Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Larsen, Renee
Master of Science - MSc
University of British Columbia
With unprecedented wildfire seasons in British Columbia during the past five years, the importance of accommodating fire in landscape management strategies has recently been recognized. Wildfire management strategies, such as prescribed burns and mechanical thinning, often do not consider the complex feedbacks and interactions among the numerous social and ecological components of wildfire-prone landscapes and the potential impacts of landscape alterations. As such, there is growing impetus to develop wildfire management strategies that balance ecological processes with social objectives. We examined how different scales of wildfire mitigation will impact social and ecological values in the Thompson-Okanagan region of British Columbia. Collaborating with local experts including Indigenous governments, municipal and regional governments, industry, and environmental organizations, we identified and mapped a range of important social (e.g., cultural, recreation, and infrastructure) and ecological (e.g., wildlife habitat, species at risk, and biodiversity) values within the Okanagan. We simulated wildfire management scenarios using Burn-P3, by incrementally increasing the proportion of mitigation applied from 0-100% in both the wildland urban interface (WUI) and entirety of the study area. The resulting outputs of Burn-P3 (burn probability and fuel consumption) were integrated into two novel spatial models that estimated wildfire severity and impacts to social and ecological values on the landscape. Wildfire mitigation at a landscape-level that treated 20 to 50% of the landscape and restored a mosaic of heterogeneous fuel compositions had the lowest burn-probability and the least social and ecological trade-offs. While there is considerable spatial variation in the impacts to the examined social and ecological values, the application of mitigation in areas extending beyond the WUI resulted in a shift to a low to mid severity wildfire regime, which, if allowed to burn, may encourage higher wildfire resilience on the landscape. This study provided insight into the wildfire social ecological system in the Okanagan and enhanced our understanding of what scales wildfire mitigation will be effective in similar landscapes and if humans and ecosystems can adapt to larger-scale implementation of mitigation.
WILDFIRE MITIGATION SCENARIOS FOR THE THOMPSON-OKANAGAN REGION OF BRITISH COLUMBIA: ASSESSING SOCIAL-ECOLOGICAL TRADE-OFFS by Renée Larsen B.Sc. (Honours), The University of British Columbia (Okanagan), 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2024 © Renée Larsen, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the thesis entitled: Wildfire Mitigation Scenarios for the Thompson-Okanagan Region of British Columbia: Assessing Social-Ecological Trade-Offs submitted by Renée Larsen in partial fulfilment of the requirements for the degree of Master of Science Examining Committee: Dr. Mathieu Bourbonnais, Irving K. Barber Faculty of Science Co-Supervisor Dr. Lael Parrott, Irving K. Barber Faculty of Science Co-Supervisor Dr. Hillary Ward, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Kevin Hanna, Irving K. Barber of Faculty of Science Supervisory Committee Member Dr. Chris Stockdale, Canadian Forest Service University Examiner iii Abstract With unprecedented wildfire seasons in British Columbia during the past five years, the importance of accommodating fire in landscape management strategies has recently been recognized. Wildfire management strategies, such as prescribed burns and mechanical thinning, often do not consider the complex feedbacks and interactions among the numerous social and ecological components of wildfire-prone landscapes and the potential impacts of landscape alterations. As such, there is growing impetus to develop wildfire management strategies that balance ecological processes with social objectives. We examined how different scales of wildfire mitigation will impact social and ecological values in the Thompson-Okanagan region of British Columbia. Collaborating with local experts including Indigenous governments, municipal and regional governments, industry, and environmental organizations, we identified and mapped a range of important social (e.g., cultural, recreation, and infrastructure) and ecological (e.g., wildlife habitat, species at risk, and biodiversity) values within the Okanagan. We simulated wildfire management scenarios using Burn-P3, by incrementally increasing the proportion of mitigation applied from 0-100% in both the wildland urban interface (WUI) and entirety of the study area. The resulting outputs of Burn-P3 (burn probability and fuel consumption) were integrated into two novel spatial models that estimated wildfire severity and impacts to social and ecological values on the landscape. Wildfire mitigation at a landscape-level that treated 20 to 50% of the landscape and restored a mosaic of heterogeneous fuel compositions had the lowest burn-probability and the least social and ecological trade-offs. While there is considerable spatial variation in the impacts to the examined social and ecological values, the application of mitigation in areas extending beyond the WUI resulted in a shift to a low to mid severity wildfire regime, which, if allowed to burn, may encourage higher wildfire resilience on the landscape. This study provided insight into iv the wildfire social ecological system in the Okanagan and enhanced our understanding of what scales wildfire mitigation will be effective in similar landscapes and if humans and ecosystems can adapt to larger-scale implementation of mitigation. v Lay Summary Social-ecological systems (SES) frameworks, which aim to understand how social and ecological values interact with each other and their landscape, are being utilized to enhance wildfire management in wildfire-prone areas. This study developed a wildfire SES model for the Okanagan region of British Columbia, Canada, by interviewing local experts. To assess the impacts of different scales of wildfire mitigation on the larger Thompson-Okanagan landscape, various wildfire management scenarios were modeled by differing the proportions of the landscape that was treated. The study underscores the need for large-scale treatments and wildfire mitigation strategies that consider both social and ecological values on the landscape. vi Preface This study was conducted by Renée Larsen under the supervision of Dr. Mathieu Bourbonnais and Dr. Lael Parrott at the University of British Columbia, Okanagan. Funding was provided by the New Frontiers in Research Fund. The research and data analysis, as well as writing were completed by Renée Larsen and supervised by Dr. Bourbonnais and Dr. Parrott. Dr. Ward and Dr. Hanna reviewed this study for clarity and content. All data analyses, including spatial analyses, were completed using R. R. Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Generative artificial intelligence was not used in the research, drafting, or preparation of the thesis. A version of Chapters 2 and 3 will submitted for peer-reviewed publication. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures .............................................................................................................................. xii List of Abbreviations ...................................................................................................................xv Acknowledgements .................................................................................................................... xvi Dedication .................................................................................................................................. xvii Chapter 1: Introduction ................................................................................................................1 1.1 Wildfire Risk and Mitigation .......................................................................................... 3 1.2 Wildfire Models .............................................................................................................. 9 1.3 Managing Complexity in Wildfire-Prone Landscapes ................................................. 14 1.3.1 Complex Social-Ecological Systems .................................................................... 14 1.3.2 “Walking on Two Legs” and Adaptive Governance ............................................ 18 1.4 Wildfire in the Thompson-Okanagan ........................................................................... 19 1.5 Knowledge Gaps and Research Objectives .................................................................. 22 Chapter 2: Conceptualization of the Wildfire SES in the Okanagan .....................................24 2.1 Introduction ................................................................................................................... 24 2.2 Methodology ................................................................................................................. 26 2.2.1 Study Area ............................................................................................................ 26 viii 2.2.2 Interview Settings and Participants ....................................................................... 28 Design ............................................................................................................... 28 Recruitment ....................................................................................................... 29 2.2.3 Interview Sessions and Data Collection ............................................................... 29 Pre-Data Collection Phase ................................................................................ 29 2.2.3.1.1 Identification of Interview Goals ................................................................ 29 2.2.3.1.2 Identification of Values and Potential Impacts ........................................... 30 Interviews .......................................................................................................... 30 2.2.4 Analysis of Interviews in NVivo .......................................................................... 31 Refinement of Expertise Sectors ....................................................................... 32 Coding Interview Responses............................................................................. 32 2.2.4.2.1 Value and Connectivity Coding .................................................................. 32 2.3 Results ........................................................................................................................... 33 2.4 Discussion ..................................................................................................................... 38 2.5 Conclusion .................................................................................................................... 51 Chapter 3: Analyzing the Social Ecological Impacts of Multiple Wildfire Mitigation Scenarios .......................................................................................................................................52 3.1 Introduction ................................................................................................................... 52 3.2 Methodology ................................................................................................................. 55 3.2.1 Study Area ............................................................................................................ 55 3.2.2 Data ....................................................................................................................... 57 Burn-P3 Model Inputs....................................................................................... 57 Wildfire Severity Data ...................................................................................... 59 ix SES values ........................................................................................................ 59 3.2.3 Analysis................................................................................................................. 60 Burn-P3 ............................................................................................................. 60 Wildfire Severity ............................................................................................... 62 SES Impacts Model........................................................................................... 64 3.2.4 Statistical Analysis ................................................................................................ 66 3.3 Results ........................................................................................................................... 68 3.3.1 Landscape Level ................................................................................................... 68 3.3.2 WUI Level Treatments ......................................................................................... 75 3.4 Discussion ..................................................................................................................... 78 3.5 Conclusion .................................................................................................................... 84 Chapter 4: Conclusions ...............................................................................................................86 Bibliography .................................................................................................................................95 Appendix A - Supplementary Material for Chapter 2 .......................................................... 125 Appendix B - Supplementary Material for Chapter 3. ......................................................... 127 x List of Tables Table 3.1. Burn-P3 Input Data ...................................................................................................... 58 Table 3.2. Summary of social and ecological data used in impacts model .................................. 60 Table 3.3. Composite burn index (CBI) representing classified wildfire severity and corresponding fuel consumption ratio .......................................................................................... 64 Table 3.4. Summary statistics (mean, median, and max) of baseline and treatment burn probabilities, fire severities, and SES impacts at a landscape scale. Non-fuels were removed from BP and wildfire severity layers. .................................................................................................... 69 Table 3.5. Paired t-test results (α=0.01) comparing burn probabilities, fire severities, and SES impacts from landscape scale (LS) scale treatments with baseline BP. Mean difference (MD) indicates the mean degree of change from the baseline scenario. The mean test statistic (TS) is a measure of difference between baseline and the treatment relative to the variability in their differences. .................................................................................................................................... 70 Table 3.6. Pearson’s correlations results (α=0.01) comparing the spatial patterns of burn probabilities, fire severities, and SES impacts from landscape scale treatments with baseline BP. A correlation coefficient (r) value close to one indicates little to no variations in spatial patterns from baseline. ................................................................................................................................ 70 Table 3.7. Summary statistics (mean, median, and max) of baseline and treatment burn probabilities, fire severities, and SES impacts at a WUI scale. Non-fuels were removed from BP and wildfire severity layers. .......................................................................................................... 75 Table 3.8. Paired t-test results (α=0.01) comparing burn probabilities, fire severities, and SES impacts from WUI scale treatments with baseline BP. Mean difference (MD) indicates the mean degree of change from the baseline scenario, where a negative value indicates an increase xi compared to baseline and a positive value a decrease from baseline. The mean test statistic (TS) is a measure of difference between baseline and the treatment relative to the variability in their differences. .................................................................................................................................... 76 Table 3.9. Pearson’s correlations results (α=0.01) comparing the spatial patterns of burn probabilities, fire severities, and SES impacts from landscape scale treatments with baseline BP. A correlation coefficient (r) value close to one indicates little to no variations in spatial patterns from baseline. ................................................................................................................................ 76 Table A.1. Sectors of expertise used to categorize experts during recruitment and coding process...................................................................................................................................................... 125 Table A.2. NVivo coding nodes that emerged during qualitative analysis of interview responses...................................................................................................................................................... 126 Table B.1. Summary of area treated in landscape and WUI mitigation scenarios where treatment level represents the approximate percentage of the landscape or WUI that was treated. ........... 127 Table B.2. Composite Burn Index form obtained from Key and Benson’s (2006) Landscape Assessment sampling and analysis methods of the FIREMON: Fire effects monitoring and inventory system. ........................................................................................................................ 128 xii List of Figures Figure 2.1. Study area in the southern interior of British Columbia. The main map panel shows a hillshade with major cities and lakes. The insert shows the position of the study area in British Columbia, Canada. ........................................................................................................................ 28 Figure 2.2. Treemap of ecological values that emerged from the interview process, where shade and size of the value indicate the hierarchy of values based on frequency of references in responses to question one (values). ............................................................................................... 34 Figure 2.3. Treemap of social values that emerged from the interview process, where shade and size of the value indicate the hierarchy of values based on frequency of references in responses to question one (values). ................................................................................................................... 35 Figure 2.4. Chord diagram summarizing connectivity between all social and ecological values identified in the expert responses to question one (values). The thickness of a line between connections represents the number of times a connection was referenced by experts. The acronyms are defined as follows: WF = wildfire, WAT = water, NHR = natural hazard reduction, FIR = forest integrity and resilience, EI = ecosystem integrity, ES= ecosystem services, CC=climate change, BD = biodiversity, RT= recreation and tourism, IC = infrastructure and communities, HLS = human life and safety, ECO = economy, AG = agriculture, FOR = forestry, CV = cultural values, AE = Aesthetic, WWH = wildlife and wildlife habitat. ............................ 36 Figure 2.5. Sankey diagram of connections between wildfire and social and ecological values in questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts). The thickness of the band represents the number of references to each connection. .................................................................................................................................... 37 xiii Figure 2.6. Sankey diagram of connection between wildfire mitigation and wildfire management themes, social values, and ecological values in questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts). The thickness of the band represents the number of references to each connection. ............................................................. 38 Figure 3.1. Study area in the southern interior of British Columbia. The main map panel shows a hillshade with major cities and lakes. The insert shows the position of the study area in British Columbia, Canada. ........................................................................................................................ 56 Figure 3.2. SES Impacts model decision tree ............................................................................... 66 Figure 3.3. Map of baseline burn probability (BP) and BP treatment ratios for landscape treatments. Positive treatment ratios (orange to red) indicate an increase in treatment BP relative to baseline and negative treatment ratios (green to blue) indicate a decrease in BP relative to baseline. ........................................................................................................................................ 71 Figure 3.4. Map of baseline wildfire severity and wildfire severity for the landscape level treatments. Where orange to red represents high wildfire severity (2 to 3), yellows represent moderate wildfire severity (1 to 2) and blue to green represents low wildfire severity (0 to 1). .. 72 Figure 3.5. Map of baseline SES impacts and SES impacts for the landscape level treatments. Where red to red represents high impact (2 to 3), yellow represents moderate impact (1 to 2), green represents low impact (0.01 to 1), and blue represents no impact (<0.01). ........................ 73 Figure 3.6. Cumulative distribution plot of landscape scale and WUI scale SES impacts for baseline SES impacts and 10 landscape-level treaments. Cumulative frequency represents the proportion of data that falls at or below the given value. ............................................................. 74 Figure 3.7. Map of baseline burn probability (BP) and BP treatment ratios for wildland urban interface (WUI) treatments. Positive treatment ratios (orange to red) indicate an increase in xiv treatment BP relative to baseline and negative treatment ratios (green to blue) indicate a decrease in BP relative to baseline. ............................................................................................................. 77 Figure B.1. Map of baseline wildfire severity and wildfire severity for the WUI level treatments. Where orange to red represents high wildfire severity (2 to 3), yellows represent moderate wildfire severity (1 to 2) and blue to green represents low wildfire severity (0 to 1). ............... 129 Figure B.2. Map of baseline SES impacts and SES impacts for the WUI level treatments. Where red to red represents high impact (2 to 3), yellows represent moderate impact (1 to 2), green represents low impact (0.01 to 1), and blue no impact (<0.01). ................................................. 130 xv List of Abbreviations BC – British Columbia BP- Burn Probability FWI – Fire Weather Index FBP – Canadian Forest Fire Behaviour Predictor System GIS – Geographic Information System NRCAN - Natural Resources Canada SES – Social Ecological System WUI – Wildland Urban Interface xvi Acknowledgements First and foremost, I would like to thank Lael Parrott and Mathieu Bourbonnais for their endless support, guidance, and patience throughout this process. Mathieu, thank you for taking the time to work through coding problems with me, parsing through the first iterations of this thesis, and all the humorous back-and-forths. Lael, thank you for the countless walks and discussions, the bike route recommendations, and for pushing me outside my quantitative research bubble to engage in qualitative research. You have both helped me grow so much as a researcher and I could not have done this without your combined support. Thank you to the New Frontier Research Fund for supporting this project. Thank you to all the regional experts who took time to share their knowledge; this project would not have been possible without your insight. I would also like to thank Marc Parisien for graciously sharing Burn-P3 inputs, and Quinn Barber for helping me troubleshoot model runs along the way. Next, I would like to thank my lab mates and peers. We started this at the worst of times, but I couldn’t think of a better group of people to go through this journey with. Thank you for being my sounding boards, accountability buddies, and laughing through the struggle with me. Outside of research, I would like to thank my partner, Trent, for listening to all my complaints and making sure I still remembered to eat and go outside when I was in the depths. I would also like to thank my friends for always believing me when I said it was “almost done” and never caring if I cancelled plans to work on my thesis. Lastly, I want to thank my family for the constant support throughout my academic career. xvii Dedication This thesis is dedicated to Trent, who never doubted my ability to finish it and who never cared if I quit. Thank you for supporting me unconditionally. 