CHARACTERISTICS OF WHITEBARK PINE (PINUS ALBICAULIS) GROWTH & DEFENSE IN DISTURBANCE-PRONE, HIGH-ELEVATION, MONTANE ECOSYSTEMS OF THE NORTHERN ROCKY MOUNTAINS by Nickolas Earl Kichas A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Ecology & Environmental Science MONTANA STATE UNIVERSITY Bozeman, Montana January 2022 ©COPYRIGHT by Nickolas Earl Kichas 2022 All Rights Reserved ii DEDICATION I dedicate this work to my incredible wife Katherine and beautiful daughter Mary Olive. Kat, you have diligently stood by me through nearly a decade of academia – through the many moves, the many highs and lows, and the constant uncertainty of what comes next – all while being a continuous source of love and inspiration. I cannot thank you enough, my darling. Thank you for your love and for taking care of me. You are my soulmate, and I could never have done this without you. You make me a better person and I cannot imagine a life without your beautiful smile greeting me at the start and end of each day. Our little Mary Olive surprised us with her arrival right in the middle of this dissertation program and I could not have asked for a better gift. Mary Olive, my little angel, thank you for teaching me to see the world in a beautiful new way and to truly appreciate the little moments. I love you with all my heart. I believe in you and know that you can accomplish all of your dreams. You are so special and have so much to offer the world. Please never underestimate yourself – realize your value and know that your Daddy loves you and supports you fully, wherever life may take you. Strive to make the world a better place and bring kindness, love, and happiness wherever you go. I would also like to acknowledge my family (my mom, brother, aunts, uncles, and in- laws) for your love and for always encouraging me and supporting me in my decisions. I would not be the person I am today without such a wonderful, loving family and I am so incredibly fortunate to have you all in my life. Mom, thank you for all your love and for all the sacrifices you’ve made for me, Jim, and Dad. We couldn’t ask for a better mother and I’m so grateful for all you do for me and my family. Jim, thanks for being my brother and always supporting and encouraging me to be my best self and helping me realize my value. Dad, although you cannot be here to celebrate this accomplishment with me, I know that you are watching from above with love and helping shape these events in my life. I love and miss you deeply and hope that I can continue to make you proud. iii ACKNOWLEDGEMENTS Thank you Dr. McWethy for your mentorship and for providing me so many opportunities to learn, develop, and grow. You helped me identify funding, supported me through my academic commitments, provided endless feedback and guidance and helped shape me into a better scientist. Dr. Pederson, thank you for being such a wonderful colleague and friend. Our conversations were always uplifting, and I am inspired by your scientific merit and capacity for critical thought. To say I couldn’t have done this without you would be a gross understatement. Thank you for challenging me to see new perspectives and be a more holistic scientist. Dr. Hood, this research would not have been possible without your guidance and expertise. Your tremendous depth of knowledge and professionalism is inspiring, and I would count myself lucky to be half the scientist you are. It has been incredibly rewarding collaborating with you on this work and I appreciate all your thoughtful and compelling input. Dr. Everett, thank you for all the meaningful, thought-provoking conversations and for all your support and coordination. The opportunities I had to experience these amazing and very special forests were life changing. This research was supported by grants from the Joint Fire Science Program Graduate Research Innovation (GRIN) program [JFSP 17-2-0114], National Science Foundation [BCS-1539820 / BCS-2029775], and USDA National Institute of Food and Agriculture [NIFA; 2013-3842420992]. Valued support was further provided by the Confederated Salish and Kootenai Tribes, specifically Rich Jannsen (Department of Natural Resources), Jim Durglo, and Tony Incashola Jr. (CSKT Tribal Forestry), which allowed us to conduct this research on Tribal lands. The Salish Cultural Committee and Salish Kootenai College provided additional support and I am deeply grateful for the opportunities I had to participate in these projects. iv TABLE OF CONTENTS 1. INTRODUCTION ...........................................................................................................1 2. CONSIDERING CONSTITUTIVE AND INDUCED DEFENSES, CHEMICAL COMPOSITION, AND MORPHOLOGICAL RESIN DUCT DEFENSES IN RELATION TO WHITEBARK PINE (PINUS ALBICAULIS) ..........................................7 Resource Allocation to Growth and Defense ...................................................................8 Constitutive and Induced Defense .................................................................................11 Chemical Composition and Variability of Oleoresin .....................................................17 Insect-Pathogen-Climate Influences and Interactions ...................................................20 Quantification and Analysis ...........................................................................................27 Research Significance for Whitebark Pine ....................................................................30 3. WHITEBARK PINE (PINUS ALBICAULIS) GROWTH AND DEFENSE IN RESPONSE TO MOUNTAIN PINE BEETLE OUTBREAKS....................................................................................................................35 Contribution of Authors and Co-Authors ......................................................................35 Manuscript Information Page ........................................................................................36 Introduction ....................................................................................................................37 Methods..........................................................................................................................39 Site Description ......................................................................................................39 Field Sampling Methods ........................................................................................40 Live/Dead Tree Pairing ..........................................................................................40 Sample Preparation ................................................................................................40 Data Analysis .........................................................................................................40 Results ............................................................................................................................41 Growth and Defense Between Live and Dead Trees .............................................41 Influence of Climate on Growth and Defense .......................................................42 Discussion ......................................................................................................................43 Influence of Climate on Growth and Defense .......................................................45 Conclusions ....................................................................................................................47 References ......................................................................................................................48 4. GROWTH AND DEFENSE CHARACTERISTICS OF LODGEPOLE PINE (PINUS CONTORTA VAR LATIFOLIA) AND WHITEBARK PINE (PINUS ALBICAULIS) IN A HIGH ELEVATION, DISTURBANCE PRONE MIXED–CONIFER FOREST IN NORTHWESTERN MONTANA, USA ......................................................50 Contribution of Authors and Co-Authors ......................................................................50 v TABLE OF CONTENTS CONTINUED Manuscript Information Page ........................................................................................52 Introduction ....................................................................................................................54 Methods..........................................................................................................................55 Site Description ......................................................................................................55 Field Sampling .......................................................................................................55 Field Sampling – Terpene Composition ................................................................56 Increment Core Analysis ........................................................................................56 Analysis of Monoterpenes and Sesquiterpenes......................................................57 Data Analysis .........................................................................................................57 Results ............................................................................................................................57 Relationship Between Constitutive Resin Chemistry and Resin Ducts ...........................................................................................................57 Chemical Composition of Constitutive Resin in Trees..........................................57 Relationship Between Tree Growth and Resin Ducts in Whitebark and Lodgepole Pine .............................................................................58 Relationship of Overstory Competition and Constitutive Resin Chemistry ....................................................................................................58 Discussion ......................................................................................................................60 Constitutive Resin Chemistry ................................................................................61 Influence of Competition on Tree Defense ............................................................61 Conclusions ............................................................................................................62 References ..............................................................................................................62 5. INCREASED WHITEBARK PINE (PINUS ALBICAULIS) GRPWTH AND DEFENSE UNDER A WARMER AND REGIONALLY DRIER CLIMATE ..................................................................................66 Contribution of Authors and Co-Authors ......................................................................66 Manuscript Information Page ........................................................................................67 Introduction ....................................................................................................................69 Materials and Methods ...................................................................................................71 Site Description ......................................................................................................71 Data Analysis .........................................................................................................71 Results and Discussion ..................................................................................................73 Growth and Defense Increase with Regional Warming and Drying ............................................................................................................73 Whitebark Pine Growth and Resin Duct Morphology Have Unique Climate Responses ..........................................................................75 Conclusions ....................................................................................................................77 References ......................................................................................................................79 6. CONCLUSIONS............................................................................................................85 vi TABLE OF CONTENTS CONTINUED REFERENCES CITED ......................................................................................................89 APPENDICES .................................................................................................................101 APPENDIX A: supplementary data (chapter 3) ..................................................102 APPENDIX B: supplementary data (chapter 4) ..................................................109 APPENDIX C: supplementary data (chapter 5) ..................................................117 vii LIST OF TABLES Table Page 1. Metrics for quantifying vertical resin duct properties in secondary xylem tissues of conifers ...................................................................................29 2. Age of live and dead whitebark pine at study locations on the Flathead Indian Reservation .............................................................................42 3. Mensuration data (mean ± S.E.) for 30 whitebark pine (P. albicaulis) and 30 lodgepole pine (P. contorta var. latifolia) sampled at a high elevation montane site on the Flathead Indian Reservation in northwestern Montana ..............................................................57 4. Spearman correlation coefficients of mono- and sesqui- terpene concentrations (μg / g fresh weight) with tree ring growth and resin duct anatomical measurements in whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia) ..................................................58 5. Spearman correlation coefficients of tree ring growth and resin duct anatomical measurements of Pinus contorta and Pinus albicaulis ...........................................................................................................60 6. Spearman correlation coefficients of stand density (Reineke’s Stand Density Index; SDI), tree size (DBH), tree ring growth (BAI), and resin duct anatomical measurements for whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia) ...................................................60 7. Generalized linear models predicting constitutive monoterpenes as a function of overstory competition ..............................................................60 8. Top climate models predicting tree growth (RWI), detrended duct production / size / area, and standardized duct metrics (duct density and relative duct area) as determined by best subsets regression ..............................................................................................76 viii LIST OF FIGURES Figure Page 1. Conceptual diagram reflecting factors that can shape resin duct properties (broadly categorized as duct size, area, production, and density) ....................................................................................30 2. Three Lakes Peak (top) and Boulder (bottom) study sites on the Flathead Indian Reservation in northwestern Montana .........................39 3. Conceptual diagram of the plot sampling protocol ............................................40 4. Example of increment cores from a pair of live (top) and dead (bottom) whitebark pine ...........................................................................41 5. Principal components analysis comparing growth and resin duct characteristics (production, size, area, density, relative area) for live and dead whitebark pine. .............................................................42 6. Time series plots showing the difference in growth and defense metrics between live and dead whitebark pine ....................................43 7. Kernel density plots for growth and defense metrics across pairs of live and dead whitebark pine ...............................................................44 8. Time series plots of growth (ring width index) and relative resin duct area (% annual ring) for live (top) and dead (bottom) whitebark pine ....................................................................................45 9. Conditional density plots describing the probability of mortality in relation to principle resin duct metrics (A) resin duct size; mm2 and (B) relative resin duct area; % annual ring for 144 whitebark pines (72 live and 72 dead) ..........................................45 10. 