Forestry, Faculty of
DSpace
UBCV
Brookes, Wesley
Master of Science - MSc
University of British Columbia
Accurate historical reconstructions of disturbance regimes are necessary because current approaches toward more sustainable forest practices attempt to model forest management after historical disturbance regimes and recovery processes. Using dendrochronological analyses, I characterized both the historical fire regime and outbreaks of western spruce budworm (Choristoneura occidentalis Freeman) within a dry mixed-conifer forest to assess whether contemporary regimes are outside the historical range of variation. By determining the degree of change between the historical fire regime and the current, I provide insight into the potential consequences that fire suppression has had on resilience to fire and budworm. Historical fires burned in 23 different years from 1619 to 1943 with a mean interval of 15 years. Beginning in the later part of the 19th century fire patterns changed, such that the current fire-free period drastically exceeds the historical maximum fire-free intervals at all but 1 plot. Fire scars and post-fire cohorts varied among plots and indicated fires burned at mixed severities through time and space. Eight western spruce budworm outbreaks initiated between 1800 and 2001 with mean a mean duration of 14 years and mean return interval of 29 years. Outbreaks of a range of durations, frequencies, and severities were well represented throughout the record with no discernable trend in time. There was no significant difference between the number and timing of infestations initiating in plot canopies or subcanopies. The simple linear regression analyses determined plot-level subcanopy tree density (R² = 0.02; p = 0.43,) and the ratio of subcanopy to canopy tree densities (R² = 0.02; p = 0.48) to be poor predictors of severity during the outbreak from 2001 to 2011. It is strongly suspected that in the absence of fire in the 20th century, the forest has become denser and has lost a substantial degree of structural diversity. In addition, accumulations of ladder and surfaces fuels have also occurred and leave the forest at increased risk of a high-severity stand-replacing fire and less resilient to budworm attack.
HISTORICAL AND CONTEMPORARY DISTURBANCE REGIMES IN CENTRAL INTERIOR DRY FORESTS OF BRITISH COLUMBIA by Wesley Brookes B.Sc.F., The University of British Columbia, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2019 © Wesley Brookes, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Historical and contemporary disturbance regimes in central interior dry forests of British Columbia Submitted by Wesley Brookes in partial fulfillment of the requirements for the degree of Master of Science in Forestry. Examining Committee: Dr. Lori Daniels, Forestry Co-supervisor Dr. Allan Carroll, Forestry Co-supervisor Dr. Bianca Eskelson, Forestry Supervisory Committee Member Dr. Nicholas Coops, Forestry Additional Examiner Dr. Dominik Roeser, Forestry Additional Examiner iii Abstract Accurate historical reconstructions of disturbance regimes are necessary because current approaches toward more sustainable forest practices attempt to model forest management after historical disturbance regimes and recovery processes. Using dendrochronological analyses, I characterized both the historical fire regime and outbreaks of western spruce budworm (Choristoneura occidentalis Freeman) within a dry mixed-conifer forest to assess whether contemporary regimes are outside the historical range of variation. By determining the degree of change between the historical fire regime and the current, I provide insight into the potential consequences that fire suppression has had on resilience to fire and budworm. Historical fires burned in 23 different years from 1619 to 1943 with a mean interval of 15 years. Beginning in the later part of the 19th century fire patterns changed, such that the current fire-free period drastically exceeds the historical maximum fire-free intervals at all but 1 plot. Fire scars and post-fire cohorts varied among plots and indicated fires burned at mixed severities through time and space. Eight western spruce budworm outbreaks initiated between 1800 and 2001 with mean a mean duration of 14 years and mean return interval of 29 years. Outbreaks of a range of durations, frequencies, and severities were well represented throughout the record with no discernable trend in time. There was no significant difference between the number and timing of infestations initiating in plot canopies or subcanopies. The simple linear regression analyses determined plot-level subcanopy tree density (R2 = 0.02; p = 0.43,) and the ratio of subcanopy to canopy tree densities (R2 = 0.02; p = 0.48) to be poor predictors of severity during the outbreak from 2001 to 2011. It is strongly suspected that in the absence of fire in the 20th century, the forest has become denser and has lost a substantial degree of structural diversity. In addition, iv accumulations of ladder and surfaces fuels have also occurred and leave the forest at increased risk of a high-severity stand-replacing fire and less resilient to budworm attack. v Lay summary In a dry forest in the central interior of British Columbia historical patterns of forest fires and outbreaks of western spruce budworm were reconstructed using tree rings. Using historical references, I critically assessed the degree of change in the 20th century, determined whether current trends are unprecedented, and provided insight into the potential consequences of these changes on forest structure and resilience to fire and budworm. Beginning in the late 19th century, fire patterns changed where the current time with no fire drastically exceeds the maximum times between fire in the past. Western spruce budworm outbreaks have been repetitive and consistent for at least the past 244 years and showed no trend in frequency, duration, or severity. During the current time with no fire, the forest has become denser and homogeneous with accumulations of fuel. These structural characteristics leave the forest less resilient to high-severity fire and budworm defoliation. vi Preface This thesis is the product of a collaboration involving my co-advisors Dr. Lori Daniels and Dr. Allan Carroll, and myself. Both Dr. Lori Daniels and Dr. Allan Carroll provided guidance on my research questions, methods, and sampling design. Dr. Lori Daniels provided extensive feedback on the revisions to chapter 1, 2, and 3 as well as guidance on the format of chapter 4. Dr. Allan Carroll provided revisions for chapter 4 along with final reviews of chapters 1, 2, and 3. Dr. Bianca Eskelson provided a final review of the completed draft thesis. I was involved with developing my research questions, methods, data collection, and data analysis of chapters 2 and 3. I was also responsible for the comprehensive literature review necessary for the overview of management implications in chapter 4. Chapters 2 and 3 are expected to be submitted for scientific publication. vii Table of contents Abstract ......................................................................................................................................... iii Lay summary ................................................................................................................................. v Preface ........................................................................................................................................... vi Table of contents ......................................................................................................................... vii List of tables.................................................................................................................................. xi List of figures ............................................................................................................................... xii Acknowledgments ...................................................................................................................... xiv Dedication .................................................................................................................................... xv 1 Historical fire and western spruce budworm in a central interior dry forest in British Columbia, Canada. ....................................................................................................................... 1 1.1 Introduction ...................................................................................................................... 1 2 Highly variable fine scale burn severity amongst frequent mixed-severity fires in a central interior dry forest in British Columbia, Canada. ....................................................... 10 2.1 Introduction .................................................................................................................... 10 2.2 Materials and methods ................................................................................................... 15 2.2.1 Study area................................................................................................................ 15 2.2.2 Sampling ................................................................................................................. 17 2.2.2.1 Forest composition and structure ..................................................................... 17 2.2.2.2 Fire scars .......................................................................................................... 18 viii 2.2.3 Reconstructing fire history and forest dynamics .................................................... 18 2.2.3.1 Sample preparation .......................................................................................... 18 2.2.3.2 Measuring and crossdating .............................................................................. 18 2.2.3.3 Identifying cohorts initiated by fire ................................................................. 20 2.2.3.4 Determining historical fire frequency and severity ......................................... 21 2.2.3.5 Variation in severity within individual fires .................................................... 22 2.3 Results ............................................................................................................................ 23 2.3.1 Forest composition .................................................................................................. 23 2.3.2 Fire history .............................................................................................................. 25 2.3.2.1 Fire occurrence and frequency ........................................................................ 25 2.3.2.2 Establishment dates and cohorts ...................................................................... 28 2.3.2.3 Fire severity through time ................................................................................ 28 2.3.2.4 Spatial reconstruction ...................................................................................... 30 2.4 Discussion ...................................................................................................................... 34 2.4.1 Historical fire frequency and severity ..................................................................... 34 2.4.2 Historical severity within fires ................................................................................ 35 2.4.3 Contemporary fire regime ....................................................................................... 38 2.5 Conclusion ...................................................................................................................... 42 3 An analysis of contemporary western spruce budworm outbreaks in a central interior dry forest in British Columbia, Canada. .................................................................................. 44 ix 3.1 Introduction .................................................................................................................... 44 3.2 Materials and methods ................................................................................................... 52 3.2.1 Study area................................................................................................................ 52 3.2.2 Sampling ................................................................................................................. 53 3.2.2.1 Reconstructing historical western spruce budworm outbreaks ....................... 53 3.2.2.2 Sample preparation .......................................................................................... 54 3.2.2.3 Measuring and crossdating .............................................................................. 54 3.2.3 Outbreak detection .................................................................................................. 55 3.2.4 Outbreak duration, frequency, and severity ............................................................ 56 3.2.5 Canopy/Subcanopy outbreak dynamics .................................................................. 57 3.3 Results ............................................................................................................................ 58 3.3.1 Forest composition .................................................................................................. 58 3.3.2 Outbreak history...................................................................................................... 60 3.3.2.1 Outbreak severity, frequency, and duration ..................................................... 60 3.3.2.2 Canopy/Subcanopy infestation dynamics ........................................................ 63 3.4 Discussion ...................................................................................................................... 65 3.4.1 Historical frequency, duration, and severity ........................................................... 65 3.4.2 Canopy-subcanopy dynamics ................................................................................. 67 3.4.3 Potential future outbreak dynamics ........................................................................ 71 3.5 Conclusion ...................................................................................................................... 73 x 4 Management and restoration ............................................................................................. 74 4.1 Chapter 2 summary ........................................................................................................ 74 4.1.1 Contributions to management ................................................................................. 74 4.1.2 Future research ........................................................................................................ 78 4.2 Chapter 3 summary ........................................................................................................ 79 4.2.1 Contributions to management ................................................................................. 79 4.2.2 Future research ........................................................................................................ 81 4.3 Conclusion ...................................................................................................................... 82 References .................................................................................................................................... 84 xi List of tables Table 2.1: A summary of forest composition and structure in 35 plots located in the control and treatment areas of the long-term mule deer experiment in Knife Creek.......................................24 Table 2.2: The fire record for the 35 plots in Knife Creek listed by severity through time then by record length..................................................................................................................................26 Table 3.1: A summary of forest composition and structure in 35 plots located in the control and treatment areas of the long-term mule deer experiment in Knife Creek.......................................59 Table 3.2: Infestation records for the 35 plots in the Knife Creek study area.............................61 Table 3.3: Summary statistics for the 8 study-area-level western spruce budworm outbreaks reconstructed at Knife Creek.........................................................................................................61 xii List of figures Figure 2.1: Location of 35 sampled plots in Knife Creek located 24 km southeast of Williams Lake, British Columbia..................................................................................................................15 Figure 2.2: Fire record from 1460 to 2015 for all plots at Knife Creek. Horizontal lines represent the plot-level fire record and are stratified by fire regime severity through time and record length. Dashed lines indicate the years prior to a fire scar forming on ≥1 sampled trees; solid lines represent the years in which ≥1 trees with ≥1 scar are present at each plot. Black triangles represent fire years, grey triangles represent the first year of each post-fire cohort, and white triangles represent the first year of each cohort. The upper bottom panel shows the percentage of plots recording ≥1 fire scars (bars) relative to the number of plots that included ≥1 scarred trees that was susceptible to scarring. The lower bottom panel shows the study area age structure histogram.........................................................................................................................27 Figure 2.3: Fire history reconstructed from fire scars and post-fire cohorts organized by severity through time and record length for the 35 plots. The Histograms include all Douglas-fir trees that were crossdated.......................................................................................................................29 Figure 2.4: Spatial pattern of fire scar evidence for the 6 most widespread fires at the 35 sampled plots. One-hectare circular plots (to scale) display the distribution of fire scar evidence. Only plots containing trees old enough to record fire are shown. Legends indicate the relative percentage of each criteria used for inferring the fire severity at each plot for each fire..............32 Figure 2.5: Spatial pattern of fire severity based on fire scar evidence (see figure 2.4) for the 6 most widespread fires at the 35 sampled plots. One-hectare circular plots (to scale) display the distribution of fire severity. Only plots containing trees old enough to record fire are shown. xiii Red indicates high severity, orange indicates moderate-high severity, yellow indicates low-moderate severity, white plots indicate no fire, bolded white plots indicate no fire with fire-scarred trees present at time of fire................................................................................................33 Figure 3.1: Location of 35 sampled plots in Knife Creek located 24 km southeast of Williams Lake, British Columbia..................................................................................................................51 Figure 3.2: Study-area level western spruce budworm outbreaks reconstructed from the 35 plots at Knife Creek. Outbreak reconstructions were truncated to a minimum sample depth of 7 plots. Left y-axis (solid line) is the mean of the percentage of trees at each plot recording a suppression; right y-axis is the sample depth (the number of plots with sufficient samples to record an outbreak)........................................................................................................................62 Figure 3.3: Lag in initial suppression of subcanopy trees relative to canopy trees during 164 plot-level outbreaks reconstructed from subcanopy and canopy trees. Negative values indicate outbreaks that initiated in the subcanopy; positive values indicate outbreaks that initiated in the canopy ...........................................................................................................................................64 Figure 3.4: Simple linear regression testing severity against the ratio of subcanopy density to canopy density and subcanopy. Scatter plots show severity against the ratio of subcanopy density to canopy density (left) (R2 = 0.02, p = 0.48) and severity against subcanopy density (right) (R2 = 0.02, p = 0.43)……………………………………………………………………...64 xiv Acknowledgments Dr. Lori Daniels, I could not begin to thank you properly for all your guidance and feedback throughout the entire writing process. These were invaluable to me. However, I must say that just as important to me were your smiles, endless patience, enthusiasm, and encouragement. Without these I would not be here writing these acknowledgements. Dr. Allan Carroll, thank you very much for being an integral part of the finishing process ensuring my chapters were well polished. I thoroughly enjoyed bumping into you in the hallways. You’ve always been so kind and supportive. Raphaël Charvardès, or should I say ‘Dr. Raphael Charvardès’? What can I say? You were an endless source of encouragement and I thank you for your kind words and teaching me the basics of Illustrator. You were a big reason why I looked forward to coming into the lab and I wish only one day to try and return the favour. You are going places, my friend! Ken Day and Cathy Koot, it seems so long ago now but I must thank you for all the support you provided during my field season. I truly admire what you do at the Alex Fraser Research Forest and you give me hope for the future of our industry. My field crew, Marc-Antoine Leclerc, Tyler Dergousoff, Marie-Eve Leclerc, Angelica Kaufman, Anna Weixelman, and Eileen Xu stuck it through the heat and ant nests! My family, including my wife, Teresa, parents, and my best friend Colin. Thank you for the unconditional love and support during this process and for regularly reminding me that there’s life beyond my computer screen! Finally, a very gracious thank you to the private donor for funding. xv Dedication To all those who are afraid to try. 1 1 Historical fire and western spruce budworm in a central interior dry forest in British Columbia, Canada. 