Forestry, Faculty of
DSpace
The forest will burn : we will restore our land
UBCV
Preston, Georgina
Master of Science - MSc
University of British Columbia
Indigenous lands in interior British Columbia (BC), Canada are being impacted by increasingly large, fast-moving, high-severity wildfires due to climate change and an increase in vegetation (fuels) resulting from two centuries of settler-colonial land management. Despite disruptions from settler-colonial laws and policies, Stswecem'c Xget'tem First Nation (SXFN) has stewarded their Traditional Territory in south-central BC since time immemorial, including through frequent application of surface fire. SXFN communities have been repeatedly threatened by wildfires throughout the last two decades, inspiring SXFN to partner with the University of British Columbia in 2021 to quantify crown fire risk in their wildland-urban interface and explore forest stewardship solutions. I quantified fuel loads and potential fire behaviour in unceded dry SXFN forests with BC government legal objectives for mule deer winter range (MDWR) and old growth management (OGMA), then explored wildfire mitigation options through simulated fuel treatments. High fuel loads surrounding SXFN communities yielded predictions for high likelihood of crown fire under the 50th, 70th, 90th, and 97.5th percentiles of fire weather. Fuels data were altered to simulate fuel treatments that paired pruning and surface fuel abatement with varying intensities of thinning-from-below; two types of thinning complied with BC legal objectives for MDWR and OGMA, and another intensively thinned the smallest trees. I remodelled fire behaviour using the simulated fuel treatments and 90th percentile fire weather data and found that fuel treatments that complied with legal objectives for MDWR and OGMA reduced predictions of active crown fire but maintained high likelihood of intense passive crown fire. Comparatively, the intensive-thinning fuel treatment which would require an exemption to the legal objectives had the most efficacy in reducing crown fire predictions, but predictions for intense passive crown fire still dominated. These findings indicate that legal objectives for MDWR and OGMA should be critically revaluated, as they limit vital fuel reduction close to SXFN communities. Of utmost importance is that SXFN be able to lead the adaptive stewardship needed in these forests by using both local knowledge and western science. This work is urgent; it is not if, but when SXFN communities will next be threatened by wildfire.
ME7 TSERTSRÉP RE NEKÉCT: ME TSECÉNTEM RE TMICW-KT (THE FOREST WILL BURN: WE WILL RESTORE OUR LAND) by Georgina Preston B.Sc.H., Environmental Science, Queen’s University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2024 © Georgina Preston, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Me7 Tsertsrép re Nekéct: Me Tsecéntem re Tmicw-kt (The Forest Will Burn: We Will Restore Our Land) submitted by Georgina Preston in partial fulfilment of the requirements for the degree of Master of Science in Forestry Examining Committee: Dr. Lori Daniels, Professor, Forest and Conservation Sciences, UBC Supervisor Dr. Harold Nelson, Associate Professor, Forest Resource Management, UBC Supervisory Committee Member Dr. Jodi Axelson, Adjunct Professor, Forest and Conservation Sciences, UBC Supervisory Committee Member John Liscomb, Forester, Stswecem’c Xget’tem First Nation Supervisory Committee Member Jennifer Grenz, Assistant Professor, Forest Resource Management, UBC Additional Examiner iii Abstract Indigenous lands in interior British Columbia (BC), Canada are being impacted by increasingly large, fast-moving, high-severity wildfires due to climate change and an increase in vegetation (fuels) resulting from two centuries of settler-colonial land management. Despite disruptions from settler-colonial laws and policies, Stswecem'c Xget'tem First Nation (SXFN) has stewarded their Traditional Territory in south-central BC since time immemorial, including through frequent application of surface fire. SXFN communities have been repeatedly threatened by wildfires throughout the last two decades, inspiring SXFN to partner with the University of British Columbia in 2021 to quantify crown fire risk in their wildland-urban interface and explore forest stewardship solutions. I quantified fuel loads and potential fire behaviour in unceded dry SXFN forests with BC government legal objectives for mule deer winter range (MDWR) and old growth management (OGMA), then explored wildfire mitigation options through simulated fuel treatments. High fuel loads surrounding SXFN communities yielded predictions for high likelihood of crown fire under the 50th, 70th, 90th, and 97.5th percentiles of fire weather. Fuels data were altered to simulate fuel treatments that paired pruning and surface fuel abatement with varying intensities of thinning-from-below; two types of thinning complied with BC legal objectives for MDWR and OGMA, and another intensively thinned the smallest trees. I remodelled fire behaviour using the simulated fuel treatments and 90th percentile fire weather data and found that fuel treatments that complied with legal objectives for MDWR and OGMA reduced predictions of active crown fire but maintained high likelihood of intense passive crown fire. Comparatively, the intensive-thinning fuel treatment which would require an exemption to the legal objectives had the most efficacy in reducing crown fire predictions, but predictions for intense passive crown fire still dominated. These findings indicate that legal iv objectives for MDWR and OGMA should be critically revaluated, as they limit vital fuel reduction close to SXFN communities. Of utmost importance is that SXFN be able to lead the adaptive stewardship needed in these forests by using both local knowledge and western science. This work is urgent; it is not if, but when SXFN communities will next be threatened by wildfire. v Lay Summary Wildfires are becoming larger and more severe in interior British Columbia (BC) due to climate change and increases in forest vegetation (fuels) over the last two centuries. Stswecem’c Xget’tem First Nation (SXFN) partnered with the University of British Columbia to quantify community wildfire risk and explore solutions to address it. Forests surrounding SXFN communities are dense with fuels that are likely to burn as crown fire during moderate to extreme weather conditions. To assess solutions to wildfire risk, fuel treatments were simulated that thinned trees at varying intensities, pruned lower branches, and reduced dead woody debris on the ground. All fuel treatments decreased predicted wildfire behaviour; however, substantial decreases required high-intensity thinning of trees, branch pruning, and dead woody debris reduction. Currently, high-intensity thinning is restricted by BC government legal objectives for forest management, demonstrating a need to revaluate legal objectives that limit vital fuel reduction needed for community safety. vi Preface Georgina Preston was the main contributor to the identification, design, field research, analyses and writing of this MSc thesis. The thesis was supported throughout by Drs. Lori Daniels, Greg Greene, Jodi Axelson, and Harry Nelson. Field methods were developed by Dr. Lori Daniels and Kea Rutherford. All data are original and were obtained from field research conducted in Stswecem’c Xget’tem First Nation Traditional Territory in British Columbia, Canada, in 2021 and 2022. Assistance with field research was provided by Katie Couvrette, Samantha Shapira, Sneha Gupta, Jaqueline Morris, and Nicole Chin. Assistance with fire behaviour modelling and creating the simulated fuel treatments was provided by Dr. Greg Greene. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures ............................................................................................................................. xiii List of Abbreviations .................................................................................................................. xv Glossary ...................................................................................................................................... xvi Positionality Statement ............................................................................................................ xviii Acknowledgements .................................................................................................................. xxiii Dedication ................................................................................................................................. xxvi Chapter 1: Introduction ............................................................................................................... 1 1.1 Community-based research ................................................................................................... 7 1.2 Research Objectives .............................................................................................................. 9 Chapter 2: Not If, But When: How Are Forests Surrounding Stswecem'c Xget’tem Communities Likely to Burn? ................................................................................................... 11 2.1 Introduction ......................................................................................................................... 11 2.2 Materials and Methods........................................................................................................ 16 2.2.1 Study Area..................................................................................................................... 16 viii 2.2.2 Plot Selection ................................................................................................................ 18 2.2.3 Field Methods ............................................................................................................... 19 2.2.4 Forest Fuel Calculations .............................................................................................. 21 2.2.5 Fire Behaviour Modelling ............................................................................................ 21 2.2.6 Comparison Fire Weather and Fuels Among Predicted Fire Types ............................ 24 2.3 Results ................................................................................................................................. 25 2.3.1 Forest Composition and Structure ............................................................................... 25 2.3.2 Fuel Characteristics ..................................................................................................... 27 2.3.3 Fire Weather ................................................................................................................. 29 2.3.4 Crown Fire Likelihood ................................................................................................. 30 2.3.5 Fire Type ...................................................................................................................... 33 2.3.6 Rate of Spread and Surface Fire Intensity.................................................................... 35 2.3.7 Comparison of Fire Weather and Fuels Among Predicted Fire Types ........................ 37 2.4 Discussion ........................................................................................................................... 39 2.4.1 CFIS Modelling Relays the Importance of Wind and Ladder Fuels ............................ 39 2.4.2 CFIS Modelling Limitations ......................................................................................... 41 2.4.3 Settler-Colonial Land Management Drove Fuel Departure ........................................ 44 2.4.4 Implications for SXFN Stewardship ............................................................................. 45 2.5 Conclusion ........................................................................................................................... 49 ix Chapter 3: Protecting Forests to Death: Simulated Fuel Treatment Efficacy in Mule Deer Winter Range and Old Growth Management Area Forests of Secwépemcúl’ewc (Interior British Columbia, Canada) ........................................................................................................ 51 3.1 Introduction ......................................................................................................................... 51 3.2 Materials and Methods........................................................................................................ 56 3.2.1 Study Area..................................................................................................................... 56 3.2.2 Plot Selection ................................................................................................................ 58 3.2.3 Field Methods ............................................................................................................... 59 3.2.4 Measured Fuel Loads ................................................................................................... 60 3.2.5 Simulated Fuel Treatments Following MDWR & OGMA Legal Objectives ................ 61 3.2.6 Simulated Intensive Thinning Fuel Treatment.............................................................. 65 3.2.7 Post-Treatment Fuel Loads .......................................................................................... 65 3.2.8 Fire Behaviour Modelling ............................................................................................ 67 3.2.9 Comparison of Fuel Loads and Fire Behaviour Among Simulated Fuel Treatments .. 68 3.3 Results ................................................................................................................................. 69 3.3.1 Forest Composition and Structure ............................................................................... 69 3.3.2 Fuel Loads After Simulated Fuel Treatments ............................................................... 70 3.3.3 Predicted Fire Behaviour ............................................................................................. 75 3.4 Discussion ........................................................................................................................... 79 x 3.4.1 MDWR and OGMA Fuel Treatments Reduce Fuels, But Crown Fire Risk Is Still High ............................................................................................................................................... 80 3.4.2 Efficacy of the Intensive Thinning Treatment Would Increase After Applying “Good Fire” ...................................................................................................................................... 82 3.4.3 Restoring Long-Term Resilience in Unceded SXFN Forests ....................................... 84 3.4.4 Implications for SXFN Land Stewardship .................................................................... 86 3.5 Conclusion ........................................................................................................................... 88 Chapter 4: Lessons Learned and Future Actions .................................................................... 90 4.1 High Fuel Loads Drive Crown Fire Predictions During Moderate to Extreme Weather .. 91 4.2 Simulated MDWR and OGMA Fuel Treatments Do Not Promote SXFN Wildfire Safety .. 93 4.3 Legal Objectives Should Be Revised in Partnership with Indigenous Rights-Holders ....... 94 4.4 SXFN Has Legal Rights to Steward Their Unceded Traditional Territory......................... 98 4.5 Benefits of Community-Led Forest Stewardship and #LANDBACK .................................. 99 4.6 Conclusion and Future Research ...................................................................................... 101 Bibliography .............................................................................................................................. 103 Appendices ................................................................................................................................. 134 Appendix A: Fuel Type Selection ............................................................................................ 134 Appendix B: Fuel Attribute Tables for Each Plot Post-Treatments ........................................ 136 xi List of Tables Table 2.1: Summary of the forest density and basal area of all plots…………………………… 26 Table 2.2: Fuel types and characteristics of all plots……………………………………………. 28 Table 2.3: Fire weather attributes for increasing percentiles of the fire weather……………….. 29 Table 2.4: Comparison of fire weather, fuel loads, and predicted fire behaviour among predicted fire types at the 90th percentile of the fire weather index………………………………………... 38 Table 3.1: Proportion of the basal area of living Douglas-fir trees that can be removed in fuel treatments from forests managed for mule deer winter range…………………………………… 64 Table 3.2: A key describing the nine simulated fuel treatments with variations in thinning intensity and the inclusion of either pruning or fine surface fuel abatement……………………………… 65 Table A.1: Typical characteristics for fuel types found at the plots…………………………… 135 Table B.1: Biophysical attributes for all plots after simulated fuel treatments………………… 136 Table B.2: Density of live conifer trees by canopy layer for all plots after simulated fuel treatments……………………………………………………………………………………… 137 Table B.3: Total and canopy layer basal area of live conifer trees and percentage of live conifer canopy layer basal area removed for all plots after simulated fuel treatments………………… 138 Table B.4: Live canopy layer Douglas-fir basal area targets for mule deer winter range stand structures compared to the reference condition and simulated fuel treatment basal areas for all plots……………………………………………………………………………………………. 139 Table B.5: Average canopy base height and total canopy bulk density for all plots after simulated fuel treatments…………………………………………………………………………………. 140 Table B.6: Fine woody surface fuels for all plots after simulated fuel treatments……………. 141 Table B.7: Median crown fire likelihood for all plots after simulated fuel treatments………… 142 xii Table B.8: Median rate of spread for all plots after simulated fuel treatments………………… 143 Table B.9: Median surface fire intensity for all plots after simulated fuel treatments…………. 144 xiii List of Figures Figure 2.1: Location of the study area in British Columbia and plot locations in the Stswecem’c Xget’tem First Nation interface…………………………………………………………………. 18 Figure 2.2: Diagram showing the configuration of forest fuels mensuration plots established at each plot………………………………….……………………………………………………... 19 Figure 2.3: Distribution of increasing percentiles of fire weather during 15 fire seasons from 2007‒2021………………………………………………………………………………………. 30 Figure 2.4: Variation in crown fire likelihood among plots and from moderate to extreme fire weather scenarios……………………………………………………………………………….. 32 Figure 2.5: Proportions of fire types predicted among plots and from moderate to extreme fire weather scenarios……………………………………………………………………………….. 34 Figure 2.6: Variation in rate of spread and surface fire intensity in moderate to extreme fire weather scenarios……………………………………………………………………………….. 36 Figure 3.1: Location of the study area in British Columbia and plot locations in the Stswecem’c Xget’tem First Nation interface…………………………………………………………………. 58 Figure 3.2: Diagram showing the configuration of forest fuels mensuration plots established at each plot………………………………….……………………………………………………... 59 Figure 3.3: Comparison of density, basal area, and canopy cover amongst simulated fuel treatments and the reference condition………………………………………………………….. 72 Figure 3.4: Comparison of canopy base height and canopy bulk density amongst simulated fuel treatments and the reference condition………………………………………………………….. 74 Figure 3.5: Comparison of fine surface fuels amongst simulated fuel treatments and the reference condition………………………………………………………………………………………… 75 xiv Figure 3.6: Proportions of fire types predicted amongst simulated fuel treatments and the reference condition………………………………………………………………………………………… 76 Figure 3.7: Comparison of predicted fire behaviour amongst simulated fuel treatments and the reference condition……………………………………………………………………………… 78 xv List of Abbreviations BC British Columbia CBD Canopy bulk density CBH Canopy base height CFIS Crown Fire Initiation and Spread model DBH Diameter at breast height EFFM Estimated fine fuel moisture FBP Canadian Fire Behavior Prediction system MDWR Mule Deer Winter Range OGMA Old Growth Management Area SXFN Stswecem'c Xget'tem First Nation UBC University of British Columbia UNDRIP United Nations Declaration on the Rights of Indigenous Peoples xvi Glossary Mule Deer Winter Range (MDWR) The government of British Columbia (BC) designated certain forests as MDWR in the 1980-90s in Stswecem’c Xget’tem (SXFN) Traditional Territory and other parts of the Cariboo Region in south-central BC. Since the 1990s, designated MDWR forests have had harvesting guidance for the purpose of maintaining or restoring mule deer winter habitat while also permitting commercial timber harvesting under specific conditions (IAMC, 1998; Dawson et al., 2007). In 2007, prescriptive legal objectives for forest harvesting and other forest management activities in MDWR were created as part of a government action regulation order (MOE, 2007). In 2022, the BC government created blanket exemptions to the legal objectives for wildfire risk reduction activities in MDWR to provide forest practitioners with more flexibility when implementing wildfire risk reduction treatments in MDWR (Swan, 2022). Forests were designated as MDWR without SXFN consultation or free, prior, and informed consent, as later defined by the United Nations Declaration on the Rights of Indigenous Peoples (UN, 2007). The legal objectives and their blanket exemptions were also not created with SXFN consultation or consent. Old Growth Management Area (OGMA) The government of British Columbia (BC) designated certain forests as OGMA in Stswecem’c Xget’tem (SXFN) Territory and other parts of the Cariboo Region in south-central BC as part of the Cariboo-Chilcotin Land Use Plan and the associated Williams Lake, South Chilcotin, and 100 Mile House Sustainable Resource Management Plans, which were completed between 1996 and 2007. In 2010, a land use order legally established areas and objectives for forest management activities in OGMA with the purpose of retaining old forest and natural succession xvii processes (MAL, 2010). In 2018, the BC government made amendments to the OGMA legal objectives to provide forest practitioners with more flexibility when implementing wildfire risk reduction treatments in OGMA (FLNRORD, 2018). Forests were designated as OGMA without Stswecem’c Xget’tem consultation or free, prior, and informed consent, as later defined by the United Nations Declaration on the Rights of Indigenous Peoples (UN, 2007). The legal objectives and their amendments were also not created with Stswecem’c Xget’tem consultation or consent. xviii Positionality Statement This research was completed by Georgina Preston in partnership with Stswecem’c Xget’tem First Nation (SXFN). Before you start reading my thesis, it is important for me to tell you who I am and why I completed this research. I am a white settler, whose ancestors immigrated in the 1820s from the northern European countries of England, Ireland, Germany, and Scotland to what is now called southern Ontario. When my ancestors moved to southern Ontario in search of a better life, they contributed to the settler-colonial exclusion of Indigenous Peoples from their homes on their ancestral lands. I grew up with substantial privilege on stolen lands within the Mississaugas of Scugog Island First Nation Traditional Territory, part of the Anishinaabek Nation and the Williams Treaty First Nations. These lands have also been the home of the Huron-Wendat and the Haudenosaunee Peoples throughout different parts of history. The access to stolen lands that my ancestors and I have had, as well as the benefits that we have derived from them, have given me many advantages in life. Being a white, English-speaking, 6th generation settler from an affluent family contributed to my ability to pursue higher education, which gave me access to career opportunities in a field that I was able to choose out of passion. This privilege allowed me to take a job in the SXFN Stewardship Department, all the way across the country, despite knowing that in an ideal world it would not be filled by a young white woman from southern Ontario. This is how I came to move to Williams Lake in Secwépemcúl’ewc (interior British Columbia) shortly after finishing my undergraduate degree in 2018. My relationship with SXFN started with my job in the Stewardship Department, where I was fortunate to learn a lot from SXFN community members, Leadership, and my fellow staff xix members. This was my introduction to SXFN’s ecological and cultural values, past and current use of fire stewardship, changing forest and grassland conditions, experiences with wildfires throughout the recent and distant past, concerns with forest management on unceded lands, struggles and successes in working collaboratively with the settler-colonial governments of British Columbia (BC) and Canada, and so much more. My pre-existing world views were immediately challenged in my work with SXFN, and eventually changed. Before this job I believed that the best way to take care of the “natural world” was to let it be through removing human activity, and to tread as lightly as possible if we couldn’t do that. This interpretation does not leave room for thousands of years of active stewardship and use of lands and waters by Indigenous Peoples, who like all humanity, are not separate from nature but are part of it. In the context of the dry Douglas-fir forests in SXFN Territory, I learned that disruptions to SXFN stewardship, which historically did not always tread lightly, have led to negative changes in those ecosystems, including increased SXFN community wildfire risk. Though what I learned greatly impacted me, I acknowledge that it was only a small subset of the deep multigenerational place-based knowledge that exists in the SXFN communities, and it was likely limited by my being a séme7 (white person/outsider). Despite being a séme7 new to SXFN Territory, my role in the Stewardship Department allowed me to witness firsthand how certain settler-colonial systems controlled by the governments of BC and Canada, can conflict with SXFN’s ability to uphold their inherent rights and exert their sovereignty over stewardship in their Traditional Territory. Seeing this was frustrating for me, let alone for the SXFN community members living it, but it did make me feel like the work we were doing in the Stewardship Department to change existing systems or get work done despite them was important. Even xx though the challenges from these systems and those actively enforcing them existed, it was inspiring to see SXFN community members advocating and working hard to make changes to benefit their People and Territory. It was also encouraging to work with government staff that were doing their best to listen to SXFN and change what they could, which for me highlighted the importance of having people working within problematic systems pushing for change from the inside. The time I spent in the SXFN Stewardship Department was intense and all encompassing. I left my position with SXFN in 2020 shortly after I started to feel major effects of burn out. While I was sad to leave, this opportunity enabled me to pursue my master’s degree at the University of British Columbia and through a formal request, to continue working in close capacity with SXFN. I was interested in how the forests surrounding SXFN communities had changed since settlers arrived, the current community wildfire risk, and how lands could be stewarded to better meet SXFN ecological and cultural values, including wildfire resilience. From my time as an employee, I knew that community wildfire risk and resilience was a top priority for SXFN after their communities were threatened by large wildfires in 2010, 2017, and 2018. This is