30-year moving correlation windows for seasonal climate data (winter/spring/summer) ...........................................................................46 11. Map of sampled trees: 30 whitebark pine (white) and 30 lodgepole pine (orange) from a high elevation, mixed-conifer stand in the Mission Range (~4.5 km east of Flathead Lake) on the Flathead Indian Reservation in northwestern Montana ........................56 ix LIST OF FIGURES CONTINUED Figure Page 12. Comparison of total constitutive concentrations (μg / g fresh weight) of monoterpenes (A) and sesquiterpenes (B) across 30 whitebark pine (P. albicaulis) and 30 lodgepole pine (P. contorta) ....................................................................................................58 13. Non-metric multidimensional scaling (NMDS) ordination plot of the first and second dimensions for the A) monoterpene and B) sesquiterpene profiles for 30 whitebark and 30 lodgepole pine trees ........................................................................................59 14. Comparison of resin duct size across the full time series (1914-2018).....................................................................................................59 15. Comparison of whitebark pine tree growth and resin duct morphology across the upper and lower quartiles of reported climate variables: warm season (May-Sep) average temperature (°C), annual average Palmer-Drought Severity Index (PDSI), cool season (Oct-Apr) total snowfall (mm), and warm season (May-Sep) maximum vapor pressure deficit (VPD) from 1924-2015 .......................................................................................................74 16. Scatterplot and time series relationships (10-year moving averages) for whitebark pine tree growth, detrended resin duct metrics that were not standardized to growth (duct production, duct size, duct area), and the primary climate variables derived from best-subsets regression from 1924-2015 ................................................77 x ABSTRACT Whitebark pine (Pinus albicaulis) is a high-elevation conifer, recognized as a foundation species due to the numerous ecological benefits it provides in subalpine environments. In whitebark pine and other conifers, resin-based defenses have long been recognized as the primary mechanism by which trees respond to bark beetle attacks and several studies have linked resin duct properties to survivorship during periods of increased beetle activity. Utilizing a unique dataset of whitebark pine collected on the Flathead Indian Reservation in northwestern Montana, we set out to investigate the following research questions: (1) Are there differences in physiology (tree growth and resin duct anatomy) between trees that persisted through recent mountain pine beetle outbreaks and trees that died? (2) Does constitutive resin chemistry differ between whitebark and co-occurring lodgepole pine and are there relationships between tree growth, resin duct anatomy and resin chemistry? (3) Does competition influence constitutive resin chemistry in either whitebark or lodgepole pine? and (4) Is whitebark pine growth and/or resin duct anatomy constrained by warmer and/or regionally drier conditions? We found that whitebark pine trees that have persisted through recent stand-level disturbance produced fewer but larger resin duct structures with greater duct area compared to trees that died. We also detected important differences in the chemical composition of resin between whitebark and lodgepole pine that generally support field observations, whereby under endemic scenarios mountain pine beetle preferentially select lodgepole pine, while under outbreak scenarios, beetles successfully colonize whitebark pine trees. We found complex relationships between tree growth, resin duct anatomy and constitutive resin chemistry that present beetles with many permutations of resin-based defenses, while competition, particularly with Engelmann spruce (Picea engelmannii) can further influence constitutive resin chemistry. Lastly, we found that whitebark pine across our study sites are experiencing increased growth and defense under warmer and regionally drier conditions. Whitebark pine at our study sites exhibit differing strategies in the allocation of resources toward growth and defense. Our results support the idea that maintaining genetic variability promotes diverse response strategies to a complex array of biophysical stressors that might leave a species vulnerable to extinction across its range. 1 INTRODUCTION An increase in the incidence of large and damaging fires in the western U.S. highlight the need to revise forest management policies and implement adaptive strategies to support resilient forest ecosystems (Franklin and Agee 2003, Stephens and Ruth 2005, Westerling et al. 2006, Littell et al. 2009, van Mantgem et al. 2009, McWethy et al. 2019). Insect and disease outbreaks and extremes between wet and dry years are occurring in concert with changing fire activity, leading to disturbance synergies with profound ecological and social consequences (Loehman et al. 2017). Developing management strategies to mitigate the impacts of these disturbance synergies requires an understanding of complex interactions between disturbances and forest ecosystem dynamics in the past and present (Kane et al. 2017). While many researchers have documented changes in disturbance regimes over the past century, this information is often limited both spatially and temporally (Conedera et al. 2009, Driscoll et al. 2010) and may not fully account for subtle but important biophysical and climatic variability across geographic gradients. Currently, results from many regional studies have been applied broadly, leading to management prescriptions that are ill-matched for the ecosystems in which they are adopted (Dawson et al. 2011, Halofsky et al. 2011). This is often the case for high-elevation mixed- conifer forest stands of the northern Rocky Mountains that experience a range of disturbance conditions (in both frequency and intensity) that are highly variable across time and space (Veblen et al. 1994, Baker 2009). Management in these diverse areas requires a multidisciplinary framework that considers numerous ecological responses to past and present perturbations within a heterogeneous matrix of vegetation and topography. 2 Whitebark pine (Pinus albicaulis) is a long-lived, high-elevation conifer that occupies subalpine ecosystems (Arno and Hoff 1989, Keane 2000, Tomback et al. 2001). Often dominating treeline, whitebark pine is considered a foundation species, with high cultural and ecological significance (Cole 1990, Mills and Doak 1993, Tomback et al. 2001). Whitebark pine is one of the few species that thrives at high altitudes (> 2,000 m a.s.l.) where they stabilize erosive slopes; promote snow accumulation; increase snowpack retention and effectively modulate moisture loss in subalpine regions, helping provide a consistent supply of water to lower elevations throughout summer months (Arno and Hoff 1989, Tomback and Kendall 2001, Bockino and Tinker 2012). Whitebark pine seeds are highly nutritious and an important food source for several species of wildlife, including grizzly bears (Ursus arctos horribilis), black bears (U. americanus), and numerous small mammals and birds. Seeds are predominately dispersed by the Clark’s nutcracker (Nucifraga Columbiana), a bird which caches seeds broadly across alpine landscapes, including disturbed areas. In these environments, forgotten seeds often germinate, forming whitebark pine forests of tomorrow. Whitebark pine is an important pioneer species, as the hearty seedlings colonize areas and ameliorate site conditions, promoting additional vegetative growth through tree island initiation (Tomback et al. 1993, Tomback et al. 2016). However, extensive whitebark pine mortality due to the introduction of a highly aggressive exotic pathogen white pine blister rust (Cronartium ribicola) and extensive mountain pine beetle (Dendroctonus ponderosae) outbreaks caused by shifting climate, along with future projections of continued population declines due to warming, led the U.S. Fish & Wildlife Service to list whitebark pine as “threatened” under the Endangered Species Act in December 2020. Managers tasked with supporting and maintaining the viability of whitebark pine into the 3 future need information on how whitebark forests respond to climate and disturbance synergies across biophysical gradients throughout its range. While insects, fire, disease, and secondary effects of drought (including increased tree stress and predisposition to other disturbances) have contributed to recent mortality of whitebark pine, these processes also play an important role in the long-term establishment and persistence of northern Rocky Mountain whitebark pine forests. However, historical records detailing the relationship between disturbance and whitebark pine ecosystem dynamics are particularly lacking. For example, a metanalysis of paleoecological data suggests that the abundance of Pinus subgenus strobus, as inferred from pollen percentages, was greater (particularly at lower elevations) during the Holocene summer insolation maximum (~9 kyrs BP) when summer temperatures were warmer, bark beetle activity was prevalent, and fire activity was high in the western U.S. (Iglesias et al. 2015). This research suggests possible complex interactions between temperature, fire, and insect dynamics that have developed over thousands of years, highlighting a need to better understand how five needle pines (P. strobus subgenus) have persisted in the past and will respond to multiple disturbances in the future. Research investigating the physiological response of whitebark pine to fire frequency, increased competition, and timing / severity of beetle outbreaks across a range of diverse environmental gradients is essential for developing effective management targets. This is especially true given that current species risk assessments for whitebark pine rely on bioclimatic models that include little information on potential physiological adaptability of the species to interactions and feedbacks between changing climatic and disturbance regimes. In addition, previous research evaluating natural physiological defenses in whitebark to insect attacks has traditionally been conducted in either 4 “pure” high-elevation stands (particularly around the Greater Yellowstone Ecosystem) or in common-garden experiments (Raffa et al. 2013, Raffa et al. 2017, Warwell and Shaw 2017, Bullington et al. 2018, Six et al. 2018, Mason et al. 2019), which often fail to capture the complex suite of interacting factors that influence mixed-conifer stands where whitebark pine co- occurs. Our study sites, located within the Flathead Indian Reservation in northwestern Montana, are characterized by mixed stands of whitebark pine, lodgepole pine (P. contorta var latifolia), subalpine fir (Abies lasiocarpa), and Engelmann spruce (Picea engelmannii) with differing legacies of fire, insects, and pathogens. While these types of ecosystems are common within the northern Rocky Mountains, little research has explored disturbance interactions within these mixed whitebark stands (Kichas et al. 2020, Kichas et al. 2021). Better understanding how physiological characteristics of whitebark pine (specifically the allocation of resources to growth and defense) influence survival following the synergistic effects of multiple disturbances is critical for resource managers tasked with managing and supporting the long-term viability of this foundation species. Across many species of North America pines, research shows that during beetle attacks, even at peak epidemic levels, there are mature individuals that remain uncolonized by beetles, and that these resistant individuals may have different defense traits (Strom et al. 2002, Hood et al. 2016). For instance, Strom et al. (2002) found oleoresin flow from progeny of mature loblolly pines (P. taeda) that "escaped" mortality during an outbreak of southern pine beetle (Dendroctonus frontalis) was higher compared to the general population. Other studies have demonstrated the importance of resin ducts as defense traits in ponderosa pine (P. ponderosa), piñon pine (P. edulis), limber pine (P. flexilis), lodgepole pine, and whitebark pine (Kane and Kolb 2010, Gaylord et al. 2013, 5 Ferrenberg et al. 2014, Hood et al. 2016, Kichas et al. 2020). Survivorship of individuals within such diverse populations suggests an intrinsic level of resistance that can be genetically passed on to the survivors’ progeny (de la Mata et al. 2017, Six et al. 2018). This is supported by research which suggests that the capacity to produce defensive features (such as resin ducts) and the composition of chemical defenses (e.g. chemotypes) are under strong genetic control, resulting from site-specific selective pressures that moderate growth and defense relationships over time (Hannrup et al. 2004, Rosner and Hannrup 2004, Moreira et al. 2014, Moreira et al. 2015, Westbrook et al. 2015, Moreira et al. 2016). Evaluating relationships between tree growth, resin duct development and oleoresin chemistry can provide valuable insight into overall capacity for these trees to resist and defend against stressors that are projected to increasingly impact this important species. Utilizing a unique dataset of whitebark pine collected on the Flathead Indian Reservation in northwestern Montana, we set out to investigate the following research questions: 1) Are there differences in physiology (tree growth and resin duct anatomy) between trees that have persisted through recent mountain pine beetle outbreaks and trees that have died? 2) Does constitutive resin chemistry differ between whitebark and co-occurring lodgepole pine and are there relationships between tree growth, resin duct anatomy and resin chemistry for either species? 3) Does competition influence constitutive resin chemistry in either whitebark or lodgepole pine? 6 4) Is whitebark pine growth and/or resin duct anatomy constrained by warmer and/or regionally drier conditions? Previous whitebark pine research has primarily focused on the relationship of mortality to white pine blister rust and mountain pine beetle (Logan et al. 2010, Bockino and Tinker 2012, Millar et al. 2012, Retzlaff et al. 2016, Tomback et al. 2016). However, little is known about how physiological characteristics of whitebark pine resource allocation to growth and defense influence survivorship to the interacting effects of climate and compounding disturbances (Millar and Delany 2019). In addition, strategies for maintaining whitebark pine stands rely heavily on bioclimatic models that project substantial losses in the distribution of whitebark pine stands throughout the northern Rocky Mountains. These models, however, fail to capture long-term physiological adaptability of this unique species, and as such, are potentially misleading. Our results address these knowledge gaps by identifying physiological mechanisms by which whitebark pine respond to complex disturbance interactions within these unique montane environments. 