1.1 Introduction Disturbances are natural to North American forests (Martin, 1989; Harvey, 1994) and their combined effects have interacted for millenia (McDonald et al., 2000). Disturbances have been defined as “any relatively discrete event that disrupts the structure of an ecosystem, community, or population, and changes resource availability or the physical environment” (White and Pickett, 1985) and can originate from abiotic and biotic factors over a broad range of scales. All disturbances can be characterized by their regime. Disturbance regimes describe disturbances using temporal and spatial attributes as well as magnitudes and interactions. The severity, intensity, frequency, as well as the duration and extent, make up a disturbance regime. Disturbances are a natural and critical process determining and maintaining both spatial and temporal heterogeneity in forested ecosystems (Hessburg et al., 1994, 2000; Franklin et al., 2002; Turner, 2010, 2013; Seidl et al., 2011) and greatly influence a forest’s susceptibility to future disturbances (Paine et al., 1998; Veblen, 2000; Bebi et al., 2003; Kulakowski and Veblen, 2007; Davies et al., 2009; Turner et al., 2010). Disturbances are integral to ecological communities and resilient forests (Martin, 1989; Harvey, 1994; Hessburg et al., 2000) by influencing forest succession, biodiversity, and biogeochemical processes like carbon sequestration and water cycling (Veblen et al., 1991; Adams et al., 2010; Turner et al., 2010, Boon, 2011; Hicke et al., 2012). Biological legacies of past disturbances can persist for centuries on the landscape such that the structure, composition, and processes of today’s ecosystems are at least partly a result of 2 them (Foster et al., 1999; Franklin et al., 2002). These legacies, the organisms, organic materials, and patterns that persist through a disturbance, influence the rate and trajectory of post-disturbance succession (Lindenmayer et al., 2008). By creating heterogeneity in structures and processes, disturbances influence the variety and diversity of species within a forest and enhance forest resilience (Holling, 1996; Turner et al., 1998; Franklin et al., 2002; Seidl et al., 2014). Natural disturbances and their regimes have functioned in a dynamic manner across the landscape in the forests of Interior Western North America to create resilient systems (Hessburg et al., 2000). However, even minor changes to disturbance regimes have the potential to substantially affect forest composition, structure, biodiversity, and productivity (Turner 2010). Land use changes, fire suppression, and climate change, can potentially change disturbance regimes, which lead to environmental conditions outside the historical range of variability (HRV) where species are ill-adapted and ecosystem function is disrupted (Landres et al., 1999; Swetnam et al., 1999). Current approaches toward more sustainable forest practices attempt to model forest management after historical disturbance regimes and recovery processes (Delong and Tanner, 1996; Lertzman et al., 1998; Landres et al., 1999; Morgan et al., 2001; Wong and Iverson, 2004; Marcoux et al., 2013). Therefore, historical reconstructions of disturbance regimes are necessary to enable more confident management prescriptions (Turner, 2010). They also help illuminate the relative importance of the key drivers behind them such as climatic variability and land-use practices (Flower et al., 2014). In-depth knowledge enables more confident predictions of the 3 number and likelihood of disturbances in the future while assisting forest managers in assessing the vulnerability of forests to these disturbances and prescribing appropriate measures to mitigate impacts (Turner, 2010). Additionally, without a historical reference it is difficult to quantify the nature and magnitude of any changes in contemporary disturbance regimes, determine whether current events fall within or outside the historical range of variability (Turner, 2010), or anticipate the consequences (Weed et al., 2013). Mounting evidence suggests factors such as land management, fire suppression, and climate change are altering disturbance regimes around the world (Baker and Veblen, 1990; Hadley and Veblen, 1993; Hagle et al., 2000; Hessburg et al., 2000; Dale et al., 2001; Turner, 2010). These factors are also believed to be affecting the regimes for two of the primary disturbance agents active in the south-central interior of British Columbia: fire and western spruce budworm Choristoneura occidentalis Freeman (Daniels, 2004; Maclauchlan et al., 2006; Campbell et al., 2006; Woods et al., 2010; Heyerdahl et al., 2012). The dry Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, forests in the south-central interior have been characterized by a mixed severity fire regime, which includes short intervals between low severity fires and long intervals between more severe stand initiating fires (Kilgore, 1981; Wright and Bailey, 1982; Daniels, 2004; Heyerdahl et al., 2012). Previous studies of Douglas-fir forests in the region have revealed median fire return intervals of 13 - 22 years (Daniels, 2004). It is believed that the low to mixed severity fires maintained open, uneven-aged forests while minimizing the accumulation of fuels that could promote higher severity fires (Gray et al., 2002; Daniels, 2004). However, since the beginning of industrial forestry and effective fire 4 suppression in the 1940s, few fires have burned and scarred trees (Gray et al., 2002). Much evidence suggests that without frequent fires, these forests have become denser, more homogenous, and have higher accumulations of fuel than they would have historically (Morgan et al., 1994; Holling and Meffe, 1996; Hessburg et al., 1999; Moore et al., 1999; Arno and Allison-Bunnell, 2002; Hessburg et al., 2005; Spies et al., 2006; Fulé et al., 2013; Harvey et al., 2017). These structural conditions have resulted in increased potential for high-severity, stand-replacing fires (Morgan et al., 1994; Holling and Meffe, 1996; Moore et al., 1999; Arno and Allison-Bunnell, 2002; Fulé et al., 2013). In addition, the warming temperatures and subsequent reduced snow packs, earlier springs, increased annual temperatures, and increased occurrence of drought within the last few decades have contributed to lengthened and more severe fire seasons (Wotton and Flannigan, 1993; Hessburg et al., 1994, 2000; Miller and Urban, 2000; Daniels, 2004; Swetnan & Baisan, 2003; Hessl et al., 2004; Brown & Wu, 2005; Westerling, 2006; Heyerdahl et al., 2008; Miller et al., 2009; Harvey et al., 2017). It is feared that climate change will exacerbate the threat of high-severity fire from changes in structure. The western spruce budworm is the most prominent defoliating insect throughout coniferous forests in western North America and is a natural component of Douglas-fir ecosystems (Steed et al., 2007). It primarily feeds on Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and true fir (Abies spp.) (Hicke et al., 2012). The life cycle of the western spruce budworm has been described in detail by Harris et al. (1985), Brookes et al. (1987), and Murdock et al. (2012). Briefly, the life cycle of the western spruce budworm lasts 1 year where adults mate and lay eggs in late July and August on the underside of needles. After roughly 10 days, the eggs hatch and larvae spin hibernaculae in which they spend the winter. The larvae emerge the following spring 5 in mid to late May near the time of budburst and begin feeding. Larvae will feed on pollen, cones, buds, and old needles until the seasons new foliage flushes and becomes available (Brookes et al., 1987; Murdock et al., 2012; Nealis, 2012). Repeated consumption of foliage results in top-kill, growth loss, regeneration failure, and in extreme cases, host mortality (Alfaro et al., 1982; Harris et al. 1985; Steed et al., 2007). Once mature, adult moths mate and take flight in July to lay their eggs (Murdock et al., 2012). Written records for British Columbia have dated the first outbreaks back to 1909 on Vancouver Island and 1916 in the south-central interior of province (Harris et al., 1985). However, recent research has revealed that western spruce budworm has been present in forests with a component of Douglas-fir for some time (Campbell et al., 2006; Axelson et al., 2015). Since records began, the western spruce budworm has affected over 60% of the Douglas-fir dominated stands in the province (Harris et al., 1985; Murdock et al., 2012). Most recent records show that 1 000 000 hectares have been defoliated since 1999 (B.C. Ministry of Forests, Lands, Natural Resource Operations, and Rural Development, 2018). Historical budworm outbreaks generally lasted between 5 and 25 years (BCMFR Western Spruce Budworm Public Information Sheet; Carlson & McCaughey, 1982; Johnson & Denton, 1975). Currently, the dry forests in the south-central interior are believed to be more susceptible to western spruce budworm. Studies have found specific support that fire suppression results in forests more susceptible to budworm attack and contributes to more severe and synchronous budworm outbreaks (Carlson et al., 1983; McCune, 1983; Swetnam and Lynch, 1989). Lengthening of the fire-free interval releases understory conifer establishment resulting in 6 multilayered stands, which exacerbate outbreaks by easing the dispersal of caterpillars downward through the canopy and favouring the chances of finding suitable food (Fredericks and Jenkins, 1988; Maclauchlan and Brooks, 2009). It also homogenizes stands thereby increasing the extent of forests densely stocked with suitable hosts (Fredericks and Jenkins, 1988; Hessburg et al., 1994; Keane et al., 2002; Youngblood and Wickman, 2002; Parker et al., 2006; Maclauchlan and Brooks, 2009). Generally, climate change is expected to contribute to a northward expansion of defoliators (Choristoneura spp.) and to affect over-winter survival, host susceptibility, and timing of larval emergence with bud break (Ryerson et al., 2003; Campbell et al., 2006; Reynolds et al., 2007; Candau and Fleming, 2011; Thomson and Benton, 2007). Climate fluctuations, especially those relating to changes in moisture availability have been identified as key drivers of outbreaks (Hard et al., 1980; Thomson et al., 1984; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003, Flower et al., 2014). Outbreaks have been associated with years with temperatures that synchronize larval emergence and bud swelling and drought (Hard et al., 1980; Thomson et al., 1984; Shepherd, 1985; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003; Flower et al., 2014). The range and duration of recent outbreaks of western spruce budworm appear to be increasing throughout British Columbia (Murdock et al., 2012). It is believed the extent, severity, and frequency of budworm outbreaks have increased in the 20th century primarily as a result of human-induced changes to forest structure and climate change (BCMFR Western Spruce Budworm Public Information Sheet; Blais, 1983; Fellin et al., 1983). Recent outbreaks have 7 infested higher elevations and more northern ranges of Douglas-fir stands that had no previous records of budworm (Maclauchlan et al., 2006; Woods et al., 2010). This is concerning for the future of these forests for the provision of ecosystem services and timber supply (Kurz et al., 2008; Dymond et al., 2010). It is particularly relevant in the south-central interior where industry is increasingly turning to Douglas-fir to fulfill timber supply in lieu of Lodgepole pine (Pinus contorta Doug. ex. Loud.) where Lodgepole pine stands were decimated by the recent mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic. However, there is a deficiency of knowledge regarding the history of western spruce budworm outbreaks in the south-central interior of British Columbia to determine with certainty whether contemporary trends are outside the historical range of variation (Axelson et al., 2014). The purpose of my research was to characterize both the historical fire regime and outbreaks of western spruce budworm at the Knife Creek block within the Alex Fraser Research Forest. By critically assessing the degree of change in the contemporary fire regime, I aimed to provide insight into the possible consequences of fire suppression on forest resilience to fire and budworm. To achieve these goals, my research was designed to answer seven inter-related questions: 1. How frequent were historical fires and how has frequency changed in the 20th century? 2. How severe were historical fires? Did severity vary through time and spatially within the most widespread fires? 3. How have changes to the fire regime in the 20th century likely influenced forest structure? 8 4. How frequent, severe, and prolonged were historical outbreaks of western spruce budworm in the Knife Creek block? 5. Have western spruce budworm outbreaks become more severe or prolonged in the 20th century? 6. Do outbreaks initiate in the canopy or subcanopy and has this changed through time? 7. Are levels of defoliation dependent upon subcanopy density? I hypothesized that at the Knife Creek block the historical fire regime included mixed severity fires through time and space and that the record would signal an absence of fire in the 20th century. I hypothesized that while western spruce budworm outbreaks were common over the last several centuries, the frequency, duration, and severity of contemporary outbreaks has increased in the 20th century. As well, increased density of subcanopy trees resulting from land-use changes and fire suppression in the 20th century has altered canopy and subcanopy outbreak dynamics. In chapter 2, I characterize the historical fire regime of the Knife Creek block in space and time, changes in the 20th century, and assess the impacts these changes have had on forest structure and susceptibility to high severity fire. In chapter 3, I characterize the historical record of western spruce budworm outbreaks and assess whether contemporary outbreaks are more frequent, severe, and longer in duration. I conduct several analyses to test the effects of subcanopy tree density on outbreak severity. 9 I complete my thesis with a 4th chapter in which I discuss the application of my findings for management and restoration. 10 2 Highly variable fine scale burn severity amongst frequent mixed-severity fires in a central interior dry forest in British Columbia, Canada. 2.1 Introduction Fire is a dominant disturbance in many forested ecosystems around the world and historically was the primary determinant of stand structure and forest dynamics in many forests in western North America (Agee 1993; Convington et al., 1994; Kushla and Ripple, 1997; Hessburg et al., 1999; Baker and Kipfmueller, 2001; Morgan et al., 2001; Daniels, 2004). Fire regimes describe the patterns of fire over space and time. The severity, intensity, frequency, as well as the spatial patterns and extent, make up a fire regime. Traditionally, fire regimes have been documented as being dominated by either frequent lower-severity fires or periodic higher-severity fires (Perry et al., 2011). However, with emerging understanding of the complexity of wildfire in dry mixed-conifer forest ecosystems, the concept of a distinct mixed-severity fire regime has been established in which low to high severity fires occur at varying frequencies (Schoennagel et al., 2004). In these forests, interactions among topography, fuels, and weather, result in variable burn severities within and among fires (Lertzman et al., 1998; Beaty and Taylor, 2001; Perry et al., 2011). The severity within individual fires can vary dramatically and leave unburnt or relatively unburnt patches (Eberhard and Woodward, 1987; Delong and Tanner, 1996) at scales of 10 to 100 meters (Lertzman et al., 1998). As well as individual fires varying spatially, mixed-severity fire regimes include periodic low- and high-severity fires over time (Hessburg et al., 2016). 11 Mixed-severity fire regimes are poorly understood compared to regimes where either low- or high-severity fires dominate (Hessburg et al., 2016). This is partly because the mixed-severity concept is relatively new and partly because of the complexity inherent to these regimes. An important question has emerged: How much high-severity and how much low-severity fire is within the historical range of variation of a mixed-severity fire regime? Although the answer will vary among ecosystems, knowing the relative importance of low- versus high-severity fires is critical to guide many aspects of forest and fire management. For example, determining whether the recent widespread and high-severity fires in dry forest ecosystems are a result of abnormal structural changes or solely extreme weather conditions has been a point of contention (Klenner et al., 2008; Williams and Baker, 2012; Fulé et al., 2013; Odion et al., 2014; Baker, 2015). Specifically, this debate centers upon whether recent fires in dry and mixed-conifer forests are outside their historic range of variability or if recent high-severity fires are an inherent part of a mixed-severity fire regime with a high degree of spatial and temporal variability (Klenner et al., 2008; Baker, 2015). Different management actions are associated with these two schools of thought. If the fire regime has been altered, determining how and why is important for taking appropriate management actions (Fulé et al., 1997; Schoennagel et al., 2004; Schoennagel and Nelson, 2011; Stevens et al., 2016). If the fire regime is not altered, management actions are unnecessary and may exacerbate other management impacts on forests (Williams and Baker, 2012; Odion et al., 2014; Baker 2015). Therefore, characterizing historical fire severity is important because current sustainable forest practices attempt to model forest management after historical disturbance patterns and recovery processes, and fire regime classifications guide forest management (Delong and Tanner, 1996; Lertzman et al., 1998; Landres et al., 1999; Morgan et al., 2001; Wong and Iverson, 2004; Marcoux et al., 2013). 