1 17 16 7 7 24 12 12 712 7 3 8 3122517 12210 ACHNOD Achnanthes nodosa (or N. seminulum?) 0 ACHRUP Achnanthes rupestoides 1 3 ACHSAC Achnanthes saccula 3 2 23 ACHSCO Achnanthes scotia 2 37 ACH01Q Achnanthes sp1QUEBEC 0 ACH11Q Achnanthes sp11QUEBEC 0 ACHSUB Achnanthes subcapitata 0 ACHTHM Achnanthes thermalis 0 ACHVNT Achnanthes ventralis 18 6 11 5 2 1 5 843 121332454 ACHMIN Achnanthidium minutissimum 18 5 3 7 5 4 8 17751113 1533 ADLBRY Adalfia bryophila 0 ADLMIN Adalfia minuscula 0 AMPCOP Amphora copulata 0 AMPLBC Amphora libyca 0 AMPVEN Amphora sp. [cf. A. veneta] 0 AULALPAulacoseiraalpigena 20 2951715263633226 14 38 20 47 69 57 62 76 58 57 59 93 AULLIR Aulacoseira lirata 20 7 14 10 12 10 34 13 35 14 35 13 10 12 7 3 8 4 6 8 7 AULUND Aulacoseira sp und 1 2 BRAMIC Brachysira microcephala 0 BRACPR Brachysira sp. [cf. B. procera] 0 CALSIL Caloneis silicula 4 2 5 11 CALBAC Caloneis bacillum 1 2 CALCAI Caloneis sp. [cf. C. aemula var. inflata] 0 CALFAS Caloneis fasciata 1 1 CALFUS Caloneis fusus 0 CALUND Caloneis sp und 4 1 231 CAVCOC Cavinula cocconeiformis 2 1 2 CAVJAE Cavinula jaernefeltii 0 CAVPSE Cavinula pseudoscutiformis 19 7 18 17 6 9 5 4 5484 25742131 CAVVIN Cavinula vincentii 1 2 CENUND Centric sp und 0

136

Sample label BY14-2-0-0.5-B BY14-2-1-1.5-B BY14-2-2-2.5B BY14-2-3-3.5-B BY14-2-4-4.5B BY14-2-5-5.5-B BY14-2-6-6.5B BY14-2-7-7.5-B BY14-2-8-8.5-B-2 BY14-2-9-9.5-B BY14-2-10-10.5-B-3 BY14-2-11-11.5-B-2 BY14-2-12-12.5-B BY14-2-13-13.5-B-3 BY14-2-14-14.5-B-3 BY14-2-16-16.5-B-4 BY14-2-18-18.5-B-4 BY14-2-20-20.5-B-4 BY14-2-22-22.5-B-3 BY14-2-24-24.5-B-4 Abbrev. Taxon name No. of lakes CYMUND Cymbell a sp und (broken) 3 2 1 1 DENTEN Denticula tenuis 0 DIAS1A Diadesmis "sp1- Antoniades" 0 DIACON Diadesmis contenta 0 DIAING Diadesmis ingeaeformis 15 2 8 4 8357 1266128487 DIAPAR Diadesmis paracontenta 2 1 1 DIALAE Diadesmis laevissima 1 2 DIAUND Diatoma sp und 0 DIATEN Diatoma tenuis 0 DIPMAR Diploneis sp. [cf. D. marginstriata] 0 DIPOVA Diploneis ovalis 0 DIPPAR Diploneis sp. [cf. D. parma] 0 ENCELG Encyonema elginense 0 ENCFOG Encyonema fogedii 0 ENCGAE Encyonema gaeumannii 0 ENCLAN Encyonema lange-bertalotti 1 1 ENCMIN Encyonema minutum 4 12 4 1 ENCPAU Encyonema paucistriatum 1 1 ENCSILEncyonemasilesiacum 18 4123321345 3 2271342 ENCUN2 Encyonema sp 1 (pic 36, QB10) 0 ENCUND Encyonema sp und(pic 31 BY17) 1 1 ENCVEN Encyonema ventricosum 0 ENCCES Encyonopsis sp. [cf. E. cesatiformis] 0 EOLSP4 Eolimna Sp.4, Lavoie et al 0 EPISOR Epithemia sorex 1 1 EUNARL Eunotia arculus 0 EUNARC Eunotia arcus 0 EUNBIL Eunotia bi lunaris 7 2 211 211 EUNBMC Eunotia bi lunaris var mucophila 0 EUNBOR Eunotia boreotenuis 0 EUNDEN Eunotia denticula 0 EUNDIO Eunotia diodon 5 3 1 2 1 1 EUNELE Eunotia elegans 0 EUNEXG Eunotia exigua 0 EUNFLE Eunotia flexulosa 0 EUNUND Eunotia girdle view (s=12-14, l=13) 0 EUNGLA Eunotia glacialis 0 EUNGRO Eunotia groenlandica 0 EUNIMP Eunotia implicata 0 EUNINC Eunotia incisa 0 EUNINT Eunotia intermedia 0 EUNMEI Eunotia meisteri 0 EUNMNR Eunotia minor 0 EUNMON Eunotia monodon 0 EUNMSP Eunotia muscicola var. perminuta 0 EUNNYM Eunotia nymanniana 1 1 EUNPAL Eunotia paludosa 2 12 EUNPEC Eunotia pectinalis 0 EUNPCM Eunotia pectinalis var. minor 0 EUNPRA Eunotia praerupta 6 1 2 13 11 EUNPRB Eunotia praerupta [cf. E. p. var. biggiba] 0 EUNPRB Eunotia praerupta var. bidens 0 EUNPRC Eunotia praerupta var. curt a 17 7 5 6 9 3 3 4 329131 41115 EUNRHO Eunotia rhomboidea 0 EUNRHY Eunotia rhynchocephala var satelles 4 1 122