how my thesis came to be. After approaching SXFN with a research partnership proposal in January 2021, we started a new chapter in our relationship. I will forever be grateful that SXFN Leadership was willing to partner with myself, Mike Stefanuk (PhD student), and our supervisor, Dr. Lori Daniels, to answer vital SXFN stewardship questions related to their forests and how they interact with wildfire. It has been one of my greatest joys to come to know the SXFN communities and the Douglas-fir dominated forests and grasslands that surround them better than I once did throughout this research journey. xxi As a new representative of a large academic institution, I thought about how I was taught to respond to requests for new relationships as an employee of the Stewardship Department. I knew that it would be important to ensure that the research I completed in SXFN Territory was done in partnership with SXFN, was consented to by SXFN, and benefited the communities first and foremost. This is what I have used to guide my research and I feel proud about what we have accomplished together. Through working closely with SXFN, I have been able to bring a new type of knowledge home to the communities, expand it more with every cross-cultural conversation, and then incorporate SXFN feedback into my understandings about the research results and their implications. This wouldn’t have been possible without strong collaboration with SXFN Leadership, staff, and community members, as well as other local forest knowledge holders in the BC government and the forestry industry. Throughout it all, it was important to both SXFN and I that this research was grounded in an understanding that SXFN has rights to govern their Peoples and steward their lands and waters for present and future generations, both Indigenous and settler alike. Place and Responsibility Data collection occurred in SXFN Territory and my field crew resided throughout Secwépemcúl’ewc during this period. However, the data analyses and writing occurred on many different Territories. I have been lucky to live on lands in Musqueam First Nation Territory, K’ómox First Nation Territory, Na-Cho Nyak Dun First Nation Territory, and Kaska Dena Territory while working on this degree. As a settler, I try to think about my responsibilities to the Indigenous rights-holders of every Territory that I travel to and reside within. This includes but is xxii not limited to the responsibility to use my privilege to amplify Indigenous voices, educate fellow settlers on the existing settler-colonial systems that continue to disproportionately harm Indigenous Peoples, and share what I know of the decolonization work being led by Indigenous Peoples to address them. I believe strongly in Indigenous Peoples’ rights to sovereignty over their own communities and their ancestral Territories and will endeavour to always advocate for this. xxiii Acknowledgements Without the communities of Stswecem’c Xget’tem First Nation (SXFN) choosing to hire me in 2018, I would not be writing this thesis today. Thank you so much to everyone from the communities and my fellow staff members for patiently teaching a 22-year-old from southern Ontario about SXFN Territory, culture, and your visions for the future. It changed my life forever. Of special recognition, thank you to my former supervisor and the current Kukpi, Marilyn Camille, former Kukpi Patrick Harry, John Liscomb, Ashley Camille, Hank Adams Jr., Harold Harry, Saidra Archie, and to everyone in the Stewardship Department, both current and past employees, for your dedication in stewarding lands and waters for the wellbeing of current and future generations. Thank you also to Elders Clara Camille and Darlene Louie, who spent time with me selecting Secwepemctsín to include in the community report for this research; without their help, this thesis would not have its beautiful title. From the Tree Ring Lab, thank you to Dr. Greg Greene, who spent countless hours on Zoom teaching me about fire behaviour modelling and co-developing my simulated fuel treatments. I came in with few skills needed to complete this part of the research, but through Greg’s mentorship and extreme patience, my capacity has grown enormously. Thank you to Dr. Lori Daniels, my supervisor, for taking me on as a student, for supporting me in forming an unprecedented research partnership with SXFN, and for teaching me skills to become an impactful science communicator. It's incredible to learn from a woman in wildfire and forest sciences who is breaking down barriers for community-led forest stewardship. Thank you to Mike Stefanuk; we went through hell and back together during the 2021 field season, leading field crews during the Covid-19 pandemic while wildfires chased us out of our study areas. Mike xxiv has been a great support and mentor throughout my whole degree. Thank you to my 2021 and 2022 field assistants, namely Katie Couvrette, Samantha Shapira, Sneha Gupta, Jackie Morris, and Nicole Chin, who worked so hard, often under uncertain and stressful circumstances, to help me collect the data required for this study. With so little field experience of my own prior to this research, my field assistants helped me learn both the hard and soft skills of field work and those lessons will stay with me forever. Thank you to the rest of the Tree Ring Lab members who have created safe and supportive spaces for our cohort, all while conducting research that I feel incredibly proud to be even remotely affiliated with. Thank you so much to Jodi Axelson, Harry Nelson, and John Liscomb from my research committee who read and re-read my thesis; your feedback made me think deeply about my project and made my research stronger. Thank you so much to all the people from the BC government and forestry industry in the Cariboo who spent time giving me feedback or answering questions that have helped me ground my research in their on-the-ground experiences. Thank you to Matt Lees and Peter Holub from BC Wildfire Service, as well as Alex Tranq and Kerri Howse from the Cariboo Region. Thank you to Mike Tomlinson and Ken Day for sharing their experiences with forest management in Mule Deer Winter Range and Old Growth Management Area. Thank you to the multiple funders of this research, including the NSERC-Canada Wildfire Research Network, British Columbia Wildfire Service, Pacific Institute for Climate Solutions, University of British Columbia Faculty of Forestry and Community Engagement Services, and MITACS Accelerate. The funders helped make this research possible, especially the community engagement portion which was extremely important to both SXFN and I and took significant xxv financial resources. Thank you also to the Namkoong family and the Heller family, who generously donated money that paid for two fellowships that I received in 2023. Thank you especially to Stswecem’c Xget’tem, who believed in this research enough to put forward their own money to be matched by MITACS Accelerate. Finally, thank you to my family and friends for their unwavering support and encouragement throughout this degree. Especially to my partner and fellow fire nerd, Randy, who put up with countless questions and discussions well past the time for “work talk” to be concluded. Randy and Joni, our beloved dog, were there to encourage and support me throughout this entire degree. Thank you. xxvi Dedication To the dry Douglas-fir forests and grasslands that I have had the pleasure of coming to know, and the People from Stswecem’c Xget’tem who are and will always be a part of them. And to my mom, who always made me believe that I was capable of changing the world. 1 Chapter 1: Introduction Fire has always and will always be part of the dry forests of Secwépemcúl’ewc, the Traditional Territory of the Secwépemc People in what many today call interior British Columbia (BC), Canada. Since time immemorial, the Secwépemc People have used fire to steward the lands they call home, burning frequently in the cool and wet seasons to care for the land, the non-human relatives they rely upon, and their own communities (FREC, 2021; Dickson-Hoyle et al., 2021; Copes-Gerbitz et al., 2023). These fires and other Secwépemc stewardship practises shaped the ecological and cultural (hereafter, “eco-cultural”) systems of Secwépemcúl’ewc, contributing to the maintenance of widespread grasslands and open forests dominated by fire-adapted species. Secwépemc fire stewardship contributed to the maintenance of a mixed-severity fire regime dominated by frequent, low-severity surface fires in dry mixed-conifer forests (Brookes et al., 2021; Copes-Gerbitz et al., 2021; Copes-Gerbitz et al., 2023). Low-severity surface fires, which frequently burned understory plants and woody debris while excluding large trees, limited dry vegetation close to settlements and other important areas (FREP, 2021; Dickson-Hoyle et al., 2021; Copes-Gerbitz et al., 2023). These fires mitigated the occurrence of high-severity crown fires, which burn through the canopies of trees (Coogan et al., 2021). Euro-Canadian settlers and settler-colonial powers disrupted Secwépemc stewardship practises through the occupation and management of land starting in the 19th century (Copes-Gerbitz et al., 2021) and have contributed to grassland encroachment by trees (Bai & Thompson, 2004; Blackstock & McAllister, 2004; Harvey et al., 2017; Hamilton & Burton, 2023) and changes in the composition and structure of forests (Day, 1998; Leclerc et al., 2021; Brookes et al., 2021). Fire exclusion and suppression policies caused the densification of dry forests by young trees (Brookes et al., 2021; Copes-Gerbitz et al., 2023), while intense livestock grazing may have 2 limited the spread of lightning ignited low-severity fires that would have periodically reduced small tree densities and consumed shrubs and dead woody surface fuels (Savage & Swetnam, 1990; Belsky & Blumenthal, 1997). Industrial forestry in the mid to late 20th century targeted the removal of large-diameter fire-adapted trees through high-grading (Day, 1998; Leclerc et al., 2021), while other industrial forestry activities have discouraged the growth of fire-resistant deciduous trees in conifer plantations (FLNRO, 2012). These settler management interventions have cumulatively shifted the land from a fire-resilient to a fire-vulnerable state, similar to other dry conifer forests in western North America (Hessburg et al., 2005; Hessburg et al., 2016; Hessburg et al., 2019). Wildfires in 2017, 2018, 2021, and 2023 in the dry interior Douglas-fir forests of Secwépemcúl’ewc have been uncharacteristically large, frequent, and have burned at high severities, perhaps indicating a shift in the fire regime to favour frequent crown fires (Abbott & Chapman, 2018; Dickson-Hoyle & John, 2021). Vegetation (hereafter, “fuels”), have increased on the landscape due to settler-colonial land management and when paired with extreme hot, dry, and windy weather periods that are exacerbated by human-caused climate change, contribute to changing wildfire behaviour patterns (Flannigan et al., 2005; Jain et al., 2022; Parisien et al., 2023). These contemporary fires have had profound impacts on air quality, water quality, soil composition, slope stability, wildlife habitat, and the many diverse species that call this area home, including humans (Coogan et al., 2019; Dickson-Hoyle & John, 2021; Hoffman et al., 2022a; Dickson-Hoyle et al., 2024). People have lost their homes and livelihoods, have had to evacuate their communities with little notice, and are dealing with physical and mental health consequences of living through wildfire-caused emergencies (McGee & Christianson, 2021). In 3 recent record-breaking wildfires seasons, civilians (2021) and firefighters (2023) have lost their life either fleeing or fighting wildfires in BC. Indigenous Peoples and other rural communities are often disproportionately impacted by wildfires, as they are often surrounded by forested or fire-prone ecosystems and poor access routes (McGee, 2021; McGee & Christianson, 2021; Batdorf & McGee, 2023). Despite ongoing efforts to mitigate climate change, insufficient actions to date makes a degree of climate change inevitable, leading to increased durations, frequencies, and severities of extremely hot, dry, and windy weather events that promote extreme wildfire behaviour (Wotton et al., 2017; Parisien et al., 2023). While continued climate change mitigation efforts should be prioritized, there is also a need to adapt ecological, cultural, social, and economic systems to the current and projected impacts (CleanBC, 2022), including increasingly large, fast spreading, high-severity wildfires. This can be done through reducing fuel quantities and continuity in vegetated ecosystems through fuel treatments, the reintroduction of frequent low-severity surface fires, the management of wildfires, or a combination of these tactics. These stewardship interventions are commonly used in efforts to reduce crown fire likelihood near communities and throughout the landscape (Agee & Skinner, 2005; Stephens et al., 2012; FPB, 2023). They can be a part of eco-cultural restoration of fire-dependent forests and grasslands, driving not only fire resiliency, but also the revitalization of biodiversity, food and medicine plants, game species, and Indigenous culture (Lewis et al., 2018; Lake & Christianson, 2019; Nikolakis & Roberts 2020; Long et al., 2021; Dickson-Hoyle et al., 2021; Maclean et al., 2023; Parminter, 2023; Tom et al., 2023). Today, many dry forest stewards are also attempting to create fuel structures that are fire-resilient during current and future climate change-induced extreme weather (Schoennagel et al., 4 2017; Daniels et al., 2020; Prichard et al., 2021; Jones et al., 2022; Eisenberg et al., 2024). Although this stewardship has been employed by Indigenous Peoples since time immemorial in interior BC, as well as by some settlers since the 19th century, there is disagreement amongst varying land management specialists about the intensity and extent of fuel removal appropriate in dry forests because of its potential to impact other ecosystem components, such as wildlife habitat and biodiversity (McIver et al., 2013; North et al., 2015; Stephens et al., 2020; Stephens et al., 2021). In the Cariboo Region of south-central BC that overlaps with northern Secwépemcúl’ewc, there is debate about the quantity and extent of fuel removal appropriate to meet wildfire risk reduction goals versus legal mule deer winter range (MDWR) habitat and old growth management area (OGMA) objectives. Following decades of industrial forestry in the 20th century that targeted large diameter Douglas-fir trees through high-grade logging, the BC government designated 462,000 ha of forest as MDWR (Dawson et al., 2023) and 432,072 ha as OGMA (BCSC, 2011) in the Cariboo Region in the 1980-90s, with many OGMAs overlapping MDWR forests. These forests have had guidance for forest management since their establishment, but the BC government introduced legal objectives to protect the valuable ecosystems from over-extraction in 2007 (MDWR) and 2010 (OGMA). The legal objectives for MDWR and OGMA have helped prevent further high-grade and clear-cut logging by the forestry industry since their introduction, but they may have also inadvertently created barriers to proactive forest stewardship that enhances fire-resilience and these barriers ultimately threaten the forest structures that they are aiming to protect (Abbott & Chapman, 2018; Melton, 2020; Leclerc et al., 2021). 5 Stswecem’c Xget’tem First Nation (SXFN) is home to two semi-remote northern Secwépemc communities called Stswecem’c (Canoe Creek) and Xget’tem (Dog Creek). Both communities are surrounded by dry forests and grasslands within river valleys that flow into the Fraser River. They are separated from each other and larger population centres such as Williams Lake and 100 Mile House by rough, winding dirt roads within the Cariboo Region of BC. SXFN community members have governed, stewarded, lived on, and utilized their 1.3 million hectare (ha) Traditional Territory since time immemorial and hold multigenerational place-based knowledge about the lands and waters they call home, despite the disruptions from settler-colonial imposed genocide and oppression (Copes-Gerbitz et al., 2021). For several generations, SXFN community members have watched their ancestral, unceded, Traditional Territory (hereafter, “Territory”) change under settler-colonial land management, much of which was approved and implemented without their involvement or consent. SXFN communities are surrounded by MDWR and OGMA forests, though they never gave free, prior, and informed consent to their establishment or the legal objectives that control forest stewardship within them. SXFN has long advocated for land and water sovereignty based on their Aboriginal Title and Rights as outlined in the 1982 Canadian Constitution Act (Constitution Act, 1982) and their rights to make stewardship decisions in their Territory as per the 2007 United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP; United Nations, 2007). Despite SXFN being disproportionately impacted by land management decisions in their Territory, as well as having deep knowledge systems that would benefit land stewardship, SXFN faces barriers from the current settler-colonial BC government to lead or sometimes even influence land stewardship in their Territory. This includes the barriers they face in stewarding their forests proactively for fuel 6 reduction to promote community wildfire safety, eco-cultural restoration, and climate change adaptation. After significant high-severity fires within their Territory throughout the past two decades, some of which threatened their communities, SXFN community members, staff, and Leadership have become increasingly concerned about the risk they face from community-threatening wildfire (DWB, 2010; SXFN, 2018; FREC, 2021). Therefore, SXFN partnered with the University of British Columbia (UBC) to co-generate western scientific knowledge on fuels in MDWR and OGMA forests surrounding their communities and how those fuels are predicted to affect wildfire behaviour under a variety of weather conditions. SXFN knowledge stipulates that they are at risk from community-threatening wildfire due to the forest fuel conditions around them (FREP, 2021) and this study provides western scientific knowledge that SXFN can use to “walk on two legs” to make informed forest stewardship decisions, with one leg being SXFN knowledge and the other being western science (Dickson-Hoyle et al., 2021). The generation of western scientific knowledge also provides scientists and government decision makers with a type of knowledge that they value and can understand. However, this research has been framed to value contributions from multiple knowledge types, as SXFN values both western science and their multigenerational place-based knowledge systems when identifying solutions to wildfire risk and climate change (FREP, 2021). Ideally, the western scientific knowledge generated in this research is used by the BC government to understand 1) the risk that rural and remote communities like SXFN currently face, 2) how their existing legal objectives limit the ability of communities to address it, and 3) that the current governance system disempowers SXFN to lead stewardship on their unceded lands (Eisenberg et al., 2024). Fire will always be a part of SXFN 7 Territory, but through this study and other SXFN advocacy initiatives, SXFN communities can become more prepared for the fuel and climate change induced fires that will continue to dominate Secwépemcúl’ewc forests in 2024 and beyond. 1.1 Community-based research Ethical community-based research is based on strong relationships and mutual respect, which take significant time and resources to build (Beveridge et al., 2021; KXSA, 2021; Hayward et al., 2021). I formed strong relationships within SXFN when I worked in their Stewardship Department from 2018-2020. When I became a student at UBC in January 2021, these relationships inspired me to propose a research partnership to SXFN Leadership to address priority wildfire research questions. SXFN has deep multigenerational place-based knowledge about the lands and waters of their Territory (SXFN, 2018) and wished to co-generate western science knowledge on the forests surrounding their communities, particularly in how these forests influence the current and future wildfire risk for SXFN in a changing climate. SXFN Leadership agreed to partner with UBC, and we formalized our partnership through signing a legally binding Indigenous Collaborative Research Partnership Agreement (ICRPA) derived from a template from the UBC Indigenous Research Support Initiative. The ICRPA upholds SXFN rights to free, prior, and informed consent to all research activities as outlined in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP; United Nations, 2007). Additionally, the ICRPA upholds First Nations Principles on Ownership, Control, Access, and Possession (FNIGC, 2020), ensuring that all data collected is legally owned by SXFN and that documentation using the data must be approved by SXFN prior to publication. Finally, the 8 ICRPA stipulates that SXFN community members must be given fair credit and compensation for their contributions to the research. As the research took place in SXFN Territory, adjacent to both SXFN communities, it was important that I centred the research on how wildfire could impact the SXFN community members. Throughout the research process, I regularly met with SXFN staff and Leadership to approve my study area, research questions, and methodologies. When my research supervisory committee was formed, SXFN elected a representative to help guide the research with community perspectives. To further bring awareness of the research to SXFN community members and to foster feedback from them, community outreach was a top priority. This was accomplished through regular social media presence on SXFN community pages, through co-organizing land tours with SXFN where UBC could provide costs for honoraria and catering, and through hosting two large community dinners in SXFN Territory with interactive discussions, research presentations, question periods, and our own version of wildfire preparedness bingo. These interactions have helped to shape my interpretations of the land that I am studying and have contributed to my understanding of the past, present, and potential future impacts of wildfire on SXFN communities. As part of my commitment to the ICRPA, I am writing a community report that synthesizes my research results in language that will be accessible for SXFN community members. The principles of ethical community-based research employed in this partnership are informed by both expectations from SXFN as well as other leaders in community-based research (Wong et al., 2020; Copes-Gerbitz et al., 2021; Beveridge et al., 2021; KXSA, 2021; Hayward et al., 2021; Saturno et al., 2023; Ignace et al., 2023). In addition to answering the specific research questions detailed in the next section, the goals of this project 9 are to not just reduce harm to the People whose lands we are working on, but provide benefits through a meaningful partnership, ownership of data, inclusion of perspectives, and the advocation for Indigenous rights and local land stewardship decision making. 1.2 Research Objectives The overall research objective of this thesis was to assess potential wildfire behaviour in the dry unceded forests of the SXFN wildland urban interface (hereafter, “interface”) and the efficacy of MDWR and OGMA fuel treatments in reducing wildfire behaviour. In Chapter 2, I quantified the fuels in MDWR and OGMA forests within a 2 kilometre radius of four SXFN reserve boundaries. I then modelled the fuels data using varying weather conditions to assess how fuels and weather may impact wildfire behaviour close to SXFN communities. The main questions addressed in this chapter were: (1) What are the fuel loads in MDWR and OGMA forests of the SXFN interface?; and (2) How do those fuel loads contribute to the likelihood of crown fire under varying fire weather conditions? The outcomes are discussed in the context of proactive forest stewardship to protect SXFN communities and the lands they rely upon from widespread high-severity wildfire. In Chapter 3, I altered the fuels data to simulate eight fuel treatments that comply with legal objectives for MDWR and OGMA, plus an intensive thinning fuel treatment that would require an exemption from those legal objectives. I remodelled the new fuel data under 90th percentile fire weather and assessed changes in predicted wildfire behaviour. The specific questions I asked were: (1) To what degree do simulated fuel treatments that comply with MDWR and OGMA 10 legal objectives reduce fuel loads and alter modelled fire behaviour at the stand-level during high fire danger?; and (2) How do fuel loads and fire behaviour differ when simulated fuel treatments intensively thin the smallest trees at the stand-level? Efficacy in reducing predicted wildfire behaviour was compared among simulated fuel treatments. I discuss the implications of complying with legal objectives for MDWR and OGMA in fuel treatments versus implementing a fuel treatment that intensively thins the forest to protect SXFN communities from widespread high-severity wildfire. In Chapter 4, I discuss the barriers and solutions to mitigating wildfire risk in the SXFN interface. I address the challenges that face SXFN when they exercise their rights to steward their Territory for wildfire resilience. Finally, I outline potential future SXFN-led research that would help them steward the dry forests and grasslands of their Territory for eco-cultural restoration, climate change adaptation, and wildfire risk reduction. 