7 CHAPTER TWO CONSIDERING CONSTITUTIVE AND INDUCED DEFENSES, CHEMICAL COMPOSITION, AND MORPHOLOGICAL RESIN DUCT DEFENSES IN RELATION TO WHITEBARK PINE (PINUS ALBICAULIS) Mounting evidence suggests recent increases in tree mortality resulting from fire exclusion, changes in climate, pathogen and insect activity, and complex interactions among these disturbance events (Mattson and Haack 1987, Westerling et al. 2006, van Mantgem and Stephenson 2007, van Mantgem et al. 2009, Allen et al. 2010, Schwandt et al. 2010, Tomback and Achuff 2010, Roy et al. 2014). As such, there is a growing interest to better understand the role of tree defense and physiological responses of trees as they relate to mortality. However, tree physiology and disturbance regimes vary substantially across both time and space, which complicates our ability to make generalized assumptions for inter- and intra- specific species response to biotic and abiotic processes. This is made more difficult by the substantial complexity of defense anatomy and diversity of chemical compounds that are produced by plants, which vary across populations, individuals, and plant organs (Trowbridge 2014, Jamieson et al. 2017). In addition, defensive structures and chemical compounds are influenced by a host of biotic and abiotic factors that collectively inflate variability and confound the evolutionary origin and development of secondary metabolites. Coevolution of chemical defenses in response to herbivores and pathogens over millions of years has resulted in a myriad of complex relationships whereby insect and pathogen activity is often intimately linked to the presence, diversity, and concentration of specific chemical compounds in host plants (Smith 2000, Six 2012, Jamieson et al. 2017). However, the production of physical defense structures 8 and chemical compounds is energetically expensive, which necessitates important trade-offs between metabolic differentiation to growth versus resource allocation to defensive features (Stamp 2003, Trowbridge 2014). While there have been several studies that have looked to quantitatively assess growth and defense characteristics for a range of species, general inconsistencies in methodologies and research findings both within and across species reflect a need for standardization of metrics and ongoing refinement of analytical techniques. This is particularly true for long-lived organisms, such as conifers that have variable, genetically-mediated metabolic processes and context- specific responses to their local environment that may select for unique combinations of defensive traits over time (Cobb et al. 1994, Mitton et al. 1998, Rosner and Hannrup 2004, Moreira et al. 2016). Resource Allocation to Growth and Defense Physical and chemical defenses are critical for mediating plant-herbivore-pathogen interactions; however, the production and proliferation of defensive structures and chemical compounds is energetically expensive with high demand for relatively limited carbon resources (Lewinsohn et al. 1991, Stamp 2003, Moreira et al. 2014, Trowbridge 2014). Given adequate resource availability, plants will preferentially allocate resources toward primary production, including diameter and height growth as well as development of leaves, cones, and course- and fine-root networks (Trowbridge 2014, Jamieson et al. 2017). However, plants simultaneously utilize carbon reserves in the production of secondary metabolites, such as anatomical features and chemical compounds (e.g., terpenoids, alkaloids, and phenols) to protect crucial tissues from damage by herbivory and pathogens. As such, there are important trade-offs regarding resource 9 allocation between growth and defense, which remains to be fully elucidated given the complex biosynthetic pathways involved in cell differentiation and chemical synthesis (Trowbridge 2014). Many hypotheses have been proposed to characterize observed biophysical processes relating to these trade-offs, including the resource allocation hypothesis (RAH), as well as the growth- differentiation balance hypothesis (GDBH). The resource allocation hypothesis argues that plants in stressful, resource-poor environments will exhibit naturally slow growth rates and will invest comparatively more carbon into the development of defensive features and chemical compounds to limit the potential loss of expensive tissues, which would be difficult to replace under such circumstances (Coley et al. 1985, Stamp 2003, Endara and Coley 2011, Moreira et al. 2014, Moreira et al. 2015, Moreira et al. 2016). This hypothesis has been supported by several studies across a range of deciduous trees and shrubs, which tend to have lower growth rates and higher levels of constitutive defenses in resource poor environments (Endara and Coley 2011). For instance, research by Moreira et al. (2015) provides support for this hypothesis as the authors found increased resin duct density and increased relative area (a metric that combines duct density and duct size) as well as decreased growth rates in juvenile maritime pine (Pinus pinaster) under a phosphorous limited setting. Additional work by Moreira et al. (2014) looked at defense characteristics in a common greenhouse experiment for 18 Pinaceae species whose native habitats occupy broad latitudinal (31°) and elevational (2300 m) gradients. The researchers found increased constitutive defenses (stem resin and needle phenolics) and decreased inducible defenses for species that naturally occur at higher latitudes and elevations, where environmental conditions correspond with slower growth rates, suggesting a heritable pattern of increased primary defenses in more resource-limited environments. 10 Alternatively, the GDBH suggests that investment in defensive features may occur with relatively minimal trade-offs to growth under resource-limited conditions (such as reduced moisture or nutrient availability) whereby growth is constrained by resource limitations, but photosynthesis continues to produce carbon compounds and this ‘carbon surplus’ may then be allocated toward physical and chemical defenses. This hypothesis has some observational support, as resin flow has been observed to increase in some species under moderate water stress, where growth would be more limited, but photosynthesis would continue to produce carbon resources, while decreasing under severe moisture stress, where assimilation would be insufficient in meeting basic metabolic demands for primary production (Loomis 1932, Herms and Mattson 1992, Stamp 2003, Gaylord et al. 2007, Gaylord et al. 2013, Hood and Sala 2015, Moreira et al. 2015). Anatomical resin ducts also tend to form in the latewood when water availability is generally reduced, which would follow this logic (Rigling et al. 2003). The GDBH has also been supported in various studies, although research is largely context dependent, given wide variability in methodologies and protocols (Ferrenberg et al. 2015). For instance, Gaylord et al. (2013) found that resin flow in twigs of piñon pine decreased under extreme (3 year) drought stress, as trees were unable to assimilate carbon for resin defenses, as well as under water-additive treatments, as the well-hydrated trees preferentially allocated resources to growth over defense, whereas resin production increased under moderate water stress, corresponding with intermediate growth. Similarly, the work by Moreira et al. (2015) also lends support for the GDBH as the maritime pines responded to phosphorous limitation through decreased growth with a corresponding increase in defensive features. Research by Hood and Sala (2015) also broadly support this hypothesis as the researchers found increased resin flow in ponderosa pine 11 trees during periods of seasonal moisture stress, where oleoresin production was greater in August (less moisture availability) than in July. Central to each hypothesis is the larger idea that resource allocation towards defense enhances plant fitness and that inter- and intra-specific variability can be explained by trade-offs between growth and defense, whereby external factors including resource availability, abiotic stress, and herbivore pressures all contribute to investment in defensive systems (Moreira et al. 2014, Trowbridge 2014, Ferrenberg et al. 2015, Jamieson et al. 2017). However, results of many studies looking to establish evidence for either hypothesis are often contradictory, as the nature of biophysical mechanisms involved in resource allocation to growth and defense are highly complex, as well as difficult to test in both field / greenhouse studies and lab experiments (Trowbridge 2014, Jamieson et al. 2017). In addition, the hypotheses themselves are not mutually exclusive and it is likely that aspects of both are correct depending on the plant species considered as well as environmental conditions influencing resource allocation, such as climate, topography, intra- and inter-specific competition, as well as a host of other biotic and abiotic pressures (Stamp 2003, Trowbridge 2014). Research addressing either hypothesis tends to be limited to small number of species in early developmental phases (e.g., seedlings) with minimal data for intermediate and mature ages classes (Ferrenberg et al. 2015). Constitutive and Induced Defense The production of resin ducts and storage of oleoresin compounds has been researched for a variety of species and has generally been distilled into two broad categories: constitutive (primary) defensive features, which are always present and provide a relatively constant barrier to attack by herbivores and pathogens, and induced (secondary) defenses, which are formed 12 locally in response to biotic disturbance (Lewinsohn et al. 1991, Moreira et al. 2014, Trowbridge 2014). The degree to which certain plants will exhibit constitutive versus induced defenses is largely controlled by evolutionary history, with pines (Pinus spp.) having characteristically greater constitutive defenses while true firs (Abies spp.) tend to exhibit greater inducibility (Lewinsohn et al. 1991, Phillips and Croteau 1999). However, the degree to which species reflect constitute versus induced defenses is strongly moderated by environmental conditions and biotic pressures and can therefor vary substantially within and among populations (Ferrenberg et al. 2017). In addition, the plethora of external factors acting on individuals and populations will collectively select for defensive phenotypes over time and may lead to incongruencies regarding what would otherwise be expected based purely on molecular phylogeny (Moreira et al. 2015). While pines and true firs broadly represent two extremes of defense phenotypes many taxa exist along a gradient of constitutive and inducible defenses and the two are not mutually exclusive (Phillips and Croteau 1999). For instance, many species of pine will biosynthesize additional defensive features, such as traumatic resin ducts and phenolic compounds, in response to insect attack or fungal inoculation (Klepzig et al. 1995, Paine et al. 1997, Phillips and Croteau 1999). These induced responses are regulated by hormones of jasmonic and salicyclic acid, which collectively signal plant defense systems. This process may take days to weeks and is inherently complicated due to antagonistic crosstalk between signaling pathways that can vary substantially within and across species (Siemens et al. 2009, Moreira et al. 2014, Liu et al. 2017b). Synthetic compounds of jasmonic and salicyclic acid have been successfully used in greenhouse experiments to stimulate induced defense and have provided an effective tool for assessing the 13 development of inducible defenses, which are characteristically difficult to assess in natural settings (Moreira et al. 2014, Moreira et al. 2015, Moreira et al. 2016, Liu et al. 2017b). Constitutive and induced defenses are the primary barrier to beetle-fungal complexes and consist of intricate anatomical resin duct structures located throughout cortex, xylem and secondary phloem tissues, with the relative frequency and distribution of these structures varying largely across taxa (Wu and Hu 1997, Warren et al. 1999). These systems contain axial and radial tube-like resin ducts, surrounded by secretory parenchyma cells that produce viscous, free- flowing liquid compounds which are quickly mobilized to isolated areas in response to damage from mechanical injury, abiotic stress, or biological agents (Raffa and Berryman 1983, Paine et al. 1997, Nagy et al. 2000, Franceschi et al. 2005, Rodríguez-García et al. 2014). Preformed resin is synthesized and stored within the lumen of resin ducts, which are lined by thin-walled secretory epithelial cells (Phillips and Croteau 1999, Franceschi et al. 2000, Nagy et al. 2000, Baier et al. 2002). Parenchyma cells, containing toxic polyphenolic compounds, are located throughout the secondary phloem and play an important role in mitigating insects and fungal activity due to their ability to immobilize enzymes produced by microorganisms (Bell 1981, Paine et al. 1997, Franceschi et al. 1998, Warren et al. 1999). There are many types of resin duct structures, including cortical resin ducts, resin ducts in the secondary xylem and phloem, as well as traumatic resin ducts and resin blisters (multicellular sac-like structures), which are produced in response to isolated injury (Bannan 1936, Lewinsohn et al. 1991, Wu and Hu 1997, Nagy et al. 2000, Baier et al. 2002). However, the nature of these structures varies widely among and within species as does the relative rate of resin synthesis and storage methods for oleoresin compounds (Bannan 1936, Phillips and Croteau 1999, Nagy et al. 14 2000). Pines for instance exhibit an elaborate system of constitutive resin structures which extend in the vertical, radial, and transverse directions throughout the xylem and phloem, often intersecting to form complex networks capable of quickly transporting large volumes of preformed and synthesized resin to various areas of the tree in response to physiological demands (Lewinsohn et al. 1991, Phillips and Croteau 1999, Perrakis and Agee 2006, Wainhouse et al. 2009). On average, species of pine also produce a much larger quantity of resin than other species such as true firs and many genera in the Cupressaceae family, although some species of pine (e.g., ponderosa) are more prolific resin producers than others (e.g., Great Basin bristlecone pine) (Lewinsohn et al. 1991, Wu and Hu 1997, Phillips and Croteau 1999, Smith 2000, Wainhouse et al. 2009, Bentz et al. 2017). In addition to intra- and inter-species variation there is supportive evidence that developmental stage (ontology) and phenology also have a significant influence on the relative proportion of carbon allocated to resin-based defenses (DeAngelis et al. 1986, Imaji and Seiwa 2010). For instance, Wainhouse et al. (2009) found that Corsican pine (P. nigra spp. laricio) produced more resin duct structures as the trees aged, compared to Sitka spruce (Picea sitchensis), which did not exhibit any notable differentiation in resin duct production across an age gradient. Lodgepole pine (P. contorta) has also been found to have fewer chemical defenses in older stem tissues, whereas chemical defenses increase in older white spruce (P. glauca) foliage (Quiring 1992, Goodsman et al. 2013). Moreira et al. (2014) found that constitutive resin defenses in the stems and needles of 18 species in Pinaceae decreased for taxa that characteristically occupy lower latitudes and elevations that correspond with higher growth-rates, while inducible defenses increased. In general, slow growth rates associated with reduced vigor 15 tend to correlate with reduced resin-based defenses, which can increase susceptibility to mortality from insects and pathogens (Kane and Kolb 2010, Perrakis et al. 2011, Ferrenberg et al. 2014, Hood and Sala 2015, Hood et al. 2015, Hood et al. 2016). The high degree of interspecific variability in natural settings is attributable to several factors, including age, genetics, and growing conditions. Conifers are locally adapted to climate and edaphic controls, which may lead to considerable variation in defensive properties across taxa even when among-population gene flow is present (Cobb et al. 1994, Mitton et al. 1998, Neale and Savolainen 2004, Ferrenberg et al. 2015, Moreira et al. 2015). For instance, tree vigor (relative growth rate) has been shown to influence defensive structures and chemical compounds. Hood and Sala (2015) found that faster growing ponderosa pine (P. ponderosae) invested relatively less carbon into resin ducts (lower production per year of growth) but had larger overall resin ducts and produced volumetrically greater amounts of resin compared to slower- growing individuals. Similar results were presented by Hood et al. (2015) and Hood et al. (2016), whereby ponderosa pine that survived bark beetle attacks had faster growth rates (30% wider ring widths in the 30 year period prior to attack) and increased resin duct size (20% larger ducts in surviving trees), and ponderosa pines in areas of reduced stand densities (through experimental reduction treatments involving thinning and prescribed fire) exhibited increased growth rates (2.4 x greater BAI in most recent five years of growth) and greater proportional resin defenses (33% larger ducts in most recent five years and a near doubling in resin duct production). Ferrenberg et al. (2014) also found an increase in resin duct size that corresponded with increased growth rates for both limber pine (P. flexilis) and lodgepole pine trees in Colorado. In another study in Crater Lake, Oregon, Perrakis et al. (2011) found that old 16 ponderosa pine (> 200 years) that were assigned greater crown class vigor ratings exhibited greater ring widths and higher overall resin flow four years after prescribed fire treatments. However, not