Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Hebda, Nicholas J. R.
Doctor of Philosophy - PhD
University of British Columbia
Wildfire regimes are changing, with fires becoming more frequent and intense globally as climate change advances, putting natural capital such as surface water at risk. Wildfires can detrimentally impact surface water quality by burning vegetation and altering soils, leading to increased erosion and release of contaminants into waterways, including reservoirs. Here, a multi-proxy paleoecological investigation assesses the risk posed by wildfire to water supply in a municipal drinking water catchment serving 400,000 people in the Capital Regional District (CRD) on Vancouver Island, British Columbia, Canada. Sediment records from Sooke Lake Reservoir and adjacent Begbie Lake were analyzed for charcoal, magnetic susceptibility, geochemistry, pollen, and diatoms to quantitatively reconstruct the terrestrial and aquatic biogeochemical impacts of five past fire events at sub-decadal resolutions. Severe fires have burned in the Sooke watershed under differing climates, including modern conditions and past warmer, drier states. These past climates serve as analogues for anticipated future conditions. All five fires had statistically significant effects on terrestrial and aquatic systems in the study watershed across diverse physical, chemical, and biological fire-related proxy indicators. Magnetic susceptibility, sediment chemistry, and fossil diatom evidence reveal post-fire increases in soil erosion and water column turbidity. Sediment concentrations of potentially toxic elements, including barium, chromium, and lead, also increased after several fires. These fire-induced changes likely persisted for decades, in some cases perhaps more than a century. Overall, this study suggests that Sooke Lake Reservoir and Begbie Lake are vulnerable to post-fire effects which last for many years before pre-fire conditions return. Wildfires pose a significant threat to water quality and water supply security in the CRD. These insights into location-specific catchment fire characteristics and impacts inform evidence-based water supply management recommendations. The novel approach developed and demonstrated in this study can be applied to assess the wildfire risk to any surface water basin with a sufficiently long and intact sedimentary record.
ASSESSING WILDFIRE RISK TO MUNICIPAL DRINKING WATER USING PALEOECOLOGY by Nicholas J. R. Hebda B.Sc., University of Victoria, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) October 2022 © Nicholas J. R. Hebda, 2022 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: ASSESSING WILDFIRE RISK TO MUNICIPAL DRINKING WATER USING PALEOECOLOGY submitted by Nicholas J. R. Hebda in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Ian Walker, Irving K. Barber Faculty of Science Supervisor Dr. Kendrick Brown, Irving K. Barber Faculty of Science Co-Supervisor Dr. David Scott, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Adam Wei, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Robert Lalonde, Irving K. Barber Faculty of Science University Examiner Dr. Mitchell Power, College of Science, University of Utah External Examiner iii Abstract Wildfire regimes are changing, with fires becoming more frequent and intense globally as climate change advances, putting natural capital such as surface water at risk. Wildfires can detrimentally impact surface water quality by burning vegetation and altering soils, leading to increased erosion and release of contaminants into waterways, including reservoirs. Here, a multi-proxy paleoecological investigation assesses the risk posed by wildfire to water supply in a municipal drinking water catchment serving 400,000 people in the Capital Regional District (CRD) on Vancouver Island, British Columbia, Canada. Sediment records from Sooke Lake Reservoir and adjacent Begbie Lake were analyzed for charcoal, magnetic susceptibility, geochemistry, pollen, and diatoms to quantitatively reconstruct the terrestrial and aquatic biogeochemical impacts of five past fire events at sub-decadal resolutions. Severe fires have burned in the Sooke watershed under differing climates, including modern conditions and past warmer, drier states. These past climates serve as analogues for anticipated future conditions. All five fires had statistically significant effects on terrestrial and aquatic systems in the study watershed across diverse physical, chemical, and biological fire-related proxy indicators. Magnetic susceptibility, sediment chemistry, and fossil diatom evidence reveal post-fire increases in soil erosion and water column turbidity. Sediment concentrations of potentially toxic elements, including barium, chromium, and lead, also increased after several fires. These fire-induced changes likely persisted for decades, in some cases perhaps more than a century. Overall, this study suggests that Sooke Lake Reservoir and Begbie Lake are vulnerable to post-fire effects which last for many years before pre-fire conditions return. Wildfires pose a significant threat to water quality and water supply security in the CRD. These insights into location-specific catchment fire characteristics and impacts inform evidence-based water supply management recommendations. The novel approach developed and demonstrated in this study can be applied to assess the wildfire risk to any surface water basin with a sufficiently long and intact sedimentary record. iv Lay Summary Public water supplies are threatened by wildfire, which is becoming more frequent and intense worldwide. After a fire, rain and wind can carry contaminants from ash and exposed soils into reservoirs, potentially affecting water quality for months or even years. In this study, mud from the bottom of a reservoir that supplies drinking water to 400,000 people on Vancouver Island in British Columbia, Canada, was examined for evidence of fire-related impacts and contamination. Changes in charcoal, chemical elements, pollen, and the remains of algae revealed that some past fires likely made the water cloudier and introduced toxic contaminants. These impacts probably lasted for years or even decades. Considering this, practical recommendations were developed for water supply managers to safeguard water resources from wildfire effects. The new approach developed in this study can be used to assess fire risk to water supply in other reservoirs. v Preface This study was conducted as part of a broader paleofire research collaboration between the Capital Regional District (in particular, Joel Ussery) and Natural Resources Canada (NRCan). The research program was conceptualized, initiated, and led by Dr. Kendrick Brown (NRCan, UBCO), and has involved the collaboration of many individuals. The advent of the COVID-19 pandemic necessitated the outsourcing of selected analyses. Nicholas Hebda (the doctoral candidate) was involved in this research from the start and contributed significantly to the first publication arising from this program (Brown et al., 2019). The concept and approach for the present dissertation were co-developed by N. Hebda and K. Brown, and the study led by N. Hebda. Methods were researched and developed by N. Hebda and Nicholas Conder (NRCan). Sample preparation, charcoal quantification, magnetic susceptibility measurements, and grain size measurements were conducted by N. Hebda and N. Conder. Geochemical analyses (isotope-ratio mass spectrometry and inductively coupled plasma mass spectrometry) were carried out at the NRCan analytical laboratory at the Pacific Forestry Centre (Victoria, BC) by Rebecca Dixon and David Dunn. Pollen slides were counted by K. Brown and Dr. Richard Hebda (University of Victoria). Diatom slides were counted by N. Hebda, Dr. Janice Brahney (Utah State University), and Biologica Environmental Services Ltd. (Victoria, BC). Analysis and synthesis of the research data and preparation of the present dissertation were conducted by N. Hebda. vi Table of Contents Abstract ................................................................................................................................................ iii Lay Summary ...................................................................................................................................... iv Preface ................................................................................................................................................... v Table of Contents ................................................................................................................................. vi List of Tables ........................................................................................................................................ xi List of Figures .................................................................................................................................... xiv Acknowledgements ........................................................................................................................... xxv 1 Introduction .................................................................................................................................. 1 1.1 Study Significance .................................................................................................................. 1 1.2 Wildfire Effects on Water Quality .......................................................................................... 2 1.2.1 Nutrients ......................................................................................................................... 4 1.2.2 Toxic Metals and Other Trace Elements ........................................................................ 5 1.3 Paleoecological Studies of Wildfire–Water Relationships ..................................................... 7 1.3.1 Characterizing the Biogeochemical Effects of Past Fires ............................................... 7 1.3.2 Inferring Fire-Induced Water Quality Effects from Sedimentary Proxies ................... 11 1.4 Study Region & Site ............................................................................................................. 13 1.4.1 Regional Wildfire Regimes Over Time ........................................................................ 13 1.4.2 Greater Victoria Water Supply Area ............................................................................ 16 1.4.2.1 Sooke Lake Reservoir ............................................................................................... 18 vii 1.4.2.2 Begbie Lake .............................................................................................................. 19 1.4.2.3 Catchment Characteristics ........................................................................................ 20 1.4.2.4 Catchment Fire History ............................................................................................ 21 1.5 Precursor Study .................................................................................................................... 21 2 Methods ....................................................................................................................................... 23 2.1 Sediment Acquisition ........................................................................................................... 23 2.2 Radiometric Dating and Age-Depth Modelling ................................................................... 24 2.3 Subsampling Methodology ................................................................................................... 26 2.4 Magnetic Susceptibility ........................................................................................................ 27 2.5 Charcoal ................................................................................................................................ 28 2.6 TC, TN, δ15N and δ13C ......................................................................................................... 29 2.7 Inorganic Elements ............................................................................................................... 29 2.8 Pollen and Spores ................................................................................................................. 30 2.9 Diatoms ................................................................................................................................ 30 2.10 Grain Size ............................................................................................................................. 31 2.11 Numerical Analyses .............................................................................................................. 32 3 Results .......................................................................................................................................... 34 3.1 Sediment Chronologies ........................................................................................................ 34 3.2 Begbie Lake Sediment Sequence A (BEGA) ....................................................................... 36 3.2.1 BEGA Charcoal, MS, TC, TN, δ13C and δ15N.............................................................. 36 3.2.1.1 BEGA Charcoal ........................................................................................................ 36 viii 3.2.1.2 BEGA MS ................................................................................................................ 38 3.2.1.3 BEGA TC, TN, δ13C and δ15N .................................................................................. 38 3.2.2 BEGA Inorganic Elements ........................................................................................... 38 3.2.3 BEGA Diatoms ............................................................................................................. 47 3.2.4 BEGA Pollen and Spores ............................................................................................. 54 3.3 Begbie Lake Sediment Sequence B (BEGB)........................................................................ 59 3.3.1 BEGB Charcoal, MS, TC, TN, δ13C and δ15N .............................................................. 59 3.3.1.1 BEGB Charcoal ........................................................................................................ 59 3.3.1.2 BEGB MS ................................................................................................................. 59 3.3.1.3 BEGB TC, TN, δ13C and δ15N .................................................................................. 61 3.3.2 BEGB Inorganic Elements ........................................................................................... 62 3.3.3 BEGB Diatoms ............................................................................................................. 69 3.3.4 BEGB Pollen and Spores .............................................................................................. 74 3.4 Begbie Lake Sediment Sequence C (BEGC)........................................................................ 79 3.4.1 BEGC Charcoal, MS, TC, TN, δ13C and δ15N .............................................................. 79 3.4.1.1 BEGC Charcoal ........................................................................................................ 81 3.4.1.2 BEGC MS ................................................................................................................. 81 3.4.1.3 BEGC TC, TN, δ13C and δ15N .................................................................................. 81 3.4.2 BEGC Inorganic Elements ........................................................................................... 82 3.4.3 BEGC Diatoms ............................................................................................................. 90 3.4.4 BEGC Pollen and Spores .............................................................................................. 95 ix 3.5 Sooke Lake Reservoir North Basin Sediment Sequence (SLRN) ........................................ 99 3.5.1 SLRN Charcoal, MS, TC, TN, δ13C and δ15N .............................................................. 99 3.5.1.1 SLRN Charcoal ........................................................................................................ 99 3.5.1.2 SLRN MS ............................................................................................................... 101 3.5.1.3 SLRN TC, TN, δ13C and δ15N ................................................................................ 101 3.5.2 SLRN Inorganic Elements .......................................................................................... 101 3.5.3 SLRN Diatoms ........................................................................................................... 108 3.5.4 SLRN Pollen and Spores ............................................................................................ 112 3.6 Sooke Lake Reservoir South Basin Sediment Sequence (SLRS) ....................................... 116 3.6.1 SLRS Charcoal, MS, TC, TN, δ13C and δ15N ............................................................. 116 3.6.1.1 SLRS Charcoal ....................................................................................................... 118 3.6.1.2 SLRS MS ................................................................................................................ 118 3.6.1.3 SLRS TC, TN, δ13C and δ15N ................................................................................. 119 3.6.2 SLRS Inorganic Elements .......................................................................................... 119 3.6.3 SLRS Diatoms ............................................................................................................ 126 4 Discussion .................................................................................................................................. 131 4.1 Individual Sediment Sequence Interpretations ................................................................... 131 4.1.1 Begbie Lake Sediment Sequence A (BEGA) ............................................................. 131 4.1.2 Begbie Lake Sediment Sequence B (BEGB) .............................................................. 141 4.1.3 Begbie Lake Sediment Sequence C (BEGC) .............................................................. 147 4.1.4 Sooke Lake Reservoir North Sediment Sequence (SLRN) ........................................ 156 x 4.1.5 Sooke Lake Reservoir South Sediment Sequence (SLRS) ......................................... 162 4.2 Comparisons Among Inferred Fires and Post-Fire Responses ........................................... 167 4.2.1 Charcoal and MS ........................................................................................................ 167 4.2.2 Sediment C, N, δ13C, and δ15N ................................................................................... 169 4.2.3 Inorganic Elements ..................................................................................................... 171 4.2.4 Diatoms....................................................................................................................... 172 4.2.5 Pollen .......................................................................................................................... 173 4.2.6 Consistent Indicators of Post-Fire Impacts ................................................................. 173 4.3 Challenges, Limitations, and Future Research ................................................................... 174 5 Conclusion ................................................................................................................................. 179 5.1 Key Findings and Implications ........................................................................................... 179 5.2 Recommendations .............................................................................................................. 183 5.3 Coda .................................................................................................................................... 185 References ......................................................................................................................................... 186 Appendix A: Summary for Policymakers ...................................................................................... 203 Key Research Findings ................................................................................................................... 203 Water Supply Management Recommendations for the Capital Regional District ......................... 204 xi List of Tables Table 1. Sediment records recovered from the Sooke watershed. ........................................................ 23 Table 2. Chronological control points for Begbie Lake and Sooke Lake Reservoir (SLR) sediment records. ................................................................................................................................................. 25 Table 3. Charcoal peaks selected for detailed analysis......................................................................... 26 Table 4. BEGA inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. ......................................................... 39 Table 5. Pearson correlation coefficients between BEGA magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. ............................................................................................ 40 Table 6. BEGA diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. ............... 47 Table 7. BEGA pollen and spore summary statistics. Only taxa with mean abundance ≥0.9% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. .............................................................................................................................................................. 54 Table 8. BEGB inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. ......................................................... 62 Table 9. Pearson correlation coefficients between BEGB magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. ............................................................................................ 63 xii Table 10. BEGB diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. ............... 70 Table 11. BEGB pollen and spore summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. .............................................................................................................................................................. 75 Table 12. BEGC inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. ......................................................... 83 Table 13. Pearson correlation coefficients between BEGC magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. ............................................................................................ 84 Table 14. BEGC diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. ............... 91 Table 15. BEGC pollen and spore summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. .............................................................................................................................................................. 96 Table 16. SLRN inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. ....................................................... 102 Table 17. Pearson correlation coefficients between SLRN magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. .......................................................................................... 103 xiii Table 18. SLRN diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. ............. 108 Table 19. SLRN pollen and spore summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. ............................................................................................................................................................ 113 Table 20. SLRS inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. ....................................................... 120 Table 21. Pearson correlation coefficients between SLRS magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. .......................................................................................... 121 Table 22. SLRS diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. ............. 126 Table 23. Summary of fires and recovery periods measured for key fire proxy indicators. Fire ages and impact intervals are based on centennial-scale age-depth models and are approximate. “N/A” indicates no significant impact. TC = total carbon, TN = total nitrogen. ........................................... 204 xiv List of Figures Fig. 1. Map of the study area showing Sooke Lake Reservoir, Begbie Lake, and coring locations. Bathymetric contours are given in meters above sea level. Darker terrestrial contours are at 20 m intervals, lighter contours at 10 m intervals. For a more detailed map of Begbie Lake, see Figure 1 in Brown et al. (2019). .............................................................................................................................. 17 Fig. 2. Age-depth models for the Begbie Lake (BEG), Sooke Lake Reservoir north basin (SLRN), and Sooke Lake Reservoir south basin (SLRS) sediment records. Irregular light blue shapes indicate 14C samples and correspond to the top x-axis. Darker blue horizontal rectangles represent 210Pb samples and correspond to the bottom x-axis. Background 210Pb activity is indicated by vertical dashes at the right edge of each panel. The black dotted line indicates mean modeled sediment age, and the grey gradient shaded areas represent 95% confidence intervals. The solid grey horizontal bar in panel A represents the Mazama tephra (Egan et al., 2015) ................................................................................ 35 Fig. 3. BEGA fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; charcoal summed area = mm2 cm-3; charcoal mean particle area = mm2; charcoal mean particle aspect ratio = unitless; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. ........................................................................................................... 37 Fig. 4. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGA inorganic elements data (black) with that expected from the broken-stick model (red). ..................... 41 Fig. 5. BEGA inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 3 caption. .................................................. 42 xv Fig. 6. Box-and-whisker plots of selected BEGA fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 5). X-axis units: see Fig. 3 caption. Thick vertical lines indicate zone median values, boxes denote interquartile ranges, thin horizontal lines represent the minimum and maximum measured values that fall within 1.5× the interquartile range, and dots indicate outliers beyond this threshold. Vertical brackets indicate statistically significant pairwise comparisons (Dunn tests, Bonferroni adjusted; α = 0.05) between zone medians. P-values are given for significant pairwise differences. ..................................................................................................... 44 Fig. 7. Box-and-whisker plots of selected BEGA inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 5). All exhibit statistically significant increases in median values from zone 1 to zone 2 and/or zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .................................................................................................................... 45 Fig. 8. Box-and-whisker plots of selected BEGA inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 5). All exhibit statistically significant decreases in median values from zone 1 to zone 2 and/or zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .............................................................................................................. 46 Fig. 9. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGA diatom data (black) with that expected from the broken-stick model (red). ........ 48 Fig. 10. BEGA diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 3 caption. ................................................................................................................................................. 49 xvi Fig. 11. Box-and-whisker plots of selected BEGA fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 10). X-axis units: see Fig. 3 caption. See Fig. 6 caption for further figure explanation. .......................................................................................................................................... 51 Fig. 12. Box-and-whisker plots of selected BEGA diatom taxa binned by diatom-derived bio-stratigraphic zones (see Fig. 10). X-axis units: %. See Fig. 6 caption for further figure explanation. . 52 Fig. 13. Box-and-whisker plots of selected BEGA diatom groups and indices binned by diatom-derived bio-stratigraphic zones (see Fig. 10). X-axis units: diatom morphologies = %; for all others, see Fig. 10 caption. See Fig. 6 caption for further figure explanation. ................................................ 53 Fig. 14. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGA pollen and spore data (black) with that expected from the broken-stick model (red). ..................................................................................................................................................... 55 Fig. 15. BEGA pollen and spore relative abundance data (coloured). Only selected taxa are shown. On the Alnus panel, bright green represents Sitka alder (Alnus alnobetula ssp. sinuata) and dark green represents red alder (Alnus rubra). On all other panels, lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 3 caption. .............................................................. 56 Fig. 16. Box-and-whisker plots of selected BEGA fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 15). X-axis units: see Fig. 3 caption. See Fig. 6 caption for further figure explanation. .................................................................................................................... 57 Fig. 17. Box-and-whisker plots of selected BEGA pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 15). X-axis units: pollen and spores = %; pollen and spore diversity = Shannon index. See Fig. 6 caption for further figure explanation. ........................... 58 xvii Fig. 18. BEGB fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. ............................................................................................................................................ 60 Fig. 19. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGB inorganic elements data (black) with that expected from the broken-stick model (red). ..................... 64 Fig. 20. BEGB inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 18 caption. ................................................ 65 Fig. 21. Box-and-whisker plots of selected BEGB fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). X-axis units: see Fig. 18 caption. See Fig. 6 caption for further figure explanation. .............................................................................................................. 66 Fig. 22. Box-and-whisker plots of selected BEGB inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). All exhibit statistically significant increases in median values from zone 1 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. ................................................................................................................................ 67 Fig. 23. Box-and-whisker plots of selected BEGB inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). All exhibit statistically significant decreases in median values from zone 1 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. ................................................................................................................................ 68 Fig. 24. Box-and-whisker plots of selected BEGB inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). All exhibit peaks in zone 2. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. ................................................................... 69 xviii Fig. 25. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGB diatom data (black) with that expected from the broken-stick model (red). ......... 71 Fig. 26. BEGB diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 18 caption. ................................................................................................................................................. 72 Fig. 27. Box-and-whisker plots of selected BEGB fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 26). X-axis units: see Fig. 18 caption. See Fig. 6 caption for further figure explanation. ................................................................................................................................ 73 Fig. 28. Box-and-whisker plots of selected BEGB diatom taxa binned by diatom-derived bio-stratigraphic zones (see Fig. 26). X-axis units: %. See Fig. 6 caption for further figure explanation. . 74 Fig. 29. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGB pollen and spore data (black) with that expected from the broken-stick model (red). ..................................................................................................................................................... 76 Fig. 30. BEGB pollen and spore relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 18 caption. ..................................................................................................................................... 77 xix Fig. 31. Box-and-whisker plots of selected BEGB fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 30). X-axis units: see Fig. 18 caption. See Fig. 6 caption for further figure explanation. .................................................................................................................... 78 Fig. 32. Box-and-whisker plots of selected BEGB pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 30). X-axis units: pollen and spores = %; pollen and spore diversity = Shannon index. See Fig. 6 caption for further figure explanation. ........................... 79 Fig. 33. BEGC fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. ............................................................................................................................................ 80 Fig. 34. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGC inorganic elements data (black) with that expected from the broken-stick model (red). ..................... 85 Fig. 35. BEGC inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 33 caption. ................................................ 86 Fig. 36. Box-and-whisker plots of selected BEGC fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. .............................................................................................................. 87 Fig. 37. Box-and-whisker plots of selected BEGC inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). All exhibit statistically significant changes in median values from zone 1 to zone 2. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .......................................................................................................................................... 88 Fig. 38. Box-and-whisker plots of selected BEGC inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). All exhibit statistically significant changes in median xx values from zone 1 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .......................................................................................................................................... 88 Fig. 39. Box-and-whisker plots of selected BEGC inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). All exhibit statistically significant changes in median values from zone 2 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .......................................................................................................................................... 89 Fig. 40. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGC diatom data (black) with that expected from the broken-stick model (red). ......... 91 Fig. 41. BEGC diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 33 caption. ................................................................................................................................................. 92 Fig. 42. Box-and-whisker plots of selected BEGC fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 41). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. ................................................................................................................................ 93 Fig. 43. Box-and-whisker plots of selected BEGC diatom taxa binned by diatom-derived bio-stratigraphic zones (see Fig. 41). X-axis units: %. See Fig. 6 caption for further figure explanation. . 94 Fig. 44. Box-and-whisker plots of selected BEGC diatom groups and indices binned by diatom-derived bio-stratigraphic zones (see Fig. 41). X-axis units: diatom morphologies = %; for all others, see Fig. 41 caption. See Fig. 6 caption for further figure explanation. ................................................ 95 Fig. 45. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGC pollen and spore data (black) with that expected from the broken-stick model (red). ..................................................................................................................................................... 96 xxi Fig. 46. BEGC pollen and spore relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 5× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 33 caption. ..................................................................................................................................... 97 Fig. 47. Box-and-whisker plots of selected BEGC fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 46). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. .................................................................................................................... 98 Fig. 48. Box-and-whisker plots of selected BEGC pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 46). X-axis units: pollen and spores = %; pollen and spore diversity = Shannon index. See Fig. 6 caption for further figure explanation. ........................... 99 Fig. 49. SLRN fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. .......................................................................................................................................... 100 Fig. 50. Comparison of the distribution of total variance from the CONISS-zoned standardized SLRN inorganic elements data (black) with that expected from the broken-stick model (red). ................... 104 Fig. 51. SLRN inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. The horizontal dashed line delineates stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; grain size median diameter = µm; for all others, see Fig. 49 caption. ............................................................................................................................................... 105 xxii Fig. 52. Box-and-whisker plots of selected SLRN fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 51). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. ............................................................................................................ 106 Fig. 53. Box-and-whisker plots of selected SLRN inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 51). All exhibit statistically significant increases in median values from zone 1 to zone 2. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .............................................................................................................................. 107 Fig. 54. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed SLRN diatom data (black) with that expected from the broken-stick model (red). ....... 109 Fig. 55. SLRN diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 49 caption. ............................................................................................................................................... 110 Fig. 56. Box-and-whisker plots of selected SLRN fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 55). X-axis units: see Fig. 49 caption. See Fig. 6 caption for further figure explanation. .............................................................................................................................. 111 Fig. 57. Box-and-whisker plots of selected SLRN diatom taxa and groups binned by diatom-derived bio-stratigraphic zones (see Fig. 55). X-axis units: %. See Fig. 6 caption for further figure explanation. ........................................................................................................................................ 112 Fig. 58. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed SLRN pollen and spore data (black) with that expected from the broken-stick model (red). ................................................................................................................................................... 