1 Chapter 1: Introduction Over the past several decades, many landscapes across North America have experienced a notable shift in wildfire behavior, marked by an increase in both the frequency and size of wildfires (Brookes et al., 2021; Coogan et al., 2019; Daniels et al., 2024; Dunn et al., 2020; Hanes et al., 2019; Parisien et al., 2023; Schoennagel et al., 2017). This new wildfire reality reflects the complex social and ecological feedbacks that shape wildfire-prone landscapes (Ager et al., 2015; Campos-Ruiz et al., 2018; Dunn et al., 2020; McGee et al., 2015); particularly, the critical relationship between humans and wildfires. Despite being a natural disturbance process, the behaviour of wildfire on many landscapes is intrinsically tied to human activities (Bowman et al., 2011; Christianson et al., 2022; Hoffman et al., 2021, 2022a; Stewart, 2002). Whether through direct or indirect actions, humans have been influencing the wildfire regimes of the landscapes that they inhabit for a millennia (Hoffman et al., 2021, 2022a; Ryan et al., 2013; Stewart, 2002). Early relationships to wildfire differed greatly from present day because Indigenous Nations recognized wildfire as a value on the landscape and utilized it as a tool to manage risk and natural resources. Colonization brought an abrupt shift to this perspective on wildfire across western North America (Christianson et al., 2022; Hoffman et al., 2022a; Lewis, 1978), as wildfire posed a threat to new colonial developments and timber supplies and therefore was viewed as a destructive force that should be erased from the landscape (Christianson, 2015; Lewis & Ferguson, 1988; Pyne, 2008). This attitude led to a century of wildfire suppression, which was accompanied by rapid development of the wildland urban interface and intensification of forestry practices that significantly altered forest composition (Baker, 1992; Cumming, 2005; Dombeck et al., 2004; Dunn et al., 2020; Keane et al., 2002). The cumulative impact of these actions on the resilience of wildfire-prone landscapes was not fully realized until recently, partly due to the cooler and wetter 2 climate that dominated much of the 20th century (Dunn et al., 2020; Hessburg et al., 2019; Parisien et al., 2023). As many regions experience changes in temperature and precipitation as a result of climate change, the consequences of the intense shift in human activities on wildfire behaviour is becoming increasingly apparent (Coogan et al., 2019; Moritz et al., 2014; Schoennagel et al., 2017; Wang et al., 2015). The heightened wildfire activity under the changing climatic conditions (i.e., increased temperatures and decreased precipitation) coupled with the rapid expansion of the wildland-urban interface and altered forest structures has resulted in wildfire suppression becoming progressively challenging on many modern landscapes (Parisien et al., 2020; Stockdale et al., 2019b; Tymstra et al., 2020). In light of these shifting wildfire regimes, there is a growing recognition of a need to coexist with wildfire as part of the natural dynamic of the landscape that human communities inhabit; this means allowing for more frequent and less intense fires so that social and ecological values are not lost to wildfire (Copes-Gerbitz et al., 2022; Daniels et al., 2024; Dunn et al., 2020; Moritz et al., 2014; Prichard et al., 2021; Steelman, 2016). Learning to coexist with wildfires will require the inclusion of fire as an ecological process in landscape management strategies (Daniels et al., 2024; Prichard et al., 2021; Sankey et al., 2018). This presents a complex challenge on western North American landscapes where the legacy of wildfire suppression and forest management practices have created homogenous forest structures with high fuel loads (Chavardès & Daniels, 2016; Hessburg et al., 2019, 2021; Rhemtulla et al., 2002; Stockdale et al., 2019a).These landscapes are not only more prone to large, high severity wildfires, but they are also less resilient to the adverse impacts of wildfire (Hessburg et al., 2019; Stockdale et al., 2019a, 2019b). Managing wildfire on these landscapes is further complicated by the presence of diverse social and ecological values that have varying levels of value to stakeholders within the region 3 (Copes-Gerbitz et al., 2021; Dunn et al., 2020; McGee et al., 2015; Spies et al., 2014; Steelman, 2016). This presents a multi-faceted social-ecological problem: how can wildfire management balance ecological processes (i.e., improving resiliency) with social objectives (i.e., protecting human values)? 1.1 Wildfire Risk and Mitigation As the frequency of large, catastrophic wildfires increases on many fire adapted landscapes across North America, it has become crucial to understand the underlying factors that influence wildfire risk and explore mitigation strategies that are adaptable to the dynamic nature of wildfires (Dunn et al., 2020; Erni et al., 2024; Hoffman et al., 2022b; Moritz et al., 2014; Ohlson et al., 2006; Prichard et al., 2021). Risk, in the context of wildfire, is generally a measure of the probability of wildfire occurrence and the potential magnitude of its impact (Ohlson et al., 2003; Thompson et al., 2012). Thus, wildfire risk assessment includes not only estimations of the likelihood of wildfire occurrence, but also involves predicting the potential scale, severity, and social and ecological consequences of wildfire (Ohlson et al., 2003; Thompson et al., 2012). Identifying the factors that influence the behaviour of wildfire is a critical component of wildfire risk assessment. Wildfire behaviour is influenced by four main factors all of which must be present for a fire to occur (Flannigan et al., 2005; Resco de Dios, 2020a; Stocks et al., 1989; Taylor & Alexander, 2006). The first factor is fuel type and accumulation, where the amount and type of fuel can influence patterns of wildfire ignition, spread, and severity (Resco de Dios, 2020b; Stocks et al., 2002). Broadly, wildfire fuels are often categorized according to how they are stratified in the environment: ground fuels, surface fuels, ladder fuels, and crown fuels (Finney et al., 2021; Stocks et al., 2002). Ground fuels are defined as material found below the forest floor (e.g., duff and roots), whereas surface fuels encompass fuels that reside on or near forest floor 4 (e.g., fallen needles, grass, and wood debris; (Finney et al., 2021; Forestry Canada Fire Danger Group, 1992). Ladder fuels represent low to intermediate fuels that allow wildfire to spread between strata, i.e., from the surface to the crown, and crown fuels are characterized as the living and dead foliage and branches of trees (Finney et al., 2021; Forestry Canada Fire Danger Group, 1992). These stratifications are also used to characterize wildfire according to what strata they are spreading through (i.e., ground, surface, and crown), which can be used to infer other characteristics including rate of spread and potential intensity (Finney et al., 2021; Stocks et al., 2002). Generally, ground fires spread more slowly, whereas surface fires can exhibit varying rates of spread and intensities (Finney et al., 2021; Ryan, 2002). For example, surface fires that occur in cured grasses tend to ignite quickly and facilitate the rapid spread of wildfire, but they are often associated with shorter burning durations (Cruz et al., 2022). In contrast, crown fires, particularly in conifer stands, tend to sustain wildfire for longer periods of time, and while they spread at intermediate rates, they can burn at high intensities and are often the most difficult to control (Finney et al., 2021; Resco de Dios, 2020b). The species composition and structural configuration of vegetation further influence the wildfire behavior, as species exhibit different morphological features, biomass loading potentials, and fire adaptations. For example, some tree species, such as black spruce (Picea mariana) and jack pine (Pinus banksiana), are often adapted to complete a life cycle within the historical fire regime (i.e., before a stand-replacing fire occurs) and may display additional adaptations that aid in their regeneration post wildfire such as serotinous or semi-serotinous cones (Gauthier et al., 1996; Rogers et al., 2015). These species generally have characteristics that can increase the likelihood of stand-replacing fires (e.g., high volume of ladder fuels; (Stocks et al., 2004). Conversely, other species, such as larch (Larix laricina), have 5 adaptations, such as self-pruning, high moisture content, and thick bark, that decrease the likelihood of stand replacing fires. Because of these adaptations, these species can often suppress the occurrence of crown fire (Beck et al., 2005; Rogers et al., 2015). This interaction between species-specific adaptations and fire behavior emphasizes the importance of fuel composition and arrangement in shaping the spread of large wildfires (Beck et al., 2005; Resco de Dios, 2020a; Stocks et al., 1989). The second main factor that influences wildfire behaviour is the availability of fuels to burn (Flannigan et al., 2005; Forestry Canada Fire Danger Group, 1992; Resco de Dios, 2020a; Stocks et al., 1989). Fuel moisture, which varies by fuel type and condition (e.g., live versus dead) and is influenced by weather over space and time controls whether the available fuel will burn (Beck et al., 2005; Resco de Dios, 2020a; Stocks et al., 1989). Therefore, for a large wildfire to persist across the landscape, a critical flammability threshold must be reached which is characterized by high fuel loads and low moisture content. Dead fuel tends to be more flammable than live fuel, and as a result initial fire spread is largely influenced by the availability and spatial arrangement of fine dead fuels (Resco de Dios, 2020a). The third factor is the presence of a wildfire ignition source (Beck et al., 2005; Flannigan et al., 2005; Resco de Dios, 2020a). The ignition of a wildfire can result from anthropogenic sources or lightning, with each responsible for roughly 50% of fires in Canada (Coogan et al., 2021). However, lightning fires are responsible for more annual area burned (Coogan et al., 2021; Stocks et al., 2002; Tymstra et al., 2020). Wildfire ignitions only occur if fuels are present and fuel moisture is sufficiently low to sustain pyrolysis (Resco de Dios, 2020a). The fourth factor is the fire environment, which encompasses the abiotic factors that influence wildfire behaviour including weather (e.g., temperature, precipitation, wind and relative 6 humidity) and topography (e.g., slope, aspect, and relief; (Resco de Dios, 2020a; Stocks et al., 1989; Taylor & Alexander, 2006). Weather is considered to be a dynamic factor that can exhibit several independent influences on wildfire behaviour (Holsinger et al., 2016; Jolly et al., 2015; Parisien et al., 2011; Taylor & Alexander, 2006). High temperatures and low humidity can desiccate fuels, which increases the probability of ignition and rate of spread, and wind can further accelerate the spread of wildfire and influence the direction in which it spreads (Beck et al., 2005; Jolly et al., 2015; Resco de Dios, 2020a; Wotton, 2009). Therefore, both these factors can contribute to the size and intensity of wildfire (Jolly et al., 2015; Ryan, 2002; Stocks et al., 1989). Precipitation patterns can further influence the probability of ignition and rate of wildfire spread by altering the moisture content of fuels. Additionally, precipitation can exert indirect influences on wildfire behaviour by influencing near surface temperatures and relative humidity (Holden et al., 2018; Jolly et al., 2015). These weather factors are considered to be dynamic and often interact with topographical features, including slope, aspect, and relief. For example, the combination of high winds and high temperatures and complex terrain can promote rapid wildfire spread. Furthermore, these factors vary temporally, with fuel being more flammable in the summer seasons and weather and ignition sources varying over very short timescales (Resco de Dios, 2020a; Taylor & Alexander, 2006; Van Nest & Alexander, 1999). If all four conditions: fuel load and structure, fuel moisture content, ignition source and fire environment are conducive, there is a high risk of wildfires developing on the landscape. Similar to the factors that influence wildfire risk, the management of wildfire risk can be divided into four major phases: prevention and mitigation, preparedness, response, and recovery (Tymstra et al., 2020). Prevention and mitigation have been identified as a critical component of wildfire mitigation, with several strategies being developed to mitigate wildfire occurrence 7 (Roberts et al., 2019; Tymstra et al., 2020). Mitigation approaches can be applied pre- or post-wildfire to help reduce the frequency and severity of future wildfires. The benefits of these treatments extend beyond biophysical (e.g., reduced wildfire risk), and can have positive social and economic outcomes (e.g., decrease in wildfire associated health issues and reduced expenditure to wildfire evacuation; Roberts et al., 2019). The most common forms of mitigation are mechanical thinning and prescribed burn (Roberts et al., 2019). Mechanical thinning is the process of selectively removing trees and other vegetation to reduce flammable fuel loads within forest (Nunamaker et al., 2007). The aim of this process is to decrease the density of fuel within the forest and reduce fuel connectivity, particularly through the removal of small-diameter trees, shrubs, and ladder fuel (Nunamaker et al., 2007), thereby reducing the potential spread rate and severity of the wildlife. Prescribed burns, also known as controlled burns, is the practice of intentionally igniting fires on the landscape under specific conditions for the purposes of specific management outcomes (Fernandes & Botelho, 2003; Wade, 1989; Weber & Taylor, 1992). Similar to mechanical thinning, prescribed fires are aimed at reducing fuel loads and minimizing the connectivity of fuels, this is achieved largely through clearing finer surface fuels which facilitate the spread of wildfires (Fernandes & Botelho, 2003). Prescribed fire can have benefits that extend beyond the reduction of wildfire risk, including improvement of wildlife habitat and rangeland (Fernandes & Botelho, 2003; Wade, 1989; Weber & Taylor, 1992). Despite the benefits associated with wildfire mitigation, there is considerable controversy surrounding the type or combination of mitigation strategies that should be applied to wildfire-prone landscapes and the overall effectiveness of these approaches. This controversy stems from the lack of consensus on risk prioritization and the cultural bias towards wildfire suppression (Dunn et al., 2020). The idea that wildfire is a critically influential process on the landscape is not 8 a novel concept; Indigenous Nations have been harnessing the power of wildfire as a tool for landscape management for a millennia (Hoffman et al., 2021, 2022a; Lake & Christianson, 2020; Lewis, 1978; Pyne, 2008). Prior to colonization, wildfire was recognized as a value on the landscape for its ability to act as a key disturbance mechanism that could provide not only ecological benefits, but could also promote the growth of food sources and reduce the risk of fires that threatened human safety (Hoffman et al., 2021, 2022a). Colonization brought forth an era of fire exclusion, and the criminalization of Indigenous wildfire stewardship (Hoffman et al., 2022a; Lake & Christianson, 2020; Lewis, 1978). Only recently, has western science started to recognize the dependency of many landscapes on wildfire as a key disturbance mechanism (Copes-Gerbitz et al., 2022; Daniels et al., 2024; Dunn et al., 2020; Lake et al., 2017). In this sense, much of wildfire management’s current focus on mitigation is borrowing from the past knowledges of Indigenous Nations. Western science is in its infancy of learning to live with wildfire, whereas Indigenous Nations have an extensive history and expertise of coexisting with wildfire (Hessburg et al., 2019; Hoffman et al., 2022a). While the need to shift away from exclusionary practices has been increasingly recognized by wildfire managers over the past decade, changes in climatic conditions (e.g., increased temperatures) combined with increased development of human infrastructure within wildland areas and changes in forest composition have increased the complexity of wildfire management (Baker, 1992; Daniels et al., 2024; Dombeck et al., 2004; Dunn et al., 2020; Hessburg et al., 2021; Keane et al., 2002). The complexity of these landscapes means that mitigation strategies must be adapted for local areas (Dunn et al., 2020; Steelman, 2016). 9 1.2 Wildfire Models Simulation modeling has emerged as a valuable tool for understanding the complex spatial and temporal dynamics of wildfires (Scheller et al., 2019). Wildfire simulation models have been designed for a variety of purposes including quantifying characteristics of fire regimes (e.g., fire size and frequency; Barbero et al., 2015; Dennison et al., 2014) understanding environmental influences on fire behavior (Ager et al., 2018; Nitschke & Innes, 2008; Parisien et al., 2010, 2011; Parks et al., 2012), and quantifying wildfire risk (Finney, 2005; Oliveira et al., 2021; Parisien et al., 2005, 2019). Wildfire simulation models can be used as an efficient and cost-effective alternative to field experimentation, providing an understanding of the causes and effects of wildfire that can inform management strategies (Scheller et al. 2019). While wildfire simulation models are not inherently spatial, but they are often coupled with geographic information systems (GIS) (Papadopoulos & Pavlidou, 2011), which allows for a multifaceted perspective of wildfire behavior and its impacts on the landscape (Scheller, 2019). GIS provides a means of visualizing and analyzing spatial data related to wildfires. Model developers can generate spatial input layers characterizing the wildfire landscape (e.g., topography, fuel types, and weather patterns) to explore how these and other modelled factors influence the spatial patterns of wildfire across the modelled area. The advantages of model outputs that are spatial is that they can provide insight about how wildfire will move and interact throughout a landscape and therefore provide crucial information about where wildfire managers should focus their efforts (Scheller, 2019). For example, by identifying areas of high BP and potential pathways of spread, wildfire managers can make more informed decisions about the placement of mitigation (Ager et al., 2016; Parisien et al., 2019; Thompson et al., 2012). Models that are developed to quantify spatial patterns of wildfire risk can help address the growing need 10 to understand wildfire risk at landscape scales and, therefore, they have been increasingly used to inform decision making in wildfire management (Parisien et al., 2019). For North American landscapes, spatially explicit models, such as FlamMap, FSim, and Burn-P3, have been developed to simulate wildfire behaviour and predict the likelihood of wildfire occurrence (i.e., burn probability [BP]; Oliveira et al., 2021). These BP models share a common framework that relies on running a large number of wildfire ignition and spread simulations to estimate the likelihood of a fire occurring across the simulated landscape (Parisien et al., 2019). The main advantage of this modelling framework is that it considers nearly all potential ignition and fire spread scenarios on the landscape, making the resulting estimates of BP robust even in complex landscapes (Parisien et al., 2019). Burn-P3 is a BP model designed to provide estimates of BP for Canadian landscapes (Parisien et al., 2005). Burn-P3 uses Monte Carlo simulation to predict wildfire risk across a landscape on a cell-by-cell basis. Monte Carlo simulations allow for the prediction of outcomes in systems with a high degree of uncertainty or randomness, which makes them well suited for modelling wildfire-prone landscapes (Parisien et al., 2005). In each simulation, values for various inputs, including those that are probabilistic in nature, are randomly chosen and used within a deterministic set of equations. A pre-defined number of simulations are run, and the results are aggerated to provide an estimate of potential outcomes (e.g., BP). The Monte Carlo simulation in Burn-P3 is comprised of three modules: the probabilistic ignitions and burning conditions modules, which are based on historical data, and the deterministic fire growth module (Natural Resources Canada, 2017; Parisien et al., 2005). The ignitions module provides information to select the location, number, and timing of the ignition for each fire growth simulation; ignitions are either randomly selected on the landscape or informed by grids or lists of ignition probability that were generated by the user. The burning conditions module provides 11 information on the environmental conditions that inform how the fire will spread through the landscape; this is defined by the fire weather list that describes daily weather (temperature, relative humidity, wind speed, wind direction, and precipitation) and Fire Weather Index (FWI) System. This module also describes the duration of the fire based on user defined values or distributions. The outputs provided from the ignitions and burning modules, combined with the mapped inputs (topography, fuels, and fire zones) provide the inputs for the fire growth module. The fire growth module in Burn-P3 is based on Prometheus, which is a fire growth and spread model designed to simulate wildfire spread in landscapes with varied terrain and fuel (Natural Resources Canada, 2017). In Burn-P3, the fire growth module uses the deterministic algorithms from Prometheus in conjunction with the Canadian Forest Fire Behaviour Prediction (FBP) System to estimate how wildfire will spread through the simulated environment (Natural Resources Canada, 2017). The FBP is a fuel typing system that describes 16 categories of fuel using the fuel association approach (i.e., defining fuels based on dominant vegetation species (Baron et al., 2024; Forestry Canada Fire Danger Group, 1992; Stocks et al., 1989). While defined by the dominant vegetation species (e.g., C7 – Ponderosa Pine-Douglas-Fir), FBP uses vegetation composition and forest structure to group fuel types that facilitate the prediction of stand level wildfire behaviour (Baron et al., 2024; Stocks et al., 1989). It should be noted that the FBP fuel types provide the most comprehensive descriptions for the boreal and sub-boreal forests for which the fuel types were originally developed (Baron et al., 2024). In other forest systems, such as the diverse conifer forests of British Columbia, the FBP descriptions of stand and fuel characteristics are often insufficient at describing the stand and fuel characteristics (Baron et al., 2024). In addition to the characterization of fuel provided by the FBP, the fire growth module also includes internal settings to adjust for vegetation states by season, specifically grass fuel types (i.e., curing, green-12 up, and fuel load) and state rules regarding the maximum number of hours in which a fire can burn (Natural Resources Canada, 2017; Parisien et al., 2005). Burn-P3 runs through the modules in order of ignition, burning conditions, and fire growth. This process represents a single iteration of the model run, a substantial number of iterations (500 to 20,000) are averaged to produce maps of annual BP (Parisien et al., 2019). As the outputs of Burn-P3 are spatial estimates of annual BP, they do not provide information on seasonal variation in BP (Parisien et