all studies support a correlation between increased growth rates and increased resin-based defenses against bark beetle induced mortality. For instance, Kane and Kolb (2010) found that while resin duct density and size were greater in ponderosa pine that survived beetle attacks, growth rates between live and dead trees did not differ and growth metrics were not a significant factor in mortality models, whereas resin duct density and size were the best predictors for ponderosa pine mortality. Ferrenberg et al. (2014) found mixed results for lodgepole pine and limber pine, with decreased growth rates associated with less robust resin duct production for lodgepole pines that were killed by bark beetles (over a five-year period prior to tree death), while limber pines killed by bark beetles exhibited greater growth rates and had fewer resin duct structures (over the past 15-20 years prior to tree death), suggesting a trade-off between resource allocation to growth and defense. Although it should be noted that studies that assess growth rates utilize varying combinations of growth metrics, including ring widths, radial growth rates, as well as basal area increments (BAI), over varying time periods (e.g., growth over five, ten, and 20 years) which can complicate interpretation and cross-study comparisons and highlights a need for standardization of methodologies relating to growth and defense research. While increased resin production may enhance a tree’s ability to survive and overcome environmental stressors, the many complexities and high degree of variability in the production of resin ducts and synthesis of resinous compounds complicate our ability to accurately quantify these relationships, particularly in older trees (Baier et al. 2002, Rosner and Hannrup 2004, 17 Gaylord et al. 2007, Kolb et al. 2007). The notable variability in carbon trade-offs between growth and defense hinders our ability to make general assumptions and further highlights the need for additional research across a range of species and geographic areas (Warren et al. 1999, Baier et al. 2002, Gaylord et al. 2007, Wainhouse et al. 2009). Chemical Composition and Variability of Oleoresin Oleoresin acts as a mechanical defense by repelling or entrapping herbivores and as a chemical defense, as oleoresin compounds contain diverse chemical assemblages, including mixtures of terpenoids, which are collectively biosynthesized from five-carbon isoprene compounds (Trapp and Croteau 2001, Mumm and Hilker 2006, Trowbridge 2014, Ferrenberg et al. 2015). Key among these are diterpenes and diterpene acids, which influence viscosity and adhesiveness, as well as volatile terpenes, such as monoterpenes and sesquiterpenes, which act as solvents for transport of molecularly heavier non-volatile diterpenes (Phillips and Croteau 1999, Franceschi et al. 2005, Schaller 2008). When exposed to the atmosphere, volatile terpenes evaporate, resulting in an accumulation of semi-crystalline resin acids, which polymerize (through oxidation) to form a physically hard barrier to insects and pathogens (Phillips and Croteau 1999). In general, production of defense structures, including preformed and wound-induced resin ducts and oleoresin compounds, is slow (occurring over weeks to months to years) and metabolically demanding, due to biological constraints of cell division and cell differentiation (Moreira et al. 2014, Hood and Sala 2015). Such processes are also resource intensive and the allocation of carbon to development of such features represents a net loss, as resin cannot be reassimilated or further metabolized after formation (Franceschi et al. 2005, Moreira et al. 2014). 18 However, other chemical compounds produced in various plant organs, such as water-soluble phenolic compounds in xylem and cortical tissues, may be biosynthesized de novo through rapid ‘plastic’ change in the metabolism of secretory cells and reassimilated following damage (Lewinsohn et al. 1991, Bonello et al. 2006, Salminen and Karonen 2011, Moreira et al. 2014). These various compounds have interacting, complimenting properties against certain aspects of the beetle-pathogen complex with terpenes and phenolics constituting the primary chemical defense in temperate conifers (Smith 2000, Raffa et al. 2013). However, the diversity of specific chemical compounds within each of these broad groupings is remarkable, with over 22,000 compounds having been described while many have yet to be recognized (Smith 2000, Trowbridge 2014). The specific role these secondary metabolites play in defense to herbivory and pathogens is poorly understood for a wide variety of species and represents an area of active research (Trowbridge 2014, Jamieson et al. 2017). For instance, terpene compounds have been shown to disrupt nervous-systems and basic metabolic processes in many types of insects (Smith 2000, Seybold et al. 2006, Trowbridge 2014) and some compounds, such as phytoecdysones (a type of triterpene) are thought to mimic insect-molting hormones, which may disrupt the development cycle of larva (Trowbridge 2014). Diterpenes have been found to inhibit fungal activity while certain phenolics (e.g., stilbenes and phenylpropanoid) impede the synthesis of beetle aggregation pheromones and tend to have strong anti-fungal properties, particularly for fungi associated with bark beetles (Hayes and Strom 1994, Hammerbacher et al. 2013, Raffa 2014, Raffa et al. 2017). 19 Within the broader groupings of defensive chemical compounds monoterpenes are common and abundant in conifers, particularly pines, and have been recognized as a key defense to pathogens and insect activity due to their strong insecticidal properties (Smith 2000, Raffa et al. 2017). Monoterpenes are primarily controlled by genes in the xylem tissue and include olefins (alkene) compounds of hepane, undecane, α-pinene, ß-pinene, δ-3-carene, sabinene, myrcene, limonene, ß-phellandrene, with various stereoisomers, such as (+)-α-pinene and (–)-α-pinene (Lewinsohn et al. 1991, Smith 2000). Monoterpene synthase (cyclase) varies across species but is generally greater in species with more organized resin-containing structures, such as pines, and lower in species with less organized structures, including true firs and species of Cupressaceae, such as western red cedar (Thuja plicata) and redwood (Sequoia sempervirens) (Lewinsohn et al. 1991). High concentrations of monoterpenes have been associated with increased tree survival during beetle attacks, although monoterpene concentrations and diversity are widely variable within and across species and may have little to no overlap for taxa within the same genera (Zhao et al. 2010, Boone et al. 2011, Ferrenberg et al. 2017). For example, Ferrenberg et al. (2017) compared monoterpenes across an elevational gradient of lodgepole pine, ponderosa pine, and limber pine in the Colorado Rocky Mountains and found that overall concentrations were greater in lodgepole pine followed by ponderosa pine with the lowest concentrations occurring in limber pine (P. flexilis). In contrast, monoterpene diversity showed an opposite trend, with the greatest diversity occurring in limber pine, followed by ponderosa pine and lodgepole pine respectively. Within the sampled populations, inter-specific monoterpene composition and abundance were greatest for lodgepole pine, followed by limber pine and then ponderosa pine (Ferrenberg et al. 2017). The researchers also considered tree size, age, and growth rates and 20 found mixed results, with monoterpene concentrations decreasing with increased size in lodgepole pine but increasing with increased age and higher growth rates. No significant patterns were found for tree properties and monoterpene concentration and diversity in ponderosa pine and limber pine, however, all three pines exhibited reduced monoterpene concentrations at higher elevations (Ferrenberg et al. 2017). Such incongruent results suggest that monoterpene synthesis is controlled by multiple mechanisms that differ across species and varies in both time and space. In addition, while certain properties of some chemical compounds are reasonably well understood resin chemistry has also been found to change over short temporal windows (e.g., growing season) as well as over longer timescales in response to disturbance, edaphic changes, or competitive interactions following successional trajectories (Campbell and Taylor 2007, Trowbridge 2014, Ferrenberg et al. 2017). Monoterpene concentrations can also change as trees age. For instance, the monoterpene myrcene and limonene have been shown to increase with increasing age, while camphene, α-phellandrene, and gammaterpinene tend to be absent in young trees but increase as the tree ages (Smith 2000). The complex interplay of these different factors limits our ability to generalize chemical defenses and warrants species- and site-specific examinations. Insect-Pathogen-Climate Influences and Interactions Physical and chemical defenses are heritable but are also shaped by complex biotic and abiotic factors that can have significant impacts on host-pest relationships and on larger ecosystem function across time and space (Trowbridge 2014). For instance, bark beetles (Curculionidae, Scolytinae) have coevolved with host species and have developed complex behaviors, whereby the insects will actively exploit certain monoterpenes as synergists or 21 precursors in the synthesis of communication and location (e.g., aggregation and host- identification) pheromones (Seybold et al. 2006). For example, mountain pine beetle (Dendroctonus ponderosae) has been found to utilize the monoterpene (−)-α-pinene as a precursor to the synthesis of their aggregation pheromone (−)-trans-verbenol, and the monoterpene myrcene as a chemical catalyst in this process (Seybold et al. 2006, Raffa 2014, Hood et al. 2016, Keeling 2016). β-myrcene and δ-3-carene have also been found to synergize pheromone synthesis as well as increase host-attraction by increasing the flight response of mountain pine beetle (Seybold et al. 2006, Borden et al. 2008). Similarly, adult mountain pine beetles will exploit ß-phellandrene to enhance host-recognition (Huber et al. 2000, Miller and Borden 2000). However, certain monoterpenes may also act as attractants for bark beetle predators or enhance predator response to bark beetle pheromones (Seybold et al. 2006). For instance, Mizell et al. (1984) reported that increases in the monoterpenes α- and β-pinene increased the flight response of the predaceous checkered beetle (Thanasimus dubius) to aggregation pheromones produced by the southern pine beetle (D. frontalis) in P. taeda trees of the southeastern U.S. Similarly, Erbilgin and Raffa (2001) found that (–)-a-pinene, (+)-a-pinene, and δ-3-carene increased the flight response of T. dubius in the Great Lakes Region of the U.S. However, these relationships are highly complex, as Erbilgin and Raffa (2001) also found that increased concentrations of the monoterpene β-myrcene interrupted the response of T dubius to bark beetle pheromones, which highlights the delicate balance of monoterpene diversity, insect ecology, and ecosystem function. Bark beetles have a known symbiotic relationship with a variety of Ascomycota fungi, including species in the genus Ophiostoma and Grosmannia that are vectored into trees during 22 attack through highly specialized exoskeletal structures (mycangia) in the beetles (Six 2003, Harrington 2005). These fungi play a complex role in catalyzing tree mortality by inhibiting transport of water and nutrients through xylem and phloem tissues (Harrington 2005, Keefover- Ring et al. 2016). The fungi further promote beetle establishment by breaking down cellulose and lignin and creating extensive networks of mycelium, which the beetles actively utilize as a food source to acquire various amino acids, vitamins, and nutrients (Beaver 1989, Six 2003, 2012). The beetle-pathogen complex has necessitated development of defensive compounds that can collectively act against each of these agents separately and further increases the complexity of resin chemistry. For instance, Keefover-Ring et al. (2016) found that ponderosa pine that were inoculated with G. clavigera responded through a relatively rapid (within 17 days) increase in concentrations of monoterpenes (22 x increase), sesquiterpenes (57 x increase), and diterpenes (35 x increase), and that these responses were far less pronounced in trees that were mechanically wounded without inoculation. Given these multifaceted, interacting relationships determining the concentrations and diversity of monoterpenes for variety of native and naïve hosts is important in understanding insect ecology, yet this information is critically lacking for a wide variety of species (Raffa et al. 2017). In addition to the chemical composition of oleoresin, physical resin duct properties (such as resin duct density and duct size) have been shown to have a significant influence on bark beetle related tree mortality. For instance, in a study in northern Arizona, Kane and Kolb (2010) found that ponderosa pine that survived drought and bark beetle attacks (primarily by Ips spp.) produced more resin ducts (29-54% increase) with a greater overall duct density (30% increase), duct width (13% increase) and relative duct area (51-66% increase) compared to trees that were 23 killed. Similarly, Hood et al. (2015) showed that ponderosa pine that survived bark beetle attack (primarily by Dendroctonus and Ips spp.) across Montana, Utah, Idaho, and Oregon produced larger resin ducts (20% increase), had greater resin duct area per ring (24% increase) and allocated more area to ducts (15% increase) compared to trees that were killed. Ferrenberg et al. (2014) found more nuanced results among lodgepole and limber pines, whereby lodgepole pines and limber pines had more resin ducts (21% and 18% respectively) over the most recent ten years of growth compared to trees that died during bark beetle attack. In addition, lodgepole pines had greater duct density per annual ring (8%) although this result was not statistically significant, while surviving limber pines had greater duct density (37%) over the most recent ten- year growth period. Interestingly, the researchers did not find any difference in resin duct size between lodgepole pines that survived compared to those that died, while surviving limber pines were found to have smaller resin ducts than trees that died. These results are somewhat surprising as resin duct size has been shown to be an important factor in other studies, although research has largely focused on lower elevation species, such as ponderosa pine, which highlights the need to consider these variables across a range of different species and environments. Climate has also been shown to have significant influence on the development of defensive structures and chemical compounds (Gaylord et al. 2013). Under drought scenarios, increased stomatal closure can reduce carbon assimilation, which can reduce resource availability for production of resin ducts and oleoresin compounds (Sala et al. 2010, Gaylord et al. 2013). Several studies have analyzed resin flow as a surrogate for defense and observations generally support reduced resin flow under drought conditions (Gaylord et al. 2007, Jactel et al. 24 2012, Gaylord et al. 2013). However, resin flow is considerably variable within and across species as viscosity is highly dependent upon air temperature, with increased viscosity occurring under higher temperatures, and can thus exhibit substantial daily-to-seasonal variability (Gaylord et al. 2007, Gaylord et al. 2013, Rodríguez-García et al. 2014). This has led many researchers to consider vertical resin ducts in the xylem as a proxy for defense, given their persistence and broad correlation with resin production and storage of oleoresin compounds (Kane and Kolb 2010, Gaylord et al. 2013, Ferrenberg et al. 2014, Gaylord et al. 2015, Hood et al. 2015, Hood et al. 2016). For instance, Gaylord et al. (2013) found that experimental 3-year drought reduced both the size and density of resin ducts in piñon pine (which corresponded with increased mortality), however, no treatment effect was detected on resin flow, which led the authors to suggest that resin flow is highly compartmentalized within pinon pines and drawing inference regarding tree-based defenses from isolated samples of resin flow should be avoided. Drought has also been correlated with increased bark beetle