114 xxiii Fig. 59. SLRN pollen and spore relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 5× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 33 caption. ................................................................................................................................... 115 Fig. 60. Box-and-whisker plots of selected SLRN fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 59). X-axis units: see Fig. 49 caption. See Fig. 6 caption for further figure explanation. .................................................................................................................. 116 Fig. 61. Box-and-whisker plots of selected SLRN pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 59). NAP = non-arboreal pollen and spores. X-axis units: %. See Fig. 6 caption for further figure explanation. ............................................................... 116 Fig. 62. SLRS fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; charcoal summed area = mm2 cm-3; charcoal mean particle area = mm2; charcoal mean particle aspect ratio = unitless; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. ......................................................................................................... 117 Fig. 63. Comparison of the distribution of total variance from the CONISS-zoned standardized SLRS inorganic elements data (black) with that expected from the broken-stick model (red). ................... 122 Fig. 64. SLRS inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed line delineates stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 62 caption. .............................................. 123 xxiv Fig. 65. Box-and-whisker plots of selected SLRS fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 64). X-axis units: see Fig. 62 caption. See Fig. 6 caption for further figure explanation. ............................................................................................................ 124 Fig. 66. Box-and-whisker plots of selected SLRS inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 64). X-axis units: parts per million. See Fig. 6 caption for further figure explanation. .................................................................................................................. 125 Fig. 67. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed SLRS diatom data (black) with that expected from the broken-stick model (red). ........ 127 Fig. 68. SLRS diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 62 caption. ............................................................................................................................................... 128 Fig. 69. Box-and-whisker plots of selected SLRS fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 68). X-axis units: see Fig. 62 caption. See Fig. 6 caption for further figure explanation. .............................................................................................................................. 129 Fig. 70. Box-and-whisker plots of selected SLRS diatom taxa and groups binned by diatom-derived bio-stratigraphic zones (see Fig. 68). X-axis units: %. See Fig. 6 caption for further figure explanation. ........................................................................................................................................ 130 xxv Acknowledgements I thank the members of my doctoral committee (Drs. Ian Walker, Kendrick Brown, David Scott, and Adam Wei) for contributing their time, expertise, and feedback to this endeavour. I am especially grateful to my co-supervisors, Dr. Brown and Dr. Walker, for their guidance, support, and patience. I extend my gratitude to the Capital Regional District (CRD) and Natural Resources Canada for providing funding, laboratory space, and analytical services for this work. In particular, I thank Joel Ussery at the CRD for championing this research. I am furthermore grateful for the valuable contributions to various aspects of this study by Nicholas Conder, Dr. Richard Hebda, Dr. Janice Brahney, Rebecca Dixon, David Dunn, Kiera Smith, and Christopher Hebda. 1 1 Introduction 1.1 Study Significance Wildfires are becoming more frequent, intense, and extensive worldwide (United Nations Environment Programme [UNEP], 2022). Although fire is an ecologically important process, wildfires of unprecedented size and severity are increasingly putting communities, infrastructure, and natural capital at risk (UNEP, 2022). This trend is predicted to continue as anthropogenic climate change advances, driving progressively more extreme wildfire activity (Swain et al., 2020; UNEP, 2022). Of particular concern is the growing threat posed by wildfires to water security (Hallema et al., 2018; Robinne et al., 2021). Wildfires can negatively impact water supply catchments by combusting vegetation and altering soil properties, resulting in increased erosion and contaminant runoff (Rust et al., 2018; Smith et al., 2011), deleteriously impacting water quality and potentially exceeding water quality standards (Health Canada, 2020). Treating these detrimental effects to water quality is often costly or unfeasible, putting water resources for billions of people around the globe at risk (Robinne et al., 2018; UNEP, 2022). Although the effects of wildfires on water quality are relatively well-studied near burn sites (e.g., Rust et al., 2018), our understanding of catchment-scale impacts of wildfires on downstream systems, including lakes and reservoirs, remains limited (Rhoades et al., 2019a). Similarly, although there are abundant observations of the short-term effects of wildfire on water quality, recent research indicates that some effects can persist for many years (Dunnette et al., 2014; Rhoades et al., 2019a) or decades (Dunnette et al., 2014; Pompeani et al., 2020). Furthermore, the effects of climate change on fire regimes are regionally diverse (UNEP, 2022), as are the effects of individual wildfires on water quality and treatability (Rhoades et al., 2019b). Therefore, there is a need to develop site-specific 2 methods of evaluating the effects of wildfires in water supply areas across multiple temporal and spatial scales. Paleoecological archives offer a site-specific, spatially-integrated means of assessing the effects of wildfires on both terrestrial and aquatic environments in water supply catchments at various timescales (Conedera et al., 2009). Lake and reservoir sediments often preserve a rich chronological record of local environmental history, including physical, chemical, and biological evidence of past wildfires and their effects. The careful study of multiple paleoenvironmental proxies in tandem can provide uniquely holistic, long-term perspectives on wildfire occurrence and effects, which can be complemented by insights gained through other avenues of investigation, such as monitoring and modelling. This thesis uses a paleoecological approach to study the millennial-scale interactions among climate, vegetation, and wildfire in a water supply catchment in western Canada, as well as the specific effects of individual wildfires on terrestrial and aquatic environments at sub-decadal temporal resolution. Resilience of the catchment to wildfire effects will be evaluated. The findings will be used to derive site-specific, meaningful, and actionable catchment management recommendations for watershed managers. 1.2 Wildfire Effects on Water Quality As Rhoades et al. (2019b) succinctly put it, “wildfires influence watersheds from top to bottom.” Wildfires can have profound effects on watersheds, from disrupting ecosystem processes (Dunnette et al., 2014; Neary et al., 2005) to altering geomorphological characteristics and hydrological regimes (Shakesby & Doerr, 2006). These effects can, in turn, compromise a catchment’s ability to supply clean water. However, wildfire behaviour and watershed processes are spatiotemporally diverse and complex, which makes generalizing the effects of wildfire on water quality challenging (Rhoades et al., 2019b). A growing body of research aims to identify existing 3 knowledge gaps and characterize wildfire effects on water quality across a wide range of spatial and temporal scales and settings. Wildfires can generate and mobilize a multitude of physical and chemical constituents, many of which possess contamination potential. These include suspended sediments, nutrients, ash constituents, dissolved organic carbon (C) and other disinfection by-product precursors, toxic metals, and various trace elements (Smith et al., 2011). Whether a constituent is deemed a contaminant, however, depends on the ecosystem service being valued (Nunes et al., 2018). This review will focus on contaminants in the context of water intended for human consumption. Contaminants are mobilized and transported during and after a fire by a variety of processes and pathways including lofting and subsequent atmospheric deposition, wind and water erosion of sediments and particle-associated contaminants, water dissolution, and large-scale movements of sediment such as debris flows or mass failures (Nunes et al., 2018). Although different mobilization processes and transport pathways may dominate in different landscapes, water erosion typically plays a major role in delivering contaminants to surface water systems (Nunes et al., 2018). The geomorphological and hydrological effects of wildfire are extensively reviewed by Shakesby & Doerr (2006) and will not be a focus of this review. In brief, however, wildfires combust vegetation, litter, and soil organic matter, which reduces precipitation interception, sublimation, and evapotranspiration, exposing mineral soils to increased water erosion. Furthermore, heat from fire can induce water repellency in soils by volatilizing organic compounds which travel downward in the soil and, upon encountering cooler strata, condense into a concentrated hydrophobic layer (Doerr et al., 2000). This water-repellent layer can reduce infiltration capacity of the soil, enhance overland flow, and accelerate soil erosion (Doerr et al., 2000). Finally, wildfires often leave behind a blanket of readily-erodible surface ash loaded with labile nutrients, toxic metals, and other ions (Bodí et al., 2014). These ashes can clog soil pore space, further preventing infiltration and enhancing overland flow. Numerous studies have demonstrated that these factors, among others, 4 can lead to increased export of water-borne contaminants from burned areas (e.g., Rust et al., 2018; Smith et al., 2011). 1.2.1 Nutrients Nutrients comprise one major group of potential water contaminants. Nitrogen (N) and phosphorus (P) are limiting nutrients for primary productivity in most aquatic systems, and elevated concentrations can lead to eutrophication and the risk of potentially toxic or aesthetically undesirable algal blooms that can negatively impact water colour, odour, and taste (Smith et al., 2011). N is naturally present in all ecosystems in both organic and inorganic forms, and its cycling involves a complex web of interconnected physical, chemical, and biological processes (Neary et al., 2005). Wildfires reduce total terrestrial N stocks through combustion and volatilization, with the magnitude of those losses generally proportional to burn temperature and the amount of organic matter combusted (Neary et al., 2005). In contrast, bioavailable N, particularly NH4+, temporarily increases following fire, and some is exported via hydrologic pathways (Dunnette et al., 2014; Neary et al., 2005). Some organic N also remains in wildfire ash (Knicker, 2011). Phosphorus is naturally present at low levels in some rocks and soils, and in organic matter. Unlike N, total terrestrial P generally does not decrease substantially after wildfire because its volatilization temperature is much higher (774+ °C; Bodí et al., 2014; Smith et al., 2011). Nevertheless, fire has been shown to increase soil concentrations of bioavailable P (Cade-Menun et al., 2000), including particulate forms (Blake et al., 2010) and, notably, in surface ash (Bodí et al., 2014; Neary et al., 2005), where it is easily eroded or dissolved. Many studies have documented increased N and P concentrations and fluxes in runoff from burned areas, in both dissolved and particulate forms (Neary et al., 2005; Smith et al., 2011). The magnitude and duration of these increases have been correlated with the degree to which canopy vegetation is burned, and the rate of post-fire vegetation recovery (Kinoshita & Hogue, 2011; Rust et 5 al., 2018, 2019). Recent studies have shown that in some cases, these elevated nutrient exports can persist for many years following a fire (Rhoades et al., 2019a; Rust et al., 2018). In a meta-analysis of 159 wildfires across the western United States, Rust et al. (2018) found that increased nutrient runoff following fire was the most consistent response of all variables measured, with concentrations of particulate N and some forms of dissolved N increasing significantly in about one third of burned watersheds in the 5 years following a fire, and both dissolved and particulate forms of P showing significant increases in a similar proportion of watersheds over the same time period. 1.2.2 Toxic Metals and Other Trace Elements Toxic metals and other trace elements constitute another critical group of potential post-fire contaminants that can negatively impact human health, water aesthetics, and aquatic ecology (Smith et al., 2011). Elements such as aluminum (Al), barium (Ba), copper (Cu), mercury (Hg), and lead (Pb) are toxic to humans when consumed in sufficient quantities, while arsenic (As) and hexavalent chromium (Cr[VI]) are known carcinogens (Smith et al, 2011). Elevated levels of certain trace elements can also have negative ecological effects; for example, dissolved metals have been shown to inhibit osmoregulation in fish (Hogstrand et al., 1994). A wide array of potentially problematic elements naturally exist at low levels in rocks, soils, and organic matter, and can be released following fire. For example, heavy metals can be released from combusted vegetation and vary based on the fuel type, degree of combustion, and soil characteristics (Pereira & Úbeda, 2010). Post-fire heavy metal releases are often more pronounced following wildfires in mature forests with long fire return intervals (FRIs) because heavy metals may have accumulated via atmospheric deposition for decades (Woodruff & Cannon, 2010). This finding has particular relevance to the discussion of wildfire risks in water supply catchments, as municipal reservoirs are often proximal to population centres, and therefore potential sources of heavy metal emissions. 6 In their review of fire impacts on water quality, Smith et al. (2011) observe that for many toxic metals, elevated concentrations in runoff may reflect high sediment loading. Indeed, metals have a strong affinity for ash and fine sediments (Bladon et al., 2014), meaning that fluxes of metals following fires may be tied to the severity of the burn and long-lasting (Biswas et al., 2007). This is corroborated by Rust et al. (2018) who, in a synthesis of data from about 20 burned watersheds in the western United States, found that in the five years following fire, dissolved metal concentrations decreased significantly in 11–60% of burned catchments, but particulate metal concentrations increased significantly after 22–60% of fires. For those fires where significant differences were observed between pre- and post-fire metal concentrations, decreases of around 2–3% were typically measured for dissolved metals, while increases in particulate forms ranged from approximately 80–500% (Rust et al., 2018). This pattern held true for most elements measured, including Cu, selenium (Se), and zinc (Zn). However, several elements, including As, iron (Fe), and manganese (Mn), exhibited significant post-fire increases of both particulate and dissolved forms. Across all measured elements, for both dissolved and particulate forms, flux increased significantly in the five years following fire due to increased stream flows (Rust et al., 2018). Overall, significant increases in total metal concentrations were found to be one of the most common changes in stream water quality following wildfire (Rust et al., 2018). In a follow-up study concerning the same dataset, Rust et al. (2019) found that for burned catchments, streamwater total metal concentrations were negatively correlated with post-fire living vegetation cover, suggesting a link between fire severity, ecosystem recovery, and metal exports. Meanwhile, dissolved metal concentrations in post-fire streamwater were found to be negatively correlated with pre-fire soil organic matter (SOM), likely due to the behaviour of post-fire soil aggregates (Rust et al., 2019). More specifically, where pre-fire SOM was ≥2%, post-fire dissolved metal concentrations decreased relative to pre-fire baselines, whereas pre-fire SOM ≤2% was correlated with large increases in post-fire dissolved metal concentrations (Rust et al., 2019). 7 Furthermore, the authors found that there was a 750% median increase in total metals exported from burned areas when pre-fire soil field capacity (the amount of water held in the soil) exceeded 17% (Rust et al., 2019). Overall, the findings highlighted the importance of thresholds in post-fire metal responses and indicated that fire severity, post-fire vegetation cover, and pre-fire soil organic matter were key explanatory variables mediating metal exports from burned areas. 1.3 Paleoecological Studies of Wildfire–Water Relationships 1.3.1 Characterizing the Biogeochemical Effects of Past Fires Observational records of water quality and wildfire disturbance in water supply catchments are typically short relative to fire disturbance cycles and climatic changes. Moreover, the timespan covered by these observations generally coincides with the modern era of active wildfire suppression in many areas, reducing the likelihood that large or impactful wildfire events are documented. Further complicating matters is the fact that wildfire occurrence, behaviour, and biogeochemical effects are diverse in both space and time, owing to site-specific variation in drivers like climate, vegetation, topography, geology, and ecosystem processes (Dunnette et al., 2014). As a result, it can be challenging for researchers and water supply managers to anticipate the ways in which watershed fire disturbance frequency and effects might change over time in response to changes in drivers like climate and vegetation. It can be similarly difficult to anticipate in what ways, to what degree, and for how long individual wildfires might affect a specific catchment’s ability to supply clean water. Paleoecological analysis of lake and reservoir sediments provides an approach to investigate these questions. Lake and reservoir sediments often preserve a continuous chronological archive of physical, chemical, and biological proxy indicators that can be quantified and interpreted to reconstruct the environmental history of the catchment, including fire disturbance history. Paleofire proxies are natural materials (e.g., charcoal, pollen grains, minerals) or parameters of materials (e.g., 8 particle size, magnetic susceptibility) that are directly generated or indirectly influenced by past fires or fire-related processes (Conedera et al., 2009). These materials are commonly deposited in lake sediments during or after a proximal wildfire via various pathways (Conedera et al., 2009). Lacustrine sediments therefore often preserve a sequential accumulation of physical, chemical, and biological paleofire proxies that record fire occurrence, drivers, and terrestrial and aquatic impacts over timescales of centuries to millennia (Birks, 1997; Conedera et al., 2009). These paleolimnological proxies can be extracted from sediment cores and systematically analyzed to enable reconstructions of past fire dynamics and effects (Conedera et al., 2009). In many cases, lake sediment records are well-preserved and can extend thousands of years back in time, facilitating the study of wildfire disturbance patterns and biogeochemical effects on sub-decadal to multi-millennial timescales. Lake sediments furthermore integrate environmental inputs from throughout their watershed, providing catchment-scale insights into the effects of wildfire on both terrestrial and aquatic environments. Insights derived from the paleoecological record can thus help contextualize modern wildfire disturbance patterns, anticipate the potential effects of wildfire on water quality in a particular catchment, and evaluate a reservoir’s resilience to these effects. Paleoenvironmental archives preserved in lacustrine sediments have long been studied to reconstruct fire histories (Conedera et al., 2009; Whitlock & Larsen, 2001). Sedimentary macrocharcoal is routinely quantified to reconstruct fire regimes under varying past environmental conditions and identify individual wildfires that occurred in the proximity of a lake (Brown & Power, 2013). Other proxy indicators, such as magnetic susceptibility (MS) as a proxy for soil erosion, are commonly quantified alongside sedimentary charcoal to facilitate the differentiation of fires occurring within versus outside the watershed, as well as low- versus high-severity fires that burned within the catchment (Dunnette et al., 2014; Sandgren & Snowball, 2005). Fossil pollen and spores (henceforth pollen) preserved in lake sediments are also commonly quantified to interpret vegetation and climate histories at local to regional scales (Conedera et al., 2009). These kinds of paleofire studies can 9 provide valuable insights into long-term dynamics and relationships among climate, vegetation, and wildfire disturbance. Typically, however, paleofire studies are conducted at relatively coarse temporal resolutions, with each sample spanning decades to centuries. While such studies are useful for determining FRIs and characterizing long-term variations in fire regimes, they are unable to resolve the shorter-term effects of individual fires. More recently, some paleoecological studies have begun to probe the catchment-scale biogeochemical effects of individual past wildfires at sub-decadal timescales (e.g., Chipman & Hu, 2019; Dunnette et al. 2014; Pompeani et al., 2020), although such studies remain relatively uncommon, perhaps due to the challenges inherent in fine-resolution sampling. An insightful study by Dunnette et al. (2014) characterized the biogeochemical effects of a series of wildfires over several millennia in a subalpine watershed in Colorado, USA. The authors quantified a wide variety of proxies including sedimentary macrocharcoal, MS, pollen, biogenic silica, total carbon (TC), total nitrogen (TN) and C and N stable isotope ratios (δ13C and δ15N) to distinguish high-severity catchment wildfires from lower-severity or extra-local fires and characterize the average biogeochemical response to each type of fire as recorded in lake sediments. It was found that high-severity catchment fires, but not low-severity or extra-local fires, were followed by significant decreases in sedimentary C and N concentrations, likely due to destruction of terrestrial organic matter and increased post-fire erosion of mineral material (Dunnette et al., 2014). This was accompanied by a significant increase in sedimentary δ15N, suggesting increased terrestrial N availability and reduced biotic N retention immediately following severe catchment fires. Within 25 years post-fire, δ15N returned to average values, probably due to strong nutrient demand from recovering forests, whereas TC and TN remained low for 35–40 years. Sediment TC-to-TN ratio (C:N) was also found to be low for 20–50 years post-fire, indicating reduced terrestrial organic matter inputs to the lake during this time. Overall, the results demonstrated that severe catchment fires could be reliably identified in the sedimentary record by their characteristic signature of approximately 10 coincident increases in charcoal, MS, and δ15N, and a corresponding decrease in TC, TN, and C:N, with the magnitude of post-fire changes scaling almost directly with inferred fire severity. Lower-severity or extra-local fires, defined as those which lacked coincident charcoal, MS, and δ15N peaks, produced weak responses across other proxies (Dunnette et al., 2014). In contrast, in a study of sediment records from three subalpine Colorado lakes, Pompeani et al. (2020) found that local fires were typically followed by modest increases in total sedimentary C, N, and S lasting about 20 years. The authors attribute elevated post-fire organic matter concentrations to a mix of terrestrial sources and increased algal productivity, as indicated by commensurate changes in C:N and δ13C, though they note that the source of post-fire sedimentary organic material was likely different in each study lake. Sedimentary concentrations of titanium (Ti) and other metals associated with terrestrial mineral input were generally found to decrease following fire, probably due to dilution with organic material. Overall, these changes were only found to be statistically significant in some of the lakes studied, and the authors emphasize that the overall biogeochemical response of these lakes to wildfire was muted, suggesting that the temporal resolution of the proxy records (~20 years/sample) may have been too coarse to resolve potential short-term post-fire responses. They highlight the need for sampling at sub-decadal timescales in future analyses to better resolve the short-term biogeochemical effects of wildfires. Chipman and Hu (2019) found similarly subtle post-fire biogeochemical responses studying five fires in a Holocene sediment record from a small boreal lake in Alaska. On average, no significant differences were found in sediment δ13C, %C, %N, C:N, or MS between pre-fire, post-fire, and fire samples. However, δ15N was found to increase significantly in fire-decade samples, echoing the results of Dunnette et al. (2014). A potential shortcoming of the studies outlined above is that in each case, proxy measurements from multiple fires were combined into composite signals by means of superposed epoch analysis or similar. While this improves statistical power by increasing effective sample sizes 11 and allows for evaluation of an “average” biogeochemical response to wildfire, it may also potentially obscure important differences among responses to varying kinds of fires, different fire proximities to the basin, or fires that occurred under dissimilar climatic and landscape conditions. Given the well-documented and significant influences of timing and volume of post-fire precipitation, fire severity, pre-fire vegetation cover, post-fire vegetation recovery, and other temporally variable factors on post-fire biogeochemical responses, it seems likely that averaging responses from numerous fires separated by centuries or millennia could inadvertently “average out” potentially meaningful differences among post-fire responses. Another notable shortcoming of the discussed studies is the generally coarse temporal resolution of the proxy records. Although some wildfire effects may persist for decades, others are likely to be more ephemeral, necessitating sub-decadal sampling to properly detect. 1.3.2 Inferring Fire-Induced Water Quality Effects from Sedimentary Proxies Connecting sedimentary paleofire proxies to specific past changes in water quality typically involves quantifying the remains of organisms that lived in the water at the time of interest and then drawing inferences based on modern relationships between those organisms and contemporary water quality parameters. For example, sedimentary records of zooplankton, such as cladocerans (e.g., Bass and Boucherle, 1984), and algae, such as diatoms (e.g., Brown et al., 2020; Enache & Prairie, 2000) and chrysophytes (e.g., Paterson et al, 1998; Rhodes & Davis, 1995) have been studied to infer the effects of past wildfires on water quality. Of these biological proxies, diatoms are perhaps the most commonly analyzed. Diatoms are abundant photosynthetic microscopic algae that are commonly studied as biological indicators of environmental change (Battarbee et al., 2001; Smol & Stoermer, 2010). Their siliceous cell walls preserve well in sediments and exhibit taxonomically diagnostic morphologies (Battarbee et al., 2001). Many diatom species are ecologically sensitive and respond rapidly to changes in key water quality parameters including nutrient availability, salinity, pH, turbidity, mixing 12 regime, and water temperature (Brown et al., 2020; Smol & Stoermer, 2010). Sedimentary diatom assemblages can therefore be studied to qualitatively infer past fire impacts on the aquatic environment, in much the same way that pollen is studied to infer terrestrial impacts. They can furthermore be analysed to quantitatively reconstruct past aquatic conditions by applying transfer functions calibrated using modern data (Cumming et al., 2015; Reavie et al., 1995; Smol & Stoermer, 2010; Voit et al., 2014; Wilson et al., 1994). Results vary substantially among studies that employ fossil diatoms to infer the aquatic effects of specific past fire events. In some cases, the diatom community does not appear to respond to fires. For example, Philibert et al. (2003a) observed no significant changes in diatom-inferred lake chemistry associated with past fire events in a Quebec catchment. Similarly, a paleoecological study of an Ontario lake by Paterson et al. (2002) found only minimal changes in the diatom species assemblage following historical watershed fires. In a Yellowstone National Park study, Bracht et al. (2008) likewise found that the diatom record exhibited no direct relationship to pollen or charcoal reconstructions. In contrast, other studies have found compelling relationships between sedimentary diatom assemblages and inferred fire events. For example, Rhodes and Davis (1995) found that both fossil diatom and chrysophyte assemblages recorded abrupt increases in lake pH coinciding with fire events in Maine, while Enache and Prairie (2000) used diatom-inferred changes in lake geochemistry to demonstrate that total P increased significantly during four historical fire events in western Quebec. In a paleoecological analysis of an Alberta lake, Philibert et al. (2003b) report increases in total diatom abundance and the benthic to planktonic taxa ratio coinciding with charcoal-inferred fire events. In a study of a Finnish lake, Korhola et al. (1996) found a significant increase in diatom-inferred pH following a historical catchment fire. Similarly, a paleoecological study of a Swedish lake by Korsman and Segerström (1998) found that variations in diatom-inferred pH and alkalinity were closely associated with changes in charcoal flux, implying that the diatom record reflects fire-induced 13 changes in lake biogeochemistry. More recently, Brown et al. (2020) found that fossil diatom assemblages in the sediments of a Wyoming lake recorded impacts of large catchment fires over the past 350 years, including changes in turbidity and mixing. Fire-like watershed disturbances such as clearcutting have also been shown to significantly alter lacustrine diatom assemblages (Laird & Cumming, 2001; Laird et al., 2001). 1.4 Study Region & Site 1.4.1 Regional Wildfire Regimes Over Time The present study was conducted on southern Vancouver Island, a part of the Coast Mountains and Islands physiographic region of British Columbia, Canada (Meidinger & Pojar, 1991), which constitutes the westernmost portion of the province. This high-relief terrain comprises mountainous Vancouver Island, Haida Gwaii, and a multitude of smaller islands, separated by a submerged coastal trough from the Coast Mountains on the mainland to the east. The region is dominated by glacial landforms and erosion surface remnants, although low-lying areas along the coastal trough, such as the Fraser Lowland, are blanketed in thick fluvial deposits (Meidinger & Pojar, 1991). The modern climate of this maritime region is mild, with cloudy, wet winters and warm, relatively dry summers. Ecologically, this unit is dominated by the Coastal Western Hemlock (CWH) zone in lowland areas and the Mountain Hemlock (MH) zone at higher elevations. A narrow strip of Coastal Douglas Fir (CDF) zone runs down the southeastern margin of Vancouver Island in the rain shadow of island ranges and the Olympic Mountains. Coastal Mountain-heather Alpine Zone (CMA) occupies the highest elevations in the Coast Mountains and north-central Vancouver Island (British Columbia Ministry of Forests and Range Research Branch, 2006). Most fire history studies from this region are centred on Vancouver Island and the Lower Mainland, coinciding with population centres. A handful of studies are available for the central coast, 14 but the northern part of this unit is poorly represented in the fire literature. The longest record of regional fire regimes and drivers is provided by Brown and Hebda (2002a,b). The authors present complete post-glacial vegetation and fire histories reconstructed from sedimentary pollen and charcoal sequences drawn from numerous sites in the CWH, CDF, and MH zones on Vancouver Island. Fire on Vancouver Island during the late-glacial period appears to have been controlled by changes in both fuel availability and climate following deglaciation. Brown and Hebda (2002a) observed that open, fire-free Pinus woodlands dominated the landscape in the cool, dry early late-glacial interval, ca. >13500 calibrated years before present (cal yr BP). This is corroborated by similar results from high-elevation sites in the MH zone (Brown & Hebda, 2003). The first evidence of fires on the landscape coincides with the replacement of open Pinus forests with closed mixed-conifer forests under cooler, moister conditions in the later late-glacial (ca. 13500-11700 cal yr BP). Fire incidence increased during the early-Holocene interval, ca. 11700–7000 cal yr BP (Brown & Hebda, 2002a,b). This increase was likely the product of a significantly warmer and drier climate - up to 3°C warmer than present (Walker & Pellatt, 2003) - but may have been partly driven by human activity (Brown & Hebda, 2002b). Open Pseudotsuga menziesii forests developed and expanded westward during the early Holocene, although Tsuga heterophylla-Picea forests persisted in wetter sites and high elevations remained relatively moist (Brown & Hebda, 2003). In the dry eastern regions of Vancouver Island, fires may have been fuel-limited. Gavin et al. (2003a) reported similar findings from a tree ring and soil charcoal study on western Vancouver Island, noting that regional climate and forest composition were likely the primary drivers of fire frequency and extent during the early-Holocene, producing larger landscape fires. A gradual decrease in fire frequency from the mid- to early late-Holocene (ca. 6000–2500 cal yr BP) is attributed to a regional moistening trend, which implies strong climatic control of the fire regime during this time (Brown & Hebda, 2002b). An increase in T. heterophylla and Cupressaceae 15 (likely Thuja plicata) pollen (and, at higher elevations, Tsuga mertensiana) beginning around 3800 cal yr BP provides evidence for cooling and continued moistening in the late-Holocene and is accompanied by a further decrease in charcoal flux, suggesting even fewer fires (Brown & Hebda, 2002b; 2003). However, in the later late-Holocene (ca. 2,000 cal yr BP to present), several sites record an increase in charcoal flux despite continued climatic moistening and cooling, a trend mirrored at high elevation sites (Brown & Hebda, 2002b; 2003). This is interpreted by Brown and Hebda (2002b) as an increase in fire activity driven by non-climate factors, and the authors suggest that humans may have been responsible for this late-Holocene decoupling of climate and fire regime. Gavin et al. (2003a) similarly observed that climatic influences on the regional fire regime were weaker during the late-Holocene but find that landform controls exerted a strong influence on fire incidence during this time, resulting in a bias to south-facing hillslopes. In a study of sedimentary and soil charcoal on western Vancouver Island, Gavin et al. (2003b) found a sharp increase in the FRI at 1100 CE, from ca. 50 to ca. 300 years, coinciding with the “Little Ice Age”. This implies strong climatic controls on the fire regime, which contradicts the late-Holocene anthropogenic control hypothesis of Brown and Hebda (2002b). Gavin et al. (2007) later noted that while climate effects on fire regimes are evident in many regions of western North America, comparisons of paleo-fire records among sites reveals a lack of synchrony in some cases, indicating that local factors substantially affect fire occurrence. Most literature concerning anthropogenic influence on fire regimes in coastal B.C. comes from the CDF zone on southeastern Vancouver Island and the southern Gulf Islands. Historical and ethnographic research strongly indicates the purposeful use of low-severity fire by Coast Salish peoples of this region to manage the landscape and maintain availability of plant and animal resources (Lucas & Lacourse, 2013; McCune et al., 2013). However, a lack of detailed archaeological and ethnographic records for southern Vancouver Island makes separation of human and climatic 16 influences on fire regimes challenging (Lucas & Lacourse, 2013). For example, an analysis of sedimentary pollen and charcoal from Roe Lake in the southern Gulf Islands identified 15 fire episodes over the last 1,300 years, all of which were of low or moderate severity. While it is possible that some of these fires were ignited by Indigenous peoples, the period of highest frequency corresponds with regionally warm, dry conditions during the Medieval Climate Anomaly, suggesting strong climatic control. Numerous studies, however, have documented that the cessation of frequent burning by Indigenous peoples and implementation of fire suppression practices by European settlers has led to conifer encroachment on many remnant historical oak savannas (Gedalof et al., 2006; Lucas & Lacourse, 2013; McCune et al., 2013; Pellatt et al., 2015). Regarding recent and future fire regimes in coastal B.C., Westerling et al. (2006) show that wildfire activity in western North America has increased since the mid-1980s, and it has been demonstrated that the area burned by fires in Canada has increased since the mid-20th century, and that at least some of this change is attributable to anthropogenic climate change (Gillett et al., 2004). Climate models predict warming throughout much of western North America and the Pacific Northwest over the next 100 years. Models of future fire risk project significant increases in fire indices for coastal B.C., including more severe fire weather and longer fire seasons (Haughian et al., 2012). 1.4.2 Greater Victoria Water Supply Area The present study was conducted in the Greater Victoria Water Supply Area, which is located northwest of Victoria, British Columbia, Canada, on southern Vancouver Island (Fig. 1A,B,C). It is comprised of the Sooke, Goldstream, and Leech watersheds and encompasses approximately 226 km2 of forested land which is owned and managed by the Capital Regional District (CRD), a local government administrative district (CRD, 2015a). 17 Fig. 1. Map of the study area showing Sooke Lake Reservoir, Begbie Lake, and coring locations. Bathymetric contours are given in meters above sea level. Darker terrestrial contours are at 20 m intervals, lighter contours at 10 m intervals. For a more detailed map of Begbie Lake, see Figure 1 in Brown et al. (2019). 18 The Sooke watershed provisions almost all the water delivered to the CRD’s approximately 400,000 customers in Greater Victoria (CRD, 2015b; CRD, 2022). It is almost entirely encompassed by the Drinking Water Protection Zone, which restricts public access to safeguard water supply. The Goldstream watershed serves as a secondary water source and is typically only used during the brief annual maintenance of infrastructure of the primary Sooke water supply system. However, in the case of an emergency in the Sooke system, the Goldstream system can serve as a backup, and has the capacity to supply about one month of water at average summer demand levels (CRD, 2015c). The Leech watershed does not currently supply water but is being prepared to supplement the Sooke system to meet anticipated increases in water demand in the coming decades (CRD, 2015d). 1.4.2.1 Sooke Lake Reservoir Sooke Lake Reservoir (SLR; 48.55° N, 123.70°W; Fig. 1A) is located within the Sooke watershed and is currently the primary water source for the Greater Victoria Water Supply System (CRD, 2015b). In use since 1915, SLR is a dammed natural lake that has had its water level raised several times over the past century (Barraclough, 1995). At full capacity, the modern reservoir surface sits at 187 m above sea level and covers 7.35 km2 (Werner et al., 2015). It has a catchment area of about 70 km2, including the reservoir itself, giving SLR a surface area to catchment ratio of approximately 1:10 (Werner et al., 2015). Before raising, the lake was substantially smaller (Fig. 1A), with a surface area to catchment ratio closer to 1:20 (Barraclough, 1995). The present reservoir is approximately 7.2 km long and 1.5 km across at its widest point and comprises three main basins which decrease in size from north to south, with a surface area ratio of about 10:3:1, respectively (Barraclough, 1995; Werner et al., 2015). The north basin is also the deepest, reaching a maximum depth of 74 m. The reservoir has a maximum total volume of 160.3 million m3, of which 92.7 million m3 are useable for water supply (i.e., available at the water supply intake; CRD, 2015b). About 25% of the water entering SLR flows in from Rithet Creek, while seven smaller creeks and streams contribute most of the remainder (Barraclough, 1995). 