al., 2005). Additionally, minimum fire size is defined by the user and determines what fires are included in the averaging of simulations (Natural Resources Canada, 2017) In Burn-P3, BP is quantified by stochastically distributing a number of escaped fires (i.e., fires greater than minimum fire size) per iteration (Parisien et al., 2005). The area burned by the fire in each simulation is recorded on a grid, where overlap with other fires is not allowed in simulation, and the total burned area is the sum of each simulation (Natural Resources Canada, 2017). The BP (Bi) of a cell is calculated by dividing the total number of times the cell burned in all simulations by the total number of simulations (N) and multiplying it by 100 (Equation 1). Bi = biN x 100 Eq. 1 The probability gives an estimate of the chance of a cell burning in a given fire year based on input variables provided. In addition to BP, Burn-P3 provides statistics on each simulated fire (e.g., area burned, ignition fuels, and number of spread days, etc.), a vector layer of all fire perimeters, and spatial estimates of fire intensity and vegetation consumption (Natural Resources Canada, 2017). Burn-P3 has been used in many different contexts of wildfire related research including examinations of landscape level wildfire susceptibility (Parisien et al., 2005, 2011, 2013), investigations on the controls of BP (Parisien et al., 2010), estimations of wildfire risk in different 13 wildfire and landscape management scenarios (Dawe et al., 2022; Reimer et al., 2019; Stockdale et al., 2019c), and exploring future wildfire-mediated landscape changes and risk scenarios (Stralberg et al., 2018; Wang et al., 2016a; Hall et al., 2018). The wide variety of research applications highlights the flexibility of Burn-P3 for modelling wildfire risk in a variety of scenarios. For example, Stockdale et al. (2019c) used Burn-P3 to understand the risk wildfire presented to seismic line areas that had been restored for caribou (Rangifer tarandus) in Cold Lake, Alberta. In addition to quantifying wildfire risk within the study area to provide guidance for the placement of future restoration, the efficacy of mitigation measures was also investigated to help land managers understand where and how to implement these measures to protect restored habitat. In another study, Burn-P3 was used to explore how the vegetation in the boreal forest region of Alberta will change in response to future wildfire and climate scenarios, and how in turn this will impact future BP (Stralberg et al., 2018). They evaluated 18 scenarios that considered fire-mediated and climate driven changes to vegetation, differing fire regimes, and future climate scenarios (as predicted by global climate models). The results of this study indicated that future wildfire regimes and climate scenarios could compound to accelerate the conversion of boreal forest to deciduous woodland and grassland over the next century. This study provided insight into the role of fire in future vegetation composition, such as in modelling scenarios where future weather was considered, but no changes were made to the number of escaped wildfires or the spread-day distributions (i.e., a constrained fire regime). In the constrained scenario, a decrease in BP and total burned area was observed for the near future, which was attributed to the transitioning of the more flammable coniferous forests of the boreal to less flammable deciduous woodlands (Stralberg et al., 2018). These studies show how Burn-P3 can be used on its own or in coordination with other models to enhance our understanding of current and future wildfire risk. 14 While models such as Burn-P3 produce robust estimates of the likelihood of wildfire on the landscape, the raw outputs of the model do not quantify the risk of wildfire to the landscape. This requires the likelihood of fire to be considered in coordination with a particular value or multiple values on the landscape (Finney, 2005). For example, Stockdale et al (2019c) quantified risk of wildfire to restored caribou habitat by using the mean fire intensity and BP to quantify fire hazard within the study area and the patterns of ignition density to delineate firesheds. Parisien et al. (2019) demonstrated an alternative approach to wildfire risk quantification by using a habitat suitability model and BP to estimate the potential impacts of wildfire to a target species (e.g., caribou). In these examples, wildfire risk is only quantified for a single value (e.g., caribou) on the landscape, which is the predominant method in wildfire risk modelling (Mahat et al., 2015; Rockweit et al., 2017). This approach is applied because the impacts of wildfire differ based on what landscape value is being considered (Parisien et al., 2019). While this method can offer more in-depth analyses of wildfire impacts, it fails to consider the interactions between values on the landscapes and the impact to the system as a whole. As a result, there is a growing need to develop tools which incorporate wildfire risk models that allow decision-makers to explore the social-ecological impacts of wildfires and wildfire management. 1.3 Managing Complexity in Wildfire-Prone Landscapes 1.3.1 Complex Social-Ecological Systems Reconciling the often opposing social and ecological objectives of wildfire management to accommodate wildfire on the landscape will require a paradigm shift to consider a broader view of wildfires as part of complex social-ecological systems (SESs; Moritz et al., 2014; Steelman, 2016). This perspective employs both the social ecological systems and complex systems science frameworks to reduce uncertainty around potential outcomes by considering multiple scales of 15 interactions and feedbacks from both ecological and social drivers and how these entities combine to produce patterns in wildfire behaviour across a given landscape (Filotas et al., 2014; Fischer, 2018; Paton et al., 2015; Spies et al., 2014). SESs are characterized by the presence of both social and ecological components, the interaction between these components that is driven by human alterations of the environment (e.g., infrastructure development), and diverse exogenous disturbance mechanisms and internal responses, all of which is connected to the interactions between various scales (e.g., temporal and spatial) of the system’s components (Janssen & Ostrom, 2006). This diverges from conventional approaches to resource management that tend to view social and ecological values as distinct entities (Akamani et al., 2016). However, separating social and ecological systems does not adequately address the complexity and uncertainty of many resource management challenges (Westley et al., 2011) as there is a dynamic interdependence between the social and ecological properties of many natural systems (Akamani et al., 2016; Janssen & Ostrom, 2006; Spies et al., 2014). Where there is a high level of complexity present within an SES, as is present in many wildfire-prone landscapes, complex ecosystems science can offer an additional lens through which to understand interactions between system components and system dynamics (Akamani et al., 2016). Complex systems science is centered around the idea that the interactions between components on a local level can produce system wide dynamics that cannot be explained through the individual behaviors of the components (Filotas et al., 2014). Similar to SESs, complex systems are defined through a set of characteristics: heterogeneity, hierarchy, self-organization and emergence, openness, adaptation, memory, non-linear thresholds, and uncertainty (Filotas et al., 2014). Examples of how each of these characteristics appear in wildfire-prone landscapes are as follows: 16 • Heterogeneity: Variations in topography, climate, and vegetation structure and composition that interacts on multiple spatial and temporal scales • Hierarchy: Levels of the landscape including the individual, stand-level, and landscape level. • Self-Organization and Emergence: Interactions between vegetation and climate can produce changes in wildfire regimes, which in turn can influence vegetational succession. • Openness: Exogenous controls on wildfire behaviour, such as climate change. • Adaptation: Wildfire-prone landscapes adapt to different frequencies and severities of wildfire. • Memory: Past wildfire events are embedded in the landscape and can influence future wildfire behavior. • Non-Linear Thresholds: Relatively small shifts in temperatures, humidity, or wind can produce non-linear responses in wildfire behaviour. • Uncertainty: There is significant uncertainty in future wildfire behaviour due to the complex interactions between the landscape components and exogenous controls such as climate change. Despite the growing focus on wildfire in complex SESs, most research focuses on ecological and climatic components, including the influence of wildfire severity, burn patterns, and/or climate change (Hanes et al., 2019; Hessburg et al., 2019; Parisien et al., 2010, 2011, 2019; Stockdale et al., 2019a; Wang et al., 2016). Few studies have developed an SES framework for understanding the relationships between the social and ecological components of wildfire. Steelman (2016) proposes a useful SES framework that was adapted from Chapin et al. (2006), 17 which illustrates the management of wildfires as an SES at multiple temporal and spatial scales. This framework suggests wildfire governance should be considered at multiple spatial and temporal scales, including broad-scale and long-term exogenous controls (e.g., long-term management practices and international climate change policy); meso-scale, slow changing variables (e.g., wildfire management, budget processes, and wildfire-urban interface development); and fine scale, fast changing variables (emergency response, houses burned, and annual area burned). These social components are linked to the ecological components through human actors and institutions (Steelman, 2016). Frameworks such as Steelman’s (2016) can be used as a starting point for the development of wildfire management policies. Studies examining wildfire as an SES in Alaska, California, and New Zealand, emphasize the importance of agencies working with local communities to develop management policies (Chapin et al., 2008; Jakes & Langer, 2012; Paveglio et al., 2009). When communities are managed collaboratively they can better adapt to both predictable and unpredictable changes (Chapin et al., 2008). Community dynamics and local knowledge were identified as key factors that influence a community's adaptive capacity to wildfire management (Jakes & Langer, 2012; Paveglio et al., 2009). Because these factors differ among communities, a one-size fits all approach to wildfire management is not feasible (Paveglio et al., 2009). Stakeholder engagement in collaborative management processes is fostered by consideration of local knowledge and conditions (Jakes & Langer, 2012; Paveglio et al., 2009). Social relationships are critical for community preparedness, adaptive capacity, and resilience (McCaffrey, 2015; Prior & Eriksen, 2013). To effectively respond to wildfire threats, communities must support each other by working together and sharing resources, which is enhanced with increased social interconnectedness (Eriksen, 2014; McCaffrey, 2015; Prior & 18 Eriksen, 2013). Similarly, open communication between agencies and communities allows for adaptive governance to be developed. Agencies should strive to learn from and work with communities to understand land-use and values to develop integrated management plans (Abrams et al., 2015; Bach et al., 2015; Copes-Gerbitz et al., 2022; Moritz et al., 2014). 1.3.2 “Walking on Two Legs” and Adaptive Governance In the context of wildfire SESs, governance can be defined as the social components that interact with the environment either directly (e.g., individual entities or organizations) or indirectly (e.g., legislation and collective perceptions) to influence wildfire outcomes (Copes-Gerbitz et al., 2022). The power of the different social components to influence outcomes within governance structures is often unequal, with centralized colonial systems holding the majority of influence (Copes-Gerbitz et al., 2022; Hoffman et al., 2022b) resulting in governance that is often incongruent with the objectives of smaller components (i.e., communities). The decentralization of power has been proposed as an solution to these rigid, and frequently problematic, centralized systems of power (Copes-Gerbitz et al., 2022; Hoffman et al., 2022b). This would facilitate a more equitable distribution of power particularly to Indigenous Nations who have faced consistent marginalization, particularly in the context of wildfire management (Dickson-Hoyle et al., 2022; Hoffman et al., 2022a, 2022b). This means looking beyond colonial management frameworks and western science and instead following principles of reconciliation that empower Indigenous-led fire stewardship. There are many Indigenous frameworks that provide a pathway for western science and wildfire management practices to coexist with Indigenous science and stewardship systems (Dickson-Hoyle et al., 2022). One such example is the “walking on two legs” framework described by the Secwépemc Elder Ronald E. Ignace, where Indigenous knowledge (the mind) guides the actions and practices (the walking) of the two legs, with Indigenous science and 19 stewardship representing one leg and western science representing the other (Dickson-Hoyle et al., 2022). This framework emphasizes coexistence of the two knowledges, while acknowledging the entrenched inequity of power between the two. A western framework that is complimentary to the “walking on two legs” framework is adaptive management. At its core, adaptive governance is a theoretical framework where management actions are regularly assessed so that policies can be continually updated and adjusted (Folke et al., 2005). Multiple stakeholders across multiple scales of management are involved in the decision-making process that is aimed at building the capability of the SES to respond to change (Akamani et al., 2016; Dunn et al., 2020). Adaptive governance seeks to integrate the concerns of multiple sectors into management policies because only considering one sector’s concerns can lead to partial solutions that can have unintended consequences (Akamani et al., 2016). Most importantly, adaptive governance is focused on learning and adapting policies continuously, recognizing that complex SESs cannot be managed through one static strategy and that the solutions in place can always be improved upon (Abrams et al., 2015; Dunn et al., 2020). Adaptive governance addresses many of the issues surrounding the management of complex SESs and offers a framework for developing meaningful policy within the context of western management practices (i.e., the western science leg; Abrams et al., 2015; Dunn et al., 2020) 1.4 Wildfire in the Thompson-Okanagan In BC, the wildfire landscape has been rapidly shifting from low to moderate severity wildfire regimes, which characterized much of the 20th century, to a wildfire regime that is characterized frequent large high severity fires (Parisien et al., 2023). Yearly wildfire activity fluctuates greatly, with severe wildfire seasons, including 2017, 2018, 2021, and 2023 negatively impacting human health and resulting in community evacuations and property losses (e.g., the 20 community of Lytton in 2021; Parisien et al., 2023). Similar to many places in western North America, this change in wildfire behaviour is the result of a complex set of interacting factors including anthropogenic climate change coupled with widespread changes in land-use, rapid urbanization of the wildland urban interface, and a legacy of wildfire suppression (Copes-Gerbitz et al., 2022; Marcoux et al., 2015; Meyn et al., 2010; Parisien et al., 2023). The complexity of relationships between these factors (e.g., climate, vegetation, and human activity) and wildfire complicates predictions of future wildfire trends (Wang et al., 2016). Paleontological records indicate that during the Holocene, increasing temperatures were associated with increases in wildfire activity (Hallett et al., 2003; Hebda, 1995; Wang et al., 2016). The increase in wildfire activity was attributed to changes in climate and vegetation, and notably the changes in temperature experienced during this period are lower than projected temperature increases over the next century (Hallett et al., 2003; Hebda, 1995; Wang et al., 2016). Before recent fire years (i.e., 2017 onwards), annual area burned had decreased in British Columbia over the last century despite temperatures rising by roughly 0.5oC, this trend varies regionally and is highly dependent on summer precipitation patterns (Nitschke & Innes, 2008; Wang et al., 2016). The characteristics of fire regimes, including frequency, extent, cycle, intensity, severity, and seasonality may be altered as a result of climate change (Nitschke & Innes, 2008). One region that is already experiencing notable shifts in wildfire behaviour, in part due to climate change, is the Thompson-Okanagan region located in the southern interior of BC (Jain et al., 2022). This area has experienced some of the most devastating wildfires, such as the Okanagan Park Wildfire, Elephant Hill Wildfire, and McDougall Creek Wildfire, in the past two decades. These large high severity wildfires near or in the wildland urban interface can not only have profound effects on human health and infrastructure, but can produce to a host of secondary impacts including the 21 alteration of hydrological regimes and wildlife habitat (Jordan, 2016; Parisien et al., 2023). The uncertainty around future wildfire regimes coupled with current changes in climate within the Thompson-Okanagan has led to the development of models to provide insight into future trends in wildfire behaviour. Modelling the spatiotemporal trends of future wildfire scenarios is particularly complex as these models must include all aspects of the fire SES. For the Thompson-Okanagan region, models have been developed that consider multiple characteristics of the wildfire SES including climate, topography, vegetation, and land-use change (Nitschke & Innes, 2008; Wang et al., 2016). Nitschke & Innes (2008) found that over the next 70 years fire regimes will shift to include more frequent and large fires; a significant departure from historic fire regimes in the area which were generally characterized by mixed wildfire regime (Hagmann et al., 2021; Hessburg et al., 2019). It should be noted that this model did not assess future vegetation patterns, which play a critical role in wildfire dynamics. Wang et al. (2016) used Burn-P3, a spatial fire simulation that integrates fuels, topography, weather, and fire ignition patterns, to produce BP surfaces, for south-central British Columbia. They found that there are several competing effects (e.g., changes in fuels, weather, and ignition patterns) related to wildfire behaviour making a single prediction of future fire regimes challenging. Therefore, they assessed the effects individually and found that fire weather will result in an increase in wildfire ignitions, but weather changes could also encourage the growth of more fire-resistant vegetation, including aspen. The study points towards a need to better understand the effects of climate change on fuel and vegetation dynamics in the Thompson-Okanagan. 22 1.5 Knowledge Gaps and Research Objectives Developing wildfire management strategies that balances the benefits of fire while minimizing its adverse effects remains a challenge for wildfire managers (Copes-Gerbitz et al., 2022; Dickson-Hoyle et al., 2022; Dunn et al., 2020; Hoffman et al., 2022b). The complexity of wildfire SESs makes identifying the impacts of management strategies on social and ecological values difficult, posing challenges to informed decision-making in wildfire management. While there is extensive literature on the ecological impacts of wildfires (e.g., Mahat et al., 2015 and Rockweit et al., 2017), few studies exist that assess the impacts on the entire SES. As most wildfire-prone landscapes have a high number of both social and ecological values, overlooking the impacts of wildfire to the entire SES during management planning could inadvertently favour certain SES components to the detriment of others. These approaches may not consider the complex feedbacks and interactions among the numerous social and ecological components of wildfire prone landscapes and the potential impacts of landscape alterations. As such, there is growing impetus to develop wildfire management strategies that balance ecological processes with social objectives (Copes-Gerbitz et al., 2022; Dickson-Hoyle et al., 2022; Hessburg et al., 2021; Hoffman et al., 2022b) In western science, testing wildfire management strategies requires considerable time and resources, creating challenges to develop proactive and adaptive management plans. This is further complicated by opposing opinions about wildfire management that can lead to low stakeholder agreement. Wildfire simulation models can help accelerate the development of landscape scale wildfire management plans due to their ability to effectively test and assess the impacts and effectiveness of different strategies. 23 To develop a model that effectively assesses the impacts of wildfire and wildfire management on the SES, it is critical to understand the SES of the study area. There are very few frameworks that exist for understanding wildfire SESs which creates barriers for model development. Using the Okanagan watershed and Thompson Plateau, situated in southern British Columbia, Canada, as a case example, this study will address following objectives: 1. Assess local organizational perspectives and values in relation to wildfire and wildfire risk to conceptualize the wildfire SES of the study area. 2. Estimate the potential impact of various scales of wildfire mitigation by developing a wildfire SES model and wildfire severity model using the outputs of Burn-P3. 24 Chapter 2: Conceptualization of the Wildfire SES in the Okanagan 2.1 Introduction Wildfire has been an important ecological process of Canadian landscapes for over a millennium (Coogan et al., 2021; Hanes et al., 2019; Price et al., 2013; Pyne, 2008; Richard, 1993). However, wildfire management over the past century has focused almost exclusively on fire suppression. This emphasis on the exclusion of wildfire on the landscape has been, in part, driven by the prioritization of human assets and protection of timber. The continual development of communities in wildfire-prone landscapes has greatly expanded the wildland urban interface (WUI), which in turn, has led to greater suppression efforts to protect human values. The legacy of fire suppression has created homogenous landscapes with high fuel loads that are vulnerable to large high-severity fires (Baker, 1992; Cassell et al., 2019; Cumming, 2005; Stockdale et al., 2019a). Climate and land-use changes are further exacerbating the risk of these destructive fires which can negatively impact social and ecological values. The higher risk of destructive wildfires in turn leads to greater fire suppression efforts creating a positive feedback loop, leading many to characterize wildfire as a wicked problem (Essen et al., 2023 & Tedim et al., 2020). Wicked problems are characterized as ill-defined problems with elusive solutions that plague policy and planning experts (Balint et al., 2011; Rittel & Webber, 1973). The lack of clear solutions to wicked problems creates inherent difficulties in determining optimal management strategies (Allen & Gould, 1973; Carroll et al., 2007; Hammer, Stewart, & Radeloff, 2009). Wildfire management has recently begun shifting from suppression to reintroduction of wildfire to the landscape under specific conditions, but this objective comes with a myriad of management challenges (Dombeck et al., 2004; Tymstra et al., 2020). 