11 Chapter 2: Not If, But When: How Are Forests Surrounding Stswecem'c Xget’tem Communities Likely to Burn? 2.1 Introduction Indigenous Territories in what many now call interior British Columbia (BC), Canada are being impacted by increasing uncharacteristically large, high-severity wildfires (Abbott & Chapman, 2018; Dickson-Hoyle & John, 2021). The 2017, 2018, 2021, and 2023 BC wildfire seasons burnt over 6.28 million hectares (ha), leading to 2.7 billion dollars in BC government suppression costs and the displacement of over 138,000 people (BCWS, 2023c). Some human displacement from 2021 is still ongoing in Lytton, Tl’kemtsin Territory, where two people lost their lives after 90% of the village burned to the ground during the 2021 Pacific Northwest “heat dome” weather event (Hoffman et al., 2022a). Most recently, the 2023 wildfire season broke several BC wildfire records and is characterized as the worst Canadian wildfire season in settler-recorded history (CIFFC, 2023). These fire seasons and the climate events that exacerbated them have had profound impacts on various ecological and cultural (hereafter, “eco-cultural”) systems, including to wildlife habitat, plant communities, watersheds, and all the people who rely on them for various benefits and services (Hoffman et al., 2022a; Dickson-Hoyle & John, 2021; Abbott & Chapman, 2018; Dickson-Hoyle et al., 2024). Rural Indigenous Peoples are disproportionately impacted by wildfire (McGee, 2021; McGee & Christianson, 2021; Batdorf & McGee, 2023), as their communities are often surrounded by abundant grassland or forest vegetation (hereafter, “fuel”), and can be separated from emergency services by rough roads that span large distances. Despite being the most impacted by fire on their ancestral lands, the oppression of Indigenous Peoples since settlers arrived in North America has limited their power over the law making and 12 land management that have contributed to the current wildfire crisis (Whyte, 2018; Copes-Gerbitz et al., 2021; Vinyeta, 2022). Climate change and settler-colonial land management have been attributed to recent extreme fire behaviour in dry forests in what is now called western North America (Stephens et al., 2020; Coogan et al., 2021; Hessburg et al., 2021; Vinyeta et al., 2022; Eisenberg et al., 2024). Settler-colonial and colonial industrialization throughout the world is responsible for climate change, drastically impacting all people, but especially the Indigenous Peoples and Territories that often find themselves at its forefront (Whyte, 2018; Vinyeta, 2022; Sultana, 2022). Climate change is increasing the frequency and duration of hot, dry, windy weather periods (Wotton et al., 2017; Coogan et al. 2019; Parisien et al., 2023) and when these weather periods pair with abundant fuel loads, high-severity wildfires are more likely to occur (Hessburg et al., 2021; Prichard et al., 2021; Stephens et al., 2020; Hagmann et al., 2021; Jones et al., 2021; Schoennagel, 2017; Vinyeta et al., 2022). The fuel loads in dry conifer forests of BC have increased over the past two centuries, largely due to settler-colonial land management, and are now conducive for crown fire, a fire that can spread quickly through the crowns of trees (Coogan et al., 2021). Many researchers have documented fire regime shifts from historical frequent, low-severity, surface fires towards the current large, fast-moving, high-severity crown fires as being connected to shifts in forest and grassland composition, structure, and fuel load (Wong & Iverson, 2004; Daniels, 2004; Harvey et al., 2017; Brookes et al., 2021; Baron et al., 2022; Copes-Gerbitz et al., 2023). Past low- and mixed-severity fire regimes are linked with Indigenous fire stewardship practises, which involves purposeful ignitions by Indigenous Peoples to enhance a variety of eco-cultural needs on the land, such as for food and medicine plants, game habitat, pest management, 13 and ceremony (Lake & Christianson, 2019; Hoffman et al., 2022b; Long et al., 2021; Copes-Gerbitz et al., 2021; Dickson-Hoyle et al., 2021; Nikolakis & Roberts, 2021). In Secwépemcúl’ewc, the lands of the Secwépemc People that overlap with interior BC, Indigenous Fire Stewardship in dry ecosystems created heterogeneous landscapes (Dickson-Hoyle et al., 2021; Brookes et al., 2021; Copes-Gerbitz et al., 2021; Copes-Gerbitz et al., 2023). Though less extensive than today, some parts of forest were dominated with high forest fuel loads that would burn infrequently at high-severity, while other areas that historically had more representation than today, were dominated by open forests with old, fire-resistant trees and diverse understory plant communities that burned frequently at low severities (Turner, 1991; Iverson et al., 2002; Copes-Gerbitz et al., 2023). When Indigenous Peoples were exposed to European diseases, removed from their lands, and had their fire stewardship practises outlawed in the 19th century, fire exclusion in BC began (Pogue, 2017; Copes-Gerbitz et al., 2021; Baron et al., 2022; Copes-Gerbitz et al., 2022a). Fire suppression (Brookes et al., 2021; Copes-Gerbitz et al., 2022a), intensive cattle grazing (Bai & Thompson, 2004; Blackstock & McAllister, 2004; Hamilton & Burton, 2023), and industrial forestry (Day, 1998; Leclerc et al., 2021) also contributed to grassland and forest structure change throughout the 20th and 21st centuries. The combination of these land management decisions with laws that excluded Indigenous Peoples from their ancestral Territories (Copes-Gerbitz et al., 2022a) homogenized previously heterogeneous landscape conditions, shifting forests from fire-resilient to fire-vulnerable states much like other dry conifer forests in western North America (Hessburg et al., 2005; Hessburg et al., 2019; Hagmann et al., 2021; Hessburg et al., 2021; Liang & Hurteau, 2023), while also 14 degrading biodiversity and wildlife habitat (Blackstock & McAlister, 2004; Dawson et al., 2007; Roerick et al., 2019; Waterhouse & Walton, 2021). Following widespread high-grade logging, a form of industrial forestry that targeted large-diameter Douglas-fir trees in interior BC during the mid to late 20th century, the BC government designated certain forests in the Cariboo Region of BC as Mule Deer Winter Range (MDWR) or Old Growth Management Area (OGMA), with some forests being designated as both. Guidance for forest management and harvesting in MDWR and OGMA was outlined in the 1998 Cariboo Chilcotin Land Use Plan Integration Report (IAMC, 1998), but this guidance was updated and legally entrenched a decade later. In 2007, highly prescriptive legal objectives for forest management and harvesting in MDWR (MOE, 2007) were created to promote the maintenance and restoration of mule deer winter habitat (Dawson et al., 2007). Shortly after in 2010, less prescriptive legal objectives for forest management in OGMA (MAL, 2010) were created to protect old growth and natural forest succession (BCSC, 2011). The legal objectives for MDWR and OGMA have played a role in protecting these forests from clear-cut and high-grade logging practises since their creation (Day, 1998; Dawson et al., 2007; Leclerc et al., 2021). However, the MDWR legal objectives have also been described as “extremely constraining to forest management” in a study on barriers to fuel treatments in the Cariboo Region of BC (Melton, 2020, p. 60). Further, MDWR and OGMA were established by settler-colonial powers without consent from Aboriginal Title and Rights holders (hereafter, “rights-holders”). While the legal objectives were designed to protect forests from ecologically destructive management, their complexity and highly prescriptive nature might be the reason that the forestry industry, communities, and the BC government have largely avoided harvesting in MDWR and OGMA 15 forests since their creation. A lack of forest stewardship in MDWR and OGMA has inadvertently maintained forest structures that were degraded by previous settler-colonial land management (Greene, 2021) and these forests are now likely to burn as high-severity crown fire (Day et al., 2010; Abbott & Chapman, 2018; Day, 2019; Melton, 2020; Leclerc et al., 2021; FREC, 2021). From 2007 – 2021, Stswecem’c Xget’tem First Nation (SXFN), home to two northern Secwépemc communities, experienced fast moving crown fires that burnt 20% of their 1.3 million hectare (ha) Traditional Territory, triggering evacuation alerts or orders in 2010, 2017, 2018, and 2021. Secwépemc Peoples have used fire to steward their lands since time immemorial (Dickson-Hoyle et al., 2021; FREC, 2021; Copes-Gerbitz et al., 2023) and have witnessed many changes in their Territories since settlers arrived in great numbers in the 19th century (FREC, 2021). SXFN is deeply aware of their vulnerability to wildfire due to the remoteness of their two communities, the rough road conditions between SXFN and emergency response services, a shortage in human capacity (Bonde, 2011), and the dense forest fuels that surround them (DWB, 2010). In response, SXFN partnered with the University of British Columbia to quantify the forest fuels and crown fire likelihood surrounding their communities using western science. SXFN citizens have deep, multigenerational place-based knowledge about their lands and are rights-holders in their Territory under the Canadian Constitution Act (Constitution Act, 1982) and the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP; United Nations, 2007; SXFN, 2018). The western science generated by this research can be used by SXFN to “walk on two legs”, the practise of utilizing both their traditional knowledge and western science in tandem (Dickson-Hoyle et al., 2021), to inform SXFN-led stewardship practises that promote wildfire resiliency. Specifically, this research was co-developed by SXFN 16 and the University of British Columbia to address two inter-related, high priority questions for SXFN: (1) What are the fuel loads in the MDWR and OGMA forests of SXFN’s wildland urban interface (hereafter, “interface”)?; and (2) How do those fuel loads contribute to the likelihood of crown fire under varying fire weather conditions? Through detailed forest measurements surrounding SXFN communities, I quantified fuel loads and used them to model potential wildfire behaviour during a range of fire weather conditions using the Crown Fire Initiation and Spread model (Alexander et al., 2006). The outcomes are discussed in the context of proactive forest stewardship to protect SXFN communities and the lands they rely upon from widespread high-severity wildfire. 2.2 Materials and Methods 2.2.1 Study Area Since time immemorial, SXFN has stewarded their Traditional Territory, an area of 1.3 million ha in northern Secwépemcúl’ewc, which overlaps with south-central interior BC. The two SXFN communities are in tributary valleys of the Fraser River, in the rain shadow of the Coastal Mountain Range, with a continental climate. Average elevation of the study area is 1062 metres above sea level. Using ClimateBC data, the mean temperatures of the warmest and coldest months were 14.9 ℃ and -6 ℃, respectively from 1991 – 2020 (Wang et al., 2016). Mean annual precipitation is 432 mm and mean May – September precipitation is 197 mm (Wang et al., 2016). Characterized by the biogeoclimatic (BEC) zone system in BC (Pojar et al., 1987), the study area spans the Interior Douglas-Fir and Bunchgrass zones. Vegetation present includes grasslands, and a mixture of open and closed forests dominated by Douglas-fir trees. Other common tree species include lodgepole pine, trembling aspen, white or hybrid spruce, paper 17 birch, and Rocky Mountain juniper (Steen & Coupé, 1997), although most mature lodgepole pines were killed during the mountain pine beetle epidemic in the early part of the 2000s. Common understory species include common juniper, prickly rose, soopolallie, kinnikinnick, and aster (Steen & Coupé, 1997). Historically, forests of SXFN Territory experienced a mixed-severity fire regime with frequent low-severity fires (Iverson et al., 2002; Bai & Thompson, 2004; Daniels, 2004; Harvey et al., 2017; Brookes et al., 2021; Copes-Gerbitz et al., 2023). These low-severity fires were part of Secwépemc fire stewardship practises (Wong & Iverson, 2004; Dickson-Hoyle, 2021; FREC, 2021; Copes-Gerbitz, 2023). Due to the colonization of Secwépemcúl’ewc by settlers, Indigenous cultural practises and the fire regimes that stemmed from them were disrupted (Dickson-Hoyle et al., 2021; Copes-Gerbitz et al., 2021). Human fires were effectively excluded from Secwépemc lands after the Bush Fire Act in the late 19th century, while other ignitions were targeted by BC government fire suppression in the 20th and 21st centuries (Copes Gerbitz et al., 2022a). Research in similar BC ecosystems has found that the combined effects of fire exclusion and suppression have led to widespread fire deficits that are associated with increases in the annual number of fires, burned area, severity, and mean fire size (Baron et al., 2022). A map of the MDWR and OGMA surrounding the study area is featured in Figure 2.1, with wildfire perimeters from 2000 – 2023 overlaid on the landscape. 18 Figure 2.1: Location of the (a) study area in British Columbia and (b) 37 plots in the wildland urban interface surrounding the communities of Stswecem’c and Xget’tem. 2.2.2 Plot Selection Using BC government land management spatial datasets in a geographic information system, I defined my study area by creating the SXFN interface, which is delineated by a 2 kilometre (km) buffer around communities in BC (BCWS, 2023a). The interface surrounding Stswecem’c is 8,297 ha and Xget’tem is 4,641 ha; the total study area is 12,938 ha. Within the study area, I differentiated forests classified as MDWR and OGMA (51%), from other land types (29%), areas burned in the last 20 years (7%; majority MDWR or OGMA), and private land (13%). I superimposed a 500-m grid across the interface and randomly selected 37 study plots using a stratified random sampling design from the forests classified as MDWR, 19 of which were also 19 classified as OGMA. Plot locations were reviewed by SXFN staff to ensure they were not in ecologically or culturally sensitive locations. 2.2.3 Field Methods Forest fuel measurements followed the National Forest Inventory (Gillis et al. 2005), enhanced to include attributes needed for fire behaviour modelling (Figure 2.2). At each plot, I established a plot centre and recorded the UTM coordinates, elevation at metres above sea level, slope angle (%), and aspect (°). Plot layout is shown in Figure 2.2. Figure 2.2: Diagram showing the configuration of forest fuels mensuration plots established at each study plot (Rutherford, 2023). Trees were sampled in nested plots around plot centre. Canopy trees (DBH ≥ 12.5 cm) were inventoried in a 0.04 ha circular plot with a radius of 11.28 metres (m). At 5 plots where density 20 was >750 trees ha -1, the plot radius was reduced to 7.98 m. Subcanopy trees (12.5 cm > DBH ≥ 5 cm) were inventoried in a 0.01 ha circular plot with a radius of 5.64 m. At 3 plots where density was >3,000 trees ha-1, the plot radius was reduced to 3.99 m. Species, DBH, tree vigour, total height, and height to the base of the live crown, which is the lowest continuous tree branch with live foliage, was recorded for each tree. In a 0.01 ha circular satellite plot (radius = 5.64 m) at the end of the first fuel transect, live tree saplings (height ≥ 1.3 m and DBH < 5 cm) were tallied by species and average DBH, total height, and height to live crown were measured. Within the satellite plot, percent cover of live and dead grasses was recorded, and average height was measured. Two 30 m transects were laid perpendicular to one another through plot centre; the first transect was oriented on a random azimuth. The slope of each transect was recorded. Canopy cover was measured at 2 m intervals along each transect (n = 30 points) using a tube densitometer to estimate percent cover of deciduous and conifer trees for each plot. Surface woody fuels were sampled along each transect using the line intercept method (Van Wagner, 1982). The sampling effort varied with wood size. Wood with diameters <0.5 cm were tallied from 0 – 2 m and 28 – 30 m; diameters from 0.5 – 7 cm were measured from 0 – 5 m and 25 – 30 m; diameters from 7 – 30 cm were measured from 0 – 10 m and 20 – 30 m; and diameters > 30 cm were measured from 0 – 30 m. Species, diameter (cm), and decay class (1 – 3 sound, 4 – 5 rotten) were recorded for each piece of wood. At 7.5 and 22.5 m along each transect, the depth of the litter and duff layers were measured. 21 2.2.4 Forest Fuel Calculations Data from live trees were used to calculate density (trees ha-1), basal area (m2 ha-1), and canopy fuel metrics for each plot; all metrics were scaled to one hectare for subsequent analyses. Canopy bulk density (CBD in kg m-3) was calculated for each tree using species-specific equations for western Canada (Ung et al., 2008). Live crown base height (CBH in m) was calculated as a weighted average for sapling, subcanopy, and canopy trees. Mean tree height (m) was calculated as weighted average for subcanopy and canopy trees only. Density and mean tree height metrics were used along with other plot characteristics (Table A.1) to assign Canadian Forest Fire Behaviour Prediction System (FBP) fuel types based on the forest structure decision key from Perrakis et al. (2018). Surface fuel load was calculated from the fine woody surface fuels (diameter < 7 cm) and ground fuels at each plot. For each woody fuel, log diameter was converted to volume, while correcting transect slope distances to horizontal distances (Van Wagner, 1982), and multiplied by the species-specific wood density (Gonzalez, 1990) to derive biomass (kg m-2). The sum of the woody fuel biomass represented the surface woody fuel load for each plot. The mean litter and duff depths were converted to volumetric fuel loads (Woodall and Monleon, 2008) and summed as the ground fuel loads (kg m-2). 2.2.5 Fire Behaviour Modelling The Crown Fire Initiation and Spread (CFIS) model (Alexander et al., 2006) was used to estimate the likelihood of crown fire occurrence and fire type for each plot. Model inputs including CBH and CBD are derived from the field data, while wind speed (km hr-1) is derived 22 from fire weather data. Estimated fine fuel moisture (EFFM) for dead surface fuels are derived from slope, aspect, canopy cover, and fire weather data using a sub-model based on Rothermel (1983). In CFIS, surface fuel consumption is represented by one of three categories (<1.0 kg m-2, 1.0‒2.0 kg m-2, and >2.0 kg m-2). I estimated surface fuel consumption (kg m-2) using equations from the FBP system that are based on fuel type (Perrakis et al., 2018) and fire weather data (FCFDG, 1992). For plots that were classified as an FBP grass fuel type, the photo load method (Keane & Dickenson, 2007) was used to estimate grass fuel load and the surface fuel consumption category. For the forested FBP fuel types, I used FBP equations (Wotton et al., 2009; de Groot et al., 2009) to estimate plot-level surface fuel consumption, then compared this value with the calculated total surface fuel load. To avoid over-estimation, the lower of the two values was used to select the CFIS surface fuel consumption category. For each plot, I extracted daily fire weather data and indices for 2007‒2021 from the Canadian gridded dataset which extrapolates weather attributes from the Churn Creek, Place Lake, and Meadow Lake weather stations to specific plot locations (Canadian Forest Service, 2023). To be consistent with BC Wildfire Service (BCWS), I analyzed fire seasons spanning from May 15th – August 31st because the study area is in the Humid Continental Highlands Ecodivision (BCWS, 2023b). I used the fire weather index (FWI) to represent the 50th, 70th, 90th, and 97.5th percentiles of weather conditions at each plot. In the FBP system, FWI is a numeric rating of potential fire intensity that integrates temperature, relative humidity, wind speed and precipitation to indicate fire danger (Van Wagner, 1987). The same FWI value can result from multiple combinations of these weather attributes. Thus, I represented fire weather associated with the 50th, 70th, 90th, and 97.5th percentiles of FWI for each plot, as follows. For the 15 fire seasons from 2007‒2021 (n = 23 1,635 days), I ranked individual days by FWI and identified four subsets of days for each plot corresponding to the 50th (45‒55th), 70th (65‒75th), 90th (85‒95th), and 97.5th (95‒100th) percentiles. To summarize fire weather of each percentile, I calculated the mean and standard deviation of the daily temperature, relative humidity, wind speed, precipitation and FWI across all plots. For each plot, multiple iterations of the model CFIS were run using the fire weather for individual days in each percentile to estimate the likelihood of crown fire occurrence, predict fire types (e.g., surface, passive crown, or active crown fire), rate of spread, and surface fire intensity. In CFIS, at plots with calculated likelihood of crown fire ≤50%, the predicted fire type is a surface fire, a fire that burns through the surface fuel layer while excluding trees (Coogan et al., 2021); plots with a likelihood of crown fire >50% are predicted to burn as a crown fire (Coogan et al., 2021). For the latter plots, an active crowning metric is calculated from the crown fire rate of spread and CBD (Van Wagner, 1977). If the calculated active crowning metric is ≤1, passive crown fire, a surface fire that includes the torching of a small group of trees (Stephens et al., 2012), is predicted; if the metric is >1, active crown fire, a fire that advances through tree fuels usually in conjunction with surface fires (Coogan et al., 2021), is predicted. Plot-level predicted fire behaviour was summarized for each of the four fire-weather percentiles as the mean and range of crown fire likelihood and the proportion predicted to be surface, passive crown, or active crown fire. Rate of spread and surface fire intensity fire behaviour were also predicted for each plot under each weather scenario. For active crown fire, rate of spread was calculated using coefficients 24 from the Canadian experimental crown fires, wind speed, CBD, and EFFM (Cruz et al., 2005). In contrast, the active crown fire rate of spread value and a negative active crowning metric were used to calculate passive crown fire rate of spread (Cruz et al., 2005). Surface fire rate of spread was calculated using an equation from the Canadian Conifer Pyrometrics model (Perrakis et al., 2023). Surface fire intensity was calculated using Byram’s (1959) fire intensity equation. 2.2.6 Comparison Fire Weather and Fuels Among Predicted Fire Types The BC government requires fuel treatment prescriptions to meet wildfire behaviour targets at the 90th percentile of fire weather (BCWS, 2023b); therefore, I investigated how different fire weather and fuel loads influenced predictions of fire type under the 90th percentile of the FWI. Data from all plots and days were pooled, then stratified according to predicted fire type (surface, passive crown, or active crown fire). Key fire weather (FWI, temperature, relative humidity, and wind speed) and fuel attributes (tree density, basal area, canopy cover, CBD, CBH, total surface fuel, fine surface fuel, and ground fuel), as well as predicted rate of spread and surface fire intensity, were compared among predicted fire types. Because these attributes did not meet the assumption of normality, I used a Kruskal-Wallis test on ranks and Dunn’s method for pairwise comparisons when significant differences were detected for median values (α = 0.05). 25 2.3 Results 2.3.1 Forest Composition and Structure Of the 37 plots within the SXFN interface, 20 plots were established around Stswecem’c, and 17 plots were established around Xget’tem; all plots were in MDWR forests, while 19 plots were also in OGMA (Table 2.1). Most plots were established in Douglas-fir-leading stands, but four were mixed-species stands with combinations of Douglas-fir, lodgepole pine, hybrid spruce, Rocky Mountain juniper, and trembling aspen present. Lodgepole pine dominated one plot. The density of all live conifer trees ranged from 175 – 9,125 trees ha-1, with a median of 2,275 trees ha-1 (Table 2.1). Canopy trees (14% of sampled trees) ranged from 125 – 1,100 trees ha-1 and subcanopy trees (17%) ranged from 0 – 2,000 trees ha-1, while saplings (67%) ranged from 0 – 8,500 trees ha-1. The basal area of live conifer trees ranged from 7 – 58 m2 ha-1, with a median of 24 m2 ha-1 (Table 2.1). Given their large diameters, canopy tree basal area ranged from 4 – 55 m2 ha-1 and represented 79% of total basal area sampled. Although denser, basal area was less for subcanopy trees (0 – 13 m2 ha-1; 12% of all basal area) and saplings (0 – 8 m2 ha-1; 9% of all basal area). 26 Table 2.1: Summary of the forest density and basal area of 37 plots in the wildland urban interface of two SXFN communities, Stswecem’c (plots 1‒20) and Xget’tem (plots 21‒37). Community Plot # Density of Live Conifers (trees ha-1) Basal Area of Live Conifers (m2 ha-1) Canopy Sub-canopy Sapling Total Canopy Sub-canopy Sapling Total Stswecem’c 1 275 400 300 975 12.5 2.6 0.1 15.2 2 200 100 0 300 6.3 0.3 0.0 6.6 3 750 2,800 0 3,550 35.4 14.9 0.0 50.3 4 125 100 0 225 19.6 0.8 0.0 20.4 5 375 1,600 300 2,275 33.6 8.6 0.3 42.5 6 750 200 300 1,250 25.2 0.5 0.4 26.1 7 850 1,900 200 2,950 43.4 8.5 0.1 52.0 8 350 200 0 550 17.5 0.7 0.0 18.2 9 300 800 1,600 2,700 16.0 3.9 0.5 20.4 10 325 1,000 1,700 3,025 24.1 4.2 1.2 29.5 11 250 2,000 1,800 4,050 15.4 12.7 2.2 30.3 12 325 1,200 7,600 9,125 26.5 4.9 5.4 36.8 13 300 300 4,300 4,900 54.8 1.3 2.1 58.2 14 225 100 300 625 31.9 0.2 0.0 32.1 15 125 3,400 2,200 5,725 19.6 16.7 1.5 37.8 16 425 300 1,700 2,425 8.6 2.1 1.2 11.9 17 425 0 3,000 3,425 41.6 0.0 2.6 44.2 18 175 1,800 800 2,775 6.4 9.8 0.6 16.8 19 325 600 6,800 7,725 22.8 1.9 4.2 28.9 20 200 1,000 5,400 6,600 6.8 4.5 4.6 15.9 Xget’tem 21 175 0 0 175 18.4 0.0 0.0 18.4 22 275 200 400 875 15.8 0.9 0.3 17.0 23 125 3,800 1,500 5,425 11.6 18.0 1.0 30.6 24 850 700 0 1,550 15.5 4.4 0.0 19.9 25 125 0 1,500 1,625 24.5 0.0 1.0 25.5 26 150 600 1,000 1,750 5.8 3.2 2.0 11.0 27 500 1,400 600 2,500 14.4 9.8 1.9 26.1 28 1100 300 400 1,800 38.8 2.4 0.3 41.5 29 600 1,100 2,600 4,300 17.9 5.6 1.8 25.3 30 375 400 200 975 20.2 2.6 0.1 22.9 31 1025 100 400 1,525 40.4 0.4 0.3 41.1 32 225 200 500 925 18.4 0.7 0.3 19.4 33 300 300 200 800 15.2 1.1 0.1 16.4 34 175 1,300 1,700 3,175 4.0 6.6 1.2 11.8 35 450 200 700 1,350 21.7 1.5 0.5 23.7 36 150 600 2,300 3,050 12.6 3.8 1.6 18.0 37 450 100 200 750 13.3 0.7 0.1 14.1 27 2.3.2 Fuel Characteristics The fuel types and characteristics varied among the 37 plots (Table 2.3; Appendix Table A.1). Most plots (n=20) were classified as the Conifer 7 (C7) fuel type; however, Conifer 3 (C3, n=13), Open 1 (O1, n=2), Conifer 4 (C4, n=1), and Mixedwood 2 (M2, n=1) fuel types were also present. At the plot level, CBH (mean height to live crown) ranged from 0.6 – 8.2 m; CBD ranged from 0.04 – 0.65 kg m-3; and fine surface fuels ranged from 0.2 – 1.98 kg m-2 (Table 2.2). 28 Table 2.2: Fuel types and characteristics of the 37 plots in the wildland urban interface of two SXFN communities Stswecem’c (plots 1‒20) and Xget’tem (plots 21‒37). Community Plot # Fuel Type CBH (m) CBD (kg m-3) Total Surface Fuels (kg m-2) Fine Surface Fuels (kg m-2) Ground Fuels (kg m-2) Stswecem’c 1 C3 1.6 0.12 1.16 0.36 0.62 2 M2 2.7 0.04 6.82 0.41 4.42 3 C3 4.4 0.46 33.48 0.61 1.46 4 O1b 1.4 0.14 9.30 0.36 0.41 5 C3 3.3 0.29 13.75 0.74 2.50 6 C3 3.5 0.23 4.22 1.24 1.30 7 C7 5.0 0.29 13.91 0.63 3.04 8 C7 2.9 0.15 7.27 0.73 1.22 9 C4 2.0 0.16 9.48 0.48 2.00 10 C3 1.5 0.19 14.30 0.59 0.99 11 C3 3.0 0.47 7.94 1.98 1.87 12 C3 1.9 0.66 16.07 0.36 3.41 13 C7 1.1 0.28 2.43 0.48 1.48 14 C7 2.0 0.15 7.90 0.78 0.93 15 C7 1.7 0.33 5.97 0.32 2.05 16 C3 1.6 0.07 6.46 0.91 1.88 17 C7 2.1 0.29 13.14 0.57 4.65 18 C7 0.9 0.16 12.74 0.49 2.69 19 C7 1.2 0.31 11.24 0.42 1.73 20 C7 0.6 0.17 12.40 0.61 2.27 Xget’tem 21 C7 8.2 0.09 1.53 0.28 0.94 22 O1b 3.1 0.11 41.63 0.70 1.33 23 C3 3.1 0.29 16.66 0.27 1.09 24 C3 4.9 0.26 4.84 0.96 1.49 25 C7 1.3 0.11 0.72 0.20 0.49 26 C3 2.5 0.13 7.93 0.25 2.10 27 C7 5.8 0.30 10.36 0.64 2.16 28 C7 6.7 0.49 5.19 0.67 4.18 29 C3 1.6 0.25 7.03 0.22 2.76 30 C7 6.1 0.29 5.45 1.02 2.81 31 C7 5.4 0.22 4.12 0.35 2.89 32 C7 1.4 0.09 20.39 0.95 0.74 33 C7 4.6 0.14 13.82 0.47 2.39 34 C3 1.3 0.13 26.71 1.06 6.28 35 C7 2.2 0.12 15.48 0.94 3.69 36 C7 1.6 0.18 6.12 0.34 2.73 37 C7 2.8 0.12 16.78 0.47 3.00 29 2.3.3 Fire Weather Though FWI is calculated based on varying weather attributes, FWI generally increased with warmer temperatures, higher wind speeds, and lower values of relative humidity and precipitation (Table 2.5). Almost all 90th and 97.5th FWI percentile days are from 2009, 2015, 2017, 2018, and 2021 (Figure 2.3). Table 2.3: Fire weather attributes for increasing percentiles of the fire weather index (FWI) during 15 fire seasons (May 15th – August 31st) from 2007‒2021. Data are pooled for all 37 plots; averages are followed by standard deviations in parentheses. Percentile class (range) Number of days (n) Temperature (℃) Wind Speed (km hr-1) Relative Humidity (%) Precipitation (mm) Fire Weather Index 50th (45-55) 6,066 19.5 (±4.0) 10.7 (±3.8) 42 (±9.6) 0.4 (±0.6) 15 (±2) 70th (65-75) 6,020 21.8 (±3.9) 10.5 (±3.5) 37 (±9.1) 0.2 (±0.3) 24 (±2) 90th (85-95) 6,047 24.2 (±3.9) 12.1 (±3.7) 29 (±6.8) 0.1 (±0.2) 37 (±4) 97.5th (95-100) 3,653 26.7 (±4.4) 14.0 (±4.7) 23 (±5.8) 0.0 (±0.1) 49 (±8) 30 Figure 2.3: Distribution of increasing percentiles of fire weather during 15 fire seasons from 2007‒2021. Data are pooled for all 37 plots. Inter-annual variation in the proportion of days with moderate (50th percentile) to extreme (97.5th percentile) during 15 fire season from 2007 – 2021. Days in the “All other days” category are within the 1st – 45th FWI percentile range, representing mild fire weather. The proportion of weather days (%) in each year is labelled for the “All other days” and 97.5th weather scenarios. 2.3.4 Crown Fire Likelihood The likelihood of crown fire varied within and among plots (Figure 2.4). Variation within each plot resulted from day-to-day variation in observed fire weather, whereas the variation among plots resulted from differences in forest structure and fuels and their interactions with fire weather. Overall, crown fire was more likely than surface fire across the range of fire weather conditions tested. Even under moderate weather conditions (50th percentile of FWI), 31 of 37 plots (84%) had median crown fire likelihoods >50%, indicating crown fire was more likely than surface fire on most days. As fire weather becomes hotter, drier, and windier, three trends were evident: 1) fewer plots and days within plots had median likelihoods of crown fire <50%, 2) an 49 56 22 51 74 49 54 40 31 62 13 39 61 58 150 0 1 0 0 0 0 4 6 0 31 8 1 3 2201020304050607080901002007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021Proportion of Weather Days(%)YearfAll other days 50th 60th 70th 80th 90th 97.5th31 increasing proportion of plots and days had median likelihoods of crown fire ≥90%, and 3) variation in likelihood of crown fire within and among plots also decreased. Specifically, at the 70th percentile, only 3 plots had median crown fire likelihoods of <50%, and at the 97.5th percentile, all but 6 plots had medians ≥90%. 