12 There is growing evidence to support the hypothesis that fire suppression in forests that historically included lower-severity fires has lead to dense stands with increased fuel accumulations and more homogeneous landscapes with increased potential for high-severity, stand-replacing fires (Morgan et al., 1994; Holling and Meffe, 1996; Moore et al., 1999; Arno and Allison-Bunnell, 2002; Fulé et al., 2013). Many dry mixed-conifer forests have shifted from structures dominated by large trees to much denser structures dominated by small trees (Fulé et al., 2013). Some dry forests in the western United States are now 2 – 10 times denser than at the time of fire exclusion and, consequently, form more continuous flammable fuel structures than in the past (Fulé et al., 2013; Hagmann et al., 2013). Tree density of some dry mixed-conifer forests in Oregon has tripled over the past 90 years while mean basal area only increased by 20% and large diameter trees (>53 cm dbh) declined (Hagmann et al., 2013). Compounding the increased potential for high-severity fire is a warming and drying trend throughout western North America (Johns et al., 1997; Mote, 2003; Westerling et al., 2003; Columbia Mountains Institute of Applied Ecology 2005; Westerling et al., 2006; Nitschke and Innes, 2008; BCFLNRO, 2015; Crockett and Westerling, 2018). Combinations of reduced snowpack, earlier springs, increased summer and winter temperatures, and increased occurrence of drought that are associated with these climate predictions will exacerbate the threat of high-severity fire from changes in structure (Wotton and Flannigan, 1993; Miller and Urban, 2000; Daniels, 2004; Swetnan & Baisan, 2003; Hessl et al., 2004; Brown & Wu, 2005; Westerling, 2006; Heyerdahl et al., 2008; Miller et al., 2009; Harvey et al., 2017). 13 Efforts to restore dry mixed-conifer forests and model historical disturbance history through thinning and prescribed burning are ongoing in the western United States and British Columbia (Hessburg et al., 2016; BCMFLNRO, 2014). However, generalization of historical fire regimes across forest types and landscapes oversimplifies fire history and may result in the inappropriate application of treatments, which can threaten site productivity and/or diminish the abundance of critical habitat (Tiedemann et al., 2000; Gray et al., 2002; Schoennagel et al., 2004; Feller, 2005 Schoennagel and Nelson, 2011). For mixed-severity fire regimes, the scale at which fires burn (1 – 1 000 ha) and the influence of bottom-up controls on burn severity within fires requires that prescriptions and restoration plans be ecosystem and stand specific (Beaty and Taylor, 2001; Schoennagel et al., 2004; Moritz et al., 2011; Perry et al., 2011; Swetnam et al., 2011; Heyerdahl et al., 2015). Therefore, ecosystem-specific reference conditions at the stand level are required for developing sound management plans and prioritizing restoration treatments (Landres et al., 1999; Swetnam et al., 1999; Keane et al., 2009; Schoennagel et al., 2004; Agee and Skinner, 2005; Schoennagel and Nelson, 2011). Historically, fires of a range of severities burned in the interior dry, montane, sub-boreal and boreal forests, east of the Coastal Mountains. Modern fire records indicate 2 500 fires per year, on average, about 50% ignited by lightning. Since the advent of effective fire suppression in the 1940’s, 92% of fires were successfully suppressed before exceeding 4 ha in size (BCWFMS, 2010, 2012). Past management focused on controlling fire and acted on the premise that all fire was bad and mostly low frequency and high-severity fire defined the character of wildfires (Holling and Meffe, 1996; BCWFMS, 2010). This, along with climate change, has potentially 14 left forests with an increased risk of high severity fire. Since 2003, 3.4 million ha have burned in British Columbia costing over $3 billion in fire suppression and resulting in tens of thousands of evacuations (Abbott and Chapman, 2017). There is great concern that fire seasons such as those of 2003 and 2017, the latter being the worst fire season on record, will become common (Abbott and Chapman, 2017). Combined, these two fire seasons burned almost 1.5 million ha, largely in dry mixed-conifer forests with tree mortality exceeding 80% in many areas (Abbott and Chapman, 2017). The threat of more frequent and severe fires threatens the lives and property of the people living in communities surrounded by dry mixed-conifer forests. The purpose of my research was to characterize the historical fire regime in an interior dry mixed-conifer forest in time and space and to quantify changes in the 20th century. By critically assessing the degree of change in the contemporary fire regime, I aimed to provide insight into the possible consequences on forest structure and resilience to fire. To achieve these goals, my research was designed to answer 3 questions: 1. How frequent were historical fires and how has frequency changed in the 20th century? 2. How severe were historical fires? Did severity vary through time and spatially within the most widespread fires? 3. How have changes to the fire regime in the 20th century influenced forest structure? To answer these questions, I used dendrochronological analyses of fire scars and increment cores sampled in 35 plots across a 98-ha study area. The large number of plots in a small study area allowed me to reconstruct fire history and forest dynamics at a fine scale and make novel interpretations of the spatio-temporal variability within and among fires. 15 2.2 Materials and methods 2.2.1 Study area My study area is located in the central interior of the province, which is largely made up of the Fraser Plateau with elevations typically ranging between 900 to 1 500 meters above sea level (masl). The landscape is characterized by level to gently rolling hills crossed with deep river valleys. The western boundary of the region comprises the Chilcotin Plateau and beyond this are the Coast Mountains, which have elevations up to 4 000 masl. The Coast Mountains along with prevailing westerly winds put the central interior in a rain shadow. However, as air masses from the Pacific Ocean move east over the region, humidity levels and precipitation slightly increase (Wang et al., 2012). Figure 2.1: Location of 35 sampled plots in Knife Creek located 24 km southeast of Williams Lake, British Columbia. 500meters1000 200 300 400PlotsCopyright 2018 GoogleImage Copyright 2018 Digitalglobe16 The Knife Creek block (hereinafter referred to as Knife Creek) is 3,487 hectares and located 24 km southeast of the town of Williams Lake. The forests here are comparable in structure and climate to many dry mixed-conifer forests in the montane forests of western North America. My study area included a 98-hectare section at the northwest boundary, which is characteristically comprised of stands of Douglas-fir and lodgepole pine in the canopy with varying densities of Douglas-fir regeneration. However, due to the recent mountain pine beetle epidemic, living, mature lodgepole pine is largely absent from the canopy. The study area has varying elevations from 761 - 879 MASL and average annual precipitation is 451 mm/yr with approximately half falling in the growing season (Environment Canada, 2017 – Williams Lake). Summer months are mostly dry but punctuated with precipitation from convective storms. July is the warmest month with a mean daily temperature of 16°C and mean monthly precipitation of 61 mm (Environment Canada, 2017 – Williams Lake). Winter months are dominated by Arctic air masses resulting in stretches of extreme cold (Steen and Coupé, 1997). December is the coldest month with a mean daily temperature of -7°C and mean monthly precipitation of 48 mm (Environment Canada, 2017 – Williams Lake). Mule deer winter range is the highest management priority in the Knife Creek block, where a long-term experiment is underway to test a silvicultural system being considered as a method of dry forest management in BC (Day 1997). In the experiment, harvesting in the treatment areas has focused on removing relatively low numbers of trees between long periods of no removals to promote a clumpy arrangement of wide-crowned trees (Day 1998). These methods are designed to remove timber while maintaining sufficient forest cover to provide areas with snow 17 interception, low snow accumulation, and forage that mule deer require (Day 1997). In 1984, 80 permanent sample plots were established c. 100 meters apart along 15 systematically placed transects creating a regular grid (Armleder and Thomson, 1984; Koot et al., 2015). This grid covers 98 hectares and includes 3 control (C#) and 3 treatment (T#) units. I sampled every other plot, which resulted in 17 in the treated units and 18 in the control units for a total of 35 plots (Figure 2.1). 2.2.2 Sampling 2.2.2.1 Forest composition and structure To quantify forest composition and structure at the plot level, I used an n-tree design (Jonsson et al., 1992). The 10 closest live canopy and subcanopy trees to plot center with diameter at breast-height (dbh) ≥12.5 cm were sampled for species and dbh. Canopy trees were classified as those visually assessed to be comprising the emergent, dominant, and codominant crown classes (Adams et al., 1994). Subcanopy trees were those in the intermediate and suppressed crown classes (Adams et al., 1994). The distance from the center of the plot to the 10th tree in each size class formed the radius of a circular plot used for calculating tree densities (Jonsson et al., 1992). To assess tree ages and growth histories, one increment core was removed from each tree. Cores were sampled between 20-30 cm from the base of the trees and coring height and diameter at coring height were recorded. When needed, multiple cores were extracted to ensure they intersected or were close to the pith. 18 2.2.2.2 Fire scars At each plot, I searched a 1-hectare circular plot (radius = 56.4 m) surrounding the plot center for living trees, snags, logs, or stumps with external fire scars (herafter “fire-scarred trees”). I identified fire scars based on morphology looking for scars that were triangular in shape at the base of the stem, had all bark missing from the scar face, and included charcoal if there were multiple scar lobes (McBride, 1983; Dietrich and Swetnam, 1984, Daniels, 2004). I removed a maximum of 5 cross- or partial-sections from each plot using a chainsaw (Cochrane and Daniels 2007), while choosing to take samples with the most and best preserved visible scars (Swetnam and Baisan, 1996). 2.2.3 Reconstructing fire history and forest dynamics 2.2.3.1 Sample preparation Fire-scar sections and cores were air-dried, repaired after drying using hot glue and wooden supports, and all cores were mounted in grooved wooden boards. Samples were sanded following standard methods so that after surfacing individual cells and ring boundaries were clearly visible (Stokes and Smiley, 1996; Swetnam et al., 1985). High-resolution digital images (2400 – 4800 dpi) were taken of cross-sections and cores, including the bark, pith, and tips of fire scars when present. 2.2.3.2 Measuring and crossdating Using the high-resolution images, I measured the annual ring increments on the cross-sections and cores to the nearest 0.01 mm using CooRecorder (Larsson, 2011a). I used CDendro (Larsson, 2011b) to crossdate the ring widths against an existing regional Douglas-fir chronology 19 (Daniels, 2004). This allowed me to identify false and missing rings and assign accurate calendar years to the tree-rings. To verify that samples were properly crossdated, I verified the dated ring-width series using the program COFECHA (Holmes, 1986). Forty-five cores (6%) and 3 cross-sections (4%) could not be crossdated yielding a correlation coefficient ≥0.25 between the sample and chronology and were excluded from further analysis. Stand age structures were derived from crossdated increment cores extracted from canopy and subcanopy trees. To achieve estimations of the establishment dates, two corrections were applied to take into account coring height and rings missing from cores that did not intersect the pith (Heyerdahl et al., 2012). To estimate the number of years for trees to grow to coring height on individual trees, I used age-on-height regression equations developed from Douglas-fir regeneration near the study area (Daniels, 2004). For cores that did not intersect the pith but included arced rings close to it, the number of rings to the pith were estimated based on ring geometry (Duncan, 1989). For increment cores that did not include arced rings but the increment core comprised ≥90% of the geometric radius of the tree at core height, the length of the missing radius and average ring width of the rings were used to estimate the number of missing rings (Norton et al., 1987). The age corrections for each tree were summed and 88% of cores had total corrections of ≤15 years. Thus, subsequent age analyses were based on 15-year classes. Fire dates were determined from the location of the tip of each fire scar within annual rings of the cross-sections (Brown and Swetnam, 1994). I assigned fire scars located at the boundary between two rings to the calendar year of the completed ring because modern fires in the region typically occur in mid to late summer (B.C. Wildfire Service-unpublished data; Heyerdahl 2012). 20 Fire-scar dates recorded by only one tree in the study area were excluded because of the chance the scar was caused by a disturbance agent other than fire (Swetnam and Baisan, 1996). 2.2.3.3 Identifying cohorts initiated by fire Even-aged cohorts provide evidence for moderate- to high-severity fires due to the favourable conditions they produce for tree establishment. For this study, an even-aged cohort was defined as ≥5 trees established in a single plot within a 15-year period, preceded by at least 15 years of no establishment (after Heyerdahl et al., 2012). Fifteen years was selected because Douglas-fir can take a long time to colonize because of seedling sensitivity to frost and shelter requirements in the study area (Klinka et al., 2000). To identify cohorts, I counted the number of trees that established in 15-year periods beginning with the pith date of the oldest tree in the plot and then shifted in 1-year increments until I reached the pith date of the youngest tree (Chavardès and Daniels, 2016). Post-fire cohorts established within 15 years of a fire-scar year in the same plot or an adjacent plot (Heyerdahl et al., 2012). The year assigned to each cohort was the establishment year of the oldest member of the cohort. Since periods of suitable climate can facilitate tree establishment independently of disturbance, the possibility that cohorts were a consequence of favourable climate rather than fire was assessed (Heyerdahl et al., 2012). Using an independent tree-ring reconstruction of precipitation in Williams Lake (Daniels and Watson, 2003), I calculated 15-year moving averages reported for the first year of each 15-year window (e.g. 1901 = average values from 1901 to 1915) and compared the annual values against the long-term mean. Positive departures indicated dry periods likely to limit establishment because a lack of summer moisture has been shown to delay seedling growth (Burns and Honkala, 1990). Each cohort was classified as establishing during a wet or dry period. Contingency tables of the 21 number of cohorts that established during wet versus dry periods were assessed against a random distribution using chi-squared goodness of fit (= 0.05) (Whitlock and Schluter, 2009). 2.2.3.4 Determining historical fire frequency and severity To quantify fire frequency, I developed plot-level fire chronologies using fire-scars and post-fire cohorts. For each plot, the length of the fire record, number of fires, the minimum, maximum, and mean number of years between fires, and the time since last fire were calculated. The length of the fire record at each plot was determined from the age of the oldest tree. The time since last fire was the number of years between 2015 (the year of sampling) and the last fire year indicated by a scar or a post-fire cohort. The maximum fire interval from each plot was used to determine whether the current fire-free interval is outside the historical range of variation. A visual representation of fire frequency and cohorts for each plot was constructed using the Fire History Analysis and Exploration System (FHAES) (Brewer et al., 2016). The program FHAES analyzes disturbance event chronologies, creates summary figures of the event data, and is used to quantitatively compare fire frequency statistics between locations. To discern whether reconstructed fire frequency differed between the mule deer control and treatment units at Knife Creek, I composited the plot level fire records for the control and treatment units and used a Welch’s 2-tail t-test to compare the mean intervals between fires (Welch, 1938). They did not differ significantly (p = 0.50, α = 0.05, df = 31), therefore I used all plots from the study area and calculated fire frequency as the mean number of years between all fire years recorded. The severity of fires at individual plots through time was inferred using criteria from Heyerdahl et al. (2012). I classified plots with only 1 fire year associated with a post-fire cohort and no fire 22 scars as those recording high-severity fire through time. Mixed-severity fire histories were assigned to plots with >1 fire year and ≥1 post-fire cohorts. Plots with low-severity regimes were plots with >1 fire scar years but no post-fire cohorts. For plots not meeting these criteria, severity was unclassifiable. Trees that established before and survived at least one fire indicated by a fire scar or post-fire cohort were classified as remnant trees (Chavardès and Daniels, 2016). The severity of the last fire at each plot was calculated as the percentage of remnant trees surviving the last fire relative to all trees in the plot (Chavardès and Daniels, 2016). For plots with no scars but with post-fire cohorts, the fire scar year associated with the post-fire cohort was used. A plot with 81 - 100% remnant trees meant the last fire was low severity, 20 - 80% meant moderate severity, and <20% meant high severity (after Sherriff and Veblen, 2006). The severity of the last fire for the study area was assessed using the proportion of plots with evidence that the last fire burned at low, moderate, or high severity. 2.2.3.5 Variation in severity within individual fires Widespread fires were defined as those that scarred trees in ≥25% of plots. The distribution of fire scars, post-fire cohorts, and remnant trees were collectively analyzed to assess spatial variation in severity within individual fires. For each fire, the presence or absence of fire scars, post-fire cohorts, remnant trees, and potential recording trees were combined to estimate severity at the plot level. Similar to the classification of fire-severity through time, plots with fire scars but no post-fire cohort indicated local low intensity fire that scarred trees but did not burn with enough intensity to initiate a post-fire cohort. Plots with fire scars and a corresponding post-fire 23 cohort indicated plots that burned at a moderate to high- intensity sufficient to provide a suitable environment for tree establishment although some trees survived. Plots with only a post-fire cohort were those plots that burned with high enough intensity to initiate a new cohort, leave no local fire scars, and destroy any fire scar evidence from previous fires. For each fire year, plots with remnant trees that did not scar or form cohorts indicated the plot did not burn or burned with an intensity too low to scar trees. This latter evidence was strongest at plots with existing scarred trees at the time of fire, because trees that have previously scarred are much more likely to scar during subsequent fires. 