activity that may amplify tree mortality, although the magnitude of bark beetle activity can vary widely depending on intensity and duration of the drought event, further highlighting the complex biotic and abiotic interactions that can cumulatively influence defense properties over time and space (Mattson and Haack 1987, Jactel et al. 2012, Gaylord et al. 2013). In addition, the timing and variability of seasonal changes in precipitation and temperature have been shown to influence resin duct development (Wimmer and Grabner 1997, Vázquez-González et al. 2021). For instance, in a study of climate, tree growth, and resin duct production in Norway spruce (P. abies), Wimmer and Grabner (1997) found that resin duct density increased during periods of above-average temperatures, particularly from June to August, while production decreased during periods of above-average 25 precipitation from May to July. This suggests a nuanced and complex relationship between resource acquisition and allocation of carbon to resin-based defenses. Abiotic disturbances are also an integral component in the development of defensive features. For example, fire has been shown to have a measurable impact on the formation of oleoresin and resin ducts and may potentially influence resin chemistry by altering nutrient availability (Cannac et al. 2009, Perrakis et al. 2011, Hood et al. 2015, Hood et al. 2016). For instance, Hood et al. (2015) found that total resin duct area increased by 80% one-year post-fire for a site in Montana and by 87% two years after fire for a site in Utah, although the timing of the response varied due to interannual variability in precipitation and temperature. This research also showed that resin duct area decreased by 15% following fire cessation (due to fire suppression) at sites in both Idaho and Oregon, suggesting that the absence of fire in fire-adapted ecosystems may diminish resin defenses over time. In another study, Perrakis et al. (2011) investigated resin flow four years following prescribed fire treatments of varying intensities (in spring and fall burn operations) for old-growth ponderosa pine and found that resin flow increased across burned trees, with a peak in resin flow two years after the prescribed fires. A previous study in Cascade ponderosa pine (Perrakis 2008) compared resin flow across various fire treatments (prescribed fire and bole charring) and mechanical fire-surrogate treatments (crown pruning and root trenching) and found that resin flow only increased in the fire treatments, suggesting that induced resin response may only be stimulated through fire-related injuries or through cambial heating. To contrast, in a series of experimental treatments using various combinations of thinning and prescribed fire in ponderosa pine dominated forests of the northern Rocky Mountains, Hood 26 et al. (2016) did not find any increase in resin duct properties (production, size, and area) or in long-term (12 year) resin flow in a ‘prescribed fire only’ treatment. In addition, the researchers observed decreased concentrations of monoterpenes (myrcene, (−)-α-pinene, 3-carene, and terpinolene) compared to replicants of thinning and combined ‘thinning-and-burning’ treatments (Hood et al. 2016). However, the authors note that fire season and the type of fire (e.g., low- intensity prescribed fires) may have contributed to a lack of an induced resin response, as the prescribed fire treatments were implemented in the late spring, whereas naturally occurring wildfires in the northern Rockies tend to ignite under drier conditions in the late summer and early fall that may increase intensity and stimulate production of resin defenses (Hood et al. 2016). In addition, resin chemistry has also been found to change with time since fire, which could explain some of these differences (Campbell and Taylor 2007). In another study, Powell and Raffa (2011) found that fire-induced injuries had a measurable impact on monoterpene concentrations in both constitutive and induced phloem tissues. Regardless of the severity of injury (low, moderate, or high) monoterpene concentrations throughout induced tissues were reduced (by as much as half) while concentrations in constitutive tissues remained relatively stable. The authors found a reduction in 4-allylanisole, which has been associated with inhibition of bark beetle aggregation pheromones, in both constitutive and induced tissues, and a simultaneous increase in (–)-α-pinene, which is a precursor for pheromone synthesis. This suggests that fire-injured trees may be less chemically defended against subsequent bark beetle colonization and that fire injuries may stimulate a chemically more favorable environment for bark beetle success. However, reductions in the monoterpene β-myrcene could confound this effect, as β-myrcene has been shown to synergize pheromone biosynthesis, which suggests that 27 more research is needed to better understand these intricate processes. These seemingly nuanced and contradictory results highlight the complexity of interacting physiological response to disturbance interactions and environmental variability and illustrate a need for additional analyses that consider such interactions across a range of species and environments. Quantification and Analysis Resin duct structures and oleoresin flow represent effective metrics for quantifying tree defense and overall resilience to pathogens and insects (Stamp 2003, Gaylord et al. 2007, Kane and Kolb 2010, Ferrenberg et al. 2014, Hood and Sala 2015, Hood et al. 2016). Research suggests that carbon allocation to resin duct production may be better measures of vigor than carbon allocation to radial growth, which can have significant impacts on tree survival during disturbance events (Gaylord et al. 2007, Wainhouse et al. 2009, Kane and Kolb 2010, Ferrenberg et al. 2014). However, methods for analyzing resin ducts and oleoresin production vary substantially and there is a general lack of uniformity in researching this topic (Nagy et al. 2000, Baier et al. 2002, Kane and Kolb 2010, Ferrenberg et al. 2014, Hood and Sala 2015). For instance, some researchers have focused on vertical resin ducts in the xylem (Nagy et al. 2000, Kane and Kolb 2010, Ferrenberg et al. 2014, Hood and Sala 2015, Hood et al. 2015, Hood et al. 2016) and radial resin ducts (Rodríguez-García et al. 2014) while others have used resin ducts in cortical tissues (Moreira et al. 2015) or in needles (Moreira et al. 2014, Moreira et al. 2016, Liu et al. 2017b), with considerable variation in what properties are measured and analyzed (e.g., duct production, density, size, and area). Such inconsistencies in resin duct methodology complicates efforts to draw conclusions across research. For example, work by Kane and Kolb (2010) and Ferrenberg et al. (2014) compared resin duct properties and growth metrics between 28 trees that survived and those that died following bark beetle attacks, however, their resin duct metrics did not specifically account for ring area, which is a point argued by Hood and Sala (2015) to develop a standard method for quantification of resin duct measures. Analysis of resin duct structures and oleoresin flow can ultimately lead to a better understanding of tree physiology and their numerous coping strategies in responding to disturbance agents. However, relatively little research has been conducted given the numerous types of resin ducts and the various physical and chemical properties associated with these defense features, particularly as they relate to growth and defense characteristics (Warren et al. 1999, Wainhouse et al. 2009, Kane and Kolb 2010). Many researchers for instance have sought to analyze vertical and radial resin ducts in the secondary xylem (Blanche et al. 1992, Wimmer and Grabner 1997, Lombardero et al. 2000, Smith 2000, Baier et al. 2002, Rosner and Hannrup 2004) yet relatively little investigation has been conducted as to the nature of resin ducts originating in secondary phloem tissue (Warren et al. 1999, Franceschi et al. 2000). Resin systems within the phloem are fundamental in the initial defense of trees to insects and pathogen attack as they contain many important phenolic compounds, which help mitigate spread of these organisms (Klepzig et al. 1995, Franceschi et al. 2000, Baier et al. 2002). Similarly, only two studies have looked to relate the chemical composition (e.g., monoterpene concentrations and diversity) of oleoresin compounds with morphological characteristics of resin duct structures (Hood et al. 2016, Mason et al. 2019). When considering the broad spectrum of species that exhibit different strategies and priorities in the development of these types of primary defense systems there is a great deal of opportunity to analyze these features to better understand their overall function and variability among and within genera. In general, changes in the physical and 29 chemical properties of resin ducts and oleoresin compounds relating to ontogeny and phenology is poorly understood for a wide range of species and will require more nuanced research across a broader range of habitats (Boege and Marquis 2005, Barton and Koricheva 2010, Muola et al. 2010, Ferrenberg et al. 2015). Hood and Sala (2015) established an effective framework for the quantification of key resin duct metrics in a study analyzing constitutive and induced resin response to simulated disturbance. Specifically, the authors categorized vertical resin ducts in secondary xylem tissues into standardized measures, where resin duct properties are related to growth rate and non- standardized measures, where resin duct properties are measured independent of growth rate (Table 1) (Hood and Sala 2015). Such a classification of key metrics for assessing resin-based defenses can help unify methodologies relating to resin duct analysis and future research should adopt this strategy to develop a more holistic view of the morphological characteristics of resin duct defenses. In addition, various physical and environmental conditions have been shown to influence resin duct formation and resin duct properties, including genetics, nutrient availability, drought and seasonal changes in precipitation and temperature, disturbance, as well as tree growth rates and herbivore pressures (Figure 1). Given the high degree of variability in analyzing resin-based defenses future research should consider this suite of biotic and abiotic factors than can influence resin-based defenses. 30 Table 1. Metrics for quantifying vertical resin duct properties in secondary xylem tissues of conifers. From Hood and Sala (2015). Variable Type1 Description Duct Size (mm2) Unstandardized Mean size of all ducts per annual ring Duct Production (no. year-1) Unstandardized Total number of ducts per annual ring Total Duct Area (mm2 year-1) Unstandardized Sum of duct area per annual ring Total number of ducts per annual ring divided Duct Density (no. mm2 year-1) Standardized by ring area2 Relative Duct Area (% annual ring) Standardized Total duct area divided by ring area2 x 100 1Type: Unstandardized – adjusted for growth rage; Standardized – unadjusted for growth rate. 2Ring Area: (ring width x core diameter). Figure 1. Conceptual diagram reflecting factors that can shape resin duct properties (broadly categorized as duct size, area, production, and density). Note: Amorphous lines for Genetic Variability are meant to highlight the considerable differences within and among species that can shape heritable resin duct properties. Adopted from Ferrenberg et al. (2014). Research Significance for Whitebark Pine Whitebark pine (P. albicaulis) is a foundation species in the northern Rocky Mountains that has been shown to influence soil hydrology, increase snowpack retention, and promote vegetative establishment in subalpine settings (Keane and Arno 1993, Campbell and Antos 2000, 31 Kendall and Keane 2000, Tomback and Kendall 2001, Kim et al. 2003, Bockino and Tinker 2012). Whitebark pine also serves as an important food source for grizzly bears and a host of other wildlife (Arno and Hoff 1989, Tomback and Kendall 2001). However, there is growing concern that whitebark pine may be largely extirpated from its current habitat over the next century due to cumulative impacts of bark beetles, fire suppression, increased competition, and the invasive pathogen white pine blister rust (Cronartium ribicola), which has already decimated populations (by as much as 80%) in some areas (Keane and Arno 1993, Kendall and Keane 2000, Macfarlane et al. 2013). In addition, modeling efforts show that the species may be significantly reduced in the Greater Yellowstone Ecosystem in the next century based on changing habitat suitability (Chang et al. 2014). Given these collective biotic and abiotic pressures there is a critical need to develop a more comprehensive assessment of growth and defense characteristics relating to this important species. In this context, Raffa et al. (2013) and Raffa et al. (2017) looked at the chemical composition of oleoresin in both lodgepole pine (the native host for mountain pine beetle), as well as whitebark pine (a naïve host) to determine how bark beetles may interact with these populations. The research found that whitebark pine is a chemically more suitable host than lodgepole pine, which may exacerbate mortality under projected epidemic scenarios. For instance, the researchers found that whitebark pine has thicker phloem on average compared to lodgepole pine, which would make it more habitable for overwintering beetle populations. Whitebark pine was found to have greater concentrations of the monoterpene (–)-α-pinene, a precursor for pheromone biosynthesis (2.4 x increase), as well as myrcene and δ-3-carene, which catalyze pheromone production (1.6 x and 2.1 x increase, respectively), and lower concentrations 32 of the phenylpropanoid 4-allylanisole, which inhibits pheromone production (2.3 x decrease) compared to lodgepole pine (Raffa et al. 2017). To contrast, lodgepole pines had greater overall concentrations of monoterpenes in both constitutive and induced tissues and responded to simulated attack by producing significantly more 4-allylanisole (3.8 x increase) to inhibit bark beetle aggregation and less myrcene (0.56 x decrease), which catalyzes pheromone synthesis (Hayes and Strom 1994, Raffa et al. 2017). Lodgepole pines also had greater concentrations of nitrogen and calcium, which have been shown to be important minerals in signaling induced defenses (Ranty et al. 2016, Raffa et al. 2017). Collectively, these findings suggest that lodgepole pines have more thorough chemical defenses against mountain pine beetle while whitebark pine are chemically more susceptible to successful bark beetle colonization, likely resulting from a less continuous and more fragmented historic exposure of the naïve host to this disturbance (Raffa et al. 2013, Raffa et al. 2017). However, despite the chemical suitability of whitebark pines for colonization, the researchers found that under endemic scenarios mountain pine beetles will preferentially attack lodgepole pines, although this preference decreased with increasing proportions of whitebark pine at the stand level (Raffa et al. 2013, Raffa et al. 2017). This is likely attributable to the greater concentrations of the monoterpene β-phellandrene in lodgepole pines (2.6 x increase), which adult pine beetles exploit in host-recognition (Miller and Borden 2000, Raffa et al. 2017). The implications of increased susceptibility of whitebark pine to bark beetles is concerning, given projections for enhanced bark beetle activity over the next several decades. Temperature has been shown to exert a strong control on the regulation of life-cycle development in mountain pine beetle populations. For instance, prolonged periods of 33 temperatures less than – 40° C will kill nearly all (~99-100 %) of beetle populations, regardless of developmental stage, whereas changes in climate that result in increased summer seasons and decreased winter periods have well researched correlations with increased bark beetle activity, as many species of these insects are able to reproduce for longer time periods under these warmer conditions (Buotte et al. 2016, 2017). A key area of concern is an observed trend in increased winter minimum temperatures, which is particularly pronounced in the northern Rocky Mountains (Pederson et al. 2009, Pederson et al. 2011, Pederson et al. 2013) and which has been correlated with recent, epidemic outbreaks (Williams and Liebhold 2002, Bentz et al. 2010). Continued increases in winter temperatures has the potential to increase the climate suitability for increased brood production, which could exacerbate tree mortality throughout the broader range of whitebark pine over the next century (Chang et al. 2014, Buotte et al. 2016, 2017). Increased temperatures in the winter combined with an earlier onset of spring have led to decreased winter snowpack in the northern Rocky Mountains and a shift to longer and drier summer seasons, which can amplify stress in trees (Westerling et al. 2006, Pederson et al. 2009, Pederson et al. 2011, Pederson et al. 2013). This drought-induced stress can reduce tree responsiveness and increase susceptibility to fire, insects and pathogens (Rigling et al. 2003, Smith et al. 2005, Fettig et al. 2007, Maloney et al. 2008, Kane and Kolb 2010). Environmental stressors in general reduce vigor, inhibit growth, and lead to premature mortality within older age classes (Perrakis and Agee 2006). Mortality in these older cohorts of whitebark pine is particularly concerning due to well-documented rates of high mortality in younger age classes as a result of the rampant spread of white pine blister rust, which preferentially targets younger trees and cone-bearing branches (Kim et al. 2003, Tomback and Achuff 2010). 