32 Figure 2.4: Variation in crown fire likelihood among plots and from moderate (top) to extreme (bottom) fire weather scenarios. Plots 1‒20 surround Stswecem’c and plots 21‒37 surround Xget’tem. For each plot, the box represents the 25th and 75th percentiles of fire weather index (FWI); the midline is the median and cross (x) is the mean; whiskers are the 5th and 95th percentiles; and circles are outliers. In each panel, dashed and solid horizontal lines indicate 50% and 90% crown fire likelihood, respectively. 33 2.3.5 Fire Type Predictions of fire type paralleled the likelihoods of crown fire, with much less surface fire than crown fire and increasing proportions of active relative to passive crown fire as weather became more extreme (Figure 2.5). Under moderate weather conditions (50th percentile of FWI), surface fire was predicted on 23% of days (n = 6,066, all plots combined), while passive and active crown fire were predicted on 34% and 43% of days, respectively. Surface fire was predicted for >50% of days at only 6 of 37 plots, while another 6 plots had no surface fire predictions (Figure 2.5a). Active crown fire was predicted at 33 plots, with potential for active crown fire on >50% of days at 16 of 37 plots. These patterns were enhanced under extreme fire weather (97.5th percentile of FWI). Surface fire was predicted on only 3% of days (n = 3,653, all plots combined), while passive and active crown fire were predicted on 15% and 82% of days, respectively. Surface fire predictions were limited to 5 plots (1, 4, 21, 22, and 25; Figure 2.5d). In stark contrast, active crown fire was 100% of the predicted fire types at 20 plots. 34 Figure 2.5: Proportions of fire types predicted among plots and from moderate (top) to extreme (bottom) fire weather scenarios. Plots 1‒20 surround Stswecem’c and plots 21‒37 surround Xget’tem. Blue bars represent surface fire predictions, yellow represent passive crown fire, and red represents active crown fire. In each panel, the dashed horizontal line indicates 50% of predictions. 35 2.3.6 Rate of Spread and Surface Fire Intensity Rate of spread and surface fire intensity (Figure 2.6) varied among plots within all fire weather conditions and increased as fire weather became more extreme. The median rate of spread under moderate weather conditions (50th percentile of FWI) was 9 m min-1, contrasting the median of rate of spread under extreme fire weather (97.5th percentile of FWI) of 31 m min-1. Similarly, median surface fire intensity was 4,538 kW m-1 and 18,238 kW m-1 under moderate and extreme fire weather conditions, respectively. Rates of spread ranged from 0 m min-1 (multiple plots; 467 days) to 58 m min-1 (plot 30; 1 day) under moderate weather conditions but increased to 3 m min-1 (plots 4 and 22; 2 days and 3 days respectively) to 111 m min-1 (plot 28; 1 day) under extreme weather conditions. Similarly, surface fire intensity ranged from 1 kW m-1 (plots 4 and 22; 9 days and 2 days respectively) to 46,184 kW m-1 (plot 30; 1 day) under moderate weather conditions and 29 kW m-1 (plot 22; 3 days) to 93,680 kW m-1 (plot 30; 1 day) under extreme weather conditions. 36 Figure 2.6: Variation in (a) rate of spread (top) and (b) surface fire intensity (bottom) in moderate to extreme fire weather (FWI) scenarios. Data are pooled for all 37 plots and 15 fire seasons from 2007‒2021. For each scenario, the box represents the 25th and 75th percentiles of FWI; the midline is the median and cross (x) is the mean; whiskers are the 5th and 95th percentiles; and circles are outliers. 37 2.3.7 Comparison of Fire Weather and Fuels Among Predicted Fire Types Fire weather and fuel load attributes varied within and among the three fire types predicted at the 90th percentile of FWI (n=6,047) (Table 2.4). A summary of statistically significant differences in median values among predicted fire types follows. Passive crown fires had higher FWI and lower relative humidity than surface and active crown fires, which did not differ significantly from each other. Temperature differed among all three fire types and was highest for surface fire and lowest for passive crown fire. Wind speed also differed among fire types and increased from surface to passive crown, then active crown fire. Overall, active crown fire was predicted for plots with the highest fuel loads, followed by passive crown, then surface fires. However, plots with passive crown fire had the highest median sapling density, but the lowest total and canopy basal areas. Crown base height was lowest for passive crown fire and did not differ significantly between surface and active crown fires. Total surface fuel loads were highest for passive crown fire, followed by active crown fire, but were much lower where surface fire was predicted. Similarly, ground fuel loads were lowest for surface fire and did not differ significantly between active and passive crown fires. As expected for fire behaviour metrics, active crown fire had the highest rate of spread and surface fire intensity; passive crown fire was intermediate; and surface fire was lowest (Table 2.4). 38 Table 2.4: Comparison of fire weather, fuel loads, and predicted fire behaviour among predicted fire types at the 90th percentile of the fire weather index. For each attribute medians are followed by 25th and 75th percentiles in brackets; values followed by the same superscript were not significantly different (α = 0.05). Attribute Surface Fire (n=349) Passive Crown Fire (n=1,474) Active Crown Fire (n=4,224) Fire Weather Fire Weather Index 37 (34, 40)a 35 (33, 39)b 37 (34, 40)a Temperature (℃) 26 (21, 27)a 24 (21, 27)c 24 (22, 27)b Relative Humidity (%) 28 (24, 32)b 30 (26, 35)a 28 (24, 33)b Wind Speed (km hr-1) 10 (8, 11)c 11 (9, 13)b 12 (10, 14)a Precipitation (mm) 0.0 (0.0, 0.2)a 0.0 (0.0, 0.2)a 0.0 (0.0, 0.2)a Fuel Loads Total Density (trees ha-1) 875 (175, 975)c 1350 (800, 2,425)b 2775 (1,525, 4,300)a Canopy Density (trees ha-1) 175 (175, 275)c 225 (175, 425)b 325 (225, 600)a Subcanopy Density (trees ha-1) 100 (0, 200)c 300 (200, 600)b 600 (200, 1,400)a Sapling Density (trees ha-1) 300 (0, 400)c 700 (200, 1,600)a 600 (200, 2,300)b Total Basal Area (m2 ha-1) 18.4 (17.0, 18.4)b 16.4 (11.8, 19.4)c 28.9 (20.0, 41.1)a Canopy Basal Area (m2 ha-1) 18.4 (15.8, 18.4)b 12.9 (6.3, 18.4)c 20.2 (15.2, 33.6)a Subcanopy Basal Area (m2 ha-1) 0.8 (0.0, 0.9)c 1.5 (0.7, 3.2)b 3.8 (0.9, 8.5)a Sapling Basal Area (m2 ha-1) 0.1 (0.0, 0.3)c 0.5 (0.1, 1.2)b 0.6 (0.1, 1.9)a Canopy Cover (%) 30 (16.7, 36.7)c 50 (50.0, 66.7)b 53 (46.7, 63.3)a Canopy Bulk Density (kg m-3) 0.11 (0.09, 0.12)c 0.12 (0.09, 0.14)b 0.26 (0.17, 0.29)a Crown Base Height (m) 3.1 (1.6, 8.2)a 2.0 (1.4, 2.7)c 2.8 (1.6, 4.6)a Total Surface Fuel (kg m-2) 1.53 (1.53, 41.63)c 9.49 (6.82, 15.48)a 9.30 (5.44, 13.82)b Fine Surface Fuel (kg m-2) 0.35 (0.28, 0.69)c 0.49 (0.41, 0.94)b 0.57 (0.36, 0.73)a Ground Fuel (kg m-2) 0.94 (0.62, 1.33)b 2.10 (1.22, 3.69)a 2.16 (1.46, 2.81)a Predicted Fire Behaviour Rate of Spread (m min-1) 3.3 (2.7, 4.3)c 8.5 (7.5, 10.3)b 26.7 (20.9, 33.6)a Surface Fire Intensity (kW m-1) 967 (31, 1,443)c 4,799 (4,081, 6,513)b 16,138 (12,036, 21,912)a 39 2.4 Discussion Excessive fuel loads in the interface surrounding SXFN, driven by areas of dense sapling and subcanopy trees, contribute to high likelihoods of crown fire, which escalate as fire weather increases from moderate to extreme conditions. Even under the 50th percentile of FWI, representing moderate or average summer weather conditions, crown fire dominated predictions, indicating that high forest fuel loads across the study area largely impact predicted fire behaviour. Consistent with well-documented relationships between fire weather and behaviour (FCFDG, 1992; Podur & Wotton, 2011; Wang et al., 2017), model simulations show that the likelihood of crown fire occurrence increases as weather becomes increasingly hot, dry, and especially, windy. Under the most extreme FWI conditions in the last 15 years (i.e. 90th and 97.5th percentiles of FWI), very little surface fire was predicted for the interface surrounding SXFN communities, and greater proportions of active crown fire were predicted than passive crown fire. The most extreme FWI scenario results are concerning for SXFN, as they represent weather conditions that dominated in recent years when wildfires burned close to their communities and are becoming more common with climate change (Figure 2.3; Wang et al., 2015; Zhang et al., 2023; Parisien et al., 2023). 2.4.1 CFIS Modelling Relays the Importance of Wind and Ladder Fuels Both fire weather and fuel structures interact to produce strong crown fire likelihood predictions in CFIS, with high wind speeds and excessive ladder fuels being critical drivers for fire behaviour results in this study. At the 90th percentile weather conditions, days predicted to burn as surface fire had lower median wind speeds than days predicted to burn as crown fires, with active crown fire days having the highest median wind speed. This finding aligns with other fire 40 behaviour research, as winds dry fine fuels, accelerate rates of surface fire spread, increase flame lengths that propagate vertically into tree crowns, and increase horizontal spread of fire from crown to crown (Beer, 1991; Fendell & Wolff, 2001; Abatzoglou et al., 2018; Ghodrat et al., 2021). In another study where wildfire behaviour was modelled for forests similar to the ones in this study, CFIS predicted more crown fire than other fire behaviour models and these results indicated that CFIS may be more sensitive to wind speed than the other models used (Scott, 2006; see 2.4.2 CFIS Modelling Limitations). Despite this, the CFIS results in this study are consistent with recently observed wind-driven fires in dry BC forests that have burned at high severities and intensities, such as the 2017 Elephant Hill fire (Dickson-Hoyle & John, 2021). Dense fuel structures have also contributed to recent large, fast moving high-severity fires dry forests of BC (Abbott & Chapman, 2018; Dickson-Hoyle & John, 2021). In particular, the densities of small trees, such as the subcanopy and sapling layers in this study, play a critical role as ladder fuels in the propagation of fire from surface fuels to larger tree canopies. During the 90th percentile of fire weather conditions, plots predicted to burn as crown fire had significantly higher median subcanopy and sapling live conifer densities than in plots predicted to burn as surface fire. As small trees typically have lower live branch heights (i.e., lower canopy base height) than large trees, the presence of high densities of subcanopy and sapling trees likely lowered average live branch height for plots, adding to the presence of ladder fuels. Plots with passive crown fire predictions at the 90th percentile of weather were associated with lowest median live branch height compared to other fire types, indicating that low live branch height is a key fuel attribute for determining the torching of individual clumps of trees that occurs in passive crown fire. In contrast, active crown fire was associated with both low median live 41 branch height and high median canopy bulk density, indicating that active crown fire is unlikely without horizontally continuous canopy fuels. For instance, plot 32 has a low canopy base height of 1.4 m and abundant surface fuels, but a low canopy bulk density of 0.09 m3 kg-1. Despite high crown fire likelihood under extreme fire weather at this plot, most days were predicted to be passive crown fire versus active crown fire likely due to its low CBD. Moving down the fuel profile, higher surface and ground fuels resulted in crown fire predictions under 90th percentile weather, and lower surface and ground fuels resulted in surface fire predictions. It is notable that the median fine surface fuel load for all plots is <1 kg m-2, the maximum fuel load targeted after a fuel treatment in BC (BCWS, 2023a). Given low median fine fuel loads in the plots, fuels from small diameter trees likely had the greatest influence of all the fuel attributes on crown fire likelihood. 2.4.2 CFIS Modelling Limitations The modelled fire behaviour in this research is a prediction given the fuel and weather data for the study locations, and the parameters of the models used. CFIS, as with all models, has limitations in its ability to predict real-world fire behaviour (Alexander & Cruz, 2013). CFIS was designed to predict stand-level crown fire behaviour in live conifer-dominated forests characteristic of those in the boreal region of Canada (Alexander et al., 2006; Alexander & Cruz, 2015). Alexander and Cruz, two of the creators of CFIS, have written that CFIS is meant to be used with data from “healthy, single-age and single stratum conifer forests” (i.e. not dead forests or live forests with variable vertical complexity) and that the model does not incorporate the effects of slope on fire behaviour (2015; p. 249). Using CFIS in this study area has limitations, as many plots were within multi-aged forests, or had substantial broadleaf or dead tree densities. 42 Additionally, the SXFN interface is within two valleys with variable topography, and this would likely impact fire behaviour there. However, it is unknown whether these model suitability factors would under or over-predict crown fire in this study area. Further limitations arise from the scope of fuel attributes used in CFIS. CFIS, like most fire behaviour models, does not account for dead trees because of an assumption that dead trees have fewer fine fuels (i.e. needles and small-diameter branches) that promote fire spread. The effect of standing dead trees on fire behaviour is unresolved in fire science (Hummel & Agee, 2003; Jenkins et al., 2012; Cohn et al., 2014; Vane et al., 2017; Stephens et al., 2018). Some studies have found that the presence of dead trees decrease fire behaviour due to the increased gaps between live tree crowns, decreases in CBD, and increase in branch height (Vane et al., 2017; Cohn et al., 2014). However, other researchers have noted that the presence of dry, dead trees killed by insect attacks, disease, or drought increases fire behaviour through increases to the surface fuel load and low fuel moisture levels (Hummel & Agee, 2003; Jenkins et al., 2012; Stephens et al., 2018). There is a need to investigate how densities of dead trees, especially abundant small dead trees, may impact fire spread and behaviour in this ecosystem, especially as Douglas-fir beetle and western spruce budworm outbreaks, among other insect outbreaks, have been abundant throughout the region (Axelson et al., 2015; Harvey et al., 2018). Another limitation of CFIS, relevant in our study area, is the focus on living biomass without accounting for the height of dead branches when calculating fuel metrics. In the dense forests in our study area, Douglas-fir trees are known to self-prune, which leads to higher live branch heights but persistently low dead branch heights. In many plots, it was observed that the average 43 base height of dead branches was lower than that of the live canopy. The vertically continuous nature of dead branches in the study area, as well as the arboreal lichens that were attached to some of them, have the potential to impact crown fire likelihood, especially under extreme fire weather. Not including the height of continuous dead branches when estimating the crown base height of a tree underestimates the presence of ladder fuels and likelihood of crown fire. Thus, there is a need to further understand the difference between how continuous dead branches, with or without lichens, and live branches act as ladder fuels to propagate surface fire into the crowns of trees. Due to known CFIS limitations, this research enhanced fire behaviour prediction modelling by using CFIS and FBP models in tandem. To customize CFIS to my study area, I used equations from the FBP system (FCFDG, 1992) that are based on unique fuel types (Perrakis et al., 2018) to calculate surface fuel consumption. By comparing the FBP surface fuel consumption calculation to the measured surface fuel quantities and choosing the lesser of the two to use as the surface fuel consumption input in CFIS, the surface fuel consumption input is kept within the confines for the unique fuel type of the plot being modelled. The incorporation of the FBP equations in surface fuel consumption estimation partially addresses the limitation of CFIS being developed based on experimental fires from primarily within boreal forest fuel types (Alexander et al., 2006) instead of the other fuel types found within this study area (Table 2.2, Table A.1). All fire behaviour modelling, using CFIS or any other model, is unable to deliver a perfect representation of complex ecological processes (Alexander & Cruz, 2013); however, wildfires in similar forest types within SXFN Territory have burned as large, fast moving, high-severity 44 active crown fires during hot, dry, and windy weather conditions within the last decade (Abbott & Chapman, 2018; Dickson-Hoyle & John, 2021). Thus, recent nearby fire behaviour and the existing dense forest conditions in the SXFN interface supports the predicted fire behaviour results from CFIS and the interpretation of high wildfire risk in the study area. 2.4.3 Settler-Colonial Land Management Drove Fuel Departure The fuel quantities in the study area starkly contrast with criteria for fire-resistant dry conifer forests (Agee & Skinner, 2005) and likely vary from their historical structures as described by SXFN knowledge holders, who have said that forests have undergone densification throughout their lifetimes (FREP, 2021; pers. comm., A. Camille). Additionally, local historical forest and fire regime reconstructions indicate that historically dry conifer forests in Secwépemcúl’ewc and other parts of interior BC were likely less vulnerable to high-severity fire than they are today (Iverson et al., 2002; Daniels, 2004; Harvey et al., 2017; Brookes et al., 2021; Copes-Gerbitz et al., 2021; Baron et al., 2022; Copes-Gerbitz et al., 2023). Multiple factors interact to explain degraded fire resiliency. Climate patterns play a role in the transition from fire resilient to fire vulnerable dry forests. Harvey et al. (2017) found that historical wide-spread fires within 20 km of SXFN communities were associated with extreme drought years and concluded that drought and fire will be more prevalent on this landscape in the future due to increasing temperatures associated with climate change. However, changes to forest structure and fuel loads are equally important and result from the cumulative impact of the past two centuries of settler-colonial land management. Dry conifer forests in SXFN Territory have followed in the trajectory of other formally frequent-fire dry conifer forests in western North America (Hessburg et al. 2021; Brookes et al., 2021; Copes-Gerbitz et al., 2022a). Indigenous fire stewardship, or cultural fire, 45 was disrupted by settler-colonial policies that removed Indigenous Peoples from their Territories and outlawed their stewardship practises in the 19th century (Dickson-Hoyle et al., 2021; Copes-Gerbitz et al., 2021; Vinyeta et al., 2022; Copes-Gerbitz et al., 2023). As a result, cohorts of small trees were able to establish throughout dry interior forests due to the exclusion of frequent surface fire. Concurrently, newly introduced livestock grazing reduced grassy and herbaceous surface fuels, which may have limited surface fire spread when ignitions occurred, indirectly aiding in the persistence of recently established small trees (Blackstock & McAllister, 2004; Hamilton & Burton, 2023). Fire suppression policies introduced in the 20th century aimed to extinguish all fire starts on the landscape to protect communities and timber interests, which also contributed to the establishment and persistence of new trees (Hessburg et al., 2019; Copes-Gerbitz et al., 2022a). Finally, industrial logging practises, including high-grading, changed forest structure further by targeting large fire-resistant Douglas-fir trees in harvesting, increasing forest vulnerability to fire (Day, 1998; Agee & Skinner, 2005; Hessburg et al., 2019). All these land management decisions were made in SXFN Territory without SXFN consent and have contributed to the fuel accumulation and fire deficit that currently puts their communities at risk. 2.4.4 Implications for SXFN Stewardship In the SXFN interface, there is high risk of intense surface fires that can readily spread into the canopy, potentially supporting intense crown fires with extreme rates of spread. This type of fire would likely threaten SXFN communities and the eco-cultural systems they rely upon. Consistent with our modelling outcomes, other research has indicated that under extreme weather, high-severity crown fires are likely (Donovan et al., 2022) and may exceed suppression capabilities (Wotton et al., 2017). Extreme rates of spread and surface fire severity predicted 46 during extreme fire weather (e.g., 97.5th percentile scenario) will affect timely suppression response effectiveness and challenge SXFN citizens, if they are required to evacuate their communities. With climate change causing higher frequencies and durations of extreme fire weather (Flannigan et al., 2009; Hanes et al., 2019; Zhang et al., 2023; Parisien et al., 2023), SXFN community vulnerability to wildfire will likely increase in the future. The results from this study indicate that proactive forest stewardship within the SXFN interface is urgently needed. The most common forest stewardship strategies to reduce crown fire likelihood involve thinning trees, pruning tree branches, reintroducing surface fire, or a combination of these tactics. However, these stewardship interventions are limited in the SXFN interface by legal objectives for forest management in MDWR and OGMA. Forests in SXFN Territory were designated as MDWR and OGMA in the late 20th century. While there were BC government guidance for the management of these forests since the 1990s (IAMC, 1998), legal objectives for forest management in MDWR and OGMA were established in 2007 and 2010, respectively. The legal objectives for MDWR allow timber to be harvested in these forests, but they are highly prescriptive in order to maintain or restore mule deer winter habitat and they prioritize the protection or recruitment of large Douglas-fir trees to do so (Dawson et al., 2007). In contrast, legal objectives for OGMA are less prescriptive but only allow harvesting in these forests for specific reasons, some of which include for fire management and fuel treatments (MAL, 2010; FLNRORD, 2018). Both the legal objectives for MDWR (MOE, 2007) and those for OGMA (MAL, 2010) allow for forests to be thinned-from-below (ie. the removal of trees from smaller diameter size classes) as a form of ecosystem maintenance or restoration (Dawson et al., 2007; Leclerc et al., 2021; Koot et al., 2015). Leclerc et al. (2021) 47 found that infilled MDWR forests 50 km northeast of Xget’tem, similar to ones in this study, had increased risk of crown fire, bark beetles, and defoliating insects. In that study, forest harvesting in accordance with MDWR legal objectives removed subcanopy trees and retained large canopy trees in clumped distributions, which mitigated both crown fire and insect risk. However, the MDWR and OGMA forests in the SXFN interface were not harvested between the 1990s, or earlier, and 2023. Part of the reason for this is that since 1998, there have been complex BC government forest management guidance for MDWR and OGMA forests. After 2007, the highly prescriptive legal objectives for MDWR (MOE, 2007) have made implementing forest stewardship within MDWR both complex and expensive for forest practitioners (Day et al., 2010; Koot et al., 2015; Day, 2019; Melton, 2020), especially in forests surrounding remote communities. Harvesting limitations from the legal objectives for MDWR and OGMA have protected the study area forests from clear-cutting and high-grade logging that was done elsewhere in SXFN Territory, but legal objectives also created barriers to proactive forest stewardship that reduces wildfire risk (Abbott & Chapman, 2018; Melton, 2020), indirectly maintaining fire-vulnerable forest structures. The forests in SXFN Territory will always burn one way or another, but the current fuel structures mean that high-severity crown fire is the most likely fate of the forests in the SXFN interface without proactive forest stewardship. Therefore, barriers posed by OGMA, and especially MDWR legal objectives must be addressed. As these eco-cultural systems used to burn frequently, restoration of frequent low-severity fire to the interface and surrounding SXFN Territory is critical. Restoring fire to dry forest ecosystems through Indigenous fire stewardship, prescribed fire, or allowing wildfires to burn under the right conditions does not only reduce 48 surface and ladder fuels that can promote crown fire (Prichard et al., 2010; Stephens et al., 2012), but has a suite of other eco-cultural benefits including enhancement of wildlife habitat, promotion of important food and medicine plants, and the strengthening of cultural connections between Indigenous Peoples and their land (McIver et al., 2013; Abbott & Chapman, 2018; Lake & Christianson, 2019; Wynecoop et al., 2019; Prichard et al., 2021; Dickson-Hoyle et al., 2021; Long et al., 2021; Copes-Gerbitz et al., 2023). Some of the forest fuel structures in the SXFN interface may be able to support the reintroduction of surface fire without prior thinning or pruning of trees. For instance, plots 21 and 22 were predicted to burn as surface fire even during extreme fire weather. However, most plots have high fuel loads that promote crown fire at even moderate weather conditions. Given these results, it is recommended that mechanical or hand thinning of dense small-medium sized trees be paired with the pruning of tree branches ahead of the reintroduction of surface fire to the study area to prevent crown fire at most plots (North et al., 2015; Harvey et al., 2018; Stephens et al., 2021). Of utmost importance is that the vital proactive forest stewardship be given the appropriate priority and resources by the BC government and that it be led by or consented by Indigenous rights-holders, as per UNDRIP (United Nations, 2007; Dickson-Hoyle et al., 2021; Lake & Christianson, 2019; Abbott & Chapman, 2018; Dickson-Hoyle & John, 2021; Copes-Gerbitz et al., 2021; Hoffman et al., 2022b) which was adopted into BC legislation in 2019 (BC, 2020). Both Indigenous knowledge and western science can be used and should be valued equally in this process, as using both knowledge types will allow Indigenous communities to “walk on two legs” when stewarding their Traditional Territories (Dickson-Hoyle et al., 2021). Undoing settler-colonial systems will take considerable time and resources, and one mechanism to reduce 49 settler-colonial land management barriers for Indigenous Peoples is through the #LandBack movement (NDN Collective, n.d.; Fisk et al., 2021). While owning the land they depend upon would reduce considerable barriers for SXFN, additional economic and human resources will likely be needed to implement forest stewardship that addresses eco-cultural restoration, climate change adaptation, and the mitigation of high-severity wildfire risk adjacent to communities (Ager et al., 2021; Copes-Gerbitz et al., 2022b). 