25 The exclusion of wildfire from the landscape has resulted in a wildfire deficit in many regions (Keane et al., 2002; Parisien et al., 2020). Wildfire is an important disturbance agent in many landscapes, influencing species diversity, forest structure, and ecological succession. For example, in western Canada, the exclusion of wildfire combined with forest harvest and landscape management has altered the fuel structure and forest diversity so that many forested areas are increasingly homogeneous and even-aged (Chavardès & Daniels, 2016; Cumming, 2005; Hanes et al., 2019; Keane et al., 2002; Rhemtulla et al., 2002). This landscape scale homogeneity can influence fire behavior by allowing wildfire to spread more rapidly and increases the chances of crown fire, which in turn increases the probability of larger more severe wildfires (Cassell et al., 2019; Wang et al., 2014). Large and severe wildfires not only pose a risk to human life but they are generally associated with more negative outcomes to social and ecological values on the landscape (Doerr & Santín, 2016). Re-introducing wildfire to western Canadian forests will require a holistic understanding of the social and ecological components of landscapes, and their interactions with wildfire and wildfire management practices. Examining wildfire and wildfire mitigation with a social ecological system (SES) framework allows for the consideration of multiple values and processes on a landscape (Dunn et al., 2020; Spies et al., 2014). Wildfire managers can use conceptual models of SESs for individual landscapes to better understand not only the interactions between wildfires and social and ecological values, but also how stakeholders and communities understand, relate to, and manage wildfire. The multidimensional perspective that SES theory offers can address uncertainties related to unintended impacts on landscape values resulting from both wildfire and wildfire mitigation (Steelman, 2016). In SES research, the conceptualization of an individual landscape’s SES is often informed by interviews with local stakeholders. However, there is limited research focusing on 26 stakeholder-driven SES conceptualization in wildfire-prone landscapes and their management, particularly in a Canadian context (Vigna et al., 2021). To address this knowledge gap, we developed a conceptual model to illustrate connections between the social and ecological values and their interactions with wildfire and wildfire mitigation in the Okanagan region of British Columbia Canada. To inform the conceptual model, structured interviews were held with local experts to better understand (1) what values are on the landscape, (2) the risk of wildfire to the identified values, and (3) the impacts of wildfire mitigation to the identified values. 2.2 Methodology 2.2.1 Study Area The study area is comprised of the Okanagan region of southern British Columbia (Figure 2.1). This region encompasses 8000 km2 spanning from Osoyoos in the south to Salmon Arm in the north. The Okanagan is characterized by deep valleys that are a result of erosion by the large rivers within the region (Fraser, Thompson, Okanagan, etc.; Meidinger & Pojar, 1991). These valleys dissect the plateaus and highlands that dominate the physiography of this region (Meidinger & Pojar, 1991). Located on the eastern side of the Coast and Cascade Mountains, the region is characterized by a significant rain shadow effect that greatly reduces precipitation (Pike et al., 2010). The region is semi-arid with mean annual precipitation ranging between 250 and 360 mm per year. The spring often has little precipitation, followed by increased precipitation in early summer, and a decline in precipitation by mid-late summer (Pike et al., 2010). The climate of this area is the warmest and driest in BC, with notably hot and dry summers, which makes it particularly susceptible to wildfires. The Okanagan contains several biogeoclimatic zones including the Engelmann Spruce – Subalpine Fir, Montane Spruce, Bunchgrass, and Interior Douglas Fir zones (Pike et al., 2010). 27 The vegetation within the Okanagan is dominated by coniferous forest, predominately ponderosa pine (Pinus ponderosa) and interior Douglas fir (Pseudotsuga menziesii), with grasses and shrublands present at lower elevations (Ireland & Petropoulos, 2015). The region has a complex ecosystem, where the combination of topography, fuel, and climate elevate the risk of wildfire in the Okanagan (Ireland & Petropoulos, 2015). Wildfire seasons in the Okanagan, and in British Columbia, have been increasing in severity and frequency since 2005 (Parisien et al., 2023), with four of the worst wildfire seasons in the past century occurring in 2017, 2018, 2021, and 2023. Climate change is expected to exacerbate this risk in the Okanagan as mean annual temperatures increase and summer precipitation decrease (Regional District of North Okanagan et al., 2020). The Okanagan includes three regional districts – North Okanagan, Central Okanagan, and South Okanagan-Similkameen (Figure 2.1). The population of these districts is 91,610; 222,162; and 90,178, respectively (Statistics Canada, 2023). Urbanization in the region has led to an increase of residential areas that intersect with wildland areas, expanding the wildland urban interface. Most wildfire management in the region is overseen by the provincial government through the BC Wildfire Service which is responsible for wildfire management on provincial and federal crown land, local government land (when requested by municipalities), Treaty settlement lands, and private lands (Copes-Gerbitz et al., 2022). The Okanagan is the unceded and ancestral home of the Syilx Okanagan Nation and includes several reserves. 28 Figure 2.1. Study area in the southern interior of British Columbia. The main map panel shows a hillshade with major cities and lakes. The insert shows the position of the study area in British Columbia, Canada. 2.2.2 Interview Settings and Participants Design We conducted structured interviews to aid in the conceptualization of the Okanagan SES. We selected interviews over other participatory methods, such as town halls, for several reasons, including limitations due to the Covid-19 pandemic and because interviews allowed us to target regional experts. The interviews were framed to gather knowledge from the perspective of the organization that the experts were affiliated with. Therefore, the responses to these interviews represent organizational knowledge about social and ecological values on the landscape and their 29 interactions with wildfire and wildfire mitigation rather than individual opinions or perspectives. The information collected helped guide the study design and quantitative modelling in Chapter 3. Recruitment The recruitment process took place from April 2021 to March 2022. Before recruitment, we identified local organizations that represented multiple social and ecological sectors on the Okanagan landscape. We identified potential candidates based on their role in the organization and connection to land management processes. Following the initial round of interviews, we identified sectors with less representation to guide additional recruitment efforts. We asked experts during the interview process to refer any experts within the region who may be interested in participating. We primarily recruited participants from the Okanagan region, as this was the anticipated study area during the recruitment process. We sent one follow-up email to potential participants who did not initially respond and made no further efforts to contact participants if there was no response to the follow-up email. A total of 33 emails were sent out with 21 survey responses. We offered experts three forms of response: email, phone, or video call. 2.2.3 Interview Sessions and Data Collection Pre-Data Collection Phase 2.2.3.1.1 Identification of Interview Goals Before the interview process, we identified the overarching goals and purpose. In the conceptual stages of the project, we acknowledged that, as ecological researchers, there would be an inherent bias in what values we identified as essential to the SES. By interviewing local experts associated with regional organizations, we hoped to identify additional values we may have overlooked and identify which values were consistently considered essential in the study area. In addition to SES value identification, we aimed to enhance our knowledge of the regional 30 perceptions of wildfire risk and wildfire mitigation through the interview process. Lastly, we wanted to explore expert opinions on wildfire and wildfire mitigation's positive and negative impacts on key landscape values. 2.2.3.1.2 Identification of Values and Potential Impacts We identified potential values on the landscape prior to the interview process to gain an understanding of how these landscape values respond to not only wildfire, but wildfire mitigation. Prior to conducting the interviews, we investigated the relationship of these values to wildfire through a literature review combined with informal conversations with SES and wildfire experts at UBC. This allowed us to better understand the current body of knowledge and compare it to interview results. Additionally, we were able to identify knowledge gaps that could be addressed with wildfire experts during the interview process. Interviews R. Larsen conducted structured interviews from the following pre-determined list of questions: Q1 Values: What are the primary values (infrastructure, agricultural land, ecosystem services, water quality, wildlife habitat, tourism, parks, and protected areas, etc.) identified as management or conservation priorities for your organization? If many values are selected, please order their importance with 1 being the most important. Q2 Wildfire Risk: Of the values identified, do you consider wildfire as a primary or secondary risk to these values now and/or in the future? If yes, how does wildfire affect or put the values you identified at risk? Q3 Mitigation Strategies: Are current wildfire mitigation strategies in the Okanagan, including the use of mechanical fuel treatments and prescribed burning, sufficient to achieve targeted risk reduction for the values you have identified? 31 Q4 Mitigation Improvements: Are there ways wildfire mitigation can be improved in the region? If yes, please explain. Q5 Mitigation Impacts: What impacts does wildfire mitigation have on landscape values? What ecosystem indicators would you use to assess these impacts? Questions were provided in the same order during every interview and follow-up questions were asked if answers were unclear or if further elaboration would be beneficial to our understanding in a particular area of focus. Initial discussion of participant’s background and relation to fire was added mid-way through the interview process as we identified a need to establish the expert’s base knowledge. Interviews were conducted from May 2021 to June 2022. For interviews conducted over the phone or email, recording and transcribing was not used because of the exploratory and informal nature of the interview. We acknowledged that recording would change the tone of the interview and may influence how much experts were willing to share on a sometimes controversial topic. Instead, detailed notes were taken during the phone and video interviews. Experts were informed that identities and answers would remain anonymous. 2.2.4 Analysis of Interviews in NVivo NVivo 12 Pro (2017) was used to analyze the interview results. This software allows for a systemic review of qualitative datasets by coding common themes among study participants (Jackson & Bazeley, 2019). The text that is coded is assigned to a node that summarizes the general theme of the text (e.g., water), and nodes can be nested under previously coded nodes. Additionally, relationships between nodes can be coded to identify relationships between themes. These nodes and relationships can be visualized within NVivo or exported for further analysis. 32 Refinement of Expertise Sectors Prior to recruitment, experts were sorted into sectors based on expertise (Table A.1 in Appendix A). Following completion of the interview process, the identified sectors were revised based on the information provided by the experts about their background. The categories were broadly defined in an attempt to capture the wide breadth of expertise, with many experts falling into multiple categories e.g., municipal government and wildfire management. The sectors of expertise were coded as attributes in NVivo, allowing for experts to be grouped, thus providing a means of comparing responses amongst sectors versus the collective response. Coding Interview Responses Interview responses were grouped by question to identify generalized themes and values. The answers to each question were then coded. Themes and values were not predefined, instead these entities were emergent during the coding process. Coded themes and values were refined throughout the research process. 2.2.4.2.1 Value and Connectivity Coding The themes that emerged during the qualitative analysis were used to create conceptual diagrams that display the hierarchy of values identified on the landscape and the connectivity between values, wildfire, and wildfire mitigation. Question one (Section 2.2.3.2) was coded to identify the social and ecological values that experts considered to be at risk from wildfire. A connection between values identified in question one was coded when experts identified a relationship between two identified values. For example, if human life and safety and infrastructure were grouped together as a priority in a response, then this was considered to be a link (or connection) between the values. Connections to wildfire were only coded if the response framed 33 wildfire as a value e.g., a discussion of how wildfire could positively impact wildlife habitat. Connectivity between wildfire and other identified values was coded separately for questions two to five because these questions were framed with a greater focus on wildfire. Connectivity was also coded for wildfire mitigation with nodes representing both values and themes 2.3 Results The sector with the highest number of responses was ecological (nine) and the sectors with the lowest number of expert responses were Indigenous (one) and economic (one; Table A.1 in Appendix A) The responses to question one (values) generated several levels of nodes. The primary nodes, which provided the broadest themes, were social and ecological, with 75 ecological references and 55 social references (Table A.2 in Appendix A). A total of 11 secondary nodes emerged under the ecological node, while six secondary nodes emerged under the social node (Figures 2.2, 2.3, 2.4, 2.5, & 2.6). These secondary nodes represented values that experts identified as at risk from wildfire. The values that were coded most frequently in the secondary nodes were wildlife and wildlife habitat (19 references) and infrastructure and communities (17 references). Five out of the 21 values that emerged in question one had over 10 references. 34 Figure 2.2. Treemap of ecological values that emerged from the interview process, where shade and size of the value indicate the hierarchy of values based on frequency of references in responses to question one (values). 35 Figure 2.3. Treemap of social values that emerged from the interview process, where shade and size of the value indicate the hierarchy of values based on frequency of references in responses to question one (values). The social and ecological values show a high amount of connectivity between all values identified in question one (Figures 2.2, 2.3, and 2.4). A total of 92 connections were coded between values. Wildfire displayed the highest amount of connectivity (20 connections referenced), with the highest connectivity (five references) to wildlife and wildlife habitat. Infrastructure and communities had the next highest number of connections referenced, with this value being referenced most frequently in conjunction with wildfire (four references) and human, life and safety (three references). The median number of connectivity references per value was four and values were connected to a median of three other values. Air quality was not referenced in relation to any other values during our interviews. 36 Figure 2.4. Chord diagram summarizing connectivity between all social and ecological values identified in the expert responses to question one (values). The thickness of a line between connections represents the number of times a connection was referenced by experts. The acronyms are defined as follows: WF = wildfire, WAT = water, NHR = natural hazard reduction, FIR = forest integrity and resilience, EI = ecosystem integrity, ES= ecosystem services, CC=climate change, BD = biodiversity, RT= recreation and tourism, IC = infrastructure and communities, HLS = human life and safety, ECO = economy, AG = agriculture, FOR = forestry, CV = cultural values, AE = Aesthetic, WWH = wildlife and wildlife habitat. In questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts), there were 38 connectivity references between wildfire and social values (nine 37 references) and ecological values (29 references; Figure 2.5). Forest integrity resilience (seven references) was the most connected ecological value to wildfire. Infrastructure and communities (five references) was the most connected social value to wildfire. Invasive species management, soils, and grasslands and rangelands emerged as new values connected to wildfire in questions two to five. Figure 2.5. Sankey diagram of connections between wildfire and social and ecological values in questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts). The thickness of the band represents the number of references to each connection. In questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts), there were 144 connectivity references coded for wildfire mitigation (Figure 2.6; Table A.3 in Appendix A). Of these references, 71 centered around connections between wildfire mitigation and management themes, while 73 were references to social and ecological values on the landscape. The most referenced management theme was prescribed and cultural burns (18 references), and the average number of theme references was nine. Ecological values were referenced 42 times, while social values were refenced 31 times. The most referenced 38 ecological value was wildlife and wildlife habitat (14 references), while the most referenced social value was infrastructure and communities (12 references). On average, each value was referenced six times. Figure 2.6. Sankey diagram of connection between wildfire mitigation and wildfire management themes, social values, and ecological values in questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts). The thickness of the band represents the number of references to each connection. 2.4 Discussion The conceptualization of ecological systems often takes place through one of two perspectives: an ecologically focused perspective, or a human focused perspective, where the human perspective is structured around value to humans and the ecological perspective is structured around the processes of the ecosystem (Folke et al., 2003, 2005; Miller et al., 2022; 39 Akamani et al., 2016). This dichotomy can pose challenges when understanding landscapes that have multiple interconnected and often interdependent social and ecological values (Folke et al., 2005; Prior & Eriksen, 2013; Westley et al., 2011; Akamani et al., 2016). The values assessed in this study are representative of the Okanagan region of British Columbia. This region has numerous social and ecological values that interact over multiple temporal and spatial scales. To consider the diversity of values within the study area through a singular lens would provide an incomplete understanding of the system and its responses to wildfire, which interacts with both social and ecological values. Despite efforts to elicit responses from experts with a variety of backgrounds (Appendix A; Table A.1), expert responses were often framed through an ecological lens. For example, ecological values were referenced at a ratio of 1.4:1 compared to social references. This may be partially attributed to the high proportion of ecological experts (n=9) who responded to the questionnaire, but the ecological framing was prevalent in responses from experts with social backgrounds as well. This emphasis on ecological processes and values may have been influenced by how the project itself was described and our backgrounds as ecological researchers. It is possible that experts may have tailored their responses to the questionnaire because the project itself had an ecological framing. Given the high proportion of WUI within the Okanagan region, it is expected that there would be the high prevalence of references from local experts to structures and communities at risk to wildfire (Figure 2.3). The WUI will likely continue to expand due to increases in urban sprawl and recreational development (Johnston et al., 2020; Johnston & Flannigan, 2017; Peter et al., 2006) and recent growth of many remote communities, including Indigenous communities (Johnston et al., 2020; Statistics Canada, 2023). Understanding the risk that wildfire poses to structures and communities extends beyond loss of assets (Johnston et al., 2020), as wildfires can 40 cause psychological trauma and adversely influence the collective mental wellbeing of the community (Coogan et al., 2019; Johnston et al., 2020; Sankey, 2018). This heavy valuation of structures and communities may contribute to conservatism in wildfire management within the region where the benefit of protecting from short term risk, largely through wildfire suppression, is prioritized over management decisions that contribute towards long term resilience to wildfire (Calkin et al., 2014; Wilson et al., 2011). The socio-political pressures to minimize damage from wildfires coupled with lack of financial incentives for wildfire mitigation, incentivizes maintaining reactionary wildfire practices and reduces the willingness of many wildfire managers to consider new strategies (Sherry et al., 2019). In recent years the Okanagan, and to a larger extent BC, has experienced higher amounts of wildfire activity with particularly destructive wildfire years in 2017, 2018, 2021, and most recently 2023 where the McDougall Creek wildfire resulted in the evacuation of over 10,700 people (Central Okanagan Emergency Operations, 2023) and destroyed or damaged approximately 190 homes. Years of high wildfire activity often result in public pressure to reduce conservative wildfire management practices focused on suppression. Recently, the BC Forests Practices Board has called on the provincial government to undergo a “paradigm shift” and manage wildfire risk at a landscape level, not just within the wildland urban interface. The BC Forest Practices Board urged the government to adopt landscape fire management aimed at building resiliency to wildfire by managing risk at multiple scales including forest stand, watershed, and landscape scales (Forest Practices Board, 2023). The overarching goal of this type of management is to increase ecosystem resiliency so that fire can resume its natural role as a disturbance mechanism. Management strategies such as this place higher prioritization on ecological values than current wildfire management strategies. 