19 Sooke Lake Reservoir is a warm monomictic lake, with winter water temperatures typically reaching a minimum of 4°C and late summer water temperatures reaching as high as 28°C (Werner et al., 2015). Winter ice cover is uncommon (Barraclough, 1995). A thermocline typically develops from April–May and stabilizes at 12–20 m depth from August–September (Lucey, 1993). Thorough vertical mixing occurs each winter, with no thermal stratification from November–April (Barraclough, 1995). The reservoir is drawn on year-round and has a mean water residence time of about two years (Barraclough, 1995). Even after multiple raisings that inundated foreshore areas, the modern reservoir remains naturally oligotrophic (chlorophyll a ≈ 1.7 μg/L; total P ≈ 4.8 μg/L) with very low turbidity, exhibiting a Secchi depth of 6–10 m (CRD, 2014). As such, water from SLR receives no fine filtration before delivery to customers, which makes this water source particularly vulnerable to fire-induced contamination (CRD, 2012; Werner et al., 2015). 1.4.2.2 Begbie Lake Begbie Lake is a small water body situated approximately 500 m north of SLR (Fig. 1A). It was selected as an additional sampling site for this study because the depth of SLR made recovering a long core from the reservoir itself logistically prohibitive. Connected to the main reservoir via a channel, the relatively shallow, calm BL satisfies many of the selection criteria for a desirable paleolimnological study site (Brown & Power, 2013). Furthermore, its proximity to SLR ensures that the two lakes share common fire, vegetation, and climate histories. Begbie Lake is roughly oval, measuring about 70 m by 100 m and reaching a maximum depth of 8.4 m at the approximate centre of the lake. Water quality in Begbie Lake is not well-studied, but a Secchi depth of 1.5 m was measured as part of the present study, suggesting mesotrophic to eutrophic conditions. A single inflowing stream brings water from the northeast, but when water levels are high, SLR and Begbie Lake mutually exchange water via their connecting channel. Before the reservoir was raised, however, Begbie Lake exclusively flowed into Sooke Lake. 20 Characteristics of the Sooke watershed at large, such as climate, vegetation, and geology (discussed below), also apply to the Begbie Lake catchment. However, Begbie Lake has a much smaller catchment than SLR, embedded within the greater Sooke watershed. Notably, the topography of the Begbie Lake catchment is subdued and only gently sloping. 1.4.2.3 Catchment Characteristics The SLR watershed comprises approximately 70 km2 of forested terrain defined by two long ridges running north-south on either side of the reservoir (CRD, 2015b; Fig. 1). The landscape varies from gently rolling to modestly hilly with bluffs and cliffs (Barraclough, 1995). About 85% of the watershed has a slope between 0 and 30%, and less than 5% of the terrain has a slope greater than 45% (Barraclough, 1995). The Sooke watershed is primarily underlain by intrusive rocks of the Westcoast Crystalline Complex, such as diorite, except at its southern end, which is comprised of volcanic rocks of the Leech River Complex (British Columbia Geological Survey, 2022). Surficial materials were deposited during the Fraser Glaciation and are present as discontinuous tills interspersed with exposed bedrock (Barraclough, 1995). Soils throughout the watershed are generally nutrient-poor, contain little calcium carbonate, and are slightly acidic, all properties which transfer to the reservoir water itself (Barraclough, 1995). Most of the watershed falls within the Eastern Very Dry Maritime subzone of the CWH biogeoclimatic zone (CWHxm1; Meidinger & Pojar, 1991). As elevation increases in the western and northern portions of the watershed, CWHxm1 is replaced by the moister CWHxm2 variant, and then the Coastal Western Hemlock Moist Maritime subzone (CWHmm1) at the highest elevations. The watershed experiences warm, dry summers and mild, moist winters with infrequent snowfall. Precipitation averages approximately 1640 mm annually, reaching a mean monthly maximum of 301 mm in November and minimum of 29 mm in July (Barraclough, 1995; Werner et al., 2015). 21 Predominant tree species in the watershed include western hemlock (Tsuga heterophylla), Douglas fir (Pseudotsuga menziesii), and western red cedar (Thuja plicata) (Meidinger & Pojar, 1991). 1.4.2.4 Catchment Fire History Significant wildfires in the modern SLR catchment are infrequent but potentially high-impact disturbance events, although the fire regime has varied with climate over time (Brown et al., 2019). A precursor study (summarized in Section 1.5) was the first to reconstruct the complete post-glacial fire history of the Sooke watershed (Brown et al., 2019). Previous work included a paleolimnological study of a short sediment core from SLR extending back several centuries (Barraclough, 1995) and a tree ring study examining watershed stand ages to estimate the dates of major disturbances, which covered a similar time period (Murray, 1994). Records of fires in the area have been kept since the 1910s (Barraclough, 1995; Smiley et al., 2016). A mean FRI of 127 years was previously calculated for the Sooke watershed (Murray, 1994), and tree ring and sedimentary evidence indicates that 1890 was perhaps the most recent major fire year (Barraclough, 1995; Murray, 1994). Smaller fires occurred in the watershed throughout the 20th century, but management activities related to timber harvesting and fire suppression have likely had a profound impact on natural disturbance regimes (Smiley et al., 2016). In August 2020, two lightning-caused wildfires burned on the ridgetop west of SLR. Both fires were small (2.1 and 5.9 ha, respectively), but their proximity to the reservoir caused concern among water supply managers. The burned areas were closely monitored for post-fire effects that might pose a risk to water quality, but only negligible impacts were observed (Scott, 2020). 1.5 Precursor Study A precursor study (Brown et al., 2019) was conducted in the Sooke watershed and provided important context for the current work by characterizing post-glacial relationships among climate, vegetation, and fire disturbance in the watershed and facilitating the selection of sedimentary fire 22 horizons for detailed analysis at fine temporal resolutions. This precursor study is briefly summarized below; please see Brown et al. (2019) for complete information. Post-glacial climate, vegetation, and fire history were reconstructed from a sediment record from Begbie Lake. Charcoal, pollen, and MS were analyzed to determine how vegetation, fire, and streamflow varied through time with climate in the Sooke watershed. A total of 59 fire events were identified over the entire post-glacial period, yielding a mean FRI of approximately 240 years. In the cold late-glacial period (≥13500 cal yr BP), the watershed was dominated by open Pinus woodlands with periodic fire disturbance. This regime transitioned to mixed conifer forests subject to high-severity fires, which prevailed until about 11600 cal yr BP. Rapid climatic warming and drying at the onset of the early Holocene led to the development of an open landscape characterized by abundant fire-adapted taxa, longer fire seasons, frequent low-severity surface fires, and occasional high-severity fires. Climate moistened in the mid Holocene around 8000 cal yr BP, facilitating forest canopy closure and a decline in fire disturbance. However, low-severity fires likely persisted in Garry oak (Quercus garryana) meadow ecosystems, which expanded locally. Climate cooled and further moistened between 6200–2500 cal yr BP, yet sedimentary charcoal flux increased, perhaps reflecting human activity and/or increased climate variability. Modern climate and vegetation conditions arose in the Sooke watershed after about 2500 cal yr BP, impacted most recently by the presence of European settlers beginning in the mid-1800s CE. Future landscapes and fire regimes of the Sooke watershed will be shaped by anthropogenic climate change, and may approximate early Holocene conditions, with extended summer droughts leading to longer fire seasons, a mixed-severity fire regime, and open vegetation comprised of fire-adapted taxa. 23 2 Methods 2.1 Sediment Acquisition Three lake sediment records were collected from the Sooke watershed (Table 1): one from Begbie Lake (BEG), one from the SLR north basin (SLRN), and one from the SLR south basin (SLRS; Fig. 1A). Table 1. Sediment records recovered from the Sooke watershed. Begbie Lake SLR North SLR South Coordinates 48.5851, -123.6836 48.5649, -123.6915 48.5251, -123.7023 Core name BEG-K BEG-1 BEG-2 SLRN-1 SLRS-1 SLRS-2 Core type Kajak Livingstone Livingstone Kajak Kajak Kajak Date collected 2014-05-13 2014-05-14 2014-05-15 2014-05-16 2019-09-12 2019-09-12 Water depth (m) 8.27 8.20 8.38 74 27 27 Total length (cm) 53 636 581 35 35 36 The Begbie Lake sediment record comprises three adjacent, overlapping sediment cores (BEG-K, BEG-1, and BEG-2) collected from the approximate centre of the lake in May 2014 using a raft-based coring platform. The uppermost sediment (BEG-K) was collected using a Kajak gravity corer (Kajak et al., 1965) to ensure intact recovery of unconsolidated material at the sediment–water interface. Sediment below 30 cm depth was collected using a modified Livingstone piston corer (Wright, 1967) in consecutive 1-m increments. Because this process is incremental, two overlapping cores (BEG-1 and BEG-2; horizontal offset = ~1 m, vertical offset = ~50 cm) were collected to ensure recovery of a continuous sediment record. The SLR north basin sediment record comprises a single sediment core (SLRN-1) collected from the deepest part of the north basin in May 2014 using a Kajak gravity corer from a raft-based coring platform. A Livingstone core could not be collected due to the water depth. The SLR south basin sediment record comprises two parallel, adjacent sediment cores (SLRS-1 and SLRS-2) collected from the deepest part of the south basin in September 2019 using a modified Kajak gravity corer from a small watercraft. A Livingstone core could not be collected due to water depth. 24 All Kajak cores were sectioned in the field using a purpose-built extrusion device. BEG-K and SLRN-1 were sampled in contiguous 5-mm increments, whereas SLRS-1 and SLRS-2 were sampled in contiguous 2.5-mm increments. Kajak core samples were transported and stored at 4°C in sealed polyethylene bags. Livingstone core sections from Begbie Lake were wrapped intact in polyethylene plastic and transported and stored in rigid boxes at 4°C. 2.2 Radiometric Dating and Age-Depth Modelling Bulk sediment was subsampled from selected Kajak core sections for lead-210 (210Pb) dating (Table 2). Subsamples were weighed and sent wet to Flett Research Ltd., Winnipeg, Canada, for 210Pb dating. Livingstone cores were photographed in the laboratory and selectively sampled for embedded plant macrofossils and bulk sediment for radiocarbon (14C) dating (Table 2). Samples were oven-dried at 60°C, weighed, and sent to one of either A. E. Lalonde AMS Laboratory, Ottawa, ON, or W. M. Keck AMS Laboratory, Irvine, CA, for accelerator mass spectroscopy (AMS) 14C dating. A tephra layer was observed at 405–415 cm depth and inferred to be from the climactic eruption of Mount Mazama, Oregon, ca. 7631 cal yr BP (Egan et al., 2015), thus providing an additional chronological control point. Age-depth relationships were modelled using the rplum package (v0.2.2; Blaauw et al., 2021) for R 4.1.1 (R Core Team, 2021), which uses Bayesian statistics to reconstruct realistic sediment accumulation histories based on combined 210Pb and 14C dates (Aquino-López et al., 2018). Bayesian priors were estimated for each age-depth model based on known sediment accumulation characteristics and the evidence provided by available chronological control points. Because the Mazama tephra was deposited by an effectively discrete and instantaneous eruption event, its thickness was disregarded during age-depth modelling. 25 Table 2. Chronological control points for Begbie Lake and Sooke Lake Reservoir (SLR) sediment records. Sediment record Sample ID Method Material Depth (cm) 210Po activity (dpm g-1) or 14C age (ybp) ±1 σ Modelled mean calibrated age (cal yr BP) Begbie BEG_top N/A N/A 0.00 N/A N/A -64 Begbie 70556 210Pb Bulk sediment 1.50 15.12 1.22 -63 Begbie 70561 210Pb Bulk sediment 3.75 18.49 0.48 -61 Begbie 70565 210Pb Bulk sediment 5.50 17.49 0.64 -53 Begbie 70568 210Pb Bulk sediment 7.25 14.48 0.48 -43 Begbie 70572 210Pb Bulk sediment 9.00 9.31 0.68 -30 Begbie 70575 210Pb Bulk sediment 10.50 7.07 0.32 -16 Begbie 70578 210Pb Bulk sediment 12.25 4.38 0.25 -1 Begbie 70582 210Pb Bulk sediment 14.25 2.31 0.19 14 Begbie 70586 210Pb Bulk sediment 16.75 1.99 0.18 35 Begbie 70592 210Pb Bulk sediment 19.00 0.92 0.11 61 Begbie 70595 210Pb Bulk sediment 22.25 0.54 0.13 88 Begbie UCIT31394 14C Plant fragment 29.0 280 20 135 Begbie UCIT31395 14C Twig 76.0 1435 20 1323 Begbie UCIT31396 14C Plant fragment 104.0 1875 25 1816 Begbie UOC-3392 14C Plant fragments 114.6 2207 31 2190 Begbie UOC-3393 14C Plant fragments 127.7 2512 31 2542 Begbie UCIT31397 14C Twig 168.5 3075 20 3279 Begbie UCIT31398 14C Bark fragment 239.0 3935 20 4392 Begbie UCIT31399 14C Plant fragment 345.0 5785 20 6578 Begbie Mazama N/A Tephra 415.0 N/A N/A 7639 Begbie UCIT31400 14C Twig 471.0 8205 25 9135 Begbie UOC-3394 14C Plant fragments 489.6 8480 35 9496 Begbie UOC-3395 14C Plant fragments 511.9 7166 47 9996 Begbie UCIT31401 14C Seed 513.5 8885 25 10034 Begbie UOC-3396 14C Plant fragments 574.0 9957 44 11777 Begbie UCIT31402 14C Twig 575.0 10240 30 11834 Begbie UCIT31403 14C Needle 621.5 11960 30 13761 SLRN-1 SLRN1_top N/A N/A 0.00 N/A N/A -64 SLRN-1 72714 210Pb Bulk sediment 0.75 37.53 0.90 -64 SLRN-1 72716 210Pb Bulk sediment 1.75 25.26 0.85 -64 SLRN-1 72718 210Pb Bulk sediment 3.00 32.79 0.85 -62 SLRN-1 72721 210Pb Bulk sediment 4.50 27.30 0.75 -58 SLRN-1 72724 210Pb Bulk sediment 6.00 18.35 0.54 -44 SLRN-1 72727 210Pb Bulk sediment 7.50 12.82 0.56 -30 SLRN-1 72730 210Pb Bulk sediment 9.25 12.68 0.50 -19 SLRN-1 72734 210Pb Bulk sediment 11.50 5.70 0.28 -2 SLRN-1 72739 210Pb Bulk sediment 14.00 2.92 0.20 16 SLRN-1 72744 210Pb Bulk sediment 16.50 2.27 0.18 36 SLRN-1 UOC-3397 14C Seed 18.75 269 31 62 SLRN-1 72749 210Pb Bulk sediment 19.00 1.47 0.14 87 SLRN-1 72754 210Pb Bulk sediment 22.75 1.18 0.13 90 SLRN-1 UOC-3398 14C Plant fragments 25.75 629 31 135 SLRS-1 SLRS1_top N/A N/A 0.000 N/A N/A -70 SLRS-1 98714 210Pb Bulk sediment 0.875 21.61 0.48 -67 SLRS-1 98715 210Pb Bulk sediment 1.875 19.33 0.55 -62 SLRS-1 98716 210Pb Bulk sediment 2.875 18.62 0.45 -55 SLRS-1 98717 210Pb Bulk sediment 3.875 19.83 0.59 -45 SLRS-1 98718 210Pb Bulk sediment 4.875 17.12 0.53 -31 SLRS-1 98719 210Pb Bulk sediment 5.875 11.27 0.43 -17 SLRS-1 98720 210Pb Bulk sediment 6.875 7.24 0.35 -4 SLRS-1 98721 210Pb Bulk sediment 7.875 4.53 0.26 6 SLRS-1 98722 210Pb Bulk sediment 9.375 2.27 0.16 16 SLRS-1 98724 210Pb Bulk sediment 11.875 1.27 0.14 28 SLRS-1 UOC-11418 14C Plant fragments 23.125 2731 23 N/A SLRS-1 UOC-11419 14C Plant fragments 28.625 2843 23 N/A SLRS-1 UOC-11807 14C Bulk sediment 34.875 3221 23 N/A 26 Only 210Pb dates were used in modeling SLRS-1 age-depth relationships because the 14C dates obtained from the core were incongruously old (ca. 2800 years older than 210Pb dates at similar depths), suggesting contamination with old carbon. 2.3 Subsampling Methodology Sediment for biogeochemical analyses was subsampled in two phases. First, the full length of each sediment record was contiguously subsampled at multi- to sub-decadal resolution (BEG-1 and BEG-2 at 10 mm; BEG-K and SLRN-1 at 5 mm; SLRS-1 at 2.5 mm). For the Begbie Livingstone cores, subsamples above the Mazama tephra (415 cm) were taken from BEG-1, while subsamples below were taken from BEG-2 due to concerns about the integrity and continuity of the BEG-1 core below this depth. For each subsample, MS and charcoal accumulation rate were quantified to identify peaks that were interpreted as discrete fire events. Pollen and spores were quantified every 10 cm for the Begbie Lake record. For more results from this first sampling phase, please see Section 1.5 and Brown et al. (2019). Next, five sediment sequences spanning five inferred fire events were manually selected for analysis at sub-decadal resolution (BEGA, BEGB, BEGC, SLRN, SLRS; Table 3). The criteria for selection were 1) an approximately coincident charcoal and magnetic peak, 2) no other fire events evident in the preceding or following ca. 50 years, and 3) peaks of approximately average magnitude for the record, except for BEGA (see Section 3.2), which was selected specifically for its extreme magnitude. Furthermore, fire events were selected to coincide with a variety of past climates. Table 3. Charcoal peaks selected for detailed analysis. Fire event Approximate depth (cm) Approximate age (cal yr BP) Relative climate Relative CHAR peak magnitude Subsampling resolution (mm) BEGA 577.8 11990 Cold, moist High 2.0 (pollen @ 5.0) BEGB 505.4 9850 Warm, dry Average 2.0 BEGC 122.4 2410 Cool, wet Average 2.0 SLRN 22.25 130 Cool, wet Average 5.0 SLRS 25.50 130 Cool, wet Average 2.5 27 These five selected sediment sequences were contiguously subsampled at 2–5 mm resolution using a purpose-built slicing system to ensure accuracy and precision. Roughly 3–7 cm of additional sediment was sampled above and below each fire horizon, comprising a window of several decades to several centuries centred on each fire event. Subsamples were analyzed for a diverse array of biogeochemical proxy indicators including charcoal, pollen, diatoms, magnetic susceptibility (MS), grain size, TC, TN, δ13C, δ15N, and a suite of inorganic elements. 2.4 Magnetic Susceptibility Volume MS, reflecting the magnetizability of the sediment when subjected to a small magnetic field, was measured using a Bartington MS2E sensor connected to a Bartington MS3 meter and logged with Bartsoft for PC (v4.0; Bartington Instruments, 2013). The MS2E sensor has an area of response of 3.8 × 10.5 mm and a depth of response of 50% at 1 mm and 10% at 3.5 mm. Before measurement, sediment was allowed to warm up to ambient lab temperature (~21°C) and the sensor head left in contact with the sediment core for at least an hour to achieve thermal equilibrium between sensor and sediment. Kajak core samples were gathered into the corners of their bags and measured with the sensor head held flat against the bag for two seconds. Care was taken to avoid air bubbles and ensure that at least 1 cm3 of sediment was positioned under the sensor head. Livingstone cores were measured intact. Before measurement, core surfaces were gently cleaned with a plastic scraper and covered with a thin sheet of polyethylene plastic wrap to prevent contamination. Each measurement lasted for two seconds, with the sensor head held flat against the sediment, its long axis oriented perpendicular to the long axis of the core. Because the sensitive area of the MS2E sensor head is 3.8 mm across, those fire events sampled at 2 mm intervals resulted in 0.9 mm of physical overlap with adjacent samples on either side. 28 A temperature drift correction was automatically applied by Bartsoft every five measurements. Every sequence was measured three to five times, and a consensus MS profile was independently calculated for each core by averaging valid temperature-corrected measurements across all runs. 2.5 Charcoal Sediment subsamples ranging from 0.5 to 3.0 cm3 were gently rinsed on a 150 μm sieve to remove the sedimentary matrix and micro-charcoal but retain the macro-charcoal particles. The fraction that passed through the sieve was collected for later pollen analysis, while the fraction retained in the sieve was prepared for charcoal analysis. Each sieved charcoal subsample was added to a gridded petri dish with a small volume of distilled water and all charcoal particles were visually identified and tabulated at 20× magnification using a Leica MZ7.5 stereomicroscope. For selected fire events (BEGA and SLRS), a microscope-mounted video camera (Leica IC-90E) connected to a computer was used in conjunction with Leica LAS X imaging software (Leica Microsystems, 2017) to measure the major and minor axes, perimeter, and area of each charcoal particle. Strict criteria for charcoal identification were observed; fragments were classified as charcoal only if they were black, brittle, and predominantly opaque, with visible sub-metallic lustre and cellular structure. The full-length charcoal time series from the Begbie Lake sediment record, as presented in Brown et al (2019), was decomposed using CharAnalysis (v0.9; Higuera, 2009). Charcoal concentration (fragments cm-3) was interpolated to the median sample resolution of the core (21 years) and converted into charcoal accumulation rate (CHAR, fragments cm-2 yr-1) by multiplying by the vertical accretion rate. The CHAR time series was statistically decomposed into background and peaks components using locally weighted scatterplot smoothing (LOWESS) with a 750-year window. The background component reflects centennial-scale changes in charcoal production, transport, and sediment mixing (Brown et al., 2019). The peaks component is comprised of local fire-induced signal and non-fire related noise. For every sample in the record, a Gaussian mixture model with a 95th-percentile separation threshold was used to separate signal from noise, thus generating a record of 29 local fire events through time. A signal-to-noise index (SNI) was used to evaluate if the record was suitable for peak detection (Kelly et al., 2011). The trend in post-glacial fire frequency was generated using a smoothing window, whereby the fires in each 1000-year period were summed and smoothed using a LOWESS smoother. A similar process was used to determine trends in FRIs (Higuera, 2009). Fine-resolution charcoal counts from individual sediment sequences were simply summarized by converting to concentrations (particles cm-3). For the sediment sequences where charcoal particle size data were recorded (BEGA and SLRS), charcoal summed area was calculated for each sample by summing the surface area of all measured charcoal particles in a sample and converting to mm2 cm-3. Mean charcoal particle size was calculated by dividing charcoal summed area by concentration. Aspect ratio (long axis divided by short axis) was also calculated for each charcoal particle, mean charcoal particle aspect ratio was calculated for each sample. 2.6 TC, TN, δ15N and δ13C Sediment subsamples of 1.0 cm3 were freeze-dried and finely ground by hand using a glass stirring rod. From each subsample, 5 mg of homogenized material was withdrawn, weighed, and sealed in a tin combustion capsule. TC and TN content were measured via combustion analysis using a Costech Instruments Elemental Combustion System. Stable isotope ratios (δ15N and δ13C) were measured using a Thermo Scientific Delta V Advantage continuous flow isotope ratio mass spectrometer. Both analyses were conducted by the Natural Resources Canada (NRCan) analytical laboratory at the Pacific Forestry Centre (Victoria, BC). Data quality was verified at regular intervals by running sample replicates and standard materials with known properties. 2.7 Inorganic Elements Inorganic elements were quantified to identify fire-induced fluctuations in sedimentary concentrations of various key substances, including elements common to wildfire ash and heavy metals and nutrients important to drinking water quality. Sediment subsamples of 1.0 cm3 were 30 freeze-dried and finely ground by hand using a glass stirring rod. Inductively coupled plasma mass spectrometry (ICP-MS) was used to measure a large and diverse suite of inorganic elements using standard techniques. Analysis was carried out by the NRCan analytical laboratory at the Pacific Forestry Centre (Victoria, BC). Elements measured included silver (Ag), Al, As, boron (B), Ba, beryllium (Be), calcium (Ca), cadmium (Cd), cobalt (Co), Cr, Cu, Fe, Hg, potassium (K), magnesium (Mg), Mn, molybdenum (Mo), sodium (Na), nickel (Ni), P, Pb, sulfur (S), Se, vanadium (V), and Zn. Not all elements were measured for every fire event. 2.8 Pollen and Spores Sediment subsamples of 0.5–3.0 cm3 were collected for pollen analysis. Pollen was not quantified for SLRS. Subsamples from BEGB and BEGC were first sieved at 150 μm to isolate macrocharcoal and the fraction that washed through the sieve was retained for pollen analysis. BEGA and SRLN pollen samples were not sieved for charcoal before pollen preparation. Each sub-sample was spiked with an exotic Lycopodium marker (20848 ± 1546 spores tablet-1; Department of Quaternary Geology, University of Lund, batch no. 1031) and processed for pollen and spores using standard techniques (Moore et al., 1991). Grains were identified and tabulated in brightfield on a Leica DM4000 B LED compound microscope at 400–630× magnification until a minimum of 300 pollen grains per slide had been tallied. A pollen reference slide collection and printed text references such as Kapp (1971) and Moore et al. (1991) aided pollen identification. 2.9 Diatoms Sediment subsamples of 1.0 cm3 were removed from the cores and freeze-dried. From each subsample, 20.0 mg of well-homogenized sediment was removed, weighed, and placed in a sterile 50 mL screw-top polypropylene centrifuge tube with 10.0 mL of 30% hydrogen peroxide (H2O2) to digest organic particles. Samples were placed in a fume hood overnight to react at ambient temperature, then placed in a heat bath at 80°C for 8 hours and gently vortexed hourly to accelerate 31 the reaction, after which they were returned to the fume hood to react and settle at ambient temperature (~21°C) until bubble formation stopped (~12–18 hrs). Samples were then filled to 50 mL with ultrapure water and centrifuged for 10 minutes at 1200 rpm, after which the supernatant was aspirated down to 10 mL. This rinse-centrifuge-aspirate cycle was repeated three times for each sample. After the third cycle, 2.0 mL of 10% hydrochloric acid (HCl) was added to each tube. Samples were vortexed and left to react for 1 hour, after which they underwent the rinse-centrifuge-aspirate cycle three more times. On the last cycle, samples were aspirated down to 20 mL final volume. Slides were made by pipetting 700 µL of homogenized sample mixture onto a round cover slip and allowing it to air-dry. Cover slips were mounted on microscope slides using Naphrax® permanent mounting medium. Slides were analyzed under differential interference contrast optics at 1000x magnification. Randomly selected fields of view were counted until a minimum of 300 diatom valves per slide were enumerated (200 for BEGB). Valves were only counted if >50% of the valve was visible. Printed and online photographic keys (e.g., Cumming et al., 1995; Krammer & Lange-Bertalot, 1986; Spaulding et al., 2021) were used to identify diatom valves to the lowest taxonomic level confidently attainable, typically species. Diatoms from the BEGA, SLRN, and SLRS sediment sequences were tabulated by Biological Environmental Services Ltd. 2.10 Grain Size A project-specific grain size sample preparation protocol was developed to address the presence of recalcitrant organic particles and abundant diatoms in the sediment. Grain size was not measured for BEGA or SLRS due to insufficient sample material. Sediment samples of approximately 0.5–1.0 cm3 were weighed wet and placed into 50 mL screw-top polypropylene centrifuge tubes. Fifteen millilitres of 30% H2O2 was added to each tube to digest organic particles for 12 hours at ambient temperature (~21°C). Samples were then placed in a 32 heat bath at 80°C to react until bubble formation stopped (~24–48 hrs). Next, samples were centrifuged at 3500 rpm for 30 minutes, aspirated down to 5.0 mL, and treated with 2.0 mL of 11 M HNO3 for 10 minutes to dissolve any remaining organic particles. Samples were then filled to 50 mL with ultrapure water, centrifuged, and again aspirated down to 5.0 mL. This cycle was repeated three times to ensure all HNO3 was removed before proceeding to the next step. Each sample was then treated with 10.0 mL of 0.5 M NaOH for 4.5 hours at 80°C to dissolve biogenic silica (i.e., diatoms). The NaOH was neutralized with an equal volume of 0.5 M HCl and the samples were again rinsed with ultrapure water, centrifuged, and aspirated. Finally, the samples were suspended in 10.0 mL of sodium hexametaphosphate ([NaPO3]6) and sonicated for 15 minutes to deflocculate particle aggregates. The finished samples were analyzed using a Beckman Coulter LS 13 320 Laser Diffraction Particle Size Analyzer. Data quality was verified at regular intervals by running sample replicates and silica standards. 2.11 Numerical Analyses Stratigraphically constrained cluster analysis was used to quantitatively define bio- and chemo-stratigraphic assemblage zones. For each fire event, pollen, diatoms, and inorganic elements data were independently analyzed using the CONISS method, an agglomerative, hierarchical zonation algorithm based on incremental sum of squares (Grimm, 1987). Zonation was performed using the rioja package’s chclust function (v0.9-26; Juggins, 2020) in R 4.1.1 on square-root transformed percentage data for pollen and diatoms, and on standardized concentration data for trace elements. Square root transformation upweights poorly-represented variables relative to abundant ones, and is considered particularly desirable for biological percentage data (Grimm, 1987), while standardization transforms each variable to have mean = 0 and σ = 1, thus weighting all variables equally. Corresponding broken stick analysis (Bennett, 1996) was conducted using rioja’s bstick function (Juggins, 2020) to evaluate the number of significant zones in each stratigraphic sequence. This procedure helped identify the number of zones that could be reliably derived based on inherent 33 structure in the dataset rather than random noise. In some cases, zones were further partitioned into marginally significant subzones at the analyst’s discretion to facilitate comparisons among proxies and fire events. For each fire event, untransformed proxy data were binned according to the resultant zones, and non-parametric analysis of variance (i.e., Kruskal-Wallis) tests were conducted using the rstatix package (v0.7.0; Kassambara, 2021) in R 4.1.1 to evaluate the null hypothesis that variable medians did not significantly differ among zones (i.e., p ≥ 0.05). For variables where p < 0.05, which indicated rejection of the null hypothesis, multiple pairwise post hoc comparisons were conducted via Dunn tests with Bonferroni adjustments to identify zones which differed significantly from one another (i.e., p < 0.05). Pearson correlation coefficients were calculated between sets of geochemical variables using the CORREL function in Microsoft Excel 365 to examine linear correlations between MS, TC, TN, and concentrations of various inorganic elements. The planktonic-to-benthic diatom ratio was calculated as an indicator of water clarity (Brown et al., 2020; Wolin & Stone, 2010). Taxa with habitat data available on diatoms.org (Spaulding et al., 2021) were classified as either planktonic or benthic, with diatoms listed as both being classified as benthic. A simple index was calculated by summing valves of each type and dividing planktonic by benthic. 34 3 Results Results are organized by sediment sequence and proxy. Many variables were analyzed, but only those variables showing statistically significant or otherwise noteworthy results are mentioned in the text. Generally, variables that exhibit no significant changes are excluded from figures, even if they are mentioned in the text. 3.1 Sediment Chronologies The Begbie Lake (BEG) sediment chronology was modeled using a mixture of 210Pb and 14C dates (Fig. 2A). Unsupported 210Pb was at or below the detection limit below 30 cm depth. The age-depth model yielded a basal mean age of 14016 cal yr BP at 634 cm. The mean 95% confidence range was 418 years, with a minimum of 0 years at 0 cm and a maximum of 960 years at 598 cm. The Sooke Lake Reservoir north basin (SLRN) sediment chronology was modeled using a mixture of 210Pb and 14C dates (Fig. 2B). Unsupported 210Pb was at or below the detection limit below 19.25 cm depth. The age-depth model yielded a basal mean age of 265 cal yr BP at 33 cm. The mean 95% confidence range was 36 years, with a minimum of 0 years at 0 cm and a maximum of 70 years at 23 cm. The Sooke Lake Reservoir south basin (SLRS) sediment chronology was modeled using only 210Pb dates (Fig. 2C). Unsupported 210Pb was at or below the detection limit below 35.75 cm depth. The age-depth model yielded a basal mean age of 158 cal yr BP at 28.75 cm. The mean 95% confidence range was 51 years, with a minimum of 0 years at 0 cm and a maximum of 94 years at 27 cm. 35 Fig. 2. Age-depth models for the Begbie Lake (BEG), Sooke Lake Reservoir north basin (SLRN), and Sooke Lake Reservoir south basin (SLRS) sediment records. Irregular light blue shapes indicate 14C samples and correspond to the top x-axis. Darker blue horizontal rectangles represent 210Pb samples and correspond to the bottom x-axis. Background 210Pb activity is indicated by vertical dashes at the right edge of each panel. The black dotted line indicates mean modeled sediment age, and the grey gradient shaded areas represent 95% confidence intervals. The solid grey horizontal bar in panel A represents the Mazama tephra (Egan et al., 2015) Depth (cm) 36 3.2 Begbie Lake Sediment Sequence A (BEGA) 3.2.1 BEGA Charcoal, MS, TC, TN, δ13C and δ15N The BEGA charcoal, MS, TC, and TN sequences (Fig. 3) each span 585.0–570.0 cm depth (ca. 12300–11650 cal yr BP) and comprise 40 contiguous samples. The error margins of the millennial-scale age-depth model (Fig. 2A) are large relative to the time scale of the features of interest, so results in this section are described in terms of sediment depth rather than modeled age. 3.2.1.1 BEGA Charcoal BEGA charcoal particle concentration (Fig. 3) first deviates from background levels at 578.0 cm and increases sharply to a pronounced maximum at 577.8 cm before gradually declining to background levels by 576.4 cm. A small secondary peak occurs at 575.0 cm. Maximum charcoal concentration (432 particles cm-3) exceeds mean background levels (about 50 particles cm-3) by a factor of more than eight. Summed charcoal area exhibits two distinct peaks: one concurrent with the charcoal particle concentration maximum at 577.8 cm and a second peak at 576.8 cm. Charcoal concentration and summed area covary throughout the profile except around this second peak, where summed charcoal area increases but particle concentration decreases. This is due to an increase in mean charcoal particle size around this horizon, where charcoal particles are on average three times larger than those elsewhere in the sequence. This change in mean size is accompanied by a change in shape, with charcoal particles becoming less elongate on average. Also noteworthy is the fact that abundant sand-sized mineral grains were observed in samples around 576.8 cm, but nowhere else in the sequence. 37 Fig. 3. BEGA fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; charcoal summed area = mm2 cm-3; charcoal mean particle area = mm2; charcoal mean particle aspect ratio = unitless; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. 38 3.2.1.2 BEGA MS The BEGA volume MS sequence (Fig. 3) exhibits a strong, sharp, approximately symmetrical peak centred on 576.8 cm. Values first deviate from background levels at 578.0 cm and return to background levels by 575.2 cm, delineating a well-defined disturbance zone spanning 2.8 cm. Maximum MS values exceed background levels by nearly two orders of magnitude. 3.2.1.3 BEGA TC, TN, δ13C and δ15N The BEGA TC and TN timeseries (Fig. 3) closely match one another. Both profiles decline sharply from background levels at 577.8 cm and reach minimum values by 577.4 cm. Values remain about 65% lower than background through 576.8 cm and increase abruptly thereafter, returning to background levels by 576.2 cm. C:N exhibits modest variability throughout the profile, with a sharp peak occurring at 576.8 cm, coinciding with the MS and summed charcoal area peaks. Stable isotope ratios of C and N likewise covary (Fig. 3). Both δ13C and δ15N increase abruptly from background levels at 577.8 cm and peak at 577.6 cm. A second, smaller peak occurs in each profile at 576.8 cm, after which δ15N rapidly declines to background values while δ13C declines more slowly, achieving background levels by 574.0 cm. 3.2.2 BEGA Inorganic Elements For fire event BEGA, the concentrations of 21 inorganic elements were measured in 40 contiguous sediment samples from 585.0–570.0 cm depth, representing ca. 12300–11650 cal yr BP (Fig. 2A). On average, Fe, S, Al, Ca, Mg, and P were present in the highest quantities, whereas only trace amounts of As, Mo, Pb, and Cd were detected (Table 4). Concentrations of these and other elements varied considerably over the sequence, and many exhibit strong correlations with MS, TC, and TN (Table 5). 39 Table 4. BEGA inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. Element N Mean Median Minimum Maximum SD Skewness Kurtosis Fe 40 34437 35045 23493 44385 3757 -0.35 1.53 S 40 22839 22766 7324 44666 8381 0.19 0.48 Al 40 16102 16897 10386 23528 3295 0.23 -0.78 Ca 40 8935 9121 4464 11968 1721 -0.34 0.22 Mg 40 7256 6724 2915 16255 3017 1.28 1.53 P 40 1415 1480 629 2236 390 -0.31 -0.48 K 40 721 733 222 948 141 -1.16 2.68 Na 40 302 301 192 452 63 0.32 -0.64 Cu 40 192 195 124 245 28 -0.63 0.17 Mn 40 188 181 139 278 33 1.29 1.11 Ni 40 95.2 94 83.3 109.5 6.9 0.34 -0.69 Zn 40 83.2 82 68.0 108.8 8.6 0.68 0.62 Ba 40 79.1 84 32.3 151.8 28.8 0.27 -0.44 Cr 40 76.9 71 36.8 142.2 23.8 1.27 1.25 V 40 60.6 62 20.4 86.4 16.4 -0.68 -0.15 Co 40 35.1 36 22.2 42.2 5.5 -1.12 0.44 B 40 32.3 33 9.4 49.8 9.3 -0.39 -0.10 As 40 8.5 9 4.4 11.0 1.9 -0.87 -0.24 Mo 40 7.8 8 1.4 15.6 4.3 0.0 -1.2 Pb 40 2.9 3 1.8 4.5 0.6 -0.3 0.1 Cd 40 1.6 2 1.5 2.5 0.2 4.1 17.6 40 Table 5. Pearson correlation coefficients between BEGA magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. Pearson correlation (r) MS TC TN MS 1.00 -0.80 -0.82 Mg 0.93 -0.86 -0.85 Cr 0.87 -0.93 -0.93 Mn 0.86 -0.91 -0.90 Al 0.75 -0.69 -0.68 Na 0.69 -0.58 -0.59 V 0.63 -0.68 -0.70 Ni 0.57 -0.44 -0.42 Ca 0.47 -0.55 -0.58 K 0.43 -0.49 -0.50 Pb 0.37 -0.48 -0.50 Ba 0.31 -0.55 -0.55 P 0.10 -0.20 -0.22 Cd 0.02 -0.13 -0.14 Fe -0.08 0.23 0.17 Zn -0.28 0.43 0.46 B -0.58 0.74 0.71 As -0.65 0.82 0.77 Mo -0.69 0.78 0.74 S -0.71 0.88 0.87 Cu -0.76 0.63 0.64 Co -0.76 0.94 0.95 TC -0.80 1.00 0.99 TN -0.82 0.99 1.00 41 Comparison of the distribution of total variance from the CONISS-zoned standardized BEGA elemental data with that expected from the broken-stick model revealed three reliable stratigraphic zones (Fig. 4; Fig. 5). According to the millennial-scale age-depth model, BEGA Elemental Zone 1 (BAE1) encompasses ca. 12300–12000 cal yr BP (585.0–577.7 cm; ca. 300 yr; n = 17), BEGA Elemental Zone 2 (BAE2) ca. 12000–11900 cal yr BP (577.7–576.3 cm; ca. 100 yr; n = 7), and BEGA Elemental Zone 3 (BAE3) ca. 11900–11650 cal yr BP (576.3–570 cm; ca. 250 yr; n = 16). Fig. 4. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGA inorganic elements data (black) with that expected from the broken-stick model (red). 42 Fig. 5. BEGA inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 3 caption. 43 The stratigraphic zones derived from the BEGA elemental data delineate statistically significant (i.e., p ≤ 0.05) changes in median values of a variety of key fire-related proxies (Fig. 6). Charcoal particle count and sediment δ15N increase from BAE1 to BAE2 and remain elevated in BAE3, while mean charcoal particle size and sediment C:N increase from BAE1 to BAE2 and return to previous levels in BAE3. Charcoal summed area, MS, and sediment δ13C increase from BAE1 to BAE2 and decrease to intermediate values in BAE3. Mean charcoal particle aspect ratio decreases from BAE1 to BAE2 and returns to previous values in BAE3, while total sediment C and N decrease from BAE1 to BAE2 and increase to intermediate values in BAE3. Although the ranking of the most prevalent elements remains mostly stable throughout the sequence, there are statistically significant differences in median concentrations of individual elements among zones. Between BAE1 and BAE2, significant increases occur in Al, Ba, Ca, Cr, K, Mg, Mn, Na, Ni, and V (Fig. 7). At the same time, significant decreases occur in As, B, Co, Cu, Mo, and S (Fig. 8). Between BAE2 and BAE3, significant increases occur in As, B, Co, Cu, Mo, S, and Zn, while significant decreases occur in Cr and Mn (Fig. 7; Fig. 8). Notably, P increases significantly between BAE1 and BAE3, but there is no significant difference between BAE2 and either BAE1 or BAE3 (Fig. 7). Cd, Fe, and Pb show no significant differences among zones. Over the three-zone sequence, Al, Ba, Ca, K, Mg, Na, Ni, and V increase from BAE1 to BAE2 and remain elevated into BAE3, although most show apparent, but not statistically significant, declines in BAE3. Cr and Mn also increase from BAE1 to BAE2 but show a statistically significant return to previous levels in BAE3. Following a similar, but inverse, change-and-recover pattern, As, B, Co, and Cu decrease from BAE1 to BAE2 and then recover to previous levels in BAE3. At the same time, Mo and S decrease from BAE1 to BAE2 and then show a statistically significant increase in BAE3, but do not fully recover to BAE1 values. 