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Sample label BY14-2-0-0.5-B BY14-2-1-1.5-B BY14-2-2-2.5B BY14-2-3-3.5-B BY14-2-4-4.5B BY14-2-5-5.5-B BY14-2-6-6.5B BY14-2-7-7.5-B BY14-2-8-8.5-B-2 BY14-2-9-9.5-B BY14-2-10-10.5-B-3 BY14-2-11-11.5-B-2 BY14-2-12-12.5-B BY14-2-13-13.5-B-3 BY14-2-14-14.5-B-3 BY14-2-16-16.5-B-4 BY14-2-18-18.5-B-4 BY14-2-20-20.5-B-4 BY14-2-22-22.5-B-3 BY14-2-24-24.5-B-4 Abbrev. Taxon name No. of lakes FRAUND Fragilaria sp und 10 7 1 2 1221311 FRAVRE Fragilaria virescens var. exigua 15 3 4 12 3245 577141385 FRUCRA Frustulia crassinervia 2 2 2 FRUSAX Frustulia saxonica 12 1 1 2 1 3 8510432 1 SPEUND Eunotia or fragilaria sp und (girdle) 0 GEIBOR Geissleria boreosiberica 0 GEISCH Geissleria schoenfeldii 4 3 12 2 GOMCAN Gomphonema sp. [cf. G. anglicum] 1 1 GOMCLA Gomphonema clavatulum 0 GOMGRA Gomphonema gracile 0 GOMMIC Gomphonema micropus 1 1 GOMPAR Gomphonema parvulum 1 1 COMPPR Gomphonema parvulum var. parvulus 1 1 COMPRO Gomphonema sp. [cf. G. productum] 0 GOMUND Gomphonema sp und 7 1 1 1 12 11 HANARC Hannaea arcus 0 HYGBAL Hygroptera balfouriana 20 31 39 61 41 35 56 21 16 911 5 72014162217241725 KARLAT Karayevia laterostrata 0 MERCIR Meridion circulare 0 NAVS1Q Navicula "sp1 Quebec" (Fallu) 14 1 3 3 5 1 21212184836 NAV40Q Navicula "sp40 Quebec" (Fallu) 4 2 1 1 3 NAVAUT Navicula aff. Utermoehlii 0 NAVAGR Navicula agrestis 0 NAVCHI Navicula sp. [cf. N. chiarae] 0 NAVCIN Navicula cincta 0 NAVCON Navicula contenta 1 1 NAVCRP Navicula cryptocephala 16 3 3 3 3 2 13 1131214 22 NAVDIA Navicula diadesmis 0 NAVDIG Navicula digitulus 20 11 17 23 11 7 26 28 15 15 24 11 11 11 24 13 12 7 8 20 11 NAVEGR Navicula egregia 0 NAVELG Navicula elginensis 0 NAVEXP Navicula explanata 1 1 NAVFES Navicula festiva 0 NAVGER Navicula gerloffii 1 2 NAVKRA Navicula krasskei 2 11 NAVLIB Navicula libonensis 0 NAVMIC Navicula micropuncta 0 NAVMIN Navicula minima 1 1 NAVCMN Navicula sp. [cf. N. miniscula] 2 12 NAVMUR Navicula muraloides 1 1 NAVPUM Navicula pupula var. mutata 0 NAVRAD Navicula radiosa 0 NAVRHY Navicula rhynchocephala 4 2 1 13 NAVSAL Navicula salinarum 0 NAVSCH Navicula schmassmannii 20 1 13 17 6 8 24 28 40 45 73 23 72 26 10 3 10 16 5 6 3 NAVSEM Navicula seminulum 19 22 15 35 46 25 40 17 603613159636011 3 2 3 716 NAVCSE Navicula sp. [cf. N. seminuloides] 1 3 NAVPTS Navicula phylleptosoma 0 NAVPSE Navicula pseudotenelloides 0 NAVMEG Naviculoid sp. [cf. Microstatus egregius] 0 NAVRHE Navicula sp. [cf. N. rhynchocephala f. elegans] 0 NAVSCH Navicula sp. [cf. N. schadei] 0 NAVSMI Navicula sp. [cf. N. seminulum var. intermedia] 0 NAVSUB Navicula submolesta 0 NAVSUB Navicula submuralis 17 4 1 9 6 9553353126266612