2.5 Conclusion Indigenous Territories in what many now call interior British Columbia, Canada are at the forefront of the impacts of climate change, including risk from high-severity crown fire that disproportionately affects Indigenous communities (McGee, 2021; Batdorf & McGee, 2023). Settler-colonial land management such as the exclusion of Indigenous Peoples and cultural fire, fire suppression, overgrazing, and industrial logging have led to the accumulation of dense flammable fuels that leave dry forests vulnerable to crown fires, especially as hot, dry weather periods become more common with climate change. The interface surrounding SXFN communities is dominated by forests designated as MDWR and OGMA by the BC government, and their legal objectives for forest management limit the proactive stewardship that can take place there. MDWR and OGMA were established in SXFN Territory and throughout their interface without SXFN consent and those within the SXFN interface have predominantly remained un-stewarded since their establishment in the late 20th century. This lack of stewardship has maintained previously degraded forest structures, consequentially contributing to the likelihood of crown fire under moderate to extreme fire weather. Virtually all forests would burn in crown fires under current fire weather extremes; ominous given that the frequency 50 and duration of extreme fire weather is increasing with climate change. Without proactive forest stewardship, these forests are a threat to the communities that they surround. As society tries to collectively implement UNDRIP in BC, Indigenous Peoples must be decision-makers and leaders in local solutions. However, there can be value in working collaboratively with settler-colonial governments to create benefits for Indigenous and settler communities alike. Multigenerational, place-based Indigenous knowledge and western science can be used to “walk on two legs” to contribute to proactive stewardship of forests, including returning regular surface fire to the land through Indigenous fire stewardship or prescribed fire. Land stewardship must be adaptive as more knowledge is generated and eco-cultural landscapes change. Future research should explore the barriers and opportunities for Indigenous-led forest and fire stewardship, as well as how forest stewardship that reduces crown fire risk can be strategically planned in the interface and across landscapes to benefit a variety of values, including, but not limited to, culture, biodiversity, wildlife habitat, watershed health, and sustainable economic development. 51 Chapter 3: Protecting Forests to Death: Simulated Fuel Treatment Efficacy in Mule Deer Winter Range and Old Growth Management Area Forests of Secwépemcúl’ewc (Interior British Columbia, Canada) 3.1 Introduction In 2023, Canada experienced its worst wildfire season in settler-recorded history, doubling previous area-burned records from 7.6 million hectares (ha) in 1989 to 18.5 million ha (CIFFC, 2023). The 2023 wildfire season coincided with extreme fire weather, which is exacerbated by human-caused climate change (Flannigan et al., 2005; Wotton et al., 2017; Wang et al., 2023; Parisien et al., 2023). When extreme weather dries vegetation and an ignition occurs, large, fast moving, high-severity wildfires are more likely, threatening lives, communities, infrastructure, and the lands and waters they all depend upon (Hoffman et al., 2022a; Coogan et al., 2019). In conjunction with climate change mitigation efforts, reducing high-severity wildfire risk for communities and other values on the landscape will require strategic reductions of dry vegetation, called fuel (Agee & Skinner, 2005; Stephens et al., 2012; Hessburg et al., 2019; Stephens et al., 2021). In northern Secwépemcúl’ewc, which overlaps the south-central interior of British Columbia (BC), Canada, there is a strong need for fuel reduction treatments (hereafter, “fuel treatments”) that reduce wildfire hazard (Filmon, 2004; Abbott & Chapman, 2018; SXFN, 2018; FPB, 2023). Similar to other dry forests in western North America, there are also recommendations to pair fuel treatments with ecological and cultural (hereafter, “eco-cultural”) restoration of forests and grasslands (Long et al., 2020; Daniels et al., 2020; Jones et al., 2021; Stephens et al., 2021; Hoffman et al. 2022b) that simultaneously adapt these systems to the current and projected impacts of climate change (Schoennagel et al., 2017; Prichard et al., 2021; Hessburg et al., 2021). 52 Historically, much of the dry conifer forests of northern Secwépemcúl’ewc experienced frequent low-severity fires that burned through understory vegetation and woody debris (hereafter, “surface fires”; Coogan et al., 2021), largely due to fire stewardship by Secwépemc People that has been ongoing since time immemorial (Wong & Iverson, 2004; Harvey et al., 2017; FREC, 2021; Dickson-Hoyle et al., 2021; Copes-Gerbitz et al., 2021). When settlers established themselves in Secwépemcúl’ewc in the 1800s, their oppressive laws and policies, as well as their unfamiliar diseases, caused extensive harm to Secwépemc People that led to a disruption in Secwépemc fire stewardship practises (Copes-Gerbitz et al., 2021). Settler-colonial policies made Indigenous fire stewardship illegal (Copes-Gerbitz et al., 2022a), while cattle grazing (Blackstock & McAlister, 2004), industrial forestry (Leclerc et al., 2021), and fire suppression (Copes-Gerbitz et al., 2022a) throughout the 19th – 21st centuries contributed to fire exclusion and shifted forest structures from being fire resilient to fire vulnerable. The current fire deficit (Chavardes et al., 2022; Baron et al., 2022) in dry forests and grasslands of northern Secwépemcúl’ewc has allowed small diameter conifer trees and dead woody debris to accumulate outside of their range of natural variability (Wong & Iverson, 2004; Brookes et al., 2021). This fuel accumulation has contributed to the shift from a mixed-severity fire regime dominated by surface fires to the recent surge in large, high-severity wildfires in Secwépemcúl’ewc (Coogan et al., 2021; Dickson-Hoyle & John, 2021; Copes-Gerbitz et al., 2022a; Baron et al., 2022; BCWS, 2023c). Stswecem’c Xget’tem First Nation (SXFN) is home to two semi-remote northern Secwépemc communities called Stswecem’c (Canoe Creek) and Xget’tem (Dog Creek). In Chapter 2, I 53 demonstrated that current forest structures surrounding SXFN communities have high likelihood to burn as crown fire, a fire that burns through the canopies of trees often in conjunction with surface fire (Coogan et al., 2021), during moderate to extreme fire weather. Large, fast-spreading, high-severity crown fires have become common in SXFN Traditional Territory in the past two decades (Dickson-Hoyle & John, 2021; FREP, 2021; BCWS, 2023c), threatening SXFN communities five times from 2010 – 2023, while dramatically altering the eco-cultural systems that SXFN People rely on for food, medicine, traditional plants, water, transportation routes, and culture. SXFN is aware of their vulnerability to crown fire due to the fuel quantities surrounding them (DWB, 2010), as well as the remoteness and the rough road conditions separating their communities from emergency response agencies (Bonde, 2011). The fuel quantities in the SXFN wildland urban interface (hereafter, “interface”), a 2 kilometre radius around their communities (BCWS, 2023a), justify the need for fuel treatments to reduce crown fire likelihood, restore their fire-dependent eco-cultural systems (Dickson-Hoyle et al., 2021), and adapt their Traditional Territory to the extreme fire weather becoming more frequent with climate change (Flannigan et al., 2005; Wotton et al., 2017; Parisien et al., 2023). There are many ways to mitigate crown fire, with the most obvious being removal of flammable fuels that act as a wildfire hazard close to homes, communities, or other critical infrastructure such as escape or egress routes. While this method may be appropriate directly surrounding houses in forested landscapes and is a component of a “FireSmart®” community (BCWS, 2019), it also might conflict with many of the other eco-cultural values that exist within the dry forests and grasslands of Secwépemcúl’ewc. Secwépemc cultural areas, traditional plants, biodiversity, wildlife habitat, watersheds, and economies, among other priority values, would be negatively impacted by total fuel removal throughout the SXFN interface. Therefore, SXFN must decide how community fuel treatments 54 will balance wildfire risk reduction with those other values, necessitating trade-offs in SXFN forest stewardship (North et al., 2015; Stephens et al., 2020; Stephens et al., 2021). The SXFN interface overlaps with the Cariboo Region of interior BC, where historical logging and current BC land use laws and policies influence how and where fuel treatments may be applied. In the mid-late 20th century, many large-diameter Douglas-fir trees were targeted in industrial high-grade logging and subsequent clearcut logging, removing the trees with the most fire resilience from the landscape (Day, 1998; Iverson et al., 2002; Leclerc et al., 2021). To protect vital mule deer winter habitat and old growth forest structures from further over-extraction, the BC government designated certain forests in SXFN Territory as Mule Deer Winter Range (MDWR) and Old Growth Management Areas (OGMA) in the late 20th century (Dawson et al., 2007; BCSC, 2011). This was done without the free, prior, and informed consent from Indigenous rights-holders, as later defined in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP; UN, 2007). Forest management and harvesting objectives for MDWR and OGMA are legally defined through separate provincial orders (MOE, 2007; MAL, 2010), but both broadly aim to protect and enhance structurally complex forests dominated by large interior Douglas-fir trees (Dawson et al., 2007). Today, 29,865 hectares (ha) of MDWR and 12,822 ha of OGMA overlap with community interfaces in the Cariboo Region (Swan, 2022). The SXFN interface is approximately 13,000 ha and 58% is classified as either MDWR, OGMA, or both. During the 2010 Dog Creek Fire, 866 ha of the SXFN interface (7%), all of which was designated as either MDWR or OGMA, burned in high-severity crown fire, amplifying concerns about wildfire risk within these areas. Despite MDWR and OGMA forests in the Cariboo Region having accumulated high fuel loads (Day et al., 2010; Abbott & Chapman, 55 2018; Day, 2019; Leclerc et al., 2021; Waterhouse & Walton, 2021), their legal objectives impact how fuel treatments are implemented and they have been perceived as being “extremely constraining to forest management” (Melton, 2020, p. 60) by forest practitioners. Further concern about wildfire risk in MDWR and OGMA is reflected in the Abbott and Chapman (2018) report, which recommends that BC decision makers review existing land use plans to ensure that the locations of MDWR and OGMA do not prevent wildfire risk reduction adjacent to communities. In response to these recommendations and the 2017, 2018, and 2021 record-breaking BC wildfire seasons, the BC government amended the OGMA legal objectives in 2018 to reduce barriers for fuel treatments (FLNRORD, 2018), while specific MDWR legal objectives were given blanket exemptions in 2022 to make implementing wildfire risk reduction in MDWR more efficient and flexible (Swan, 2022). Little research has been done to assess legal objective-compliant fuel treatments or their efficacy in reducing crown fire likelihood or other fire behaviour metrics. This research is designed to address this knowledge gap. In the interest of protecting communities, vitally important wildlife habitat, and old growth forests from widespread high-severity crown fire, this study was co-developed by SXFN and the University of British Columbia to answer the following questions: (1) To what degree do simulated fuel treatments that comply with MDWR and OGMA legal objectives reduce fuel loads and alter modelled fire behaviour at the stand-level during high fire danger?; and (2) How do fuel loads and fire behaviour differ when simulated fuel treatments intensively thin the smallest trees at the stand-level? To answer these questions, I simulated nine fuel treatments that incorporated surface fuel abatement, pruning, and varying intensities of thinning. I then modelled potential wildfire behaviour at the 90th percentile of fire weather using the Crown Fire Initiation 56 and Spread model (Alexander et al., 2006). Efficacy in reducing predicted wildfire behaviour was compared among simulated fuel treatments. I discuss the implications of complying with MDWR and OGMA legal objectives in fuel treatments versus implementing a fuel treatment that intensively thins the forest to protect SXFN communities from widespread high-severity wildfire. 3.2 Materials and Methods 3.2.1 Study Area Since time immemorial, SXFN has stewarded their Traditional Territory, an area of 1.3 million ha in northern Secwépemcúl’ewc, which overlaps with south-central interior BC. The two SXFN communities are in tributary valleys of the Fraser River, in the rain shadow of the Coastal Mountain Range, with a continental climate. Average elevation of the study area is 1062 metres above sea level. Using ClimateBC data, the mean temperatures of the warmest and coldest months were 14.9 ℃ and -6 ℃, respectively from 1991 – 2020 (Wang et al., 2016). Mean annual precipitation is 432 mm and mean May – September precipitation is 197 mm (Wang et al., 2016). Characterized by the biogeoclimatic (BEC) zone system in BC (Pojar et al., 1987), the study area spans the Interior Douglas-fir and Bunchgrass zones. Vegetation present includes grasslands, and a mixture of open and closed forests dominated by Douglas-fir trees. Other common tree species include lodgepole pine, trembling aspen, white or hybrid spruce, paper birch, and Rocky Mountain juniper (Steen & Coupé, 1997), although most mature lodgepole pines were killed during the mountain pine beetle epidemic in the early part of the 2000s. Common understory species include common juniper, prickly rose, soopolallie, kinnikinnick, and aster (Steen & Coupé, 1997). 57 In response to the widespread overextraction of large diameter Douglas-fir trees throughout the Cariboo Region of BC, certain forests in SXFN Territory were designated as MDWR or OGMA by the BC government in the late 20th century. MDWR and OGMA each have separate legal objectives for forest management, but in the SXFN interface they both aim to maintain or restore forests dominated by large, old Douglas-fir trees (Dawson et al., 2007; BCSC, 2011). The MDWR in the SXFN interface is classified as the low-moderate snowpack zone and the legal objectives are defined in the Ungulate Winter Ranges, Cariboo Chilcotin Land Use Plan, Shallow and Moderate Snowpack government action regulation order (MOE, 2007). All OGMA in the interface is classified as being permanent, static, and legal, as opposed to the rotating, transitional and non-legal OGMA that occurs elsewhere in SXFN Territory (BCSC, 2011). The legal objectives for permanent, static, and legal OGMA are defined in the Land Use Objectives for the Cariboo-Chilcotin Land Use Plan Area land use order (MAL, 2010). A map of the MDWR and OGMA surrounding the study area is featured in Figure 3.1, with wildfire perimeters from 2000 – 2023 overlaid on the landscape. 58 Figure 3.1: Location of the (a) study area in British Columbia and (b) 37 plots in the wildland urban interface surrounding the communities of Stswecem’c and Xget’tem. 3.2.2 Plot Selection Using BC government land management spatial datasets in a geographic information system, I defined my study area by creating the SXFN interface, which is delineated by a 2 kilometre (km) buffer around communities in BC (BCWS, 2023a). The interface surrounding Stswecem’c is 8,297 ha and Xget’tem is 4,641 ha; the total study area is 12,938 ha. Within the study area, I differentiated forests classified as MDWR and OGMA (51%), from other land types (29%), areas burned in the last 20 years (7%; majority MDWR or OGMA), and private land (13%). I superimposed a 500-m grid across the interface and randomly selected 37 study plots using a stratified random sampling design from the forests classified as MDWR, 19 of which were also 59 classified as OGMA. Plot locations were reviewed by SXFN staff to ensure they were not in ecologically or culturally sensitive locations. 3.2.3 Field Methods Forest fuel measurements followed the National Forest Inventory (Gillis et al. 2005), enhanced to include attributes needed for fire behaviour modelling. At each plot, I established a plot centre and recorded the UTM coordinates, elevation at metres above sea level, slope angle (%), and aspect (°). Figure 3.2 shows the layout of the plot. Figure 3.2: Diagram showing the configuration of forest fuels mensuration plots established at each study plot (Rutherford, 2023). Trees were sampled in nested plots around plot centre. Canopy trees (DBH ≥ 12.5 cm) were inventoried in a 0.04 ha circular plot with a radius of 11.28 m. At 5 plots where density was >750 trees ha-1, the plot radius was reduced to 7.98 m. Subcanopy trees (12.5 cm > DBH ≥ 5 cm) were inventoried in a 0.01 ha circular plot with a radius of 5.64 m. At 3 plots where density was 60 >3,000 trees ha-1, the plot radius was reduced to 3.99 m. Species, DBH, tree vigour, total height, and height to the base of the live crown, or the lowest continuous tree branch with live foliage, was recorded for each tree. In a 0.01 ha circular satellite plot (radius = 5.64 m) at the end of the first fuel transect, live tree saplings (height ≥ 1.3 m and DBH < 5 cm) were tallied by species and average DBH, total height, and height to live crown were measured. Within the satellite plot, percent cover of live and dead grasses was recorded, and average height was measured. Two 30 m transects were laid perpendicular to one another through plot centre; the first transect was oriented on a random azimuth. The slope of each transect was recorded. Canopy cover was measured at 2 m intervals along each transect (n = 30 points) using a tube densitometer to estimate percent cover of deciduous and conifer trees for each plot. Surface woody fuels were sampled along each transect using the line intercept method (Van Wagner, 1982). The sampling effort varied with wood size. Wood with diameters <0.5 cm were tallied from 0 – 2 m and 28 – 30 m; diameters from 0.5 – 7 cm were measured from 0 – 5 m and 25 – 30 m; diameters from 7 – 30 cm were measured from 0 – 10 m and 20 – 30 m; and diameters > 30 cm were measured from 0 – 30 m. Species, diameter (cm), and decay class (1 – 3 sound, 4 – 5 rotten) were recorded for each piece of wood. At 7.5 and 22.5 m along each transect, the depth of the litter and duff layers were measured. 3.2.4 Measured Fuel Loads Data from live trees were used to calculate density (trees ha-1), basal area (m2 ha-1), and canopy fuel metrics for each plot; all metrics were scaled to one hectare for subsequent analyses. Canopy bulk density (CBD in kg m-3) was calculated for each tree using species-specific equations for 61 western Canada (Ung et al., 2008). Live crown base height (CBH in m) was calculated as a weighted average for sapling, subcanopy, and canopy trees. Mean tree height (m) was calculated as weighted average for subcanopy and canopy trees only. Density and mean tree height metrics were used along with other plot characteristics (Table A.1) to assign Canadian Forest Fire Behaviour Prediction System (FBP) fuel types based on the forest structure decision key from Perrakis et al. (2018). Surface fuel load was calculated from the fine woody surface fuels (diameter < 7 cm) and ground fuels at each plot. For each woody fuel, log diameter was converted to volume, while correcting transect slope distances to horizontal distances (Van Wagner, 1982), and multiplied by the species-specific wood density (Gonzalez, 1990) to derive biomass (kg m-2). The sum of the woody fuel biomass represented the surface woody fuel load for each plot. The mean litter and duff depths were converted to volumetric fuel loads (Woodall and Monleon, 2008) and summed as the ground fuel loads (kg m-2). 3.2.5 Simulated Fuel Treatments Following MDWR & OGMA Legal Objectives Eight fuel treatments were developed to represent forest harvesting that complies with the legal objectives for MDWR, and OGMA that overlaps with MDWR, without exemptions. These treatments included combinations of forest thinning, pruning of live trees, and abatement of surface fuels. Post-treatment conditions were simulated by altering the inventoried forest fuels data for each study plot, as follows. 62 Thinning Treatments Thinning-from-below, the removal of trees below a certain DBH, is permitted in MDWR and in OGMA that overlaps with MDWR (MOE, 2007; MAL, 2010). Two thinning treatments were developed using the legal objective criteria outlined in general wildlife measures 9 of the MDWR government action regulation order (U-5-001, U-5-002 and U-5-003 in MOE, 2007). One thinning treatment removed a maximum of 25% of the basal area of live canopy layer trees (DBH ≥ 12.5 cm), as this is what is allowed in the legal objectives for MDWR (hereafter “25% thinning treatment”; MOE, 2007). The second thinning treatment removed a maximum of 35% of the basal area of live canopy layer trees (DBH ≥ 12.5 cm) to reflect the 2022 blanket exemptions to MDWR legal objectives in fuel treatments, allowing an additional 10% of basal area to be removed (hereafter “35% thinning treatment”; Swan, 2022). The blanket exemptions from 2022 do not apply to OGMA forests, but all OGMA plots were still subjected to the 35% thinning treatment for simplicity. Treatments were designed to reduce fuel loads from trees while complying with detailed legal objectives that vary by plot based on pre-treatment forest composition, tree size, stand structure, biogeoclimatic unit, slope angle, and aspect (MOE, 2007). After consulting with local forest managers (pers. comm., K. Day), the thinning treatments used six steps applied to tree data from each plot: 1. All dead conifer trees with DBH < 37.5 cm were removed. 2. Living conifer trees with DBH < 12.5 cm other than Douglas-fir, including lodgepole pine, hybrid spruce and Rocky Mountain juniper were removed. 3. Ninety percent of living Douglas-fir trees with DBH < 12.5 cm were removed, which is the maximum percentage allowed for the sapling and subcanopy tree layers (Table 3.1; Swan, 2022). 63 4. Living conifer trees with 12.5 ≥ DBH ≥ 37.5 cm other than Douglas-fir were removed from smallest to largest based on DBH. Removal continued until 25% (or 35%) of the total pre-harvest basal area of all live conifer canopy trees was achieved; if these thresholds were not achieved, I proceeded to step 5. 5. Living Douglas-fir trees with 12.5 ≥ DBH ≥ 22.4 cm were removed from smallest to largest based on DBH classes (Table 3.1; Swan, 2022). Within DBH classes in this size range, trees were removed up to a maximum of 85% of the basal area (Table 3.1; Swan, 2022). Removal continued until 25% (or 35%) of the total pre-harvest basal area of all live conifer canopy trees was achieved; if these thresholds were not achieved, I proceeded to step 6. 6. Living Douglas-fir trees with 22.5 ≥ DBH ≥ 37.4 cm were also removed from the smallest to largest based on DBH classes (Table 3.1; Swan, 2022). However, the amount removed from each DBH class was the smaller of two values: (a) the maximum percentage of basal area for each class (Table 3.1; Swan, 2022) or (b) an alternate maximum basal area for removal from each class calculated using the BDq method for managing uneven-aged forests (Guldin, 1991) that has been adapted for different types of MDWR (MOE, 2007). Removal continued until 25% (or 35%) of the total pre-harvest basal area of all live conifer canopy trees was achieved. If these thresholds were not achieved, I calculated the total basal area and percentage of basal area that was removed. All broadleaved trees were retained, as were all living and dead conifer trees with DBH ≥ 37.5 cm. Variable numbers of living conifer trees with DBH < 37.5 cm were retained in the simulated 25% versus the 35% thinning treatments. 64 Table 3.1: Guidance on the proportion of the basal area of living Douglas-fir trees that can be removed from forests managed for mule deer winter range in British Columbia (Swan, 2022). DBH Class Percent of basal area to be removed Midpoint (cm) Range (cm) Minimum (%) Maximum (%) 5 2.5‒7.4 45 90 10 7.5‒12.4 45 90 15 12.5‒17.4 30 85 20 17.5‒22.4 20 85 25 22.5‒27.4 0 75 30 27.5‒32.4 0 30 35 32.5‒37.4 0 20 Pruning Treatments Trees remaining after the thinning treatments were either pruned to increase the base height of the live crown or not pruned. For individual trees with height ≥ 7 m, pruning was simulated by increasing crown base height values to 3.5 m. For trees with height < 7 m, pruning height was calculated as the midpoint between live crown base height and the total tree height, to a maximum of 3.5 m. Surface Fuel Treatments Within each combination of thinning and pruning treatments, fine woody surface fuels (diameter < 7 cm) were abated or not abated. Abatement reduced fine surface fuels (diameter < 7 cm) to a maximum of 1.0 kg m-2 at the plot level, as required in BC fuel treatments to reduce wildfire risk (BCWS, 2022a). For treatments with no abatement, the surface fuels generated by thinning and pruning augmented the surface fuels measured in each plot (see 3.2.7 Post-treatment Fuel Loads). 65 3.2.6 Simulated Intensive Thinning Fuel Treatment One additional simulated treatment was developed to specifically represent intensive thinning treatments that are applied in seasonally-dry forests of BC to reduce crown fire risk (Rutherford, 2023). I removed all small trees in the sapling and subcanopy layers that could act as ladder fuels and thinned the live conifer canopy layer trees to retain the largest trees at a density of 150 trees ha-1 (tph; BC, 2016; Greene, 2021; Brookes et al., 2021). At 3 plots that had a pre-existing canopy layer density of 125 trees ha-1, no further reduction in canopy layer trees occurred. Residual trees were pruned to 3.5 m and surface fuels were abated to 1.0 kg m-2 as recommended by BCWS (BCWS, 2023a). All nine simulated fuel treatments are represented in Table 3.2. Table 3.2: A key describing the nine simulated fuel treatments with variations in thinning intensity and the inclusion of either pruning or fine surface fuel abatement. The 25% and 35% thinning treatments refer to the amount of live conifer basal area that may be removed from each plot under legal objectives for Old Growth Management Area and Mule Deer Winter Range. The 150 trees ha-1 (tph) thinning treatment refers to the intensive thinning treatment. Pruning is to a minimum of 3.5 m and fine surface fuel abatement is to a maximum of 1.0 kg m-2. Colours in the table correspond to the colours in box plots representing each simulated fuel treatment in the results section. Simulated Fuel Treatments Thinning 25% 35% 25% 35% 25% 35% 25% 35% 150 tph Pruning + + + + + Abatement + + + + + 3.2.7 Post-Treatment Fuel Loads After each simulated treatment, I used the modified tree data to reflect thinning and pruning in each plot and recalculated tree basal area, density, mean height, canopy bulk density, and canopy base height at the one-hectare scale. As well, post-treatment canopy cover (%) was estimated for each retained tree using species-specific equations (Gray et al. 2021). Tree-level canopy covers were scaled to one hectare and summed to estimate percent canopy cover. Although canopy 66 cover estimates account for overlap among trees in different height classes (Gray et al. 2021), the post-treatment estimates exceeded measured canopy cover for 10 of 37 plots. For these plots, the measured canopy cover value was used to represent pre- and post-treatment conditions. For treatments with no surface fuel abatement, the additional leaf litter and fine woody surface fuel loads generated by thinning and pruning were estimated and combined with the measured plot-level surface fuel loads. I assumed that the biomass of thinned trees with DBH ≥ 12.5 cm (i.e., minimum diameter for merchantable trees) was removed from the plot, while trees with DBH < 12.5 cm contribute to surface fuel at the plot (pers. comm., M. Tomlinson; pers. comm., J. Liscomb). Foliar and branch biomass from thinned trees with DBH < 12.5 cm and stem biomass from thinned trees with DBH < 7 cm (i.e., maximum diameter for fine woody surface fuel) were calculated using Ung et al.’s (2008) species-specific equations and scaled to one hectare. These biomass values were added to fine surface fuel measurements (kg m-2) to represent an increase in surface fuels post thinning. Similarly, for treatments with pruning, I calculated the biomass of branches pruned from the retained trees using Ung et al.’s (2008) species-specific equations. The biomass of the pruned branches was scaled to one hectare and added to fine surface fuel measurements (kg m-2) to represent the increase in surface fuels post pruning. After each treatment, plot-level fuel types were reclassified using Perrakis et al. (2018). The subset of treated plots with canopy cover < 26% were assigned a grass (O1) fuel type with abatement or a slash (S1) fuel type without abatement. 