2.3 Results 2.3.1 Forest composition Douglas-fir constituted 100% of the trees in all but 1 plot that included trembling aspen (Populous tremuloides Michx.). Douglas-fir trees were up to 149 cm in diameter and 456 years of age (Table 2.1). Tree densities in the control area varied dramatically and were 633±766 trees ha-1 (mean ± standard deviation) with 182±84 trees ha-1 in the canopy and 450±784 trees ha-1 in the subcanopy. All plots in the control had subcanopy densities of ≤ 581trees ha-1 except for 1 plot that had a density of 3 537 trees ha-1. Due to harvesting, as part of the treatment for mule deer (Day, 1998), mean tree densities in the treatment area were generally lower with 376±297 trees ha-1 with 152±85 trees ha-1 in the canopy and 224±299 trees ha-1 in the subcanopy. 24 Table 2.1: Summary of forest composition and structure in 35 plots located in the control and treatment areas of the long-term mule deer experiment in Knife Creek. Canopy Subcanopy TotalControl 1 28.9 ± 11.7 [46.3] 116 ± 24 [144] 158 113 2713 24.3 ± 10.6 [49.8] 104 ± 24 [160] 237 263 5005 30.3 ± 12.0 [55.0] 207 ± 71 [373] 278 345 6237 34.4 ± 16.8 [69.5] 150 ± 45 [302] 204 188 3929 28.5 ± 9.2 [43.8] 122 ± 15 [139] 108 3537 364510 32. 8 ± 20.5 [82.1] 185 ± 96 [352] 54 273 32714 30.2 ± 14.5 [62.5] 154 ± 26 [228] 85 120 20517 30.3 ± 19.4 [70.7] 146 ± 21 [194] 172 158 33018 26.6 ± 10.0 [44.6] 134 ± 11 [146] 353 81 43421 24.3 ± 9.8 [44.7] 149 ± 40 [305] 138 393 53122 32.3 ± 21.5 [81.9] 158 ± 70 [340] 46 581 62723 27.9 ± 8.5 [44.0] 139 ± 5 [144] 162 376 53824B 33.0 ± 29.3 [46.4] 153 ± 31 [220] 245 278 52327 26.1 ± 16.6 [90.4] 153 ± 23 [172] 237 537 77428 25.2 ± 12.4 [48.1] 109 ± 20 [163] 188 345 53330 27.0 ± 14.5 [55.0] 132 ± 35 [212] 129 111 24031 26.2 ± 10.1 [44.3] 139 ± 13 [185] 197 268 46533 23.6 ± 10.0 [43.2] 144 ± 20 [177] 300 136 436Treatment 2 27.2 ± 13.4 [70.4] 114 ± 23 [161] 126 118 24414 36.5 ± 25.0 [80.8] 166 ± 77 [356] 140 241 38114B 32.7 ± 19.6 [92.6] 166 ± 49 [347] 105 289 39416 26.2 ± 17.5 [89.9] 131 ± 9 [139] 254 92 34616B 29.5 ± 14.3 [74.6] 156 ± 41 [309] 70 360 43019 29.4 ± 20.3 [86.6] 124 ± 74 [328] 71 141 21220 24.7 ± 10.8 [57.1] 128 ± 18 [169] 278 43 32122 35.5 ± 22.3 [75.9] 165 ± 116 [457] 78 140 21824 33.7 ± 21.3 [68.8] 139 ± 66 [291] 134 1326 146024B 25.3 ± 9.2 [43.2] 137 ± 7 [151] 306 249 55527 36.4 ± 23.9 [109.8] 133 ± 34 [241] 180 185 36528 29.0 ± 9.8 [49.7] 142 ± 3 [148] 225 46 27129 29.9 ± 16.2 [60.6] 208 ± 120 [391] 71 229 30033 32.1 ± 20.5 [89.9] 134 ± 50 [261] 116 126 24235 33.5 ± 15.8 [65.2] 215 ± 64 [292] 278 34 31237 29.0 ± 13.4 [66.1] 134 ± 31 [209] 94 62 15649 27.0 ± 16.2 [71.8] 135 ± 51 [289] 52 130 182Tree densities (ha-1 )Location Plot ID DBH (cm)Mean ± SD [Max.]Age (yrs)Mean ± SD [Max.]25 2.3.2 Fire history 2.3.2.1 Fire occurrence and frequency Of the 35 plots, 29 contained fire-scarred trees (Table 2.2, Figure 2.2). In total, 79 fire-scarred cross-sections yielded 367 fire scars of which 305 had clearly visible fire scar tips that could be precisely dated and 282 scars corresponded to ≥1 other fire-scarred tree in the study area. Fire histories at individual plots ranged from 150 to 555 years, based on the inner ring date of oldest tree in each plot. One to 10 fire scar years were recorded per plot. At the 27 plots with ≥2 fires, mean fire intervals ranged from 18 to 111 years, however the majority of plots (73%) had mean intervals <40 years. Current plot-level fire-free intervals (72-184 years) exceeded their corresponding historical maximum fire intervals (23-113 years) at all but one plot. Four plots had only 1 fire-scar year and 6 included no fire scars. Based on the composite fire record, including data from all plots combined, fires burned in 23 different years from 1619 to 1943 with intervals between 7 and 32 years and a mean interval of 15 years. (Figure 2.2). Widespread fires occurred in 1790, 1817, 1831, 1840, 1863, and 1905 with 19, 13, 13, 19, 30, and 13 out of the 35 sites containing evidence (scars and/or post-fire cohorts) of these fires, respectively. The fire in 1863 stands out as the most widespread fire year when 41% of cross sections and 86% of plots contained evidence of this fire. 26 Table 2.2: Fire record for the 35 plots in Knife Creek listed by severity through time then by record length. *Indicates a fire year and subsequent metrics associated with post-fire cohort. Fire-scar years (n) Post-fire cohorts (n) Mean (yrs) Range (yrs) Year Time since fire Remnants (%) SeverityT33 9 0 1584-2015 30 9-60 Low 1905 111 67 ModerateC28 7 0 1615-2015 41 14-113 Low 1863 153 5 HighC07 4 0 1635-2015 29 23-42 Low 1905 111 94 LowC03 8 0 1646-2015 27 13-42 Low 1905 111 29 ModerateT14B 7 0 1656-2015 27 9-47 Low 1863 153 65 ModerateC10 8 0 1673-2015 24 13-32 Low 1863 153 39 ModerateT22 2 0 1712-2015 111 111 Low 1840 176 22 ModerateT24 5 0 1715-2015 29 23-42 Low 1905 111 69 ModerateC21 7 0 1743-2015 25 13-32 Low 1905 111 100 LowC27 7* 1 1460-2015 37* 9-113* Mixed 1840* 175* 0* High*T27 6 1 1558-2015 53 32-73 Mixed 1895 121 84 LowC31 11* 1 1587-2015 19* 9-37* Mixed 1863* 153* 5* High*T14 5 1 1592-2015 29 23-42 Mixed 1905 111 94 LowT49 3 1 1597-2015 37 32-42 Mixed 1905 111 58 ModerateC24B 7* 1 1621-2015 26* 9-42* Mixed 1943 73 100 LowC05 4 1 1651-2015 38 23-50 Mixed 1905 111 84 LowT16B 9 1 1666-2015 18 11-32 Mixed 1863 153 26 ModerateT20 4 1 1666-2015 38 23-50 Mixed 1905 111 79 ModerateC18 2 1 1691-2015 73 73 Mixed 1863 153 0 HighC22 7 1 1712-2015 18 9-26 Mixed 1863 153 21 ModerateC01 6 1 1721-2015 28 23-42 Mixed 1930 86 85 LowC23 7* 1 1722-2015 19* 12-32* Mixed 1863* 153* 0* High*T37 6 1 1726-2015 23 9-42 Mixed 1905 111 75 ModerateT19 5 1 1732-2015 29 23-42 Mixed 1905 111 53 ModerateT28 3 1 1748-2015 52 23-80 Mixed 1943 73 100 LowC14 2* 1 1788-2015 23* 23* Mixed 1863 153 42 ModerateT16 2* 1 1800-2015 32* 32* Mixed 1863* 153* 0* High*C30 1* 1 1804-2015 - - High 1863* 153* 15* High*C09 1 1 1818-2015 - - High 1863 153 0 HighC17 1* 1 1822-2015 - - High 1863* 153* 28* Moderate*T24B 1* 1 1865-2015 - - High 1863* 153* 0* High*T29 0 0 1625-2015 - - Unclassified - - - -T35 0 0 1724-2015 - - Unclassified - - - -C33 1 0 1823-2015 - - Unclassified 1863 153 26 ModerateT02 0 0 1855-2015 - - Unclassified - - - -Fire evidence Fire return intervals Last firePlot Fire record Fire severity through time27 Figure 2.2: Fire record from 1460 to 2015 for all plots at Knife Creek. Horizontal lines represent the plot-level fire record and are stratified by fire regime severity through time and record length. Dashed lines indicate the years prior to a fire scar forming on ≥1 sampled trees; solid lines represent the years in which ≥1 trees with ≥1 scars are present at each plot. Black triangles represent fire years, grey triangles represent the first year of each post-fire cohort, and white triangles represent the first year of each cohort. The upper bottom panel shows the percentage of plots recording ≥1 fire scar (bars) relative to the number of plots that included ≥1 scarred tree that was susceptible to scarring. The lower bottom panel shows the study area age structure histogram. 28 2.3.2.2 Establishment dates and cohorts Trees established between 1559 and 1962, however, 77% of all trees have established since the most widespread fire in 1863 (Table 2.1, Figure 2.3). Only 2% of trees recruited into the canopy since the last fire 1943. Among the 35 plots, cohorts established from 1844 to 1899. Twenty-two were deemed post-fire cohorts and 2 were not associated with a fire. Post-fire cohort dates ranged from 1844 to 1877. Nine cohorts comprised the oldest trees in their respective plots. The contingency analysis revealed that cohort establishment was significantly associated with dry, rather than wet, periods (p = 0.07, α = 0.05, df = 1). 2.3.2.3 Fire severity through time Fire scars and post-fire cohorts varied among plots and indicated fires burned at a range of severities through time. Eighteen plots (54%) were classified as mixed severity, 4 (11%) as high severity, 9 (26%) as low severity and 4 (11%) were unclassifiable (Table 2.2). The mixed severity plots included the oldest and most recent recorded fires (1619-1943). Eleven of the mixed-severity plots recorded ≥5 fire scar years and had 1 post-fire cohort, which established after the 1840 (3) and 1863 (18) fires (Table 2). The plots with low-severity fires through time recorded between 2 and 9 fires and had no post-fire cohorts. The percentage of remnant trees after the most recent fire in each plot revealed a range of severities. The most recent fire at 8 plots was low severity, 15 were moderate severity, and 6 were high severity. Three plots had neither a fire-scar year nor a post-fire cohort and could not be classified. Both plot-level fire histories through time and severity of the last fire indicate a mixed-severity fire regime for the study area. 29 Figure 2.3: Fire history reconstructed from fire scars and post-fire cohorts organized by severity through time and record length for the 35 plots. The Histograms include all Douglas-fir trees that were crossdated. Year of establishment (upper limit of 15-year bins in calendar years)Frequency (number of trees)C01 (n = 20)C03 (n = 17)C09 (n = 19)T16 (n = 19)20100T35 (n = 19)T24B (n = 20)T29 (n = 20)C30 (n = 20)T19 (n = 17)T14 (n = 18)LowMixedHighC07 (n = 15)2010020100201002010020100201001450 1600 1750 1900201001450 1600 1750 1900 1450 1600 1750 1900T02 (n = 20)1450 1600 1750 1900T22 (n = 17)C27 (n = 19) T27 (n = 19)T33 (n = 18)C31 (n = 20)C24B (n = 17) C05 (n = 19)T14B (n = 20)T16B (n = 19) T20 (n = 19)C10 (n = 18)C18 (n = 19)C22 (n = 19)T24 (n = 16)C23 (n = 20) T37 (n = 20)C21 (n = 19)T28 (n = 19)C33 (n = 19)1450 1600 1750 1900T49 (n = 19)C28 (n = 20)C14 (n = 19)C17 (n = 18)Fire-scar yearPost-fire cohortDouglas-firCohortNoClass30 2.3.2.4 Spatial reconstruction The spatial distribution of fire scars, combined with cohorts revealed variation in fire severity within and among the 6 most widespread fires (Figures 2.4 and 2.5). Of the 35 plots, 29 included trees that established prior to 1790 and had the potential to record fire. The fire in 1790 scarred trees in 19 (62%) plots, with a post-fire cohort establishing in one of them. Although 9 other plots included living trees and 3 plots contained previously scarred trees that were susceptible to fire, no scars formed. Both plots with and without scars were distributed throughout the study area. In 1817, 31 plots had the potential to record fire and trees were scarred in 13 (42%) of them. Of the 16 (58%) plots that did not record fire, 10 included previously scarred trees. All but one plot with fire evidence was located in the north and west parts of the study area, although plots without fire evidence were adjacent to and between plots with scars. In 1831 and 1840, 33 plots included trees with potential to record fire. The fire of 1831 resulted in fire scars in 13 (39%) plots. All but 1 plot with fire evidence were located in the north and east parts of the study area. Twelve (36%) of the 19 (60%) plots without fire evidence included previously scarred trees. Three plots recorded this as their last fire and all had <20% remnant trees, which indicates this fire burned with patches of high severity. The fire of 1840 left evidence at 20 (61%) of 33 plots with the potential to record fire. Scars formed in 16 plots and a post-fire cohort established in 1 of them. Post-fire cohorts formed in another 3 plots without a scar from this year. Plots with cohorts were located in the north and 31 east parts of the study area, while scars formed throughout the study area. The 1840 fire was the last recorded in 2 plots; severity was classified as moderate in 1 plot (22% remnants) and high in the other (0% remnants). Of the 11 (35%) of plots without fire evidence, 7 (22%) included trees with existing scars. Plots without fire evidence were located throughout the study area, adjacent to and between plots with scars. All 35 plots included trees with the potential to record the 1863 and 1905 fires. Evidence of the 1863 fire in 30 (86%) plots indicated it was widespread and relatively severe (Figure 2.5). Fire scars formed in 24 (69%) plots and were accompanied by cohorts in 11(31%) plots. Cohorts established in another 6 plots, for a total of 17 (49%) plots with post-fire cohorts. Although fire scars formed throughout the study area, cohorts establishing after the 1863 fire were primarily in the south and west parts of the study area. The 1863 fire was the last recorded in 15 plots; severity was classified as moderate in 7 plots and high in 8 plots. Plots without fire evidence were located throughout the study area, adjacent to and between plots with scars and cohorts. The 1905 fire was relatively low in severity. Trees were scarred in 13 (37%) of the 35 plots, but no cohorts resulted. All plots, except 1, were in the west and north of the study area and included relatively young cohorts that had established after the 1840 and 1863 fires. The 1905 fire was the last recorded in 11 plots; it was classified as low-severity in 4 plots (>81% remnant trees per plot) and moderate severity in 7 plots (61±17% remnant trees per plot). Of the 22 plots with no evidence of fire, 16 plots contained previously existing scarred trees. 32 Figure 2.4: Spatial pattern of fire scar evidence for the 6 most widespread fires at the 35 sampled plots. One-hectare circular plots (to scale) display the distribution of fire scar evidence. Only plots containing trees old enough to record fire are shown. Legends indicate the relative percentage of each criteria used for inferring the fire severity at each plot for each fire. 0 500 1000Meters33 Figure 2.5: Spatial pattern of fire severity based on fire scar evidence (see figure 2.4) for the 6 most widespread fires at the 35 sampled plots. One-hectare circular plots (to scale) display the distribution of fire severity. Only plots containing trees old enough to record fire are shown. Red indicates high severity, orange indicates moderate-high severity, yellow indicates low-moderate severity, white plots indicate no fire, bolded white plots indicate no fire but with fire-scarred trees present at time of fire. 0 500 1000Meters1817 18311840 1863 1905179034 2.4 Discussion 2.4.1 Historical fire frequency and severity The temporal trends are consistent with other research on historical fire regimes of Douglas-fir forests in the Cariboo Region (Iverson et al. 2002; Daniels, 2004; Harvey et al., 2017), as well as the North Thompson-Okanagan Region, which includes the hottest and driest climates in B.C. (Gray and Riccius, 1999; Heyerdahl et al., 2007, 2012; Pogue 2017). Similar regimes with frequent surface fires have also been reconstructed for mixed-conifer forests that include Douglas-fir in southeastern B.C. (Marcoux et al., 2013; Greene and Daniels, 2017) and the Pacific Northwest of the US (Heyerdahl et al., 2001; Agee, 2003; Wright and Agee, 2004). The use of mean composite intervals in historical fire reconstructions in dry forest ecosystems has been criticized for overestimating fire frequency and underestimating fire return intervals (e.g., Baker, 2012), because the mean intervals often combine fire scar evidence from plots over large areas (>100ha) and fires in dry forests are often patchy (<80 ha) (Spies et al. 2006). However, the high density of plots and the small size of my study area allows greater confidence that the mean composite interval accurately reflects the historical frequency. Although moderate- to high-severity fires were reconstructed in the mid-1800s, there was no evidence of a widespread high-severity event that initiated the forest across the study area over the 396-year reconstruction. This reconstruction of the mixed-severity fire regime differs from the classical interpretation that frequent low-severity fires are punctuated by periodic high-severity fires that initiate even-aged forests at intervals of c. 250 years (e.g. Natural Disturbance Type 4 in BCMOF 1996; Shinneman and Baker, 1997; Klenner et al., 2008; Williams and Baker, 2012; Odion et al., 2014). Instead, the spatio-temporal reconstructions showed frequent low- or 35 moderate-severity fires that included fine-scale patches that burned at higher-severity and generated cohorts. Similar spatial patterns have been found in other studies where evidence of smaller high-severity patches between 0.1 ha and 100 ha have been found within low- to moderate-severity fire (Scholl and Taylor, 2010; Colins and Stephens, 2010; Halofsky et al., 2011; Harvey et al., 2017). 2.4.2 Historical severity within fires In dendroecological reconstructions with appropriate sampling and research design, the timing, severity and spatial extent of past fires are inferred from the presence and location of fire-scarred trees and post-fire cohorts (Swetnam et al., 1999, 2011; Sherriff and Veblen, 2006; Beaty and Taylor, 2008; Heyerdahl et al., 2012; Chavardes and Daniels, 2016). The absence of scars and cohorts from a plot can be equally informative, but yield multiple possible interpretations including: fire did not burn that plot, fire burned at low severity leaving no scars or cohorts, or fire burned but the evidence no longer persists (Gutsell and Johnson, 1996; Baker and Ehle, 2001; Stephens et al., 2010). The latter explanation is a widely recognized limitation of dendroecological reconstructions of historical fires. Subsequent disturbances, tree mortality, and wood decay diminish the quantity and quality of physical evidence of past fires, with cumulative effects moving back through time (Swetnam et al., 1999; Veblen, 2002; Falk et al., 2007, 2011). At Knife Creek, only half of the plots included trees that established prior to 1700. The decreasing number of plots with the potential to record fires partly explains the reduction in the number of fire scars in the early part of the record. Thus, the lack of fire evidence between 1460 and 1700 does not necessarily indicate absence of fire over that period. Given this limitation, the 36 following discussion focuses on the fires that burned from 1790 through 1905, for which multiple lines of evidence persist. In Knife Creek, scars and cohorts indicated the six most widespread fires from 1790 to 1863. The fire evidence from these 6 fires revealed substantial internal heterogeneity with which individual fires burned at scales of 10 to 102 meters. Individual trees and clumps of trees survived even the most widespread and severe fires of 1840 and 1863. The fire in 1863 resulted in cohorts at 17 plots; 4 and 1 cohorts were associated with the fires in 1840 and 1790, respectively. The establishment of cohorts suggests that these fires burned with patches of high severity sufficient to cause overstory tree mortality and improve levels of light and nutrients available to survivors and seedlings (Harvey et al., 2017). Subsequent to 1863, five fires (1876, 1895, 1905, 1930, 1943) burned at low to moderate severity and/or were localized, scarring trees at only 1 or 2 plots. The fire in 1905 is the exception, which scarred trees at 13 plots but generated no cohorts, indicating it was more widespread but burned at mostly low to moderate severity. Evidence of the 1790 fire included fire scars in 19 plots, but only one plot included a cohort. One interpretation is that the presence of scars but absence of cohorts suggests the 1790 fire burned primarily at low severity. However, if it burned at moderate or high severity and generated post-fire cohorts in multiple plots, subsequent fires would have eliminated that evidence. The temporal proximity of the 5 fires between 1790 and 1840 has important implications for the interpretation of fire severity based on the absence of cohorts. Due to the high frequency of fires burning at intervals of 9 to 15 years, the absence of cohorts in plot-level 37 age structures does not necessarily mean a cohort did not form. Of the 19 plots with scars from the 1790 fire, 5 burned again in 1805, 11 in 1817, 9 in 1831, and 12 in 1840. Nine plots burned three times and 5 plots burned four times during this 50-year interval. Thus, it stands to reason that the cumulative effects of these subsequent fires may have destroyed any cohorts that established after the 1790 fire. Interestingly, the single plot with a cohort that established after the 1790 fire and persisted to be sampled is the only plot with no evidence of a subsequent fire until 1840. It is possible that any additional cohorts from the 1790 fire would have persisted had fires not burned from 1805 to 1840. The same can be said for all but the most recent fires represented in my fire history reconstruction. In contrast to the 1790 fire, the 1863 fire was classified as a moderate- to high-severity event based on the post-fire cohorts evident at 17 sites. Only 23% of sampled trees persisted through this fire, thus post-fire establishment accounts for 77% of the current living canopy and subcanopy trees that largely comprise the current closed-canopy forest structure. The lack of cohorts before, and after, this fire suggest it may have been unusual, perhaps representing a periodic stand-initiating event within the mixed-severity fire regime. In this case, development of a post-fire even-aged stand would explain the absence of subsequent fires (Odion et al., 2014). An alternative interpretation is that the severity of the 1863 fire was not unusual. As described above, equally high-severity fires earlier in the record may no longer be evident due to subsequent fires. Since the 1863 fire was the last fire to burn at 43% of plots, the cohorts that resulted from it may have persisted due to exclusion of fires during much of the 20th century. 