44 Fig. 6. Box-and-whisker plots of selected BEGA fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 5). X-axis units: see Fig. 3 caption. Thick vertical lines indicate zone median values, boxes denote interquartile ranges, thin horizontal lines represent the minimum and maximum measured values that fall within 1.5× the interquartile range, and dots indicate outliers beyond this threshold. Vertical brackets indicate statistically significant pairwise comparisons (Dunn tests, Bonferroni adjusted; α = 0.05) between zone medians. P-values are given for significant pairwise differences. 45 Fig. 7. Box-and-whisker plots of selected BEGA inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 5). All exhibit statistically significant increases in median values from zone 1 to zone 2 and/or zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 46 Fig. 8. Box-and-whisker plots of selected BEGA inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 5). All exhibit statistically significant decreases in median values from zone 1 to zone 2 and/or zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. Some individual elements do not show statistically significant changes between certain zones, but, when grouped with related elements, they do. For example, Cd and Pb do not individually show any significant changes, but when summed together with As, these toxic elements collectively exhibit a statistically significant decrease from BAE1 to BAE2, and a significant increase from BAE2 to BAE3 (Fig. 8). 47 3.2.3 BEGA Diatoms For fire event BEGA, diatom valves were counted in 39 contiguous sediment samples from 585.0–570.0 cm depth, representing ca. 12300–11650 cal yr BP (Fig. 2A). A total of 369 unique taxa were found, of which 318 (86%) were identified to at least genus level and 212 (57%) were identified to species level or lower. In terms of valves, 12050 valves were counted in total, of which 10714 (89%) were unambiguously assigned to a unique taxon, 10390 (86%) were identified to at least genus level, and 9590 (80%) were identified to species level or lower. The dominant taxa, defined here as those comprising ≥2% of the valves on average, were Staurosira venter (19.8%), Staurosirella pinnata (15.6%), Pseudostaurosira brevistriata (12.4%), an undetermined (undet.) diatom in girdle view (8.3%), and an undet. Aulacoseira species (2.1%) (Table 6). Relative abundance of these and other taxa varied considerably over the sequence. Araphid taxa consistently comprised about 40–70% of the valves in each sample, with symmetric biraphid, centric, and monoraphid morphologies accounting for most of the remainder. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 2.2 to 3.5. Table 6. BEGA diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Staurosira venter 39 19.78 19.67 7.03 31.35 5.69 0.04 -0.32 Staurosirella pinnata 39 15.62 15.28 3.97 30.41 5.57 0.48 0.40 Pseudostaurosira brevistriata 39 12.35 9.71 0.96 29.57 7.25 0.53 -0.45 Undet. girdle view A 39 8.25 8.28 0.99 16.61 3.63 0.26 -0.10 Aulacoseira undet. A 39 2.12 2.21 0.00 7.31 1.66 0.84 1.06 Aulacoseira ambigua 39 1.54 1.61 0.00 4.53 1.16 0.66 0.29 Aulacoseira perglabra 39 1.54 1.30 0.00 5.41 1.29 0.93 0.77 Monoraphid undet. B 39 1.53 0.97 0.00 8.58 1.88 1.83 3.94 Aulacoseira valida 39 1.46 1.32 0.00 4.70 1.07 0.98 1.50 Achnanthidium minutissimum 39 1.38 1.23 0.00 3.63 0.92 0.68 -0.24 Staurosira construens 39 1.17 0.95 0.00 3.19 0.85 0.85 -0.11 Undet. girdle view E 39 1.15 0.00 0.00 5.81 1.70 1.40 0.76 Undet. girdle view B 39 1.08 0.00 0.00 6.91 1.62 1.85 3.69 48 Comparison of the distribution of total variance from the CONISS-zoned square-root transformed BEGA diatom data with that expected from the broken-stick model reveals no reliable stratigraphic zones, but the three-zone configuration comes closest to being significant (Fig. 9). According to the millennial-scale age-depth model, BEGA Diatom Zone 1 (BAD1) encompasses ca. 12300–12000 cal yr BP (585–577.9 cm; ca. 300 yr; n = 15), BEGA Diatom Zone 2 (BAD2) ca. 12000–11850 cal yr BP (577.9–575.3; ca. 150 yr; n = 13), and BEGA Diatom Zone 3 (BAD3) ca. 11850–11650 cal yr BP (575.3–570; ca. 200 yr; n = 11) (Fig. 10). Fig. 9. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGA diatom data (black) with that expected from the broken-stick model (red). 49 Fig. 10. BEGA diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 3 caption. 50 The stratigraphic zones derived from the BEGA diatom data delineate statistically significant (i.e., p ≤ 0.05) changes in median values for a variety of key fire-related proxies (Fig. 11). Charcoal mean particle area and sediment C:N increase from BAD1 to BAD2 and return to previous levels in BAD3. Charcoal particle count, charcoal summed area, sediment volume MS, and sediment δ13C increase from BAD1 to BAD2 and decrease to intermediate values in BAD3, while δ15N increases and remains elevated. Conversely, charcoal particle mean aspect ratio decreases from BAD1 to BAD2 before returning to previous levels in BAD3, and total sediment C and N decrease from BAD1 to BAD2, recovering to intermediate values in BAD3. All proxies show clear evidence of disruption in BAD2. Although the ranking of dominant diatom taxa remains relatively consistent throughout the BEGA sequence, there are statistically significant differences in median abundance of certain individual taxa among zones (Fig. 12). Between BAD1 and BAD2, meaningful increases occur in Encyonopsis albertana, Brachysira ocalanensis, monoraphid undet. B, Achnanthidium pusillum, and undet. girdle view B. At the same time, decreases occur in Staurosira venter, Brachysira vitrea, and undet. girdle view E. Between BAD2 and BAD3, Pseudostaurosira brevistriata shows a significant increase, while decreases occur in Stauroneis bryocola, monoraphid undet. B, Achnanthidium undet. A, and undet. girdle view B. Over the three-zone sequence, only two taxa, monoraphid undet. B and undet. girdle view B, show a noteworthy change-and recover pattern. Various other taxa exhibit statistically significant differences among zones, but their counts are generally too low (i.e., <3 valves/sample) to warrant confidence or be considered meaningful. 51 Fig. 11. Box-and-whisker plots of selected BEGA fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 10). X-axis units: see Fig. 3 caption. See Fig. 6 caption for further figure explanation. 52 Fig. 12. Box-and-whisker plots of selected BEGA diatom taxa binned by diatom-derived bio-stratigraphic zones (see Fig. 10). X-axis units: %. See Fig. 6 caption for further figure explanation. 53 Grouping diatoms by morphology increases effective sample sizes, and therefore statistical power, yielding better-defined differences among zones (Fig. 13). As a group, araphid diatoms decrease significantly from a median abundance of 56% in BAD1 to 47% in BAD2, and then increase to 59% in BAD3. In contrast, symmetric biraphid diatoms increase from a median abundance of 11% in BAD1 to 14% in BAD2 before decreasing to 10% in BAD3. A similar pattern is observed in monoraphid taxa, which nearly double from 7% in BAD1 to 12% in BAD2, then drop to 6% in BAD3. Biraphid asymmetric diatoms likewise increase from 2% in BAD1 to 6% in BAD2, then exhibit an apparent, though not statistically significant, decline to 4% in BAD3. Notably, overall taxonomic diversity also shows this “change and recover” pattern, with Shannon’s H increasing from 2.9 to 3.2 between BAD1 and BAD2, then falling to 2.6 in BAD3. Fig. 13. Box-and-whisker plots of selected BEGA diatom groups and indices binned by diatom-derived bio-stratigraphic zones (see Fig. 10). X-axis units: diatom morphologies = %; for all others, see Fig. 10 caption. See Fig. 6 caption for further figure explanation. 54 3.2.4 BEGA Pollen and Spores For fire event BEGA, pollen and spores were counted in 26 contiguous sediment samples from 585.0–570.0 cm depth, representing ca. 12300–11650 cal yr BP (Fig. 2A). A total of 38 unique taxa were identified. The dominant taxon by far was Pinus contorta, comprising about 70% of the assemblage on average, followed by Alnus at 18%. Both remained dominant throughout the sequence (Table 7). Abies, Salix, Tsuga mertensiana, Tsuga heterophylla, Picea, and Isoetes were all minor constituents averaging around 1–2% of the assemblage. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 0.8 to 1.5. Table 7. BEGA pollen and spore summary statistics. Only taxa with mean abundance ≥0.9% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Pinus contorta 26 69.7 69.0 50.3 80.6 6.4 -0.7 2.2 Alnus 26 17.7 17.7 9.5 28.9 4.4 0.4 0.3 Abies 26 1.9 1.9 0.3 6.0 1.1 1.9 6.2 Salix 26 1.4 1.3 0.3 3.4 0.7 1.4 2.0 Tsuga mertensiana 26 1.4 1.3 0.0 3.3 0.9 0.4 -0.6 Isoetes 26 1.3 1.0 0.0 4.3 1.2 1.4 1.1 Picea 26 0.9 0.7 0.0 3.2 0.7 1.5 3.7 Tsuga heterophylla 26 0.9 0.7 0.0 2.5 0.7 0.9 0.3 Comparison of the distribution of total variance from the CONISS-zoned square-root transformed BEGA pollen data with that expected from the broken-stick model reveals three marginally significant stratigraphic zones (Fig. 14). According to the millennial-scale age-depth model, BEGA Pollen Zone 1 (BAP1) encompasses ca. 12300–12010 cal yr BP (585.0–578.25; ca. 290 yr; n = 9), BEGA Pollen Zone 2 (BAP2) ca. 12010–11880 cal yr BP (578.25–575.75; ca. 130 yr; n = 5), and BEGA Pollen Zone 3 (BAP3) ca. 11880–11650 cal yr BP (575.75–570.0; ca. 230 yr; n = 12) (Fig. 15). 55 Fig. 14. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGA pollen and spore data (black) with that expected from the broken-stick model (red). The stratigraphic zones derived from the BEGA pollen data delineate statistically significant changes (i.e., p ≤ 0.05) in median values of a variety of key fire-related proxies (Fig. 16). Charcoal mean particle area and sediment C:N increase from BAP1 to BAP2 and return to previous levels in BAP3. Charcoal summed area, sediment volume MS, and sediment δ13C increase from BAP1 to BAP2 and decrease to intermediate values in BAP3, while charcoal particle count and δ15N increase and remain elevated. Conversely, charcoal particle mean aspect ratio decreases from BAP1 to BAP2 before returning to previous levels in BAP3, and total sediment C and N decrease from BAP1 to BAP2, recovering to intermediate values in BAP3. 56 Fig. 15. BEGA pollen and spore relative abundance data (coloured). Only selected taxa are shown. On the Alnus panel, bright green represents Sitka alder (Alnus alnobetula ssp. sinuata) and dark green represents red alder (Alnus rubra). On all other panels, lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 3 caption. 57 Fig. 16. Box-and-whisker plots of selected BEGA fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 15). X-axis units: see Fig. 3 caption. See Fig. 6 caption for further figure explanation. 58 Although the ranking of dominant taxa remains relatively consistent throughout the BEGA sequence, there are statistically significant differences in median abundance of certain individual taxa among zones (Fig. 17). Between BAP1 and BAP2, meaningful increases occur in Pinus contorta and an unidentified herb, while decreases occur in Alnus and Tsuga heterophylla. Between BAP2 and BAP3, P. contorta declines and Alnus increases, both returning to BAP1 levels. Salix also increases significantly. T. heterophylla shows an apparent, but not statistically significant increase. Several other minor taxa show statistically significant, but likely not meaningful, changes among zones. Pollen taxonomic diversity decreases significantly from BAP1 to BAP2 and increases again from BAP2 to BAP3. Fig. 17. Box-and-whisker plots of selected BEGA pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 15). X-axis units: pollen and spores = %; pollen and spore diversity = Shannon index. See Fig. 6 caption for further figure explanation. 59 3.3 Begbie Lake Sediment Sequence B (BEGB) 3.3.1 BEGB Charcoal, MS, TC, TN, δ13C and δ15N The BEGB charcoal, MS, C, and N sequences (Fig. 18) each span 511.0–491.0 cm sediment depth (ca. 9970–9530 cal yr BP) and comprise 101 contiguous 0.2-cm samples. The error margins of the millennial-scale age-depth model (Fig. 2A) are large relative to the time scale of the features of interest, so results in this section are described in terms of sediment depth rather than modeled age. 3.3.1.1 BEGB Charcoal The BEGB charcoal particle count (Fig. 18) exhibits a distinct peak at 505.4 cm and a secondary region of elevated charcoal with two sub-peaks at 499.4 cm and 497.6 cm. Mean charcoal concentration across the profile is 126 particles cm-3. The profile first diverges from initial background levels around 509 cm, where it enters a region characterized by elevated charcoal concentrations and increased inter-sample variability. This persists until 505.8 cm, at which point charcoal concentration rises rapidly to the primary peak of 422 particles cm-3 at 505.4 cm. Concentrations then quickly decline to around 200 particles cm-3 at 504.8 cm, before returning to initial background levels at around 503 cm. A secondary region of elevated charcoal begins at 499.4 cm with an increase to 208 particles cm-3. Concentrations decline thereafter to around 120 particles cm-3 before again rising to 218 particles cm-3 at 497.6 cm. This is followed by a rapid decline to approximately initial background levels where the profile remains through its end. 3.3.1.2 BEGB MS The BEGB volume MS profile (Fig. 18) closely resembles the charcoal profile, with a few noteworthy differences. The primary magnetic peak (9.51x10-5 SI) occurs at 504.4 cm depth, 1.0 cm above the corresponding charcoal peak. The secondary peaks occur at 499.6 cm (6.97x10-5 SI) and 60 497.4 cm (7.78x10-5 SI), placing them 0.2 cm above and below the corresponding charcoal peaks, respectively. Fig. 18. BEGB fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. 61 3.3.1.3 BEGB TC, TN, δ13C and δ15N The BEGB TC and TN profiles (Fig. 18) closely resemble one another and are the approximate inverse of the corresponding charcoal and MS profiles. The TC and TN profiles begin to decline from initial baseline levels around 506 cm and reach their respective minima (C = 12.7%; N = 0.85%) by 504.6 cm. This trails the charcoal maximum by 0.6 cm and approximately coincides with the MS maximum. Both C and N profiles exhibit a slow partial recovery through 497.6 cm before quickly dropping to local secondary minima at 497.2 cm. This decline follows closely on the secondary charcoal peak (497.6 cm) and secondary magnetic peak (497.4 cm). Thereafter, both C and N profiles gradually recover and approximately reach initial baseline levels around 495 cm. The BEGB C:N ratio exhibits two distinct peaks: a primary peak at 504.4 cm, and a secondary peak at 497.2 cm (Fig. 18). These peaks align with the corresponding MS peaks and follow closely on the charcoal peaks. The BEGB δ13C profile (Fig. 18) is generally stable, averaging around -30.6. A pronounced minimum of -33.5 occurs at 502.6 cm, but since it comprises a single sample, it may be a spurious data point. Two modest peaks of -29.3 and -28.6 and are evident at 500.2 cm and 496.4 cm, respectively. However, neither appears to coincide with significant features in other proxies. The δ15N profile is more variable (Fig. 18), exhibiting distinctly elevated regions at approximately 509.2–508.4 cm, 504.6–503.8 cm, and 499.2–498.0 cm. The second of these regions roughly coincides with charcoal, MS, and C:N maxima, and TC and N minima. The elevated regions around 509.2–508.4 cm and 499.2–498.0 cm are each followed by abrupt declines, yielding local minima that persist for several samples before rapidly recovering. 62 3.3.2 BEGB Inorganic Elements For fire event BEGB, concentrations of 24 inorganic elements were measured in 28 contiguous sediment samples from 510.0–499.0 cm depth, representing ca. 9950–9700 cal yr BP (Fig. 2A). On average, Fe, Al, S, Ca, Mg, and P were present in the highest quantities, whereas only trace amounts of As, Se, Pb, Cd, Be, Ag, and Hg were detected (Table 8). Many elements exhibit strong correlations with MS, TC, and TN (Table 9). Although evidently present in substantial quantities, K was excluded from subsequent analyses due to data quality concerns. Table 8. BEGB inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. Element N Mean Median Minimum Maximum SD Skewness Kurtosis Fe 28 24455 24738 19983 27049 1789 -0.7 0.1 Al 28 17342 17565 13797 20996 2256 0.0 -1.2 S 28 13582 12615 10278 19187 2450 0.5 -0.9 Ca 28 9947 9901 8238 11471 917 0.1 -1.0 Mg 28 7332 7795 4219 9803 1887 -0.5 -1.2 P 28 1104 1107 978 1179 57 -0.6 -0.4 Na 28 327 339 264 381 34 -0.5 -0.9 Mn 28 208 211 161 252 30 -0.2 -1.3 Cu 28 146 145 132 159 7 0.0 -0.4 Ni 28 122 126 87 145 14 -1.0 1.2 Cr 28 106 110 72 132 20 -0.6 -1.2 V 28 96.1 94.8 89.0 105.2 4.2 0.5 -0.6 Zn 28 83.8 83.2 65.2 103.9 7.5 0.4 1.9 Ba 28 62.6 61.6 50.6 71.4 5.1 -0.2 -0.3 B 28 50.4 49.8 41.6 60.0 6.5 0.1 -1.6 Co 28 39.9 38.9 31.5 49.0 4.4 0.2 -0.2 As 28 4.0 3.8 3.3 4.9 0.5 0.5 -1.1 Se 28 3.8 3.8 3.1 4.8 0.5 0.1 -1.1 Pb 28 2.5 2.5 2.3 2.9 0.1 0.6 -0.4 Cd 28 0.8 0.8 0.2 2.0 0.5 1.1 0.9 Be 28 0.5 0.5 0.4 0.5 0.0 -0.5 0.1 Ag 28 0.2 0.2 0.2 0.3 0.0 0.2 -1.3 Hg 28 0.1 0.1 0.0 0.1 0.0 0.3 -0.8 63 Table 9. Pearson correlation coefficients between BEGB magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. Pearson correlation (r) MS TC TN MS 1.00 -0.92 -0.88 Mn 0.96 -0.91 -0.87 Mg 0.94 -0.84 -0.79 Al 0.93 -0.94 -0.91 Cr 0.87 -0.74 -0.67 Na 0.84 -0.81 -0.77 Ca 0.81 -0.80 -0.79 Ba 0.68 -0.81 -0.84 P 0.65 -0.60 -0.62 Be 0.61 -0.64 -0.65 Ni 0.60 -0.48 -0.45 Zn 0.39 -0.44 -0.46 Pb 0.30 -0.53 -0.55 Fe 0.26 -0.07 -0.02 Co 0.08 0.09 0.11 Cu 0.02 -0.22 -0.24 V -0.27 0.35 0.41 Hg -0.50 0.40 0.39 Cd -0.51 0.43 0.37 Ag -0.63 0.42 0.34 S -0.66 0.80 0.78 As -0.79 0.86 0.85 B -0.85 0.85 0.82 TN -0.88 0.98 1.00 Se -0.90 0.81 0.78 TC -0.92 1.00 0.98 Comparison of the distribution of total variance from the CONISS-zoned standardized BEGB elemental data with that expected from the broken-stick model reveals a maximum of four reliable stratigraphic zones (Fig. 19; Fig. 20). According to the millennial-scale age-depth model, BEGB Elemental Zone 1 (BBE1) encompasses ca. 9950–9860 cal yr BP (510.0–505.7 cm; ca. 90 yr; n = 8), BEGB Elemental Zone 2 (BBE2) ca. 9860–9840 cal yr BP (505.7–504.9; ca. 20 yr; n = 4), BEGB 64 Elemental Zone 3 (BBE3) ca. 9840–9790 cal yr BP (504.9–502.9 cm; ca. 50 yr; n = 10), and BEGB Elemental Zone 4 (BBE4) ca. 9790–9700 cal yr BP (502.9–499 cm; ca. 90 yr; n = 6). Fig. 19. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGB inorganic elements data (black) with that expected from the broken-stick model (red). The stratigraphic zones derived from the BEGB elemental data delineate statistically significant changes (i.e., p ≤ 0.05) in median values of a variety of key fire-related proxies (Fig. 21). Charcoal particle count peaks in BBE2 and then declines, while MS, C:N, and δ15N peak in BBE3. Total sediment C and N reach minima in BBE3. Although the ranking of the most prevalent elements remains mostly stable throughout the sequence, there are statistically significant differences in median concentrations of individual elements among zones. These can be grouped into three general types. 65 Fig. 20. BEGB inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 18 caption. 66 Fig. 21. Box-and-whisker plots of selected BEGB fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). X-axis units: see Fig. 18 caption. See Fig. 6 caption for further figure explanation. The first group comprises those elements which show a significant increase from BBE1 to BBE3 and includes Al, Ba, Be, Ca, Cr, Fe, Mg, Mn, Na, and P (Fig. 22). All these elements exhibit an apparent, though not statistically significant, increase from BBE1 to BBE2, then peak in BBE3, followed by an apparent, though not statistically significant, decline in BBE4. Primary wildfire ash constituents Ca and Mg, when combined, also exhibit this trend (Fig. 22). The only exception among the above elements is Ba, which does decline significantly in BBE4. 67 Fig. 22. Box-and-whisker plots of selected BEGB inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). All exhibit statistically significant increases in median values from zone 1 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 68 The second group comprises elements which exhibit minima in BBE3, showing a significant decrease from BBE1 and/or BBE2 to BBE3. This group includes Ag, As, B, Cd, Hg, S, and Se (Fig. 23). Toxic elements As, Hg, and Pb, when summed together, also strongly show this pattern. Fig. 23. Box-and-whisker plots of selected BEGB inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). All exhibit statistically significant decreases in median values from zone 1 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 69 The third group comprises elements which peak in BBE2, then show a significant decline from BBE2 to BBE4. This group includes Cu and Pb (Fig. 24). Zn also appears to follow this pattern, although it does not exhibit any statistically significant differences among zones. Fig. 24. Box-and-whisker plots of selected BEGB inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 20). All exhibit peaks in zone 2. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 3.3.3 BEGB Diatoms For fire event BEGB, diatom valves were counted in 17 non-contiguous sediment samples from 510.0–499.0 cm depth, representing ca. 9950–9700 cal yr BP (Fig. 2A). A total of 143 unique taxa were found, of which 113 (79%) were identified to at least genus level and 81 (57%) were identified to species level or lower. In terms of valves, 3795 valves were counted in total, of which 3010 (79%) were identified to at least genus level, and 2897 (76%) were identified to species level or lower. The predominant taxa, defined here as those comprising ≥2% of the valves on average, were Discostella stelligera (31.6%), Staurosirella pinnata (15.8%), an undet. araphid taxon in girdle view (araphid undet. girdle view F; 6.9%), Staurosira venter (6.7%), two other undet. araphid taxa in girdle view (araphid undet. girdle view E and G; 3.9% and 3.1 %, respectively), Aulacoseira perglabra (2.8%), Aulacoseira ambigua (2.3%), and Staurosira construens (2.2%) (Table 10). Relative abundance of these and other taxa varied considerably over the sequence. Araphid taxa consistently 70 comprised about 40–50% of the valves in each sample and centric valves about 35–45%, with a wide variety of other morphologies accounting for the remainder. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 2.1 to 2.7. Comparison of the distribution of total variance from the CONISS-zoned square-root transformed BEGB diatom data with that expected from the broken-stick model does not reveal any reliable stratigraphic zones (Fig. 25). The four-zone configuration (Fig. 26) was selected for analysis because it best corresponds to the elemental data zonation and its significance is approximately equal to that of the two-zone and three-zone configurations. Moreover, the three-zone model is undesirable because it yields a middle zone comprised of only a single sample, which precludes meaningful hypothesis testing. Table 10. BEGB diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Discostella stelligera 17 31.59 30.00 24.63 44.99 5.49 1.51 1.99 Staurosirella pinnata 17 15.75 14.78 8.49 21.57 3.57 -0.17 -0.37 Araphid undet. girdle view F 17 6.89 6.90 0.00 12.69 3.29 0.05 0.12 Staurosira venter 17 6.72 6.57 1.51 14.49 3.51 0.67 0.14 Araphid undet. girdle view E 17 3.89 2.96 0.00 14.78 4.20 1.29 1.50 Araphid undet. girdle view G 17 3.13 2.04 0.00 11.56 3.38 1.25 1.10 Aulacoseira perglabra 17 2.78 2.07 0.00 6.74 2.18 0.44 -0.82 Aulacoseira ambigua 17 2.30 2.31 0.00 4.93 1.41 0.17 -0.36 Staurosira construens 17 2.20 1.89 0.00 5.91 1.87 0.46 -1.01 Araphid undet. girdle view I 17 1.70 0.99 0.00 7.55 2.03 1.59 3.12 Pseudostaurosira brevistriata 17 1.33 1.01 0.00 3.45 1.09 0.60 -0.47 Aulacoseira humilis 17 1.20 0.50 0.00 8.49 2.12 2.84 9.28 Araphid undet. girdle view A 17 1.16 0.49 0.00 5.25 1.52 1.40 1.77 71 Fig. 25. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGB diatom data (black) with that expected from the broken-stick model (red). 72 Fig. 26. BEGB diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 18 caption. 73 According to the millennial-scale age-depth model, BEGB Diatom Zone 1 (BBD1) encompasses ca. 9950–9850 cal yr BP (510.0–505.5 cm; ca. 100 yr; n = 4), BEGB Diatom Zone 2 (BBD2) ca. 9850–9845 cal yr BP (505.5–505.3 cm; ca. 5 yr; n = 1), BEGB Diatom Zone 3 (BBD3) ca. 9845–9830 cal yr BP (505.3–504.5 cm; ca. 15 yr; n = 4), and BEGB Diatom Zone 4 (BBD4) ca. 9830–9710 cal yr BP (504.5–499.0 cm; ca. 120 yr; n = 8) (Fig. 26). The stratigraphic zones derived from the BEGB diatom data delineate statistically significant (i.e., p ≤ 0.05) changes in median values of a handful of key fire-related proxies (Fig. 27). Charcoal particle count increases from BDD1 to BDD3 and then decreases from BDD3 to BDD4. MS and sediment C:N also increase from BDD1 to BDD3 but remain elevated in BDD4. Total sediment C and N both decrease from BDD1 to BDD3 and remain low in BDD4. Fig. 27. Box-and-whisker plots of selected BEGB fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 26). X-axis units: see Fig. 18 caption. See Fig. 6 caption for further figure explanation. 74 Despite the diversity of the BEGB diatom assemblage, the ranking of dominant taxa remains relatively consistent throughout the sequence and there are few meaningful differences in median abundance of individual taxa among zones (Fig. 28). One notable exception is Staurosira venter, which decreases significantly from BBD1 to BBD3. Other noteworthy exceptions are the spikes in Discostella stelligera and araphid undet. girdle view C in BBD2, and the decline in Sellaphora laevissima from BBD1 to BBD2. None of these changes are statistically significant, but this is likely due to BBD2 having a sample size of one. Various other taxa do exhibit statistically significant differences among zones, but their counts are generally too low (i.e., <3 valves/sample) to warrant confidence or be considered meaningful. Fig. 28. Box-and-whisker plots of selected BEGB diatom taxa binned by diatom-derived bio-stratigraphic zones (see Fig. 26). X-axis units: %. See Fig. 6 caption for further figure explanation. Even when valves are grouped by morphology, statistically significant differences among zones do not emerge. However, in BBD2, the conspicuous spike in centric taxa and corresponding drop in araphid taxa, and, to a lesser extent, asymmetric biraphid taxa, is noteworthy (Fig. 26). It is evident that the diatom assemblage in BBD2 is atypical compared to the other zones, but the low sample size confounds rigorous quantification of this difference. 3.3.4 BEGB Pollen and Spores For fire event BEGB, pollen and spores were counted in 36 contiguous sediment samples from 510.0–499.0 cm depth, representing ca. 9950–9700 cal yr BP (Fig. 2A). A total of 65 unique 75 taxa were identified. The dominant taxon was Alnus, comprising 28% of the assemblage on average, followed by Pseudotsuga menziesii (19%), Pinus contorta (12%), Pteridium (11%), and Isoetes (11%) (Table 11). Alnus and Pseudotsuga remained dominant throughout the sequence. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 2.1 to 2.3. Table 11. BEGB pollen and spore summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Alnus 36 27.58 27.79 20.10 33.97 3.89 -0.14 -1.07 Pseudotsuga 36 18.59 18.78 13.04 24.86 2.87 -0.14 -0.64 Pinus contorta 36 12.39 12.50 7.55 16.29 2.06 -0.22 -0.44 Pteridium 36 11.12 11.13 6.67 16.04 2.33 -0.13 -0.56 Isoetes 36 10.73 9.50 5.14 19.44 4.10 0.66 -0.65 Rosaceae 36 2.67 2.59 1.16 5.49 1.03 0.65 0.14 Poaceae 36 2.62 2.33 0.24 6.04 1.25 0.80 0.68 Tsuga heterophylla 36 1.75 1.54 0.27 5.14 0.96 1.52 3.51 Cyperaceae 36 1.74 1.64 0.51 4.00 0.78 0.88 1.02 Salix 36 1.59 1.64 0.24 3.49 0.75 0.51 0.66 Cupressaceae 36 1.34 1.16 0.26 2.67 0.64 0.53 -0.46 Monolete fern undiff. 36 1.21 1.10 0.00 3.43 0.79 0.55 0.32 Undet. herb 36 1.18 1.09 0.00 2.31 0.51 0.33 0.22 Comparison of the distribution of total variance from the CONISS-zoned square-root transformed BEGB pollen data with that expected from the broken-stick model reveals two significant stratigraphic zones, with a third being marginally significant (Fig. 29). The three-zone configuration was selected for analysis. According to the millennial-scale age-depth model, BEGB Pollen Zone 1 (BBP1) encompasses ca. 9950–9860 cal yr BP (510.0–505.9 cm; ca. 90 yr; n = 9), BEGB Pollen Zone 2 (BBP2) ca. 9860–9790 cal yr BP (505.9–502.9 cm; ca. 70 yr; n = 15), and BEGB Pollen Zone 3 (BBP3) ca. 9790–9710 cal yr BP (502.9–499.0 cm; ca. 80 yr; n = 12) (Fig. 30). 76 Fig. 29. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGB pollen and spore data (black) with that expected from the broken-stick model (red). The stratigraphic zones derived from the BEGB pollen data also delineate statistically significant changes (i.e., p ≤ 0.05) in the median values of several key fire-related proxies (Fig. 31). Charcoal particle count and C:N increase from BBP1 to BBP2 and then decrease from BBP2 to BBP3, while MS increases from BBP1 to BBP2 and remains elevated in BBP3. Sediment TC and TN decrease from BBP1 to BBP2 and remain low through BBP3. 77 Fig. 30. BEGB pollen and spore relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 18 caption. 78 Fig. 31. Box-and-whisker plots of selected BEGB fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 30). X-axis units: see Fig. 18 caption. See Fig. 6 caption for further figure explanation. Although the ranking of dominant taxa remains relatively consistent throughout the BEGB pollen sequence, there are statistically significant differences in median abundance of several individual taxa among zones (Fig. 32). Between BBP1 and BBP2, meaningful increases occur in Alnus, Poaceae, and Polystichum, while meaningful decreases occur in Cyperaceae and Isoetes. Between BBP2 and BBP3, Tsuga heterophylla and Abies increase significantly, while Isoetes continues to decline. Several other minor taxa show statistically significant, but probably not meaningful, changes among zones. Pollen taxonomic diversity increases significantly from BBP1 to BBP2 and total non-arboreal pollen (NAP) decreases from BBP2 to BBP3. 79 Fig. 32. Box-and-whisker plots of selected BEGB pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 30). X-axis units: pollen and spores = %; pollen and spore diversity = Shannon index. See Fig. 6 caption for further figure explanation. 3.4 Begbie Lake Sediment Sequence C (BEGC) 3.4.1 BEGC Charcoal, MS, TC, TN, δ13C and δ15N The BEGC charcoal, MS, C, and N sequences (Fig. 33) each span 126.0–116.0 cm sediment depth (ca. 2500–2250 cal yr BP) and comprise 51 contiguous 0.2-cm samples. The error margins of 80 the millennial-scale age-depth model (Fig. 2A) are large relative to the time scale of the features of interest, so results in this section are described in terms of sediment depth rather than modeled age. Fig. 33. BEGC fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. 81 3.4.1.1 BEGC Charcoal The BEGC charcoal profile (Fig. 33) spans 126.0–116.0 cm sediment depth, sampled in 0.2-cm increments. It exhibits a double peak with local charcoal concentration maxima at 122.4 cm (74 particles cm-3) and 121.4 cm (86 particles cm-3). Overall, this profile exhibits high inter-sample variability, and the prominence of the largest peaks is relatively low. Mean charcoal concentration across the profile is 41 particles cm-3. 3.4.1.2 BEGC MS The BEGC volume MS profile (Fig. 33) contains a single large peak, in contrast to the double-peaked charcoal profile. The single magnetic peak emerges from the background at 123.4 cm, reaches its maximum value (3.38 x10-5 SI) at 122.6 cm, and returns to baseline values by 120.8 cm. This magnetic maximum precedes the first charcoal peak by 0.2 cm. 3.4.1.3 BEGC TC, TN, δ13C and δ15N The BEGC total sediment C and N profiles (Fig. 33) closely resemble one another and are the approximate inverse of the corresponding charcoal and MS profiles, with some noteworthy exceptions. Both profiles follow a broad curve, initially declining to minimum values at 122.8 cm depth. This minimum slightly precedes the MS and charcoal maxima at 122.6 cm and 122.4 cm, respectively. Thereafter, C and N levels gradually increase, reaching initial levels by the end of the section. However, because the profiles do not appear to begin in equilibrium, it’s impossible to discern from these results whether the initial and final values represent a baseline state or a transitional state. The BEGC C:N profile (Fig. 33) follows a similar trend to the individual C and N profiles. This is the opposite trend to that observed in the BEGB event, where the C:N ratio instead mirrored 82 the charcoal and MS profiles. However, it is worth noting that a localized C:N peak at 122.8 cm roughly corresponds with BEGC MS and charcoal peaks. Variations in the BEGC δ13C and δ15N profiles are relatively small in magnitude, and do not appear to coincide with notable changes in other proxies (Fig. 33). 3.4.2 BEGC Inorganic Elements For fire event BEGC, concentrations of 24 inorganic elements were measured in 20 contiguous sediment samples from 120.4–126.0 cm sediment depth, representing ca. 2500–2360 cal yr BP (Fig. 2A). On average, Fe, S, Ca, Al, Mg, and P were present in the highest quantities, whereas only trace amounts of As, Se, Cd, Be, Ag, and Hg were detected (Table 12). Generally, element concentrations are only weakly correlated with MS, but more strongly correlated with TC and TN (Table 13). Although apparently present in substantial quantities, K was excluded from subsequent analyses due to data quality concerns. Also excluded was Pb, as all samples measured below the detection limit of the instrument. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGC elemental data with that expected from the broken-stick model reveals a maximum of five reliable stratigraphic zones (Fig. 34). However, the five-zone configuration yields a zone comprised of only a single sample, and two zones comprised of two samples. These low sample sizes preclude meaningful hypothesis testing among groups. In response, the uppermost three zones were instead classified as subzones (i.e., 3a, 3b, and 3c) and grouped together for subsequent analysis. Using this configuration, BEGC Elemental Zone 1 (BCE1) encompasses ca. 2500–2420 cal yr BP (126.0–122.9 cm; ca. 80 yr; n = 7), BEGC Elemental Zone 2 (BCE2) ca. 2420–2380 cal yr BP (122.9–121.3 cm; ca. 40 yr; n = 8), and BEGC Elemental Zone 3 (BCE3) ca. 2380–2360 cal yr BP (121.3–120.4 cm; ca. 20 yr; n = 5), with BCE3a (121.3–120.9 cm; ca. 10 yr; n = 2), BCE3b (120.9–120.7 cm; ca. 5 yr; n = 1), and BCE3c (120.7–120.4 cm ca. 8 yr; n = 2) (Fig. 35). 83 Table 12. BEGC inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. Element N Mean Median Minimum Maximum SD Skewness Kurtosis Fe 20 16928 17105 13289 19035 1338 -0.7 1.6 S 20 15565 15183 14278 18246 1167 1.0 0.1 Ca 20 6499 6465 6129 7105 263 0.7 0.1 Al 20 6397 6366 5973 7232 259 1.7 5.0 Mg 20 992 987 888 1141 76 0.5 -0.7 P 20 641 627 568 916 78 2.4 7.7 Na 20 235 238 160 304 41 -0.1 -0.8 Mn 20 138 137 130 146 5 0.4 -0.6 B 20 134 133 108 158 12 0.0 -0.1 Cu 20 49.1 48.5 36.7 62.5 6.4 0.4 0.0 V 20 46.7 46.2 36.8 54.2 4.2 -0.2 0.4 Ni 20 43.8 43.3 40.3 50.2 2.5 0.8 0.6 Zn 20 36.0 35.9 31.7 43.2 2.9 0.6 0.4 Co 20 35.6 34.9 33.7 39.1 1.6 0.8 -0.4 Cr 20 29.0 29.0 25.1 35.5 2.9 0.7 0.0 Ba 20 19.2 19.0 14.2 26.5 2.7 0.8 2.0 As 20 5.9 5.9 5.3 6.9 0.3 0.9 2.1 Se 20 2.8 2.8 2.5 3.5 0.3 0.8 -0.1 Cd 20 0.65 0.50 0.27 2.43 0.48 3.1 10.8 Be 20 0.21 0.22 0.12 0.30 0.05 -0.5 0.4 Ag 20 0.08 0.07 0.02 0.12 0.02 0.4 0.2 Hg 20 0.04 0.04 0.00 0.08 0.03 -0.2 -1.6 84 Table 13. Pearson correlation coefficients between BEGC magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. Pearson correlation (r) MS TC TN MS 1.00 -0.47 -0.32 Hg 0.21 0.40 0.28 Cd 0.16 0.20 0.12 Na 0.15 -0.27 -0.31 Be -0.03 0.06 -0.06 B -0.04 0.09 0.00 V -0.08 0.40 0.39 Al -0.16 -0.12 -0.14 Mg -0.31 0.43 0.37 TN -0.32 0.92 1.00 As -0.36 0.77 0.70 Ag -0.38 0.57 0.53 Mn -0.38 0.03 0.07 Fe -0.39 0.84 0.76 Ca -0.41 0.07 0.14 Cr -0.41 0.59 0.59 Se -0.43 0.71 0.58 Cu -0.44 0.67 0.55 Ba -0.44 0.47 0.39 TC -0.47 1.00 0.92 S -0.47 0.82 0.75 P -0.51 0.74 0.67 Co -0.52 0.72 0.63 Ni -0.53 0.67 0.60 Zn -0.63 0.37 0.29 85 Fig. 34. Comparison of the distribution of total variance from the CONISS-zoned standardized BEGC inorganic elements data (black) with that expected from the broken-stick model (red). 86 Fig. 35. BEGC inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 33 caption.87 The stratigraphic zones derived from the BEGC elemental data demarcate few statistically significant (i.e., p ≤ 0.05) changes in median values of other fire-related proxies (Fig. 36). Only MS increases significantly from BCE1 to BCE2, and sediment C:N decreases significantly across the same interval. Notably however, charcoal particle count exhibits an apparent, though not statistically significant, increase from BCE1 to BCE2. Fig. 36. Box-and-whisker plots of selected BEGC fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. Although the ranking of the most prevalent elements remains mostly stable throughout the BEGC sequence, there are statistically significant differences in median concentrations of individual elements among zones. Between BCE1 and BCE2, Hg increases while Zn decreases (Fig. 37). Mg increases significantly from BCE1 to BCE3 while Be decreases (Fig. 38). The BCE2 to BCE3 transition marks more substantial changes, with significant increases in Ag, Ca, Co, Cu, Mn, Ni, P, and S (Fig. 39), and significant decreases in Hg (Fig. 37), Cd, and Na (Fig. 39). The overall pattern is that of subtle change between BCE1 and BCE2, but substantial change between BCE2 and BCE3. 88 Fig. 37. Box-and-whisker plots of selected BEGC inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). All exhibit statistically significant changes in median values from zone 1 to zone 2. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. Fig. 38. Box-and-whisker plots of selected BEGC inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). All exhibit statistically significant changes in median values from zone 1 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 89 Fig. 39. Box-and-whisker plots of selected BEGC inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 35). All exhibit statistically significant changes in median values from zone 2 to zone 3. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 90 3.4.3 BEGC Diatoms For fire event BEGC, diatom valves were counted in 16 non-contiguous sediment samples from 120.4–126.0 cm sediment depth, representing ca. 2500–2360 cal yr BP (Fig. 2A). A total of 116 unique taxa were found, of which 108 (93%) were identified to at least genus level and 82 (71%) were identified to species level or lower. In terms of valves, 4975 valves were counted in total, of which 4327 (87%) were identified to at least genus level, and 3329 (67%) were identified to species level or lower. The predominant taxa, defined here as those comprising ≥3% of the valves on average, were Placoneis clementis (17.0%), Discostella stelligera (9.9%), an undet. Pinnularia species (Pinnularia undet B1; 7.1%), an undet. Aulacoseira species (Aulacoseira undet. B2; 6.8%), an undet. araphid taxon in girdle view (araphid undet. girdle view B2; 6.4%), Stauroneis gracilis (5.1%), Aulacoseira pusilla (4.0%), Aulacoseira perglabra (3.6%), and Sellaphora alastos (3.4%) (Table 14). Relative abundance of these and other taxa varied considerably over the sequence. The majority of the samples were comprised of symmetric biraphid (30–50%), centric (15–35%), and araphid (10–30%) valves, with a wide variety of other morphologies accounting for the remainder. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 2.5 to 3.2. Comparison of the distribution of total variance from the CONISS-zoned square-root transformed BEGC diatom data with that expected from the broken-stick model reveals four marginally significant stratigraphic zones (Fig. 40). According to the millennial-scale age-depth model, BEGC Diatom Zone 1 (BCD1) encompasses ca. 2500–2460 cal yr BP (126.0–124.5 cm; ca. 40 yr; n = 2), BEGC Diatom Zone 2 (BCD2) ca. 2460–2390 cal yr BP (124.5–121.6 cm; ca. 70 yr; n = 8), BEGC Diatom Zone 3 (BCD3) ca. 2390–2360 (121.6–120.2; ca. 30 yr; n = 5) and BEGC Diatom Zone 4 (BCD4) comprises a single sample at about 2360 cal yr BP (120.4 cm) (Fig. 41). Because a sample size of one precludes meaningful between-group comparisons, BCD4 is not considered in subsequent analyses. 