67 3.2.8 Fire Behaviour Modelling Similar to Chapter 2, the Crown Fire Initiation and Spread (CFIS) model (Alexander et al., 2006) was used to predict potential fire behaviour at each of the 37 plots at the 90th percentiles of fire weather data. Each plot was modelled using the original fuels data measured in the field (hereafter, “reference conditions”) and the fuel loads calculated following the nine simulated treatments, as follows. Plot-level model inputs, CBH, CBD and canopy cover, were derived from the measured field data or calculated following each simulated fuel treatment, while wind speed (km hr-1) is derived from weather data. Estimated fine fuel moisture (EFFM) for dead surface fuels are derived from slope, aspect, canopy cover, and fire weather data using a sub-model based on Rothermel (1983). In CFIS, surface fuel consumption is represented by one of three categories (<1.0 kg m-2, 1.0 – 2.0 kg m-2, and >2.0 kg m-2). For both measured and simulated fuels at each plot, I estimated surface fuel consumption (kg m-2) using equations from the FBP system that are based on fuel type (i.e. C3, C4, C7, M2, O1, S1; Wotten et al., 2009; de Groot et al., 2009; Perrakis et al., 2018). I compared the estimated plot-level surface fuel consumption with the calculated surface fuel load and used the lower of the two values to select the CFIS surface fuel consumption category. For plots that were classified as an FBP grass fuel type (i.e. O1), the photo load method (Keane & Dickenson, 2007) was used to estimate grass fuel load and the surface consumption category. 68 For each plot, I extracted daily fire weather data and indices for May 15 to August 31, 2007‒2021 from the Canadian gridded dataset which extrapolates weather attributes from the Churn Creek, Place Lake, and Meadow Lake weather stations to specific plot locations (Canadian Forest Service, 2023) and used the FWI to represent the 90th percentile of weather conditions at each plot (BCWS, 2023a). The FWI is a numeric rating of potential fire intensity that integrates temperature, relative humidity, wind speed and precipitation to indicate fire danger (Van Wagner, 1987). The same FWI value can result from multiple combinations of these weather attributes. Thus, I ranked individual days by FWI in the 15-year dataset for each plot and identified a subset of days corresponding to the 90th (85‒95th) percentile for each plot. For each simulated treatment, 6,047 iterations of the model CFIS were run to represent this range of fire weather and the 37 plots. Each model run predicted fire type (i.e., surface, passive crown, or active crown fire) and estimated the likelihood of crown fire occurrence at the plot level, which I then summarized to the treatment level. As well, I calculated the rate of fire spread and surface fire intensity for each plot and summarized this data at the treatment level. Rate of spread was calculated based on the type of fire CFIS predicted for the plot. Rates of spread for active and passive crown fire were calculated using equations from Cruz et al. (2005), while rates of spread for surface fire were calculated using an equation from the Canadian Conifer Pyrometrics model (Perrakis et al., 2023). Surface fire intensity was calculated for all fire types using Byram’s (1959) fire intensity equation. 3.2.9 Comparison of Fuel Loads and Fire Behaviour Among Simulated Fuel Treatments I investigated the differences in fuel loads (e.g., density, basal area, canopy cover, CBD, CBH, fine surface fuels) among the reference condition forest, the eight simulated MDWR and OGMA 69 fuel treatments, and the intensive fuel treatment. To understand the efficacy of the fuel treatments, I compared crown fire likelihood, rate of spread, and surface fire intensity among the reference condition forest and the nine fuel treatments. These fuel and fire behaviour metrics did not meet the assumption of normality, so I used a Kruskal-Wallis test on ranks and Tukey method for pairwise comparisons when significant differences were detected for median values (α = 0.05). 3.3 Results 3.3.1 Forest Composition and Structure Of the 37 plots, 20 plots were in the interface around Stswecem’c, and 17 plots were around Xget’tem; all plots were in MDWR forests, while 19 plots were also in OGMA (Tables 2.2, 2.3). Most plots were dominated by Douglas-fir; four were mixed-species stands including Douglas-fir, lodgepole pine, hybrid spruce, Rocky Mountain juniper, and trembling aspen; and one plot was dominated by lodgepole pine. The median density of live conifers was 2,275 trees ha-1 (range = 175 – 9,125 trees ha-1) and median basal area was 24 m2 ha-1 (ranged = 7 – 58 m2 ha-1). The density of saplings and subcanopy trees exceeded that of canopy trees in most plots. Although large-diameter canopy trees dominated the basal areas, the abundance of subcanopy trees and saplings contributed an average of 21% of basal area in all plots (Table 2.1 and Figure 3.3) 70 3.3.2 Fuel Loads After Simulated Fuel Treatments 3.3.2.1 Thinning Relative to the reference condition forest plots, all simulated thinning treatments significantly reduced the basal area, density, and canopy cover of live conifer trees (Figure 3.3, Tables B.1-3). Treatment effects generally increased with simulated thinning intensity; however, there were few significant differences among the three levels of thinning. Total live conifer basal area was reduced significantly from a median of 24 m2 ha-1 in the reference condition to 15 and 14 m2 ha-1 in the 25% and 35% thinning treatments, respectively, and 13 m2 ha-1 in the intensive thinning treatment (Figure 3.3; Table B.3). There were no significant differences in residual basal area among the treatments. Although MDWR and OGMA treatments were permitted to remove up to 25% or 35% of the basal area of live canopy conifer trees, actual removal medians were 25% (range = 3 ‒ 25%) and 30% (range = 3 ‒ 35%) given constraints on trees removed from each size class, or the majority of basal area being represented by trees with DBH > 37.4 cm. As a result, there was no significant difference in the average basal areas removed between the 25% and 35% thinning treatments (data not shown). In contrast, reducing canopy tree density to c. 150 trees ha-1 at each plot removed a median of 47% (range = 4 ‒ 85%) of the basal area, which was significantly more than the 25% and 35% thinning treatments. Consistent with changes in basal area, total live conifer tree density decreased significantly from a median of 2,275 to 399 and 368 trees ha-1 in the 25% and 35% thinning treatments, respectively. There was no significant difference in post-treatment average density between these 71 two treatments. The further reduction in density of live trees to a median of 150 trees ha-1 was significant (Figure 3.3, Table B.2). Similarly, canopy cover decreased significantly from a median of 53% in the reference condition to 30% in both the 25% and 35% treatments, and 28% in the intensive thinning treatment. Canopy cover did not differ significantly among thinning treatments (Figure 3.3, Table B.1). 72 Figure 3.3: Comparison of density, basal area, and canopy cover among treatments and the reference condition. For each treatment, the box represents the 25th and 75th percentiles of the forest structure attribute; the midline is the median and cross (x) is the mean; whiskers are the 5th and 95th percentiles; and circles are outliers. Median values for treatments with the same superscript were not significantly different (α = 0.05). 73 3.3.2.2 Thinning and Pruning Treatments that combined thinning with pruning, generally increased CBH and decreased CBD (Figure 3.4, Table B.5). The median CBH for the reference condition was 2.2 m and CBH increased to 3.8 m with 25% and 35% thinning alone, although this increase was not significant. By combining the 25% and 35% thinning with pruning, median CBH significantly increased to 4.5 m. In the intensive thinning fuel treatment, removing all saplings and subcanopy trees and pruning the large canopy trees that were retained resulted in significantly higher CBHs (median = 6.3 m) than in the 25% and 35% thinning treatments and the reference condition. Conversely, CBD significantly decreased from a median of >0.18 in the reference condition to <0.10 kg m-3 in all treatments. There was no significant difference among any of the thinning and pruning treatments, although intensive thinning to a density of 150 trees ha-1 yielded the lowest median CBD at 0.067 kg m-3. 74 Figure 3.4: Comparison of canopy base height and canopy bulk density among treatments and the reference condition. For each treatment, the box represents the 25th and 75th percentiles of the forest structure attribute; the midline is the median and cross (x) is the mean; whiskers are the 5th and 95th percentiles; and circles are outliers. Median values for treatments with the same superscript were not significantly different (α = 0.05). 3.3.2.3 Thinning, Pruning, and Surface Fuel Abatement The fine surface fuel loads measured in the reference condition were significantly lower than those in all treatments. Fine surface fuel loads increased significantly from a median of 0.57 kg m-2 in the reference condition to ≥1.81 kg m-2 following the 25% and 35% thinning and pruning 75 treatments, without abatement (Figure 3.5; Table B.6). Although median fine surface fuel loads were greater with pruning relative to the thinning only treatment, these differences were not significant. Fine fuel loads were reduced significantly to a median of 1.0 kg m-2 in all abatement treatments, with no significant difference among them. Figure 3.5: Comparison of fine surface fuels among treatments and the reference condition. For each treatment, the box represents the 25th and 75th percentiles of fine surface fuel quantities; the midline is the median and cross (x) is the mean; whiskers are the 5th and 95th percentiles; and circles are outliers. Median values for treatments with the same superscript were not significantly different (α = 0.05). 3.3.3 Predicted Fire Behaviour 3.3.3.1 Fire Type The predictions of active crown fire decreased from 70% for the reference condition to <30% for all treatments; however, passive crown fire predictions remained ≥55% (Figure 3.6). The MDWR and OGMA legal objective-compliant treatment that yielded the highest surface fire 76 (16%) and lowest active crown fire (21%) predictions was the 35% thinning, pruning, and surface fuel abatement treatment. Out of all of the simulated fuel treatments, the intensive thinning treatment yielded the highest surface fire (34%) and the lowest active crown fire (11%) predictions (Figure 3.6). Figure 3.6: Proportions of fire types predicted among treatments and the reference conditions. Blue bars represent surface fire predictions, yellow represent passive crown fire, and red represents active crown fire. 3.3.3.2 Crown Fire Likelihood, Rate of Spread, and Surface Fire Intensity The simulated fuel treatments impacted crown fire likelihood, rate of spread, and surface fire intensity predictions differently, with the greatest reductions in fire behaviour achieved by the intensive thinning treatment (Figure 3.7; Tables B.7-9). Median crown fire likelihood was not significantly different between the reference condition (96%) and the 25% and 35% thinning treatments that did not include pruning and abatement (97% for both). Median crown fire 77 likelihood significantly and progressively decreased when 25% and 35% thinning was combined with pruning (95% for both), surface fuel abatement (94% and 91% for 25% and 35% thinning treatments, respectively), or both (90% and 89% for 25% and 35% thinning treatments, respectively). The lowest median crown fire likelihood was for the intensive thinning treatment (71%). Median rate of spread was significantly faster in reference condition (22 m min-1) relative to any of the treatments (Figure 3.7, Table B.8). There was variation among the treatments that were compliant with legal objectives for MDWR and OGMA, but generally median rates of spread decreased with increased thinning intensity (25% versus 35%) and with the additive effects of pruning and surface fuel abatement (range = 14–15 m min-1). The slowest median rate of spread was in the intensive thinning treatment (13 m min-1). Median surface fire intensity was significantly higher in thinning and pruning treatments without surface fuel abatement (range = 13,713 – 14,500 kW m-1), but significantly lower in treatments with abatement (range = 5,717 – 6,971 kW m-1) relative to the reference condition (median 12,722 kW m-1; Figure 3.7, Table B.9). Among treatments with abatement, surface fire intensities decreased significantly as the intensity of thinning increased. 78 Figure 3.7: Comparison of predicted fire behaviour among treatments and the reference conditions. For each treatment, the box represents the 25th and 75th percentiles of the predicted fire behaviour metric; the midline is the median and cross (x) is the mean; whiskers are the 5th and 95th percentiles; and circles are outliers. Treatments with the same superscript above the box plots were not significantly different (α = 0.05). 79 3.4 Discussion The departure from historically fire-resilient forest structures, the current high fuel loads, and the projected impacts from climate change all justify proactive forest stewardship in the SXFN interface to reduce the likelihood of large, fast-moving, high-severity crown fires. However, the BC government legal objectives for MDWR and OGMA forests represent a trade off on wildfire hazard reduction in the simulated fuel treatments from this study. Tree densities are significantly reduced in thinning treatments compliant with legal objectives for MDWR and OGMA (i.e. 25% and 35% thinning treatments), but significant subsequent increases to surface fuel loads would occur without abatement. Because of this, MDWR and OGMA thinning treatments alone did not significantly reduce the likelihood of crown fire relative to the reference condition, and surface fire intensity significantly increased. Even when MDWR and OGMA thinning treatments were paired with pruning and surface fuel abatement, crown fire likelihoods remained high (median ≥89%), although there was a substantial shift from active to passive crown fire predictions. Despite significantly lower density of live conifer trees in the intensive thinning treatment, residual basal areas, canopy covers, canopy bulk densities, and fine surface fuels were not significantly different than those resulting from the MDWR and OGMA treatments. The intensive thinning treatment increased surface fire and decreased active crown fire predictions substantially, but passive crown fire predictions still dominated. Of all the simulated fuel treatments, as well as the reference conditions, the intensive thinning treatment had the highest efficacy in achieving wildfire risk reduction. 80 3.4.1 MDWR and OGMA Fuel Treatments Reduce Fuels, But Crown Fire Risk Is Still High The simulated MDWR and OGMA fuel treatments significantly reduced fuels relative to the reference conditions, but still produced predictions for high likelihood of crown fire when modelled during high fire danger weather using CFIS. I hypothesize that the reason residual stand conditions were likely to sustain crown fire is because too many small- and medium-sized trees were retained following the MDWR and OGMA treatments. Removing trees with DBH < 22.5 cm in compliance with Table 3.1 resulted in the retention of many small trees, especially in the subcanopy and sapling layers. Though 90% of live Douglas-fir tree densities were removed in these layers, the 21 plots with reference conditions that had extremely high live conifer densities (>1,000 trees ha-1) retained high densities of small trees after the simulated treatment. This is important because small diameter trees typically have lower canopy base heights and act as ladder fuels to propagate surface fire into crown fire (Agee & Skinner, 2005; Stephens et al., 2012; Jain et al., 2012; Rutherford, 2023). While dense residual small trees likely contributed to the high crown fire likelihoods predicted by CFIS, their contribution to ladder fuels may be mitigated in real MDWR and OGMA fuel treatments. For example, a local innovative forestry practise is to configure thinning of understory trees to retain small, thinned clumps that are distanced from larger, older trees (pers. comm., M. Tomlinson). These clumpy patterns of residual trees in actual MDWR and OGMA treatments disrupt the horizontal and vertical continuity of ladder fuels and may reduce the likelihood of crown fire within stands, while also providing visual screening for mule deer (Dawson et al., 2007). However, this nuance is not represented when modelling fire behaviour in CFIS, since fuel load inputs represent average stand conditions rather than being spatially explicit. 81 In all MDWR and OGMA treatments, active crown fire predictions substantially decreased, but passive crown fire represented the highest proportion of fire type predictions. This has the potential to impact SXFN eco-cultural values as high-intensity passive crown fire can cause substantial tree mortality through injuries to the crown, stem, and root tissue of affected trees (Stephens & Finney, 2002; Hood et al., 2018; Varner et al., 2021). Of the eight MDWR and OGMA treatments, the two thinning, pruning, and surface fuel abatement treatments had the lowest median crown fire likelihoods (89-90%). There was no significant difference between the 25% and 35% thinning treatments, with the exception that active crown fire proportions were slightly higher in the 25% thinning treatment due to the extra retention of canopy layer conifer trees. This is important because the blanket exemptions to the legal objectives for fuel treatments in MDWR do not apply to OGMA that overlaps with MDWR (Swan, 2022). This means that without alternative exemptions to the MDWR legal objectives, OGMA that overlaps with MDWR can only have a maximum of 25% of live conifer basal area (12.5 ≥ DBH > 37.5 cm) removed in a fuel treatment versus the 35% permitted by the blanket exemptions. Consistent with high likelihood of crown fire, the MDWR and OGMA treatments yielded moderate rates of spread and intense surface fires that would challenge a timely, safe, and effective firefighter response, and under the worst-case scenario, challenge the safety of community evacuations. Following the MDWR and OGMA treatments, median predicted rates of spread ranged from 14 – 15 m min-1. Such rates of spread near or in the SXFN interface would challenge suppression response time, as provincial wildland firefighters must travel >100 km, mostly over rough, winding, gravel roads, to respond to a fire in either community. Anecdotally, accessing my field plots from Williams Lake, BC (the closest wildland fire base) required 2 to 6 82 hours, depending on road conditions. Similarly, aerial fire fighting support takes c. 30 minutes to SXFN communities from wildland fire bases in Williams Lake or 100 Mile House, BC (pers. comm., J. Liscomb). These response times are concerning for SXFN, as external help is often slow to arrive in emergencies (Bonde, 2011) and has forced SXFN citizens to defend their communities from nearby fires for several days without support from BC Wildfire Service (BCWS) in the past (pers. comm., J. Liscomb). Median surface fire intensities increased after MDWR and OGMA thinning and pruning, consistent with research showing that unabated logging slash generated by fuel treatments increases fire risk (Stephens et al., 2012; Collins et al., 2014; Jain & Graham, 2015; Moreau et al., 2022). Median surface fire intensities in the reference condition and the MDWR and OGMA treatments without abatement are >10,000 kW m-1 (Figure 3.7, Table B.9). Wildland fire suppression agencies in Canada refer to fires burning at this intensity as being in fire intensity class 6, the most intense class of fires (Taylor & Alexander, 2016). After all the treatments that included surface fuel abatement, median surface fire intensities were lower than those of the reference condition, but they were still all >4,000 kW m-1, falling within fire intensity class 5. BCWS does not attempt to directly suppress fires with fire intensity classes of 5 or 6 due to firefighter safety and would instead need to use aerial support to protect SXFN communities during this type of fire (Beck et al., 2002; BCWS, 2023a). 3.4.2 Efficacy of the Intensive Thinning Treatment Would Increase After Applying “Good Fire” Consistent with other research on intensive thinning in dry mixed conifer forests, wildfire behaviour was significantly reduced in the intensive thinning treatment under high fire danger 83 relative to MDWR and OGMA treatments and the reference condition (Johnson et al., 2011; Prichard & Kennedy, 2014; Moreau et al., 2022; North et al., 2022; Urza et al., 2023; Rutherford, 2023). Although intensive thinning significantly reduced live conifer densities, the residual basal areas, canopy covers, and canopy bulk densities were not significantly different than those estimated for the MDWR and OGMA fuel treatments. In contrast, canopy base height was significantly higher in the intensive thinning treatment compared to the MDWR and OGMA treatments. These findings are the result of removing the smallest trees, while retaining large trees to achieve a target of 150 trees ha-1. The large trees that were retained by the intensive thinning fuel treatment disproportionately contribute to basal area, canopy cover, and canopy bulk density within each plot, while the trees from smaller size classes that contribute to lower crown base heights and act as ladder fuels were more intensively removed. The differences in thinning intensity and the sizes of trees removed versus retained contributed to the efficacy of the intensive thinning treatment in reducing fire behaviour predictions relative to the MDWR and OGMA treatments and the reference condition. However, the intensive thinning treatment still yielded high predictions of passive crown fire, and rate of spread and surface fire intensity values were similar to those of the MDWR and OGMA treatments that incorporated pruning and abatement. Therefore, concerns for SXFN community safety still exist following the simulated intensive thinning fuel treatment. Further reductions in passive crown fire predictions require either pruning tree branches to increase the gap between surface fuels and tree canopies or further reducing fine surface fuels to reduce surface fire intensities and flame lengths (Stephens et al., 2012; Jain et al., 2012; BCWS, 2023a). Across all treatments, pruning height was set to 3.5 m, the highest achieved by most forestry practitioners in the Cariboo Region (pers. 84 comm., M. Tomlinson). Therefore, to further reduce passive crown fire in this study area there would need to be a greater reduction in fine surface fuels. Local forestry practitioners aim to reduce fine fuel loads to 1.0 kg m-2 post-fuel treatment at the guidance of BCWS (BCWS, 2023a), which is often achieved by piling the thinned small trees, branches, and other woody debris and burning those piles in the winter (pers. comm., K. Day). As an alternative to pile burning, further reduction in fine fuels could be achieved by applying cultural or prescribed surface fire (i.e. “good fire”) under appropriate fire weather conditions (Agee & Skinner, 2005; Johnson et al., 2011; Stephens et al., 2012; Knapp et al., 2017; Lydersen et al., 2017; Stephens et al., 2020; Ritter et al., 2023). By returning surface fire to these forests, future wildfires that burn into treated areas, even under high fire danger, would have lower surface fire intensities and crown fire potential because of the reduction of fine fuels, while potentially having positive impacts on fire-adapted plants that are important to SXFN (Wynecoop et al., 2019; Dickson-Hoyle et al., 2021; Dickson-Hoyle et al., 2024). Additionally, closely monitored wildfires burning through recently thinned forests under low-to-moderate fire weather may also burn as surface fire and contribute to eco-cultural restoration of interior Douglas-fir forests (North et al., 2021). Regardless, all wildfires burning in the SXFN interface during hot, dry, or windy weather conditions would be priority for full suppression response in the interest of community safety (BCWS, 2023a). 3.4.3 Restoring Long-Term Resilience in Unceded SXFN Forests Unceded SXFN forests were designated as MDWR and OGMA in response to the over-extraction of large Douglas-fir trees in the Cariboo Region throughout the 20th century by the forestry industry (Day, 1998; Le Clerc et al., 2021). In MDWR, snow interception and thermal 85 cover from large trees are important habitat components, while the foliage from large Douglas-fir trees provides a vital food source for mule deer during the winters (Dawson et al., 1990; Armleder et al., 1994; Dawson et al., 2007; Waterhouse & Walton, 2023). Additionally, studies on winter habitat use by deer in the 1980-90s indicate that deer use old growth forest more as snow depth increases (Armleder et al., 1994). To maintain and restore habitat, the MDWR land management handbook describes specific forest harvesting types allowed in MDWR forests (Dawson et al., 2007). This research focused on thinning-from-below (general wildlife measures 9) versus the clumpy single tree selection harvesting type (general wildlife measures 10), which allows harvest of trees with DBH > 37.5 cm (MOE, 2007). In clumpy single tree selection harvesting, there are minimum post-harvest basal area targets for live Douglas-fir trees with DBH ≥ 12.5 cm and they vary based on the physical characteristics of the harvest area, such as snowpack zone, MDWR stand structure class, biogeoclimatic subzone, and slope and aspect (Dawson et al., 2007). Thinning-from-below treatments do not have to meet these targets post-harvest, but the maximum removal of live Douglas-fir trees in size classes between 22.5 ≥ DBH < 37.5 cm is calculated using the post-harvest targets and the BDq method (see 3.2.5 Simulated Fuel Treatments Following MDWR & OGMA Legal Objectives). It is notable that most reference conditions in the SXFN interface lacked large trees due to past high-grade logging and this resulted in measured basal areas that were below post-harvest targets (Table B.4). In these stands clumpy single tree selection harvesting would not be possible without an exemption to the legal objectives (MOE, 2007), as the deficit in large overstory trees makes it impossible to meet long-term post-harvest basal area targets, and this was my justification for simulating thinning-from-below treatments in this research instead. Despite thinning-from-below treatments not needing to meet the basal area targets after harvest, higher basal areas have been found to be an important 86 indicator of snow interception, which is vital for mule deer winter habitat (Dawson et al., 2023). I detected no significant differences between the residual basal areas and canopy covers in simulated MDWR thinning versus intensive thinning treatments. Thus, carefully designed and implemented intensive thinning treatments applied in the interface have the potential to both reduce crown fire risk and gradually restore desired habitat attributes in MDWR, including overstory tree survival after fire (Collins et al., 2014) which would benefit snow interception, thermal cover, and food sources for deer during winter. A lack of stewardship in MDWR and OGMA forests that overlap with unceded SXFN land perpetuates current forest structures and fuels, putting these eco-cultural systems at risk of crown fire (Agee, 2002; Ganz et al., 2022). Restricting thinning treatments that could reduce wildfire risk with greater efficacy exacerbates the wildfire problem. While there are often trade offs between wildfire mitigation and habitat values, other research on fuel treatments in dry forests of western North America has yielded examples of where fuel treatments can meet both wildfire mitigation and eco-cultural goals (McIver et al., 2013; North et al., 2015; Stephens et al., 2020; Stephens et al., 2021; Yemshanov et al., 2023), and specifically in mule deer winter habitat (Roerick et al., 2019; Bristow et al., 2020). For the Cariboo region of BC, my results suggest that MDWR and OGMA legal objectives need to be critically revaluated, particularly in the interface, for fuel treatments to meet both wildfire mitigation and mule deer habitat goals. 