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Sample label BY14-2-0-0.5-B BY14-2-1-1.5-B BY14-2-2-2.5B BY14-2-3-3.5-B BY14-2-4-4.5B BY14-2-5-5.5-B BY14-2-6-6.5B BY14-2-7-7.5-B BY14-2-8-8.5-B-2 BY14-2-9-9.5-B BY14-2-10-10.5-B-3 BY14-2-11-11.5-B-2 BY14-2-12-12.5-B BY14-2-13-13.5-B-3 BY14-2-14-14.5-B-3 BY14-2-16-16.5-B-4 BY14-2-18-18.5-B-4 BY14-2-20-20.5-B-4 BY14-2-22-22.5-B-3 BY14-2-24-24.5-B-4 Abbrev. Taxon name No. of lakes PINDIV Pinnul aria divergentissima 0 PINGRU Pinnul aria grunowii 0 PININM Pinnularia intermedia 0 PINOBS Pinnul aria obscura 1 2 PINRAB Pinnul aria rabenhorstii 0 PINSCH Pinnul aria schroeterae 4 1111 PIN182 Pinnul aria Pl 182 Ettl - unidentified sp 0 PINUND Pinnul aria Sp1 0 PINMES Pinnularia sp [cf. P. mesolepta] 2 34 PINMIC Pinnularia sp [cf. P. microstauron] 12 3 1 4 3 1 31 12111 PINMM3 Pinnularia mesolepta morphotype 3 16 2 1 2 2 6 2 9 11122 412 111 PINUND Pinnul aria sp und 5 4 6 3 1 1 PIN65A Pinnul aria Pl 65 Ant on - unidentified sp 1 1 PIN46L Pinnul aria und-Lavoie Pl46 "?" 12 1 2 1 3 11231934 PLAEXP Placoneis explanata 0 PLALAN Plantothidium lanceolatum 0 PLNOES Planothidium oestrupii 0 PLNPER Planothidium sp. [cf. P. peragalloi] 0 PSABRO Psammnothidium broenlundense 0 PSABIO Psammothidium bioretii 1 2 PSACHI Psammothidium chlidanos 0 PSASUB Psammot hidium subatomoi des 20 15 20 14 10 8 8 10 2 4 9 4 11 11 21 13 15 16 14 11 11 PSAVEN Psammothidium ventrale 0 PSEBRE Pseudostaurosira brevistriata 6 1 51131 PSEPSE Pseudostaurosira pseudoconstruens 2 1 2 PUNLAN Punctastriata sp. [cf. P. lancettula] 0 ROSPET Rossithidium petersenii 9 3 3 1 241 31 1 ROSPUS Rossithidium pussilla 18 3 8 10 12 8 4 7 812393 43173 SELBLA Sellaphora blackfordensis 0 SELPU3 Sellaphora pupula (complex) forme 3 (Lavoie) 2 1 1 SELPSE Sellaphora pseudonana 6 10 1 35 21 SELR/B Sellaphora rectangularis? or blackfordensis? 2 31 SELUND Sellaphora sp und 1 1 STAS1A Stauroneis sp1 (Antoniades) 0 STAS2A Stauroneis sp2 (Antoniades) 2 43 STAAGR Stauroneis agrestis 0 STAAMP Stauroneis amphicephala 2 2 2 STAGRA Stauroneis gracilis 0 STAJAR Stauroneis jarensis / S. reichardtii 10 1 1 4 2 114 2 21 STAKRI Stauroneis krigerii 0 STANEOStauroneisneohyalina 15 12231135435125 2 STASMI Stauroneis smithii 0 STATHE Stauroneis thermicoloides 0 STAUND Stauroneis sp und 0 STCONC Staurosira construens 0 STVENT Staurosira venter 20 275 622 781 359 436 551 283 300 243 529 293 355 329 272 210 250 252 239 236 181 STPINN Staurosirella pinnata 19 9 17 35 57 21 29 16 232219 9181116 511121112 STPINL Staurosirella pinnata - long form 2 21 STPINI Staurosirella pinnata var intercedens 5 43233 SURBOH Surirella bohemica 0 SURROB Surirella roba 0 SURAMO Surirella sp. [cf. S. amoena] 0 AURANG Surirella sp.[cf.S. angusta] 0 SURUND Surirella sp und (broken) 1 2 SURMIN Surirella sp. [cf. S. minuta] 0