91 Table 14. BEGC diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Placoneis clementis 16 16.96 18.72 2.96 37.93 9.48 0.41 0.01 Discostella stelligera 16 9.91 5.54 2.02 35.98 9.90 1.67 2.17 Pinnularia undet. B1 16 7.05 6.77 0.97 13.80 3.14 0.37 0.57 Aulacoseira undet. B2 16 6.81 6.85 0.32 12.27 3.61 -0.30 -0.29 Araphid undet. girdle view B2 16 6.40 6.56 2.40 15.24 3.48 0.97 1.21 Stauroneis gracilis 16 5.11 4.60 0.87 12.13 3.09 0.85 0.36 Aulacoseira pusilla 16 3.96 3.74 0.00 9.04 2.75 0.17 -0.93 Aulacoseira perglabra 16 3.59 3.87 1.74 5.43 1.15 -0.29 -0.79 Sellaphora alastos 16 3.40 3.01 0.74 6.98 1.56 0.79 0.89 Amphora undet. B1 16 2.74 2.52 0.00 6.69 1.97 0.46 -0.71 Araphid undet. girdle view B4 16 2.27 1.83 0.00 5.26 1.87 0.37 -1.26 Araphid undet. girdle view B1 16 1.83 2.05 0.00 3.90 1.14 -0.10 -0.73 Aulacoseira undet. B1 16 1.32 1.48 0.00 2.21 0.71 -0.42 -1.14 Staurosira venter 16 1.29 1.15 0.00 2.37 0.60 -0.14 0.31 Staurosirella pinnata 16 1.28 0.63 0.00 5.49 1.43 1.89 4.22 Pinnularia undet. B2 16 1.17 1.19 0.00 2.11 0.76 -0.27 -1.38 Cymbopleura sublanceolata 16 1.12 0.94 0.00 3.03 0.93 0.51 -0.41 Araphid undet. girdle view B5 16 1.12 0.30 0.00 5.09 1.51 1.44 1.74 Pinnularia interrupta 16 1.10 0.99 0.00 2.69 0.75 0.33 -0.21 Pseudostaurosira brevistriata 16 1.09 0.55 0.00 5.26 1.38 2.00 4.90 Fig. 40. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGC diatom data (black) with that expected from the broken-stick model (red). 92 Fig. 41. BEGC diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 33 caption. 93 The stratigraphic zones derived from the BEGC diatom data delineate statistically significant changes (i.e., p ≤ 0.05) in the median values of numerous key fire-related proxies (Fig. 42). Notably, charcoal is not among them. Nonetheless, sediment MS increases significantly between BCD1 and BCD2, while total sediment C, N, C:N, and δ15N decrease. Fig. 42. Box-and-whisker plots of selected BEGC fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 41). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. The ranking of dominant diatom taxa shows considerable change throughout the sequence, yet there are few statistically significant differences in median abundance of individual taxa among zones (Fig. 43). No significant differences were observed between BCD1 and BCD2, although this is likely due to the small sample size (n = 2) of BCD1. Between BCD1 and BCD3, however, significant increases were observed in Aulacoseira undet. B2 and Aulacoseira pusilla. Between BCD2 and BCD3, Araphid undet. girdle view B3 increased significantly, while Placoneis clementis decreased significantly. 94 Fig. 43. Box-and-whisker plots of selected BEGC diatom taxa binned by diatom-derived bio-stratigraphic zones (see Fig. 41). X-axis units: %. See Fig. 6 caption for further figure explanation. Grouping diatoms by morphology increases effective sample sizes, and therefore statistical power, yielding better-defined differences among zones (Fig. 44). As a group, araphid diatoms increased significantly from a median abundance of 14% in BCD2 to 27% in BCD3, while centric diatoms similarly increased significantly from 19% to 32% across the same interval. In contrast, asymmetric biraphid taxa decreased significantly from 11% of the valve count in BCD2 to 6% in BCD3. Overall diatom diversity, as measured by the Shannon H index, shows a significant increase from 2.6 in BCD1 to 3.0 in BCD2 and remains high in BCD3. 95 Fig. 44. Box-and-whisker plots of selected BEGC diatom groups and indices binned by diatom-derived bio-stratigraphic zones (see Fig. 41). X-axis units: diatom morphologies = %; for all others, see Fig. 41 caption. See Fig. 6 caption for further figure explanation. 3.4.4 BEGC Pollen and Spores For fire event BEGC, pollen and spores were counted in 21 contiguous sediment samples from 120.4–126.0 cm sediment depth, representing ca. 2500–2360 cal yr BP (Fig. 2A). A total of 33 unique taxa were identified. The dominant taxon by far was Pseudotsuga menziesii, comprising about 50% of the assemblage on average, followed by Tsuga heterophylla (15%), Pinus contorta (12%), Alnus (12%), Cupressaceae (5%), and Pteridium (1%) (Table 15). P. menziesii, T. heterophylla, P. contorta, and Alnus remained dominant throughout the sequence. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 1.3 to 1.9. Comparison of the distribution of total variance from the CONISS-zoned square-root transformed BEGC pollen data with that expected from the broken-stick model reveals no reliable stratigraphic zones (Fig. 45). The three-zone configuration was selected for analysis to remain 96 consistent with other fire events. According to the millennial-scale age-depth model, BEGC Pollen Zone 1 (BCP1) encompasses ca. 2500–2430 cal yr BP (126.0–123.3 cm; ca. 70 yr; n = 6), BEGC Pollen Zone 2 (BCP2) ca. 2430–2400 cal yr BP (123.3–122.1 cm; ca. 30 yr; n = 6), and BEGC Pollen Zone 3 (BCP3) ca. 2400–2360 cal yr BP (122.1–120.4 cm; ca. 40 yr; n = 9) (Fig. 46). Table 15. BEGC pollen and spore summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Pseudotsuga menziesii 21 48.15 49.61 35.70 59.50 5.93 -0.40 -0.20 Tsuga heterophylla 21 15.40 15.56 8.94 21.32 2.77 -0.25 0.99 Pinus contorta 21 12.41 12.22 9.48 18.06 2.02 1.08 1.74 Alnus 21 12.36 12.05 7.33 18.69 2.88 0.42 -0.19 Cupressaceae 21 5.39 5.25 3.01 9.16 1.89 0.51 -0.82 Pteridium 21 1.25 1.34 0.17 2.51 0.69 -0.03 -1.05 Fig. 45. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed BEGC pollen and spore data (black) with that expected from the broken-stick model (red). 97 Fig. 46. BEGC pollen and spore relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 5× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 33 caption. 98 The stratigraphic zones derived from the BEGC pollen data delineate statistically significant (i.e., p ≤ 0.05) changes in median values of two other key fire-related proxies: sediment MS increases significantly between BCP1 and BCP2, while sediment C:N decreases between the same zones (Fig. 47). Notably, charcoal particle count does not change significantly between the zones. Fig. 47. Box-and-whisker plots of selected BEGC fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 46). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. Although the ranking of dominant taxa remains relatively stable throughout the BEGC pollen sequence, there are statistically significant differences in median abundance of several individual taxa among zones (Fig. 48). Between BCP1 and BCP2, meaningful increases occur in Alnus, Quercus, and Pteridium, the latter of which remains elevated through BCP3. Between BCP1 and BCP3, T. heterophylla and Cyperaceae decline significantly. Several other minor taxa show statistically significant, but likely not meaningful, changes among zones. Pollen taxonomic diversity increases significantly from BCP1 to BCP2 but total non-arboreal pollen remains stable throughout the sequence. 99 Fig. 48. Box-and-whisker plots of selected BEGC pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 46). X-axis units: pollen and spores = %; pollen and spore diversity = Shannon index. See Fig. 6 caption for further figure explanation. 3.5 Sooke Lake Reservoir North Basin Sediment Sequence (SLRN) 3.5.1 SLRN Charcoal, MS, TC, TN, δ13C and δ15N The SLRN charcoal, MS, C, and N sequences (Fig. 49) each span 26.0–20.0 cm sediment depth (ca. 175–100 cal yr BP) and comprise 12 contiguous 0.5-cm samples. The error margins of the centennial-scale age-depth model (Fig. 2B) are large relative to the time scale of the features of interest, so results in this section are described in terms of sediment depth rather than modeled age. 3.5.1.1 SLRN Charcoal The SLRN charcoal profile (Fig. 49) comprises a relatively small number of samples (n = 12), and as such the charcoal peak appears less pronounced than those in the the Begbie Lake fire 100 events. The charcoal maximum of 67 particles cm-3 occurs at 22.25 cm depth. Mean charcoal concentration across the profile is 44 particles cm-3, comparable to that of BEGC. Fig. 49. SLRN fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. 101 3.5.1.2 SLRN MS The SLRN volume MS profile (Fig. 49) generally corresponds closely to the charcoal profile. The maximum value (1.41 SI) occurs at 22.75 cm, one sample before the corresponding charcoal peak. MS remains elevated until 21.75 cm, before declining towards background values. 3.5.1.3 SLRN TC, TN, δ13C and δ15N The SLRN TC and N profiles (Fig. 49) closely resemble one another and are the approximate inverse of the corresponding charcoal and MS profiles. Both profiles begin to decline from baseline levels at 23.25 cm. The C profile reaches its minimum value (8.25%) at 22.25 cm, while the N profile reaches its minimum value (0.62%) at 21.75 cm. Both profiles upturn thereafter and return to baseline levels by the end of the section. The SLRN C:N profile (Fig. 49) reveals a relatively linear trend of increasing C relative to N, with no apparent correlation with the charcoal or MS profiles. The SLRN δ13C profile exhibits a generally linear increase over time, but the absolute change is very small. The δ15N profile exhibits a generally elevated section from 22.75–21.25 cm which approximately corresponds with the elevated segment of the MS profile. 3.5.2 SLRN Inorganic Elements For fire event SLRN, concentrations of 24 inorganic elements were measured in 10 contiguous sediment samples from 25.0–20.0 cm sediment depth, representing ca. 160–105 cal yr BP (Fig. 2B). On average, Fe, A1, Ca, Mn, Mg, and P were present in the highest quantities, whereas only trace amounts of Pb, As, Se, Be, Cd, Ag, and Hg were detected (Table 16). Many elements are moderately-to-strongly correlated with MS, TC, and TN (Table 17). The ranking of predominant elements remained stable throughout the sequence. 102 Table 16. SLRN inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. Element N Mean Median Minimum Maximum SD Skewness Kurtosis Fe 11 42469 41568 32708 56928 7138 0.6 0.2 Al 11 23975 23767 22072 25662 1250 0.1 -1.3 Ca 11 11156 11164 10673 11612 358 -0.3 -1.5 Mn 11 6785 6304 5922 8173 848 0.6 -1.4 Mg 11 5255 5245 4657 5903 407 0.3 -1.2 P 11 2047 1937 1701 2686 298 1.1 0.7 S 11 797 772 666 1089 120 1.6 3.0 K 11 669 664 468 795 101 -0.5 -0.1 Ba 11 642 624 561 757 63 0.6 -0.5 Na 11 616 616 587 677 26 1.2 2.0 Ni 11 90.3 83.1 79.2 142.2 18.6 2.6 7.2 V 11 76.1 76.3 65.1 82.8 5.2 -0.7 0.7 Zn 11 68.6 68.5 65.3 73.4 2.2 0.7 1.4 Cr 11 62.9 61.7 54.9 74.1 5.1 1.0 1.7 Cu 11 57.2 57.1 51.8 60.7 2.6 -0.8 0.3 B 11 48.6 47.1 32.9 68.0 8.8 0.7 2.3 Co 11 33.3 31.2 27.4 51.6 6.9 2.1 5.3 Pb 11 3.6 2.8 1.5 7.9 2.2 1.3 0.3 As 11 3.3 3.2 3.0 4.1 0.3 1.6 2.2 Se 11 1.7 1.6 1.4 1.9 0.2 -0.2 -0.8 Be 11 0.7 0.7 0.6 0.7 0.0 -1.4 2.5 Cd 11 0.5 0.4 0.3 1.2 0.2 1.8 3.2 Ag 11 0.2 0.2 0.2 0.2 0.0 0.3 -0.2 Hg 11 0.1 0.1 0.1 0.2 0.0 0.7 0.2 Comparison of the distribution of total variance from the CONISS-zoned standardized SLRN elemental data with that expected from the broken-stick model reveals two reliable stratigraphic zones, or three marginally reliable zones (Fig. 50). The two-zone configuration was selected for analysis to avoid untenably low sample sizes. In this configuration, SLRN Elemental Zone 1 (SNE1) encompasses ca. 160–135 cal yr BP (25.0–22.5 cm; ca. 25 yr; n = 5) and SLRN Elemental Zone 2 (SNE2) spans ca. 135–105 cal yr BP (22.5–20.0 cm; ca. 30 yr; n = 5) (Fig. 51). 103 Table 17. Pearson correlation coefficients between SLRN magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. Pearson correlation (r) MS TC TN MS 1.00 -0.67 -0.72 Cd 0.74 -0.28 -0.33 Be 0.64 -0.38 -0.53 Cr 0.58 -0.15 -0.36 Pb 0.57 -0.52 -0.64 Mg 0.54 -0.75 -0.78 Cu 0.51 -0.54 -0.60 Al 0.46 -0.60 -0.72 V 0.45 -0.60 -0.63 K 0.43 -0.61 -0.60 Ba 0.42 -0.25 -0.59 Hg 0.23 -0.02 -0.33 B 0.18 0.13 0.08 Ca 0.14 -0.49 -0.29 As 0.07 0.22 -0.14 Ni -0.02 0.36 0.03 Na -0.02 0.11 0.36 Ag -0.23 0.41 0.16 Zn -0.24 0.13 0.03 S -0.35 0.63 0.26 Co -0.36 0.58 0.40 Se -0.48 0.53 0.24 Fe -0.55 0.32 0.63 TC -0.67 1.00 0.89 Mn -0.70 0.44 0.68 TN -0.72 0.89 1.00 P -0.83 0.53 0.72 The stratigraphic zones derived from the SLRN elemental data delineate statistically significant (i.e., p ≤ 0.05) changes in median values of a handful of fire indicators (Fig. 52). Neither charcoal particle count nor MS change significantly between SNE1 and SNE2, but grain size median diameter and total sediment N show significant decreases, while sediment C:N shows a significant increase. 104 Fig. 50. Comparison of the distribution of total variance from the CONISS-zoned standardized SLRN inorganic elements data (black) with that expected from the broken-stick model (red). 105 Fig. 51. SLRN inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. The horizontal dashed line delineates stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; grain size median diameter = µm; for all others, see Fig. 49 caption. 106 Fig. 52. Box-and-whisker plots of selected SLRN fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 51). X-axis units: see Fig. 33 caption. See Fig. 6 caption for further figure explanation. Although the ranking of the most prevalent elements remains mostly stable throughout the SLRN sequence, there are statistically significant differences in median concentrations of individual elements between zones. Increases are observed in Al, Ba, Be, Cu, Hg, K, Mg, Pb, and V between SNE1 and SNE2, while only Fe decreases significantly (Fig. 53). Some individual elements do not show statistically significant changes between zones, but when grouped with related elements, they do (Fig. 53). For example, Ca shows no significant change between SNE1 and SNE2, but when summed with other primary wildfire ash constituents K and Mg, they collectively exhibit a statistically significant increase. Similarly, the toxic elements As and Cd show no significant change individually, but when summed with toxic metals Hg and Pb, they collectively exhibit a statistically significant increase from SNE1 to SNE2. 107 Fig. 53. Box-and-whisker plots of selected SLRN inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 51). All exhibit statistically significant increases in median values from zone 1 to zone 2. X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 108 3.5.3 SLRN Diatoms For fire event SLRN, diatom valves were counted in 10 non-contiguous sediment samples from 25.0–20.0 cm sediment depth, representing ca. 160–105 cal yr BP (Fig. 2B). A total of 151 unique taxa were found, of which 134 (89%) were identified to at least genus level and 109 (72%) were identified to species level or lower. In terms of valves, 3380 valves were counted in total, of which 3283 (97%) were identified to at least genus level, and 2848 (84%) were identified to species level or lower. The predominant taxa, defined here as those comprising ≥2% of the valves on average, were Aulacoseira pusilla (22.3%), Discostella stelligera (19.8%), Cyclotella distinguenda (10.9%), an undet. Aulacoseira taxon in valve view (Aulacoseira undet. valve view C; 8.3%), Lindavia michiganiana (7.1%), Pantocsekiella rossii (4.7%), Lindavia intermedia (2.5%), and another undet. Aulacoseira taxon in valve view (Aulacoseira undet. valve view F; 2.4%) (Table 18). Relative abundance of these and other taxa varied considerably over the sequence. The vast majority of the valves in each sample were centric (80–93%), with a variety of other morphologies accounting for the remainder. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 2.3 to 2.8. Table 18. SLRN diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Aulacoseira pusilla 10 22.32 22.32 17.82 29.26 3.90 0.48 -0.79 Discostella stelligera 10 19.82 19.05 15.79 24.68 3.26 0.42 -1.23 Cyclotella distinguenda 10 10.88 9.70 0.00 25.16 8.19 0.79 -0.09 Aulacoseira undet. valve view C 10 8.30 7.26 5.23 13.07 2.93 0.71 -1.12 Lindavia michiganiana 10 7.14 6.22 3.92 13.18 2.71 1.31 1.78 Pantocsekiella rossii 10 4.74 4.74 1.29 8.04 2.16 -0.13 -0.66 Lindavia intermedia 10 2.47 2.43 0.99 4.10 0.93 0.18 -0.27 Aulacoseira undet. valve view F 10 2.37 2.26 0.99 4.29 1.08 0.68 -0.37 Aulacoseira alpigena 10 1.59 1.46 0.00 3.86 1.36 0.49 -1.06 Cyclotella atomus 10 1.46 1.14 0.32 3.25 1.03 0.70 -0.65 Pantocsekiella ocellata 10 1.17 0.98 0.63 2.27 0.55 1.10 0.42 109 Comparison of the distribution of total variance from the CONISS-zoned square-root transformed SLRN diatom data with that expected from the broken-stick model does not reveal any reliable stratigraphic zones (Fig. 54). The three-zone configuration was selected for analysis to remain consistent with other fire events. According to the millennial-scale age-depth model, SLRN Diatom Zone 1 (SND1) encompasses ca. 160–145 cal yr BP (25.0–23.5 cm; ca. 15 yr; n = 3), SLRN Diatom Zone 2 (SND2) ca. 145–125 cal yr BP (23.5–22.0 cm; ca. 20 yr; n = 3), and SLRN Diatom Zone 3 (SND3) ca. 125–105 cal yr BP (22.0–20.0 cm; ca. 20 yr; n = 4) (Fig. 55). Fig. 54. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed SLRN diatom data (black) with that expected from the broken-stick model (red). The stratigraphic zones derived from the SLRN diatom data delineate few statistically significant (i.e., p ≤ 0.05) changes in other fire-related proxies (Fig. 56). Sediment MS increases significantly from SND1 to SND2, and sediment C:N increases significantly from SND1 to SND3, but charcoal does not vary significantly among zones. 110 Fig. 55. SLRN diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 49 caption. 111 Fig. 56. Box-and-whisker plots of selected SLRN fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 55). X-axis units: see Fig. 49 caption. See Fig. 6 caption for further figure explanation. Similarly, despite the diversity of the assemblage and the fact that the ranking of dominant taxa changes considerably throughout the sequence, there are very few statistically significant differences in median abundance of individual taxa among zones (Fig. 57). Cyclotella distinguenda and Aulacoseira perglabra decrease significantly from SND1 to SND3, while Aulacoseira undet. valve view D increases significantly from SND2 to SND3. Pantocsekiella rossii also shows an apparent, though not statistically significant increase, from SND2 to SND3. Even when valves are grouped by morphology, statistically significant differences among zones do not emerge, with the one exception being monoraphid valves, which increase significantly from SND1 to SND2 and remain elevated through SND3. 112 Fig. 57. Box-and-whisker plots of selected SLRN diatom taxa and groups binned by diatom-derived bio-stratigraphic zones (see Fig. 55). X-axis units: %. See Fig. 6 caption for further figure explanation. 3.5.4 SLRN Pollen and Spores For fire event SLRN, pollen and spores were counted in 10 contiguous sediment samples from 25.0–20.0 cm sediment depth, representing ca. 160–105 cal yr BP (Fig. 2B). A total of 26 unique taxa were identified. The dominant taxon was Pseudotsuga menziesii, comprising about 34% of the assemblage on average, followed by Pinus contorta (21%), Cupressaceae (14%), Tsuga heterophylla (11%), Alnus (7%), Abies (4%), Pteridium (2%), Pinus monticola (2%), and monolete fern spores (2%) (Table 19). This ranking of dominant taxa remained mostly stable throughout the sequence. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 1.7 to 2.0, averaging 1.9. 113 Table 19. SLRN pollen and spore summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Pseudotsuga menziesii 10 33.98 34.16 26.75 41.28 4.25 0.11 0.10 Pinus contorta 10 20.62 20.51 17.42 24.32 2.31 0.23 -1.20 Cupressaceae 10 14.19 13.06 8.39 22.49 4.15 0.73 0.37 Tsuga heterophylla 10 11.20 11.42 8.77 12.44 1.08 -1.22 2.12 Alnus 10 7.34 7.46 5.28 9.75 1.48 0.09 -0.58 Abies 10 3.80 3.85 2.33 5.55 1.08 0.02 -0.97 Pteridium 10 2.35 2.15 1.34 3.75 0.80 0.79 -0.50 Pinus monticola 10 2.02 2.09 0.35 2.98 0.77 -1.06 1.47 Monolete fern undiff. 10 1.71 1.62 0.87 3.19 0.81 0.78 -0.48 Comparison of the distribution of total variance from the CONISS-zoned square-root transformed SLRN pollen data with that expected from the broken-stick model reveals no reliable stratigraphic zones (Fig. 58). Nonetheless, the three-zone configuration was selected for analysis to preserve consistency and facilitate comparison with other proxies and fire events. According to the millennial-scale age-depth model, SLRN Pollen Zone 1 (SNP1) encompasses ca. 160–140 cal yr BP (25.0–23.0 cm; ca. 20 yr; n = 4), SLRN Pollen Zone 2 (SNP2) ca. 140–120 cal yr BP (23.0–21.5; ca. 20 yr; n = 3), and SLRN Pollen Zone 3 (SNP3) ca. 120–105 cal yr BP (21.5–20.0 cm; ca. 15 yr; n = 3) (Fig. 59). The stratigraphic zones derived from the SLRN pollen data delineate statistically significant (i.e., p ≤ 0.05) changes in the median values of two key fire-related proxies: charcoal particle count and MS (Fig. 60). Median charcoal particle count shows an apparent increase from SNP1 to SNP2, and then a statistically significant decline from SNP2 to SNP3. Sediment MS shows a similar pattern, with a statistically significant increase from SNP1 to SNP2, and then an apparent decline between SNP2 and SNP3. 114 Fig. 58. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed SLRN pollen and spore data (black) with that expected from the broken-stick model (red). Although fluctuations are apparent in the abundance of individual taxa in the SLRN pollen sequence, there are no statistically significant differences in median values among zones at the α = 0.05 level. This may be due to insufficient statistical power stemming from low per-zone sample sizes. It is nonetheless worth noting that P. contorta shows an apparent decrease from a median abundance of about 22% in SNP1 to 19% SNP2, followed by recovery in SNP3 (Fig. 61). Furthermore, Poaceae pollen is only present in SNP2, albeit in very small amounts (<1%). Overall NAP appears to drop from about 6–7% in SNP1 and SNP2 to about 4% in SNP3. Diversity, however, shows no meaningful change throughout the sequence. 115 Fig. 59. SLRN pollen and spore relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 5× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: pollen and spores, including non-arboreal (NAP) = %; pollen and spore diversity = Shannon index; for all others, see Fig. 33 caption. 116 Fig. 60. Box-and-whisker plots of selected SLRN fire-related proxies binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 59). X-axis units: see Fig. 49 caption. See Fig. 6 caption for further figure explanation. Fig. 61. Box-and-whisker plots of selected SLRN pollen taxa and diversity binned by pollen-and-spore-derived bio-stratigraphic zones (see Fig. 59). NAP = non-arboreal pollen and spores. X-axis units: %. See Fig. 6 caption for further figure explanation. 3.6 Sooke Lake Reservoir South Basin Sediment Sequence (SLRS) 3.6.1 SLRS Charcoal, MS, TC, TN, δ13C and δ15N The SLRS charcoal, MS, C, and N sequences (Fig. 62) each span 28.0–16.0 cm sediment depth (ca. 150–45 cal yr BP) and comprise 49 contiguous 0.25-cm samples. The error margins of the centennial-scale age-depth model (Fig. 2C) are large relative to the time scale of the features of interest, so results in this section are described in terms of sediment depth rather than modeled age. 117 Fig. 62. SLRS fire-related proxies plotted by depth (top) and age (bottom). X-axis units: charcoal particle count = particles cm-3; charcoal summed area = mm2 cm-3; charcoal mean particle area = mm2; charcoal mean particle aspect ratio = unitless; magnetic susceptibility (MS) = SI units; total carbon (TC) and total nitrogen (TN) = %; TC-to-TN ratio (C:N) = unitless; carbon and nitrogen stable isotope ratios (δ13C, δ15N) = ‰. 118 3.6.1.1 SLRS Charcoal At the original 0.25-cm sampling resolution, the SLRS charcoal data are highly variable (Fig. 62). The charcoal concentration profile exhibits a section of elevated peak values from 25.5 to 20.5 cm. Similarly, charcoal summed area exhibits a section of elevated peak values from 25.75 to 19.0 cm. Overall, a distinct fire signal is difficult to discern at this sampling resolution. When the charcoal count data are smoothed using a simple LOWESS smoother (span = 0.25), a clearer signal emerges (Fig. 62). The SLRS charcoal particle count sequence exhibits a region of generally elevated charcoal values centred on ca. 22.5 cm. Charcoal concentration first deviates from presumed background levels around 26 cm sediment depth and returns to background levels by 18 cm. Maximum smoothed charcoal count concentration (ca. 19 particles cm-3) exceeds mean background levels (ca. 10 particles cm-3) by a factor of approximately two. Smoothed charcoal summed area exhibits a similar trend, albeit with a higher degree of variability. Three peaks are apparent in the smoothed charcoal summed area profile: one each at 25.0 cm, 22.0 cm, and 19.0 cm. Mean charcoal particle size approximately correlates with the summed charcoal area timeseries. Mean charcoal particle aspect ratio, a measure of charcoal particle shape, exhibits no discernable trend. 3.6.1.2 SLRS MS The SLRS sediment sequence exhibits a region of generally elevated MS values centred on 23.25 cm (Fig. 62). Values first deviate from presumed background levels at 24.50 cm and return to background levels at 21.75 cm, delineating a variable yet well-defined impact zone spanning 2.75 cm. This span is comparable to the MS responses observed in other analyzed fire episodes. Here, maximum MS values exceed background levels by a factor of approximately four. The variability exhibited by MS values in the elevated segment may be attributable to the relatively low bulk density of the sediment at this depth. Alternatively, it may be attributable to the measurement protocol, in 119 which MS was measured with samples sectioned into discrete bags rather than on the contiguous surface of an intact core. 3.6.1.3 SLRS TC, TN, δ13C and δ15N The SLRS TC and N timeseries generally match one another, with a few notable deviations (Fig. 62). Both profiles decline sharply from background levels starting at 24.75 cm and reach minimum values at 22.5 cm. Values trend upwards thereafter, although neither profile achieves pre-disturbance levels by the end of sampled section. Noteworthy deviations include a sharp spike in TN in a single sample at 23.5 cm, and a moderate spike in TC in a single sample at 19.0 cm. These divergences between the two profiles are reflected in the C:N ratio, which is generally very consistent but exhibits a sharp trough at 23.5 cm and a sharp peak at 19.0 resulting from the aforementioned deviations, respectively. Stable isotopes ratios of C and N exhibit highly variable results. The δ13C profile yields no apparent trend or systematic response (Fig. 62). Conversely, δ15N levels remain generally high from 28.75 cm to 23.0 cm before trending downwards to a minimum value at 17.75 cm and trending upwards thereafter. Notably, a prominent δ15N peak occurs at 23.5 cm, coincident with the aforementioned TN peak and C:N trough (Fig. 62). 3.6.2 SLRS Inorganic Elements For the fire event SLRS, concentrations of 24 inorganic elements were measured in 29 contiguous sediment samples from 19.5–26.5 cm sediment depth, representing ca. 140–70 cal yr BP (Fig. 2C). On average, Fe, A1, Ca, S, Mg, and P were present in the highest quantities, whereas only trace amounts of Mo, As, Pb, Se, Cd, Ag, and Sb were detected (Table 20). Generally, element concentrations are only weakly correlated with MS, but moderately-to-strongly correlated with TC and TN (Table 21). The ranking of predominant elements remained stable throughout the sequence. 120 Table 20. SLRS inorganic elements summary statistics. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are concentrations in parts per million. Skewness and kurtosis are unitless. Sorted by mean concentration in descending order. Element N Mean Median Minimum Maximum SD Skewness Kurtosis Fe 29 16180 16183 14869 17512 704 0.1 -1.0 Al 29 13118 13148 11945 13824 410 -0.6 1.0 Ca 29 5321 5380 4771 5675 226 -0.5 -0.5 S 29 3823 3777 3486 4292 226 0.4 -0.9 Mg 29 2989 3027 2728 3212 125 -0.4 -0.7 P 29 1011 1009 941 1099 33 0.4 1.2 Mn 29 265 261 223 377 33 1.7 3.7 K 29 243 269 75 435 101 0.0 -1.1 Na 29 231 233 178 292 31 0.0 -0.8 Ba 29 74.5 72.8 69.1 82.2 4.1 0.5 -1.2 V 29 58.5 58.2 52.5 69.9 3.1 1.5 5.3 Zn 29 42.3 41.9 36.5 51.2 2.8 0.7 3.1 Ni 29 39.5 39.1 36.6 53.1 2.9 4.0 19.6 B 29 38.4 39.0 30.6 47.0 3.9 -0.1 0.0 Cu 29 34.8 34.1 32.0 44.1 2.3 2.5 9.1 Cr 29 32.8 33.0 27.7 40.1 2.6 0.5 1.4 Co 29 14.5 14.4 12.9 19.8 1.2 3.1 13.6 Mo 29 2.84 2.83 2.55 3.37 0.20 0.9 0.9 As 29 2.63 2.64 2.45 2.83 0.09 0.1 0.2 Pb 29 2.20 2.25 1.41 3.18 0.48 -0.1 -0.8 Se 29 1.07 1.05 0.96 1.30 0.08 1.0 1.8 Cd 29 0.25 0.26 0.23 0.28 0.01 -0.1 -0.8 Ag 29 0.11 0.11 0.10 0.13 0.01 -0.3 -0.2 Sb 29 0.03 0.03 0.02 0.06 0.01 2.5 9.0 Comparison of the distribution of total variance from the CONISS-zoned standardized SLRS elemental data with that expected from the broken-stick model reveals a maximum of four reliable stratigraphic zones (Fig. 63). According to the millennial-scale age-depth model, SLRS Elemental Zone 1 (SSE1) encompasses ca. 140–110 cal yr BP (26.5–23.6 cm; ca. 30 yr; n = 12), SLRS Elemental Zone 2 (SSE2) comprises a single sample at ca. 110 cal yr BP (23.6–23.4 cm), SLRS Elemental Zone 3 (SSE3) encompasses ca. 110–85 cal yr BP (23.4–20.9 cm; ca. 35 yr; n = 10), and SLRS Elemental Zone 4 (SSR4) spans ca. 85–70 cal yr BP (20.9–19.5 cm; ca. 15 yr; n = 6) (Fig. 64). 121 Table 21. Pearson correlation coefficients between SLRS magnetic susceptibility (MS), total carbon (TC), total nitrogen (TN), and concentrations of inorganic elements. Sorted by strength of positive correlation with MS in descending order. Pearson correlation (r) MS TC TN MS 1.00 -0.36 -0.31 Mg 0.51 -0.44 -0.37 Cr 0.38 0.03 0.11 Na 0.35 -0.75 -0.66 Ag 0.30 0.27 0.34 Zn 0.20 0.01 0.00 Cd 0.19 0.34 0.32 Ca 0.18 0.08 0.24 Al 0.15 -0.27 -0.22 Ni 0.15 0.07 0.23 P 0.13 0.31 0.36 Mn 0.13 -0.10 -0.04 Co 0.13 0.20 0.32 V 0.09 0.12 0.26 Fe 0.07 0.58 0.62 Cu 0.07 0.20 0.28 Mo 0.02 0.57 0.63 Sb -0.10 0.09 -0.14 Pb -0.13 -0.01 -0.10 K -0.14 0.04 0.06 Ba -0.20 -0.56 -0.60 S -0.25 0.76 0.78 Se -0.25 0.68 0.73 As -0.28 0.05 -0.07 TN -0.31 0.89 1.00 B -0.33 -0.09 -0.08 TC -0.36 1.00 0.89 The stratigraphic zones derived from the SLRN elemental data delineate statistically significant (i.e., p ≤ 0.05) changes in median values of several fire indicators (Fig. 65). Neither charcoal particle count nor MS change significantly, but total sediment C and N show significant decreases from SSE1 to SSE3 and SSE4, while sediment C:N shows a significant increase from SSE1 to SSE3. Furthermore, C:N shows an apparent strong minimum in SSE2. Sediment δ15N declines significantly from SSE1 to SSE4. 122 Fig. 63. Comparison of the distribution of total variance from the CONISS-zoned standardized SLRS inorganic elements data (black) with that expected from the broken-stick model (red). 123 Fig. 64. SLRS inorganic elements concentration data (blue). Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed line delineates stratigraphic zones determined by constrained cluster analysis (CONISS) of standardized inorganic elements concentration data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: elements = parts per million; for all others, see Fig. 62 caption. 124 Fig. 65. Box-and-whisker plots of selected SLRS fire-related proxies binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 64). X-axis units: see Fig. 62 caption. See Fig. 6 caption for further figure explanation. Although the ranking of the most prevalent elements remains mostly stable throughout the SLRS sequence, there are statistically significant differences in median concentrations of individual elements among all zones except SSE2 due to its sample size of one (Fig. 66). Notably however, many elements exhibit an apparent increase from SSE1 to SSE2, and some elements, including Ag, Co, Ni, P, Se, and Zn, reach their maxima in SSE2. Between SSE1 and SSE3, Ba and Na increase significantly while Fe, K, S, and Se decrease significantly. Ag, Co, Cr, Mg, and Zn all decrease significantly from SSE3 to SSE4. Between SSE1 and SSE4, only Ba shows a significant increase while Ca, Cr, Fe, S, and Se all show declines. Overall, if SSE2 is ignored, an apparent “change and partial recovery” pattern is exhibited by K and Na while Ba, Ca, Cr, Fe, S, and Se show approximately linear trends throughout the sequence. The overall trends for Ag, Co, Mg, and Zn are ambiguous. When grouped, major wildfire ash constituents Ca, K, and Mg show a statistically significant decline from SSE1 to SSE4, but toxic elements show no significant change. 125 Fig. 66. Box-and-whisker plots of selected SLRS inorganic elements binned by inorganic-elements-derived chemo-stratigraphic zones (see Fig. 64). X-axis units: parts per million. See Fig. 6 caption for further figure explanation. 126 3.6.3 SLRS Diatoms For the fire event SLRS, diatom valves were counted in 25 non-contiguous sediment samples from 19.5–26.5 cm sediment depth, representing ca. 140–70 cal yr BP (Fig. 2C). A total of 204 unique taxa were found, of which 180 (88%) were identified to at least genus level and 156 (76%) were identified to species level or lower. In terms of valves, 7671 valves were counted in total, of which 6583 (86%) were identified to at least genus level, and 6166 (80%) were identified to species level or lower. Table 22. SLRS diatoms summary statistics. Only taxa with mean abundance ≥1% are shown. Arithmetic mean, median, minimum, maximum, and standard deviation (SD) are relative abundances in %. Skewness and kurtosis are unitless. Sorted by mean abundance in descending order. Taxon N Mean Median Minimum Maximum SD Skewness Kurtosis Staurosira venter 25 21.47 21.82 14.14 28.85 3.86 -0.10 -0.39 Staurosirella pinnata 25 16.33 16.18 10.49 25.25 3.35 0.92 1.39 Undet. girdle view A 25 8.68 8.47 1.33 18.95 4.69 0.31 -0.49 Pseudostaurosira brevistriata 25 4.53 3.62 1.00 12.13 3.06 0.99 0.17 Aulacoseira pusilla 25 3.75 3.29 1.30 7.89 1.70 0.86 -0.01 Aulacoseira undet. valve view F 25 3.53 3.28 1.60 6.56 1.32 0.71 -0.18 Aulacoseira ambigua 25 2.73 2.63 0.65 5.81 1.49 0.26 -1.10 Pseudostaurosira elliptica 25 2.20 2.53 0.00 6.98 2.06 0.80 0.00 Achnanthidium minutissimum 25 1.93 1.90 0.98 3.99 0.68 1.46 2.60 Lindavia michiganiana 25 1.93 1.97 0.33 3.29 0.82 -0.06 -0.60 Sellaphora saugerresii 25 1.83 1.62 0.33 3.46 0.89 0.34 -0.86 Discostella stelligera 25 1.80 1.95 0.00 3.62 1.03 -0.06 -0.71 Undet. girdle view N 25 1.66 1.32 0.00 4.89 1.23 0.95 1.01 Psammothidium microscopicum 25 1.57 1.31 0.00 4.65 1.09 1.18 1.78 Pantocsekiella ocellata 25 1.42 1.64 0.00 2.91 0.94 0.08 -1.59 Aulacoseira perglabra 25 1.37 0.66 0.00 7.21 1.81 1.77 3.34 Symmetric biraphid undet. H 25 1.37 1.32 0.33 2.58 0.65 0.24 -0.54 Staurosira pseudoconstruens 25 1.33 1.30 0.32 2.97 0.77 0.31 -0.82 Psammothidium levanderi 25 1.32 1.31 0.00 3.29 0.95 0.78 0.05 The predominant taxa, defined here as those comprising ≥2% of the valves on average, were Staurosira venter (21.5%), Staurosirella pinnata (16.3%), an undet. taxon in girdle view (undet. girdle view A; 8.7%), Pseudostaurosira brevistriata (4.5%), Aulacoseira pusilla (3.8%), an undet. Aulacoseira taxon in valve view (Aulacoseira undet. valve view F; 3.5%), Aulacoseira ambigua (2.7%), and Pseudostaurosira eliptica (2.2%) (Table 22). Relative abundance of these and other taxa varied 127 considerably over the sequence. Most of the valves in each sample were araphid (35–60%) or centric (15–25%), with a variety of other morphologies accounting for the remainder. Rarefied taxonomic diversity, as measured by the Shannon index, ranged from 2.8 to 3.1. Comparison of the distribution of total variance from the CONISS-zoned square-root transformed SLRS diatom data with that expected from the broken-stick model does not reveal any reliable stratigraphic zones (Fig. 67). The three-zone configuration was selected for analysis to remain consistent with other fire events. According to the millennial-scale age-depth model, SLRS Diatom Zone 1 (SSD1) encompasses ca. 140–110 cal yr BP (26.5–23.6 cm; ca. 30 yr; n = 11), SLRS Diatom Zone 2 (SSD2) ca. 110–90 cal yr BP (23.6–21.1; ca. 20 yr; n = 10), and SLRS Diatom Zone 3 (SSD3) ca. 90–70 cal yr BP (21.1–19.5 cm; ca. 20 yr; n = 4) (Fig. 68). Fig. 67. Comparison of the distribution of total variance from the CONISS-zoned square-root-transformed SLRS diatom data (black) with that expected from the broken-stick model (red).128 Fig. 68. SLRS diatoms relative abundance data (coloured). Only selected taxa are shown. Lighter-coloured shading is 3× exaggeration. Other selected proxies are plotted for comparison (grey). Ages are approximate. Horizontal dashed lines delineate stratigraphic zones determined by constrained cluster analysis (CONISS) of square-root-transformed diatom percentage data. At right is the hierarchy of sample dissimilarity calculated by CONISS. X-axis units: diatom taxa = %; diatom diversity = Shannon index; diatom plankton-to-benthon ratio = unitless; for all others, see Fig. 62 caption. 129 The stratigraphic zones derived from the SLRS diatom data delineate several statistically significant (i.e., p ≤ 0.05) changes in median values of key fire indicators (Fig. 69). Between SSD1 and SSD2, sediment MS increases significantly while total sediment C and N decrease and remain low through SSD3. Sediment δ15N declines significantly from SSD1 to SSD3. Charcoal particle count does not vary significantly among zones. Fig. 69. Box-and-whisker plots of selected SLRS fire-related proxies binned by diatom-derived bio-stratigraphic zones (see Fig. 68). X-axis units: see Fig. 62 caption. See Fig. 6 caption for further figure explanation. Despite the diversity of the assemblage, there are only a handful of statistically significant differences in median abundance of individual taxa among zones (Fig. 70). Between SSD1 and SSD2, Psammothidium subatomoides increases significantly while Punctastriata mimetica, Nitzschia fonticoloides, and Psammothidium didymum decrease. Between SSD2 and SSD3, Psammothidium didymum increases, returning to SSD1 values, while Sellaphora saugerresii and Sellaphora nigri decline. Also worth noting is the significant increase in Pseudostaurosira elliptica from SSD1 to 130 SSD3. Several other taxa exhibit statistically significant differences among zones, but their counts are generally too low (i.e., <3 valves/sample) to warrant confidence or be considered meaningful. Grouping diatoms by morphology reveals a significant increase in monoraphid valves from SSD1 to SSD2, and a significant decline in Nitzschioid taxa across the same interval. Fig. 70. Box-and-whisker plots of selected SLRS diatom taxa and groups binned by diatom-derived bio-stratigraphic zones (see Fig. 68). X-axis units: %. See Fig. 6 caption for further figure explanation. 131 4 Discussion The following discussion is divided into three overarching sections. The first section concerns independently interpreting each inferred fire event by drawing comparisons among proxies within a sediment sequence. The nature of each individual fire, its terrestrial and aquatic impacts, and the character and duration of post-fire recovery will be inferred. The second section concerns comparisons among all five fire sequences, outlining commonalities and key differences and invoking explanatory factors such as landscape development, vegetation, climate, and basin size to interpret varying post-fire responses. The comparisons and interpretations in these sections will be guided by the novel conceptual framework developed for this study, namely statistical comparisons of proxy values among stratigraphic zones in the fire sequences, which facilitates quantitative, rather than merely qualitative, identification of the most critical fire impacts and permits characterization of post-fire recovery. The third section contains a discussion of the various challenges and limitations encountered in the course of this study and proposes some potential refinements to the approach and directions for future research. Finally, a concluding chapter will take the insights developed from the paleoenvironmental record in the discussion sections and apply these findings to the modern watershed in the context of protecting current and future water supply by providing evidence-based, actionable management recommendations. Overall, this approach represents an original application of paleoenvironmental data to practical modern water supply management. 