3.4.4 Implications for SXFN Land Stewardship This research allows SXFN to “walk on two legs” to steward their lands using pre-existing SXFN traditional knowledge as well as the western science generated by this study. By 87 analyzing fire behaviour predictions for simulated MDWR and OGMA fuel treatments during 90th percentile fire weather, the efficacy of these treatments was determined to be low compared to the simulated intensive thinning fuel treatments. Intensively thinning forests in strategic locations throughout the SXFN interface may have more efficacy in reducing crown fire but will likely require the application of good fire, through cultural or prescribed burning, to further reduce surface fire intensities that currently promote passive crown fire. Outside of stand-level risk reduction described in this research, treating the interface and surrounding SXFN Traditional Territory with varying fuel reduction intensities would increase landscape heterogeneity. This is important because fire behaviour and spread direction on a landscape with a diverse mosaic of fuels and fuel structures could be slowed and altered (Knapp et al., 2017; Harvey et al., 2017; Brookes et al., 2021; Jones et al., 2021; Stephens et al., 2021; Urza et al., 2023; Ott et al., 2023). Applying fuel treatments that comply with legal objectives for MDWR and OGMA could increase fuel structure diversity in parts of the landscape but cannot represent the most extreme fuel removal scenarios; instead, fuel treatments that incorporate intensive thinning as well as pruning and surface fuel abatement should be prioritized closest to community values (Ager et al., 2010; Stephens et al., 2020; Urza et al., 2023; Ott et al., 2023). Unfortunately, fuel treatments targeting small-diameter trees are expensive in BC, sometimes costing $5,000 ha-1 or more (Copes-Gerbitz, 2022b). As it would be too expensive, operationally infeasible (North et al., 2015), and perhaps even inappropriate for SXFN eco-cultural values (SXFN, 2018) to implement fuel treatments throughout the entirety of the SXFN interface, there will likely always be portions of the interface that have high densities of fuel. Thus, there is a need to target strategic locations, areas close to community values or existing fuel-sparse areas within the interface or greater landscape, with intensive fuel removal that will alter fire behaviour and increase safety 88 for both SXFN communities and firefighters when it is necessary to employ suppression tactics (Moghaddas & Craggs, 2007). 3.5 Conclusion Adaptively and proactively stewarding dry forests in northern Secwépemcúl’ewc is necessary to mitigate negative impacts of high-severity crown fire on communities, wildlife, watersheds, and eco-cultural landscapes that provide multitudes of services (Wong & Iverson, 2004; Stephens & Moghaddas, 2005; North et al., 2015; Hessburg et al., 2016; Schoennagel et al., 2017; Stephens et al., 2018; Prichard et al., 2021; Hagmann et al., 2021; Hessburg et al., 2021). In SXFN Traditional Territory, and especially on lands adjacent to their communities, SXFN has the right to determine their own community wildfire safety targets and meet them through community-led stewardship (United Nations, 2007; SXFN, 2018). However, SXFN stewardship sovereignty is challenged by legal objectives for MDWR and OGMA that are inadvertently protecting MDWR and OGMA forests to death. The legal objectives limit fuel removal that is needed to restore and adapt these eco-cultural systems to meet the challenges caused by settler-colonial land management and climate change. For remote Indigenous communities surrounded by dense flammable fuels in MDWR or OGMA forests, this is a social and environmental justice issue, especially in an era where Indigenous rights and sovereignty are being continually advanced by Indigenous Peoples (Fisk et al., 2021; Fletcher et al., 2021; Vinyeta, 2022; Jacobs et al., 2022; Gordon et al., 2023). In the interest of truly protecting communities, vitally important wildlife habitat, and old growth forests from eco-cultural conversions caused by widespread high-severity crown fire, barriers to SXFN-led forest stewardship must be addressed. Future research could include assessing effectiveness of real MDWR and OGMA fuel treatments in reducing wildfire 89 behaviour, or investigating the social, economic, and other barriers there are for small remote Indigenous communities to implement forest stewardship or fuel treatments in their Traditional Territories. 90 Chapter 4: Lessons Learned and Future Actions This research project was a partnership between Stswecem’c Xget’tem First Nation (SXFN) and the University of British Columbia. I quantified fuel loads and potential fire behaviour in the Mule Deer Winter Range (MDWR) and Old Growth Management Area (OGMA) forests of the unceded SXFN wildland urban interface (hereafter, “interface”), then explored wildfire mitigation options through simulated fuel treatments. Fire has always been a part of SXFN Traditional Territory and SXFN stewardship practises, but the large, fast-spreading crown fires of the last two decades have repeatedly endangered SXFN’s two semi-remote communities, while also altering their connection to surrounding unceded lands and the ecological and cultural (hereafter, “eco-cultural”) benefits that they provide. As SXFN communities are small and semi-remote, emergency response by external agencies is often slow to arrive (Bonde, 2011), leading to the necessity of a community-led response to wildfire emergencies in their early stages (Dickson-Hoyle & John, 2021). Knowing this, SXFN is currently preparing their communities for wildfire emergencies, while also making strategies to proactively steward the forests closest to their communities by reducing fuels to mitigate uncontrolled wildfire spread and promote lower-severity fires, even during extreme fire weather (Prichard et al., 2020; Prichard et al., 2021). The settler-colonial government of British Columbia (BC) considers the unceded SXFN lands in their interface as Crown land, meaning that SXFN is required to comply with provincial laws and policies when implementing stewardship in forests closest to their communities. After industrial forestry targeted large Douglas-fir trees in the 20th century, concerns for wildlife habitat and biodiversity arose (Dawson et al., 2023). In response, the BC government designated >450,000 91 hectares (ha) of forest as MDWR or OGMA in the Cariboo Region of interior BC in the late 20th century (Dawson et al., 2023; BCSC, 2011). Legal objectives for the management of these forests were developed by the BC government to promote the maintenance or restoration of mule deer winter habitat (MOE, 2007) and old growth forest structures (MAL, 2010). Over half of the SXFN interface (58%) is designated as MDWR or OGMA and since their establishment, little forest management has occurred there. The lack of management, or stewardship, has maintained forest structures in MDWR and OGMA that have been degraded by forestry and fire suppression and are poorly suited for both wildlife and wildfire resilience. For instance, an unintended consequence has been that mule deer are avoiding the dense thickets of small Douglas-fir trees within MDWR during winter months (Waterhouse & Walton, 2021; Waterhouse & Walton, 2023). In interviews with local practitioners, legal objectives for MDWR and OGMA were identified as barriers to fuel treatments in the Cariboo Region of BC (Melton, 2020). While amendments to support fuel treatment implementation were made in 2018 to OGMA legal objectives and blanket exemptions to MDWR legal objectives were created in 2022, concerns about balancing wildlife needs with wildfire risk reduction persist in these forests (Day et al., 2010; Abbott & Chapman, 2018; Day, 2019; Melton, 2020). This thesis quantified wildfire risk in MDWR and OGMA forests of the SXFN interface (Chapter 2) and demonstrated the limited efficacy of simulated wildfire mitigation treatments that complied with existing legal objectives versus a treatment that would require extensive exemptions (Chapter 3). 4.1 High Fuel Loads Drive Crown Fire Predictions During Moderate to Extreme Weather In Chapter 2, I determined that there are high quantities of flammable forest fuels at 37 plot locations in the MDWR and OGMA forests of the SXFN interface. Specifically, I inventoried 92 high densities of small live conifer trees (DBH < 12.5 cm) and low densities of live conifer trees in the canopy layer (DBH > 12.5 cm; Table 2.1). Infilling of forest fuels beyond the natural range of variation (Wong & Iverson, 2004) in dry Douglas-fir forests have been attributed to settler-colonial land management, including Indigenous land dispossession, the exclusion of Indigenous fire and forest stewardship (FREC, 2021; Dickson-Hoyle et al., 2021; Copes-Gerbitz et al., 2021; Copes-Gerbitz et al., 2022b), overgrazing by livestock (Savage & Swetnam, 1990; Belsky & Blumenthal, 1997), aggressive fire suppression (Brookes et al., 2021; Copes-Gerbitz et al., 2023), and industrial forestry (Day, 1998; Leclerc et al., 2021). I modelled stand-level fire behaviour using data that I collected from detailed forest measurements and weather data representing four increasingly extreme fire season weather scenarios. The models predicted high likelihoods of intense, fast moving, crown fire in forests surrounding SXFN communities at all weather scenarios (Figures 2.4-2.6). The high potential for crown fire, even under average summer fire weather, indicates that forest fuels may be the main driver of crown fire potential in MDWR and OGMA forests of the SXFN interface. In addition to these results, the well documented departure from historical forest structures (Iverson et al., 2002; Daniels, 2004; Harvey et al., 2017; Brookes et al., 2021; Copes-Gerbitz et al., 2022b) and the reality of climate change exacerbating extreme fire weather (Flannigan et al., 2005; Jain et al., 2022; Wang et al., 2023; Parisien et al., 2023) justify the need for proactive and adaptive forest stewardship that mitigates aggressive crown fires, restores eco-cultural systems, and adapts them to climate change. However, there are both known and unknown barriers to accomplishing this much-needed proactive forest stewardship (Melton, 2020; Hoffman et al., 2022b; Copes-Gerbitz et al., 2022b). 93 4.2 Simulated MDWR and OGMA Fuel Treatments Do Not Promote SXFN Wildfire Safety In Chapter 3, I determined that simulated fuel treatments that comply with legal objectives for MDWR and OGMA do not promote SXFN community wildfire safety, especially in the interface. In this chapter, I simulated two types of fuel treatments; one that complied with MDWR and OGMA legal objectives (without applying for an exemption to the legal objectives) and another that intensively thinned the smallest trees within stands but retained c. 150 trees ha-1 of the largest diameter classes. Both thinning types were paired with the pruning of tree branches and abatement of surface fuels. I found that while density of residual trees was significantly lower in the intensive thinning treatments, the stand basal area and canopy cover were not significantly different among the different thinning treatments (Figure 3.3). However, post-treatment basal areas were below the long-term targets for MDWR at 26 of 37 plots, showing a persistent legacy of industrial logging in the 20th century and indicating that it will require many decades to restore MDWR to desired forest structures (Table B.4). I remodelled fire behaviour using fuels data from the simulated fuel treatments and 90th percentile fire weather data and found that MDWR and OGMA fuel treatments reduced predictions of active crown fire but maintained high likelihood of intense passive crown fire. Comparatively, the intensive-thinning fuel treatment most greatly reduced crown fire predictions, but predictions for high-intensity passive crown fire still dominated (Figures 3.6-3.7). These results are concerning for SXFN, as high-intensity passive crown fire has the potential to cause substantial tree mortality (Stephens & Finney, 2002; Hood et al., 2018; Varner et al., 2021) and may be difficult for firefighters to suppress while trying to prevent spread towards the communities (Beck et al., 2002; BCWS, 2023a). I hypothesized that further reduction of fine fuels by returning cultural or prescribed fire following intensive thinning would decrease fire intensity and promote more surface fires, even 94 during 90th percentile fire weather. Restoration of surface fire potentially has numerous additional benefits to the surrounding eco-cultural system that support SXFN communities (Wynecoop et al., 2019; Nikolakis et al., 2020; Long et al., 2021; Dickson-Hoyle et al., 2021; Dickson-Hoyle et al., 2024). 4.3 Legal Objectives Should Be Revised in Partnership with Indigenous Rights-Holders Identifying better ways to steward for multiple objectives are needed, given that the predicted fire behaviour in simulated MDWR and OGMA fuel treatments would challenge SXFN community safety, and that widespread crown fire would degrade desired MDWR and OGMA forest structures. The residual basal areas in intensive fuel treatments are not significantly different than those in MDWR and OGMA fuel treatments, so pairing intensive thinning with pruning and the reintroduction of cultural or prescribed fire is a viable solution to reducing crown fire likelihood. This stewardship would also restore fire-resilient forest structures favoured by mule deer by retaining the largest Douglas-fir trees. Despite SXFN wishing to experiment with various intensities of thinning in the forests of their Traditional Territory, a form of adaptive stewardship, the intensive thinning treatment simulated in this research is not legally allowed in MDWR forests without substantial exemptions to the current legal objectives. Exemptions are available to the legal objectives associated with MDWR, especially if the exemption request is related to wildfire risk reduction. However, the exemption process can take several months due to the negotiating of exemption conditions (pers. comm., J. Liscomb), adding time and additional transaction costs to the implementation of already expensive, vital forest stewardship (Ager et al., 2021; Copes-Gerbitz et al., 2022b). There are no exemptions allowed for the OGMA legal objectives, but OGMA legal objectives are less prescriptive than those for MDWR (MAL, 2018). 95 Therefore, fuel treatments in OGMA that does not overlap with MDWR may be less constrained than in MDWR. As MDWR overlaps with 55% of the SXFN interface, MDWR legal objectives are barriers to the timely implementation of intensive thinning fuel treatments in forests where they are most needed. These legal objectives were developed to protect and restore wildlife habitat and biodiversity, however they did not consider the wildfire risk to rural communities, Indigenous and settler alike. An unintended consequence is that hazardous, fire-susceptible forest structures adjacent to those communities cannot be mitigated without exemptions that take time and significant effort to be approved. Given the current wildfire crisis in BC, these legal objectives need to be revised by Indigenous rights-holders and the BC government to better address contemporary issues. The government guarantee that legal objective exemptions will always be approved for wildfire risk reduction is not what has been experienced in practise (Bings, 2023). Nor is it a respectful solution, as the ultimate decision-making power regarding the stewardship of these lands is still being held by the BC government and not by the local Indigenous rights-holders that will be most impacted by these decisions. However, if SXFN, other Indigenous rights-holders, and the BC government worked together using Indigenous knowledge, western science, and knowledge from forest and fire professionals, more inclusive land use policies and legal objectives are possible in Secwépemcúl’ewc. Restoration of fire-resilient forests and the protection of mule deer winter range and old growth forest structures can be achieved throughout the landscape without compromising community safety or other eco-cultural values. Ideally, through a collaborative process of revising the legal objectives, SXFN would gain the flexibility to adaptively steward their unceded lands without having to navigate the complex, time-96 constraining exemption process to current legal objectives. This barrier reduction for community-led stewardship may lead to some strategic parts of the landscape being intensively thinned to promote crown fire resistance, while other forests may be maintained closer to what the MDWR and OGMA legal objectives currently allow, but without being constrained by them. Due to the high expense of fuel treatments (Copes-Gerbitz et al., 2022b), as well as access constraints, it is impossible to thin the entire SXFN interface, let alone their greater Traditional Territory, and it will take many years to implement all of the fuel treatments desired by the communities (North et al., 2015). However, by strategically implementing diverse forest stewardship throughout the Traditional Territory, the resulting heterogeneous landscape composed of varying forest structures, grasslands, wetlands, water bodies, and other human infrastructure could provide many benefits, including landscape fire resiliency as the climate changes (Knapp et al., 2017; Harvey et al., 2017; Brookes et al., 2021; Jones et al., 2021; Stephens et al., 2021; FPB, 2023). It is important to note that the simulated fuel treatments in this study cannot perfectly represent a real fuel treatment and predicted wildfire mitigation efficacy may differ from effectiveness when fuel treatments are impacted by real wildfire. At present, a 300 m wide, 400 ha fuel break is being implemented in MDWR and OGMA forests south of the Xget’tem reserves, overlapping with the study area of this research. The fuel break follows legal objectives for MDWR and OGMA thinning-from-below. The fuel break prescription specifies a median density target of 808 residual live conifer trees ha-1 (range = 187 – 970), which is well above the median density of live conifers in the simulated fuel treatments of this study. As a result, this treatment area does not provide a true break in forest fuels. An exemption request was made to remove more basal 97 area from larger trees in a particularly dense treatment unit, but the original request was denied by the BC government. Instead, after several months the BC government made an exemption decision that came with conditions and differed from the original request through reducing less basal area in the interest of protecting mule deer habitat (Bings, 2023). So far, the implementation of this treatment has been expensive, with estimates of ~$6,100 ha-1 (pers. comm., M. Tomlinson), due to the combination of hand and mechanical thinning treatments, the transportation of merchantable trees to sawlog markets (90 km) and pulp markets (>200 km), stumpage fees owed to the BC government for harvesting merchantable trees on Crown land, the chipping and transportation of unmerchantable trees to a pellet plant (90 km), and the grinding of residual logging slash via portable grinder. The total cost of the treatment at that price per hectare would be $2.44 million. While the effectiveness of wildfire mitigation in this fuel break cannot be determined without it encountering a real wildfire, SXFN staff are concerned that not enough small to medium sized trees are being removed to meet community safety goals, especially under climate change (pers. comm., J. Liscomb). Forest practitioners responsible for the harvesting prescription acknowledge that by following the legal objectives for MDWR and OGMA in this treatment, as well as other government forestry standards such as those associated with post-harvest stocking standards, there has been a substantial wildfire risk reduction compared to pre-treatment conditions. However, they also feel that wildfire risk reduction could be further improved within the fuel break (pers. comm., M. Tomlinson). It was hypothesized that an improved fuel treatment would retain fewer small trees (DBH < 12.5 cm), increase separation between surface and ladder fuels and the crowns of large trees, and have basal area targets that are flexible depending on the pre-treatment forest structures (pers. comm., M. Tomlinson). It is likely that this would increase the economic efficiency of the treatment, as more work could be 98 done mechanically versus by hand, and that harvesting machine operators might not have to apply the same level of care when operating around small trees, something that has slowed down fuel treatment implementation here (pers. comm., M. Tomlinson). 4.4 SXFN Has Legal Rights to Steward Their Unceded Traditional Territory Regardless of whether MDWR and OGMA compliant or intensive fuel treatments will be effective at reducing wildfire risk, the legal objectives limit the flexibility of SXFN to exercise their legal rights in stewarding their unceded lands. SXFN and other Secwépemc Nations in Secwépemcúl’ewc never gave their free, prior, or informed consent to have their forests designated as MDWR or OGMA by the BC government, or to their legal objectives for forest management. Additionally, SXFN forest knowledge, which is derived from many generations of adaptive stewardship in and beyond this study area, and SXFN preference, given their current and future reliance on these forests for various benefits, is not reflected in the legal objectives (SXFN, 2018; FREP, 2021). SXFN has Aboriginal Title and Rights to use and exert jurisdiction over their unceded Traditional Territory and they are protected under section 35 of the Canadian Constitution Act (Constitution Act, 1982; McNeil, 2016). Additional SXFN land rights are reaffirmed by the United Nations Declaration on the Rights of Indigenous Peoples (UN, 2007), which was recently incorporated into provincial legislation in BC under the Declaration of the Rights on Indigenous Peoples Act (BC, 2020). This Act upholds that First Nations have rights to “own, use, develop and control lands and resources within their territories in BC” (BC, 2020; p. 14). Further, SXFN has a government-to-government agreement with BC that dictates their right to jointly manage the land and natural resources within their Traditional Territory with the province (Yecweminul'ecw Land and Resource Use Agreement, 2018). The legal rights that 99 SXFN hold are an acknowledgement that the People of SXFN were here long before settlers arrived, never ceded their lands or waters to the BC or Canadian governments, continue to depend on these lands and waters for their physical, mental, and cultural wellness, and have multigenerational place-based knowledge relating to land and water stewardship that does not just benefit them, but rather everyone who lives on, works or recreates in, SXFN Territory and beyond. After two centuries of harm from settler-colonial systems, both BC and Canadian governments have stated their intention to advance reconciliation with Indigenous Peoples. BC recognition and support of SXFN rights to exert stewardship sovereignty and benefit from their unceded Traditional Territory is a way to do this. 4.5 Benefits of Community-Led Forest Stewardship and #LANDBACK Outside of the legal rights that SXFN holds, there are other reasons why land stewardship led by local people can produce benefits for those people and indirectly the greater population (Ostrom et al., 1999). As local people are most impacted by local stewardship, or a lack of stewardship, directly adjacent to their communities or throughout their ancestral lands, they have a long-term vested interest in making stewardship decisions that fulfill their responsibility to future generations (Anton & Lawrence, 2016; SXFN, 2018; Long et al., 2020; FREP, 2021). This is demonstrated by the fact that while Indigenous Peoples steward only 22% of the land on this planet, 80% of world biodiversity is found on those lands (Trewin et al., 2021). Community Forest Agreement tenure holders in BC are a positive example of communities stewarding their forests for multiple eco-cultural benefits unique to their locality and many are leaders in advancing local wildfire governance (McIlveen & Bradshaw, 2009; Furness et al., 2015; Devisscher et al., 2021; Dickson-Hoyle et al., 2023). SXFN is currently advocating for a 100 Community Forest Agreement with BC that crosses two timber supply areas to encompass both their communities, as this would give them exclusive harvesting rights and access to lower stumpage rates within the area-based tenure, making forest stewardship such as fuel treatments more affordable. However, Crown land policies and legal objectives would still apply within the tenure, maintaining the lack of flexibility that SXFN has to intensively thin in MDWR and OGMA forests without provincial exemptions. The SXFN community wildfire risk identified in this research stems from abundant forest fuels on lands managed by settler-colonial governments that have not yet reconciled how to best support SXFN sovereignty. If full control of lands in the SXFN interface was returned to SXFN, it would be less challenging for the communities to address their fuel problem. The #LandBack movement has gained notoriety as Indigenous Peoples fight for access to, and sovereignty over the lands and waters that their ancestors have stewarded since time immemorial (NDN Collective, n.d.; Fisk et al., 2021; Gordon et al., 2023; Jacobs et al., 2022; Vinyeta, 2022; Leonard et al., 2023). With the majority of BC considered as Crown land, there are many opportunities for lands to be returned to Indigenous governments and communities. Alternatively, 7,800 ha of private ranch land surrounding the Stswecem’c community was returned to SXFN in 2023 through their advocacy and participation in the modern-day Treaty process. Because these lands are now owned by SXFN, experimental thinning and burning prescriptions are being created for them in 2024. These lands, which surround Stswecem’c, will be proactively stewarded to meet both present and future eco-cultural goals, including safety from community-threatening wildfire. Without having to work within MDWR and OGMA legal objectives or other provincial land use policies, #LandBack facilitates proactive, adaptive, 101 community-led stewardship. It is important to note that while many barriers are addressed with #LandBack, implementing proactive forest stewardship is expensive and economic barriers may still exist for SXFN. This economic challenge is exasperated on the unceded Crown lands in SXFN Territory, where the communities are forced to pay stumpage to the BC government for the volume of merchantable trees that are harvested. On unceded Crown lands, especially forests closest to the communities, the barriers to timely, adaptive, and affordable fuel treatment implementation must be identified and overcome for landscape-scale fire-resiliency to be achieved. It is important that this vital work is led by or partnered with Indigenous rights-holders and other local communities who have place-based knowledge that strengthens land stewardship. More importantly, local communities should have control over decisions that will disproportionately impact them, whether it be their homes and infrastructure or the lands and waters that they rely upon for food, water, culture, and general survival. 