38 Interpretation of the relative severity of the 1863 fire is important for understanding the current structural state of forests in Knife Creek and determining if the historical fire regime has been altered. Comparison of evidence among plots corroborates the interpretation that the 1863 fire was a mostly moderate-to-high in severity but also included patches that likely did not burn. The 17 plots with post-fire cohorts had the fewest survivors (mean 9%, range 0-28%) and the 13 plots where fire scars formed also had low survivorship (mean 32%, range 6-74%). Most trees that survived the 1863 fire were in the subset of 5 plots without a fire scar or post-fire cohort. The persistence of fire-scarred trees and cohorts formed in 1863 was influenced by the number, size, and severity of subsequent fires. After 1863, 4 of 5 fires scarred trees in only 1 or 2 plots. Only the 1905 fire had the potential to substantively impact the structure of forests because it was the only widespread fire. However, multiple lines of evidence suggest it burned mostly at low severity and resulted in little impact on the size and age structure of the forest. On average, 74% (range 29-100%) of trees survived at the 13 (37%) plots with scars and no post-fire cohorts established after the 1905 fire. Combined these lines of evidence indicate the 1863 fire was relatively high in severity, but it did not initiate a new even-aged stand. Instead, the near absence of fires in the 20th century explains the contemporary forest age and size structures. 2.4.3 Contemporary fire regime The absence of fire scars and new cohorts in the 20th century is strong evidence of a change in fire regime at Knife Creek (Swetnam, et al., 1999). From 1905 to 2015, all plots included relatively young and small subcanopy trees and 19 (54%) plots included live, fire-scarred trees that would have been especially susceptible to scarring in the event of a fire. The absence of widespread fire after 1863 closely corresponds to other historic fire reconstructions (Heyerdahl et 39 al., 2012; Marcoux et al., 2013; Charvardes and Daniels, 2016; Harvey et al., 2017) and the historical land use in the area (Parminter, 1991; Day, 1997; Gray et al., 2002). The absence of fire in Knife Creek after 1863 coincides with the disruption of Indigenous traditional land use following the Gold Rush in the 1860s in combination with agricultural developments including cattle grazing and more effective fire suppression beginning in the 1900s (Parminter, 1991; Day, 1998; Gray et al., 2002). The Knife Creek block lays within the traditional territory of the T’exelcemc, members of the Secwepemc Nation (NStQ, 2014). The forest contains sacred ceremonial grounds and the Jones Creek river valley, which is adjacent to the block, was a well-established travel route between seasonal camps (oral history share with and reported by Day, 2007). Therefore, the historical fire record reconstructed in this study has likely captured fires deliberately set for purposes of land care and resource management (Parminter, 1978; Arno, 1985; Daigle and Penty, 1996; Turner, 1999; Day, 2007; Turner and Krannitz, 2001). Similar to other parts of British Columbia (Turner, 1999), fire was likely used to manage forests for food and medicinal plants and for hunting animals at or near Knife Creek. During the Gold Rush and continuing through to the 1940s, the Cariboo region was rapidly colonized by Europeans as ranches and road houses were established along the Cariboo Wagon Road, which lays adjacent to the Knife Creek block (Day, 1998). To provide travelers and miners with food and other supplies, many of these ranchers used controlled burning to promote the expansion of meadows and grasslands to provide feed for livestock (Parminter, 1991; Day, 1998). In 1874, the Bush Fire Act was implemented, which introduced fines or imprisonment if a purposely-set fire resulted in damage to private or crown land (Parminter, 1991). However, the 40 Act had no organized enforcement and was not enforced unless two thirds of a district petitioned to request action (Parminter, 1991). It was not until the Forest Act of 1912 that there was financial support to equip and maintain a fire prevention force (Parminter, 1991). Since the 1940s, firefighting technology improved substantially, and both fire scar and documentary records indicate the majority of fires have been successfully suppressed (Iverson et al., 2002; Daniels, 2004; BCWS 2010, 2012). Tree-ring evidence, consistent with oral history and documentary records, indicate changes in human land-use and fire suppression have altered the fire regime at Knife Creek relative to the historical range of variation. As observed in other dry mixed-conifer forests, contemporary fire regimes have led to changes in forest structure that have made dry forests less resilient to fire (Morgan et al., 1994; Holling and Meffe, 1996; Moore et al., 1999; Arno and Allison-Bunnell, 2002; Schoennagel et al., 2004; Fulé et al., 2013). There is compelling evidence that in the absence of frequent fires, a substantial degree of forest structural diversity in Knife Creek has been lost (Hessburg et al., 1999, 2005; Spies et al., 2006; Harvey et al., 2017). Over the last fire-free period, it is likely canopy cover and continuity has increased, structure has become more layered, and open areas with grass and shrubs in the understory have been replaced by trees. (Hessburg et al., 1999; Spies et al., 2006). In addition, accumulations of ladder and surface fuels have also occurred (Hessburg et al., 1999; Hessburg et al., 2005; Spies et al., 2006; Harvey et al., 2017). Plots within Knife Creek that have not been harvested have a density of 307±137 trees ha-1 (mean ± standard deviation) with 182±84 trees ha-1 in the canopy and 450±784 trees ha-1 in the subcanopy. These high densities, particularly subcanopy densities, leave Knife Creek at 41 increased risk of a high-severity stand-replacing fire (Spies et al., 2006; Harvey et al., 2017) and necessitate treatments to mitigate this threat. The re-introduction of fire to the landscape via managed wildfire, prescribed fire, and/or other forms of fuel mitigation can reduce chances of high-severity fire while being an effective component of ecological restoration (Covington et al., 1997; Moore et al., 1999; Swetnam et al., 1999; Stephens and Moghaddas, 2005; Johnson et al., 2011; Hessburg et al., 2016). Management objectives for Knife Creek should aim to restore the historical diversity of forest structures by reducing stand density and increasing stand-level heterogeneity (Millar et al., 2007; Halofsky et al., 2011; Klenner et al., 2008; Hessburg et al., 2016). Based on my results, treatments should remove a significant portion of the canopy. Before the most widespread and severe fire in 1863, unharvested areas of the Knife Creek were potentially 77% less dense than they are today. Decreasing the density of unharvested areas of Knife Creek by up to 70% would result in a mean tree density of 92 trees ha-1, which is congruent with historical densities suggested to have been common for dry forest types and for the reduction in crown fire potential (Covington and Moore, 1994; Harrod et al., 1999; Arno and Allison-Bunnell, 2002; Fulé et al., 2009; Johnson et al., 2011). However, this is not entirely in line with treatments for mule deer winter habitat where treated areas have been left with 196 ± 111 (Mean ± SD) trees per hectare. Therefore, a potential compromise will be necessary if treatments are to satisfy more than 1 management priority. Either way, for thinning treatments to be effective at reducing crown fire potential, the removal of large surface fuels is necessary (Agee and Skinner, 2005; Hessburg et al., 2016; Leclerc et al., 2017). The large amount of fuels left behind after thinning has been found to significantly increase the risk of high-severity fire (Agee and Skinner, 2005; Leclerc et 42 al., 2017). To restore the fine scale and diverse mosaic of forest structures that would have resulted from the historical mixed-severity fire regime, my results suggest treatments should vary at scales between 1 to 20 ha and be congruent with the historical mean fire frequency of 15 years. 2.5 Conclusion This study contributes to a growing body of literature on fire regimes in dry forests in British Columbia (Gray and Riccius, 1999; Iverson et al., 2002; Daniels, 2004; Arsenault and Klenner, 2005; Heyerdahl et al. 2007; Klenner et al., 2008; Daniels and Gedalof, 2012; Heyerdahl et al., 2012; Marcoux et al., 2013, 2015; Greene and Daniels, 2017; Harvey et al., 2017; Pogue, 2017). A decade ago, there was little published information on the disturbance history in dry forest ecosystems (Klenner et al., 2008) and the geographical extent of historical mixed-severity fire regimes in the dry Douglas-fir forests was poorly understood (Wong and Iverson, 2004). My dendroecological reconstructions of 35 plots within a 98-ha study area revealed historical fires that burned at a wide range of frequencies and severities at both the plot- and study-area scales. The 23 fires that were recorded between 1619 and 1943 included multiple low- to moderate-severity fires that scarred trees, while the most widespread fire in 1863 was moderate in severity with patches of higher severity that generated cohorts at local scales only. These results indicate the severity of fires varied at fine spatial scales, and offer little support for the notion that periodic, high-severity, stand-initiating events were a component of the mixed-severity fire regime at Knife Creek. Many studies consider fires in the late 1800s relatively severe because they generated new cohorts of trees, and, thus, emphasize the importance of high-severity fires in a mixed-severity fire regime. In my study area, the most wide-spread and severe fire was not a 43 stand-initiating fire. Cohorts that established after the 1863 fire persisted due to an absence of fire in the 20th century and are an artifact of fire exclusion and suppression. The focus of future restoration and management in Knife Creek should aim to reduce stand densities while increasing fine-scale stand-level heterogeneity. 44 3 An analysis of contemporary western spruce budworm outbreaks in a central interior dry forest in British Columbia, Canada. 3.1 Introduction Defoliating insects are a natural component of forested ecosystems throughout the world. The western spruce budworm (Choristoneura occidentalis Freeman) is the most prominent defoliating insect in coniferous forests in western North America (Steed et al., 2007). It primarily feeds on Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and true fir (Abies spp.) (Hicke et al., 2012). The life cycle of the western spruce budworm lasts 1 year (Harris et al., 1985; Brookes et al., 1987; Murdock et al., 2012). Adults mate and lay eggs in late July and August on the underside of needles (Harris et al., 1985; Murdock et al., 2012). After roughly 10 days, the eggs hatch and larvae spin hibernaculae in which they spend the winter (Harris et al., 1985; Brookes et al., 1987; Murdock et al., 2012). The larvae emerge the following spring in mid- to late-May near the time of budburst and begin feeding (Harris et al., 1985; Brookes et al., 1987; Murdock et al., 2012). Larvae preferentially feed on new foliage, but will feed on pollen, cones, buds, and old needles until buds flush (Brookes et al., 1987; Murdock et al., 2012; Nealis, 2012). Once mature in July, adult moths mate and disperse to lay their eggs (Harris et al., 1985). The western spruce budworm is an endemic defoliator of interior Douglas-fir forests, but periodically experiences landscape-scale outbreaks (Harris et al., 1985). Tree-ring studies have shown that over the last 500 years, outbreaks have been consistent and repetitive with a mean return interval of around 30 years (Axelson et al., 2015; Campbell et al., 2006; Alfaro et al., 2014). These outbreaks vary in severity but typically last between 1 and 13 years and occur 45 throughout the range of Douglas-fir (Fellin and Dewey, 1982; Harris et al., 1985; Maclauchlan et al., 2006; Volney and Flemming, 2007; Axelson et al., 2015; Maclauchlan et al., 2018). Repeated consumption of foliage can cause growth loss, regeneration failure, and in extreme cases kill part or all of a tree (Alfaro et al., 1982; Harris et al., 1985; Steed et al., 2007). In addition, by decreasing tree vigour, defoliation can also increase a tree’s susceptibility to other pests and diseases (Maclauchlan et al., 2018). Western spruce budworm outbreaks are influenced by ecological processes operating across multiple spatial scales (Nealis and Régnière, 2009; Flower et al., 2014; Senf et al., 2017). Individual host-insect interactions combined with regional-scale variations in climate all impact the susceptibility of a forest to defoliation (Swetnam and Lynch, 1993; Shepherd, 1994; Ryerson et al., 2003; Nealis and Régnière 2009; Flower et al., 2014; Senf, 2017). For example, stands located within regions where climate promotes synchronous budworm emergence with bud swelling are more susceptible to defoliation (Hard et al., 1980; Thomson et al., 1984; Shepherd, 1985, 1992; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003). At the stand scale, canopy composition and structure are the most significant factors contributing to a forest’s susceptibility to defoliation (Swetnam and Lynch, 1993; Shepherd, 1994; Alfaro et al., 2001; Nealis and Régnière 2009; Senf, 2017). Highly susceptible stands are dense, multilayered, and with a high proportion of mature Douglas-fir (Swetnam and Lynch, 1993; Shepherd,1994; Heppner and Turner, 2006; Maclauchlan et al., 2006; Senf et al., 2017). Increased density and multilayered stands with high proportions of Douglas-fir sustain larger populations of western spruce budworm for longer periods by easing the dispersal of caterpillars downward through the canopy, improving the chances of finding suitable food, and reducing mortality (Carlson et al., 46 1983; McCune, 1983; Brookes et al., 1987; Fredericks and Jenkins, 1988; Swetnam and Lynch, 1989, 1993; Maclauchlan and Brooks, 2009; Nealis et al., 2009; Nealis and Régnière, 2009; Senf, 2017). Dense forests also lead to increased moisture stress and leave trees less resilient to drought and at higher risk of attack by insects such as western spruce budworm and Douglas-fir beetle (Dendroctonus pseudotsugae Hopkins) (Carlson et al., 1983; McCune, 1983; Fredericks and Jenkins, 1988; Swetnam and Lynch, 1989; Hessburg et al. 1994; Keane et al., 2002; Youngblood and Wickman, 2002; Guarin and Taylor, 2005; Parker et al., 2006; Maclauchlan and Brooks, 2009; Perry et al., 2011; Murdock et al., 2012; Flower et al., 2014; Senf et al., 2017). Individual trees growing on the driest sites and/or with low productivity exhibit low vigour and are believed to be more susceptible because they have less resources to develop the secondary metabolites used for tree defense (Alfaro et al., 2001; Senf et al., 2017). Until the late 1950s, the extent and severity of western spruce budworm outbreaks were determined from ground-based surveys (Harris et al., 1985; Alfaro et al., 2014). In 1959, systematic aerial surveys were initiated by the Canadian Forest Service (1959-1996), and later by the Province of British Columbia (1999 to present), to more efficiently record the location and severity of forest disturbances (B.C. Ministry of Forests Lands and Natural Resource Operations and Rural Development, 2018). In addition to historical documents and aerial surveys, dendroecological reconstructions have been able to identify periods of prolonged growth suppression (Swetnam and Lynch, 1989; Campbell et al., 2006; Flower et al., 2014; Axelson et al., 2015), which are thought to be the signals of past western spruce budworm outbreaks. 47 Records of western spruce budworm outbreaks in British Columbia extend back to 1909 on Vancouver Island and 1916 in the south-central interior of province (Harris et al., 1985). Recent tree-ring research has suggested repeated western spruce budworm outbreaks in interior Douglas-fir forests for several centuries (Campbell et al., 2006; Axelson et al., 2015; Maclauchlan et al., 2018). Since the documentary records began, the western spruce budworm has affected close to 60% of the Douglas-fir dominated stands in the province (Harris et al., 1985; Murdock et al., 2012). In general, western spruce budworm outbreaks appear to be increasing in frequency, duration, extent, and severity (Swetnam et al., 1995; Maclauchlan, 2003; Maclauchlan et al., 2006, 2011, 2018; Marciniak, 2015). Over the last 2 decades, based on aerial surveys there appears to have been a shift in the occurrence of western spruce budworm outbreaks into higher latitudes and higher elevations putatively due to climate change (Marciniak, 2015). The most recent outbreak in British Columbia (1987-2015) lasted 29 years and progressed well outside the historical range of previously recorded outbreaks (Maclauchlan et al., 2011). By the 1990s, over 200 000 ha exhibited signs of moderate to severe defoliation (Westfall and Ebata, 2000-2011) and by 2003 the outbreak peaked with 500 000 ha affected (Maclauchlan et al., 2006). Early observations of this outbreak suggested that the levels of defoliation in the north were particularly severe (Maclauchlan et al., 2018). Outbreaks such as this, along with the understanding that western spruce budworm was not historically found in the south-central interior (Erickson, 1992), has resulted in the perception that contemporary frequencies, durations, extent, and severities of western spruce budworm outbreaks are increasing (Maclauchlan et al., 2006). This trend is concerning due to possible negative impacts on timber supply and future ecosystem services (Kurz et al., 2008; Dymond et al., 2010). This is 48 particularly relevant in the Cariboo Forest Region where industry is increasingly turning to Douglas-fir to fulfill timber supply in lieu of lodgepole pine (Pinus contorta Doug. ex. Loud.), which was decimated by the recent mountain pine beetle epidemic. Currently, the dry mixed-conifer forests of western North America, including the interior of British Columbia, are believed to be increasing in susceptibility to western spruce budworm and, as a consequence, are experiencing more frequent and severe outbreaks than before the beginning of industrial forestry and fire suppression (Carlson et al., 1983; McCune, 1983; Anderson et al., 1987; Fredericks and Jenkins, 1988; Swetnam and Lynch, 1989; Hessburg et al., 1994; Keane et al., 2002; Youngblood and Wickman, 2002; Parker et al., 2006; Maclauchlan and Brooks, 2009; Murdock et al., 2012; Flower et al., 2014). Harvesting practices and fire suppression combined have increased the extent and homogeneity of dense, multi-layered forests composed of suitable hosts (Fredericks and Jenkins, 1988; Hessburg et al., 1994; Shepherd, 1994; Keane et al., 2002; Youngblood and Wickman, 2002; Parker et al., 2006; Maclauchlan and Brooks, 2009; Neilis and Régnière, 2009). Natural stand development post-clearcut along with an absence of thinning can produce dense stands with high crown closure (Swetnam and Lynch, 1993; MacLauchlan and Brooks, 2009; Nealis and Régnière, 2009; Senf et al., 2017). Similarly, fire suppression lengthens the fire-free interval within dry mixed-conifer forests and allows the growth of understory trees creating dense multilayered stands (Iverson et al., 2002). Evidence suggests that due to the abundance of foliage, these multilayered stands may support higher budworm populations (Shepherd, 1994; Maclauchlan and Brooks, 2009). Within the Knife Creek block, tree-ring evidence indicates changes in human land-use and fire suppression have substantially altered the fire regime relative to the historical range of variation (Chapter 2). The 49 current fire free interval is unprecedented in the last 555 years, and in this time a substantial degree of forest structural diversity in Knife Creek has likely been lost (Chapter 2), as noted elsewhere in the region (Hessburg et al., 1999, 2005; Spies et al., 2006; Harvey et al., 2017). Specifically, host tree density has increased, and structure has become more layered due to an increase in subcanopy density (Chapter 2), suggesting that the forest has become more susceptible to a severe western spruce budworm outbreak. The warming and drying trend for the south-central interior of British Columbia potentially compounds forest structural changes to further increase susceptibility of forests to budworm defoliation (Johns et al., 1997; Mote, 2003; Columbia Mountains Institute of Applied Ecology 2005; BCFLNRO, 2015). It is hypothesized that warming temperatures will contribute to a northward expansion of defoliators (Choristoneura spp.) by improving timing of larval emergence with bud break while improving over-winter survival and host susceptibility (Ryerson et al., 2003; Campbell et al., 2006; Reynolds et al., 2007; Candau and Fleming, 2011; Thomson and Benton, 2007). Evidence of this has already been recorded with recent outbreaks infesting higher elevations and more northern ranges of Douglas-fir stands, where no previous records of exposure exist (Maclauchlan et al., 2006; Woods et al., 2010; Marciniak, 2015). In addition to warmer temperatures, changes in moisture availability such as drought have also been identified as key factors contributing to outbreaks (Hard et al., 1980; Thomson et al., 1984; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003, Maclauchlan et al., 2006; Maclauchlan and Brooks, 2009; Flower et al., 2014; Senf et al., 2017). 