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Appendix D: Diatom assemblage descriptive metrics for 25 surface samples and core BY14-2

Species Shannon-Wiener Total Site Rarefaction richness Diversity Index Hill's N Abundance 2 (S) (H') BY01 17.661 697 19 0.771 1.363 BY04 31.912 576 33 1.743 2.704 BY10 31.206 571 32 2.267 5.481 BY14-1 25.101 590 26 1.912 3.605 BY14-2 37.283 513 - - - BY15 29.253 893 33 1.677 2.957 BY16 22.382 982 27 1.140 1.687 BY17 29.006 1209 33 1.773 2.862 BY18 29.655 537 30 1.580 2.279 BYS01 35.803 663 39 2.415 6.171 BYS02 29.450 566 30 2.534 9.038 BYS03 33.802 548 34 2.377 4.737 BYS06 29.394 672 31 2.164 4.020 QB01 27.402 617 29 1.943 4.253 QB02 43.946 613 46 2.624 5.841 QB03 32.566 569 33 2.198 4.328 QB04 19.553 603 20 1.589 2.796 QB05 24.450 591 25 1.755 2.888 QB06 25.761 556 26 2.100 5.419 QB07 33.915 537 34 2.095 4.979 QB08 47.121 567 48 2.656 8.361 QB10 26.873 606 28 3.135 15.317 QB12 56.579 555 58 1.873 4.221 QB15 14.000 517 14 2.935 9.726 QBS01 23.675 559 24 0.986 2.099 QBS02 21.346 641 22 1.932 4.628 BY mean 28.719 708.7 30.6 1.863 3.909 BY median 29.422 626.5 31.5 1.843 3.281 BY min 17.661 537.0 19.0 0.771 1.363 BY max 35.803 1209.0 39.0 2.534 9.038 BY stdev 4.956 210.8 5.0 0.533 2.179 QB mean 30.553 579.3 31.3 2.140 5.758 QB median 26.873 569.0 28.0 2.095 4.628 QB min 14.000 517.0 14.0 0.986 2.099 QB max 56.579 641.0 58.0 3.135 15.317 QB stdev 12.101 35.6 12.5 0.582 3.570 Mean 29.672 641.4 31.0 2.007 4.870 Median 29.253 590.0 30.0 1.943 4.253 Min 14.000 517.0 14.0 0.771 1.363 Max 56.579 1209.0 58.0 3.135 15.317 Stdev 9.239 159.2 9.4 0.565 3.072

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Appendix D (continued)

Shannon- Species Diatom Total Wiener Depth (cm) Rarefaction richness Hill's N concentration Abundance Diversity 2 (S) (valves/g) Index (H') 0.0-0.5 37.283 513 39 2.112 3.316 3.11E+09 1.0-1.5 33.275 1010 40 1.875 2.578 4.56E+09 2.0-2.5 35.036 1274 47 1.882 2.595 5.01E+09 3.0-3.5 38.088 750 45 2.252 3.997 3.07E+09 4.0-4.5 33.256 746 39 1.890 2.807 2.92E+09 5.0-5.5 41.954 1084 51 2.300 3.680 1.82E+09 6.0-6.5 35.798 583 38 2.290 3.980 2.13E+09 7.0-7.5 37.599 697 42 2.455 4.854 2.39E+09 8.0-8.5 42.293 539 44 2.458 4.509 1.59E+09 9.0-9.5 36.908 1159 44 2.321 4.298 1.65E+09 10.0-10.5 33.606 605 36 2.241 3.931 1.93E+10 11.0-11.5 33.640 702 39 2.057 3.549 3.23E+09 12.0-12.5 40.583 711 47 2.339 4.226 2.59E+09 13.0-13.5 39.367 536 42 2.199 3.614 1.25E+10 14.0-14.5 37.000 440 37 2.184 3.903 1.06E+10 16.0-16.5 39.816 577 43 2.390 4.640 3.45E+09 18.0-18.5 34.844 507 36 2.157 3.711 4.38E+09 20.0-20.5 42.659 488 44 2.273 3.829 4.36E+09 22.0-22.5 48.442 556 52 2.571 4.978 3.53E+09 24.0-24.5 47.567 502 50 2.529 5.655 4.02E+09 Mean 29.672 641.4 31.0 2.007 4.870 4.81E+09 Median 29.253 590.0 30.0 1.943 4.253 3.34E+09 Min 14.000 517.0 14.0 0.771 1.363 1.59E+09 Max 56.579 1209.0 58.0 3.135 15.317 1.93E+10 Stdev 9.239 159.2 9.4 0.565 3.072 4.39E+09

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Aerial photographs showing BY study sites (taken in July 1982) were reproduced with the permission of Natural Resources Canada 2010, courtesy of the National Air Photo Library.

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