34 The overall pressures influencing whitebark pine mortality warrant special consideration for this important species, yet relatively little research exists relating growth and defense characteristics across a range of habitats. Understanding resin defense systems is of particular importance in this regard as these advanced biochemical structures represent the primary defense mechanism of whitebark pine to biotic disturbance. Knowledge of resin duct structures and oleoresin production can provide valuable insight into overall defensibility of these trees to damaging agents, which is especially important given the sensitivity of whitebark pine to unforeseeable changes in disturbance regimes. In addition, linkage of morphological characteristics of resin ducts with resin chemistry (e.g., monoterpene concentrations and diversity) is an area of limited research and one that could benefit from future studies. 35 CHAPTER THREE WHITEBARK PINE (PINUS ALBICAULIS) GROWTH AND DEFENSE IN RESPONSE TO MOUNTAIN PINE BEETLE OUTBREAKS Contribution of Authors and Co-Authors Manuscript in Chapter 3 Author: Nickolas Earl Kichas Contributions: Conceptualization, Investigation, Data production, Formal analyses, Methodology, Visualization, Writing – original draft, Funding acquisition Co-Author: Sharon M. Hood Contributions: Investigation, Formal analyses, Methodology, Visualization, Resources, Writing – review & editing Co-Author: Gregory T. Pederson Contributions: Investigation, Formal analyses, Methodology, Visualization, Resources, Writing – review & editing Co-Author: Richard G. Everett Contributions: Project administration, Resources Co-Author: David B. McWethy 36 Contributions: Investigation, Supervision, Funding acquisition, Writing – review & editing Manuscript Information Page Nickolas E. Kichas, Sharon M. Hood, Gregory T. Pederson, Richard G. Everett and David B. McWethy Forest Ecology & Management Status of Manuscript: Prepared for submission to a peer-reviewed journal Officially submitted to a peer-reviewed journal Accepted by a peer-reviewed journal X Published in a peer-reviewed journal 37 38 39 40 41 42 43 44 45 46 47 48 49 50 CHAPTER FOUR GROWTH AND DEFENSE CHARACTERISTICS OF WHITEBARK PINE (PINUS ALBICAULIS) AND LODGEPOLE PINE (PINUS CONTORTA VAR LATIFOLIA) IN A HIGH- ELEVATION, DISTURBANCE-PRONE MIXED-CONIFER FOREST IN NORTHWESTERN MONTANA, USA Contribution of Authors and Co-Authors Manuscript in Chapter 4 Author: Nickolas Earl Kichas Contributions: Conceptualization, Investigation, Formal analysis, Methodology, Visualization, Writing - original draft, Funding acquisition. Co-Author: Amy M. Trowbridge Contributions: Conceptualization, Investigation, Formal analysis, Methodology, Resources, Writing - review & editing. Co-Author: Kenneth F. Raffa Contributions: Investigation, Supervision, Writing - review & editing. Co-Author: Shealyn C. Malone Contributions: Formal analysis, Investigation, Writing - review & editing. Co-Author: Sharon M. Hood Contributions: Conceptualization, Investigation, Methodology, Writing - review & editing. Co-Author: Gregory T. Pederson Contributions: Resources, Writing - review & editing. 51 Co-Author: Richard G. Everett Contributions: Project administration, Resources. Co-Author: David B. McWethy Contributions: Conceptualization, Methodology, Supervision, Funding acquisition, Writing - review & editing. 52 Manuscript Information Page Nickolas E. Kichas, Amy M. Trowbridge, Kenneth F. Raffa, Shealyn C. Malone, Sharon M. Hood, Richard G. Everett, David B. McWethy, and Gregory T. Pederson Forest Ecology & Management Status of Manuscript: Prepared for submission to a peer-reviewed journal Officially submitted to a peer-reviewed journal Accepted by a peer-reviewed journal X Published in a peer-reviewed journal 53 54 55 56 57 58 59 60 61 62 63 64 65 66 CHAPTER FIVE INCREASED WHITEBARK PINE (PINUS ALBICAULIS) GROWTH AND DEFENSE UNDER A WARMER AND REGIONALLY DRIER CLIMATE Contribution of Authors and Co-Authors Manuscript in Chapter 5 Author: Nickolas Earl Kichas Contributions: Conceptualization, Investigation, Data production, Formal analysis, Methodology, Visualization, Writing - original draft, Funding acquisition. Co-Author: Gregory T. Pederson Contributions: Conceptualization, Investigation, Formal analysis, Methodology, Resources, Writing - review & editing. Co-Author: Sharon M. Hood Contributions: Investigation, Methodology, Writing - review & editing. Co-Author: Richard G. Everett Contributions: Project administration, Resources. Co-Author: David B. McWethy Contributions: Methodology, Supervision, Funding acquisition, Writing - review & editing. 67 Manuscript Information Page Nickolas E. Kichas, Gregory T. Pederson, Sharon M. Hood, Richard G. Everett, and David B. McWethy Geophysical Research Letters Status of Manuscript: Prepared for submission to a peer-reviewed journal X Officially submitted to a peer-reviewed journal Accepted by a peer-reviewed journal Published in a peer-reviewed journal 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 CONCLUSIONS In response to widespread mortality, there is an urgent need to develop strategies for maintaining distributions of whitebark pine throughout its contemporary range (Keane et al. 2012, Goeking and Izlar 2018, Goeking et al. 2018). For instance, scientists have developed genetic strains resistant to white pine blister rust and are monitoring planting efforts to better understand how these strains perform in natural settings over time (Kim et al. 2003, Bower and Aitken 2008, Liu et al. 2016, Liu et al. 2017a). These are important efforts that will greatly assist managers in retaining and promoting whitebark pine within the complex environments where it is found. However, seedling growth is often the primary characteristic selected for within these breeding programs and the preferential selection for these two traits (growth and rust resistance) likely fails to account for diverse defensive strategies (both physical and chemical defenses) that differ considerably across genotypes (Bower and Aitken 2008). For example, in our manuscript investigating stand dynamics within whitebark pine populations, we found that trees that have persisted through recent stand-level mountain pine beetle activity and white pine blister rust infection produced anatomically larger resin ducts with a greater annual investment in these resin duct features (Kichas et al. 2020). However, these trees did so at the expense of growth, exhibiting lower overall growth rates compared to trees that died. This suggests a complex trade- off between the allocation of resources to growth and defense, however, such trade-offs are poorly understood within natural ecosystems. Changes in the composition and structure of whitebark pine stands, resulting from fire suppression, increased competition from shade-tolerant species, and shifting insect and pathogen interactions all highlight a critical need to better understand the evolutionary tradeoffs between 86 the investment of resources into growth and defense in whitebark pine. Developing a better understanding of how these physiological properties differ within and across whitebark pine populations is critical for establishing successful strategies for restoration. This is especially true given the dynamic nature of mixed-conifer stands within the northern Rocky Mountains. Our results lend insight into different growth and defense strategies, and specifically resin duct characteristics and resin chemistry that may be beneficial to increasing survivorship and promoting whitebark pine establishment and persistence within natural, disturbance-prone mixed-conifer ecosystems. In addition, our research provides novel insight on whitebark pine growth and defense characteristics utilizing data from a region of the northern Rocky Mountains where the species is currently underrepresented in the academic literature. There are six key points that can be drawn from this work: 1) Whitebark pine trees that have persisted through recent stand-level disturbance produced fewer but larger resin duct structures with greater relative duct area compared to trees that died. 2) There are important differences in the chemical composition of constitutive resin between whitebark and lodgepole pine that generally support field observations. Under endemic scenarios, mountain pine beetle preferentially select lodgepole pine, while under eruptive outbreak scenarios, mountain pine beetle are successful in colonizing higher-elevation whitebark pine trees. 3) There are complex relationships between tree growth, resin duct anatomy and constitutive resin chemistry that present beetles with many permutations of resin-based defenses. 87 4) Competition, particularly with Engelmann spruce (Picea engelmannii) can influence constitutive resin chemistry in whitebark pine. 5) Whitebark pine in subalpine settings of the northern Rocky Mountains are exhibiting increased growth as well as increased defense under warmer and regionally drier conditions. Evidence from this research suggests whitebark pine employs a complex array of physiological strategies in response to changing disturbance and climatic conditions. The broader study region from which our trees were sampled has been affected by numerous large-scale bark beetle outbreaks, occurring in the 1930s, 1960s-1980s, and most recently 2000s-2010s. There was strong evidence for both mountain pine beetle and white pine blister rust mortality across each of our study sites, with the majority (~ 82%) of whitebark pine trees having been killed prior to our initial sampling effort. In this context, producing larger resin duct structures and investing a greater proportion of annual carbon into resin-based defenses is advantageous in persisting through this biotic stressor. However, under different environmental pressures investment in resin ducts at the expense of growth could be disadvantageous. For instance, during periods of endemic beetle activity stand competition could reflect a greater stress and select for individuals that can more efficiently allocate resources to height and radial growth, at the expense of energetically demanding resin-based defenses. Climate can further modulate growth and resin duct morphology and adds another layer of complexity to the evolutionary filtering of this unique species. Whitebark pine in the northern Rocky Mountains have evolved with these environmental stressors over hundreds of thousands of years and exhibit high phenotypic plasticity. As such, it is essential to maintain as much stand-level genetic variability 88 as possible in order to buffer against future unpredictable changes in climate and disturbance regimes that may negatively impact this species. Managers tasked with preserving and promoting whitebark pine populations are presented with increasingly limited options for achieving successful outcomes. Many management prescriptions rely on surficial performance indicators (primarily growth) that may fail to account for the important role of physical and chemical defenses in moderating disturbance interactions. Few studies have investigated patterns of growth and defense using multiple proxies (resin duct anatomy and resin chemistry) and our work highlights a need to expand research to a broader range of species and environments. 89 REFERENCES CITED 90 Allen, C. D., A. K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D. D. Breshears, E. H. Hogg, P. Gonzalez, R. Fensham, Z. Zhang, J. Castro, N. Demidova, J.-H. Lim, G. Allard, S. W. Running, A. Semerci, and N. Cobb. 2010. A Global Overview of Drought and Heat-Induced Tree Mortality Reveals Emerging Climate Change Risks for Forests. Forest Ecology and Management 259:660-684. Arno, S. F., and R. J. Hoff. 1989. Silvics of Whitebark Pine (Pinus albicaulis).in F. S. United States Department of Agriculture, editor. lntermountain Research Station, Ogden, UT. Baier, P., E. Führer, T. Kirisits, and S. Rosner. 2002. Defence Reactions of Norway Spruce against Bark Beetles and the Associated Fungus Ceratocystis polonica in Secondary Pure and Mixed Species Stands. Forest Ecology and Management 159:73-86. Baker, W. L. 2009. Fire Ecology in Rocky Mountain Landscapes. 1 edition. Island Press, Washington, D.C. Bannan, M. W. 1936. Vertical Resin Ducts in the Secondary Wood of the Abietineae. The New Phytologist 35:11-46. Barton, K. E., and J. Koricheva. 2010. The Ontogeny of Plant Defense and Herbivory: Characterizing General Patterns Using Meta-Analysis. The American Naturalist 175:481- 493. Beaver, R. A. 1989. Insect–Fungus Relationships in the Bark and Ambrosia Beetles. Pages 121- 143 Insect-fungus Interactions. Bell, A. A. 1981. Biochemical Mechanisms of Disease Resistance. Annual Review of Plant Physiology 32:21-81. Bentz, B. J., S. M. Hood, E. M. Hansen, J. C. Vandygriff, and K. E. Mock. 2017. Defense Traits in the Long-Lived Great Basin Bristlecone Pine and Resistance to the Native Herbivore Mountain Pine Beetle. New Phytologist 213:611-624. Bentz, B. J., J. Régnière, C. J. Fettig, E. M. Hansen, J. L. Hayes, J. A. Hicke, R. G. Kelsey, J. F. Negrón, and S. J. Seybold. 2010. Climate Change and Bark Beetles of the Western United States and Canada: Direct and Indirect Effects. BioScience 60:602-613. Blanche, C. A., P. L. Lorio, R. A. Sommers, J. D. Hodges, and T. E. Nebeker. 1992. Seasonal Cambial Growth and Development of Loblolly Pine: Xylem Formation, Inner Bark Chemistry, Resin Ducts, and Resin Flow. Forest Ecology and Management 49:151-165. Bockino, N. K., and D. B. Tinker. 2012. Interactions of White Pine Blister Rust and Mountain Pine Beetle in Whitebark Pine Ecosystems in the Southern Greater Yellowstone Area. Natural Areas Journal 32:31-40. Boege, K., and R. J. Marquis. 2005. Facing Herbivory as You Grow Up: The Ontogeny of Resistance in Plants. Trends in Ecology and Evolution 20:441-448. Bonello, P., T. R. Gordon, D. A. Herms, D. L. Wood, and N. Erbilgin. 2006. Nature and Ecological Implications of Pathogen-Induced Systemic Resistance in Conifers: A Novel Hypothesis. Physiological and Molecular Plant Pathology 68:95-104. Boone, C. K., B. H. Aukema, J. Bohlmann, A. L. Carroll, and K. F. Raffa. 2011. Efficacy of Tree Defense Physiology Varies with Bark Beetle Population Density: A Basis for Positive Feedback in Eruptive Species. Canadian Journal of Forest Research 41:1174-1188. Borden, J. H., D. S. Pureswaran, and J. P. Lafontaine. 2008. Synergistic Blends of Monoterpenes for Aggregation Pheromones of the Mountain Pine Beetle (Coleoptera: Curculionidae). Journal of Economic Entomology 101:1266-1275. 91 Bower, A. D., and S. N. Aitken. 2008. Ecological Genetics and Seed Transfer Guidelines for Pinus Albicaulis (Pinaceae). American Journal of Botany 95:66-76. Bullington, L. S., Y. Lekberg, R. A. Sniezko, and B. Larkin. 2018. The Influence of Genetics, Defensive Chemistry and the Fungal Microbiome on Disease Outcome in Whitebark Pine Trees. Molecular Plant Pathology. Buotte, P. C., J. A. Hicke, H. K. Preisler, J. T. Abatzoglou, K. F. Raffa, and J. A. Logan. 