50 Determining if dry mixed-conifer forests are particularly susceptible to western spruce budworm defoliation is an integral factor that must be considered as part of any management plan (Shepherd, 1994). Highly susceptible stands have the potential to act as epicenters of outbreaks by producing an abundance of gravid dispersing females that increase the susceptibility of surrounding stands (Shepherd, 1994; Senf et al., 2017). My research aimed to determine whether contemporary outbreak trends of frequency, duration, and severity, are outside the historical range of variation as well as to gain some insight into the outbreak dynamics between canopy and subcanopy trees. My research focused on the Knife Creek block of the Alex Fraser Research Forest and addressed 4 questions: 1. How frequent, severe, and prolonged were historical outbreaks of western spruce budworm in the Knife Creek block? 2. Have western spruce budworm outbreaks become more frequent, prolonged, and severe in the 20th century? 3. Do infestations initiate in the canopy or subcanopy and has this changed through time? 4. Are levels of defoliation dependent upon subcanopy density? I predicted that western spruce budworm outbreaks have been common at the Knife Creek block for the last several centuries, but that the frequency, duration, and severity of contemporary outbreaks have increased due to an increased density of subcanopy trees resulting from land-use changes and fire suppression in the 20th century. I also predicted that if contemporary subcanopy 51 densities are contributing to increased levels of defoliation, that levels of defoliation will increase with measures of subcanopy density. An increase in the frequency, duration, and severity of outbreaks in the 20th century as well as evidence of altered canopy-subcanopy dynamics would necessitate a re-assessment of current forest management. Specifically, it would provide support for restoration and management prescriptions aimed at increasing structural diversity and reducing host and subcanopy densities in interior Douglas-fir forests. A greater understanding of the western spruce budworm outbreak dynamics between canopy and subcanopy trees will allow us to better predict and manage future outbreaks. Figure 3.1: Location of 35 sampled plots in Knife Creek located 24 km southeast of Williams Lake, British Columbia. 500meters1000 200 300 400PlotsCopyright 2018 GoogleImage Copyright 2018 Digitalglobe52 3.2 Materials and methods 3.2.1 Study area My study area is located in the central interior of the province, which is largely made up of the Fraser Plateau with elevations typically ranging between 900 to 1 500 meters above sea level (masl). The landscape is characterized by level to gently rolling hills crossed with deep river valleys. The western boundary of the region comprises the Chilcotin Plateau and beyond this are the Coast Mountains, which have elevations up to 4 000 masl. The Coast Mountains along with prevailing westerly winds put the central interior in a rain shadow. However, as air masses from the Pacific Ocean move east over the region, humidity levels and precipitation slightly increase (Wang et la., 2012). The Knife Creek block (hereinafter referred to as Knife Creek) is 3,487 hectares and located 24 km southeast of the town of Williams Lake. The forests here are comparable in structure and climate to many dry mixed-conifer forests in the montane forests of western North America. My study area included a 98-hectare section at the northwest boundary, which is typically comprised of stands of Douglas-fir and lodgepole pine in the canopy with varying densities of Douglas-fir regeneration. However, due to the recent mountain pine beetle epidemic, living, mature lodgepole pine is largely absent from the canopy. The study area has varying elevations from 761 to 879 masl and average annual precipitation is 451 mm/yr with approximately half falling in the growing season (Environment Canada, 2017 – Williams Lake). Summer months are mostly dry but are punctuated with precipitation from convective storms. July is the warmest month with a mean daily temperature of 16°C and mean monthly precipitation of 61 mm (Environment Canada, 2017 – Williams Lake). Winter months are dominated by Arctic air 53 masses resulting in stretches of extreme cold (Steen and Coupé, 1997). December is the coldest month with a mean daily temperature of -7°C and mean monthly precipitation of 48 mm (Environment Canada, 2017 – Williams Lake). Mule deer winter range is the highest management priority in Knife Creek, where a long-term experiment is underway to test a silvicultural system being considered as a method of dry forest management in BC (Day 1997). In the experiment, harvesting in the treatment areas has focused on removing relatively low numbers of trees between long periods of no removals to promote a clumpy arrangement of wide-crowned trees (Day 1998). These methods are designed to remove timber while maintaining sufficient forest cover to provide areas with snow interception, low snow accumulation, and forage that mule deer require (Day 1997). In 1984, 80 permanent sample plots were established c. 100 meters apart along 15 systematically placed transects creating a regular grid (Armleder and Thomson, 1984; Koot et al., 2015). This grid covers 98 hectares and includes 3 control (C#) and 3 treatment (T#) units. I sampled every other plot, which resulted in 17 in the treated units and 18 in the control units for a total of 35 plots (Figure 3.1). 3.2.2 Sampling 3.2.2.1 Reconstructing historical western spruce budworm outbreaks To reconstruct outbreaks of western spruce budworm from tree rings, I used the increment cores sampled to reconstruct forest dynamics (Chapter 2). They were sampled from 35 plots to reconstruct fire history and forest dynamics. I used an n-tree design (Jonsson et al., 1992) to sample the 10 closest live canopy and subcanopy trees to plot center with ≥12.5 cm diameter at 54 breast-height (dbh). Canopy trees were classified as those visually assessed to comprise the emergent, dominant, and codominant crown classes (Adams et al., 1994). Subcanopy trees were those in the intermediate and suppressed crown classes (Adams et al., 1994). To assess tree ages and growth histories, one increment core was removed from each tree. Cores were sampled between 20-30 cm from the base of the trees and coring height and diameter at coring height were recorded. When needed, multiple cores were extracted to ensure they intersected or were close to the pith. 3.2.2.2 Sample preparation Cores were air-dried, repaired after drying using hot glue and wooden supports, and all cores were mounted in grooved wooden boards. Samples were sanded following standard methods so that after surfacing, individual cells and ring boundaries were clearly visible (Stokes and Smiley 1996; Swetnam et al., 1985). High resolution digital images (2400 – 4800 dpi) were taken and included the bark and pith when present. 3.2.2.3 Measuring and crossdating Using the high-resolution images, I measured the annual ring increments on the cores to the nearest 0.01 mm using the program CooRecorder (Larsson, 2011a). I used the program CDendro (Larsson, 2011b) to crossdate the ring widths against an existing regional Douglas-fir chronology (Daniels, 2004). This allowed me to identify false and missing rings and assign accurate calendar years to the tree-rings. To verify that samples were properly crossdated, I statistically verified the dated ring-width series using the program COFECHA (Holmes, 1986). Forty-five 55 cores (6%) that could not be crossdated with a correlation coefficient ≥0.25 were excluded from further analysis. 3.2.3 Outbreak detection Defoliation caused by western spruce budworm reduces tree photosynthetic capacity and can severely suppress radial growth (Kramer and Kozlowski, 1979). Historical outbreaks of western spruce budworm were inferred from periods of prolonged growth suppression in tree-ring series from individual trees (Alfaro et al., 1982; Swetnam and Lynch, 1989; Holmes and Swetnam, 1996; Axelson et al., 2015). Each ring-width series was standardized using a negative exponential curve and a 50-year 50% frequency response cubic spline in the program ARSTAN (Cook and Peters, 1981; Cook et al., 2007; Axelson et al., 2015). Standardizing preserves the long-term growth trend while removing biological and geometric trends so that other factors causing growth variation, such as insects, fire, and climate, can be detected (Veblen et al., 1991). The climate-driven component of the radial growth pattern was taken into account using a non-host species that is not affected by the budworm but still sensitive to the same climatic factors as the host (Campbell et al., 2005; Axelson et al., 2015). Convention is to use non-host chronologies from the same or adjacent stands (Swetnam and Lynch, 1989). For this study we used a lodgepole pine chronology developed in the 1990s (Daniels, 2004) because few lodgepole pine grow in the study area and most mature trees were killed by mountain pine beetle since the 1990s. Lodgepole pine and Douglas-fir make a suitable host-non-host pair because they exhibit positive correlations between radial growth and previous July to current June precipitation (Griesbauer and Green, 2010; Lo et al., 2010; McLane et al., 2011, Axelson et al., 2015). 56 To detect suppressions potentially caused by western spruce budworm, individual standardized Douglas-fir ring-width series were compared with the regional standardized lodegpole pine chronology using the program OUTBREAK (Holmes and Swetnam, 1996, Axelson et al., 2015). OUTBREAK applies user-defined criteria to detect prolonged growth reductions in host trees that are asynchronous with the non-host chronology. To identify growth reductions potentially caused by budworm, 2 user-defined criteria initially identified by Axelson et al. (2015) were applied: (1) reduction in growth was ≥ −1.28 standard deviations below average and (2) growth suppression was sustained ≥ 8 years. Detected suppressions were confirmed by visually re-evaluating the cores to ensure the host trees exhibited a significant reduction in growth in the years identified using the program OUTBREAK (Holmes and Swetnam, 1996). Visual verification reduced the chances of false positive detections. After accounting for biological, geometric, and climatic factors influencing radial growth rates, I inferred growth reductions in Douglas-fir were due to defoliation by the western spruce budworm (Axelson et al., 2015; Flower et al., 2014; Swetnam and Lynch, 1989). 3.2.4 Outbreak duration, frequency, and severity Plot-level infestations were reconstructed for the periods with a minimum sample depth of 4 trees (Axelson et al., 2015). The initiation year of an infestation was the first year in which ≥15% of trees at each plot recorded a suppression in radial growth (Axelson et al., 2015) preceded by 5 consecutive years where <15% of trees recorded growth suppression (Gray et al., 1999). The cessation of an infestation was the last year the number of trees showing suppressed growth was ≥15% (Axelson et al., 2015) followed by 5 consecutive years where <15% exhibited suppression (Gray et al., 1999; Axelson et al., 2015). Duration was calculated as the number of 57 years between and including initiation and cessation dates. Return intervals were determined as the number of years intervening each consecutive infestation initiation date. The maximum percentage of ring series from each plot recording an infestation at any given year within the outbreak was used as a proxy to classify the severity. Minimum thresholds of 15, 50, and 75% of ring series recording an infestation at each plot determined if they were low, moderate, or high severity, respectively (Axelson et al., 2015). Summary infestation statistics including durations and return intervals were calculated for each plot. To develop a study-area outbreak record, I compiled the plot-level infestation records then truncated the record to a minimum of 7 plots (20%) to keep with the minimum sample depth at the plot level and make the inferences of a study-area level outbreak more robust. To determine the duration, return intervals, and severity, I used the percentage of trees recording an infestation at each plot per year and calculated the mean across all plots. Then I applied the same criteria that were used with the plot-level infestation records. 3.2.5 Canopy/Subcanopy outbreak dynamics To determine if outbreak dynamics differed between canopy and subcanopy trees, I conducted three tests. For each plot, I identified the subset of infestations that included both subcanopy and canopy trees. For each, I identified the first canopy and subcanopy tree to show ring suppression and recorded the first year (e.g., initiation dates) of both trees. I compared the mean initiation dates of suppressions first detected in canopy versus subcanopy trees using a t-test (Whitlock and Schluter, 2009). The mean initiation dates of both canopy and subcanopy outbreaks were used to determine if the more recent increase in subcanopy density is contributing to suppressions. If so, 58 the mean date for suppressions initiating in the subcanopy should be more recent. The initiation lag between subcanopy and canopy trees was to provide a clearer picture of where the effects of infestations are initiating. The lag between strata was calculated as the initiation date of the subcanopy tree subtracted from the initiation date of the corresponding canopy tree such that values of zero indicated simultaneous initiation; negative values indicated infestations that began in the subcanopy; and, positive values indicated those that began in the canopy. Additionally, to test whether levels of total canopy defoliation increase along with increasing subcanopy densities I used simple linear regression to test whether the severity of the most recent outbreak (2001 – 2011) was affected by the density of subcanopy trees. At the plot level, severity was measured as the mean percentage of trees exhibiting suppressed rings from 2001 to 2011 and was regressed against (1) the density of subcanopy trees, and (2) the ratio of subcanopy versus canopy tree ratios. Data were log transformed to help better meet assumptions of simple linear regression. 3.3 Results 3.3.1 Forest composition Douglas-fir comprised 100% of the trees in all but 1 plot that included trembling aspen (Populous tremuloides Michx.). Douglas-fir trees were up to 149 cm in diameter and 456 years of age (Table 3.1). Tree densities in the control area were 633±766 trees ha-1 (mean ± standard deviation) with 182±84 trees ha-1 in the canopy and 450±784 trees ha-1 in the subcanopy. All plots in the control had subcanopy densities of ≤ 581trees ha-1 except for 1 plot that had a density of 3537 trees ha-1. Due to harvesting, as part of the treatment for mule deer (Day, 1998), mean tree densities in the treatment area were generally lower with 376±297 trees ha-1 with 152±85 trees ha-1 in the canopy and 224±299 trees ha-1 in the subcanopy. 59 Table 3.1: Summary of forest composition and structure in 35 plots located in the control and treatment areas of the long-term mule deer experiment in Knife Creek. Canopy Subcanopy TotalControl 1 28.9 ± 11.7 [46.3] 116 ± 24 [144] 158 113 2713 24.3 ± 10.6 [49.8] 104 ± 24 [160] 237 263 5005 30.3 ± 12.0 [55.0] 207 ± 71 [373] 278 345 6237 34.4 ± 16.8 [69.5] 150 ± 45 [302] 204 188 3929 28.5 ± 9.2 [43.8] 122 ± 15 [139] 108 3537 364510 32. 8 ± 20.5 [82.1] 185 ± 96 [352] 54 273 32714 30.2 ± 14.5 [62.5] 154 ± 26 [228] 85 120 20517 30.3 ± 19.4 [70.7] 146 ± 21 [194] 172 158 33018 26.6 ± 10.0 [44.6] 134 ± 11 [146] 353 81 43421 24.3 ± 9.8 [44.7] 149 ± 40 [305] 138 393 53122 32.3 ± 21.5 [81.9] 158 ± 70 [340] 46 581 62723 27.9 ± 8.5 [44.0] 139 ± 5 [144] 162 376 53824B 33.0 ± 29.3 [46.4] 153 ± 31 [220] 245 278 52327 26.1 ± 16.6 [90.4] 153 ± 23 [172] 237 537 77428 25.2 ± 12.4 [48.1] 109 ± 20 [163] 188 345 53330 27.0 ± 14.5 [55.0] 132 ± 35 [212] 129 111 24031 26.2 ± 10.1 [44.3] 139 ± 13 [185] 197 268 46533 23.6 ± 10.0 [43.2] 144 ± 20 [177] 300 136 436Treatment 2 27.2 ± 13.4 [70.4] 114 ± 23 [161] 126 118 24414 36.5 ± 25.0 [80.8] 166 ± 77 [356] 140 241 38114B 32.7 ± 19.6 [92.6] 166 ± 49 [347] 105 289 39416 26.2 ± 17.5 [89.9] 131 ± 9 [139] 254 92 34616B 29.5 ± 14.3 [74.6] 156 ± 41 [309] 70 360 43019 29.4 ± 20.3 [86.6] 124 ± 74 [328] 71 141 21220 24.7 ± 10.8 [57.1] 128 ± 18 [169] 278 43 32122 35.5 ± 22.3 [75.9] 165 ± 116 [457] 78 140 21824 33.7 ± 21.3 [68.8] 139 ± 66 [291] 134 1326 146024B 25.3 ± 9.2 [43.2] 137 ± 7 [151] 306 249 55527 36.4 ± 23.9 [109.8] 133 ± 34 [241] 180 185 36528 29.0 ± 9.8 [49.7] 142 ± 3 [148] 225 46 27129 29.9 ± 16.2 [60.6] 208 ± 120 [391] 71 229 30033 32.1 ± 20.5 [89.9] 134 ± 50 [261] 116 126 24235 33.5 ± 15.8 [65.2] 215 ± 64 [292] 278 34 31237 29.0 ± 13.4 [66.1] 134 ± 31 [209] 94 62 15649 27.0 ± 16.2 [71.8] 135 ± 51 [289] 52 130 182Tree densities (ha-1 )Location Plot ID DBH (cm)Mean ± SD [Max.]Age (yrs)Mean ± SD [Max.]60 3.3.2 Outbreak history 3.3.2.1 Outbreak severity, frequency, and duration Six hundred and fifty-five cores were successfully crossdated with the longest ring-width series from 1559 to 2015. Combined, the 35 plot-level infestation histories included 192 infestation records between 1775, the first year sample depth reached 4 trees in at least one plot, to 2011 (Table 3.2). At individual plots, I detected 4 to 9 infestations with durations of 16±8 years and return intervals of 30±10 years. Infestations varied in magnitude with at least 2 severity classes recorded at all plots. Among all plot level infestation records, 63 were classified as low in severity, 63 and 66 were classified as moderate and high in severity, respectively. At the study-area level, 8 outbreaks initiated between 1800 and 2001 (Figure 3.2, Table 3.3). Durations ranged from 8 to 28 years; however, the majority of outbreaks (75%) lasted ≥11 years. Return intervals ranged from 18 to 42 years, with a mean return interval of 29±9 years. Three (38%) of the 8 outbreaks were classified as low severity and initiated in 1818, 1875, and 2001. Three outbreaks (38%) were moderate severity and initiated in 1800, 1851, and 1959 and 2 outbreaks (25%) were high severity and initiated in 1895 and 1934. 