4.6 Conclusion and Future Research Fire has and always will be a part of SXFN Territory, but SXFN citizens have the right to determine how they will best co-exist with fire in the years to come. Despite not being responsible for industrialization or the accumulation of hazardous fuels on their unceded lands in the last two centuries, SXFN and many other rural Indigenous communities are being disproportionately impacted by climate change and subsequently, wildfire. This is a social and environmental injustice that must be addressed through our society upholding Indigenous rights and supporting Indigenous Peoples today. This can be done through the BC government providing funds and other supports for community-led stewardship, through collaboratively amending legal objectives and land management policies that act as barriers with Indigenous 102 rights-holders, and by respecting the necessity of free, prior, and informed consent from Indigenous rights-holders for land management laws, policies, or activities planned within their Traditional Territories. Future research should continue to be partnered with SXFN and other Indigenous rights-holders and should be prioritized on the SXFN private ranch lands that are undergoing land stewardship in 2024. There are opportunities and benefits in the future research being partnered with the BC government, fire specialists, or members of the forestry industry. Research should explore the effectiveness of fuel treatments in dry forests of Secwépemcúl’ewc after they encounter wildfire. Special focus could be on comparing effectiveness of fuel treatments that comply with MDWR and OGMA legal objectives, versus fuel treatments that incorporate more intensive thinning. The stand and landscape scale eco-cultural impacts of fuel treatments and wildfire as they relate to Indigenous communities should also be explored, as this will help Indigenous Peoples lead forest stewardship in fire-prone parts of their Territories with an understanding of how it may impact a wide range of their values. One eco-cultural value that could be prioritized is mule deer use of intensively thinned forests versus MDWR-compliant thinned, or untreated forests. While I reviewed the legal objectives for MDWR and OGMA closely during this research, a formal analysis on the legal objectives and how they impact the implementation of forest stewardship in the Cariboo Region under the current challenges posed by climate change (e.g., increased fire severity) is crucial. 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Frontiers for Global Change, 6, 1186616. https://doi.org/10.3389/ffgc.2023.1186616 Zhang, X., Zhou, T., Zhang, W., Ren, L., Jiang, J., Hu, Shuai., Zhang, L., and Man, W. (2023). Increased impact of heat domes on 2021-like heat extremes in North America under global warming. Nature Communications, 14, 1690. https://doi.org/10.1038/s41467-023-37309-y 134 Appendices Appendix A: Fuel Type Selection Plots were assigned a FBP fuel type based on methods in Perrakis et al. (2018). Mixture of tree species, time since logging or fire, average stand height (from trees with DBH ≥ 5cm), crown closure (canopy cover used as proxy), biogeoclimatic zone, density, mountain pine beetle attack status, and the percentage of dead trees metrics are used to select the fuel type that best matches data derived from the plot. Most plots are Douglas-fir leading (80% of basal area composition) and are classified as C7 fuel types. However, two Douglas-fir leading stands have canopy covers <26% and are classified as O1, the grassland fuel type. In contrast, twelve Douglas-fir leading plots have canopy covers >55% with average stand heights of 4 – 12 m. Depending on the percentage of dead trees present, the plots were classified as C4 (n = 1; >34% dead) or C3 (n = 11) fuel types. One plot is lodgepole pine leading and is also classified as C3 fuel type. The four mixed species plots are classified differently based on their ratio of conifer to broadleaf trees and their leading species. Two are classified as C7, one is C3, and the last is M2. Table A1 describes the general fuel characteristics linked to each fuel type assigned to plots in this study. There is not a perfect fuel type for Douglas-fir leading stands infilled with small-diameter trees, despite this being a common forest structure throughout the study area (Baron et al., 2024). 135 Table A.1: Typical characteristics for fuel types found at the plots (FCFDG, 1992). It is important to note that the fuel type name does not always indicate the dominant tree species in a plot assigned with that fuel type. Fuel Type Forest floor and organic layer Surface and ladder fuels Stand structure and composition C7 (Ponderosa Pine-Douglas-fir) Continuous needle litter, absent to shallow organic layer. Discontinuous grasses, herbs, except in conifer thickets, where absent; light woody fuels; tree crowns separated from ground except in thickets. Open ponderosa pine and Douglas-fir stands; mature uneven-aged; assoc. sp. Western larch, lodgepole pine; understory conifer thickets. C3 (Mature Jack or Lodgepole Pine) Continuous feather moss and/or Cladonia; deep, compacted organic layer. Sparse conifer understory may be present; sparse down woody fuels; tree crowns separated from the ground. Fully stocked jack or lodgepole pine stands; mature. M1/2 (Boreal Mixedwood) Continuous leaf litter in deciduous portions of stands; discontinuous feather moss and needle litter in conifer potions of stands; organic layers shallow, uncompacted to moderately compacted. Moderate shrub and continuous herb layers; low to moderate dead, down woody fuels; conifer crowns extend nearly to ground; scattered to moderate conifer understory. Moderately well-stocked mixed stand of boreal conifers (e.g. black/white spruce, balsam/subalpine fir) and deciduous species (e.g., trembling aspen, white birch). Fuel types area differentiated by season and percent conifer/deciduous sp. Composition. C4 (Immature Jack or Lodgepole Pine) Continuous needle litter, moderately shallow organic layer. Moderate shrub/herb cover; continuous vertical crown fuel continuity; heavy standing dead and down, dead woody fuel. Dense jack or lodgepole pine stands; immature. O1a/b (Grass) Continuous dead grass litter; organic layer absent to shallow and moderately compacted. Continuous standing grass (current year crop). Standard loading is 0.35 kg/m2, but other loading can be accommodated; percent cured or dead must be estimated. Sparse or scattered shrubs and down woody fuel. Subtypes for both early spring matted grass (a) and late summer standing cured grass (b) area included. Scattered trees, if present, do not appreciably affect fire behaviour. 136 Appendix B: Fuel Attribute Tables for Each Plot Post-Treatments Table B.1: Biophysical attributes of the 37 plots. Mule deer winter range (MDWR) stand structure classes and old growth management areas (OGMA) are derived from BC government land management spatial datasets in a geographic information system. Canopy cover values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. Community Plot Elevation (masl) Aspect (°) Slope (%) MDWR Stand Structure OGMA Canopy Cover (%) Stswecem'c 1 861 198 34 Low No 67 21 18 23 2 1107 86 5.5 Moderate Yes 77 77 77 47 3 1160 170 10.6 High Yes 57 57 50 38 4 1087 167 59.9 Low Yes 23 23 23 23 5 1241 22 8.4 Moderate No 63 41 41 45 6 1131 225 16.6 High Yes 67 61 47 30 7 1282 264 8 Low No 77 75 57 47 8 827 297 70.2 Low No 50 31 25 25 9 1124 198 7.6 High Yes 73 27 23 33 10 1095 279 5.7 High Yes 60 39 39 39 11 1061 212 8.6 High Yes 60 24 24 27 12 1154 209 6.5 Low No 70 41 41 40 13 1256 162 20.6 Moderate Yes 63 63 63 58 14 1028 193 10.6 Moderate Yes 37 37 37 37 15 1091 154 8.5 High Yes 53 24 24 30 16 1175 206 8.4 High Yes 47 20 18 5 17 1186 104 40.3 Moderate No 77 65 65 34 18 1151 194 8.1 Low No 53 18 18 18 19 1173 274 10.4 Low Yes 47 41 41 34 20 1189 253 2.7 Moderate No 47 17 14 20 Xget'tem 21 864 267 36.6 High No 30 30 30 30 22 1052 116 10.5 High No 17 17 17 17 23 1063 257 1.9 High Yes 57 20 20 24 24 1060 313 15.3 High No 73 57 50 7 25 994 228 19.6 Moderate Yes 37 27 27 34 26 1045 15 32 High Yes 53 13 11 19 27 1003 341 24.2 High Yes 67 25 19 14 28 1073 164 13.8 High No 43 43 43 23 29 1127 350 10.2 Low No 63 44 38 21 30 763 48 36.3 High No 47 32 30 35 31 792 23 24 High Yes 47 47 47 34 32 915 218 46.3 Moderate Yes 50 25 25 31 33 947 314 32.9 High Yes 40 19 16 20 34 1053 340 5 High No 57 21 20 20 35 1080 108 8.5 Moderate No 50 45 43 28 36 1064 348 5 High No 40 19 19 25 37 1023 177 13.4 High No 30 30 30 19 Median 1073 206 11 NA NA 53 30 30 28 25th Percentile 1023 162 8 NA NA 47 21 20 20 75th Percentile 1151 267 24 NA NA 63 44 43 34 137 Table B.2: Density of live conifer trees by canopy layer in the 37 plots. Density values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. Plot Canopy (trees ha-1) Subcanopy (trees ha-1) Sapling (trees ha-1) Total (trees ha-1) 1 275 115 104 150 400 40 40 0 300 30 30 0 975 185 174 150 2 200 99 74 150 100 10 10 0 0 0 0 0 300 109 84 150 3 750 353 284 150 2,800 280 280 0 0 0 0 0 3,550 633 564 150 4 125 91 91 125 100 10 10 0 0 0 0 0 225 101 101 125 5 375 121 121 150 1,600 110 110 0 300 30 30 0 2,275 261 261 150 6 750 399 313 150 200 20 20 0 300 20 20 0 1,250 439 353 150 7 850 412 238 150 1,900 150 150 0 200 0 0 0 2,950 562 388 150 8 350 197 165 150 200 10 10 0 0 0 0 0 550 207 175 150 9 300 160 151 150 800 80 80 0 1,600 160 160 0 2,700 400 391 150 10 325 149 149 150 1,000 80 80 0 1,700 170 170 0 3,025 399 399 150 11 250 112 112 150 2,000 200 200 0 1,800 180 180 0 4,050 492 492 150 12 325 148 148 150 1,200 120 120 0 7,600 760 760 0 9,125 1,028 1,028 150 13 300 246 246 150 300 30 30 0 4,300 430 430 0 4,900 706 706 150 14 225 179 179 150 100 10 10 0 300 20 20 0 625 209 209 150 15 125 61 61 125 3,400 300 300 0 2,200 220 220 0 5,725 581 581 125 16 425 275 229 150 300 10 10 0 1,700 170 170 0 2,425 455 409 150 17 425 323 323 150 0 0 0 0 3,000 300 300 0 3,425 623 623 150 18 175 68 66 150 1,800 180 180 0 800 80 80 0 2,775 328 326 150 19 325 196 196 150 600 40 40 0 6,800 680 680 0 7,725 916 916 150 20 200 107 89 150 1,000 100 100 0 5,400 530 530 0 6,600 737 719 150 21 175 111 111 150 0 0 0 0 0 0 0 0 175 111 111 150 22 275 164 151 150 200 0 0 0 400 10 10 0 875 174 161 150 23 125 59 59 125 3,800 380 380 0 1,500 150 150 0 5,425 589 589 125 24 850 582 504 150 700 70 70 0 0 0 0 0 1,550 652 574 150 25 125 59 59 125 0 0 0 0 1,500 140 140 0 1,625 199 199 125 26 150 84 77 150 1,200 120 120 0 400 40 40 0 1,750 244 237 150 27 400 207 155 150 2,100 210 210 0 0 0 0 0 2,500 417 365 150 28 1,100 623 496 150 300 30 30 0 400 30 30 0 1,800 683 556 150 29 600 319 245 150 1,100 80 80 0 2,600 260 260 0 4,300 659 585 150 30 375 194 188 150 400 40 40 0 200 20 20 0 975 254 248 150 31 1,025 540 421 150 100 10 10 0 400 40 40 0 1,525 590 471 150 32 225 96 96 150 200 20 20 0 500 50 50 0 925 166 166 150 33 300 162 145 150 300 30 30 0 200 20 20 0 800 212 195 150 34 175 132 118 150 1,300 100 100 0 1,700 150 150 0 3,175 382 368 150 35 450 357 263 150 200 20 20 0 700 0 0 0 1,350 377 283 150 36 150 111 111 150 600 50 50 0 2,300 230 230 0 3,050 391 391 150 37 450 272 216 150 100 10 10 0 200 0 0 0 750 282 226 150 Median 300 162 151 150 400 40 40 0 400 40 40 0 2,275 399 368 150 25th Percentile 200 111 104 150 200 10 10 0 200 0 0 0 975 212 209 150 75th Percentile 425 275 238 150 1,200 110 110 0 1,700 170 170 0 3,425 590 564 150 138 Table B.3: Total and canopy layer basal area of live conifer trees and percentage of live conifer canopy layer basal area removed in the 37 plots. Basal area values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. Plot Canopy (m2 ha-1) Canopy Percent Removal (m2 ha-1) Total (m2 ha-1) 1 12.5 9.4 8.8 10.2 25% 29% 33% 15.2 9.6 9.1 10.2 2 6.3 4.8 4.1 5.5 25% 35% 17% 6.7 4.8 4.2 5.5 3 35.4 26.6 23.1 17.1 25% 35% 66% 50.3 28.1 24.5 17.1 4 19.6 18.2 18.2 19.6 7% 7% 4% 20.4 18.2 18.2 19.6 5 33.6 28.7 28.7 29.9 14% 14% 30% 42.5 29.3 29.3 29.9 6 25.2 18.9 16.4 11.3 25% 35% 57% 26.1 19.0 16.5 11.3 7 43.4 32.6 28.2 25.7 25% 35% 51% 52.0 33.2 28.9 25.7 8 17.5 13.2 11.4 11.0 25% 35% 40% 18.3 13.2 11.5 11.0 9 16.0 12.1 11.1 12.8 25% 31% 37% 20.4 12.5 11.5 12.8 10 24.1 20.7 20.7 20.8 14% 14% 30% 29.5 21.1 21.1 20.8 11 15.4 12.5 12.5 13.6 19% 19% 55% 30.2 14.0 14.0 13.6 12 26.5 22.8 22.8 23.0 14% 14% 38% 36.8 23.9 23.9 23.0 13 54.8 53.3 53.3 46.1 3% 3% 21% 58.2 53.6 53.6 46.1 14 31.9 30.1 30.1 28.9 6% 6% 10% 32.2 30.1 30.1 28.9 15 19.6 18.7 18.7 19.6 5% 5% 48% 37.9 20.3 20.3 19.6 16 8.6 6.5 5.6 3.6 25% 35% 70% 11.9 6.7 5.8 3.6 17 41.6 35.3 35.3 21.2 15% 15% 52% 44.2 35.6 35.6 21.2 18 6.4 4.8 4.8 6.0 25% 26% 64% 16.8 5.9 5.8 6.0 19 22.8 18.6 18.6 16.5 18% 18% 43% 28.9 19.2 19.2 16.5 20 6.8 5.1 4.4 6.0 25% 35% 62% 15.9 6.0 5.3 6.0 21 18.4 16.8 16.8 17.7 9% 9% 4% 18.4 16.8 16.8 17.7 22 15.8 11.9 11.0 12.0 25% 31% 30% 17.0 11.9 11.0 12.0 23 11.6 10.5 10.5 11.6 9% 9% 62% 30.6 12.4 12.4 11.6 24 15.5 11.6 10.1 3.0 25% 35% 85% 19.8 12.0 10.5 3.0 25 24.5 23.5 23.5 24.5 4% 4% 4% 25.5 23.6 23.6 24.5 26 5.8 4.4 4.1 5.8 25% 30% 47% 11.1 4.9 4.6 5.8 27 11.2 8.4 7.3 6.8 25% 35% 71% 23.8 9.6 8.5 6.8 28 38.8 29.1 25.2 10.6 25% 35% 75% 41.5 29.4 25.5 10.6 29 17.9 13.5 11.6 8.7 25% 35% 66% 25.3 14.1 12.3 8.7 30 20.2 15.2 14.6 14.5 25% 28% 37% 22.9 15.4 14.8 14.5 31 40.4 30.3 26.3 13.9 25% 35% 66% 41.1 30.4 26.3 13.9 32 18.4 15.1 15.1 16.9 18% 18% 13% 19.4 15.2 15.2 16.9 33 15.2 11.4 10.2 11.4 25% 33% 30% 16.4 11.5 10.3 11.4 34 4.0 3.0 2.6 3.6 25% 35% 70% 11.8 3.7 3.3 3.6 35 21.7 16.3 14.1 11.9 25% 35% 50% 23.6 16.4 14.2 11.9 36 12.6 10.8 10.8 12.6 15% 15% 30% 18.0 11.3 11.3 12.6 37 13.3 10.0 8.6 6.7 25% 35% 53% 14.1 10.0 8.7 6.7 Median 18 15 14 13 25% 30% 47% 24 15 14 13 25th Percentile 13 11 10 9 14% 14% 30% 17 11 10 9 75th Percentile 25 23 23 20 25% 35% 62% 32 24 24 20 139 Table B.4: Post-harvest live canopy layer Douglas-fir basal area targets for mule deer winter range (MDWR) stand structures compared to the reference conditions and simulated post-treatment basal areas in the 37 plots. Basal area values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. Community Plot MDWR Stand Structure Post-Harvest Target (m2 ha-1) Reference Condition (m2 ha-1) Post-Simulated Fuel Treatment (m2 ha-1) Stswecem'c 1 Low 16 12 9 9 10 2 Moderate 22 6 5 4 6 3 High 29 35 27 23 17 4 Low 16 20 18 18 20 5 Moderate 22 32 29 29 30 6 High 29 25 19 16 11 7 Low 16 36 33 28 26 8 Low 16 18 13 11 11 9 High 29 16 12 11 13 10 High 29 24 21 21 21 11 High 27 15 12 12 14 12 Low 16 26 23 23 23 13 Moderate 22 55 53 53 46 14 Moderate 22 32 30 30 29 15 High 29 20 19 19 20 16 High 29 0 0 0 0 17 Moderate 22 42 35 35 21 18 Low 16 6 5 5 6 19 Low 16 23 19 19 17 20 Moderate 22 7 5 4 6 Xget'tem 21 High 27 18 17 17 18 22 High 27 16 12 11 12 23 High 29 12 11 11 12 24 High 29 15 12 10 3 25 Moderate 22 24 23 23 24 26 High 27 14 8 7 7 27 High 29 6 4 4 6 28 High 27 39 29 25 11 29 Low 16 16 13 12 9 30 High 27 20 15 15 14 31 High 27 40 30 26 14 32 Moderate 22 18 15 15 17 33 High 27 15 11 10 11 34 High 27 2 2 2 2 35 Moderate 22 16 16 14 8 36 High 29 13 11 11 13 37 High 27 13 10 9 7 MDWR Stand Structure Post-Harvest Target Median High (n = 20) 27 or 29 16 12 11 12 Moderate (n = 9) 22 24 23 23 21 Low (n = 8) 16 19 16 15 14 140 Table B.5: Average canopy base height (CBH) and total canopy bulk density (CBD) in the 37 plots. CBH and CBD values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. MDWR and OGMA treatments (progressively lighter greens and blues) from left to right are thinning, followed by thinning and pruning. Plot CBH (m) CBD (kg m-3) 1 1.6 2.9 2.7 4.0 3.8 4.7 0.124 0.063 0.058 0.057 0.052 0.064 2 2.7 2.8 2.8 3.5 3.5 3.5 0.039 0.021 0.018 0.018 0.016 0.024 3 4.4 5.0 4.9 5.4 5.3 6.2 0.462 0.176 0.153 0.171 0.148 0.084 4 1.4 1.7 1.7 3.5 3.5 3.5 0.140 0.099 0.099 0.086 0.086 0.125 5 3.3 5.2 5.2 5.5 5.5 7.1 0.293 0.108 0.108 0.103 0.103 0.102 6 3.5 5.1 5.1 5.2 5.2 6.4 0.233 0.150 0.126 0.149 0.125 0.078 7 5.0 7.5 7.4 7.6 7.5 8.8 0.287 0.133 0.111 0.132 0.110 0.084 8 2.9 4.3 4.2 4.5 4.5 4.4 0.146 0.093 0.076 0.091 0.075 0.072 9 2.0 3.7 3.6 4.5 4.4 7.5 0.159 0.066 0.061 0.062 0.058 0.059 10 1.5 2.7 2.7 3.5 3.5 5.3 0.189 0.075 0.075 0.068 0.068 0.067 11 3.0 3.7 3.7 4.3 4.3 6.2 0.468 0.097 0.097 0.090 0.090 0.069 12 1.9 2.8 2.8 3.5 3.5 11.0 0.659 0.140 0.140 0.103 0.103 0.091 13 1.1 2.8 2.8 3.6 3.6 6.7 0.276 0.186 0.186 0.177 0.177 0.134 14 2.0 4.2 4.2 4.5 4.5 5.3 0.148 0.124 0.124 0.122 0.122 0.118 15 1.7 2.2 2.2 3.5 3.5 5.4 0.334 0.084 0.084 0.073 0.073 0.065 16 1.6 2.6 2.6 3.5 3.5 3.7 0.074 0.021 0.019 0.015 0.014 0.008 17 2.1 4.5 4.5 5.1 5.1 9.3 0.286 0.169 0.169 0.160 0.160 0.085 18 0.9 2.2 2.2 3.7 3.7 3.7 0.159 0.033 0.033 0.025 0.025 0.027 19 1.2 2.2 2.2 3.5 3.5 6.3 0.306 0.115 0.115 0.099 0.099 0.075 20 0.6 1.0 0.9 3.5 3.5 4.3 0.173 0.036 0.031 0.025 0.021 0.026 21 8.2 8.0 8.0 8.0 8.0 8.0 0.086 0.069 0.069 0.069 0.069 0.079 22 3.1 8.1 8.0 8.1 8.0 8.7 0.112 0.063 0.058 0.063 0.058 0.061 23 3.1 3.4 3.4 4.0 4.0 6.2 0.290 0.048 0.048 0.043 0.043 0.030 24 4.9 5.7 5.6 5.7 5.7 6.4 0.257 0.147 0.128 0.146 0.127 0.034 25 1.3 2.1 2.1 3.5 3.5 5.5 0.105 0.063 0.063 0.060 0.060 0.069 26 2.5 3.5 3.4 4.3 4.3 5.6 0.126 0.039 0.036 0.036 0.033 0.041 27 5.8 6.9 6.7 7.1 7.0 8.3 0.295 0.083 0.072 0.082 0.071 0.048 28 6.7 9.6 10.3 10.0 10.6 12.0 0.489 0.350 0.346 0.347 0.345 0.133 29 1.6 3.4 3.4 4.2 4.2 6.8 0.246 0.088 0.077 0.080 0.068 0.048 30 6.1 7.6 7.6 7.6 7.6 8.6 0.294 0.139 0.136 0.138 0.135 0.092 31 5.4 7.3 7.3 7.4 7.5 11.8 0.221 0.152 0.128 0.151 0.127 0.083 32 1.4 2.7 2.7 3.6 3.6 4.3 0.095 0.053 0.053 0.049 0.049 0.058 33 4.6 7.0 6.8 7.1 7.0 8.3 0.143 0.086 0.073 0.085 0.072 0.083 34 1.3 4.3 4.4 6.0 6.1 10.1 0.132 0.033 0.029 0.018 0.015 0.011 35 2.2 4.5 5.0 5.0 5.4 5.5 0.122 0.075 0.062 0.073 0.061 0.038 36 1.6 3.8 3.8 4.6 4.6 9.4 0.185 0.063 0.063 0.057 0.057 0.062 37 2.8 3.9 4.0 4.6 4.6 5.0 0.116 0.075 0.065 0.070 0.061 0.048 Median 2.2 3.8 3.8 4.5 4.5 6.3 0.185 0.084 0.075 0.080 0.071 0.067 25th Percentile 1.6 2.8 2.7 3.6 3.6 5.3 0.126 0.063 0.058 0.057 0.057 0.048 75th Percentile 3.5 5.2 5.2 5.7 5.7 8.3 0.290 0.133 0.124 0.122 0.110 0.084 141 Table B.6: Fine woody surface fuels in the 37 plots. Fine surface fuel values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. MDWR and OGMA treatments (progressively lighter greens and blues) from left to right are thinning, thinning and pruning, thinning and abatement, and thinning, pruning, and abatement. Plot Fine Surface Fuels (kg m-2) 1 0.36 1.02 1.11 1.11 1.20 1.00 1.00 1.00 1.00 1.00 2 0.41 0.72 0.81 0.76 0.84 0.72 0.81 0.76 0.84 0.68 3 0.61 3.21 3.78 3.29 3.85 1.00 1.00 1.00 1.00 1.00 4 0.36 0.61 0.61 0.88 0.88 0.61 0.61 0.88 0.88 0.70 5 0.74 2.17 2.17 2.18 2.18 1.00 1.00 1.00 1.00 1.00 6 1.24 2.37 2.76 2.38 2.77 1.00 1.00 1.00 1.00 1.00 7 0.63 3.14 3.76 3.14 3.76 1.00 1.00 1.00 1.00 1.00 8 0.73 1.75 2.04 1.77 2.06 1.00 1.00 1.00 1.00 1.00 9 0.48 2.09 2.26 2.12 2.28 1.00 1.00 1.00 1.00 1.00 10 0.59 1.81 1.81 1.83 1.83 1.00 1.00 1.00 1.00 1.00 11 1.98 3.25 3.25 3.27 3.27 1.00 1.00 1.00 1.00 1.00 12 0.36 2.46 2.46 2.52 2.52 1.00 1.00 1.00 1.00 1.00 13 0.48 1.18 1.18 1.23 1.23 1.00 1.00 1.00 1.00 1.00 14 0.78 1.61 1.61 1.64 1.64 1.00 1.00 1.00 1.00 1.00 15 0.32 1.67 1.67 1.71 1.71 1.00 1.00 1.00 1.00 1.00 16 0.91 1.29 1.33 1.32 1.36 1.00 1.00 1.00 1.00 1.00 17 0.57 2.09 2.09 2.12 2.12 1.00 1.00 1.00 1.00 1.00 18 0.49 1.50 1.51 1.57 1.57 1.00 1.00 1.00 1.00 1.00 19 0.42 2.22 2.22 2.26 2.26 1.00 1.00 1.00 1.00 1.00 20 0.61 2.29 2.39 2.35 2.45 1.00 1.00 1.00 1.00 1.00 21 0.28 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.85 22 0.70 1.59 1.75 1.59 1.75 1.00 1.00 1.00 1.00 1.00 23 0.27 2.14 2.14 2.16 2.16 1.00 1.00 1.00 1.00 1.00 24 0.96 1.63 1.86 1.63 1.86 1.00 1.00 1.00 1.00 1.00 25 0.20 0.53 0.53 0.54 0.54 0.53 0.53 0.54 0.54 0.41 26 0.25 0.92 0.98 0.94 0.99 0.92 0.98 0.94 0.99 0.77 27 0.64 2.66 2.82 2.66 2.83 1.00 1.00 1.00 1.00 1.00 28 0.67 2.80 3.37 2.84 3.39 1.00 1.00 1.00 1.00 1.00 29 0.22 1.75 2.02 1.79 2.06 1.00 1.00 1.00 1.00 1.00 30 1.02 2.34 2.44 2.34 2.44 1.00 1.00 1.00 1.00 1.00 31 0.35 2.17 2.77 2.17 2.78 1.00 1.00 1.00 1.00 1.00 32 0.95 1.73 1.73 1.80 1.80 1.00 1.00 1.00 1.00 1.00 33 0.47 2.20 2.41 2.20 2.41 1.00 1.00 1.00 1.00 1.00 34 1.06 1.94 2.02 2.07 2.13 1.00 1.00 1.00 1.00 1.00 35 0.94 2.01 2.33 2.04 2.35 1.00 1.00 1.00 1.00 1.00 36 0.34 1.50 1.50 1.52 1.52 1.00 1.00 1.00 1.00 1.00 37 0.47 1.07 1.28 1.14 1.33 1.00 1.00 1.00 1.00 1.00 Median 0.57 1.81 2.02 1.83 2.06 1.00 1.00 1.00 1.00 1.00 25th Percentile 0.36 1.50 1.50 1.52 1.52 1.00 1.00 1.00 1.00 1.00 75th Percentile 0.74 2.22 2.41 2.26 2.44 1.00 1.00 1.00 1.00 1.00 142 Table B.7: Median crown fire likelihood in the 37 plots for every modelling run. Crown fire likelihood values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. MDWR and OGMA treatments (progressively lighter greens and blues) from left to right are thinning, thinning and pruning, thinning and abatement, and thinning, pruning, and abatement. Plot Median Crown Fire Likelihood (%) 1 (n = 165) 60 95 96 89 91 95 96 89 91 84 2 (n = 164) 86 85 85 77 77 85 85 77 77 97 3 (n = 163) 94 91 91 89 89 91 91 89 89 92 4 (n = 164) 68 64 64 32 32 64 64 32 32 32 5 (n = 163) 96 96 96 95 95 96 96 95 95 85 6 (n = 161) 97 90 96 89 96 90 96 89 96 91 7 (n = 165) 91 64 65 62 63 64 65 62 63 64 8 (n = 165) 90 97 97 96 97 97 84 96 82 83 9 (n = 163) 93 99 99 97 98 99 92 97 86 83 10 (n = 163) 95 99 99 99 99 96 96 92 92 78 11 (n = 163) 98 99 99 98 98 93 93 90 90 94 12 (n = 166) 99 99 99 99 99 99 99 99 99 30 13 (n = 162) 96 98 98 96 96 98 98 96 96 74 14 (n = 163) 98 98 98 98 98 91 91 89 89 82 15 (n = 163) 99 100 100 99 99 98 72 94 94 97 16 (n = 161) 100 99 99 99 99 99 99 99 99 99 17 (n = 163) 99 93 93 90 90 93 93 90 90 56 18 (n = 164) 99 99 99 98 98 96 96 90 90 90 19 (n = 165) 100 99 99 98 98 99 99 98 98 88 20 (n = 165) 100 100 100 98 98 98 98 89 89 83 21 (n = 162) 23 26 26 26 26 26 26 26 26 26 22 (n = 163) 43 2 2 2 2 2 2 2 2 1 23 (n = 164) 85 99 99 98 98 93 93 89 89 63 24 (n = 164) 88 82 86 81 86 82 86 81 86 60 25 (n = 164) 78 97 97 92 92 97 97 92 92 15 26 (n = 164) 85 88 89 86 87 52 55 48 50 29 27 (n = 164) 87 98 98 96 96 28 29 18 19 8 28 (n = 162) 85 41 30 36 26 41 30 36 26 2 29 (n = 163) 92 98 98 97 97 98 98 97 97 45 30 (n = 164) 93 81 81 81 81 81 81 81 81 68 31 (n = 163) 95 84 83 82 82 84 83 82 82 18 32 (n = 162) 94 99 99 97 97 92 92 86 86 79 33 (n = 164) 96 82 84 80 82 42 45 39 42 22 34 (n = 163) 99 98 97 93 92 97 97 93 92 34 35 (n = 163) 98 96 95 95 94 96 95 95 94 93 36 (n = 165) 100 98 98 96 96 87 30 80 80 12 37 (n = 162) 99 98 98 96 96 98 98 96 96 75 Median of All Results 96 97 97 95 95 94 92 90 90 71 25th Percentile 86 87 88 82 83 78 72 71 70 32 75th Percentile 99 99 99 98 98 98 98 97 96 91 143 Table B.8: Median rate of spread in the 37 plots. Rate of spread values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. MDWR and OGMA treatments (progressively lighter greens and blues) from left to right are thinning, thinning and pruning, thinning and abatement, and thinning, pruning, and abatement. Plot Median Rate of Spread (m min-1) 1 (n = 165) 9 15 15 15 16 15 15 15 16 14 2 (n = 164) 12 11 11 11 11 11 11 11 11 18 3 (n = 163) 23 19 7 19 7 19 7 19 7 13 4 (n = 164) 25 11 11 3 3 11 11 3 3 3 5 (n = 163) 20 28 28 11 11 28 28 11 11 11 6 (n = 161) 20 7 30 7 30 7 30 7 30 13 7 (n = 165) 21 8 9 8 9 8 9 8 9 13 8 (n = 165) 8 11 13 11 13 11 13 11 13 13 9 (n = 163) 7 15 15 15 16 15 15 15 16 16 10 (n = 163) 19 14 14 15 15 14 14 15 15 15 11 (n = 163) 24 11 11 12 12 11 11 12 12 15 12 (n = 166) 27 33 33 31 31 33 33 31 31 5 13 (n = 162) 20 19 19 18 18 19 19 18 18 8 14 (n = 163) 32 31 31 30 30 31 31 30 30 30 15 (n = 163) 22 13 13 14 14 13 13 14 14 15 16 (n = 161) 14 18 18 18 18 18 18 18 18 17 17 (n = 163) 21 19 19 7 7 19 19 7 7 12 18 (n = 164) 7 18 18 18 18 18 18 18 18 18 19 (n = 165) 34 29 29 11 11 29 29 11 11 14 20 (n = 165) 31 17 18 18 18 17 18 18 18 18 21 (n = 162) 5 5 5 5 5 5 5 5 5 5 22 (n = 163) 5 3 3 3 3 3 3 3 3 3 23 (n = 164) 21 17 17 17 17 17 17 17 17 18 24 (n = 164) 20 7 8 8 8 7 8 8 8 18 25 (n = 164) 29 15 15 15 15 15 15 15 15 4 26 (n = 164) 23 13 14 13 15 13 14 5 14 5 27 (n = 164) 8 14 15 15 15 3 3 3 3 3 28 (n = 162) 37 4 3 4 3 4 3 4 3 3 29 (n = 163) 20 12 13 13 14 12 13 13 14 5 30 (n = 164) 38 33 33 33 33 33 33 33 33 12 31 (n = 163) 33 31 30 31 30 31 30 31 30 4 32 (n = 162) 10 15 15 15 15 15 15 15 15 14 33 (n = 164) 26 12 13 12 13 5 5 5 5 5 34 (n = 163) 8 18 18 18 18 18 18 18 18 4 35 (n = 163) 9 14 15 14 15 14 15 14 15 17 36 (n = 165) 31 15 15 16 16 15 3 16 16 3 37 (n = 162) 27 13 14 14 15 13 14 14 15 16 Median of All Results 22 15 15 14 15 14 14 14 14 13 25th Percentile 11 11 11 11 11 10 9 9 9 4 75th Percentile 30 23 22 21 20 23 22 21 20 17 144 Table B.9: Median surface fire intensity in the 37 plots. Surface fire intensity values are for the reference conditions (grey), simulated 25% (green), 35% (blue), and intensive (pink) thinning treatments. MDWR and OGMA treatments (progressively lighter greens and blues) from left to right are thinning, thinning and pruning, thinning and abatement, and thinning, pruning, and abatement. Plot Median Surface Fire Intensity (kW m-1) 1 (n = 165) 2,532 7,128 7,772 7,834 8,474 4,355 4,496 4,530 4,659 4,330 2 (n = 164) 5,216 4,739 4,607 4,667 4,529 4,670 4,545 4,667 4,529 10,482 3 (n = 163) 13,902 16,489 6,290 16,401 6,387 14,137 5,335 14,061 5,470 9,392 4 (n = 164) 294 127 127 39 39 127 127 39 39 39 5 (n = 163) 14,055 26,561 26,561 10,285 10,285 23,496 23,496 9,012 9,012 9,055 6 (n = 161) 15,284 5,940 23,377 5,979 23,334 5,060 20,512 5,097 20,474 9,247 7 (n = 165) 15,223 7,644 8,593 7,674 8,635 6,753 7,628 6,780 7,658 10,593 8 (n = 165) 4,422 8,882 12,484 8,961 12,626 7,578 3,826 7,648 3,854 3,934 9 (n = 163) 4,081 14,192 19,532 14,591 20,140 12,532 4,593 12,870 4,711 13,133 10 (n = 163) 9,203 9,816 9,816 10,360 10,360 8,261 8,261 8,715 8,715 8,772 11 (n = 163) 23,838 17,515 17,515 18,734 18,734 3,388 3,420 3,646 3,646 12,369 12 (n = 166) 16,628 32,313 32,313 30,479 30,479 28,061 28,061 26,468 26,468 3,931 13 (n = 162) 11,836 14,899 14,899 15,043 15,043 13,881 13,881 13,754 13,754 5,975 14 (n = 163) 16,230 21,658 21,658 21,597 21,597 17,675 17,675 17,625 17,625 17,515 15 (n = 163) 14,189 14,364 14,364 15,919 15,919 3,857 933 4,233 4,233 12,703 16 (n = 161) 9,806 16,753 16,822 16,597 16,549 15,307 15,284 15,098 15,003 14,016 17 (n = 163) 14,813 17,777 17,777 6,703 6,703 15,843 15,843 5,847 5,847 10,226 18 (n = 164) 4,799 22,484 22,528 23,265 23,294 5,363 5,366 5,469 5,470 5,448 19 (n = 165) 22,128 26,097 26,097 10,242 10,242 23,323 23,323 9,074 9,074 11,185 20 (n = 165) 22,475 23,818 24,697 24,761 25,312 5,222 5,294 5,358 5,357 5,349 21 (n = 162) 1,693 2,661 2,661 2,661 2,661 2,661 2,661 2,661 2,661 2,482 22 (n = 163) 41 29 29 29 29 29 29 29 29 29 23 (n = 164) 8,478 16,087 16,087 16,689 16,689 4,992 4,992 5,142 5,142 5,418 24 (n = 164) 14,505 6,768 7,083 6,807 7,118 5,392 6,220 5,420 6,251 5,354 25 (n = 164) 5,938 4,622 4,622 4,767 4,767 4,622 4,622 4,767 4,767 1,060 26 (n = 164) 16,902 18,944 21,572 19,132 21,773 3,877 4,233 1,608 4,096 1,387 27 (n = 164) 4,331 13,148 13,452 13,284 13,573 918 971 930 984 764 28 (n = 162) 27,633 3,804 3,232 3,629 3,232 3,350 2,815 3,328 2,815 993 29 (n = 163) 10,536 11,403 12,539 12,284 13,512 10,190 11,215 11,002 11,926 1,387 30 (n = 164) 32,577 31,726 31,606 31,695 31,574 27,996 27,889 27,968 27,861 10,112 31 (n = 163) 21,511 29,520 28,587 29,476 28,537 26,045 25,222 26,006 25,177 3,328 32 (n = 162) 5,046 10,852 10,852 11,370 11,370 4,389 4,389 4,470 4,470 7,457 33 (n = 164) 17,829 16,541 18,832 16,615 18,908 1,608 1,608 1,387 1,608 1,387 34 (n = 163) 7,222 16,822 17,165 17,235 17,096 12,967 13,095 15,005 14,878 2,933 35 (n = 163) 6,999 12,737 14,087 12,934 14,206 11,372 12,431 11,537 12,524 14,172 36 (n = 165) 19,839 18,872 18,872 19,837 19,837 4,454 58 4,663 4,663 993 37 (n = 162) 18,587 11,607 13,229 12,291 13,764 11,308 12,100 11,728 12,446 4,760 Median of All Results 12,722 14,267 14,500 12,713 12,368 6,971 5,717 6,714 5,818 5,782 25th Percentile 5,886 7,656 8,018 7,538 8,060 4,119 3,739 4,163 4,068 1,844 75th Percentile 19,253 21,726 21,874 20,676 21,453 15,096 14,065 13,938 13,411 10,964
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Daniels, Lori D.
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