2016. Climate Influences on Whitebark Pine Mortality from Mountain Pine Beetle in the Greater Yellowstone Ecosystem. Ecological Applications 26:2507–2524. Buotte, P. C., J. A. Hicke, H. K. Preisler, J. T. Abatzoglou, K. F. Raffa, and J. A. Logan. 2017. Recent and Future Climate Suitability for Whitebark Pine Mortality from Mountain Pine Beetles Varies Across the Western U.S. Forest Ecology and Management 399:132-142. Campbell, E. M., and J. A. Antos. 2000. Distribution and Severity of White Pine Blister Rust and Mountain Pine Beetle on Whitebark Pine in British Columbia. Canadian Journal of Forest Research 30:1051-1059. Campbell, E. S., and J. C. A. Taylor. 2007. Monoterpene Production in Redberry Juniper Foliage following Fire. Rangeland Ecology & Management 60:104-109. Cannac, M., T. Barboni, L. Ferrat, A. Bighelli, V. Castola, J. Costa, D. Trecul, F. Morandini, and V. Pasqualini. 2009. Oleoresin Flow and Chemical Composition of Corsican pine (Pinus nigra subsp. laricio) in Response to Prescribed Burnings. Forest Ecology and Management 257:1247-1254. Chang, T., A. J. Hansen, and N. Piekielek. 2014. Patterns and Variability of Projected Bioclimatic Habitat for Pinus albicaulis in the Greater Yellowstone Area. PLoS One 9:e111669. Cobb, N. S., J. B. Mitton, and T. G. Whitham. 1994. Genetic Variation Associated with Chronic Water and Nutrient Stress in Pinyon Pine. American Journal of Botany 81. Cole, D. N. 1990. Recreation in Whitebark Pine Ecosystems: Demand, Problems, and Management Strategies. Pages 305-309 in U. S. F. Service, editor. Intermountain Research Station, Ogden, UT. Coley, P. D., J. P. Bryant, and F. S. Chapin. 1985. Resource Availability and Plant Antiherbivore Defense. Science 230:895-899. Conedera, M., W. Tinner, C. Neff, M. Meurer, A. F. Dickens, and P. Krebs. 2009. Reconstructing Past Fire Regimes: Methods, Applications, and Relevance to Fire Management and Conservation. Quaternary Science Reviews 28:555-576. Dawson, T. P., S. T. Jackson, J. I. House, I. C. Prentice, and G. M. Mace. 2011. Beyond Predictions: Biodiversity Conservation in a Changing Climate. Science 332:53-58. de la Mata, R., S. M. Hood, and A. Sala. 2017. Insect Outbreak Shifts the Direction of Selection from Fast to Slow Growth Rates in the Long-Lived Conifer Pinus ponderosa. Proceedings of the National Academy of Science 114:7391-7396. DeAngelis, J. D., T. E. Nebeker, and J. D. Hodges. 1986. Influence of Tree Age and Growth Rate on the Radial Resin Duct System in Loblolly Pine (Pinus taeda). Canadian Journal of Botany 64:1046-1049. Driscoll, D. A., D. B. Lindenmayer, A. F. Bennett, M. Bode, R. A. Bradstock, G. J. Cary, M. F. Clarke, N. Dexter, R. Fensham, G. Friend, M. Gill, S. James, G. Kay, D. A. Keith, C. MacGregor, J. Russell-Smith, D. Salt, J. E. M. Watson, R. J. Williams, and A. York. 2010. 92 Fire Management for Biodiversity Conservation: Key Research Questions and Our Capacity to Answer Them. Biological Conservation 143:1928-1939. Endara, M.-J., and P. D. Coley. 2011. The Resource Availability Hypothesis Revisited: A Meta- Analysis. Functional Ecology 25:389-398. Erbilgin, N., and K. F. Raffa. 2001. Modulation of Predator Attraction to Pheromones of Two Prey Species by Stereochemistry of Plant Volatiles. Oecologia 127:444-453. Ferrenberg, S., J. M. Kane, and J. M. Langenhan. 2015. To Grow or Defend? Pine Seedlings Grow Less But Induce More Defences When a Key Resource is Limited. Tree Physiology 35:107-111. Ferrenberg, S., J. M. Kane, and J. B. Mitton. 2014. Resin Duct Characteristics Associated with Tree Resistance to Bark Beetles Across Lodgepole and Limber Pines. Oecologia 174:1283- 1292. Ferrenberg, S., J. M. Langenhan, S. A. Loskot, L. M. Rozal, and J. B. Mitton. 2017. Resin Monoterpene Defenses Decline within Three Widespread Species of Pine (Pinus) along a 1530-m Elevational Gradient. Ecosphere 8:e01975. Fettig, C. J., K. D. Klepzig, R. F. Billings, A. S. Munson, T. E. Nebeker, J. F. Negrón, and J. T. Nowak. 2007. The Effectiveness of Vegetation Management Practices for Prevention and Control of Bark Beetle Infestations in Coniferous Forests of the Western and Southern United States. Forest Ecology and Management 238:24-53. Franceschi, V. R., T. Krekling, A. A. Berryman, and E. Christiansen. 1998. Specialized Phloem Parenchyma Cells in Norway Spruce (Pinaceae) Bark are an Important Site of Defense Reactions. American Journal of Botany 85:601-615. Franceschi, V. R., P. Krokene, E. Christiansen, and T. Krekling. 2005. Anatomical and Chemical Defenses of Conifer Bark Against Bark Beetles and Other Pests. New Phytologist 167:353- 375. Franceschi, V. R., P. Krokene, T. Krekling, and E. Christiansen. 2000. Phloem Parenchyma Cells are Involved in Local and Distant Defense Responses to Fungal Inoculation or Bark-Beetle Attack in Norway Spruce (Pinaceae). American Journal of Botany 87:314-326. Franklin, J. F., and J. K. Agee. 2003. Forging a Science-Based National Forest Fire Policy. Issues in Science and Technology 20. Gaylord, M. L., T. E. Kolb, and N. G. McDowell. 2015. Mechanisms of Piñon Pine Mortality after Severe Drought: A Retrospective Study of Mature Trees. Tree Physiology 35:806-816. Gaylord, M. L., T. E. Kolb, W. T. Pockman, J. A. Plaut, E. A. Yepez, A. K. Macalady, R. E. Pangle, and N. G. McDowell. 2013. Drought Predisposes Pinon-Juniper Woodlands to Insect Attacks and Mortality. New Phytologist 198:567-578. Gaylord, M. L., T. E. Kolb, K. F. Wallin, and M. R. Wagner. 2007. Seasonal Dynamics of Tree Growth, Physiology, and Resin Defenses in a Northern Arizona Ponderosa Pine Forest. Canadian Journal of Forest Research 37:1173-1183. Goeking, S., and D. Izlar. 2018. Pinus albicaulis Engelm. (Whitebark Pine) in Mixed-Species Stands throughout Its US Range: Broad-Scale Indicators of Extent and Recent Decline. Forests 9:131. Goeking, S. A., D. K. Izlar, and T. C. Edwards. 2018. A Landscape-Level Assessment of Whitebark Pine Regeneration in the Rocky Mountains, USA. Forest Science fxy029:1-13. 93 Goodsman, D. W., I. Lusebrink, S. M. Landhausser, N. Erbilgin, and V. J. Lieffers. 2013. Variation in Carbon Availability, Defense Chemistry and Susceptibility to Fungal Invasion along the Stems of Mature Trees. New Phytologist 197:586-594. Halofsky, J. E., D. C. Donato, D. E. Hibbs, J. L. Campbell, M. Donaghy Cannon, J. B. Fontaine, J. R. Thompson, R. G. Anthony, B. T. Bormann, L. J. Kayes, B. E. Law, D. L. Peterson, and T. A. Spies. 2011. Mixed-Severity Fire Regimes: Lessons and Hypotheses from the Klamath-Siskiyou Ecoregion. Ecosphere 2:1-19. Hammerbacher, A., A. Schmidt, N. Wadke, L. P. Wright, B. Schneider, J. Bohlmann, W. A. Brand, T. M. Fenning, J. Gershenzon, and C. Paetz. 2013. A Common Fungal Associate of the Spruce Bark Beetle Metabolizes the Stilbene Defenses of Norway Spruce. Plant Physiology 162:1324-1336. Hannrup, B., C. Cahalan, G. Chantre, M. Grabner, B. Karlsson, I. L. Bayon, G. L. Jones, U. Müller, H. Pereira, J. C. Rodrigues, S. Rosner, P. Rozenberg, L. Wilhelmsson, and R. Wimmer. 2004. Genetic Parameters of Growth and Wood Quality Traits in Picea abies. Scandinavian Journal of Forest Research 19:14-29. Harrington, T. C. 2005. Ecology and Evolution of Mycophagous Bark Beetles and their Fungal Partners. Pages 257-291 in F. E. Vega and M. Blackwell, editors. Ecological and Evolutionary Advances in Insect-Fungal Associations. Oxford University Press. Hayes, J. L., and B. L. Strom. 1994. 4-Allylanisole as an Inhibitor of Bark Beetle (Coleoptera: Scolytidae) Aggregation. Journal of Economic Entomology 87:1586-1594. Herms, D. A., and W. J. Mattson. 1992. The Dilemma of Plants: To Grow or Defend. The Quarterly Review of Biology 67:283-335. Hood, S. M., S. Baker, and A. Sala. 2016. Fortifying the Forest: Thinning and Burning Increase Resistance to a Bark Beetle Outbreak and Promote Forest Resilience. Ecological Applications 26:1984-2000. Hood, S. M., and A. Sala. 2015. Ponderosa Pine Resin Defenses and Growth: Metrics Matter. Tree Physiology 35:1223-1235. Hood, S. M., A. Sala, E. K. Heyerdahl, and M. Boutin. 2015. Low-Severity Fire Increases Tree Defense Against Bark Beetle Attacks. Ecology 96:1846–1855. Huber, D. P. W., R. Gries, J. H. Borden, and H. D. Pierce Jr. 2000. A Survey of Antennal Responses by Five Species of Coniferophagous Bark Beetles (Coleoptera: Scolytidae) to Bark Volatiles of Six Species of Angiosperm Trees. Chemoecology 10:103-113. Iglesias, V., T. R. Krause, and C. Whitlock. 2015. Complex Response of White Pines to Past Environmental Variability Increases Understanding of Future Vulnerability. PLoS One 10:e0124439. Imaji, A., and K. Seiwa. 2010. Carbon Allocation to Defense, Storage, and Growth in Seedlings of Two Temperate Broad-Leaved Tree Species. Oecologia 162:273-281. Jactel, H., J. Petit, M.-L. Desprez-Loustau, S. Delzon, D. Piou, A. Battisti, and J. Koricheva. 2012. Drought Effects on Damage by Forest Insects and Pathogens: A Meta-Analysis. Global Change Biology 18:267-276. Jamieson, M. A., L. A. Burkle, J. S. Manson, J. B. Runyon, A. M. Trowbridge, and J. Zientek. 2017. Global Change Effects on Plant-Insect Interactions: The Role of Phytochemistry. Current Opinion in Insect Science 23:70-80. 94 Kane, J. M., and T. E. Kolb. 2010. Importance of Resin Ducts in Reducing Ponderosa Pine Mortality from Bark Beetle Attack. Oecologia 164:601-609. Kane, J. M., J. M. Varner, M. R. Metz, and P. J. van Mantgem. 2017. Characterizing Interactions Between Fire and Other Disturbances and Their Impacts on Tree Mortality in Western U.S. Forests. Forest Ecology and Management 405:188-199. Keane, R. E. 2000. The Importance of Wilderness to Whitebark Pine Research and Management. Pages 84-92 in U. S. F. Service, editor. Rocky Mountain Research Station, Ogden, UT. Keane, R. E., and S. F. Arno. 1993. Rapid Decline of Whitebark Pine in Western Montana: Evidence from 20-Year Remeasurements. Western Journal of Applied Forestry 8:44-47. Keane, R. E., D. F. Tomback, C. A. Aubry, A. D. Bower, E. M. Campbell, C. L. Cripps, M. B. Jenkins, M. F. Mahalovich, M. Manning, S. T. McKinney, M. P. Murray, D. L. Perkins, D. P. Reinhart, C. Ryan, A. W. Schoettle, and C. M. Smith. 2012. A Range-Wide Restoration Strategy for Whitebark Pine (Pinus albicaulis).in U. S. D. o. Agriculture, editor. Forest Service, Rocky Mountain Research Station. Keefover-Ring, K., A. Trowbridge, C. J. Mason, and K. F. Raffa. 2016. Rapid Induction of Multiple Terpenoid Groups by Ponderosa Pine in Response to Bark Beetle-Associated Fungi. Journal of Chemical Ecology 42:1-12. Keeling, C. I. 2016. Bark Beetle Research in the Postgenomic Era. Pages 265–294 in C. Tittiger and G. Blomquist, editors. Pine Bark Beetles - Advances in Insect Physiology. Simon Fraser University, Burnaby, BC, Canada. Kendall, K. C., and R. E. Keane. 2000. The Decline of Whitebark Pine. Pages 123-145 in D. F. Tomback, S. F. Arno, and R. E. Keane, editors. Whitebark Pine Communities: Ecology and Restoration. Island Press, Washington, DC. Kichas, N. E., S. M. Hood, G. T. Pederson, R. G. Everett, and D. B. McWethy. 2020. Whitebark Pine (Pinus albicaulis) Growth and Defense in Response to Mountain Pine Beetle Outbreaks. Forest Ecology and Management 457. Kichas, N. E., A. M. Trowbridge, K. F. Raffa, S. C. Malone, S. M. Hood, R. G. Everett, D. B. McWethy, and G. T. Pederson. 2021. Growth and Defense Characteristics of Whitebark Pine (Pinus albicaulis) and Lodgepole Pine (Pinus contorta var latifolia) in a High- Elevation, Disturbance-Prone Mixed-Conifer Forest in northwestern Montana, USA. Forest Ecology and Management 493. Kim, M.-S., S. J. Brunsfeld, G. I. McDonald, and N. B. Klopfenstein. 2003. Effect of White Pine Blister Rust (Cronartium ribicola) and Rust-Resistance Breeding on Genetic Variation in Western White Pine (Pinus monticola). Theoretical and Applied Genetics 106:1004-1010. Klepzig, K. D., E. L. Kruger, E. B. Smalley, and K. F. Raffa. 1995. Effects of Biotic and Abiotic Stress on Induced Accumulation of Terpenes and Phenolics in Red Pines Inoculated with Bark Beetle-Vectored Fungus. Journal of Chemical Ecology 21:601-626. Kolb, T. E., J. K. Agee, P. Z. Fulé, N. G. McDowell, K. Pearson, A. Sala, and R. H. Waring. 2007. Perpetuating Old Ponderosa Pine. Forest Ecology and Management 249:141-157. Lewinsohn, E., M. Gijzen, and R. Croteau. 1991. Defense Mechanisms of Conifers. Plant Physiology 96:44-49. Littell, J. S., D. McKenzie, D. L. Peterson, and A. L. Westerling. 2009. Climate and Wildfire Area Burned in Western US Ecoprovinces, 1916-2003. Ecological Applications 19:1003-1021. 95 Liu, J.-J., R. A. Sniezko, M. Murray, N. Wang, H. Chen, A. Zamany, R. N. Sturrock, D. P. Savin, and A. Kegley. 2016. Genetic Diversity and Population Structure of Whitebark Pine (Pinus albicaulis Engelm.) in Western North America. PLoS One 11:e0167986. Liu, J.-J., R. A. Sniezko, A. Zamany, H. Williams, N. Wang, A. Kegley, D. P. Savin, H. Chen, and R. N. Sturrock. 2017a. Saturated Genic SNP Mapping Identified Functional Candidates and Selection Tools for the Pinus monticola Cr2 Locus Controlling Resistance to White Pine Blister Rust. Plant Biotechnology Journal. Liu, J.-J., H. Williams, X. R. Li, A. W. Schoettle, R. A. Sniezko, M. Murray, A. Zamany, G. Roke, and H. Chen. 2017b. Profiling Methyl Jasmonate-Responsive Transcriptome for Understanding Induced Systemic Resistance in Whitebark Pine (Pinus albicaulis). Plant Molecular Biology 95:359-374. Loehman, R. A., R. E. Keane, L. M. Holsinger, and Z. Wu. 2017. Interactions of landscape disturbances and climate change dictate ecological pattern and process: spatial modeling of wildfire, insect, and disease dynamics under future climates. Landscape Ecology 32:1447-1459. Logan, J. A., W. W. Macfarlane, and L. Willcox. 2010. Whitebark Pine Vulnerability to Climate- Driven Mountain Pine Beetle Disturbance in the Greater Yellowstone Ecosystem. Ecological Applications 20:895–902. Lombardero, M. J., M. P. Ayres, P. L. Lorio Jr, and J. J. Ruel. 2000. Environmental Effects on Constitutive and Inducible Resin Defences of Pinus taeda. Ecology Letters 3:329-339. Loomis, W. F. 1932. Growth-Differentiation Balance vs Carbohydrate-Nitrogen Ratio. Pages 240- 245 in Proceedings of the American Society for Horticultural Science. Macfarlane, W. W., J. A. Logan, and W. R. Kern. 2013. An Innovative Aerial Assessment of Greater Yellowstone Ecosystem Mountain Pine Beetle-Caused Whitebark Pine Mortality. Ecological Applications 23:421–437. Maloney, P. E., T. F. Smith, C. E. Jensen, J. Innes, D. M. Rizzo, and M. P. North. 2008. Initial Tree Mortality and Insect and Pathogen Response to Fire and Thinning Restoration Treatments in an Old-Growth Mixed-Conifer Forest of the Sierra Nevada, California. Canadian Journal of Forest Research 38:3011-3020. Mason, C. J., K. Keefover-Ring, C. Villari, J. G. Klutsch, S. Cook, P. Bonello, N. Erbilgin, K. F. Raffa, and P. A. Townsend. 2019. Anatomical Defences against Bark Beetles Relate to Degree of Historical Exposure between Species and are Allocated Independently of Chemical Defences within Trees. Plant Cell & Environment 42:633-646. Mattson, W. J., and R. A. Haack. 1987. The Role of Drought in Outbreaks of Plant-Eating Insects. BioScience 37:110-118. McWethy, D. B., T. Schoennagel, P. E. Higuera, M. Krawchuk, B. J. Harvey, E. C. Metcalf, C. Schultz, C. Miller, A. L. Metcalf, B. Buma, A. Virapongse, J. C. Kulig, R. C. Stedman, Z. Ratajczak, C. R. Nelson, and C. Kolden. 2019. Rethinking resilience to wildfire. Nature Sustainability 2:797-804. Millar, C. I., and D. L. Delany. 2019. Interaction between Mountain Pine Beetle-Caused Tree Mortality and Fire Behavior in Subalpine Whitebark Pine Forests, Eastern Sierra Nevada, CA; Retrospective Observations. Forest Ecology and Management 447:195-202. Millar, C. I., R. D. Westfall, D. L. Delany, M. J. Bokach, A. L. Flint, and L. E. Flint. 2012. Forest Mortality in High-Elevation Whitebark Pine (Pinus albicaulis) Forests of Eastern 96 California, USA; Influence of Environmental Context, Bark Beetles, Climatic Water Deficit, and Warming. Canadian Journal of Forest Research 42:749-765. Miller, D. R., and J. H. Borden. 2000. Dose-Dependent and Species-Specific Responses of Pine Bark Beetles (Coeoptera: Scolytidae) to Monoterpenes in Association with Phermones. The Canadian Entomologist 132:183-195. Mills, L. S., and D. F. Doak. 1993. The Keystone-Species Concept in Ecology and Conservation. BioScience 43:219-224. Mitton, J. B., M. C. Grant, and A. M. Yoshino. 1998. Variation in Allozymes and Stomatal Size in Pinyon (Pinus edulis, Pinaceae), Associated with Soil Moisture. American Journal of Botany 85:1262-1265. Mizell, R. F., 3rd, J. L. Frazier, and T. E. Nebeker. 