61 Table 3.2: Infestation records for the 35 plots in the Knife Creek study area. Table 3.3: Summary statistics for the 8 study-area-level western spruce budworm outbreaks reconstructed at Knife Creek. Mean Range Mean Range Low Moderate HighC01 4 1882-2011 17 11-27 35 26-41 1 2 1C03 4 1906-2011 12 10-15 23 18-26 2 1 1C05 9 1799-2011 13 8-27 25 15-43 5 1 3C07 5 1871-2011 15 8-26 32 18-45 2 2 1C09 4 1892-2011 17 10-28 35 23-42 1 1 2C10 9 1775-2011 13 10-16 26 16-42 4 0 5C14 4 1851-2011 27 14-42 35 23-44 0 2 2C17 5 1868-2011 13 10-16 32 19-44 2 1 2C18 5 1876-2011 15 8-23 25 19-39 1 2 2C21 6 1863-2011 12 10-17 25 17-37 2 3 1C22 8 1791-2011 15 8-31 30 20-47 3 3 2C23 5 1877-2011 14 10-27 26 20-33 3 0 2C24B 6 1858-2011 15 9-27 29 18-41 3 1 2C27 6 1854-2011 14 8-24 30 20-41 3 1 2C28 4 1905-2011 14 10-19 29 19-45 1 2 1C30 6 1869-2011 14 8-22 25 19-36 3 1 2C31 4 1879-2011 15 8-22 36 32-38 1 0 3C33 5 1862-2011 16 8-28 32 22-42 2 2 1T02 4 1889-2011 17 11-27 35 26-41 1 1 2T14 8 1800-2011 14 8-27 25 14-41 2 3 3T14B 6 1832-2011 16 8-28 33 20-42 3 1 2T16 5 1876-2011 16 10-31 32 21-46 1 3 1T16B 6 1847-2011 14 11-22 31 20-50 3 1 2T19 4 1883-2011 18 10-28 36 25-41 1 2 1T20 5 1878-2011 15 8-26 31 17-41 0 3 2T22 6 1788-2011 22 11-36 42 27-61 2 2 2T24 4 1880-2011 19 11-28 34 26-42 1 2 1T24B 6 1878-2011 12 8-15 25 17-36 1 4 1T27 5 1878-2011 14 10-16 27 17-40 2 1 2T28 5 1875-2011 20 10-37 32 20-42 0 4 1T29 8 1775-2011 17 8-47 29 18-55 2 2 4T33 4 1861-2011 23 8-53 32 24-39 0 2 2T35 6 1775-2011 21 14-29 35 29-45 1 3 2T37 5 1870-2011 16 10-31 32 19-43 1 2 2T49 6 1866-2011 12 10-16 25 18-36 3 2 1PlotPeriod ofrecordNumber ofOutbreaksDurations (yrs) SeveritiesReturn intervals (yrs)Outbreak Number Start End Duration (yrs) Return Interval (yrs) Severity1 1800 1807 8 - Moderate2 1818 1826 9 18 Low3 1851 1867 17 33 Moderate4 1875 1885 11 24 Low5 1895 1908 14 20 High6 1934 1949 16 39 High7 1959 1986 28 25 Moderate8 2001 2011 11 42 Low62 Figure 3.2: Study-area level western spruce budworm outbreaks reconstructed from the 35 plots at Knife Creek. Outbreak reconstructions were truncated to a minimum sample depth of 7 plots. Left y-axis (solid line) is the mean of the percentage of trees at each plot recording a suppression; right y-axis is the sample depth (the number of plots with sufficient samples to record an outbreak).63 3.3.2.2 Canopy/Subcanopy infestation dynamics Of the 192 plot-level infestations, 164 were recorded as suppressions of both subcanopy and canopy trees. The mean initiation dates of infestations first indicated by canopy and subcanopy trees were in the years 1930 (±43 years) and 1933 (±43 years), respectively, and were not significantly different (t-test, p = 0.52). Of the 164 infestations, 59 initiated in the canopy, 59 initiated in the subcanopy, and 46 initiated simultaneously (Figure 3.3). For infestations initiating in the canopy, canopy trees became suppressed 5.3±4.6 years before those in the subcanopy. For outbreaks initiating in the subcanopy, subcanopy trees became suppressed 3.9±4.6 years before those in the canopy revealing wide variation in initiation dates. The regression analyses using log transformed data determined plot-level subcanopy tree density (R2 = 0.02; p = 0.43) and the ratio of subcanopy to canopy tree densities (R2 = 0.02; p = 0.48) to be poor predictors of the mean percentage of trees exhibiting suppression during the outbreak from 2001 to 2011 (Figure 3.4). 64 Figure 3.3: Lag in initial suppression of subcanopy trees relative to canopy trees during 164 plot-level infestations reconstructed from subcanopy and canopy trees. Negative values indicate outbreaks that initiated in the subcanopy; positive values indicate outbreaks that initiated in the canopy. Figure 3.4: Simple linear regression testing severity against subcanopy density and the ratio of subcanopy density to canopy density. Scatter plots show severity against the ratio of subcanopy density to canopy density (left) (R2 = 0.02, p = 0.48) and severity against subcanopy density (right) (R2 = 0.02, p = 0.43). -25 -20 -15 -10 -5 0 5 10 1550454035302520151050Lag in initiation of outbreaks in subcanopy relative to canopy trees (years)Frequency (n)65 3.4 Discussion 3.4.1 Historical frequency, duration, and severity Dendrochronological analysis confirms that the western spruce budworm has been an important driver of forest dynamics at Knife Creek in the south-central interior of British Columbia for at least the past 242 years. This result is consistent with documentary records and other tree-ring reconstructions that have all indicated the budworm has been present in the southern and central interior of the province for some time (Harris et al., 1985; Erickson, 1992; Parfett et al., 1994; Campbell et al., 2005, 2006; Maclauchlan et al., 2018). From 1775 to 2011, 8 study-area level outbreaks were recorded, which is equivalent (Campbell et al., 2006) or similar to the number presented in Axelson et al. (2015), and Maclauchlan et al. (2018) over comparable periods. The reconstructed outbreak history in this study closely mirrors the documentary records (Harris et al., 1985; Erickson, 1992; Parfett et al., 1994; Westfall and Ebata, 2000-2011) and other tree-ring reconstructions in (Axelson et al., 2015; Maclauchlan et al., 2018) and around (Campbell et al., 2006; Maclauchlan et al., 2018) the south-central interior. The Knife Creek reconstruction captures what appears to have been one of the most widespread and severe outbreaks in the south and central interior of the province between 1934 and 1949; an event that is well-documented in FIDS records and appears in other reconstructions as a particularly widespread and severe event (FIDs records pre 2000s; Harris et al., 1985; Campbell et al., 2006; Axelson et al., 2015; Maclauchlan et al., 2018). Furthermore, the most recent reconstructed outbreak was also detected and mapped by the provincial aerial survey from the early 2000s to 2011 (Westfall and Ebata, 2000-2011). 66 Slight discrepancies between the number and timing of recorded outbreaks in this study and others is expected due to the varying impacts that budworm populations have across the landscape (Maclauchlan et al., 2018). Many studies have illustrated that the timing and duration of outbreaks are synchronous across regions (Campbell et al., 2005; Flower et al., 2014; Ellis and Flower; 2017), but nonetheless, the heterogenous distribution of budworm populations result in varying impacts on host trees (Maclauchlan et al., 2018). Therefore, the timing and extent of outbreaks within the record of this study and others differ slightly due to variations in their impacts at smaller scales (Swetnam and Lynch, 1993; Ryerson et al., 2003; Campbell et al., 2006; Axelson et al., 2015). This study along with others has provided evidence that contemporary outbreaks in the south-central interior are not outside the historical range of variation (Anderson et al., 1987; Swetnam et al., 1995; Maclauchlan et al., 2006; Maclauchlan and Brooks, 2009; Axelson et al., 2015). Throughout the reconstructed record, outbreaks of a range of frequencies, durations, and severities were well-represented across plots and at the study area scale with no discernable trend. The number of years between outbreak initiations varied widely through time, ranging from 18 to 42 years, and the 2 longest intervals occurred in the 20th century. In addition, the time between the cessation and initiation of a subsequent outbreak has remained relatively consistent (15±8 years). The durations of outbreaks also varied substantially over time with a range of 8 – 28 years (14±6). Contemporary outbreaks (1950-present) have been among the least severe. The most severe outbreaks occurred between 1900 and 1950 where maximum means of 77 and 86% trees per plot with suppressed rings attributed to budworm defoliation. The 2 outbreaks that occurred after 1950 had maximum means of 32 and 51%. 67 3.4.2 Canopy-subcanopy dynamics Throughout its range, western spruce budworm outbreaks have been reported as increasing in frequency (Swetnam et al., 1995; Maclauchlan and Brooks, 2009), duration (Anderson et al., 1987; Maclauchlan et al., 2006, Maclauchlan and Brooks, 2009), and severity (Anderson et al., 1987; Swetnam et al., 1995; Maclauchlan and Brooks, 2009). These apparent changes have mostly been attributed to changes in forest structure (Hadley and Veblen, 1993; Swetnam and Lynch, 1993; Shepherd, 1994; Alfaro, 2001; Heppner and Turner, 2006; Nealis and Régnière, 2009), particularly as a result of fire exclusion. In the absence of fire, historical structures that were once patchy and heterogeneous have become multilayered, dense, and homogeneous (Hessburg et al., 1999; Keane et al., 2002; Hessburg et al., 2005; Spies et al., 2006; Harvey et al., 2017). Dense and homogeneous stands favour the budworm by increasing the number and extent of suitable hosts (Keane et al., 2002; Maclauchlan and Brooks, 2009; Senf et al., 2017). In addition, the increased density of understory trees support budworm populations by decreasing budworm dispersal mortality (Swetnam and Lynch, 1993; Shepherd, 1994; Maclauchlan and Brooks, 2009; Nealis and Régnière, 2009). Tree-ring evidence along with oral history and documentary records indicate that drastic changes in human land-use and fire suppression have altered the fire regime at Knife Creek relative to the historical range of variation (Chapter 2). The absence of fire scars and establishment of new host-tree cohorts in the 20th century (Chapter 2) provides clear evidence of a change in fire regime at Knife Creek (Swetnam, et al., 1999). Over the last century, canopy cover and continuity have increased, and structure has become denser and more multilayered (Hessburg et 68 al., 1999; Spies et al., 2006). Given the progressively denser subcanopy through the 20th century, it was suspected that infestations would begin to occur more frequently in the subcanopy and that these increased densities would result in increased outbreak severity. However, I found that the number initiating in the canopy and subcanopy were equal, the mean date of subcanopy infestation initiations was not different from that of canopy initiations, and subcanopy density did not significantly impact severity. The results of the canopy-subcanopy analyses suggest other factors besides subcanopy densities are having a more significant impact on susceptibility to budworm outbreaks. There is ample evidence that within regionally homogenous climates, factors such as increased subcanopy density and vertical complexity can facilitate or dampen the expansion of outbreaks but that these effects are not as influential as larger scale factors (Shepherd, 1994; Senf et al., 2017), such as climate that extend beyond the stand scale (Hard et al., 1980; Thomson et al., 1984; Shepherd, 1985, 1992; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003; Maclauchlan et al., 2006; Raffa et al., 2008; Maclauchlan and Brooks, 2009; Flower et al., 2014; Senf et al., 2017). Regarding stand-level factors, Senf et al. (2017) found that host abundance and proximity to the previous year’s defoliation were by far the most influential. This, I believe may partly explain the lack of any relationship between subcanopy densities and severity in my study. The size of my study area (98 ha) and the tight spacing of my plots leads to a scenario where any effects of subcanopy density were likely overridden by proximity. Additionally, we cannot be certain at what densities subcanopies begin to influence levels of defoliation, how sensitive budworm populations are to varying densities of subcanopy trees, and therefore whether we should expect to see any signal of varying severity originating from the different densities at 69 individual plots. Excluding 2 of my outlying plots with 1 326 and 3 537 subcanopy trees per hectare, mean subcanopy densities were 214 ± 136 (Mean ± SD) trees per hectare. These differences between the subcanopy densities at individual plots may not be large enough to influence budworm populations to the degree in which detectable variation in levels of defoliation can occur. Overriding these, could potentially be the stress of a colder climate. The Knife Creek block is located near the northernmost extent of the historical budworm distribution so there is potential that the stress of colder climate may be offsetting any benefits of increased food supply. It is difficult to interpret canopy-subcanopy outbreak dynamics from tree-ring reconstructions because variability surrounding budworm effects on canopy and subcanopy trees can contribute to discrepancies in radial growth reduction and subsequently infestation initiation dates. The intensity and timing of defoliation, the age and type of foliage that is removed, and the physiological state of each individual tree all influence the effect of defoliation on radial growth (Mason et al., 1997). Also, during the initial stages of an outbreak, while budworm populations are still growing, radial losses due to low levels of defoliation may be undetectable (Alfaro et al., 1982). Trees typically have stored photosynthate and enough foliage to compensate for minor losses so that defoliation must reach 50% before detectable radial growth suppression occurs (Alfaro et al., 1982). Therefore, the subcanopy trees, having smaller crowns and being suppressed due to competition for light and other resources, are potentially less vigorous and more sensitive to additional stressors. It is then possible that the infestations where the initiations began with a subcanopy tree simply exhibited suppressed radial growth earlier due to a greater sensitivity to defoliation. The result of this source of error could be an overestimation 70 of the number of infestations recorded as initiating in the subcanopy. The corrected chronologies of Douglas-fir may also contain variation in ring widths unrelated to budworm defoliation because the non-host species used to correct for climate is not exact. This potentially resulted in slight variations in the initiation and cessation of outbreak dates. There likely exist other forms of variability in the corrected Douglas-fir chronology, which may be misinterpreted as different initiation dates between subcanopy and canopy trees, low severity outbreaks, or have outright resulted in the reporting of false outbreaks. However, as mentioned earlier, the latter is unlikely because levels of crown defoliation must be around 50% before suppressions in radial growth can be detected (Alfaro et al., 1982). In dendroecological reconstructions with appropriate sampling and research design, the timing, duration, and severity of past outbreaks are inferred from the presence of radial growth reductions after both geometric and climatic corrections (Campbell et al., 2006; Axelson et al., 2015; Maclauchlan et al., 2018). These periods of growth suppression can be extremely revealing, but there are several limitations to tree-ring reconstructions of budworm outbreaks that can potentially influence results (Swetnam and Lynch, 1993). First, is that only surviving trees were sampled and so mortality has not been captured in outbreaks (Swetnam and Lynch, 1993). Subsequent disturbances, tree mortality, and wood decay diminish the quantity and quality of physical evidence of past disturbances, with cumulative effects moving back through time (Swetnam et al., 1999; Veblen, 2002; Falk et al., 2007; 2011). For this reason, it is difficult to determine whether the apparent high severity outbreaks earlier in the record were severe or whether what we are interpreting as high severity is only an artifact of disappearing evidence. Since severity at the plot level was measured as the percent of trees at each plot recording a 71 suppression, the decreasing number of trees with the potential to record outbreaks may partly explain the apparent increased severity of outbreaks in the early part of the record. For example, there is more confidence in the interpretation that an outbreak was severe when 15 out of 20 trees experienced radial suppression as opposed to 3 out of 4. Given this limitation, the discussion focuses on the severity of the outbreaks beginning in the 20th century when 74% of trees were able to record outbreaks. In addition, they do not measure those characteristics of outbreaks that cannot be measured using ring widths, such as range expansion and synchrony between larval emergence and host bud burst. Therefore, the results of this study do not entirely preclude changes to contemporary outbreak characteristics in these regards. 3.4.3 Potential future outbreak dynamics Much research predicts increasing frequency, duration, and severity of outbreaks into the future because of climate change combined with past and current forest practices (Swetnam and Lynch, 1993; Iverson et al., 2002; Maclauchlan et al., 2006; MacLauchlan and Brooks, 2009; Nealis and Régnière, 2009; Woods et al., 2010; Senf et al., 2017). Current literature maintains that stand factors such as species composition, stand structure, and site quality all influence the dynamics of outbreaks (Hadley and Veblen, 1993; Swetnam and Lynch, 1993; Shepherd, 1994; Alfaro, 2001; Heppner and Turner, 2006; Nealis and Régnière, 2009). Within regionally homogeneous climates, factors at the stand and landscape scales can facilitate or dampen the expansion of outbreaks (Shepherd, 1994; Senf et al., 2017), but these effects are not as influential as larger scale factors (Shepherd, 1994; Senf et al., 2017), such as climate that extend beyond the stand scale (Hard et al., 1980; Thomson et al., 1984; Shepherd, 1985, 1992; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003; Maclauchlan et al., 2006; Raffa et al., 2008; Maclauchlan 72 and Brooks, 2009; Flower et al., 2014; Senf et al., 2017). Therefore, my finding that contemporary outbreaks at Knife Creek are not unprecedented does not negate the potential that future outbreaks will become more frequent, prolonged, and severe. Research on the dynamics of outbreaks provide ample evidence that warming temperatures will contribute to a northward expansion of defoliators (Choristoneura spp.) by improving timing of larval emergence with bud break (Hard et al., 1980; Thomson et al., 1984; Shepherd, 1985; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003) while improving over-winter survival and host susceptibility (Ryerson et al., 2003; Campbell et al., 2006; Reynolds et al., 2007; Candau and Fleming, 2011; Thomson and Benton, 2007). Evidence of this has already been recorded with recent outbreaks infesting higher elevations and more northern ranges of Douglas-fir stands where no previous records of exposure exist (Maclauchlan et al., 2006; Woods et al. 2010; Marciniak, 2015). In addition to warmer temperatures, changes in moisture availability such as drought have also been identified as key factors contributing to outbreaks (Hard et al., 1980; Thomson et al., 1984; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003, Maclauchlan et al., 2006; Maclauchlan and Brooks, 2009; Flower et al., 2014). The association of western spruce budworm with drought is cause for concern and continued monitoring since records for the region for the past few decades have seen warming temperatures, earlier snowmelt, and subsequent increased occurrences of drought (Westerling et al., 2006). Future predictions for the Cariboo Region of British Columbia include a continued warming trend of 0.5°C per decade along with drier summers, which will only serve to exacerbate these trends (Johns et al., 1997; Mote, 2003; Columbia Mountains Institute of Applied Ecology 2005; BCMFLNRO, 2015). 