4.1 Individual Sediment Sequence Interpretations 4.1.1 Begbie Lake Sediment Sequence A (BEGA) In the late-glacial BEGA sediment sequence, the peak in charcoal particle concentration at 577.8 cm (ca. 11990 cal yr BP; Fig. 3) exhibits an increase by a factor of eight over background charcoal concentrations and coincides with a peak in summed charcoal area, providing strong 132 evidence of a fire event that was severe, large, nearby, or some combination of these characteristics (Dunnette et al., 2014; Whitlock et al., 2006). The high particle count but relatively low mean particle size suggests that this charcoal peak may reflect aeolian deposition of small, light charcoal fragments lofted into the smoke plume during the fire. MS, a proxy for soil erosion, gradually begins to increase at this sedimentary horizon but does not peak until 1 cm (ca. 50 years) later in the profile, which lends further evidence to the hypothesis that this initial charcoal concentration maximum was the result of aerial deposition at the time of the fire rather than post-fire water erosion and transport. Due to various taphonomic processes, charcoal focusing in lacustrine sediments typically takes anywhere from 5-30 years post-fire, eventually forming a distinct fire-related charcoal peak (Whitlock & Millspaugh, 1996). Accordingly, it is posited that the BEGA fire occurred around 12010 cal yr BP. One centimeter (ca. 50 years, if the millennial-scale age-depth model accurately reflects sedimentation rates at finer temporal scales) above the initial charcoal peak, charcoal concentration decreases while summed charcoal area reaches a maximum, a result of a roughly threefold increase in mean particle size (Fig. 3). This signature may reflect post-fire erosion and overland transport of charcoal, and/or a significant precipitation event. This interpretation is supported by many of the other proxies, including MS, which shows a sharp, symmetrical peak at the same horizon (Fig. 3), indicating erosion-driven input of mineral material concurrent with the increase in mean charcoal particle size. Furthermore, abundant coarser, sand-sized mineral grains were observed exclusively in the sediment samples associated with this horizon. Other key fire-related proxies corroborate the interpretation suggested by the charcoal and MS data (Fig. 3). For example, well-defined C and N minima spanning 577.8–576.8 cm (ca. 50 years) suggest a high-severity catchment fire in which terrestrial organic matter was destroyed, resulting in a subsequent relative increase of mineral input to Begbie Lake driven by enhanced post-fire erosional processes. This aligns with observations by Dunnette et al. (2014), who measured significant 133 decreases in sedimentary C and N concentrations persisting for 35–40 years following severe catchment fires. Information about sediment organic matter provenance can be gleaned from sedimentary C:N and δ13C to further characterize post-fire ecosystem dynamics (Meyers, 1997; Pompeani et al., 2020). Pre-fire sediment C:N values for the BEGA sequence (~15–16; Fig. 3) are typical of a mix of both autochthonous (i.e., freshwater algae; C:N ≈ 4–10) and allochthonous (i.e., land plants; C:N ≈ 20–75) organic input (Meyers, 1997). Sediment C:N values first increase subtly around 580 cm (ca. 12080 cal yr BP), suggesting a slight relative shift towards terrestrial organic matter sources that preceded the fire event. A sharp post-fire C:N peak at 576.8 cm (ca. 11940 cal yr BP) coincides with maximum MS values, indicating an abrupt increase in the relative contribution of erosion-derived terrestrial sources to organic matter deposition, consistent with increased terrestrial mineral inputs indicated by other proxies. A rapid drop in sedimentary C:N values thereafter implies that this pulse of terrestrial organic input was relatively short (<10 years), although C:N values do not fully return to background levels until about 575 cm (ca. 11830 cal yr BP), approximately 180 years after the inferred fire event. Sediment δ13C values for the BEGA sequence (about -33 to -28‰; Fig. 3) are likewise typical of a mixture of lacustrine algae (δ13C ≈ -30 to -25‰) and C3 land plant (δ13C ≈ -32 to -22‰) sources (Meyers, 1997), with the latter including temperate conifers that dominated the area. Values increase sharply in tandem with the initial charcoal particle concentration peak at 577.8 cm and then decline slightly before increasing to a secondary peak coincident with the MS, charcoal mean particle size, and C:N maxima at 576.8 cm. Like the increase in C:N, this post-fire section of increased δ13C suggests a slight relative shift towards terrestrial organic matter sources. The entire section of elevated δ13C values spans approximately 90 years. From the combined TC, TN, C:N, and δ13C data it can be inferred that there was an overall post-fire decrease in organic matter input to the sediments relative to mineral material, and algal 134 productivity may have declined, or terrestrial organic input may have increased, as the C:N and δ13C indicate a relative shift towards more terrestrially-sourced carbon following the fire. These results run contrary to observations by Dunnette et al. (2014), who recorded decreases in sedimentary C:N following fires, indicating a relative reduction of terrestrial organic matter inputs and/or a post-fire increase in algal productivity. In contrast, Pompeani et al. (2020) observed higher sediment C:N and δ13C following fires at Summit Lake, CO, suggesting a predominantly terrestrial source for post-fire organic material, similar to that observed in the BEGA sediments. Notably however, the authors found that the source of post-fire sedimentary organic matter differed among their three study lakes, with one lake showing a shift towards in-lake algal productivity following fire, one showing a shift towards terrestrial sources, and one showing a mix. The evidence suggests that wildfires can have variable impacts on sediment organic matter provenance depending on the nature of the fire, the watershed, and the lake itself (Pompeani et al., 2020). Sedimentary δ15N can similarly record changes in post-fire ecosystem dynamics and nutrient cycling, as demonstrated by Dunnette et al. (2014), who found that high-severity catchment fires were reliably followed by increases in sedimentary δ15N. In the BEGA sequence, sedimentary δ15N is elevated from 577.8–576.4 cm (ca. 11990–11910 cal yr BP), coinciding closely with similar changes in other fire impact proxies (Fig. 3). The δ15N profile furthermore shows a strong positive correlation with δ13C, suggesting reduced terrestrial N retention for ca. 80 years following the inferred fire event, perhaps a consequence of vegetation combustion and slow post-fire recovery of the terrestrial ecosystem. Dunnette et al. (2014) and Chipman and Hu (2019) recorded similar increases in sedimentary δ15N after severe catchment fires, although in both cases, the elevated δ15N only persisted for several decades. Sedimentary concentrations of inorganic elements in the BEGA sequence show significant changes at the inferred fire horizon. Indeed, the chemo-stratigraphic zones that were derived from the standardized element concentration data exhibit remarkably close temporal alignment with significant 135 changes in other proxies including charcoal, MS, TC, TN, δ13C, δ15N, diatoms, and pollen (Fig. 5). The boundary between BEGA Elemental Zone 1 (BAE1) and BEGA Elemental Zone 2 (BAE2) falls at 577.7 cm (ca. 11990 cal yr BP), immediately after the initial charcoal peak. From this, it can be inferred that BAE1 is the zone which records pre-fire conditions, and BAE2 is the zone which records post-fire impacts. In BEGA Elemental Zone 3 (BAE3), many elements that changed significantly in BAE2 exhibit apparent recovery to approximately pre-fire levels. BAE2 encompasses the MS peak, and the concentrations of many elements are strongly correlated with MS, TC, and TN (Table 5). Strong positive correlations (r > 0.7) between elements and MS include Mg, Cr, Mn, and Al. These same elements are also strongly negatively correlated with TC and TN, and all exhibit a statistically significant increase in concentration from BAE1 to BAE2 (Fig. 7), indicating that their influx likely increased due to enhanced post-fire erosion of soils (Smith et al., 2011; Wolf et al., 2008), and perhaps also wildfire ash (Bodí et al., 2014). All these elements have been observed to increase in runoff and downstream sediments following wildfires (Gallaher & Koch, 2004; Pelletier et al., 2020; Rust et al., 2018). These elements also show statistically significant declines across the BAE2–BAE3 zone transition (Fig. 7) at 576.3 cm (ca. 11910 cal yr BP), indicating that whatever processes were driving their increased deposition after the fire returned to a pre-fire state within about 100 years. This is in good temporal agreement with previously-discussed proxies of erosional input from terrestrial sources in the BEGA sequence. A second group of elements, including Na, V, Ni, Ca, K, and Ba also show statistically significant increases from BAE1 to BAE2 (Fig. 7) and are moderately positively correlated (0.3 < r < 0.7) with MS (Table 5). Sedimentary Pb exhibits similar correlations, though it does not show a statistically significant increase from BAE1 to BAE2. Unlike the more strongly correlated elements, this group does not exhibit statistically significant declines in BAE3, and instead remained elevated for the remainder of the sequence (ca. 350 years), suggesting that whatever processes may have driven their increased deposition did not recover to pre-fire conditions even after a prolonged interval 136 of post-fire recovery. While these elements may be partially associated with increased post-fire erosion, their persistent high levels suggest that other factors may be contributing. A third group of elements that includes As, B, Co, Cu, Mo, and S exhibits statistically significant declines from BAE1 to BAE2 (Fig. 8). These elements also all show moderate-to-strong positive correlations (r > 0.5) with TC and TN, and moderate-to-strong negative correlations (r < -0.5) with MS (Table 5). Zn shows a similar but weaker pattern. This group of elements also increases significantly from BAE2 to BAE3, indicating post-fire recovery within about 100 years of the fire, effectively showing a response that is the inverse of Mg, Cr, Mn, and Al. A reasonable interpretation is that the elements in this third group were primarily delivered to the sediments via organic matter deposition, which was reduced post-fire. Certainly, this is likely the case for S, which is a significant component of organic matter and has been shown to be closely correlated with sedimentary C and N concentrations following fires (Pompeani et al., 2020). However, As, Co, Mo, and Zn are generally reported to increase in runoff and sediments following wildfire, and Cu exhibits variable responses, increasing after some fires but not others (Gallaher & Koch, 2004; Neary et al., 2005; Pelletier et al., 2020; Rust et al., 2018). In this context, the decreases following the BEGA fire are somewhat anomalous. An important consideration when interpreting these results is sediment diagenesis and potential mobility of elements post-deposition within the sedimentary profile. For example, although some studies have shown As to increase in runoff and downstream sediments following wildfire (Gallaher & Koch, 2004; Pelletier et al., 2020; Rust et al., 2018), it is highly mobile in sediments; resuspension of As from sediments can be substantial, and it is estimated that <20% of external As inputs are buried (Cornett et al., 1992), which may account for the decrease in As concentration in BAE2. As a result, sedimentary As concentrations must be interpreted with caution. Sedimentary P, Cd, and Fe concentrations show no significant changes across the inferred fire horizon, and no meaningful correlations with other fire-related proxies. In the case of Cd, this is likely because concentrations are generally at or below the detection limit of the instrumentation. In the case 137 of P and Fe, it is probably because both are highly mobile in the sediments and their behaviour is coupled (Ding et al., 2016), making the interpretation of sedimentary P concentrations, in particular, fraught with difficulties (Ginn et al., 2012). Overall, the elemental data from the BEGA sequence appears to exhibit two main modes of variability: one that is associated with inorganic input to the sediments, and another that is associated with the organic component. This aligns well with the results of Pompeani et al. (2020), who found similar patterns in Holocene paleofire records from three Colorado lakes. In terms of post-fire recovery, many, though not all, elemental concentrations return to roughly pre-fire levels within approximately 100 years of the inferred fire event. However, the precise length of the recovery window is impossible to discern due to the large uncertainties inherent in the 14C-derived age-depth model at this depth (95% CI ≈ 650 years). Nonetheless, the evidence indicates that ecosystem recovery following the BEGA fire event likely occurred on the scale of multiple decades or even a century. Cluster analysis of the BEGA diatom data delineates three assemblage zones which, despite broken-stick analysis suggesting that they are not reliably distinct (Fig. 9), nonetheless correspond closely to the chemo-stratigraphic zones derived from the inorganic elements data, and with important changes in other fire-related proxies (Fig. 10). The boundary between BEGA Diatom Zone 1 (BAD1) and BEGA Diatom Zone 2 (BAD2) occurs one sample (ca. 10 years) before the corresponding chemo-stratigraphic zone transition and the primary charcoal peak, suggesting that all these proxies are responding to the same fire event. Relative abundances of a variety of diatom taxa show statistically significant changes at the inferred fire horizon (Fig. 12). Notably, Staurosira venter, a dominant species which comprises 23% of the diatom assemblage in BAD1, significantly declines to 16% in BAD2, concurrent with a statistically significant increase in overall diatom community diversity (Fig. 13). The ratio of planktonic to benthic diatoms, a reliable proxy for turbidity when lake levels are relatively stable (Brown et al., 2020), varies throughout the profile, but peaks sharply and 138 significantly in BAD2 (Fig. 13), concurrent with the primary charcoal peak. This peak suggests that an increase in turbidity, likely due to fire-induced erosion, drove a corresponding increase in diatom community diversity, perhaps as a result of increased nutrient availability. BAD2 lasts approximately 150 years, somewhat longer than the corresponding chemo-stratigraphic zone (BAE2). The transition from BAD2 to BEGA Diatom Zone 3 (BAD3) is characterized by lower diatom-inferred turbidity and a significant decline in diatom diversity. The BEGA pollen record spans a known period of abrupt global climate change, and as such is expected to include two different climatic zones, with the age-depth model indicating that the sequence crosses the rapid and strong warming at the start of the post-glacial Holocene Epoch, ca. 11700 cal yr BP (Hebda, 1995; Brown et al., 2008). In doing so, the pollen record is expected to contain evidence of the cool, possibly cold, and moist climate of the Late Glacial Younger Dryas in the lower portion, grading into the warm dry, climate of the early Holocene in the upper portion. Although only the lower portion of this record is discussed in detail here, pollen was counted across a period spanning ca. 900 years in total, from ca. 12300–11400 cal yr BP, in order to temporally situate the BEGA fire relative to the presumed Younger Dryas – early Holocene climate transition. Understanding the climatic conditions under which the BEGA fire occurred is critical in the interpretation of its characteristics and impacts, especially with regards to terrestrial vegetation. The extended BEGA pollen counts are available as supplementary data. The pollen data indicate that the BEGA section does indeed capture the Younger Dryas – early Holocene climate transition, with the Holocene beginning in this record around 11700 cal yr BP, which is in good agreement with dates reported elsewhere in the literature (Hebda, 1995; Brown et al., 2008). The BEGA fire event, however, appears to precede this transition by approximately 300 years, indicating that climate warming probably did not play a major role in the conditions or impacts of this specific fire. Therefore, in order to focus on the effects of the fire itself, and to better align with data collected for other proxies, stratigraphically constrained cluster analysis was conducted on only 139 the pollen data from the lower portion of the BEGA record, i.e., that from the Younger Dryas. These samples are dominated by lodgepole and/or shore pine (Pinus contora) and alder, including both red alder (Alnus rubra) and Sitka alder (Alnus alnobetula subsp. sinuata) type pollen (Table 7). These two alder types were differentiated based on general size and wall thickness characteristics, although the separation was not strict, and both are grouped together in subsequent numerical analyses. Nevertheless, there are clear and somewhat systematic variations in Sitka alder abundance and its relative occurrence compared to red alder (Fig. 15). Generally, red alder is more abundant than Sitka before the inferred fire, while Sitka alder is more abundant in the decades after. In the sample immediately following the charcoal peak, Sitka alder occurs much more abundantly than red alder. The sharp reversal in ratio of Sitka to red alder at the fire horizon suggests a significant disturbance by fire and a local positive response by Sitka alder, which favours cool, disturbed sites (Uchytil, 1989). Other conifer pollen types also occur, though much less frequently, and include hemlocks (Tsuga spp.), true fir (Abies) and spruce (Picea). Two other types, willow (Salix) pollen and bracken fern (Pteridium) spores, are notable elements in some samples. The overall assemblage is indicative of cool conditions characteristic of the Younger Dryas in this region, as suggested by the abundance of Sitka alder typical of high cool elevations and disturbed sites, such as avalanche tracks (Uchytil, 1989). The three marginally significant bio-stratigraphic zones derived from these pollen data show remarkably close temporal alignment with the stratigraphic zones derived from the inorganic elements and diatom datasets (Fig. 15). The transition between BEGA Pollen Zone 1 (BAP1) and BEGA Pollen Zone 2 (BAP2) occurs at 578.25 cm (ca. 12010 cal yr BP), immediately preceding the primary charcoal peak and falling one sample (ca. 10 years) before the BAE1–BAE2 and BAD1–BAD2 transitions. All other proxies examined also show clear evidence of fire-induced disruption in BAD2. Furthermore, some of the changes in the BAP2 pollen and spore data are consistent with those 140 expected of major fire disturbance. For example, Tsuga heterophylla, a fire-sensitive species, declines significantly from BAP1 to BAP2, while pollen of an undet. herb taxon increases significantly (Fig. 17), suggesting a relatively severe fire that destroyed trees and opened the canopy. P. contorta, a prolific producer of windborne pollen, also increases significantly in BAP2, perhaps as the result of relative enrichment of the regional P. contorta pollen rain due to the combustion of other species closer to the lake. BAP2 also sees a significant decline in pollen diversity, further supporting this interpretation. Overall, however, non-arboreal pollen does not change significantly across the inferred fire horizon, suggesting relatively subtle or spatially restricted fire effects on the terrestrial ecosystem, despite the strong responses from other fire-related proxies. BAP2 persists for approximately 130 years. The transition from BAP2 to BAP3 is characterized by apparent recovery of key taxa to pre-fire levels. Interestingly, the pollen sample at the level of the fire and several pollen samples above it show an abundance of fungal hyphae and what appear to be older reworked (remobilized) conifer pollen grains that presumably originated from disturbed organic soils (R. Hebda, personal communication). These both could be strong indicators of ground surface disruption by the fire in a cool late-glacial climate. In summary, a generally consistent interpretation of the BEGA fire event emerges from the assembled proxy data. The physical, chemical, and biological indicators all show strong evidence of fire-induced effects on the terrestrial and aquatic ecosystems of Begbie Lake and its catchment around 12010 cal yr BP. This was likely a severe catchment fire that burned in a cool, moist late-glacial landscape with relatively poorly-developed soils conducive to significant post-fire erosion. An initial pulse of charcoal and ash was delivered to the lake around the time of the fire, perhaps via atmospheric deposition. A secondary pulse of charcoal, minerals, ash, and terrestrial organic material, including remobilized pollen grains, was delivered to the sediments some time later, perhaps associated with a major precipitation event. The pollen record reveals changes in terrestrial vegetation 141 around the primary charcoal peak that are consistent with wildfire disturbance, including a sharp increase in Sitka alder, a species typical of cool, disturbed sites. There is, however, no strong increase in non-arboreal herbaceous species as might be expected following a fire. Algal productivity in the lake, as indicated by C:N values, was probably reduced following the fire, perhaps due to increased erosion-associated turbidity. This is corroborated by the fossil diatom record, which reveals a peak in diatom-inferred turbidity coinciding with the primary charcoal peak. Post-fire recovery of both terrestrial and aquatic ecosystems was probably slow and variable, with various physical, chemical, and biological proxies apparently taking many decades to return to pre-fire levels. 4.1.2 Begbie Lake Sediment Sequence B (BEGB) In the BEGB sediment sequence, the initial charcoal concentration peak at 505.4 cm (ca. 9850 cal yr BP) exceeds background levels by a factor of about four (Fig. 18) and is followed closely by a prominent MS peak at 504.4 cm (ca. 9830 cal yr BP), providing strong evidence of a high-severity fire event within the watershed around 9860 cal yr BP, during the early Holocene. Climate at this time was significantly warmer and drier than the preceding BEGA sediment sequence, and a mixed-severity fire regime likely prevailed in the watershed (Brown et al., 2019). Two smaller charcoal peaks at 499.4 cm (ca. 9720 cal yr BP) and 497.6 cm (ca. 9680 cal yr BP), with corresponding MS peaks, likely reflect later fires, or may represent major precipitation-driven erosion events after the initial fire. MS, a proxy for soil erosion, declines after the initial peak, yet remains elevated relative to pre-fire values between the first and subsequent charcoal peaks. This suggests either a prolonged period (>100 years) of moderately increased soil erosion following the initial fire, or a shorter period of greatly increased erosion and a correspondingly high sedimentation rate, in which case the amount of time represented by the section is less than what is indicated by the age-depth model. While increased sedimentation rates are expected following a severe catchment fire, the fact that sediment appearance and texture in this section are consistent with that preceding the charcoal peak suggests that a sudden and dramatic increase in post-fire sedimentation is not likely 142 responsible for the persistently high MS values. It seems more likely that modest, yet long-lasting post-fire increases in catchment soil erosion, and corresponding decreases in terrestrial organic inputs due to biomass combustion, are responsible for the prolonged section of elevated MS. In any case, due to the uncertain nature of the secondary charcoal peaks, pollen, diatom, and inorganic element analyses were limited to the sediment section around the primary charcoal peak at 505.4 cm exclusively. Sediment TC and TN in the BEGB sequence (Fig. 18) are strongly negatively correlated with MS (Table 9), likely reflecting the combustion of terrestrial organic material (Dunnette et al., 2014) and perhaps suggesting reduced post-fire algal productivity resulting from increased lake turbidity due to enhanced soil erosion (Brown et al., 2020). TC and TN minima correspond closely with both charcoal and MS maxima, providing further evidence of a severe catchment fire. Sedimentary C:N values in the BEGB sequence (~12–15; Fig. 18) are typical of organic material derived from a combination of land plants and lacustrine algae (Meyers, 1997). An initial increase in sedimentary C:N around 508 cm seems to precede the inferred fire event by several decades, a phenomenon also observed in the BEGA sediment sequence. Whether this apparent increase truly occurred before the fire or whether this is a taphonomic artifact is difficult to discern from the available evidence. Regardless, the sediment C:N profile also exhibits sharp peaks that coincide closely with charcoal and MS maxima, suggesting brief but distinct fire-induced shifts towards increased terrestrial organic matter input relative to algal organic matter input. This is in good agreement with similar shifts observed in the BEGA sequence and results from Pompeani et al., (2020). Sediment δ13C values from the BEGB sequence (-34 to -28‰; Fig. 18) are likewise typical of organic material comprised of mixed inputs from C3 land plants and freshwater algal sources (Meyers, 1997). Sedimentary δ13C in the BEGB sequence does not show any significant change around the inferred fire horizon, and a distinct but brief decrease at 503 cm does not appear to 143 coincide with any major changes in other fire-related proxies. Overall, δ13C in the BEGB sequence is not strongly correlated with any other indicators, suggesting that the variation of this proxy was not strongly influenced by the BEGB fire, and may have been controlled by other factors. Similarly, sediment δ15N in the BEGB sequence (Fig. 18) shows no strong systematic relationship to other fire-related proxies. However, a subtle increase in δ15N that occurs shortly after the charcoal maximum and corresponds with MS and C:N maxima and TC and TN minima may be indicative of reduced terrestrial biotic N retention resulting from vegetation combustion, suggesting a severe fire. Other variations in the BEGB δ15N profile do not appear to correspond with changes in other fire-related proxies and may instead have been influenced by factors other than wildfire. Inorganic element concentrations in the BEGB sedimentary sequence show significant changes at the inferred fire horizon (Fig. 20). The chemo-stratigraphic zones derived from the elemental concentration data exhibit close temporal alignment with significant changes in other fire-related proxies including charcoal, MS, TC, TN, and C:N (Fig. 21). The boundary between BEGB Elemental Zone 1 (BBE1) and BEGB Elemental Zone 2 (BBE2) falls at 505.7 cm (ca. 9850 cal yr BP), one sample before the first charcoal peak and on the rising limb of the MS profile. BEGB Elemental Zone 3 (BBE3) coincides with maximum MS values and minimum TC and N values. From this, it can be inferred that BBE1 is the zone which records pre-fire conditions, and BBE2 and BBE3 are likely the zones which record post-fire impacts. BEGB Elemental Zone 4 (BBE4) corresponds with relatively lower charcoal and MS values, although MS remains somewhat elevated through this section relative to pre-fire values, as previously discussed. Many elements in the BEGB sequence, including Al, Ba, Be, Ca, Cr, Mg, Mn, Na, and P, exhibit an “increase and recover” pattern, rising significantly from BBE1 to BBE3 and then showing apparent, but generally not statistically significant, declines in BBE4 (Fig. 22). These elements also all show medium-to-strong positive correlations (r > 0.5) with MS and medium-to-strong negative correlations (r < -0.5) with TC and TN (Table 9), suggesting that post-fire soil and ash erosion was 144 their likely source. Ni shows similar correlations but no statistically significant increase in BBE3, and Fe likewise exhibits a similar yet more subtle pattern and is only weakly positively correlated with MS, perhaps due to post-depositional movement in the sediments. Nonetheless, it is likely that the primary delivery pathway for all these elements was post-fire erosion of inorganic material. Another group of elements in the BEGB sequence exhibit a “decrease and recover” pattern, with statistically significant minima occurring in zone BBE3 (Fig. 23). This group includes Ag, As, B, Cd, Hg, S, and Se, and all show medium-to-strong negative correlations (r < -0.5) with MS and weak-to-strong positive correlations (r > 0.3) with TC and TN (Table 9). V shows similar, but weaker, correlations. It is likely that this group of elements is associated with organic material in the BEGB fire event. Interestingly, several elements show statistically significant concentration maxima in BBE2 rather than BBE3, including Cu, Pb, and Zn (Fig. 24). Since MS is still rising in BBE2 and reaches its maximum in BBE3, it is unlikely that mineral erosion was the primary delivery pathway for the three elements that peak in BBE2, and their correlation with MS is predictably weak. At the same time, however, all three elements exhibit weak-to-moderate negative correlations with TC and TN, indicating that they are not associated with organic material, either. Whatever the source of these elements may have been, it peaked earlier than the other erosion-associated elements. Overall, the elemental concentration data from the BEGB sequence generally resemble those from the BEGA sequence, with clear evidence of fire effects and a distinct delineation between those elements associated with post-fire inorganic input and those associated with organic material. Several noteworthy differences, however, will be discussed in Section 4.2. Despite broken-stick analysis suggesting that the BEGB diatom data do not contain any reliably distinct bio-stratigraphic zones (Fig. 25), cluster analysis nevertheless revealed diatom assemblage zones which correspond closely to the chemo-stratigraphic zones derived from the 145 inorganic elements data, and with important changes in other fire-related proxies (Fig. 26). Most significantly, BEGB Diatom Zone 2 (BBD2) is comprised of a single sample that corresponds to maximum charcoal concentration and likely represents several years immediately following the fire. The zone boundary between BEGB Diatom Zone 1 (BBD1) and BBD2 is also significant because it occurs one sample after the zone boundary between BBE1 and BBE2, indicating that fire-associated geochemical changes immediately preceded changes in the diatom community. Diatom community diversity is notably lower in BBD2 than in BBD1 (Fig. 26), perhaps as a consequence of reduced benthic habitat, related to a narrowed photic zone constrained by increased post-fire turbidity (Brown et al., 2020). Indeed, the planktonic:benthic diatom ratio, which can be interpreted as a proxy for turbidity when lake levels are relatively stable (Brown et al., 2020), increases sharply at the transition from zone BBD1 to BBD2 and reaches its highest value in BBD2 (Fig. 26), suggesting heightened post-fire turbidity with a consequently reduced photic zone, limiting light penetration to the benthic communities. These effects appear to be short-lived (<10 years), because over the following two samples the diatom-inferred turbidity index decreases to pre-fire levels, and diatom community diversity also recovers to pre-fire levels. Diatom-inferred turbidity increases once again, however, several samples later at the boundary between BEGB Diatom Zone 3 (BBD3) and BEGB Diatom Zone 4 (BBD4), concurrent with maximum MS values. This once again suggests that turbidity increased appreciably in Begbie Lake in response to enhanced post-fire erosion, affecting water quality to a sufficient degree as to have measurable impacts on the diatom community. In particular, the araphid species Staurosira venter was a major constituent of the diatom community in BBD1 (11%) but shows a sizeable and statistically significant decline from BBD1 to BBD3, where it comprises a mere 4% of the assemblage. Overall, diatom-inferred turbidity throughout the BEGB profile is moderately negatively correlated (r = -0.44) with diatom community diversity, and there is substantial evidence for fire-induced erosion affecting diatom community composition. 146 The BEGB pollen data are typical of the early Holocene in this region and indicate that the local vegetation was fire-adapted (Brown et al., 2019). The assemblage is dominated by alder, Douglas fir (Pseudotsuga menziesii), lodgepole pine, and bracken fern (Pteridium), with relatively abundant grasses and other herbaceous taxa (Table 11). Non-arboreal pollen and spores comprise 30–40% of the total count, suggesting an open-canopy forest with a grassy understory, or perhaps an aspect-differentiated mosaic (Brown et al., 2019). The three significant bio-stratigraphic zones derived from the BEGB pollen data exhibit close temporal alignment with key changes in other fire-related proxies, including the zones derived from the inorganic elements and diatom data, and with charcoal and MS maxima (Table 11). The boundary between BEGB Pollen Zone 1 (BBP1) and BEGB Pollen Zone 2 (BBP2) occurs two samples (ca. 10 years) before the primary charcoal peak and one sample before the first chemo-stratigraphic zone transition, which is consistent with the timing observed in the BEGA fire event and provides strong evidence of fire disturbance at this horizon. Statistically significant changes in several key wildfire-related taxa occur between BBP1 and BBP2, although the overall fire signal in the pollen data is subtle. Alder, a common early seral genus and colonizer of disturbed sites, increases significantly between BBP1 and BBP2, as does grass pollen, suggesting an even more open landscape in the wake of the fire (Fig. 32). Several other herbaceous taxa also increase across this zone boundary, lending support to this interpretation. Furthermore, several grains of Onagraceae pollen, likely fireweed (Chamaenerion spp.), at 504.8 cm (ca. 9835 cal yr BP) may reflect a successional response to the fire. However, no significant changes are observed in fire-sensitive species such as western hemlock, nor in common post-fire responders such as bracken fern, which comprises a significant proportion of the assemblage. Furthermore, no meaningful change is observed in overall non-arboreal pollen. This is perhaps because the landscape was already quite open and fire-adapted before the BEGB fire event, and so the impact of the fire on the vegetation, and thus pollen and spore assemblage, was small. BBP2 spans ca. 70 years, and the transition from BBP2 to BEGB Pollen Zone 3 (BBP3) exhibits several signs of late-successional post-147 fire ecosystem recovery, including statistically significant increases in western hemlock and true fir (Fig. 32). In summary, a consistent interpretation of the BEGB fire event is provided by the proxy data. The physical, chemical, and biological proxies in the BEGB sediment sequence all show strong evidence of fire-induced effects on the terrestrial and aquatic ecosystems of Begbie Lake and its catchment around 9860 cal yr BP. The BEGB fire was probably a severe catchment fire that burned in a warm, dry, fire-adapted landscape. The relatively open vegetation was likely conducive to significant post-fire erosion, and turbidity appears to have increased following the fire, affecting water quality and diatom community composition. At the same time, a variety of inorganic elements likely derived from soil and wildfire ash erosion increased in the sediments of the lake, and it can be inferred that the overlying water was similarly affected. The effects on the fire on terrestrial vegetation are more subtle, but evidence of fire impacts and recovery are revealed in statistically significant changes in the pollen of several key taxa. Post-fire recovery of both terrestrial and aquatic ecosystems seems to have been slow, with various physical, chemical, and biological proxies apparently taking decades to return to pre-fire levels, and others exhibiting significantly altered levels through the end of the profile, well over a century after the fire. On this basis, BEGA and BEGB show similar fire responses despite differences in vegetation and climate. 4.1.3 Begbie Lake Sediment Sequence C (BEGC) The BEGC sediment sequence was deposited during the late Holocene around 2420 cal yr BP, under different climate and landscape conditions than either of the previous two fires. By this time, regional climate had cooled and moistened to essentially modern conditions, leading to closed-canopy, mixed-conifer forest interspersed with Garry oak (Quercus garryana) meadow ecosystems maintained by frequent but low-intensity Indigenous burning (Brown et al., 2019; McCune et al., 2013). 