41 While emphasis on ecological values in relation to wildfire risk may not be represented in the current wildfire management strategies, our results indicate that experts do stress the importance of ecological values on the landscape (Figure 2.3). Many experts we interviewed recognized that while wildfire plays a role in maintaining long-term ecosystem health, it can also paradoxically act both as a threat and a benefit to wildlife and wildlife habitat depending on the size, severity, and species in question. For example, prescribed burns have been found to increase the quality (i.e., crude protein content and digestibility) of winter forage for mountain sheep and mule deer in mountain shrub and grassland communities, with the most benefits experienced one year after the fire (Hobbs & Spowart, 1984). Conversely, a study conducted in the Okanogan County of Washington found that mule deer select for burned areas in the summer due to increases in forage but avoid burned areas in the winter because of greater snow accumulation (Ganz et al., 2022). While many studies focus on ungulate responses to wildfire, impacts of fires on habitat and wildlife vary considerably. For example, bird populations can both increase and decrease post-fire and often show distinct preferences for areas affected by low or mixed-severity burns (Smucker et al., 2005). Wildfires not only influence habitat conditions, but also affect biodiversity and landscape heterogeneity. High-severity fires generally have a detrimental impact on these ecological attributes, whereas low and mixed-severity fires can enrich both biodiversity and landscape heterogeneity in fire-adapted ecosystems (Hoffman et al., 2021).The nuanced benefit that wildfire may provide to wildlife and wildlife habitat was recognized by experts we interviewed, as evidenced by Expert 5: “Large wildfires are not a new phenomenon and these provided “much needed” extended early seral habitat that many species of biota used/required historically. However, this has 42 largely been reversed due to human consumptive activities and wildfire may well now be an ecological process that reduces critical habitats (i.e. old forest) for wildlife.” and Expert 1: “Wildfire should be recognized as a value required by most wildlife in the region as a necessary and important natural disturbance type. There is very little evidence suggesting that wildfire does not benefit the endemic species in the region and is often cited as a limiting factor contributing to range and population reductions. The negative narrative needs to be adjusted at least in terms of wildlife here.” While there were many references to the risk (or benefit) of wildfire on wildlife and wildlife habitat, ecosystem services, and water, recreation and tourism also emerged as prominent values in the responses to question one (values). There is an increasing awareness of the impact of wildfires to popular tourist destinations such as the study area (Hystada & Keller, 2006). The Okanagan valley regularly draws tourists from across Canada because of its geographic location in a major tourism corridor (Canadian Rockies to Canada’s west coast), numerous wineries and other agritourism, and opportunities for outdoor recreation (Cristófol et al., 2020; Getz & Brown, 2006; Hystada & Keller, 2006; Senese, 2010). Regional districts within the area are often articulating urban planning objectives centered around recreation and ecosystem (RDCO, 2017; RDNO, 2022; RDOS 2023). These activities, however, also make the area vulnerable to the economic, health, and environmental impacts of wildfires. Wildfires can have both immediate and long-term consequences on tourism and recreational activities. In the immediate aftermath of a wildfire, there is often a decline in tourist visits due to safety concerns, air quality issues, and damaged recreational infrastructures (Hystada & Keller, 2006). Over the long term, perceptions of the area as ‘risky’ or long-term aesthetic impacts to areas can deter potential visitors. Given 43 numerous direct and indirect impact that wildfire can have on recreation and tourism, and the importance to the local economy it is not surprising that these values emerged from the interview process. The strong attachment of individuals to their local environment combined with the Okanagan region being home to both government and academic institutions that conduct research in the environmental and biological spheres may have played a role in the frequent references to ecosystem services and water resources in the responses to question one (values). The Okanagan is an arid environment, making water conservation and management a regional priority. Water availability to both humans and aquatic species is of particular importance within the region (Melnychuk et al., 2017). There are numerous groups and societies, including the Okanagan Basin Water Board and the Fraser Basin Councils, that are devoted to the protection of water resources within the region. Climate change is exacerbating concerns around water availability, leading to a greater emphasis on how natural disasters such as wildfire can influence hydrology and water quality (Robinne et al., 2020). Wildfires have been shown to significantly alter the hydrological cycle, affecting both water quality and quantity within burned watersheds. In terms of water quality, wildfires can lead to increased sedimentation, nutrient loading, and the release of contaminants in water bodies (Paul et al., 2022; Smith et al., 2011). Water quality changes can have downstream impacts affecting both human consumption and aquatic ecosystems. Wildfires can also lead to changes in the timing and magnitude of streamflow (Robinne et al., 2020). The reduction in vegetation resulting from high severity wildfire can increase runoff, consequently altering hydrological regimes (e.g., higher peak flows and increased likelihood of flash floods). Additionally, wildfire can impact infiltration of water into soils due to changes to vegetation, litter and duff layers, and water repellency of 44 surface soils (Bladon et al., 2014). However, the specific hydrological responses can vary depending on factors such as fire severity, topography, pre-existing soil moisture conditions, and time since the fire (Bladon et al., 2014). These impacts to the hydrological cycle have the potential to worsen under climate change, therefore understanding the hydrological impacts of wildfires is crucial for developing sustainable water management and disaster mitigation strategies. Water is just one of numerous ecosystem services that exist within the Okanagan region (Field & Parrott, 2022). While experts in our study generally did not specify which services were most at risk from wildfires, the terms consistently appeared in interview responses. Current wildfire regimes (i.e., higher frequency of high severity wildfires) in many regions can have adverse effects on ecosystem services (Elimbi Moudio et al., 2021; Pereira et al., 2021; Steelman, 2016). In their literature review, Vukomanovic & Steelman (2019) identified several mechanisms by which fire can affect ecosystem services, such as changes in land cover, water quality and quantity, nutrient balances, and sedimentation rates. Although there are multiple potentially adverse impacts of wildfire to ecosystem services, wildfire can be an ecosystem service itself (Pausas & Keeley, 2019). Wildfire is integral to many ecosystems where it can provide services such as creation and maintenance of open habitat structures, supporting the growth and diversity of food sources such as bulbs and tubers, regulation of catastrophic wildfires, promoting biodiversity and habitat heterogeneity, and cultural values (Pausas & Keeley, 2019). The lower number of references to the remaining values that emerged in question one (values) does not mean that these values are not important in the region, but it does indicate that these values were viewed by the experts in the study as less at risk from wildfire. For example, while some experts viewed wildfire as a secondary risk to both agricultural productivity and profitability, others postulated that the prevalence of irrigation systems could help reduce wildfire 45 risk in agricultural areas. The lower frequency of references to values such as human life and safety may be attributed to experts only speaking to the risk in terms of their organization’s values as we did not interview anyone from the health sectors. Wildfires have well-documented health impacts that extend beyond immediate fire-related injuries. Smoke and particulate matter from wildfires can lead to respiratory issues, exacerbate pre-existing health conditions like asthma and chronic obstructive pulmonary disease, and contribute to cardiovascular problems (Black et al., 2017). Additionally, the mental health toll, including stress and anxiety related to evacuations and property loss, should not be underestimated. An interesting theme that emerged in question one (values) and that continued throughout the responses to all questions was the idea of wildfire as a value on the landscape itself. This perspective can be seen in a quote from Expert 5: “Fire has been part of the ecosystem for millennia, and is a value in and of itself. So separating fire from other ecological values is a very Western approach to management. A priority for our organization is to re-establish the practice of prescribed fire, as a necessary part of the ecosystem. In a fire dependent landscape, being without fire for so long has left the system in a decadent and decrepit state. Part of the problem Western science has is trying to prioritize values, when all of the values contained in the ecosystem are interdependent.” The concept of wildfire as an interdependent value on the landscape represents a holistic approach to wildfire dynamics that is present in Indigenous wildfire stewardship, which has been practiced across Indigenous cultures for thousands of years (Lake & Christianson, 2020). We are now seeing this idea emerge in western management practices with a call from many wildfire experts to reintroduce fire to the landscape (Copes-Gerbitz et al., 2021; Daniels et al., 2024; Dunn 46 et al., 2020; McWethy et al., 2019; Moritz et al., 2014; Schoennagel et al., 2017). In fire adapted landscapes, wildfire acts as a key disturbance mechanism that influences vegetative succession (Johnson, 1992; Moreno et al., 1994; Pausas & Keeley, 2009). The absence of fire can impact the ecological resilience of fire adapted landscapes, including the study area, where, prior to colonization, Indigenous Nations have used fire as a risk and resource management tool (Hoffman et al., 2021, 2022a; Lake & Christianson, 2020; Kimmerer & Lake, 2001; Turner et al., 2013). Today, Indigenous fire stewardship is used by Indigenous Nations to improve ecological diversity and mitigate fire risk in many regions across British Columbia (Burr, 2022) and Canada, but significant barriers impede the progress of Indigenous fire stewardship (Hoffman et al., 2022a). Hoffman et al., (2022a) summarized five key barriers to Indigenous fire stewardship in Canada: (1) poor understanding of the relationship between Indigenous peoples and fire (Hoffman et al., 2021; Lake et al., 2017; Stacey et al., 2019), (2) power imbalances between Indigenous Nations and the colonial government systems that monopolize decision making related to wildfire management, (3) lack of training programs centered around Indigenous fire stewardship, (4) the historical and continued criminalization and fining of cultural burning practices (Hoffman et al., 2021; Lake, 2021; Lewis, 1978; Turner, 1999) and liabilities placed on Nations and individuals by government agencies, and (5) capacity limitations following years of exclusionary practices by colonial governments (Hoffman et al., 2022a). Historically, wildfire has been a crucial component of many landscapes across Canada due in part to Indigenous wildfire stewardship, and has provided significant ecological and cultural values. The results of the interviews reveal that experts place an importance on many social and ecological values in relation to wildfire risk, including wildfire. To effectively mitigate the risk of wildfire to the identified values, a holistic approach to wildfire management that is both inclusive 47 of Indigenous knowledge and considers the critical role that wildfire plays on the landscape is vital. This involves collaborating with Indigenous Nations to overcome barriers, enabling the reintegration of Indigenous fire stewardship practices on the landscape. Building on this understanding of wildfire as a value and Indigenous fire stewardship, we further conceptualized how wildfire is valued on the landscape by exploring the interconnectedness of wildfire with other landscape values identified in question one (values). Wildfire was frequently mentioned in relation to other values on the landscape. While the question was structured such that experts were asked for input on social and ecological values considered at risk to fire, rather than valuation of fire itself, we attempted to control for this by only coding instances where a specific connection between wildfire and a value was mentioned. Wildfire was strongly connected to most values and had the highest connectivity to wildlife and wildlife habitat, ecosystem integrity, and infrastructure and communities. The emergence of ecosystem integrity during the connectivity analysis indicates the key role of fire in the ecology of the study area. Similarly, in the coding of responses from questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts), which were more directly framed around wildfire, several new values were identified. For example, grasslands and rangelands not only emerged as a new landscape value but show a high level of connectivity to wildfire. This relationship highlights the distinct responses and influences individual ecosystems can exert on wildfire behavior and wildfire risk. Questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts) also revealed an increased focus on the relationship between forest resilience and integrity and wildfire. Traditional definitions of resilience tend to be ecologically oriented, emphasizing a system's ability to maintain its existing structure and processes after disturbance 48 (McWethy et al., 2019; Schoennagel et al., 2017). In the context of wildfire management, relying solely on traditional, ecologically-based resilience may not be sufficient. Such approaches often yield reactive policies that might not be sustainable given the current trends in wildfire behavior (McWethy et al., 2019). Management strategies that are underpinned by adaptive and transformative resilience are often more suited to address the complexities of the contemporary wildfire landscape (McWethy et al., 2019). Adaptive resilience allows for adjustments to management practices and system characteristics to better cope with changing fire dynamics, thereby enhancing long-term sustainability (Schoennagel et al., 2017). Transformative resilience calls for a fundamental reassessment and restructuring of human-ecological systems, with the aim of adapting to new fire regimes that may be fundamentally different from historical norms (McWethy et al., 2019; Schoennagel et al., 2017). These two types of resilience offer a more nuanced approach to managing wildfire risks and impacts, accounting for the entirety of social and ecological system of landscapes. Shifting how we frame landscape resilience to consider more adaptive and transformative qualities may lead to more effective and sustainable wildfire management policies. To further understand the current understanding of wildfire management within the Okanagan, questions two to five (wildfire risk, mitigation strategies, mitigation improvements, and mitigation impacts) were also coded to identify potential relationships between wildfire mitigation and values and management themes. Most notably, we see the emergence of specific mitigation tactics: prescribed burn and mechanical treatment. Prescribed burns were coded as the most connected theme, while wildlife and wildlife habitat were, once again, recognized as the most connected value. The emergence of prescribed burning as the most connected theme to wildfire mitigation indicates that many experts are considering the complexity and impacts of applying fire 49 on the landscape. Some experts viewed prescribed burns positively such as responses from Expert 6: “Thinning and prescribed burning have largely positive outcomes.” and Expert 10: “We need more prescribed burns - to mimic natural cycles of ~25 years. Manual thinning does not replicate natural fires.” Conversely, other experts discussed the drawbacks of prescribed burns as a mitigation tactic. For example, Expert 20 discussed the multitude of complications and barriers with conducting prescribed burns: “Prescribed burning is not a significant fuel management tool in the Okanagan for many reasons. These include, liability, inexperience, cost, smoke management, visuals, change in harvesting methods (partial cut instead of clearcuts), licensee timber allocations, poor support from the BC Wildfire Service and many others. I do not see it having a significant role in the future of fuel management in B.C., at least near populated centers and near valuable timber or infrastructure.” Whereas Expert 5 discussed the temporal and spatial scale issues that arise when trying to conduct wildfire mitigation including prescribed burns: “I believe that the mechanical and prescribed fire treatments currently being used are anachronisms from a bygone era when these kinds of treatments may have been somewhat effective (very questionable). If you do not believe this, simply calculate how much area is treated annually and plot these treatments on a 1:500,000 scale map of the Okanagan. The treated areas will be largely invisible. Go a step further and calculate the last 5-10 years 50 of treatments and see how that looks. The literature on this varies, but indicates retreatment is necessary between 3-10 years if the activity is to be effective. At the provincial scale, there are 650,000 ha of WUI, and each year about 20-30,000 ha are “treated”. There are relatively effective ways to manage wildfire but it will likely take a very different approach than is currently used to address values protection.” The responses demonstrate the complexity of reintroducing wildfire back onto the landscape. Wildfire experts agree that suppression of wildfire is no longer feasible on fire-adapted landscapes, but there is little consensus on the what management practice would best allow for the re-integration of wildfire into these landscapes (Prichard et al., 2021). There is also uncertainty around current fire management and fuel treatment practices (Spies et al., 2018), particularly when it comes to the efficacy and suitability of large-scale treatments outside the WUI (Bradley et al., 2016; DellaSala et al., 2017; Moritz et al., 2014; Schoennagel et al., 2017). Prichard et al. (2021) addressed some of the most common questions relating to the adaptation of North American forests to climate change and wildfire. While mechanical thinning and prescribed burns are highly effective in many landscapes, the current scale of application does not sufficiently address the risk of wildfire on these landscapes (Prichard et al. 2021). Often there are policy or management constraints that limit where prescribed burns or mechanical treatments can be applied. Consequently, when scaling up treatments it is critical for wildfire managers to use tools which identify areas that would most benefit from treatments. Spatial optimization of treatments can increase efficacy of treatments by over 50% (Krofcheck et al., 2018). Our research is aimed at supporting this spatial optimization by providing a better understanding of the wildfire SES. A framework to support landscape level wildfire management through spatial optimization is presented in Chapter 3. 51 2.5 Conclusion Understanding the wildfire SES on wildfire-prone landscapes can contribute to more effective management strategies. Our study was aimed at conceptualizing the complex interactions between wildfire and social and ecological values in the Okanagan. The results of our interviews showed the complexity of the Okanagan SES with a total of 11 ecological and six social nodes emerging during interviews. Ecological values, particularly wildlife and their habitats, were more frequently cited in relation to wildfire risks than social values. This finding underscores the necessity of a holistic wildfire management approach that integrates both ecological and social perspectives Many experts recognized the critical role that wildfire plays in the ecosystem and identified wildfire itself as a value on the landscape. However, this perspective of wildfire as a value on landscapes is just beginning to emerge in western management practices (Dunn et al., 2020; McWethy et al., 2019; Moritz et al., 2014; Schoennagel et al., 2017), contrasting with millennia of Indigenous fire stewardship practices (Lake & Christianson, 2020). Exclusion of wildfire from the landscapes like the Okanagan is no longer feasible, therefore western management practices must shift to promote the reintegration of wildfire on the landscape. This transition will likely require the implementation of prescribed burns and mechanical thinning on a larger scale (Prichard et al., 2021). To increase the efficacy of these treatments, a comprehensive understanding of the values at risk on the landscape is required (Prichard et al., 2021). Our findings contribute to a more nuanced understanding of wildfire on the Okanagan landscape and how it interacts with various social and ecological values, and highlight the importance of inclusive and adaptive wildfire management within the region. 52 Chapter 3: Analyzing the Social Ecological Impacts of Multiple Wildfire Mitigation Scenarios 3.1 Introduction As large, high-severity wildfires increase in frequency in many fire-adapted landscapes, the need for effective wildfire management has become more pronounced (Dunn et al., 2020; Hoffman et al., 2022b; Moritz et al., 2014; Parisien et al., 2023; Prichard et al., 2021).The preceding century of wildfire management in North America has focused almost exclusively on excluding wildfire from the landscape with the intended benefit of reducing the harmful effects of wildfires on human values (Calkin et al., 2014; Parisien et al., 2023; Pyne, 2008). The unintended consequences of fire exclusion (e.g., increased fuel build-up and altered wildfire regimes) combined with land-use and climatic changes have highlighted the need for more proactive wildfire management aimed at building resiliency to wildfires (Calkin et al., 2014; Coogan et al., 2019; Hessburg et al., 2021). Rebuilding a landscape’s resiliency to wildfire is mired with challenges, particularly considering the disparate stakeholders involved and uncertainty around future ecological and climatic issues. The current structure of forests in many wildfire-prone landscapes are conducive to large, high severity wildfires, particularly under changing climatic conditions (Chavardès & Daniels, 2016; Coop et al., 2020; Hagmann et al., 2021; Hessburg et al., 2021; Parisien et al., 2020). While not all stand-replacing fire is detrimental (Turner, 2010), the benefits of this type of fire are often dependent on it occurring within the historical fire regime of the ecosystem (Hagmann et al., 2021). When the frequency of stand replacing fires exceeds the historical fire regime of an landscape, it can trigger shifts in vegetation and adversely impact ecosystem services and human values (Hagmann et al., 2021; Stevens-Rumann & Morgan, 2019). 53 Conversely, wildfire that aligns with a landscape’s historic wildfire fire regime (e.g., higher frequency low to mid-severity fires) can provide numerous ecological benefits and build resiliency on the landscape. Currently, wildfire management strategies are shifting to promote the occurrence of these ecologically beneficial ‘good fires’ on the landscape (Dunn et al., 2020; Hessburg et al., 2021; Moritz et al., 2014). To effectively promote 'good fires' on the landscape and re-establish wildfire as a value within the social-ecological system (SES), a shift from reactionary (i.e., suppression) to proactive (i.e., mitigation) wildfire management is required. Wildfire mitigation (e.g., prescribed fire and mechanical thinning) is generally aimed are reducing hazardous fuel within a forest so that wildfire occurrences are more easily managed (Stephens et al., 2020). While these treatments generally do reduce wildfire risk within treated areas, the current scale of wildfire mitigation application does not adequately address the risk of wildfires on many landscapes in North America (Prichard et al., 2021). To effectively scale up wildfire mitigation, researchers propose targeting mitigation to areas on the landscape with multiple landscape values at risk (Ager et al., 2013, 2016; Heirs et al., 2003; Kolden et al., 2016). Spatial optimization can not only increase efficacy of treatments, but it can also increase the cost efficacy of wildfire mitigation, as the cost and efforts associated with wildfire mitigation are only offset when multiple values benefit (Hunter & Taylor, 2022). This more holistic approach diverges from the current prioritization of human assets with applications almost exclusively in the wildland urban interface (WUI). To spatially optimize wildfire mitigation, the complex feedbacks and interactions between the numerous social and ecological components of wildfire-prone landscapes and the potential impacts of the landscape alterations must be considered. The spatiotemporal variability of wildfires can present challenges for wildfire mitigation planning, as the effectiveness of wildfire mitigation is contingent on a wildfire encountering the 54 mitigated area (Parisien et al., 2019; Prichard et al., 2021). As a result, quantifying the spatial patterns and likelihood of wildfires can help inform wildfire management and mitigation planning. Wildfire behaviour models, such as Burn-P3, produce burn probability (BP) surfaces that can support operation management decisions, long-term planning, and research (Parisien et al., 2019). Additional insights on the potential impacts of wildfire to landscape values - such as habitat, hydrological, and aesthetic alterations - can be gained from analyzing the specialized outputs of Burn-P3 (i.e., fire intensity, surface fuel consumption, canopy fuel consumption, and canopy fuel consumed), though few studies have attempted to quantify these impacts (Parisien et al., 2019). In the new wildfire reality, it has become increasingly important to develop tools that enhance our understanding of how wildfire risk interacts spatially with social and ecological values on the landscapes. Such tools allow for the exploration of the social-ecological impacts of wildfires and wildfire mitigation, and can be used to guide the spatial optimization of wildfire management. This research aims to understand the spatial interactions between wildfire, wildfire mitigation, and social and ecological values in the Thompson-Okanagan region of BC. We examine the effects of various scales of wildfire mitigation on wildfire behaviour and landscape values. We utilized Burn-P3 to estimate BP and quantify fuel consumption, and subsequently developed models that use Burn-P3's outputs for quantifying wildfire severity, as well as its impacts to social and ecological values on the landscape. The results display the responses of BP, wildfire severity, and social and ecological values to wildfire mitigation at various scales. The outcomes of this research will contribute to developing landscape-level adaptive management strategies that consider multiple landscape values. 