73 3.5 Conclusion The results of this study do not support the hypothesis that the contemporary forest structure at Knife Creek has contributed to higher frequencies, durations, and severities of western spruce budworm outbreaks. Infestation initiation dates and severity do not appear to be significantly impacted by increased subcanopy densities. However, there remains great potential for future outbreaks to become more severe. Future research and management should focus on the processes and interactions across multiple scales (stand, landscape, and regional) to improve our understanding and capacity to predict the spatiotemporal patterns of outbreaks (Senf et al., 2017). These will help further illuminate the relative influence of each on a forest’s susceptibility to infestations. Additionally, an improved understanding of the relative importance of the canopy and subcanopy layers to budworm populations will help refine future management strategies at the stand scale. 74 4 Management and restoration 4.1 Chapter 2 summary Historical fires burned regularly within the interior Douglas-fir forests of the Knife Creek block with a mean interval of 15 years between 1619 and 1943. For most of that period, the majority (80%) of fires were either low or mixed severity. The spatial analysis indicated severity varied within individual widespread fires, with substantial internal heterogeneity at scales of 10 to 102 meters. The spatio-temporal reconstructions showed that the frequent low and moderate impact fires included fine-scale patches that burned at higher-severity and generated cohorts. However, beginning in the later part of the 19th century fire patterns changed. The current fire-free period substantially exceeds the historical maximum fire-free intervals at all but 1 plot. Although some trees established between 1559 and 1962, the majority (76%) of trees established between the most widespread fire in 1863 and the last recorded fire in 1943. Only 2% of trees have recruited into the canopy since 1943. I conclude that the near absence of fire in the 20th century explains the contemporary forest age and size structures. 4.1.1 Contributions to management Tree-ring evidence, consistent with oral history and documentary records, indicate that changes in human land-use and fire suppression have substantially altered the fire regime at Knife Creek relative to the historical range of variation. The absence of fire in Knife Creek after 1863 coincides with the disruption of Indigenous traditional land use following the Gold Rush in the 1860s in combination with agricultural developments including cattle grazing and more effective fire suppression beginning in the 1900s (Parminter, 1991; Day, 1998; Gray et al., 2002). 75 My results suggest that in the absence of frequent fires, a substantial degree of forest structural diversity in Knife Creek has been lost. Over the last fire-free period, it is likely that canopy cover and continuity has increased, structure has become more layered, and historically open areas with grass and shrubs in the understory have been replaced by trees (Hessburg et al., 1999; Spies et al., 2006). In addition, accumulations of ladder and surfaces fuels have also occurred (Hessburg et al., 1999, 2005; Spies et al., 2006; Harvey et al., 2017). Plots within Knife Creek that have not been harvested have a density of 307±137 trees ha-1 (mean ± standard deviation) with 182±84 trees ha-1 in the canopy and 450±784 trees ha-1 in the subcanopy. These high densities, particularly subcanopy densities, leave Knife Creek at increased risk of a high-severity stand-replacing fire (Spies et al., 2006; Harvey et al., 2017) and necessitate treatments to mitigate this threat. Furthermore, although moderate-to-high severity fires occurred in Knife Creek in the mid-1800s, there was no evidence of a widespread high-severity event that initiated the forest across the study area over the 396-year reconstruction. The most widespread fire in 1863 was moderate in severity with patches of higher severity that generated cohorts at local scales only. These results indicate the severity of fires varied at fine spatial scales and are contrary to the classical interpretation of mixed-severity fire regimes, which entails frequent low-severity fires that are punctuated by periodic high-severity fires that initiate even-aged forests at intervals of c. 250 years (e.g. Natural Disturbance Type 4 in BCMOF 1995; Shinneman and Baker, 1997; Klenner et al., 2008; Williams and Baker, 2012; Odion et al., 2014). 76 My spatio-temporal reconstructions showed that the Knife Creek area burned at low- or moderate-severity with fine-scale patches that burned at higher-severity, generating cohorts. Then, the cohorts that established after the 1863 fire persisted due to an absence of fire in the 20th century and are therefore an artifact of fire exclusion and suppression. Similar spatial patterns have been revealed in other studies where evidence of smaller high-severity patches between 0.1 ha and 100 ha have been found within low- to moderate-severity fires (Scholl and Taylor, 2010; Colins and Stephens, 2010; Halofsky et al., 2011; Harvey et al., 2017). My results provide strong evidence that our interpretation of mixed-severity fire regimes in dry mixed-conifer forests is incorrect and, therefore, current forest management in stands considered to support mixed-severity fire regimes needs to be re-assessed. Based on my results, if forest management is to mimic past fire disturbance in these landscapes, harvesting needs to be non-contiguous and promote heterogeneity in forest structures in line with these fine scale historical fire patterns. The results of my spatial reconstruction reveal the complexity in assessing the impacts of land-use change and fire exclusion on specific areas to prioritize prescriptions for restoration or fuels mitigation in mixed-severity fire regimes (Schoennagel et al., 2004, Veblen, 2002). Systems that generalize historical fire regimes across forest types and landscapes tend to oversimplify fire history and may encourage the inappropriate application of treatments. This can then threaten site productivity and/or result in diminished abundance of critical habitat (Tiedemann et al., 2000; Gray et al., 2002; Schoennagel et al., 2004; Feller, 2005 Schoennagel and Nelson, 2011). For example, mule deer winter range is the highest management priority in the Knife Creek block and is being considered as a method of dry forest management throughout the province of B.C. (Day 1997). Timber harvesting focuses on low-volume removals and long re-entry 77 intervals that promote a clumpy arrangement of wide-crowned trees (Day 1998). These harvest methods have been designed to allow the removal of timber while maintaining low snowfall areas and forest cover to provide snow interception and forage that mule deer require (Day 1997). Areas within the Knife Creek block that have been treated for mule deer winter habitat have lower mean tree densities that are congruent with historical densities suggested to have been common for dry forest types and for the reduction in crown fire potential (Covington and Moore, 1994; Harrod et al., 1999; Arno and Allison-Bunnell, 2002; Johnson et al., 2011). However, the general application of these methods to dry forests across the southern interior without knowledge of historical reference conditions may have unintended negative consequences on ecological processes and forest resilience (Tiedemann et al., 2000; Gray et al., 2002; Schoennagel et al., 2004; Feller, 2005 Schoennagel and Nelson, 2011). To develop sound management plans and/or prioritize restoration treatments, it is critical that historical reference conditions at the stand level are available (Landres et al., 1999; Swetnam et al., 1999; Keane et al., 2009; Schoennagel et al., 2004; Agee and Skinner, 2005). Having a clearer understanding of the historical fire regime allows us to predict what structural conditions were most likely within the historical range of variation and help avoid the inappropriate application of treatments. For instance, areas known to be more prone to low-severity fires would theoretically have been affected most by fire suppression/exclusion and have experienced the most substantial changes to forest structure and fuel accumulation (Hessburg et al., 2016). Additionally, fuel reduction treatments may not be necessary where we conclude surface and ladder fuel accumulation has been due to gradual infilling and not persistence due to fire suppression (Daniels, 2004; Baker, 2012; Marcoux et al., 2013). Unfortunately, while 78 knowledge of the historic fire regime before fire suppression and modern land-use practices is ideal (Landres et al., 1999; Swetnam et al., 1999; Keane et al., 2009; Schoennagel et al., 2004; Agee and Skinner, 2005), this information is costly and often unavailable (Klenner et al., 2008). Nonetheless, more thorough planning is required to ensure areas targeted for restoration are prioritized based on conditions at the stand level. For example, where historical knowledge is unavailable, ridge tops, areas adjacent to grasslands, and south facing slopes can be prioritized for restoration and/or fuels mitigation (Heyerdahl et al., 2015; Harvey et al., 2017). 4.1.2 Future research The results of my spatial analysis have highlighted a key component missing in many previous studies on fire regimes. Mixed-severity fires and their effects are not homogenous within fire boundaries and result in high structural heterogeneity. Therefore, including a spatial dimension in future studies is essential to guide both future forest management and restoration that aim to mimic mixed-severity fire regimes. In addition to temporal scales, definitions of fire regimes and any management based on these definitions need to consider stand, landscape, and regional spatial scales by including specific criteria on the spatial patterns of live and dead trees as well as the density of tree size classes and species (Spies et al., 2006). To further develop these definitions, future studies aiming to characterize mixed-severity fire regimes through time should include a fine-scale spatial aspect. Clearer definitions developed through future studies will help ensure management and restoration targets aiming to mimic historical fire disturbance fall within the historical range of variation. 79 In general, restoration prescriptions should aim to produce the diverse mosaic of forest structures across the landscape that would have reflected the historical fire regime. The success of any restoration attempt should also include the reduction in crown fire potential in areas valued for cultural, social, or economic reasons (Daniels, 2004). A landscape forest structure reflecting the full diversity of mixed-severity fires in time and space will also be more resistant and resilient to current and future disturbances (Hessburg et al., 2016). 4.2 Chapter 3 summary The reconstruction of western spruce budworm outbreaks confirms that the effects of defoliation have been an important driver of forest dynamics at the Knife Creek block for at least the past 244 years. Outbreaks occurred regularly every 18 to 42 years and lasted between 8 and 28 years. During this period, there was no discernable trend in either frequency, duration, or severity, leading me to conclude that contemporary outbreaks at Knife Creek are not unprecedented. In addition, my analyses of canopy-subcanopy dynamics did not lend any credence to the hypothesis that contemporary forest structure, specifically a denser subcanopy, is contributing to higher levels of defoliation. 4.2.1 Contributions to management Contemporary outbreaks are not unprecedented and as such do not necessitate an urgent re-assessment of current forest management or development of specific prescriptions aimed to reduce budworm populations. However, this does not negate the increasing body of evidence indicating that warming temperatures and increased occurrences of drought will contribute to a northward expansion of defoliators (Choristoneura spp.) by improving timing of larval 80 emergence with bud break and increasing over-winter survival and host susceptibility (Hard et al., 1980; Thomson et al., 1984; Campbell, 1993; Swetnam and Lynch, 1993; Ryerson et al., 2003; Campbell et al., 2006; Maclauchlan et al., 2006; Reynolds et al., 2007; Thomson and Benton, 2007; Maclauchlan and Brooks, 2009; Candau and Fleming, 2011; Flower et al., 2014). Although the influence of climate is out of a forest manager’s control, there exists great potential for mitigating the impacts of budworm outbreaks with forest management practices (Swetnam and Lynch, 1993; Shepherd,1994; Heppner and Turner, 2006; Maclauchlan et al., 2006; Senf et al., 2017). The overarching management objectives for dry interior forests should aim to reduce insect habitat quality and quantity while prioritizing restoration in stands that have seen repeated and severe defoliation and/or assessed to be highly susceptible (Shepherd, 1994). In these stands, the aim should be to diminish western spruce budworm population growth potential by reducing the quantity of suitable host foliage, increasing mortality during dispersal, enhancing natural predators, and decreasing foliage quality (Swetnam and Lynch, 1993; Muzika and Liebhold, 2000; Maclauchlan et al., 2006; Robert et al., 2012; Senf et al., 2017). Restoration of these stands can be achieved through a combination of selective harvesting reforestation, even-aged stand management, and prescribed fire. Selective harvesting in conjunction with reforestation with non-host species could help decrease the density and continuity of suitable hosts while creating a more diverse species composition and potentially reduce budworm synchrony and subsequent defoliation intensity across the landscape (Carlson et al., 1983; Shepherd, 1994; Robert et al., 2012; Senf et al., 2017). In addition to selective harvesting and reforestation, even-aged stand management has the potential to further mitigate levels of defoliation by reducing subcanopy densities. The result here would be to increase budworm 81 mortality during dispersal downward through the canopy by lowering the chances of finding suitable food (Carlson et al., 1983; Fredericks and Jenkins, 1988; Swetnam and Lynch, 1989; Maclauchlan and Brooks, 2009, Nealis and Régnière, 2009; Senf, 2017). In addition to even-aged stand management, thinning to increase tree vigour is also believed to be effective at reducing stand susceptibility to defoliation by decreasing foliage quality (Shepherd, 1994). Once thinning has been completed, prescribed fire in line with the historical fire regime can be used to maintain the desired densities by preventing the gradual infilling of subcanopy trees. The management strategies being practiced at Knife Creek for mule deer winter range are congruent with management and restoration to mitigate budworm impacts by reducing the quantity of suitable host foliage, increasing mortality during dispersal, and decreasing foliage quality. In the Knife Creek block, areas treated for mule deer winter habitat are on average 36% less dense and have 50% less subcanopy trees. 4.2.2 Future research Future research and management should focus on the processes and interactions influencing budworm populations across multiple scales that include stand, landscape, regional, and climatic variables. An improved understanding of these factors and their interactions will improve our understanding and capacity to predict the spatiotemporal patterns of outbreaks (Senf et al., 2017). However, these interactions are difficult to quantify because these processes will likely interact so that the combined effect cannot be predicted by their sum individually. Considering the pool of evidence supporting the influence of a forest’s structural attributes on susceptibility to defoliation and the results of my study, it is clear an improved understanding of the nuances and relative importance of subcanopy densities to budworm populations is needed. Specifically, we 82 do not know at what level subcanopy densities begin to influence levels of defoliation. Neither do we know what kind of relationship subcanopy densities have with outbreak severity or whether the relationship between subcanopy density and defoliation levels is linear. Here is where a clearer understanding behind subcanopy densities and levels of defoliation could shed more light on the results of this study and subsequently further illuminate the relative influence of subcanopy density on a forest’s susceptibility to infestations. This is necessary in order to guide future budworm management strategies at the stand scale. 4.3 Conclusion As observed in other dry mixed-conifer forests, contemporary fire regimes have led to changes in forest structure that have made dry forests less resilient to fire (Morgan et al., 1994; Holling and Meffe, 1996; Moore et al., 1999; Arno and Allison-Bunnell, 2002; Schoennagel et al., 2004; Fulé et al., 2013). Generally, the historic mixed-severity fire regime is thought to have promoted open structures with large scattered fire-tolerant trees by hindering the establishment of shrubs and trees (Fulé et al., 2013). At finer scales the forest structure was successionally diverse and contained patches of open parkland, canopy gaps, and patches of young even-aged forest, dense understory trees, and shrubs (Youngblood, 2001; Hessburg et al., 2005, 2007; Spies et al., 2006; Baker, 2012; Hagmann et al., 2013) that would have also reduced the quality, quantity, and continuity of suitable hosts for the western spruce budworm. The focus of future restoration and management for the Knife Creek block as well as for other dry, mixed-conifer forests with mixed-severity fire regimes should be to reduce stand densities, increase stand-level heterogeneity, and increase the historic diversity of forest structures across 83 the landscape (Millar et al., 2007; Halofsky et al., 2011; Klenner et al., 2008; Hessburg et al., 2016). Creating structural heterogeneity at the stand and landscape levels can be achieved by developing management strategies that restore the historic diversity of forest structures and species that are believed to have resulted from the historic fire regime. Managed wildfire, prescribed fire, and/or other forms of fuel mitigation such as thinning or the mechanical removal of surface fuels can reduce chances of high-severity fire while being effective components of restoration (Covington et al., 1997; Moore et al., 1999; Swetnam et al., 1999; Stephens and Moghaddas, 2005; Johnson et al., 2011; Hessburg et al., 2016). Alongside these methods, selective harvesting, even-aged stand management, thinning, and targeted reforestation can be used in combination to achieve these goals. Treatments should be applied heterogeneously across similar landscapes to promote successionally diverse landscapes by mimicking historical frequent low-severity surface fires as well as patches created by high-severity fire (Churchill et al., 2013; Ryan et al., 2013; Hessburg et al., 2016; Harvey et al., 2017). The efficacy and longevity of any restoration will depend upon the intensity of the treatment and other environmental factors such as site productivity (Wright and Agee, 2004; Spies et al., 2006; Johnson, 2008; Hessburg et al., 2016), since over time the trees and understory grow back and replace surface and ladder fuels and canopy fuel continuity (Agee and Skinner, 2005; Collins et al., 2010; Miller et al., 2009). 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Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Historical and contemporary disturbance regimes in central interior dry forests of British Columbia
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