148 The BEGC sediment profile exhibits a bifurcated charcoal peak and a MS maximum that slightly precedes the first charcoal peak (Fig. 33). MS is highest at 122.6 cm (ca. 2410 cal yr BP), and the first of two charcoal peaks occurs one sample (ca. 4 years) later at 122.4 cm. The second, slightly larger charcoal peak occurs one centimeter (ca. 25 years) after the initial charcoal peak. Between the high values, charcoal concentration drops to a low point. The charcoal therefore seems to record two distinct deposition events, possibly transported via two separate pathways as described for the BEGA fire event, wherein the initial peak may have been delivered via atmospheric deposition at the time of the fire, and the second peak may have been delivered by subsequent water erosion of the burned landscape. Unfortunately, charcoal particle size data was not recorded for the BEGC sequence, making it difficult to infer the likely delivery pathway of each charcoal peak. Furthermore, the relative timing of the MS peak, a proxy for soil erosion, seems to contradict the two-pathway interpretation; if the first charcoal peak does indeed reflect atmospheric deposition and the second charcoal peak reflects water-eroded material, one might expect the MS maximum to coincide with the second charcoal peak, rather than the first. Yet charcoal taphonomy is often complex, and substantial remobilization and redistribution of charcoal particles can occur for decades post-fire before sufficient charcoal is focused and buried in the sediments to form a distinct peak (Whitlock & Millspaugh, 1996). Furthermore, depending on how the charcoal is (re)mobilized, it is possible for abundant charcoal to be delivered to the sediments without much mineral debris. Therefore, the timing of the MS maximum relative to the charcoal peaks does not preclude a “one fire, two peaks” interpretation. Another possible explanation is that the second charcoal peak represents a separate fire event. The absence of a second MS peak may seem to contradict this interpretation. However, Dunnette et al. (2014), in their biogeochemical study of Holocene wildfire impacts, characterized two types of fire events with differing MS signals. One type was defined by a sedimentary charcoal peak with a corresponding MS peak, which they interpreted as high-severity fires specifically within the lake 149 catchment. The other type produced a charcoal peak without a corresponding MS peak, which they interpreted as “local” fires occurring within about 1 km of the lake, but that did not burn within the catchment or were not of sufficient severity to burn through the duff and result in erosion of the surface mineral materials. It is possible that the second charcoal peak in the BEGC sediment sequence represents the latter type of fire, i.e., an out-of-catchment or low-severity fire. One might expect that in the moister late Holocene environment of the BEGC sequence, with higher primary productivity and thicker soil organic layers, a more intense fire would be required to burn through the duff, leading to overall fewer fires producing MS signals. The fact that charcoal concentration drops to low values between the charcoal peaks in the BEGC sequence lends additional support to the interpretation that the second charcoal peak represents an independent fire event. Ultimately, both the “one fire, two charcoal peaks” interpretation and the “two separate fires” interpretation could fit the BEGC charcoal and MS data, so consideration of other proxies is required to provide additional clarity. TC and TN in the BEGC sequence are at their lowest around the first charcoal peak (Fig. 33), much like the BEGA and BEGB fire events. However, the TC and TN declines in the BEGC sequence precede the charcoal peaks by several centimetres (ca. 65 years) and show only weak negative correlations (C: r = -0.47; N: r = -0.32) with MS (Table 13). Sediment C:N values (~10.5–12.1) indicate a mix of algal and terrestrial organic sources, but are skewed towards lacustrine algal input, which typically has C:N values <10 (Meyers, 1997). Sediment C:N in the BEGC sequence decreases towards the initial charcoal peak and slowly increases thereafter (Fig. 33), showing strong positive correlation with TC (r = 0.84) and moderate positive correlation with TN (r = 0.56), whereas strong negative correlations between C:N and TC and TN were observed in the BEGA and BEGB sequences. This suggests that with the BEGC fire event, post-fire organic input to the sediments shifted towards autochthonous algal sources and that terrestrial sources contributed relatively less. 150 Sediment δ13C and δ15N values in the BEGC sequence vary but show no significant changes around the charcoal-inferred fire horizon(s) and are not meaningfully correlated with other proxies (Fig. 33). This further supports the interpretation that this was a low-severity or extra-catchment fire or fires, as characterized by Dunnette et al. (2014). The BEGC inorganic elements data are distinctly different from those of the BEGA and BEGB fire events. None of the analyzed elements show meaningful positive correlations with MS (Table 13), indicating that erosion-derived mineral material was likely not a significant component of the post-fire sedimentary environment. Conversely, many elements, including Ag, As, Co, Cr, Cu, Fe, Ni, P, S, and Se, show moderate-to-strong positive correlations (r > 0.5) with TC and TN, suggesting an association with sediment organic material, perhaps derived from algae, as indicated by sediment C:N values. The five chemo-stratigraphic (sub)zones derived from the elemental concentration data exhibit close temporal alignment with other fire-related proxies, with the most significant variation occurring in the upper few centimetres of the profile, after the second charcoal peak (Fig. 35). Broken-stick analysis indicates that these zones are significant (Fig. 34), and many individual elements show statistically significant differences among zones when the uppermost three zones are treated as subzones and grouped (see Section 3.4.2). The boundary between BEGC Elemental Zone 1 (BCE1) and BEGC Elemental Zone 2 (BCE2) occurs at 122.9 cm (ca. 2420 cal yr BP), one sample before the MS maximum and two samples (ca. 10 years) before the first charcoal peak. This relatively close temporal alignment indicates that BCE1 is likely a zone recording pre-fire conditions, and BCE2 is a zone recording post-fire effects. However, only two elements show statistically significant changes across this horizon – Hg increases, and Zn decreases (Fig. 37) – indicating that the effects of this fire on sediment chemistry were minimal. In contrast, the transition from BCE2 to BEGC Elemental Zone 3 (BCE3) is marked by a variety of statistically significant changes in elemental concentrations, including increases in Ag, Ca, Co, Cu, Mn, Ni, P, and S, and decreases in Hg, Cd, and 151 Na (Fig. 39). This boundary occurs at 121.3 cm (ca. 2380 cal yr BP), immediately after the second, larger charcoal peak, but is not associated with any meaningful change in MS. Sediment C:N, however, decreases significantly between BCE1 and BCE2, but shows no significant change between BCE2 and BCE3. The overall impression that emerges is that the sediment organic matter source shifted towards autochthonous inputs around the time of the first charcoal peak and MS peak, perhaps due to fire burning off terrestrial organic material. However, most of the inorganic elements contained within the sediments did not change substantially until after the second charcoal peak, some 25 years later, and these changes were not associated with shifting organic matter provenance or an influx of terrestrial mineral material. Cluster analysis of the BEGC diatom data delineates four marginally significant assemblage zones (Fig. 40) which partially correspond to the chemo-stratigraphic zones derived from the inorganic elements data, and with key changes in other fire-related proxies (Fig. 41). The boundary between BEGC Diatom Zone 1 (BCD1) and BEGC Diatom Zone 2 (BCD2) occurs at 124.5 cm (2460 cal yr BP) and precedes the MS peak and first charcoal peak by almost two centimetres (ca. 50 years). This transition therefore probably does not reflect a fire-induced change in the diatom assemblage, an interpretation which is corroborated by the fact that it does not correspond to any other stratigraphic zone transitions derived from the inorganic elements or pollen data. However, BCD2 does encompass the first charcoal peak and the MS maximum at its upper end, leading to a statistically significant increase in MS between BCD1 and BCD2. Furthermore, the BCD1–BCD2 transition delineates statistically significant decreases in sedimentary TC, TN, C:N, and δ15N (Fig. 42). The timing and nature of these changes suggests that some factor(s) unrelated to the inferred BEGC wildfire caused a change in the character of the organic component of the Begbie Lake sediments and diatom community, or vice versa, at the BCD1–BCD2 transition. Diatom diversity also increases significantly between BCD1 and BCD2, although the planktonic:benthic diatom ratio remains stable, indicating that the increasing diversity was probably not associated with changes in turbidity. There is 152 no simple explanation for the diatom assemblage changes observed at the BCD1–BCD2 transition, and the timing demonstrates that changes in the diatom community can occur without fires. The boundary between BCD2 and BEGC Diatom Zone 3 (BCD3) occurs at 121.6 cm (ca. 2390 cal yr BP), immediately before the second, larger charcoal peak, and one sample before the transition from BCE2 to BCE3. This transition is associated with significant changes in the concentration of many elements in the sediments, indicating a strong association between charcoal, diatom community composition, and sediment chemistry. Causation among these factors is difficult to establish, but a straightforward interpretation of the timeline suggests that changes in the diatom community occurred contemporaneously with an influx of charcoal, both of which immediately preceded changes in sedimentary elemental concentrations. However, despite the apparent change in diatom community composition implied by the assemblage zone boundary, diatom community diversity does not change significantly between BCD2 and BCD3. Conversely, the planktonic:benthic diatom ratio exhibits a sharp increase across this boundary and sustained higher values in BCD3, suggesting a rapid increase in turbidity. Curiously, neither MS nor sediment C:N change meaningfully at the BCE2–BCE3 boundary, suggesting that eroding terrestrial sediment was not a significant contributor to these changes. It is possible that the changes in the diatom community were instead precipitated by other fire-associated factors, such as changes in light availability, water temperature, or water column mixing, potentially caused by combustion of shading and wind-occluding riparian vegetation (Brown et al., 2020). BCD3 spans about 30 years, and the BCD3 to BEGC Diatom Zone 4 (BCD4) transition is characterized by apparent declines in diatom diversity and planktonic:benthic ratio, although BCD4 comprises only a single sample, precluding conventional statistical analysis. BCD4 corresponds closely with inorganic-elements-derived subzone BCE3c, once again indicating a strong association between diatom community composition and sedimentary chemistry characteristics of the BEGC 153 sequence. Other fire-related proxies, including charcoal and MS, do not seem to change meaningfully in BCD4. The BEGC pollen and spore data are characteristic of the late Holocene in this region and indicative of a relatively cool, moist climate (Hebda, 1995). Changing climate through the mid- and late-Holocene facilitated a shift towards closed-canopy forests with flora resembling the modern assemblage (Brown et al., 2019). The BEGC pollen assemblage (Table 11) is dominated by Douglas fir, western hemlock, red alder, shore pine, and cupressaceous pollen, likely western redcedar (Thuja plicata). Non-arboreal pollen and spores generally comprise <5% of the assemblage, most of which is bracken fern. The local presence of Garry oak is also recorded in the pollen data, likely growing in meadow ecosystems that occupied dry, exposed sites, and were maintained by Indigenous burning practices (McCune et al., 2013). Despite the moister climate, various studies report increased regional biomass burning at this time, a phenomenon which has been attributed to both increased climate variability and human activity (Brown et al., 2019; Brown & Hebda, 2002a; Gavin et al., 2003a). Although the broken-stick analysis suggests that the BEGC pollen and spore data do not contain any reliable bio-stratigraphic zones (Fig. 45), cluster analysis nevertheless revealed three assemblage zones which correspond reasonably well with the zones derived from the inorganic elements data, the diatom data, and with changes in other key fire-related proxies (Fig. 46). The boundary between BEGC Pollen Zone 1 (BCP1) and BEGC Pollen Zone 2 (BCP2) occurs at 123.3 cm (ca. 2430 cal yr BP), two samples (ca. 10 years) before the corresponding chemo-stratigraphic zone transition (BCE1–BCE2). BCP2 encompasses the MS peak and the first charcoal peak, although the BCP1–BCP2 boundary precedes the magnetic peak by three samples (ca. 15 years) and the charcoal peak by four samples (ca. 20 years). Nevertheless, BCP2 is evidently a zone recording fire effects, and the BCP1–BCP2 transition is characterized by changes in the pollen assemblage that are consistent with fire disturbance. These include an apparent, though not statistically significant, decline in fire-sensitive western hemlock, a statistically significant increase in red alder – an early-154 seral and riparian species – and a statistically significant increase in bracken fern, a common early post-fire responder species (Fig. 48). Pollen and spore diversity also shows a statistically significant increase from BCP1 to BCP2. Notably, Garry oak pollen also shows a statistically significant increase in BCP2, a possible indicator of local Indigenous cultural burning (McCune et al., 2013). The BCP2 to Begbie Pollen Zone 3 (BCP3) transition occurs at 122.1 cm (ca. 2400 cal yr BP), one sample (ca. 5 years) after the first charcoal peak and three samples (ca. 15 years) before the second charcoal peak. This transition is characterized by a further decline in western hemlock and the apparent, though not statistically significant, recovery of alder pollen, Garry oak pollen, and assemblage diversity to approximately pre-fire (BCP1) levels. Bracken fern, however, remains high in BCP3. Overall, BCP3 exhibits characteristics of both short-term post-fire impacts (i.e., declining western hemlock pollen, elevated bracken fern spore counts) and longer-term post-fire recovery (i.e., the recovery of alder and Garry oak pollen to pre-fire concentrations). It is therefore difficult to discern from the pollen data whether the second charcoal peak represents a second independent fire. If a second fire did occur, its effects on the pollen assemblage were subtle. In summary, the BEGC sediment sequence exhibits different characteristics than either the BEGA or BEGB sequences. Notably, there is no strong mineral erosion signal associated with the inferred BEGC fire event(s). A charcoal peak and associated MS peak provide evidence of a likely catchment fire around 2420 cal yr BP and other proxies show notable changes around this time, but the nature of these changes does not indicate a large influx of terrestrial mineral or organic material to Begbie Lake. Although this was likely a catchment fire, the relatively stable sedimentary δ15N values in the BEGC profile indicate that it was probably not severe. Furthermore, the presence of a second charcoal peak approximately 25 years after the first, without a corresponding MS peak, proves challenging to interpret due to seemingly mixed signals among proxies. This second charcoal peak may have arisen from re-mobilization of charcoal from the initial fire event, or it may represent a separate fire. Both interpretations are supported by the data, but 155 overall, the two-fire interpretation is perhaps a better fit for the available evidence. The multi-proxy data seem to suggest that the second charcoal peak may represent a separate, low-severity fire event, possibly occurring outside the Begbie Lake catchment. This interpretation is supported by the pollen and spore assemblage in BCP3, which reflects a mixture of longer-term post-fire ecosystem recovery (presumably associated with the first fire event) and shorter-term post-fire impacts (presumably associated with a second fire event). If this was indeed the case, then the occurrence of two local, low-severity fires within about 25 years of one another could be indicative of potential Indigenous influence on the landscape. Whether the BEGC sequence represents one or two separate fires, the main mode of variation among proxies seems to be associated with autochthonous organic material, rather than erosion-derived inorganic material. This is evidenced by strong links between sediment organic matter provenance, diatom assemblage composition, and sedimentary element concentrations, and weak associations between MS and other proxies. The changes in the diatom community seem to precede the changes in sedimentary element concentrations and may have been a consequence of changes in light availability, water temperature, or lake mixing regime resulting from combustion of shading and wind-occluding riparian vegetation. Overall, the evidence indicates that despite an apparent lack of substantial erosional input following the BEGC fire or fires, there was nonetheless a discernable impact on both the water column and sediments of Begbie Lake. This occurred under a relatively cool and moist climatic regime with a denser flora and better-developed soils than the BEGA or BEGB sequences, indicating that Begbie Lake is likely sensitive to fire even under modern vegetation and climate. However, the fire effects recorded in the BEGC sequence generally appear to be of lower magnitude, or have different characteristics, than those earlier fires occurring on more open landscapes with less-developed soils. 156 4.1.4 Sooke Lake Reservoir North Sediment Sequence (SLRN) The SLRN sediment sequence was recovered from the deepest part of the north basin of SLR (74 m water depth; Fig. 1), approximately 2 km south of Begbie Lake. The SLRN sequence was deposited under modern climate and vegetation conditions comparable to those of the BEGC sequence, but under different limnological circumstances. Sooke Lake is much larger and deeper than Begbie Lake, consequently holding a much greater volume, and its watershed, which encompasses Begbie Lake, is many times larger. Nevertheless, both lakes share a common environmental history. In the SLRN sediment sequence, the charcoal concentration maximum at 22.25 cm depth (ca. 1820 CE) exceeds background levels by a factor of about two (Fig. 49). MS reaches its maximum value one sample (0.5 cm; ca. 6 years) earlier, although it remains high in the charcoal peak sample too. Together, these proxies indicate a likely wildfire in the Sooke Lake catchment around 140–150 cal yr BP (ca. 1800–1810 CE), considering the probable lag between fire and sedimentary charcoal peak formation. This potentially aligns with several documented prominent fire years in the region, including 1790 CE, in which wildfires are recorded to have occurred in several parts of the Sooke Lake watershed (Barraclough, 1995). Other known regionally significant fire years include 1800 CE, 1803 CE, and 1810 CE (Barraclough, 1995). The timing of this inferred fire is corroborated by sedimentary TC and TN values which decline markedly in the charcoal peak sample and remain low for the next two samples (1 cm; ca. 12 years; Fig. 49), indicating a period of relatively reduced organic matter input to the sediments. Sediment C:N values (12–14) are characteristic of organic matter derived from mixed land plant and lacustrine algal sources (Meyers, 1997), with a bias towards algal sources. Sedimentary C:N values increase gradually from the time of the inferred fire, and may indicate a slight post-fire increase in the relative contribution of land-plant derived organic matter to the sediments. Sediment δ13C also increases gradually up the sedimentary profile (Fig. 49), but the total change is slight, and no meaningful variation occurs at the inferred fire horizon, suggesting that there was no fire-induced 157 shift in terrestrial organic matter sources. In contrast, sediment δ15N exhibits a systematic increase concurrent with the MS maximum at 22.75 cm and remains elevated to the end of the profile (2.5 cm; ca. 30 years). This increase in sedimentary δ15N may reflect a severe catchment fire (Dunnette et al., 2014), but individual samples before the inferred fire horizon also show δ15N fluctuations of similar magnitude. This variation suggests that non-fire ecosystem processes could produce a similar signal. Stratigraphically constrained cluster analysis of the sedimentary inorganic elements data from the SLRN sequence yielded two reliable chemo-stratigraphic zones (Fig. 50). The zone boundary between SLRN Elemental Zone 1 (SNE1) and SLRN Elemental Zone 2 (SNE2) occurs at 22.5 cm (ca. 1818 CE), immediately after the MS maximum and immediately before the charcoal maximum (Fig. 51). This change strongly suggests that SNE1 is the zone that records pre-fire conditions, and SNE2 is the zone that captures post-fire effects. Sedimentary concentrations of numerous inorganic elements show statistically significant increases between these two zones, including Al, Ba, Be, Cu, Hg, K, Mg, Pb, and V (Fig. 53). These elements exhibit moderate-to-strong positive correlations with MS (Table 17), except for Hg, which is only weakly positively correlated, and all show negative correlations with TC and TN. Elevated levels of all these elements have been observed in runoff and downstream sediments following wildfires (Gallaher & Koch, 2004; Pelletier et al., 2020; Rust et al., 2018), and K and Mg are major constituents of wildfire ash (Bodí et al., 2014). Considered together, these factors suggest that enhanced post-fire erosion of soil and ash was likely responsible for the increased influx of these elements to the Sooke Lake sediments in the SLRN sequence. This interpretation is corroborated by the concurrent increase in terrestrially-sourced organic matter inferred from increasing sedimentary C:N values. A small but statistically significant decrease in median mineral grain size between SNE1 and SNE2 also likely reflects terrestrial disturbance, suggesting a subtle fire-induced fining of inorganic sedimentary inputs. Sedimentary concentrations of these elements remain elevated through the end of the sequence (ca. 30 years), suggesting that 158 post-fire ecosystem and water quality recovery can take decades even in a water body as large as Sooke Lake. Iron decreases significantly from SNE1 to SNE2 and is moderately negatively correlated with MS (r = -0.55) and positively correlated with TC (r = 0.32) and TN (r = 0.63), but changes in sedimentary Fe concentrations are difficult to interpret due to potential mobility within the sedimentary profile (Ding et al., 2016). Many other elements show no significant change between SNE1 and SNE2, even those that exhibit moderate-to-strong positive correlations with TC or TN, such as Co, Mn, P, S, and Se (Table 17). It seems that the primary mode of variation in the SLRN elemental data is associated with erosion-derived mineral input. The fossil diatom assemblage in the SLRN sediment sequence is diverse and dominated by planktonic taxa, as expected of a large, deep lake (Table 18). Although broken-stick analysis suggests that the SLRN diatom dataset does not contain any reliable bio-stratigraphic zones (Fig. 54), cluster analysis delineates three assemblage zones which correspond reasonably well to inferred fire-related variations in other proxies (Fig. 55). The transition from SLRN Diatom Zone 1 (SND1) to SLRN Diatom Zone 2 (SND2) occurs at 23.5 cm (ca. 1806 CE), which is one sample (ca. 6 years) before the MS maximum and two samples (ca. 12 years) before the charcoal maximum. This relatively close timing suggests that the change in the diatom assemblage implied by the stratigraphic zone boundary may have been a consequence of the inferred fire. The SND1–SND2 boundary also corresponds closely with the chemo-stratigraphic zone transition (SNE1–SNE2) at 22.5 cm (ca. 1818 CE), further linking probable fire effects to measurable changes in the Sooke Lake water column. Surprisingly, the planktonic:benthic diatom ratio shows a sharp decline in the sample preceding the SND1–SND2 zone boundary, which is typically interpreted as an indicator of decreasing turbidity (Brown et al., 2020). This inference runs contrary to the geochemical data, which indicate increased erosion-derived mineral input, and therefore probably increased turbidity, at this sediment horizon. The reasons for this apparent contradiction between the proxies are unclear. However, the planktonic:benthic diatom 159 ratio is potentially sensitive to other limnological variables, including water level, light availability, and water temperature, and the changes observed in the BEGC diatom assemblage may reflect changes in these variables, rather than turbidity. Whatever the case, SND2 comprises three samples (ca. 20 years) in total. The transition from SND2 to SLRN Diatom Zone 3 (SND3) occurs at 22.0 cm (ca. 1824 CE), immediately after the charcoal maximum. This transition exhibits few statistically significant changes among individual taxa in the assemblage, nor do diatom community diversity or the planktonic:benthic ratio change meaningfully. Nevertheless, the close temporal alignment of this bio-stratigraphic zone transition with the charcoal peak is noteworthy. The timing of the SND2–SND3 transition relative to the charcoal peak suggests that SND3 reflects changes in the diatom community immediately following the fire. However, SND2 also appears to show evidence of fire impacts, suggesting that SND3 may instead reflect longer-term (ca. 20-year) post-fire effects on the diatom assemblage, or perhaps even post-fire recovery. The distinction depends on the timing of charcoal deposition relative to the deposition of frustules from diatoms affected by the fire. Diatoms respond rapidly to environmental changes (Julius & Theriot, 2010), and if their frustules sink rapidly and are quickly incorporated into the sediments, then changes in the fossil diatom assemblage may precede changes in sedimentary charcoal concentration. The fact that the MS peak in the SLRN sequence precedes the charcoal peak suggests that there may have been a significant lag between the fire event and the formation of a charcoal peak in the sediments. The large size of Sooke Lake and its watershed supports this interpretation, as charcoal would likely take longer to migrate into the lake and be focused into the sediments than it would in a smaller lake such as Begbie Lake. Accordingly, SND3 probably represents the ongoing, long-term influence of post-fire effects on the diatom community in Sooke Lake, rather than changes in the diatom community immediately following the fire. Overall, the SLRN diatom assemblage reveals subtle but detectable changes in the diatom community of Sooke Lake. Although challenging to interpret, the timing of these changes relative to 160 key changes in other fire-related proxies strongly suggests that they are associated with the inferred fire and its impacts. This provides a strong indication that wildfire can and has affected the water column and aquatic ecosystem of Sooke Lake under modern climatic and vegetation conditions. The SLRN pollen and spore assemblage is characteristic of the modern watershed vegetation and similar to that of the BEGC assemblage (Table 19). It is dominated by Douglas fir, Pinus contorta (likely shore pine), western redcedar, western hemlock, and red alder, with true fir, western white pine (Pinus monticola), and spruce as minor constituents. Non-arboreal pollen and spores comprise 5–10% of the assemblage and are primarily represented by bracken fern and undifferentiated monolete fern spores. Broken-stick analysis suggests that the SLRN pollen dataset contains no significant bio-stratigraphic zones (Fig. 58), but the CONISS zonation nevertheless produces three zones, as with the diatoms, that show close temporal alignment with key changes in other proxies (Fig. 59). The SLRN Pollen Zone 1 (SNP1) to SLRN Pollen Zone 2 (SNP2) transition occurs at 23.0 cm (ca. 1812 CE), positioning it immediately before the MS peak and between the first diatom-derived zone boundary (SND1–SND2; 23.5 cm; ca. 1806 CE) and the inorganic elements-derived zone boundary (SNE1–SNE2; 22.5 cm; ca. 1818 CE). This close temporal alignment with changes in other fire-related proxies strongly suggests that the changes between SNP1 and SNP2 are attributable to wildfire effects. However, no individual taxa show significant changes across the SNP1–SNP2 zone boundary, perhaps reflecting the large pollen and spore source area for Sooke Lake relative to the size of the inferred fire. The low number of samples in each zone likely also plays a role in preventing statistically significant differences from emerging in the analysis. Nevertheless, shore pine shows an apparent decrease from SNP1 to SNP2, and grass pollen is present only in SNP2, suggesting a more open environment after the inferred fire. SNP2 spans three samples (ca. 20 years), and the transition from SNP2 to SLRN Pollen Zone 3 (SNP3) occurs at 21.5 cm, one sample after the SND2–SND3 diatom zone transition. SNP3 is characterized by a return of P. contorta pollen to pre-161 fire levels, the disappearance of grass from the record, and an overall decline in non-arboreal pollen, all potential indicators of stand development and terrestrial ecosystem recovery. In summary, the SLRN sediment sequence exhibits inferred fire effects that are consistent with those observed in earlier sediment sequences, but generally in a more subtle way. A charcoal peak with an associated MS maximum provides reliable evidence of a catchment fire around 1800–1810 CE. This inference is corroborated by concurrent declines in sedimentary TC and TN, and a subtle but detectable δ15N signature that suggests the fire may have been severe. Furthermore, historical records document several regionally significant fire years around this time, including fires in several parts of the Sooke Lake watershed in 1790 CE. Significant changes in the sedimentary concentrations of various fire- and erosion-associated elements align closely with the charcoal and MS maximums, strongly implicating enhanced post-fire erosion. Changes in the fossil diatom assemblage occur around the same time, and although their specific nature is difficult to interpret, the close temporal alignment of these changes with other key fire indicators suggests that that the water column in Sooke Lake was affected by the fire, whether by physical or chemical means. Changes in the pollen and spore assemblage are subtle but likewise align closely with key changes in the other fire-related proxies and suggest a more open environment in the years after the inferred fire, followed later by stand development and canopy closure as terrestrial ecosystem recovery progressed. Taken together, these coincident changes among multiple physical, chemical, and biological fire-related proxies provide strong evidence of a significant wildfire event in the Sooke Watershed around 1800-1810 CE that had detectable, wide-reaching impacts on the terrestrial environment, the water column, and the sediments of Sooke Lake, even at such large catchment and basin scales. These post-fire effects occurred in Sooke Lake itself under modern climate and vegetation conditions and appear to have persisted for two to three decades, and therefore have significant implications for modern water supply management. 162 4.1.5 Sooke Lake Reservoir South Sediment Sequence (SLRS) The SLRS sediment sequence was recovered from the deepest part of the south basin of SLR (27 m water depth; Fig. 1A), approximately 7 km south of Begbie Lake and 5 km south of the SLRN coring location. This coring site was selected to facilitate analysis of wildfire effects near the CRD water supply intake, which is situated at the southern end of the SLR south basin (Fig. 1A). The SLRS sediment sequence was likely deposited in the mid-19th century CE, under modern climate and vegetation conditions comparable to those of the SLRN sequence. By this time, however, European settlers had begun exploiting natural resources in the Sooke watershed and the adjacent Leech watershed (Barraclough, 1995). The discovery of gold in the Leech River in 1864 CE spurred a gold rush that lasted until 1866 CE, which may have produced significant landscape disturbance in both the Leech and Sooke watersheds. The SLRS charcoal sequence varies considerably and does not exhibit a distinct charcoal peak (Fig. 62). Instead, the section from 25.5–20.5 cm (ca. 1820–1870 CE) exhibits a series of elevated peak charcoal concentration values which are approximately double the magnitude of those in the preceding and following sections. Summed charcoal area shows a similar trend. When the charcoal count data are smoothed using a simple LOWESS algorithm, a clearer charcoal signal becomes evident (Fig. 62). Smoothed charcoal concentration first deviates from presumed background levels around 26 cm (ca. 1817 CE) and returns to background levels by 18 cm (ca. 1890 CE), exhibiting a lengthy region of generally elevated charcoal centred on approximately 22.5 cm (ca. 1850 CE). This long section of elevated charcoal values is atypical of a single catchment fire, but may represent a series of smaller fires, or some other kind of watershed disturbance that remobilized older charcoal. On its own, the charcoal data are insufficient to infer a distinct fire event, but other proxies provide additional information to help contextualize the charcoal signal. The choice of sediment section to analyze was informed by the prominent MS peak at 24.25 cm (ca. 1833 CE; Fig. 62), suggestive of an episode of enhanced soil erosion which may have been 163 related to wildfire activity or could have been the result of some other disturbance in the watershed. The 210Pb age-depth model developed for the SLR south basin (Fig. 2C) indicates that this MS peak likely dates to the early 1830s CE, but model uncertainty is substantial at this depth (95% CI = 90 years) due to unsupported 210Pb activity approaching background levels. Taken at face value however, this age estimate suggests that the MS peak likely predates landscape disturbance by European settlers in the watershed, who did not establish a permanent settlement in the region until 1843 CE (Barraclough, 1995). Significantly, the MS peak coincides with the section of generally elevated charcoal values, occurring two samples (0.5 cm; ca. 5 years) after maximum LOWESS-smoothed charcoal concentration values, which suggests that the MS peak may indeed have been the product of enhanced post-fire erosion. Sediment TC and TN in the SLRS sequence (Fig. 62) are strongly positively correlated with one another, but only weakly negatively correlated with MS (Table 21). Nevertheless, TC and TN show sharp declines starting at 24.75 cm (ca. 1828 CE), coincident with maximum LOWESS-smoothed charcoal concentration and two samples (ca. 5 years) before the MS maximum. TC and TN values remain depressed throughout the entire section of elevated charcoal relative to the previous section (ca. 75 years), except for a brief TN peak at 23.5 cm (ca. 1840 CE). Sediment C:N values (13–15) are similar to those of the SLRN sediment sequence, and likewise reflect organic matter derived from a mix of both land plants and lacustrine algae (Meyers, 1997). The C:N profile is generally stable (Fig. 62) with only two notable features: a valley at 23.5 cm (ca. 1840 CE) resulting from a single-sample TN peak, and a peak at 19 cm (ca. 1885 CE) resulting from a single-sample TC peak. The C:N minimum at 23.5 cm does not align with any noteworthy charcoal profile features, but does fall within the region of generally elevated charcoal counts. It also falls within the region of elevated MS values, three samples (0.75 cm; ca. 7 years) after the MS maximum. This C:N minimum may therefore reflect a brief fire-induced shift towards relatively more algal organic matter influx to the sediments, perhaps due to terrestrial organic matter 164 losses from combustion or nutrient enrichment leading to increased algal productivity. The C:N peak at 19 cm occurs near the end of the section of elevated charcoal and is situated one sample (0.25 cm; ca. 2 years) after prominent peaks in charcoal summed area and charcoal particle size. This peak may therefore reflect terrestrial carbon contributed by the charcoal. Sediment δ13C values in the SLRS profile are variable (Fig. 62), but do not exhibit close associations with other fire-related proxies, indicating that the character of terrestrial carbon input likely did not change significantly in response to the inferred fire(s). Sediment δ15N values (Fig. 62) decline noticeably at 22.75 cm (ca. 1845 CE) and remain low for the remainder of the profile. This decline occurs within the section of elevated MS and two samples after the C:N minimum. The lack of a distinct δ15N peak indicates that the inferred fire event was likely not severe, but the decline may be reflective of increasing biotic N retention as the terrestrial ecosystem recovered following fire (Dunnette et al., 2014). Stratigraphically constrained cluster analysis of the sedimentary inorganic elements data from the SLRS sequence yielded four significant chemo-stratigraphic zones (Fig. 63) which show close temporal alignment with key changes in other fire-related proxies (Fig. 64). The boundary between SLRS Elemental Zone 1 (SSE1) and SLRS Elemental Zone 2 (SSE2) occurs at 23.6 cm (ca. 1840 CE), immediately before the C:N minimum and corresponding TN peak, and three samples (0.65 cm; ca. 6 years) after the MS maximum. Significantly, SSE2 comprises a single sample, and although this precludes conventional hypothesis testing of differences between zones, many elements nevertheless show a readily apparent peak in this sample only, before declining to SSE1-equivalent levels in SLRS Elemental Zone 3 (SSE3). These elements include Ag, Co, Cu, Mn, Mo, Ni, P, Se, V, and Zn (Fig. 64). None of these elements are major wildfire ash constituents, and none of them correspond strongly to MS (Table 21); only Mg shows a noteworthy positive correlation with MS (r = 0.51), while Ba, Fe, Mo, S, and Se are moderately-to-strongly positively correlated (r > 0.5) with TC and TN. The lack of strong correlations between sediment element concentrations and MS suggests that 165 the distinct changes observed in SSE2 cannot be attributed to terrestrial ash or soil erosion. However, SSE2 corresponds closely to changes in the diatom assemblage (discussed below), and the concurrent C:N minimum indicates a relative shift towards autochthonous organic input to the sediments, suggesting that changes in the character or magnitude of algal productivity in Sooke Lake may be responsible for the changing element concentrations in SSE2. The transition from SSE3 to SLRS Elemental Zone 4 (SSE4) occurs at 20.9 cm (ca. 1865 CE), towards the end of the section of elevated charcoal and one sample (0.4 cm; ca. 5 years) before a prominent charcoal concentration peak at 20.5 cm (Fig. 64). No other proxies show notable changes at this horizon. The SSE3–SSE4 transition is generally characterized by further declines of many elements, some statistically significant, including Ag, Co, Cr, Mg, Ni, and Zn (Fig. 66). There is substantial overlap between the group of elements that decline significantly from SSE3 to SSE4, and the elements that peak in SSE2, suggesting a common influence on their abundance. Like SSE2, the SSE3–SSE4 transition aligns closely with changes in the diatom assemblage, again indicating links between conditions in the water column, algal ecology, and element concentrations in the sediments. Broken-stick analysis suggests that the SLRS diatom dataset contains no significant stratigraphic zones (Fig. 67), but stratigraphically constrained cluster analysis nevertheless reveals zone boundaries that correspond closely with the chemo-stratigraphic zones derived from the inorganic elements data (Fig. 68). The diatom assemblage is dominated by benthic taxa (Table 22), in contrast to the SLRN assemblage. This is likely due to the south basin of Sooke Lake being smaller and shallower than the north basin, especially before water levels were raised (Fig. 1A). The SLRS Diatom Zone 1 (SSD1) to SLRS Diatom Zone 2 (SSD2) transition occurs at 23.6 cm (ca. 1840 CE), the same depth as the first element zone transition (SSE1–SSE2), indicating an association between changing diatom community composition and changing sedimentary element concentrations. This bio-stratigraphic boundary also corresponds with a brief but noteworthy drop in sediment C:N, which indicates a shift towards organic matter derived from freshwater algae relative to that derived from 166 land plants, suggesting that diatom productivity may have increased at this time. The SSD1-SSD2 boundary is characterized by statistically significant changes in a handful of diatom taxa (Fig. 70), but neither diatom community diversity nor the planktonic:benthic ratio change significantly between these zones. The same is true of the SSD2 to SLRS Diatom Zone 3 (SSD3) boundary, which occurs at 21.1 cm (ca. 1863 CE). However, like the SSD1–SSD2 transition, the SSD2–SSD3 boundary exhibits a close association with the sedimentary element concentration data, occurring one sample (ca. 2 years) before the SSE3–SSE4 transition. Overall, although the diatom data on their own do not reveal the specific nature of the inferred disturbance, the remarkable alignment of the diatom-derived stratigraphic zones with the element-derived zones indicates a close link between these two proxies, and suggests that both may have been influenced by a common driver, perhaps wildfire. In summary, the SLRS sediment sequence contains ample evidence of watershed disturbance that influenced the water column and sediments of the Sooke Lake south basin in the mid-19th century CE. The lack of a single distinct charcoal peak makes it difficult to definitively interpret this disturbance as a wildfire event, but the reasonably close temporal alignment of various physical, chemical, and biological indicators of fire nonetheless provides compelling evidence of the effects of a catchment fire or fires from the 1820s through the 1870s CE. Historical documents record regionally significant fire years in 1818 CE and 1840 CE, and in 1860 CE fires burned in several parts of the Sooke watershed (Barraclough, 1995). The SLRS sediment sequence could reflect any or all of these fire episodes, due to the large uncertainty in the age-depth model. However, other kinds of watershed disturbance besides fire could leave similar signatures in the sediments, potentially including placer mining, which is known to have occurred in the adjacent Leech River watershed in the 1860s CE and cannot be ruled out in the Sooke Watershed. Another potential source of the signals in the SLRS sequence is sediment slumping. The SLRS core was recovered from the deepest part of the SLR south basin, which is a steep-sided depression that may be prone to sediment slumping (Fig. 1). The fact that anomalously old 14C dates were obtained from two microfossils in the SLRS 167 sequence (Section 2.2; Table 2) suggests that the SLRS chronology may have been disturbed. Moreover, southern Vancouver Island is a tectonically active region prone to frequent mild earthquakes and infrequent severe earthquakes (Clague et al., 2000), which could trigger sediment flows on steep lakebed slopes. Regardless of whether the SLRS sediment sequence reflects wildfire effects or some other kind of watershed disturbance, it records marked and detectable impacts on biological and chemical proxies reflecting conditions in both the water column and the sediments of Sooke Lake, under modern climate and vegetation regimes. 4.2 Comparisons Among Inferred Fires and Post-Fire Responses The sediment sequences and the inferred fire effects recorded therein exhibit common characteristics, but also key differences. Careful consideration of how, when, and why these ecosystem responses to wildfire differ reveals insights about the influence of climate, soils, vegetation, limnological variables, and potential anthropogenic effects on the character of wildfires and post-fire impacts in the Sooke watershed. These insights are critical for guiding the development of evidence-driven watershed management recommendations in the context of modern water supply. 4.2.1 Charcoal and MS By design, all five analyzed sediment sequences contain sections of increased charcoal concentration, because a distinct charcoal peak was one of the selection criteria for identifying likely fire horizons for detailed study. However, analysis at sub-decadal temporal resolution revealed that the BEGB and BEGC profiles each contain two distinct charcoal peaks, despite presenting as a single peak in the initial lower-resolution analysis. In both cases, it is difficult to discern whether the second peak represents a separate fire, or remobilization of charcoal from the first fire. Whatever the case, the high-resolution analysis suggests that post-fire charcoal deposition patterns may often be more complex than what is apparent from lower-resolution analysis. 