55 3.2 Methodology 3.2.1 Study Area The study was conducted in a 30,051 km2 area of the Thompson Okanagan region of British Columbia (Figure 3.1). This region is characterized by deep valleys resulting from erosion by large rivers (Meidinger & Pojar, 1991). These valleys dissect the plateaus and highlands that dominate the physiography of this region (Meidinger & Pojar, 1991). Situated on the eastern side of the Coast and Cascade Mountains, the region is characterized by a significant rain shadow effect that greatly reduces precipitation (Pike et al., 2010). The region is semi-arid, with mean annual precipitation between 250 and 360 mm per year. Precipitation is seasonally variable, with most precipitation in the early summer and little accumulation during the spring and mid-late summer (Pike et al., 2010). The climate of the area is the warmest and driest in BC, with notably hot and dry summers, which makes it particularly susceptible to wildfires (Xu & van Kooten, 2013). The region's biogeoclimatic zones include Engelmann Spruce – Subalpine Fir, Montane Spruce, Bunchgrass, and Interior Douglas Fir (Pike et al., 2010). The vegetation within the region is dominated by coniferous forest, which is predominately ponderosa pine (Pinus ponderosa) and interior Douglas fir (Pseudotsuga meziesii), with grasses and shrublands present at lower elevations (Ireland & Petropoulos, 2015). The combination of topography, fuel, and climate elevates the risk of wildfire in the Okanagan (Ireland & Petropoulos, 2015). Climate change is expected to exacerbate wildfire risk as mean annual temperatures increase and summer precipitation decreases (Regional District of North Okanagan et al., 2020). Historically, the dry mixed conifer forests of interior British Columbia experienced a mixed-severity fire regime (Brookes et al., 2021). This pattern typically involved frequent low-severity surface fires with recurrence intervals of less than 50 years, that were often the result of Indigenous fire stewardship 56 (Brookes et al., 2021). High-severity fires and stand replacing fires occurred less frequently, with recurrence intervals around 250 years (Brookes et al., 2021). The wildfire regime has been shifting within the study region to more frequent high severity wildfires (Brookes et al., 2021; Parisien et al., 2023). In the past few years, wildfire activity increases have been observed in the Okanagan region and across British Columbia (Parisien et al., 2023) with particularly devastating wildfire seasons in 2017, 2018, 2021, and 2023. The most recent wildfire season (2023) had several notable wildfires, including the McDougall Creek wildfire, which led to the evacuation of over 10,700 residents (Central Okanagan Emergency Operations, 2023), the destruction or damage of around 190 homes, and a burned area of approximately 13,970 hectares of land as of October 15, 2023. Figure 3.1. Study area in the southern interior of British Columbia. The main map panel shows a hillshade with major cities and lakes. The insert shows the position of the study area in British Columbia, Canada. 57 3.2.2 Data Burn-P3 Model Inputs Burn-P3 requires data on fuels, topography, ignition sources and distributions, weather conditions, and wildfire zones to simulate wildfire behavior (Parisien et al., 2005). We used inputs previously developed for a 2016 study conducted in the same study area (Wang et al. 2016), provided by Natural Resources Canada (NRCAN). We updated three inputs from this study: the forest fire behavior predictor (FBP) fuels layer, the fuel lookup dictionary, and the daily burning hours. We updated the FBP layer to Natural Resources Canada's (NRCAN) 2019 FBP layer, manually modified the fuel lookup dictionary to incorporate new fuels present in the 2019 FBP update, and revised the daily burning hours by season calculations using methods outlined by Brett Moore from NRCAN, available on GitHub (Moore, 2022). Table 3.1 describes the input data used in Burn-P3 for this study. 58 Table 3.1. Burn-P3 Input Data Variable Details Data Type Data source Reference Mapped Inputs Digital Elevation Model Digital elevation model of study area’s topography 100 m resolution raster BC Ministry of Forests, Lands, and Natural Resource Operations Wang et al. 2016 Parisien et al. 2005 Fire Zones Three geographic zones characterized by unique fire regimes and fire weather conditions: (1) subalpine forest (2) grassland and dry forest (3) moist temperate forest. 100 m resolution raster NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Weather Zones Three zones of distinct fire weather 100 m resolution raster NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Canadian Forest Fire Behaviour Prediction Layer Canadian Forest Fire Behaviour Prediction System fuel types with burn scars. Resampled to 100 m resolution. 250 m resolution raster resampled to 100 m resolution NRCAN 2019; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Ignitions Module Ignitions Locations Probability surface of fire ignitions categorized by season (spring and summer) and cause (anthropogenic and lightning) 100 m resolution raster NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Escaped Fire Rates Proportion of fires ignitions by season, cause, and fire zone Frequency distribution NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Ignitions Distribution of the number of escaped fires Frequency distribution NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Burning Conditions Module Daily Fire Weather List Daily fire weather and Fire Weather Index System ratings are categorized by season and fire zone. List of conditions NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Spread Days Distribution of duration of burning per weather zone ranging from 1 to 7 day Frequency distribution NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Wind Direction Grids of the approximate wind speed and direction as influenced by topography 100 m resolution raster NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 Fire Growth Model Daily Burning Hours Calculated proportion of the number of hours (4 or 5 hours) per day of fire growth by season. Frequency distribution Generated Wang et al. 2016 Parisien et al. 2005 Fire seasons Time periods for fire weather, grass curing, and grass green-up List of conditions NRCAN 2020; Wang et al. 2016 Wang et al. 2016 Parisien et al. 2005 59 Wildfire Severity Data Fire severity, modeled according to the methods outlined in Section 3.2.3.2, was represented as 100 m gridded representations (rasters). The wildfire severity model inputs consisted of 100 m resolution rasters of maximum and median fuel consumption (in kg/m²) for both surface and canopy fuels, as generated by Burn-P3. Intermediate fuel consumption data was not available for use in the model. SES values Based on the values identified in the structured surveys (Chapter 2), we developed 100 m resolution rasters of social and ecological values on the landscape. These rasters were generated from various publicly available vector datasets, including urban areas, conservation ranking, critical wildlife habitat and ecosystems (Table 3.2). The generation of these rasters involved assigning values based on the presences or count of features within each 100 m cell. For example, urban areas were classified as binary (present = 1, absent = 0), while other values such as conservation rank were assigned value (0.5, 0.75, or 1) based on an attribute that described the conservation rank of a polygon (Table 3.2). Table 3.2 summarizes the data used in the SES model. 60 Table 3.2. Summary of social and ecological data used in impacts model Variable Details Data source Human Data Community Watersheds Boundary of drainages that have water diverted for human consumption. Raster generated based on absence (0) or presence (1). GeoBC, 2022 Parks and Greenspaces Local and regional greenspaces managed by municipalities or regional districts. Raster generated based on absence (0) or presence (1). GeoBC, 2022 Heritage Conservation Areas Areas designated for heritage conservation purposes by local governments. Raster generated based on absence (0) or presence (1). GeoBC, 2022 Recreation Areas Inventory of recreational features on the landscape rating for significance. Raster generated using recreation significance rating attribute where high = 1, medium =0.5) GeoBC, 2022 Aesthetics Visual landscape inventory that documents the sensitivity of an area’s aesthetics to visual alteration. Raster generated using visual sensitivity class attribute where very high sensitivity = 1, high sensitivity =0.75, and moderate sensitivity =0.5). Low sensitivity areas were not included. GeoBC, 2022 Indigenous Reservations Boundaries of reservations lands. Raster generated based on absence (0) or presence (1). GeoBC, 2022 Urban Boundaries of urban or built-up areas. Raster generated based on absence (0) or presence (1). NRCAN, 2019 Ecological Data Species and Ecosystems at Risk Known locations of species and ecological systems at risk as defied by BC’s Conservation Data Centre. Raster generated based on conservation status where blue = 0.5 and red = 1. GeoBC, 2022 Biodiversity Corridors Biodiversity corridors within the Kamloops Forest District that shows dispersal routes of wildlife. Raster generated based on absence (0) or presence (1). GeoBC, 2022 Conservation Polygons ranking conservation status of ecosystems within the Okanagan. Raster generated used the conservation rating attribute where very high =1, high = 0.75, and moderate = 0.5. The ranking is based on Terrestrial Ecosystem Mapping (TEM), Sensitive Ecosystems Inventory (SEI), and Vegetation Resource Inventory (VRI), and other biophysical criteria (e.g., elevation and aspect). GeoBC, 2022 Critical Habitat Areas of critical habitat for species at risk. Raster classified based on number of critical habitats present within a cell, where 1 = 7 to 10 habitats, 0.75 = 4 to 7 habitats, and 0.5 = 1 to 3 habitats. GeoBC, 2022 Mule Deer Winter Range Modelled winter ranges for mule deer. Raster classified on forage capability attribute where high (H) =1 and moderate (M) =0.5. GeoBC, 2022 Old Growth Management Areas Boundaries of Legal Old Growth Management Areas. Raster generated based on absence (0) or presence (1). GeoBC, 2022 Sensitive Fish Watersheds Watersheds where development activities may negatively impact fish. Raster generated based on absence (0) or presence (1). GeoBC, 2022 3.2.3 Analysis Burn-P3 We used Burn-P3 (Parisien et al., 2005) to evaluate the effect of different scales of mitigation (i.e., 10 to 100%) on burn probability (BP) and to assess the potential impacts on values 61 identified in the SES. Burn-P3 is a spatial wildfire simulation model that quantifies burn probability for landscape raster cells using fuel, weather, topography, and wildfire behavior inputs (Table 3-1) in three modules: two probabilistic modules (ignitions and burning conditions) and one deterministic module (fire growth; Table 3-1). Burn-P3 provides an estimate of BP based on Monte Carlo simulations where a substantial number (e.g., 20,000) of wildfire growth simulations are repeated and BP is then calculated for each cell on the landscape based on the number of times that cell burned during simulations (Parisien et al., 2005). The resulting BP layers give an indication of wildfire risk on a cell-by-cell basis. Additional gridded outputs can be generated that give information on wildfire behaviour, including vegetation consumption and fire intensity. We used the baseline input variables from Wang et al.’s 2016 study that encompassed the same study area during modelling, with the exception of FBP and burning hours per season (Section 3.2.2.1, Table 3-1). To avoid edge effects, we implemented a 15 km buffer (Wang et al., 2016; Parisian et al., 2011) that allowed fires to initiate outside the study area and subsequently spread into the study area. Further details about the input variables and parameters of the Burn-P3 model can be found in Section 3.2.2.1. To evaluate the potential effects of fire mitigation on BP, we tested the influence of mitigation treatments in 10% intervals, ranging from 10% to 100%, at two scales: the landscape (the entire study area) and the WUI. We used WUI maps for the region developed by the BC Wildfire Service, which defined the WUI as a 2 km buffer around infrastructure with combustible fuels. Because Burn-P3 cannot directly accommodate mitigation treatments (e.g., forest thinning or prescribed burning) in fuel classes, we changed the fuel class in the Burn-P3 fuel layer to aspen (Populus tremuloides Michx.) as a proxy for mitigation while holding all other variables constant (Stockdale et al., 2019c). Aspen was chosen over other FBP fuel types because it supports lower 62 rates of wildfire spread (Fechner & Barrows, 1976), particularly in the summer when foliage and understory vegetation is green (Hély et al., 2000; Nesbit et al., 2023). Additionally, aspen are less likely to support crown fires as they have less crown fuel available and higher moisture content in their leaves than conifer needles (Nesbit et al., 2023; Van Wagner, 1967). This reflects the goals of most mitigation treatments to reduce fire spread and severity (Prichard et al., 2017, 2021). For each mitigation scenario (e.g., 10% treatment scale) for the landscape and the WUI, we ran Burn-P3 for 20,000 iterations, which reduced the pixel-wise variation and most closely resembled the distribution of fires that had occurred previously in the region (Wang et al., 2016), resulting in twenty BP surfaces. For each scenario, we applied the proxy fuel treatment using buffers of three sizes: 2.04 ha, 16.62 ha, and 52.18 ha, which represented the 10th, 50th, and 90th percentiles of mitigation treatments applied in the region from 2013 to 2019. These proxy treatments were applied randomly throughout the landscape, with areas of higher BP being prioritized for smaller scales of treatment (e.g., 10%). Non-fuel areas, such as urban, rock outcrop, and water, were excluded from mitigation. The total area treated per scenario is provided in Table B.1 in Appendix B. Wildfire Severity In addition to BP, Burn-P3 also provides information on potential fuel consumption for both surface and canopy fuels. We used the composite burn index to develop a wildfire severity model using vegetative outputs from Burn-P3 (Section 3.2.2.2). The composite burn index (Table B.2 in Appendix B) is widely used to classify wildfire severity in the field from 0 (low severity) to 3 (high severity; Han et al., 2021). The CBI provided a framework to translate the vegetative outputs from Burn-P3 into a measure of wildfire severity. Due to the data limitations 63 of Burn-P3, we only utilized metrics from the CBI that quantified vegetation consumption or loss as a percentage. We converted the fuel consumption outputs of Burn-P3, provided as kg/m3, into a percentage of fuel consumed. This was achieved by dividing the scenario’s median fuel consumption layer by the maximum fuel consumption of the baseline layer: 𝐂𝐚𝐧𝐨𝐩𝐲 𝐅𝐮𝐞𝐥 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐢𝐨 = Scenario Median Canopy Fuel Consumption (kgm3)Maximum Baseline Canopy FuelConsumption (kgm3)x 100 Eq. 2 𝐒𝐮𝐫𝐟𝐚𝐜𝐞 𝐅𝐮𝐞𝐥 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐢𝐨 = Scenario Median Surface Fuel Consumption (kgm3)Maximum Baseline Surface Fuel Consumption (kgm3)x 100 Eq. 3 The CBI calculates total burn severity by averaging the wildfire severity at five forest layers (or strata): substrates, herbs, low shrubs and trees (HST) less than 3 feet, intermediate trees, and big trees (Appendix B). For each of these layers there are several fuel components described such as duff, and medium fuels within the substrate strata. As our fuel consumption layers did not provide this level of information, we averaged the applicable indices to provide a singular surface fuel consumption index and canopy fuel consumption index: 𝐂𝐁𝐈 𝐒𝐮𝐫𝐟𝐚𝐜𝐞 𝐅𝐮𝐞𝐥 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 =Substrates + Herbs, Shrubs, and Trees < 3 ft.2Eq. 4 i. Substrates =Light Fuel Consumed+Medium Fuel Consumed+Heavy Fuel Consumed 3 ii. Herbs, low shrubs, trees (HST) < 3ft = (100 − Frequncy % Living) 𝐂𝐁𝐈 𝐂𝐚𝐧𝐨𝐩𝐲 𝐅𝐮𝐞𝐥 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 = Intermediate Canopy Mortality + Large Tree Canopy Mortality2 Eq. 5 The averaged CBI consumption values for surface and canopy fuel were used to create a series of thresholds. These thresholds (Table 3.3) enabled us to translate the Burn-P3 fuel consumption ratios into a continuous burn severity scale. 64 Table 3.3. Composite burn index (CBI) representing classified wildfire severity and corresponding fuel consumption ratio The sliding scale equation (6) that was utilized for this conversion is as follows: BSS = CBInew min + (x − xmin) x (CBInew max − CBInew minxmax − xmin) Eq. 6 Where, BSS is the burn severity scale for a given pixel, x is the Burn-P3 fuel consumption ratio for a given cell, x min and x max are the minimum and maximum fuel consumption thresholds, and CBInew min and CBInew max are the corresponding CBI thresholds as outlined in Table 3.3. This equation allowed us to reclassify the surface and canopy fuel consumption data into layers representing wildfire severity. Total wildfire severity for each scenario was produced by averaging the values of surface and canopy wildfire severity. 𝐂𝐁𝐈 𝐓𝐨𝐭𝐚𝐥 𝐅𝐮𝐞𝐥 𝐂𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 = Surface Fuel Consumption + Canopy Fuel Consumptio Ratio2 Eq. 7 SES Impacts Model To model the social and ecological impacts of wildfire, we utilized a decision tree (Figure 3.2) that assigned a level of potential impact to all of the values at risk within a single cell based on the predicted wildfire characteristics (i.e., burn probability and wildfire severity). These characteristics were used to describe wildfire risk and degree of impact, respectively. Although wildfire severity is generally used to characterize impacts to ecological values (McWethy et al., 2019), for this study we also used wildfire severity to approximate the degree of impact to human Fuel strata CBI Severity Fuel Consumption Ratio Surface 0 0 1 0.183 2 0.525 2.5 0.800 3 0.830 Canopy 0 0 1 0.125 2 0.550 2.5 0.750 3 1 65 values. By integrating wildfire risk and wildfire severity into one composite layer we captured both the likelihood and potential severity of wildfire, which provided a more holistic prediction of the potential impact of wildfire on each cell. Both wildfire severity and BP were used to assign a level of potential impact. For example, low-severity fires can be ecologically beneficial, and indirectly socially beneficial as they reduce the risk of future high severity fires that can adversely impact human values (Wu et al., 2023). Therefore, we classified low severity fires as low impact regardless of probability. Conversely, we classified high-severity fires that had a high to moderate BP as high impact because of the potential adverse impacts on social values and, if they occur outside the historical fire regime, ecological values (Hagmann et al., 2021; Stevens-Rumann & Morgan, 2019). High severity wildfires that had low BP were classified as moderate impact to acknowledge their potential to adversely affect social and ecological values in the event that they occur. The integrated wildfire behavior layer that resulted from the decision tree showed potential wildfire impact from zero (low potential impact) to three (high potential impact). Note, the decision tree did not assess impacts to values individually as that fell outside the scope of this study. The integrated wildfire behaviour layer was used in combination with an SES layer to assess the potential impact of wildfire across the SES. To generate the SES layer, presence-based values, such as urban areas, were assigned a binary value (1 for present, 0 for absent), while more qualitative values such as aesthetics were assigned a scaled score (0.5, 0.75, or 1) based on attributes that described their sensitivity or significance (Section 3.2.2.3). The individually rescaled value layers were then summed to create a layer that reflected the spatial configuration of social and ecological values that were considered at risk from wildfire. To accommodate for the varying spatial extents of the datasets, we normalized values on the landscape by dividing the total 66 number of values present on a cell by the total number of values possible on that cell and then rescaled the resulting layer from zero to one (Equation 6). The resulting layer showed the potential occurrence of social and ecological values from zero to one on a continuous scale. The SES layer was then multiplied by the integrated wildfire behaviour layer to create a layer that displayed the potential impact to social and ecological values across the landscape. This process was applied individually to all wildfire mitigation scenarios so that the response to different scales of mitigation could be assessed. Low Severity?Moderate Severity?NoHigh Severity?NoLow Burn Probability?YesLow ImpactLow Burn Probability?YesModerate ImpactYes No High ImpactModerate Burn Probability?High Burn Probability?NoYesYesNoYesYes Figure 3.2. SES Impacts model decision tree 3.2.4 Statistical Analysis BP data often displays a high degree of spatial autocorrelation, therefore, we limited our statistical analysis between baseline conditions and the mitigation scenarios for the three models to a subset of the data (Parisien et al., 2011; Wang et al., 2016). To understand the degree of autocorrelation in the baseline BP layer, we ran a Moran’s I test which indicated that the baseline 67 burn-probability data had high positive spatial autocorrelation (p-value < 2.2x10-16, I = 0.92). We fit a variogram to a random sample of the baseline BP data (n=500,000) using a spherical model (residual square root = 0.001). We used the range (range = 21,236 m, nugget = 0.016, partial sill = 0.018) of the variogram to determine the maximum number of points we could randomly sample. The range indicates the distance beyond which the data points do not influence each other, i.e., they are no longer autocorrelated. Within the extent of our landscape, we could fit a maximum of 66 points while maintaining a minimum distance of 21.5 km between points. We randomly sampled 66 points 100 times on the landscape to examine changes in BP, wildfire severity, and SES impacts for each mitigation scenario at both the WUI and landscape level as the wildfire severity and SES impacts data showed similar autocorrelation as the BP. We used paired t-tests to quantify whether the proportion area treated in each scenario had an influence on mean BP, wildfire severity, and SES impacts compared to the baseline conditions for both the landscape and WUI scales (Wang et al., 2016a). On the same subset of data, we performed Pearson’s correlation tests to test for differences in distributions between the treatment and baseline scenarios, where a high correlation coefficient indicated that the treatment displayed a similar distribution as baseline conditions (Wang et al., 2016a). We square-root transformed the sub-samples of BP, wildfire severity, and SES impacts data to normalize it prior to running the t-tests and Pearson’s correlation tests. To better visualize change in BP between different treatment and baseline scenarios, we used a method outlined by Parks et al. (2012) to generate treatment ratios which showed the degree of departure from baseline BP for each mitigation scenario. Treatment Ratio = {− (Bi Ti⁄ ) + 1, x < 0(Ti Bi⁄ ) − 1, x ≥ 0 Eq. 7 68 Where, Bi represents the baseline BP value of an individual pixel and Ti represents the treatment BP of that same pixel. The result is a surface that shows the magnitude of change in BP, where a treatment ratio of positive one would indicate that the treatment BP was twice the value of the baseline BP, while a treatment ratio of negative one would indicate that the treatment BP was half the value of the baseline BP (Parks et al., 2012). 3.3 Results 3.3.1 Landscape Level The baseline BP scenario predicted several areas of high BP within the northeast of the study area, with probabilities ranging from 0.015 to 0.030 (Figure 3.3). The results of the paired t-tests indicated that every level of treatment at the landscape level produced statistically significant differences in mean BP when compared to baseline BP (p-value < 0.01). The mean difference values from the paired t-tests revealed the degree to which the mitigation scenarios differed from each other and the baseline scenario (Table 3-5). The mean difference in BP between the baseline scenario and the treatments gradually increases, with the highest relative change between the 10% and 20% treatment levels. The averaged results of the Pearson’s correlation tests were also statistically significant at all treatment levels, but the mean correlation value did not show a linear change from baseline (Table 3.6). The predicted baseline wildfire severity was largely moderate (1 to 2), with patches of high wildfire severity present in the northeast region of the map (similar to BP) and along the western edge (Figure 3.4). Landscape level treatment generally resulted in a shift to lower wildfire severity (0.5 to 1), particularly in areas of high predicted baseline wildfire severity. The paired t-tests indicated that a statistically significant shift from mean baseline wildfire severity was not present until 50% landscape level treatment (p<0.01), with a mean difference from baseline wildfire 69 severity of 0.105. The results of the Pearson’s correlation test were largely non-significant, with the exception of the 10% landscape treatment (mean correlation = 0.62, p-value < 0.01). The baseline scenario showed that the areas of highest SES impact (class two and three) on the landscape were found on the eastern half of the study area, with a small area of high SES impact in northwest (Figure 3.5). The results of the paired t-test showed a statistically significant reduction in landscape level impact beginning at the 30% treatment level (p < 0.01; Table 3.4). Furthermore, at this scale of treatment, over 50% of the landscape achieved an SES impact class at or below 0.5, with approximately 80% not exceeding an impact class of one (Figure 3.6a). As treatment scale increases, the SES impacts continue to decrease with the highest shifts from baseline occurring between 30% to 50% treatment level (Figure 3.5, Figure 3.6a, Table 3.4 to 3.5) after which the degree of change, mean, and median values of the treatments begin to plateau. The Pearson correlation test indicated that the landscape treatments produced statistically significant differences in the spatial pattern of SES impacts, but the correlation coefficients remained relatively static between treatments. Table 3.4. Summary statistics (mean, median, and max) of baseline and treatment burn probabilities, fire severities, and SES impacts at a landscape scale. Non-fuels were removed from BP and wildfire severity layers. Treatment BP Fire Severity SES Impact Level Mean Median Max Mean Median Max Mean Median Max Baseline 0.00273 0.00230 0.02565 1.366 1.500 3.000 0.701 0.593 2.921 10% 0.00222 0.00210 0.01760 1.348 1.421 3.000 0.652 0.593 2.921 20% 0.00182 0.00180 0.00685 1.290 1.249 3.000 0.558 0.474 2.844 30% 0.00173 0.00165 0.01020 1.244 1.212 3.000 0.500 0.444 2.844 40% 0.00147 0.00140 0.00670 1.193 1.161 3.000 0.436 0.444 2.844 50% 0.00146 0.00135 0.00815 1.146 1.108 3.000 0.436 0.415 2.921 60% 0.00130 0.00120 0.00795 1.094 1.024 3.000 0.403 0.395 2.794 70% 0.00115 0.00105 0.00565 1.042 0.953 3.000 0.378 0.395 2.844 80% 0.00123 0.00110 0.00745 0.994 0.911 3.000 0.396 0.395 2.794 90% 0.00112 0.00100 0.00600 0.961 0.887 3.000 0.379 0.395 2.794 100% 0.00089 0.00080 0.00495 0.883 0.758 1.500 0.358 0.395 2.667 70 Table 3.5. Paired t-test results (α=0.01) comparing burn probabilities, fire severities, and SES impacts from landscape scale (LS) scale treatments with baseline BP. Mean difference (MD) indicates the mean degree of change from the baseline scenario. The mean test statistic (TS) is a measure of difference between baseline and the treatment relative to the variability in their differences. Table 3.6. Pearson’s correlations results (α=0.01) comparing the spatial patterns of burn probabilities, fire severities, and SES impacts from landscape scale treatments with baseline BP. A correlation coefficient (r) value close to one indicates little to no variations in spatial patterns from baseline. Treatment BP Fire Severity SES Impact Level P-Value MD TS P-Value MD TS P-Value MD TS 10% <0.01 0.004 3.72 0.466 0.006 0.256 0.361 0.025 1.105 20% <0.01 0.008 4.54 0.214 0.035 1.592 0.030 0.080 3.059 30% <0.01 0.009 6.38 0.05 0.057 2.605 <0.01 0.120 4.599 40% <0.01 0.012 7.18 0.01 0.083 3.463 <0.01 0.158 5.802 50% <0.01 0.013 9.09 <0.01 0.105 4.424 <0.01 0.162 6.368 60% <0.01 0.015 9.47 <0.01 0.133 5.435 <0.01 0.183 7.045 70% <0.01 0.017 9.8 <0.01 0.156 6.313 <0.01 0.198 7.526 80% <0.01 0.016 11.09 <0.01 0.178 7.162 <0.01 0.186 7.307 90% <0.01 0.017 11.63 <0.01 0.196 8.006 <0.01 0.197 7.575 100% <0.01 0.021 12.75 <0.01 0.235 9.485 <0.01 0.211 7.978 Treatment BP Fire Severity SES Impact Level P-Value r P-Value r P-Value r 10% <0.01 0.868 <0.01 0.623 <0.01 0.894 20% <0.01 0.555 0.076 0.406 <0.01 0.852 30% <0.01 0.747 0.034 0.439 <0.01 0.853 40% <0.01 0.58 0.114 0.331 <0.01 0.844 50% <0.01 0.764 0.112 0.344 <0.01 0.869 60% <0.01 0.678 0.116 0.309 <0.01 0.871 70% <0.01 0.575 0.176 0.265 <0.01 0.875 80% <0.01 0.729 0.163 0.277 <0.01 0.880 90% <0.01 0.692 0.175 0.242 <0.01 0.879 100% <0.01 0.597 0.230 0.206 <0.01 0.882 71 Figure 3.3. Map of baseline burn probability (BP) and BP treatment ratios for landscape treatments. Positive treatment ratios (orange to red) indicate an increase in treatment BP relative to baseline and negative treatment ratios (green to blue) indicate a decrease in BP relative to baseline. 72 Figure 3.4. Map of baseline wildfire severity and wildfire severity for the landscape level treatments. Where orange to red represents high wildfire severity (2 to 3), yellows represent moderate wildfire severity (1 to 2) and blue to green represents low wildfire severity (0 to 1). 73 Figure 3.5. Map of baseline SES impacts and SES impacts for the landscape level treatments. Where red to red represents high impact (2 to 3), yellow represents moderate impact (1 to 2), green represents low impact (0.01 to 1), and blue represents no impact (<0.01). .74 Figure 3.6. Cumulative distribution plot of landscape scale and WUI scale SES impacts for baseline SES impacts and 10 landscape-level treaments. Cumulative frequency represents the proportion of data that falls at or below the given value. a) b) 75 3.3.2 WUI Level Treatments Treatments at the WUI-level, while affecting localized changes, did not lead to significant shifts in mean BP at a landscape scale. The results of the paired t-test indicated that WUI treatments did not significantly reduce mean BP, mean wildfire severity, or mean SES impacts when compared to the baseline scenario across all scales of treatment of (Table 3.7 & Table 3.8). Additionally, pockets of increased wildfire risk were observed on the BP maps (Figure 3.7). With regards to fire severity, the WUI treatments did not show statistically significant alteration of the landscape-level fire severity in any scenario, but the Pearson’s correlation co-efficient did consistently reduce in magnitude as treatment levels increased (p-value < 0.01 for all treatments; Table 3.9). This shows that while mean wildfire severity may not have been significantly altered, the spatial patterns have changed from baseline. Despite the intent to reduce SES impact within targeted treatments within WUI, this scale of treatment did not translate into notable landscape-wide reductions in wildfire severity or impacts to the SES (Tables 3.7 to 3.8; Figure 3.6b). Table 3.7. Summary statistics (mean, median, and max) of baseline and treatment burn probabilities, fire severities, and SES impacts at a WUI scale. Non-fuels were removed from BP and wildfire severity layers. Treatment BP Fire Severity SES Impact Level Mean Median Max Mean Median Max Mean Median Max Baseline 0.00273 0.00230 0.02565 1.366 1.500 1.500 0.701 0.593 2.921 10% 0.00268 0.00225 0.02490 1.398 1.500 1.500 0.733 0.593 2.921 20% 0.00270 0.00225 0.02620 1.418 1.500 1.500 0.737 0.593 2.921 30% 0.00271 0.00220 0.02735 1.390 1.500 1.500 0.714 0.593 2.921 40% 0.00258 0.00210 0.02620 1.377 1.480 1.480 0.701 0.593 2.921 50% 0.00253 0.00210 0.02495 1.369 1.440 1.440 0.692 0.593 2.921 60% 0.00248 0.00205 0.02420 1.363 1.406 1.406 0.683 0.593 2.921 70% 0.00246 0.00200 0.02470 1.352 1.354 1.354 0.673 0.593 2.921 80% 0.00245 0.00200 0.02440 1.350 1.317 1.317 0.675 0.593 2.921 90% 0.00243 0.00200 0.02280 1.346 1.313 1.313 0.670 0.593 2.921 100% 0.00243 0.00200 0.02530 1.333 1.256 1.256 0.672 0.593 2.921 76 Table 3.8. Paired t-test results (α=0.01) comparing burn probabilities, fire severities, and SES impacts from WUI scale treatments with baseline BP. Mean difference (MD) indicates the mean degree of change from the baseline scenario, where a negative value indicates an increase compared to baseline and a positive value a decrease from baseline. The mean test statistic (TS) is a measure of difference between baseline and the treatment relative to the variability in their differences. Table 3.9. Pearson’s correlations results (α=0.01) comparing the spatial patterns of burn probabilities, fire severities, and SES impacts from landscape scale treatments with baseline BP. A correlation coefficient (r) value close to one indicates little to no variations in spatial patterns from baseline. Treatment BP Fire Severity SES Impact Level P-Value MD TS P-Value MD TS P-Value MD TS 10% 0.523 0.0002 0.373 0.256 -0.014 -1.526 0.355 -0.014 -1.121 20% 0.499 0.0001 0.135 0.148 -0.022 -1.995 0.27 -0.017 -1.304 30% 0.571 0.0002 0.146 0.426 -0.009 -0.85 0.451 -0.009 -0.612 40% 0.133 0.0015 1.991 0.478 -0.004 -0.368 0.511 -0.001 -0.084 50% 0.07 0.0019 2.444 0.479 0.001 -0.003 0.506 0.002 0.178 60% 0.032 0.0023 2.804 0.508 0.003 0.155 0.489 0.006 0.367 70% 0.047 0.0026 2.818 0.433 0.008 0.491 0.39 0.009 0.581 80% 0.02 0.0028 3.152 0.397 0.011 0.654 0.372 0.01 0.658 90% 0.013 0.0032 3.354 0.418 0.011 0.692 0.418 0.011 0.725 100% 0.014 0.0031 3.178 0.277 0.018 1.185 0.447 0.011 0.714 Treatment BP Fire Severity SES Impact Level P-Value r P-Value r P-Value r 10% <0.01 0.948 <0.01 0.835 <0.01 0.952 20% <0.01 0.945 <0.01 0.801 <0.01 0.952 30% <0.01 0.895 <0.01 0.760 <0.01 0.939 40% <0.01 0.935 <0.01 0.776 <0.01 0.943 50% <0.01 0.920 <0.01 0.746 <0.01 0.937 60% <0.01 0.913 <0.01 0.722 <0.01 0.934 70% <0.01 0.894 <0.01 0.714 <0.01 0.930 80% <0.01 0.900 <0.01 0.692 <0.01 0.932 90% <0.01 0.889 <0.01 0.684 <0.01 0.931 100% <0.01 0.880 <0.01 0.692 <0.01 0.928 77 Figure 3.7. Map of baseline burn probability (BP) and BP treatment ratios for wildland urban interface (WUI) treatments. Positive treatment ratios (orange to red) indicate an increase in treatment BP relative to baseline and negative treatment ratios (green to blue) indicate a decrease in BP relative to baseline. 78 3.4 Discussion We investigated how different scales of wildfire mitigation impacted two facets of wildfire potential and effects: BP and wildfire severity, as well as potential cumulative impacts on social and ecological values within the study area. Our goal was to provide a framework for wildfire managers to further understand the impact of different scales of wildfire mitigation on wildfire behaviour and social and ecological values on the landscape. To reduce the frequency of large wildfires (i.e., ≥100,000 ha) from occurring, wildfire experts suggested that 50% or more of the landscape would require some form of mitigation (Bourbonnais et al., 2021). However, few studies have attempted to quantify the scale of mitigation required to produce significant shifts in wildfire potential and behaviour. As we continue to experience record-breaking wildfire seasons, understanding the scale of treatments required and how wildfire behaviour responds to mitigation can help inform wildfire management planning and assess the effectiveness of mitigation efforts (Prichard et al., 2021). We examined wildfire mitigation at two different levels: the landscape and the WUI. While there is increasing acknowledgment in both the scientific and non-scientific communities (e.g., provincial and municipal governments) that wildfire treatments need to be developed on a landscape-level (Forest Practices Board, 2023; Hessburg et al., 2021; Prichard et al., 2021), the level of effort required for mitigation combined with the lack of funding in Canada has resulted in most wildfire mitigation being conducted within the WUI (Coogan et al., 2021; Johnston & Flannigan, 2017; Tymstra et al., 2020). We found that landscape-level treatments had a more pronounced effect on reducing BP, wildfire severity, and social and ecological impacts than the WUI-level treatments. Applied at the appropriate scale (i.e., 50%), landscape level treatments 79 results in significant decreases in BP, wildfire severity, and impacts to social and ecological values (SES impacts) compared to baseline conditions. This percentage of treatment represents the threshold where BP, wildfire severity, and SES impacts saw a statistically significant change from baseline. The focus of this discussion will largely be on the landscape-level treatment scenarios. The baseline BP exhibited significant positive clustering which suggests that areas of high BP tend to occur in similar areas (Wang et al., 2016). Clustering of high-BP areas can challenge fire suppression efforts as it increases the risk of large uncontrolled fires. Identifying the spatial patterns of high BP clusters can help address management challenges presented by the spatiotemporal variability of wildfire by increasing the probability that wildfire mitigation is applied in areas that will experience wildfire during the lifespan of the treatment (Parisien et al., 2019; Prichard et al., 2021). Our findings support this, as mitigation was initially applied to the highest BP (75th percentile) and produced a statistically significant decrease in BP from baseline at the two lowest treatment levels (10 and 20%); these changes were observable in the clusters of high BP, i.e., the northeast region of the study area. The Pearson’s correlation test found that both the 10% and 20% treatment levels had significantly different distributions of BP, with the 20% treatment level showing the highest departure from baseline of all the treatments. Mitigation at these levels targeted the 75th percentile of BP, thus the 20% treatment level represents a scenario where mitigation targets landscape patches with the highest probability of burning. This result aligns with previous studies that point towards higher efficacy of mitigation when treatments priority areas based on potential threat of wildfire (Krofcheck et al., 2018; Ott et al., 2023). BP continues to decrease compared to baseline as treatments levels increase until 60% landscape level treatment. Between 70% and 90% treatment levels there are still significant decreases in BP from baseline, but the magnitude (indicated by the mean difference) is similar 80 between these treatment levels. This plateau indicates a potential threshold for the scale of mitigation applied in the study area, i.e., after 60% landscape level treatment the benefits of additional mitigation (reduction in BP) may not be substantial enough to offset the cost and effort of that mitigation. Despite the significant reductions in BP detected at the 10% and 20% treatment levels, the results of the paired t-test indicate that wildfire severity only showed a significant decrease from baseline at treatment levels of 50% and higher. The lack of statistically detectable differences in the wildfire severity treatment may result from several factors, including the model design, or the ecology of the study area that historically promotes a mixed severity fire regime (Brookes et al., 2021). We experienced several limitations when using the model outputs of Burn-P3 to estimate wildfire severity. First was the lack of outputs that quantified intermediate fuel consumption; as a result, we estimated wildfire severity from surface and canopy fuel consumption. Second, we applied mitigation by changing the fuel types to a less flammable fuel type (i.e., aspen), a method which has been previously employed in studies analyzing BP and wildfire hazard (i.e., intensity x BP; Stockdale et al., 2019c), but has yet to be validated for wildfire severity. The wildfire severity model used fuel consumption ratios to quantify potential severity, which resulted in reduced wildfire severity for most fuel types. When cells classified as grasslands in Burn-P3’s fuel layer are converted to aspen, this increases the amount of fuel available to burn on these pixels, and consequently, can result in an increase the estimated wildfire severity. To compensate for this disparity in fuel quantification, we masked all treatment wildfire severity layers with the baseline wildfire severity of grasses. This resulted in more significant fractions of the landscape remaining unchanged from the baseline scenario. Wildfire severity is driven by many factors, including weather patterns, lightning strikes, topography, fire history, previous 81 disturbance (e.g., harvesting, insect mortality), and forest structure (Klenner, 2008). Within BC, the available fuel on the landscape also has a strong influence on potential wildfire severity (Hessburg et al., 2019). While the goal of wildfire mitigation is often to promote low-severity fires that have reduced ecological impact, if the landscape was adapted to a different wildfire regime (e.g., mixed severity), then the promotion of exclusively low severity fire may not provide a sufficient level of disturbance to build resiliency to wildfire on landscapes (Hessburg et al., 2015, 2019), particularly as other drivers of wildfire severity (i.e., climate) experience unprecedented changes (Hessburg et al., 2015). The study area, like many wildfire-prone landscapes, has experienced altered fire behavior (Parisien et al., 2023) that, when coupled with climate change, can produce disturbances that cause a system wide change in the ecosystem (i.e., a departure from its steady state; Hessburg et al., 2019). To build resiliency to wildfire in the study area and other wildfire-prone landscapes that exhibit complexity in their ecosystem it is imperative to understand the mechanisms that influence resilience and resistance on the landscape and their historical steady states (Beller et al., 2019; Hessburg et al., 2019). Historically, the dry conifer forests of BC were defined by a patchwork of heterogeneous forest stands that formed patches at broad (i.e., surface fuels and ground fuels), meso (i.e. patches of successional stages), and fine (i.e., clumps of tree regeneration or mortality) scales (Hagmann et al., 2021; Hessburg et al., 2019). This historical forest structure was mediated through wildfire as a disturbance mechanism, and the resulting multi-scale patchwork of vegetation fostered not only resistance to wildfire but, also resiliency through facilitating a mix of wildfire severities and frequencies (Brookes et al., 2021; Hessburg et al., 2019). Over the past 100 years, exclusion of fire, land-use changes including logging practices and expanded agriculture, and insect outbreaks have significantly altered the historical 82 heterogeneous forest structure in BC’s dry conifer forests, creating a more homogenous forest that is characterized by dense forests (patches) with high fuel loads that exhibit little diversity in stand age and species (Hessburg et al., 2005; Prichard et al., 2021) when combined with increases in severe fire weather conditions (drier and hotter), creates potential for high-severity fires in low resiliency forest structure. The study area generally aligns with this altered dry conifer landscape dominated by three FBP fuel types of C3-Mature Jack or Lodgepole Pine (~10,533 km2), C7-Ponderosa Pine Douglas Fir (~6930 km2), and O-1a-Matted Grass (~4057 km2; Figure 3-1). The baseline scenario is dominated by patches of mid-severity fire with high severity clusters in the northeast and along the western edge of the study area (Figure 3.4). The introduction of landscape level treatments resulted in a shift to a mosaic of low to mid severity fire with some high severity patches continuing to persist regardless of treatment level. This shows a potential increase in resiliency as a result of landscape level treatments, particularly at treatment scales of 50% and higher. Additionally, the Pearson’s correlation coefficient shows that between the baseline and 10% landscape treatment scenario there is a statistically significant difference in the distribution of wildfire severity, with a larger degree of change compared to baseline than what was observed modelled BP values (Table 3.6). This provides support for how mitigation efforts such as prescribed burns, which reduce the availability of fuels and help create heterogeneous stand conditions, can reduce wildfire severity, particularly in areas of high wildfire severity risk (Hessburg et al., 2021; Prichard et al., 2021). Therefore, the results of our study, can provide insight into how the scale of wildfire mitigation can influence spatial patterns of wildfire severity within the study region, which can aid rebuilding resiliency on the landscape. The last component of our research focused on quantifying the potential impact of wildfire on social and ecological values within the region and observing how this impact changes with 83 increased mitigation. Initial mapping of the SES showed clustering of values, particularly around populated centers. Similar to BP, mitigation at a landscape scale showed statistically significant shifts from baseline at lower treatment scales (i.e., 20%) and a potential threshold for the efficacy of mitigation was observed at 50% (Figure 3.6a). While the spatial pattern of impacts was significantly different compared to the baseline scenario (Table 3.6), the degree of change was relatively consistent indicating that prioritizing mitigation based on BP alone may not be sufficient in a complex SES. Though the model provided a coarse estimation of impact, the findings point towards the validity of conceptualizing wildfire-prone landscapes as an SES to provide guidance on mitigation prioritization, configuration, and application scale. The lifespan during which fuel treatments are effective has been questioned, as only 1% of total area burned in the United States has received fuel treatment, indicating that a wildfire may not be regularly encountering treated areas (Barnett et al., 2016; Kolden et al., 2016; Prichard et al., 2021) This questioning points towards a larger issue with regards to wildfire mitigation; the scale and pace of treatment is not keeping pace with the scale of the problem (Ott et al., 2023; Prichard et al., 2021). As the primary focus of wildfire mitigation and suppression efforts are on protecting human values within the WUI (Calkin et al., 2014; Wilson et al., 2011), which, as evidenced by the WUI scenarios in this study, creates spatial disconnect between wildfire mitigation and wildfire occurrence (among other wildfire behavior metrics). While current wildfire management strategies may not be able to implement the scale of landscape-level treatment that would foster resiliency at a broad scale (Hessburg et al., 2021; Prichard et al., 2021), implementing strategic treatments across more extensive areas of wildfire-prone landscapes can increase efficacy and benefits to both social and ecological values (Bowman et al., 2020; Krofcheck et al., 2018; Prichard et al., 2021; Smith et al., 2016). These treatments have the potential to indirectly diminish 84 the risk of wildfires to nearby communities, enhance forest resiliency, increase the efficacy of wildfire suppression, and mitigate the risks of crown fires and smoke impacts (Ager et al., 2010; Stevens-Rumann et al., 2016). The economic trade-offs of wildfire mitigation were not within the scope of this study, but it is worth noting that the expenditures on wildfire suppression are continuing to increase with the 2023 Canadian wildfire season costing over $1 billion, which brings into question why more funding has not been allocated to proactive measures such as mitigation. The benefits of wildfire mitigation are realized if a wildfire encounters a treated area; therefore, to increase efficacy, both the scale and spatial configuration of wildfire treatment should be considered at the level of individual landscapes (Parisien et al., 2019, 2023). Predictive risk modelling can aid in offsetting the cost of wildfire mitigation by increasing the likelihood that wildfire will encounter a treated area (Parisien et al., 2019). Previous studies have shown that mitigation that targets areas of predicted high wildfire severity can reduce the total area required to be treated by 50% (Krofcheck et al., 2019; Prichard et al., 2021). The success of risk targeted mitigation is contingent on regular maintenance of the treated areas and on allowing frequent, low severity wildfire to return to the surrounding landscape (Agee & Skinner, 2005; Barros et al., 2018). The economic cost of wildfire mitigation may be further offset when treatment occurs in areas where multiple values are at risk (Hunter & Taylor, 2022). Models such as the one developed in this study can support more economically, socially, and ecologically beneficial mitigation planning. 