168 The SLRS charcoal profile is the only one to potentially coincide with European resource exploitation in the Sooke watershed. It differs from the others in that it contains a long section of elevated charcoal, rather than a distinct peak. This may represent a single fire and subsequent charcoal remobilization, or a series of fires, or could reflect other watershed disturbances, potentially anthropogenic. Alternatively, its unique character may arise from the different depositional environment of Sooke Lake’s south basin, which contains a steep-sided depression potentially prone to sediment slumping. All five analyzed sediment sequences contain MS peaks, again by intent. Like charcoal, a distinct MS peak was one of the selection criteria for identifying likely fire horizons. There are, however, noteworthy differences in the timing of the MS peak relative to the initial charcoal peak. In the BEGA, BEGB, and SLRS sequences, the MS maximum occurs well after the initial charcoal peak. In contrast, in the BEGC and SLRN sequences, the MS maximum slightly precedes the first charcoal peak. The timing of the MS maximum relative to the initial charcoal peak may reflect the primary delivery pathway of the charcoal. Charcoal may be delivered to the lake either atmospherically at the time of the fire or by later water transport, whereas a MS peak in the sediments is only likely to be formed via water erosion of terrestrial mineral material. For example, in the cases where the initial charcoal concentration peak precedes the MS peak (i.e., BEGA, BEGB, and perhaps SLRS), the initial pulse of charcoal may have been delivered to the lake primarily via atmospheric deposition. This is well-demonstrated by the BEGA charcoal and MS data (Fig. 3). In the cases where the MS peak coincides with or slightly precedes the initial charcoal concentration peak (i.e., BEGC and SLRN), the charcoal may have been primarily delivered to the lake via water erosion sometime after the fire. This kind of inference about the primary charcoal delivery pathway may, in turn, indicate something about fire proximity to the lake, or perhaps about the volume and relative timing of post-169 fire precipitation. Further study is warranted to verify and tease apart the relative influence of these various factors. Furthermore, the apparent length of time represented by the MS peak varies by fire. After the BEGA, BEGB, and BEGC fires, MS values remain substantially elevated above pre-fire values for well over a century. However, in the case of the SLRN and SLRS fires, MS values only appear to remain elevated for approximately 30 years. This discrepancy between the duration of MS signals in the Begbie Lake and Sooke Lake sediments may be the consequence of dilution in the much larger Sooke Lake water body and in-basin transport and re-suspension of mineral matter, or possibly the result of different transport distances arising from Sooke Lake’s much larger watershed. The difference could be otherwise linked to landscape factors such as faster vegetation recovery following the modern fires. 4.2.2 Sediment C, N, δ13C, and δ15N Sediment TC and TN show consistent responses across all inferred fire events. In every sediment sequence analyzed, TC and TN decline at or around the inferred fire horizon, presumably reflecting the combustion of terrestrial organic material and a relative increase in mineral material influx to the sediments resulting from enhanced erosion. The duration of these effects, however, varies by fire. In the BEGA, BEGB, and BEGC sediment sequences, TC and TN appear to remain depressed for a century or more following fire. The SLRS sediment sequence also exhibits long TC and TN recovery times, failing to return to pre-disturbance levels by the end of the profile (ca. 75 years). In the SLRN sequence however, the recovery time appears to be much shorter, about 25 years. These differences in post-fire C and N dynamics among fire events cannot be readily explained by lake, landscape, or climatic factors. The shorter TC and TN recovery period of the SLRN fire aligns well with results from Dunnette et al. (2014) and Pompeani et al. (2020), whereas the longer recovery times of the other sediment sequences are not reported elsewhere in the literature. It is also important to note that even the direction of the response can vary. Dunnette et al. (2014) found similar post-fire 170 declines in sedimentary TC and TN to those in this study. Pompeani et al. (2020), however, found the opposite pattern, with sedimentary TC and TN increasing, rather than decreasing, after fires. Sediment C:N ratio shows variable response across the fires too. In the BEGA, BEGB, and SLRN sediment sequences, C:N increases following fire, whereas in the BEGC and SLRS sediment sequences, C:N decreases. Sediment C:N ratio provides information about sediment organic matter provenance, with lower values (>10) indicating lacustrine algal sources, and higher values (<20) indicating land plant sources (Meyers, 1997). The sedimentary C:N increases following the BEGA, BEGB, and SLRN fire events indicate a relative post-fire shift towards terrestrially-sourced organic material. Accordingly, these fires are also the ones that show the strongest erosional response in the inorganic elements data, with many elements associated with post-fire erosion increasing in concentration following the fire (Fig. 7; Fig. 22; Fig. 53), and many being strongly positively correlated with MS (Table 5; Table 9; Table 17), a proxy for soil erosion. Conversely, the BEGC and SLRS sediment profiles show post-fire decreases in C:N, indicating a shift towards freshwater algal organic material sources, an interpretation corroborated by correspondingly weak terrestrial erosional signals and little evidence of mineral input. In most cases, post-fire C:N effects appear to be short-lived relative to observed changes in other proxies, lasting for about 5–10 years. The exception is the SLRN profile, in which C:N does not fully recover to pre-fire levels by the end of the sequence (ca. 20 years). In most of the analyzed sediment profiles, δ13C shows no significant change. The one exception is the BEGA sequence, where δ13C increases markedly post-fire, possibly indicating a slight relative shift towards sediment organic matter derived from C4 land plants (Meyers, 1997). Sediment δ15N also increases sharply after the BEGA fire and shows subtle but noteworthy increases around the BEGB and SLRN fire horizons. Dunnette et al. (2014) observed that high-severity catchment fires are typically followed by increases in sedimentary δ15N and decreases in C:N, which suggests that the BEGA, BEGB, and SLRN fires may have been more severe than the BEGC and 171 SLRS fires. The BEGA, BEGB, and SLRN fires each occurred under different climates and vegetation types, indicating that high-severity fires can occur in the Sooke Watershed under a wide range of environmental conditions. The SLRN fire is especially noteworthy in that it occurred under modern environmental conditions and its effects are recorded in the sediments of Sooke Lake itself, which demonstrates that despite its large volume, the modern reservoir is likely susceptibly to post-fire impacts. 4.2.3 Inorganic Elements In every analyzed sediment sequence, inorganic element concentrations exhibit changes that are closely temporally aligned with other key indicators of fire. Some elements show relatively consistent responses between fires, whereas others vary widely. Mg, a major constituent of wildfire ash (Bodí et al., 2014), shows statistically significant increases after four of five fires, and an apparent increase after the fifth. Ba likewise increases significantly after four of five fires. Al, Ca, Mn, and Pb increase following three of five fires. Across all fires, Al, Ba, Ca, Cr, Mg, Mn, Ni, P, Pb, and V either increase or show no change, whereas As, B, and Mo either decrease or show no change. Ag, Be, Cd, Cu, Fe, Co, Hg, K, Na, S, Se, and Zn exhibit varying responses, increasing after some fires, and decreasing after others. Overall, post-fire changes in sediment inorganic element concentrations can be grouped into two main categories. In the first category, the sedimentary concentrations of many inorganic elements are positively correlated with MS, and many of the elements that show significant increases are constituents of soils and wildfire ash (e.g., Al, Cr, Mg), suggesting that increased post-fire erosion was likely the primary pathway by which these elements were delivered to the sediments. The BEGA, BEGB, and SLRN fire events fall into this category, and therefore may represent more severe fires overall, an interpretation supported by the δ15N data. In the second category, the concentrations of most inorganic elements are only weakly correlated with MS, and more strongly correlated with sediment TC, suggesting that organic matter transport and deposition may have been the primary 172 pathway by which these elements entered the sediments. The BEGC and SLRS fire events fall into this category. Recovery times to pre-fire values also vary substantially among fires and elements. In some cases, such as the SLRS sequence, sediment concentrations of most elements appear to recover to pre-disturbance levels after a few years. In other cases, such as the BEGA and BEGB fire events, recovery appears to take many decades, and indeed some elements do not achieve pre-disturbance levels by the end of the sequence. In general, post-fire sedimentary inorganic element recovery times seem to have been longer in Begbie Lake, and shorter in the Sooke Lake. However, several sediment sequences (i.e., BEGB, BEGC, SLRN) appear to truncate before post-fire sedimentary element concentrations reached an equilibrium state, so a rigorous comparison of recovery times is not possible. 4.2.4 Diatoms Fossil diatom assemblages vary significantly among sediment sequences, especially between those from Begbie Lake and those from Sooke Lake, likely reflecting the great difference in size and volume. Nevertheless, in each analyzed sequence, the diatom community exhibits changes that are closely temporally aligned with key changes in other fire-related proxies. The nature of these changes varies by fire. Both the BEGA and BEGB sequences record significant changes in diatom community diversity associated with fire disturbance. In the BEGA, BEGB, and BEGC diatom records, increases in the planktonic:benthic ratio coincide with other fire indicators and suggest fire-associated increases in turbidity (Brown et al., 2020). Sooke Lake fossil diatom assemblages exhibit no fire-associated increases in diatom-inferred turbidity, which may be due to its large volume compared to Begbie Lake. Like many of the other fire-related proxies analyzed, changes in the fossil diatom assemblage appear to persist for decades. Furthermore, diatom-inferred total phosphorus (TP) reconstructions were computed for each sediment sequence based on a 251-lake British Columbia calibration dataset assembled by Cumming 173 et al. (2015). Several model variants were explored, but the full results are not presented in this thesis due to concerns over divergent results among models and the applicability of the training set to the Begbie Lake and Sooke Lake fossil diatom datasets. Nevertheless, the preliminary results did not suggest any significant changes in TP associated with fires. These results may be refined and published in a future manuscript. Overall, the diatom data are varied and complex, and further analysis is required to extract additional information about potential fire-associated changes in lake nutrient levels and productivity. 4.2.5 Pollen Fossil pollen and spore assemblages likewise vary significantly among the analyzed sediment sequences, reflecting changing climate and fire regimes, and the gradual post-glacial revegetation and development of the landscape. Of all the proxies analyzed, pollen and spores exhibit perhaps the most statistically subtle fire responses. Although the specific changes vary by fire, each sequence nevertheless seems to show some variations in assemblage that are consistent with fire disturbance, and that are temporally aligned with changes in other key fire indicators. Generally, these post-fire variations suggest an opening of the canopy, followed by indicators of gradual stand development and terrestrial ecosystem recovery. 4.2.6 Consistent Indicators of Post-Fire Impacts The fire events examined here exhibit a broad range of impacts and ecosystem responses. The nature of wildfire in the Sooke watershed is evidently complex, varying in severity, character, and duration of post-fire impacts on terrestrial and aquatic environments. Nevertheless, across all the sediment sequences and proxies examined, common trends emerge. All the fire events investigated show convincing evidence of fire disturbance, indicated by concurrent changes across multiple physical, chemical, and biological proxies. Increases in sediment charcoal and MS are evident in all profiles, concurrent with declines in TC and TN. Inorganic elements, diatoms, and pollen sequences 174 exhibit changes that are consistent with fire disturbance and temporally aligned with changes in charcoal and MS. The BEGA and BEGB fires generally exhibit the strongest signals across the largest range of proxies, and the longest recovery times. Nevertheless, there are also clear signs of fire disturbance in the Sooke Lake sediments, indicating that despite its size, it is sensitive to wildfire impacts. In both Begbie Lake and Sooke Lake, post-fire recovery of all proxies appears to take place on the scale of years to decades. 4.3 Challenges, Limitations, and Future Research Despite the overall success of the approach presented in this study, the investigation encountered challenges, from which several limitations became apparent. Some of these shortcomings are related to study site and sample material constraints, others are related to technical methods, and yet others are inherent to the paleoenvironmental approach. Some of these limitations and associated questions provide opportunities for future research. First, analyzing non-varved sediment at sub-decadal time scales is difficult. Beyond the practical challenge of reliably sampling sediments at millimeter-scale resolutions, many timeseries that appear relatively stable at lower sampling intervals in fact resolve to be highly variable and complex upon closer inspection. For example, several features in the charcoal time series that appeared as single, distinct peaks at 1-cm (ca. 20-year) sampling resolution turned out to be complex and multi-peaked when examined at 2-mm (ca. 4-year) resolution. This issue introduces challenges of interpretation and calls into question the consistency and reliability of the proxy archives. Various elements of proxy taphonomy, sediment diagenesis, and undisturbed continuity of the depositional record, including potential bioturbation, can impact the reliability of sedimentary sequences at such fine scales. Coring site selection no doubt strongly influences the suitability of the depositional environment for the kinds of analyses conducted in this study, and the stringent requirements of fine-resolution sampling may limit the range of localities within a basin in which these techniques can be 175 meaningfully applied. Critically, however, the identification of fire events, impacts, and recoveries in this study illustrates that these challenges can be overcome. Second, constraining sediment chronologies at fine time scales is challenging due to the relatively large (often multi-decadal) uncertainties inherent in radiometric dating, particularly 14C dating. This is especially relevant when there is reason to believe that sedimentation rates may fluctuate substantially over a short period of time because of changes to sediment supply. Such changes may occur in association with fires and across fire horizons as a consequence of fire-induced variations in erosion rates and primary productivity. This inherent uncertainty limits the ability of this study to quantify post-fire recovery timelines with precision. Future work could investigate the viability of using other proxies, such as pollen accumulation rates of well-represented taxa with long-distance dispersal not affected by the fire (e.g., Pinus), to constrain chronologies more tightly at fine time scales across fire horizons. Third, diatoms are a challenging proxy to work with, especially for the novice analyst. Diatoms are an extremely diverse group of organisms, and fossil diatom assemblages may easily contain several hundred unique taxa. The task of learning diatom frustule characteristics and identifications is accordingly labour-intensive, and while keys and resources to aid with diatom identification are gradually becoming more complete and accessible (e.g., Spaulding et al., 2021), descriptions of many taxa can still only be found in aging print sources using increasingly outdated taxonomy and nomenclature, some of which lack English translations (e.g., Krammer & Lange-Bertalot, 1986). Furthermore, because the diatom flora is so diverse, large sample counts are often required to obtain sufficient statistical power for subsequent analyses, increasing the analytical time required and limiting the practical applications of the approach. Even more challenging, many diatom taxa are poorly studied, which limits their usefulness as indicators of environmental change. This limitation can be partially overcome by employing indices and transfer functions which integrate information from numerous taxa, but this approach still relies on those taxa being present in sufficient quantities 176 in samples, and upon the availability of models. Overall, the diatom analysis in this study was extremely time-consuming, and the resulting dataset is large, complex, and challenging to interpret. Nonetheless, it provides a valuable proxy indicator for conditions in the water column, and likely contains a wealth of untapped environmental information. Future efforts may be made to further augment diatom sample sizes and analyze the data using more powerful numerical techniques. Future work may also explore the use of sedimentary pigments as a complementary or alternative proxy to fossil diatoms (e.g., Charette & Prepas, 2003). Fourth, the numerical approach employed in this study may constrain its ability to discern meaningful changes in proxy time series or distinguish subtle signals from noise. Although a variety of numerical approaches were explored and evaluated, it was ultimately decided to use relatively straightforward and conservative statistical methods, at the potential cost of statistical power. The choice was made to use traditional, univariate statistics to test for differences in proxy values among stratigraphic zones because it was easier to implement, interpret and communicate the results to stakeholders than multivariate methods such as ordinations. Stratigraphically constrained cluster analysis by means of CONISS was likewise selected for identifying stratigraphic zones because of its relative intuitiveness, ease of implementation, and proven utility. However, the technique does have limitations, including the fact that it may delineate zone boundaries that are difficult to interpret, especially with proxies that may be changing in a gradual fashion over a long period of time, as is often the case with post-fire recovery. Future work might further explore alternative techniques, such as constrained ordinations and change-point analysis, in order to corroborate or augment the findings presented here. Fifth, changes recorded by proxies in the sediments may not always reflect conditions in the overlying water column. For example, post-fire influxes of dissolved contaminants may substantially affect water quality but may leave little trace in the sediments if they are rapidly flushed from the lake, which suggests that lakes with longer water residence times may be better-suited to sediment 177 sampling and analysis. Conversely, post-fire influxes of particulate contaminants may leave a substantial signal in the sediments but may settle out of the water column quickly and so have little lasting impact on water quality. Fossil diatoms are a useful tool for inferring changes in the water column, but some nutrients may not enter the lake in bioavailable forms, and some contaminants may have little or no influence on diatom productivity or community composition. For these reasons, it is very difficult, if not impossible, to quantify concentrations of contaminants in the water column based on sedimentary data alone. Calibration studies based on modern-day species-environment relationships can help in this regard (e.g., Cumming et al., 2015), but are labour-intensive. Sixth, while the sedimentary records from both Begbie Lake and Sooke Lake did preserve records of terrestrial and aquatic fire-induced impacts, not all results obtained from Begbie Lake can be easily extended to Sooke Lake, and vice versa. Although both lakes share common climate, vegetation, and fire histories by virtue of their proximity to one another, their vastly different morphometries, catchment scales, and watershed topographies mean that extrapolating findings from one to the other has limitations. This issue is especially involved when considering the magnitude and duration of post-fire aquatic impacts, whereas records of terrestrial proxies, such as pollen, are more likely to be consistent between the lakes. Ideally, a long core spanning the entire post-glacial history of the watershed from SLR should be investigated, but the 74-m depth of SLR makes sampling these sediments technically challenging. A calibration study correlating the same fire events recorded in the sediments of both Begbie Lake and SLR over the past few centuries would be valuable for developing a better understanding of the similarities and differences in post-fire responses between the two lakes. Finally, it is important to bear in mind that the past is not a perfect analogue for the future. This is a limitation inherent to all paleoenvironmental studies. For example, although the early Holocene climate may approximate future conditions forecast for the watershed, the primary climate forcing mechanism 10000 years ago (i.e., insolation) was fundamentally different than that today (i.e., atmospheric CO2). Furthermore, human disturbance of the landscape has increased markedly since the 178 early Holocene, and especially in the last century. For example, atmospheric deposition of heavy metals throughout the industrial era may result in significantly larger fluxes of dangerous contaminants, such as lead and mercury, to SLR after a fire than those observed in pre-industrial sediments. Likewise, the introduction of invasive plant species has irreversibly changed fuel characteristics in the Sooke watershed, and the incidence of human fire ignitions is likely higher today than in the past. While past records are undoubtedly valuable for establishing baseline reference conditions and characterizing ranges of natural variability, these discrepancies and others must be carefully considered when looking to the past to inform plans for the future. Nonetheless, despite these various limitations and considerations, this study yielded broadly reliable, insightful, and useful results. Further refinements and expansions of the approach pioneered here will no doubt serve to improve the granularity, accuracy, and applicability of the approach and results. 179 5 Conclusion 5.1 Key Findings and Implications This study demonstrates that wildfires have demonstrably direct impacts on sediments in Begbie Lake and Sooke Lake, which must reflect changes in the water column. Accordingly, watershed fires pose a threat to water quality and water supply security in the CRD. This threat may increase in the future under projected climate change impacts where fire activity is likely to increase (Brown et al., 2019). Sedimentary records reveal that impactful fires have burned in the Sooke watershed under a variety of climates, including during modern conditions as well as past warm, dry environments that can serve as first-order analogues for projected future conditions. All of the past fires analyzed had measurable effects on both the terrestrial and aquatic environments of the watershed, including Begbie Lake and Sooke Lake. Concurrent changes were observed across a diverse array of physical, chemical, and biological indicators, which suggests that fire impacts were systemic and significant. Furthermore, these fire-induced changes appear to have persisted for decades, in some cases perhaps more than a century. The overall impression is that Begbie Lake and Sooke Lake are vulnerable to post-fire effects and take many years to recover to pre-fire conditions. All five analyzed sediment sequences exhibit evidence of watershed disturbance, though some more strongly than others. In the small area and volume of Begbie Lake, the BEGA fire event (ca. 12010 cal yr BP) and BEGB fire event (ca. 9860 cal yr BP) produced strong sedimentary signals indicative of severe catchment fires and significantly enhanced soil erosion. Changes in sedimentary element concentrations and the fossil diatom assemblage indicate elevated post-fire turbidity and suggest that these fires had appreciable impacts on water quality and the algal ecosystem of Begbie Lake. In contrast, the BEGC fire event (ca. 2420 cal yr BP) produced subtle sedimentary signals 180 indicative of a low-severity fire with evidence of only mild fire-induced soil erosion, although changes in the fossil diatom assemblage nonetheless indicate increased post-fire turbidity. Despite being recorded in the same lake, the character of the BEGA and BEGB fire events differs from the BEGC fire event. Pollen and spore data indicate that the former fires occurred on relatively open landscapes with less-well-developed soils, whereas the latter fire occurred on a densely-vegetated landscape with well-developed soil organics. This suggests that climate and landscape-related controls may be more influential than limnological characteristics in determining the impacts of fires on aquatic environments in the Sooke watershed. The BEGB fire is especially interesting in this context, as it occurred under warmer, drier climatic conditions than present, which may prove to be similar to future summertime watershed conditions brought about by anthropogenic climate change. If this is the case, then the strong post-fire effects associated with the BEGB fire could be indicative of the nature and magnitude of future fire impacts in the Sooke watershed. A compounding factor may be the effects of climate change on the timing and volume of post-fire precipitation. Regionally, the fire season is projected to become drier and longer, and extreme precipitation events more frequent (Brown et al., 2019; Vines et al., 2017). Under these conditions, future fire impacts may be even more severe than those observed in the sedimentary records, and watershed managers may have a shorter window of time to mitigate the impacts of summer and autumn fires before wet-season rainstorms occur. In Sooke Lake, the SLRN fire event (ca. 1800 CE) exhibits similar evidence of post-fire effects across a wide variety of proxy indicators. Although Sooke Lake is many times larger than Begbie Lake, the SLRN fire nevertheless produced sedimentary signals indicative of a severe catchment fire and increased post-fire erosion. Concurrent changes in the fossil diatom assemblage indicate that this fire influenced the water column and algal ecosystem in Sooke Lake, although there is no evidence of increased turbidity. Overall, the sedimentary signature of the SLRN fire is similar in character to that of the BEGA and BEGB fire events, but considerably more subtle. This attenuated disturbance signal 181 may be a consequence of the dense vegetation and well-developed soils of the modern watershed, as hypothesized with the BEGC fire event. It may also be the result of a dilution effect due to the much larger volume of Sooke Lake. The SLRS sediment sequence (ca. 1830 CE) also records subtle disturbance signals, although the lack of a distinct charcoal peak makes it difficult to unambiguously interpret this disturbance as wildfire. Nevertheless, the SLRS sediment sequence, like the SLRN sequence, exhibits evidence of disturbance across physical, chemical, and biological proxies, reflecting altered conditions in both the water column and the sediments of Sooke Lake, under modern climate and vegetation regimes. Therefore, even SLR is likely sensitive to post-fire impacts, and catchment fire is an issue for which management action must be taken. The nature of the fire effects recorded in the Begbie Lake and Sooke Lake sediments provides insight into the specific pathways and systems most impacted by wildfires in the Sooke watershed, and in doing so provides guidance for prioritizing mitigation activities, pre-fire interventions, and post-fire monitoring to safeguard water supplies. The sedimentary data demonstrate that increased erosion rates should be anticipated following severe catchment fires, which may adversely impact reservoir water quality by increasing turbidity and affecting algal community composition and productivity. Fluxes of various elements to the reservoir may likewise increase following fire, many of which may be toxic to humans or have the potential to negatively impact water aesthetics. For example, sedimentary Ba concentrations increased significantly after four of five fires analyzed. Consumption of Ba can be deleterious to kidney health and potentially lead to vasoconstriction, convulsions, and temporary paralysis (Health Canada, 2020; Smith et al., 2011). Health Canada mandates a maximum acceptable concentration (MAC) of 2.0 mg/L for Ba in drinking water, and while it is impossible to infer from sedimentary data alone whether the MAC was exceeded in the Sooke Lake water column, the significant increases recorded in the sediments nonetheless highlights Ba as a likely element of concern following wildfire, and a candidate for close post-fire monitoring. Other toxic elements that show significant sedimentary increases after multiple fires in the Sooke 182 watershed include Cr, Cu, Hg, Mn, and Pb. Some, such as Hg and Pb, are dangerous even in extremely small quantities (<0.005 mg/L). The data indicate that all these elements are at risk of increasing significantly in the reservoir following catchment fires, potentially exceeding MAC thresholds. The CRD water supply system currently has no capacity to treat water for these likely contaminants. Equally significant is the discovery that post-fire recovery of terrestrial and aquatic systems in the Sooke watershed takes many years. All analyzed sediment sequences indicate that significant post-fire impacts persist for decades, and in some cases, more than a century. This appears to be the case for both Begbie Lake and Sooke Lake across a range of physical, chemical, and biological proxies, under past and modern environmental conditions. However, the specific duration of post-fire effects varies substantially by proxy and by fire event. For example, in the SLRS sediment sequence, a variety of inorganic elements show substantial increases in only a single sample following the inferred fire, likely representing a post-fire response lasting <2.5 years. In contrast, in the BEGA sediment sequence, both diatoms and pollen exhibit zones of post-fire impact persisting for nearly 150 years. This significant natural variability in the duration of post-fire impacts among fire events and proxies, combined with wide confidence intervals in the age-depth models, precludes precise quantification of recovery timelines. Nevertheless, even a conservative assessment suggests that water supply managers must be prepared for adverse water quality impacts lasting for years following a severe catchment fire. This is corroborated by studies in other watersheds that have observed multi-year post-fire increases in nutrient and heavy metal fluxes in stream flows (e.g., Rust et al., 2018) and multi-decadal post-fire biogeochemical impacts recorded in lake sediments (e.g., Dunnette et al., 2014; Pompeani et al., 2020). The Goldstream Water Supply Area, which serves as the current backup water supply for the Sooke Water Supply Area, can provide at most several months of water to the CRD distribution system (CRD, 2015c). The present study indicates that this is nowhere near sufficient capacity to ensure uninterrupted water supply in the event of a significant (i.e., large and 183 severe) wildfire in the Sooke watershed. It is therefore critical to limit fire and its impacts in the watershed and develop larger water volume contingency plans. 5.2 Recommendations The findings of the present study emphasize the crucial importance of wildfire risk mitigation and preparedness in the Sooke watershed. Wildfires have had appreciable impacts on Sooke Lake in the past, and likely will again in the future. Furthermore, the effects of climate change and anthropogenic ignitions are likely to exacerbate future fire occurrence and impacts. The regional population is projected to grow by approximately 170,000 people by 2050, expanding the wildland-urban interface, elevating the risk of human ignitions, and increasing demand on the CRD water supply (CRD, 2022). Considered together, these factors demonstrate that it is more critical now than ever to take proactive measures to safeguard water supply in the CRD. With this goal in mind, several key water supply management recommendations emerge from the results presented here. First, the CRD should continue to invest in wildfire prevention and mitigation measures in the Sooke watershed. The paleoenvironmental data clearly demonstrate that the threat posed by wildfire to water supply is credible and must be taken seriously. To that end, the CRD is already engaged in fuel management activities including the creation of fire breaks to control fire spread and fuel reduction activities, such as ladder fuel removal, around key facilities (Brown et al., 2019). The present study affirms the value of these programs and highlights their increasing importance in the face of climate change and ensuing landscape transformations. Second, the CRD should have well-developed contingency plans in place for monitoring and rapidly treating burn sites and downslope areas in the event of a catchment fire, in order to mitigate potential impacts on SLR. The paleoenvironmental data indicate that enhanced water erosion of ash and soils will likely be a significant source of contamination following future high-severity fires. Moreover, previous paleoecological studies in the watershed (Brown et al., 2019) and regional 184 climate change projections (Vines et al., 2017) suggest that in the future, fire seasons may become longer and drier, and extreme precipitation events may become more frequent, suggesting that watershed managers will likely have a shorter window of opportunity to respond to fires and treat burn sites to mitigate runoff. Mechanical treatments of burned areas such as mulching, slope stability enhancement, plantings, and pre-fire establishment of sediment detention systems in key tributaries, like Rithet Creek, are all potential means of mitigating post-fire water quality impacts in SLR. The data presented here provide guidance for identifying and prioritizing the most effective treatments, although the feasibility of specific mitigation activities must be fully assessed, which is beyond the scope of this study. Post-fire streamflow and water quality monitoring should also focus on the elements of concern identified in the sedimentary data, as they have demonstrably increased following past fires in the watershed. Third, the CRD should expand the capacity of its backup water supply systems. The findings of the present study indicate that significant post-fire water quality impacts have previously persisted for years or even decades in the Sooke watershed. The Goldstream Water Supply Area, which serves as the current backup for the Sooke Water Supply Area, likely does not have sufficient capacity to ensure uninterrupted water supply in the event of a severe wildfire that compromises water quality in SLR. The Leech Water Supply Area is currently being developed to meet future water supply needs, and although its extra capacity is unlikely to be required for several decades, the present study highlights its critical importance as a fallback system in the nearer future. Restoration efforts are underway to prepare the Leech watershed for supplying water, but in light of the data presented in the current study, bringing the Leech Water Supply Area on-line as soon as possible should be a top priority. Fourth, the CRD should strongly consider the construction of a water filtration plant. Current water quality in SLR is exceptional, but the paleoenvironmental data suggest that it can become compromised. Water in the CRD distribution system currently undergoes coarse filtration to remove 185 very large particles and ultraviolet and chemical treatment to address biological contaminants, but there exists no capacity to remove suspended solids, heavy metals, or other chemical contaminants. The present study demonstrates that previous fires in the Sooke watershed have triggered significantly increased fluxes of toxic elements to Begbie Lake and Sooke Lake. Future severe catchment fires are likely to have similar impacts. Construction of a water treatment plant would undoubtedly be costly but provide an invaluable safeguard against fire-induced water supply contamination. This is especially critical considering the potentially years-long duration of post-fire water quality impacts, the relatively small capacity of the current fallback water supply system, and the long timeline for preparation of the Leech watershed. 5.3 Coda The methods developed and demonstrated here also have broad applicability beyond the Sooke watershed and the CRD. This investigation demonstrates the broader value of paleofire research to modern water supply security and water resource management. The key advantage of this approach is its ability to quantify the actual nature and duration of impacts from real fires on specific water supply catchments and reservoirs, establishing ranges of responses. The quantitative approach and statistical analysis enable the derivation of evidence-based watershed management practices that are tailored to the unique geography, geology, ecology, and limnology of a specific locality. 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Journal of Sedimentary Research, 37(3), 975–976. https://doi.org/10.1306/74D71807-2B21-11D7-8648000102C1865D 203 Appendix A: Summary for Policymakers Key Research Findings • Impactful wildfires burned in the Sooke watershed, a major municipal water supply, under a variety of climates and vegetation types, including modern conditions and past warmer, drier environments (Table 23). • These wildfires had measurable physical, chemical, and biological impacts on both small (Begbie) and large (Sooke) lakes, as inferred from sedimentary records (Table 23). • These fire-induced sedimentary changes reflect changes in the overlying water column, and therefore water quality. • Inferred fire impacts of particular concern for water supply management include: o Increased erosion rates following severe catchment fires, which led to increased in-lake turbidity and changes in community composition and productivity of diatom algae. o Increased fluxes of many elements of concern following fires, including toxic heavy metals and ash-associated cations which may affect water aesthetics or treatability. • In all examples of fires, significant post-fire impacts persisted for years to decades. 204 Table 23. Summary of fires and recovery periods measured for key fire proxy indicators. Fire ages and impact intervals are based on centennial-scale age-depth models and are approximate. “N/A” indicates no significant impact. TC = total carbon, TN = total nitrogen. Fire Event BEGA BEGB BEGC SLRN SLRS Age (cal yr BP) 12010 9860 2440 140 140 Climate Cool & moist Warm & dry Cool & wet Cool & wet Cool & wet Vegetation Mixed conifer forest Open Douglas fir parkland Modern conifer forest Modern conifer forest Modern conifer forest Inferred fire severity Very high High Low Moderate Low Approximate recovery interval (years) Magnetic susceptibility 150 >140 80 40 25 TC & TN 100 >100 100 25 >75 C:N 10 10 100* >20 <5 δ13C 100 N/A N/A N/A N/A δ15N 100 20 N/A 30 N/A Inorganic elements 100 55 >20 >25 25 Diatoms 150 20 >20 >20 25 Vegetation 130 70 >30 20 Not analyzed * BEGC TC and TN values start to decline several decades before other fire proxy indicators, indicating that they may be primarily responding to non-fire drivers. Water Supply Management Recommendations for the Capital Regional District 1. Continue to invest in wildfire prevention and mitigation measures in the Sooke watershed. 2. Develop contingency plans for monitoring and rapidly treating burn sites and downslope areas. 3. Expand the capacity of backup water supply systems. 4. Strongly consider the construction of a water filtration plant.
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Assessing wildfire risk to municipal drinking water using paleoecology
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