3.5 Conclusion The overarching goal of this research was to support wildfire managers in the development of landscape-level wildfire management strategies that consider multiple values on the landscape. By considering the landscape from a more holistic perspective, the benefits of wildfire 85 management can be multiplied for various values on the landscape. These strategies should be aimed at enhancing forest resilience and promoting an active managed wildfire regime (Daniels et al., 2024; Hessburg et al., 2015, 2019; Prichard et al., 2021). Wildfire management that only utilizes fuel reduction treatments such as forest thinning and prescribed burns is insufficient to address the current scale of wildfire risk, therefore other management strategies such as cultural prescribed burning, and changes to forest composition (e.g., species types) and configuration (e.g., patchwork mosaic) are needed to promote a more wildfire-resilient landscape. These strategies can be applied over larger spatial extents and encourage the return of ecologically beneficial wildfire to the landscape (Hoffman et al., 2022b; Kolden et al., 2016; Prichard et al., 2021). There is no single solution to current wildfire management issues, but adaptive management strategies that are tailored to specific landscapes and ecosystems can offer opportunities to promote the return of active, ecologically beneficial fire regimes and wildfire resilient landscapes. Our research can contribute to developing more effective management strategies that are adapted to landscapes by providing insights into the spatial configurations of BP, wildfire severity and the social and ecological values at risk on the landscape. 86 Chapter 4: Conclusions As wildfire management begins to shift from reactionary to proactive management strategies, understanding the efficacy and potential impacts of different wildfire mitigation scenarios can help inform management decisions, particularly in landscapes with multiple values at risk from wildfire. Historically, western wildfire management has been focused on the protection of human assets, and has not considered the landscape holistically (Christianson, 2015; Dombeck et al., 2004; Dunn et al., 2020; Hoffman et al., 2022b; Lewis & Ferguson, 1988; Pyne, 2008). If wildfire mitigation is to increase in scale as many experts suggest (Prichard et al., 2021; Bradley et al. 2016; DellaSala et al., 2017; Moritz et al., 2014; Schoennagel et al., 2017), the broader implications of management decisions on both social and ecological values should be considered. Social-ecological systems (SESs) frameworks can be used to enhance our understanding of fire prone landscapes by not only defining the values at risk on the landscape, but also how these values may interact with wildfire and mitigation (Bach et al., 2015; Dunn et al., 2020; Filotas et al., 2014; Spies et al., 2014; Steelman, 2016; Vigna et al., 2021). Stakeholder driven development of wildfire SES frameworks can provide further information on local perceptions of wildfire risk to values on the landscape (Vigna et al., 2021). This research explored how SES frameworks could be applied to understand wildfire risk and mitigation within the Thompson-Okanagan region of British Columbia through both qualitative and quantitative research methods. In Chapter 2, a conceptual model of the Okanagan region’s social and ecological system was developed using expert responses to structured interviews. The Okanagan region displayed a complex SES with a total of 17 social and ecological values identified as at risk from wildfire. Ecological values were more frequently referenced by experts in relation to wildfire risk than social values, with the most referenced values being wildlife 87 and wildlife habitat, as well as infrastructure and communities. Fire itself also emerged as a prominent value on the landscape, which mirrors the current shift amongst wildfire experts of viewing wildfire exclusively as a negative disturbance on the landscapes (Copes-Gerbitz et al., 2021; Dunn et al., 2020; Hessburg et al., 2021; McWethy et al., 2019; Moritz et al., 2014; Prichard et al., 2021). The conceptualization of the wildfire SES presented here builds upon previous work within western North America that strives to develop wildfire management by considering the coupled social and ecological systems on the landscape (Dombeck et al., 2004; Dunn et al., 2020; Hessburg et al., 2021; Spies et al., 2014; Steelman, 2016). Our research provided a methodology for SES conceptualization that considers the perspectives of multiple stakeholders on the landscape, therefore providing a model that both reflects knowledge of not only ecologists and wildfire experts, but other organizations that may be underrepresented in wildfire risk and management research (Vigna et al., 2021). The applicability of community driven SES frameworks has been demonstrated in other regions of the world, where they have been used to inform wildfire preparedness of the community, adaptive capacity, and quantitative modelling efforts (Jakes & Langer, 2012; Paveglio et al., 2009; Prior & Eriksen, 2013), but this is the first effort to conceptualize a wildfire SES in a Canadian context. Wildfire mitigation and its potential impacts on the landscape were also discussed during the interview process. Several values and themes emerged with the highest number of references to prescribed and cultural burning. The responses of the experts displayed the consideration of the complex dynamics of using fires to mitigate the risks associated with future wildfires. This complexity produces uncertainty around the application of mitigation on the landscape, particularly with regard to scale and spatial configuration (Pritchard et al., 2021, Parisian et al., 2019). While our research in Chapter 2 did not directly address these management questions, we 88 were able to identify important social and ecological values that are considered at risk to wildfire. We also enhanced our understanding of perceptions of wildfire mitigations and the potential implications of mitigation to the identified landscape values. The SES framework addressed uncertainties and potential impacts related to wildfire mitigation, and while it may not provide an entirely comprehensive view due to limitations with interview responses, it does provide a basis for wildfire SES research within Canada, particularly in interior BC, that can be further developed. Beyond enhancing our understanding of the landscape, SES models have been used to inform adaptive wildfire management strategies that are aimed at increasing landscape resiliency (Chapin et al., 2008; Moritz et al., 2014; Steelman, 2016). Chapter 3 built upon the results of Chapter 2 to examine different wildfire management scenarios and the spatial relationships between wildfire and the wildfire SES. In Chapter 3, we used Burn-P3 to understand how patterns of burn probability (BP) and wildfire severity changed in response to various scales of randomly applied mitigation and the potential impacts of wildfire to social and ecological values on the landscape. Burn-P3 has been utilized to understand wildfire risk in a range of contexts (Parisien et al., 2005, 2011, 2013) including how risk changes in response to different management scenarios such as suppression (Reimer et al., 2019), the implementation of targeted mitigation (Stockdale et al., 2019c), and current forest management (Dawe et al., 2022). We built upon the frameworks provided in these studies to explore questions related to scale, spatial optimization, and potential impacts to the wildland urban interface (WUI). Mitigation was applied at both the WUI and landscape level, with the landscape level treatments showing the most notable reductions in BP, wildfire severity, and SES impacts. Similar to Wang et al.’s (2016) study on the same landscape, we found high BP areas were clustered on the landscape, which can challenge wildfire suppression by increasing the 89 likelihood of large uncontrolled wildfires, but does present opportunities for the spatial optimization wildfire mitigation (Parisien et al., 2019; Prichard et al., 2021). The response of BP to randomized mitigation was significant at all landscape scale treatment levels, but the decrease from baseline plateaued around 60% of landscape treatment which suggests an upper limit for mitigation benefits. We integrated the outputs of Burn-P3 into secondary models to quantify both wildfire severity and potential risks to social and ecological values on the landscape. Previous studies have used Burn-P3 to quantify wildfire hazard (Parisien et al., 2019; C. Stockdale et al., 2019c); we present a novel method to quantify wildfire severity using vegetative outputs from Burn-P3. The results of this modelling showed that wildfire severity had a higher threshold for treatment (70%), which may be the result of limitations in our modelling approach and the multitude of factors that influence wildfire severity. With the exception of the 10% treatment level, treatment did not produce a statistically significant shift in the spatial patterns wildfire severity patterns when compared to baseline. Despite this, it did produce a notable shift in areas of high wildfire severity to mid to low severity fire. These results show that mitigation that targets areas of high wildfire severity risk can promote a mixed wildfire severity regime that is similar to the historic regime of the Okanagan (Hagmann et al., 2021; Hessburg et al., 2019). Finally, the spatial relationship between landscape values and wildfire risk was examined for the mitigation scenarios. Previous applications of Burn-P3, and other wildfire risk models, generally quantify risk in relation to a singular value on the landscape (Parisien et al., 2019; Stockdale et al., 2019c). While this can be useful in providing more nuanced responses with regards to that singular value, it provides limitations when assessing the landscape through an SES framework. Similar to BP, there was clustering of values on the landscapes. Our results show that 90 mitigation applied to 20 to 50% of the landscape had the best efficacy for reducing impacts to social and ecological values on the landscape. This modelling did not capture the individual responses of values to wildfire; however, it does provide a holistic view of how the landscape’s SES responds to baseline wildfire and different wildfire mitigation scenarios. It provides evidence for the efficacy of landscape level management in decreasing impacts to social and ecological values on the landscape and promoting wildfire resiliency (Krofchek 2019, Pritchard et al., 2021; Bowman et al., 2020; Smith et al., 2016; Hessburg et al., 2021). The frameworks provided in Chapter 3 can help inform the spatial optimization of wildfire mitigation and address uncertainties related to the unintended impacts of wildfire and wildfire mitigation to landscape values. 4.1 Contributions and Management Implications SES conceptualization and spatial modelling can provide a multi-dimensional understanding of wildfire risk on the landscape (Dunn et al., 2020; Ganz et al., 2022; Moritz et al., 2014; Parisien et al., 2005, 2019; Prichard et al., 2021; Scheller et al., 2019). Our research has several implications for the management of wildfire within the Okanagan region of BC, and to similar wildfire-prone landscapes across Canada. Wildfire management is shifting from an exclusive focus on suppression to a higher emphasis on mitigation (Dunn et al., 2020; Hessburg et al., 2021; Moritz et al., 2014). Wildfire managers have acknowledged the presence of wildfire on the landscape is unavoidable, and therefore management strategies that reduce the risk of large catastrophic wildfires are being prioritized. However, wildfire mitigation is not being applied at a broad enough scale to address the risk of large destructive wildfire on many landscapes across western North America (Prichard et al., 2021). The significant financial and labour costs of wildfire mitigation highlight the need for greater efficacy. Our research can contribute to more 91 effective wildfire mitigation through spatial optimization by identifying areas that have the highest probability of destructive high-severity fire and how these areas may overlap with values at risk. Our findings highlight the importance of applying targeted and large-scale wildfire mitigation beyond the extent of the WUI to address the risk of wildfire to the SES. Previous studies have shown how conceptualizing the wildfire social ecological system can enhance our understanding of how wildfires interact with social and ecological values on the landscape (Dunn et al., 2020; Spies et al., 2014). We present the first conceptualization of the wildfire SES for the Okanagan region of BC, with implications for management, including providing insights on the values considered at risk from wildfire, the importance of these values to various organizations within the region, and perceptions of wildfire management within the region. The results also highlight the complexity of wildfire’s role on the landscape and the need for management strategies that aim to build adaptive or transformative resiliency on the landscape. This type of management will require collaboration across multiple stakeholders including local and provincial government and Indigenous Nations. The spatial optimization of wildfire mitigation can improve efficacy of treatments and provide greater cost benefits in the short-term (Ager et al., 2013, 2016; Heirs et al., 2003; Smith et al., 2016; Hunter and Taylor, 2022). Our modelling provided insight on the spatial configuration of the wildfire SES, and can be used to identify areas where multiple values are at risk from wildfire. We showed that landscape scale mitigation applied to 30% - 50% of the landscape has the highest overall benefits in terms of reduction of BP, wildfire severity, and impacts to values on the landscape, by limiting the potential for large destructive fires to develop This adds to the body of literature calling for increases in the scale of wildfire mitigation (Forest Practices Board, 2023; Hessburg et al., 2021; Prichard et al., 2021), which can be enhanced through spatial 92 optimization. Spatial optimization will involve prioritizing regions for mitigation based on the BP and wildfire severity patterns (i.e., regions with high BP and high wildfire severity clustering) and the number of SES values on the landscape. This type of prioritization will lead to better allocation of wildfire management resources and increase the efficacy of the mitigation. The results of this research can be used to support adaptive and transformative resilience in wildfire management strategies. This will require moving away from suppression focused management strategies in favour of strategies that recognize the value of wildfire on the landscape and encourage the return of ‘good fire’. These strategies should by underpinned by the decentralization of power that empowers marginalized groups in wildfire management, particularly Indigenous Nations (Copes-Gerbitz et al., 2022; Hoffman et al., 2022b). Non-colonial frameworks, such as “Walking on Two Legs”, can provide guidance to western management on how to coexist with Indigenous fire stewardship and how power can be decentralized so that management is not constrained by jurisdiction or organizational hierarchy (Copes-Gerbitz et al., 2022). 4.2 Future Research Understanding the relationship between wildfire and the social and ecological values on the landscape is complex, especially in landscapes where there are disparate stakeholders and a lack of consensus about wildfire management strategies. The effects of climate change, land-use changes, and a century of wildfire suppression has altered the resiliency of many fire-adapted landscapes. The low resiliency of these landscapes has created unprecedented wildfire risk with more frequent large, high-severity fires occurring outside the historical wildfire regimes (Baron et al., 2022). Therefore, understanding the complex interactions between wildfire and the values at risk on the landscape is critical for developing effective wildfire mitigation strategies. 93 Understanding how wildfire behaviour changes in response to different scales of mitigation can further enhance these strategies. Our research provides a framework for conceptualizing the wildfire SES of a landscape and understanding how different scales of mitigation on the landscape can impact wildfire risk on the landscape. To further understand the impact of wildfire on social and ecological values on the landscape, future research should consider: 1) The refinement of the wildfire severity and SES impacts models through validation and improving impact characterization. The wildfire severity model could be validated by comparing the wildfire severity of fires that occurred post-2019 to the predicted wildfire severity of that area. The SES impacts model could be refined by characterizing and calibrating the individual impacts of wildfire to each value. 2) Assessing the role of climate change and forest succession on wildfire behaviour. Changing climatic conditions will influence future wildfire behaviour as well as forest succession dynamics (Hagmann et al., 2021; Hessburg et al., 2019; Parisien et al., 2023). Wang et al. (2016) investigated the impact of climate change on future BP within the Thompson-Okanagan region, but they did not include forest succession changes in their scenarios. Climate change has the potential to alter species composition, distribution, and productivity on many landscapes, which will alter the fuel characteristics of a landscape (Hagmann et al., 2021; Stralberg et al., 2018). Future research should consider these potential successional changes and their impact on wildfire behaviour. 3) Targeted wildfire mitigation strategies that assess how wildfire behaviour and impacts change in response to mitigation that are spatially optimized. This study did not spatially optimize the mitigation based on patterns of wildfire severity or the configuration with social and ecological values on the landscape. Spatially optimizing the wildfire mitigation 94 based on baseline modelling of wildfire severity and SES impacts would provide further insights into the efficacy of different scales of wildfire mitigation. 4) The impacts of different types of wildfire mitigation, such as prescribed and cultural burning and mechanical thinning. Our study did not characterize the response of wildfire to different types of wildfire mitigation and simply considered shifting portions of the landscape to a less flammable state. Characterizing the response of wildfire to different types of wildfire mitigation can provide insight on the potential effectiveness of different wildfire management strategies. Additionally, the impact of mitigation to social and ecological values can vary based on the type of mitigation. 5) A focused analysis of the WUI would improve our understanding of how mitigation conducted in the WUI interacts with wildfire risk at the landscape-scale. Our study showed that mitigation that is confined to the WUI does not significantly reduce wildfire risk on the landscape. Analyzing spatial patterns only within the WUI would provide insight into the spatial optimization and scale at which treatments may need to be conducted to reduce the intensity of wildfires that enter the WUI. 95 Bibliography Abrams, J. 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Sectors of expertise used to categorize experts during recruitment and coding process. Sector of Expertise Number of Expert Responses Agriculture 2 Community 6 Ecological 9 Indigenous 1 Wildfire 1 126 Table A.2. NVivo coding nodes that emerged during qualitative analysis of interview responses. Question Node 1 Node 2 Node 3 References 1 Social Recreation and Tourism - 12 Infrastructure and Communities - 17 Human, Life, and Safety - 8 Economic - 7 Forestry 2 Agriculture 2 Cultural Values - 4 Aesthetics - 6 Ecological Wildlife & Wildlife Habitat - 19 Wildfire - 6 Water - 12 Species at Risk - 2 Natural Hazards Risk Reduction - 1 Forest Integrity and Resilience - 5 Ecosystems as a Whole - 9 Ecosystems Services - 11 Conservation - 2 Climate Change - 4 Biodiversity - 3 Other Scale - 1 Wildfire residual impacts - 1 OCP Guiding Principles - 1 2 Primary Risk - - 24 Secondary Risk - - 14 No Risk - - 2 Ecological Benefit - - 4 3 Sufficient - - 5 Insufficient - - 13 4 Unknown - - 1 Reintroduction of Fire to Landscape - - 3 More Inclusion of Indigenous Values - - 2 More Consideration of Values at Risk - - 6 Increased Scale - - 9 Increased Public Education - - 6 Increased Proactivity - - 5 Increased Collaboration of Government - - 1 Changes to Forest Management Practices - - 8 Better Urban Planning - - 5 127 Appendix B - Supplementary Material for Chapter 3. Table B.1. Summary of area treated in landscape and WUI mitigation scenarios where treatment level represents the approximate percentage of the landscape or WUI that was treated. Treatment Level Area Treated (km2) Landscape WUI 10 2,761 560 20 5,539 1,112 30 8,577 1,638 40 11,386 2,207 50 14,480 2,731 60 17,286 3,305 70 20,051 3,854 80 22,023 4,368 90 24,350 4,912 100 27,581 5,452 128 Table B.2. Composite Burn Index form obtained from Key and Benson’s (2006) Landscape Assessment sampling and analysis methods of the FIREMON: Fire effects monitoring and inventory system. 129 Figure B.1. Map of baseline wildfire severity and wildfire severity for the WUI level treatments. Where orange to red represents high wildfire severity (2 to 3), yellows represent moderate wildfire severity (1 to 2) and blue to green represents low wildfire severity (0 to 1). 130 Figure B.2. Map of baseline SES impacts and SES impacts for the WUI level treatments. Where red to red represents high impact (2 to 3), yellows represent moderate impact (1 to 2), green represents low impact (0.01 to 1), and blue no impact (<0.01).
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bourbonnais, Mathieu
Parrott, Lael
Wildfire mitigation scenarios for the Thompson-Okanagan region of British Columbia : assessing social-ecological trade-offs
Text