1984. Response of the Clerid Predator Thanasimus dubius (F.) to Bark Beetle Pheromones and Tree Volatiles in a Wind Tunnel. Journal of Chemical Ecology 10:177-187. Moreira, X., K. A. Mooney, S. Rasmann, W. K. Petry, A. Carrillo-Gavilán, R. Zas, L. Sampedro, and V. Novotny. 2014. Trade-Offs Between Constitutive and Induced Defences Drive Geographical and Climatic Clines in Pine Chemical Defences. Ecology Letters 17:537- 546. Moreira, X., L. Sampedro, R. Zas, and I. S. Pearse. 2016. Defensive Traits in Young Pine Trees Cluster into Two Divergent Syndromes Related to Early Growth Rate. PLoS One 11:e0152537. Moreira, X., R. Zas, A. Solla, and L. Sampedro. 2015. Differentiation of Persistent Anatomical Defensive Structures is Costly and Determined by Nutrient Availability and Genetic Growth-Defence Constraints. Tree Physiology 35:112-123. Mumm, R., and M. Hilker. 2006. Direct and Indirect Chemical Defence of Pine against Folivorous Insects. Trends in Plant Science 11:351-358. Muola, A., P. Mutikainen, L. Laukkanen, M. Lilley, and R. Leimu. 2010. Genetic Variation in Herbivore Resistance and Tolerance: The Role of Plant Life-History Stage and Type of Damage. Journal of Evolutionary Biology 23:2185-2196. Nagy, N. E., V. R. Franceschi, H. Solheim, T. Krekling, and E. Christiansen. 2000. Wound- Induced Traumatic Resin Duct Development in Stems of Norway Spruce (Pinaceae): Anatomy and Cytochemical Traits. American Journal of Botany 87:302-313. Neale, D. B., and O. Savolainen. 2004. Association Genetics of Complex Traits in Conifers. Trends in Plant Science 9:325-330. Paine, T. D., K. F. Raffa, and T. C. Harrington. 1997. Interactions Among Scolytid Bark Beetles, their Associated Fungi, and Live Host Conifers. Annual Review of Entomology 42:179- 206. Pederson, G. T., J. L. Betancourt, and G. J. McCabe. 2013. Regional Patterns and Proximal Causes of the Recent Snowpack Decline in the Rocky Mountains, U.S. Geophysical Research Letters 40:1811-1816. Pederson, G. T., L. J. Graumlich, D. B. Fagre, T. Kipfer, and C. C. Muhlfeld. 2009. A Century of Climate and Ecosystem Change in Western Montana: What do Temperature Trends Portend? Climatic Change 98:133-154. 97 Pederson, G. T., S. T. Gray, C. A. Woodhouse, J. L. Betancourt, D. B. Fagre, J. S. Littell, E. Watson, B. H. Luckman, and L. J. Graumlich. 2011. The Unusual Nature of Recent Snowpack Declines in the North American Cordillera. Science 333:332-335. Perrakis, D. D. B. 2008. Fire-Bark Beetle Interactions: Exploring Links Between Fire Injury, Resin Defenses, and Beetle-Induced Mortality in Ponderosa Pine Forests. University of Washington. Perrakis, D. D. B., and J. K. Agee. 2006. Seasonal Fire Effects on Mixed-Conifer Forest Structure and Ponderosa Pine Resin Properties. Canadian Journal of Forest Research 36:238-254. Perrakis, D. D. B., J. K. Agee, and A. Eglitis. 2011. Effects of Prescribed Burning on Mortality and Resin Defenses in Old Growth Ponderosa Pine (Crater Lake, Oregon): Four Years of Post-Fire Monitoring. Natural Areas Journal 31:14-25. Phillips, M. A., and R. B. Croteau. 1999. Resin-Based Defenses in Conifers. Trends in Plant Science - Reviews 4:1360-1385. Powell, E. N., and K. F. Raffa. 2011. Fire Injury Reduces Inducible Defenses of Lodgepole Pine against Mountain Pine Beetle. Journal of Chemical Ecology 37:1184-1192. Quiring, D. T. 1992. Rapid Change in Suitability of White Spruce for a Specialist Herbivore, Zeiraphera canadensis, as a Function of Leaf Age. Canadian Journal of Zoology 70:2132- 2138. Raffa, K. F. 2014. Terpenes Tell Different Tales at Different Scales: Glimpses into the Chemical Ecology of Conifer - Bark Beetle - Microbial Interactions. Journal of Chemical Ecology 40:1-20. Raffa, K. F., and A. A. Berryman. 1983. The Role of Host Plant Resistance in the Colonization Behavior and Ecology of Bark Beetles (Coleoptera: Scolytidae). Ecological Monographs 53:27-49. Raffa, K. F., C. J. Mason, P. Bonello, S. Cook, N. Erbilgin, K. Keefover-Ring, J. G. Klutsch, C. Villari, and P. A. Townsend. 2017. Defence Syndromes In Lodgepole - Whitebark Pine Ecosystems Relate To Degree Of Historical Exposure To Mountain Pine Beetles. Plant Cell & Environment 40:1791-1806. Raffa, K. F., E. N. Powell, and P. A. Townsend. 2013. Temperature-Driven Range Expansion of an Irruptive Insect Heightened by Weakly Coevolved Plant Defenses. Proceedings of the National Academy of Science 110:2193-2198. Ranty, B., D. Aldon, V. Cotelle, J.-P. Galaud, P. Thuleau, and C. Mazars. 2016. Calcium Sensors as Key Hubs in Plant Responses to Biotic and Abiotic Stresses. Frontiers in Plant Science 7:327. Retzlaff, M. L., S. B. Leirfallom, and R. E. Keane. 2016. A 20-Year Reassessment of the Health and Status of Whitebark Pine Forests in the Bob Marshall Wilderness Complex, Montana, USA.in U. S. D. o. Agriculture, editor. U.S. Forest Service, Rocky Mountain Research Station. Rigling, A., H. Brühlhart, O. U. Bräker, F. Theodor, and F. H. Schweingruber. 2003. Effects of Irrigation on Diameter Growth and Vertical Resin Duct Production in Pinus sylvestris L. on Dry Sites in the Central Alps, Switzerland. Forest Ecology and Management 175:285- 296. 98 Rodríguez-García, A., R. López, J. A. Martín, F. Pinillos, and L. Gil. 2014. Resin Yield in Pinus pinaster is Related to Tree Dendrometry, Stand Density and Tapping-Induced Systemic Changes in Xylem Anatomy. Forest Ecology and Management 313:47-54. Rosner, S., and B. Hannrup. 2004. Resin Canal Traits Relevant for Constitutive Resistance of Norway Spruce Against Bark Beetles: Environmental and Genetic Variability. Forest Ecology and Management 200:77-87. Roy, B. A., H. M. Alexander, J. Davidson, F. T. Campbell, J. J. Burdon, R. Sniezko, and C. Brasier. 2014. Increasing Forest Loss Worldwide from Invasive Pests Requires New Trade Regulations. Frontiers in Ecology and the Environment 12:457-465. Sala, A., F. Piper, and G. Hoch. 2010. Physiological Mechanisms of Drought-Induced Tree Mortality are Far from Being Resolved. New Phytologist 186:274-281. Salminen, J.-P., and M. Karonen. 2011. Chemical Ecology of Tannins and other Phenolics: We Need a Change in Approach. Functional Ecology 25:325-338. Schaller, A. 2008. Induced Plant Resistance to Herbivory. 1 edition. Springer, Dordrecht, Netherlands. Schwandt, J. W., I. B. Lockman, J. T. Kliejunas, and J. A. Muir. 2010. Current Health Issues and Management Strategies for White Pines in the Western United States and Canada. Forest Pathology 40:226-250. Seybold, S. J., D. P. W. Huber, J. C. Lee, A. D. Graves, and J. Bohlmann. 2006. Pine Monoterpenes and Pine Bark Beetles: A Marriage of Convenience for Defense and Chemical Communication. Phytochemistry Reviews 5:143-178. Siemens, D. H., R. Haugen, S. Matzner, and N. Vanasma. 2009. Plant Chemical Defence Allocation Constrains Evolution of Local Range. Molecular Ecology 18:4974-4983. Six, D. L. 2003. Bark Beetle-Fungus Symbioses. Page 368 in K. Bourtzis and T. A. Miller, editors. Insect Symbiosis. CRC Press. Six, D. L. 2012. Ecological and Evolutionary Determinants of Bark Beetle-Fungus Symbioses. Insects 3:339-366. Six, D. L., C. Vergobbi, and M. Cutter. 2018. Are Survivors Different? Genetic-Based Selection of Trees by Mountain Pine Beetle During a Climate Change-Driven Outbreak in a High- Elevation Pine Forest. Frontiers in Plant Science 9:993. Smith, R. H. 2000. Xylem Monoterpenes of Pines: Distribution, Variation, Genetics, Function. Pages 1-454 in U. S. D. A. F. Service, editor. Pacific Southwest Research Station, Albany, California. Smith, T. F., D. M. Rizzo, and M. P. North. 2005. Patterns of Mortality in an Old-Growth Mixed- Conifer Forest of the Southern Sierra Nevada, California. Forest Science 51:266-275. Stamp, N. 2003. Out Of The Quagmire Of Plant Defense Hypotheses. The Quarterly Review of Biology 78:23-55. Stephens, S. L., and L. W. Ruth. 2005. Federal Forest-Fire Policy in the United States. Ecological Applications 15:532-542. Strom, B. L., R. A. Goyer, L. L. Ingram, G. D. L. Boyd, and L. H. Lott. 2002. Oleoresin Characteristics of Progeny of Loblolly Pines that Escaped Attack by the Southern Pine Beetle. Forest Ecology and Management 158:169-178. Tomback, D. F., and P. Achuff. 2010. Blister Rust and Western Forest Biodiversity: Ecology, Values and Outlook for White Pines. Forest Pathology 40:186-225. 99 Tomback, D. F., S. F. Arno, and R. E. Keane. 2001. Whitebark Pine Communities: Ecology and Restoration. Island Press, Washington, D.C. Tomback, D. F., and K. C. Kendall. 2001. Biodiversity Losses: The Downward Spiral. Pages 243- 262 in D. F. Tomback, S. F. Arno, and R. E. Keane, editors. Whitebark Pine Communities: Ecology and Restoration. Island Press, Washington, DC. Tomback, D. F., L. M. Resler, R. E. Keane, E. R. Pansing, A. J. Andrade, and A. C. Wagner. 2016. Community Structure, Biodiversity, and Ecosystem Services in Treeline Whitebark Pine Communities: Potential Impacts from a Non-Native Pathogen. Forests 7:21. Tomback, D. F., S. K. Sund, and L. A. Hoffmann. 1993. Post-Fire Regeneration of Pinus albicaulis: Height–Age Relationships, Age Structure, and Microsite Characteristics. Canadian Journal of Forest Research 23:113-119. Trapp, S., and R. Croteau. 2001. Defensive Resin Biosynthesis in Conifers. Annual Review of Plant Physiology and Plant Molecular Biology 52:689-724. Trowbridge, A. M. 2014. Evolutionary Ecology of Chemically Mediated Plant-Insect Interactions. Pages 143-176 Ecology and the Environment. van Mantgem, P. J., and N. L. Stephenson. 2007. Apparent Climatically Induced Increase of Tree Mortality Rates in a Temperate Forest. Ecology Letters 10:909-916. van Mantgem, P. J., N. L. Stephenson, J. C. Byrne, L. D. Daniels, J. F. Franklin, P. Z. Fulé, M. E. Harmon, A. J. Larson, J. M. Smith, A. H. Taylor, and T. T. Veblen. 2009. Widespread Increase of Tree Mortality Rates in the Western United States. Science 323:521-524. Vázquez-González, C., L. Sampedro, V. Rozas, J. Voltas, and R. Zas. 2021. Population Differentiation in Climate Sensitivity of Resin Duct Formation during Growth Resumption in Pinus pinaster. Dendrochronologia 67. Veblen, T. T., K. S. Hadley, E. M. Nel, T. Kitzberger, M. Reid, and R. Villalba. 1994. Disturbance Regime and Disturbance Interactions in a Rocky Mountain Subalpine Forest. The Journal of Ecology 82. Wainhouse, D., J. T. Staley, R. Jinks, and G. Morgan. 2009. Growth and Defence in Young Pine and Spruce and the Expression of Resistance to a Stem-Feeding Weevil. Oecologia 158:641-650. Warren, J. M., H. L. Allen, and F. L. Booker. 1999. Mineral Nutrition, Resin Flow and Phloem Phytochemistry in Loblolly Pine. Tree Physiology 19:655-663. Warwell, M. V., and R. G. Shaw. 2017. Climate-Related Genetic Variation in a Threatened Tree Species, Pinus albicaulis. American Journal of Botany 104:1205-1218. Westbrook, J. W., A. R. Walker, L. G. Neves, P. Munoz, M. F. Resende, Jr., D. B. Neale, J. L. Wegrzyn, D. A. Huber, M. Kirst, J. M. Davis, and G. F. Peter. 2015. Discovering Candidate Genes that Regulate Resin Canal Number in Pinus taeda Stems by Integrating Genetic Analysis across Environments, Ages, and Populations. New Phytologist 205:627-641. Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam. 2006. Warming and Earlier Spring Increase Western US Forest Wildfire Activity. Science 313:940-943. Williams, D. W., and A. M. Liebhold. 2002. Climate Change and the Outbreak Ranges of Two North American Bark Beetles. Agricultural and Forest Entomology 4:87-99. Wimmer, R., and M. Grabner. 1997. Effects of Climate on Vertical Resin Duct Density and Radial Growth of Norway Spruce (Picea abies). Trees 11:271-276. Wu, H., and Z.-h. Hu. 1997. Comparative Anatomy of Resin Ducts of the Pinaceae. Trees 11. 100 Zhao, T., P. Krokene, N. Björklund, B. Långström, H. Solheim, E. Christiansen, and A.-K. Borg- Karlson. 2010. The Influence of Ceratocystis polonica Inoculation and Methyl Jasmonate Application on Terpene Chemistry of Norway spruce, Picea abies. Phytochemistry 71:1332-1341. 101 APPENDICES 102 APPENDIX A SUPPLEMENTARY DATA FOR CHAPTER 3 “WHITEBARK PINE (PINUS ALBICAULIS) GROWTH AND DEFENSE IN RESPONSE TO MOUNTAIN PINE BEETLE OUTBREAKS” 103 Figure S1. Principal components analysis of live and dead whitebark pine (144 samples, 72 live, 72 dead) for the 20–, 10– and 5–year windows prior to mortality of the dead pair. Ellipses are 95th percentile confidence intervals for each grouping (live - black; dead - red). Variables factor maps (right panels) illustrate the relative contribution of growth and defense metrics to the ordinations. 104 Figure S2. Relationship of average vertical resin ducts metrics and growth for live and dead whitebark pine across the full time period. Pairs of live and dead whitebark pine are color-coded by site: 3LK (magenta) and BLD (cyan). Note: variables were log-transformed to meet the assumptions of linear regression. Table S1. Generalized linear models predicting survival of whitebark pine during disturbance outbreaks. Log likelihood reflects the probability of the data given the model, while K represents the relative number of sample parameters for each model. Top five models are shown out of ten models developed. Model AIC ΔAIC LogL K 5year_Size + Total_Relative_Area 88.2 0 –41.1 2 5year_Size + Total_Relative_Area + 5year_Area 89.7 1.4 –40.8 3 10year_Size + Total_Relative_Area + 5year_Area + 5year_Series 95.5 7.3 –42.8 4 10year_Size + Total_Relative_Area 97.1 8.8 –42.5 2 10year_Size + Total_Relative_Area + 5year_Area 98.1 9.9 –45.1 3 Abbreviations: Area (resin duct area); Relative_Area (relative resin duct area); Size (resin duct size); Series (ring width index); 5year (5–year window prior to mortality); 10year (10–year window); Total (all overlapping years). 105 Table S2. Pearson’s product moment correlation coefficients for growth and defense metrics and climate variables across both live and dead whitebark pine. Correlations were assessed across the full chronology (1911–2004; 93 years) as well as an early 20th century period (1911–1960; 49 years) and late 20th century period (1961–2004; 43 years). Climate data was obtained from PRISM at a spatial resolution of 4 km using the AN version of the dataset. Winter: previous December, January, February, March / Spring: April, May / Summer: June, July, August Time Period Full (1911–2004) 1911–1960 1961–2004 Growth Winter Precip Live –0.41 (p < 0.0001) –0.18 (p = 0.213) –0.57 (p < 0.0001) Dead –0.01 (p = 0.949) –0.01 (p = 0.962) 0.08 (p = 0.605) Winter z–PDSI Live –0.38 (p = 0.0002) –0.28 (p = 0.05) –0.48 (p = 0.0009) Dead –0.09 (p = 0.379) –0.11 (p = 0.469) 0.04 (p = 0.795) Spring Precip Live 0.01 (p = 0.944) –0.19 (p = 0.169) 0.12 (p = 0.446) Dead –0.22 (p = 0.03) –0.19 (p = 0.182) –0.06 (p = 0.683) Spring Temp Live 0.36 (p = 0.0003) 0.41 (p = 0.003) 0.39 (p = 0.008) Dead 0.19 (p = 0.05) 0.33 (p = 0.019) 0.1 (p = 0.517) Spring z–PDSI Live –0.11 (p = 0.312) –0.31 (p = 0.029) –0.01 (p = 0.95) Dead –0.26 (p = 0.013) –0.27 (p = 0.061) –0.1 (p = 0.517) Summer Precip Live 0.09 (p = 0.353) 0.11 (p = 0.465) 0.07 (p = 0.655) Dead –0.06 (p = 0.557) 0.07 (p = 0.626) 0.05 (p = 0.761) Summer z–PDSI Live 0.07 (p = 0.516) –0.03 (p = 0.843) 0.1 (p = 0.509) Dead –0.1 (p = 0.33) –0.01 (p = 0.95) 0.02 (p = 0.903) Duct Production Winter Precip Live –0.27 (p = 0.009) –0.22 (p = 0.12) –0.47 (p = 0.001) Dead –0.14 (p = 0.183) –0.22 (p = 0.12) –0.05 (p = 0.762) Winter z–PDSI Live –0.33 (p = 0.001) –0.3 (p = 0.036) –0.44 (p = 0.003) Dead –0.23 (p = 0.028) –0.3 (p = 0.034) –0.15 (p = 0.33) Spring Precip Live –0.21 (p = 0.04) –0.18 (p = 0.204) 0.02 (p = 0.899) Dead –0.27 (p = 0.008) –0.23 (p = 0.104) –0.14 (p = 0.371) Spring Temp Live 0.11 (p = 0.29) 0.04 (p = 0.802) 0.26 (p = 0.087) Dead 0.09 (p = 0.386) 0.09 (p = 0.514) 0.02 (p = 0.88) Spring z–PDSI Live –0.22 (p = 0.035) –0.17 (p = 0.229) –0.06 (p = 0.678) Dead –0.27 (p = 0.008) –0.25 (p = 0.086) –0.15 (p = 0.34) Summer Precip Live –0.26 (p = 0.012) –0.22 (p = 0.134) –0.23 (p = 0.137) Dead –0.16 (p = 0.112) –0.09 (p = 0.516) –0.01 (p = 0.943) 106 Table S2 Continued Summer z–PDSI Live –0.28 (p = 0.006) –0.25 (p = 0.08) –0.23 (p = 0.13) Dead –0.19 (p = 0.067) –0.14 (p = 0.344) –0.05 (p = 0.759) Duct Size Winter Precip Live –0.26 (p = 0.01) –0.06 (p = 0.674) –0.46 (p = 0.002) Dead 0.13 (p = 0.207) 0.19 (p = 0.182) 0.22 (p = 0.146) Winter z–PDSI Live –0.26 (p = 0.011) –0.17 (p = 0.241) –0.42 (p = 0.005) Dead –0.002 (p = 0.988) 0.04 (p = 0.78) 0.12 (p = 0.429) Spring Precip Live 0.06 (p = 0.597) –0.21 (p = 0.14) 0.15 (p = 0.324) Dead –0.24 (p = 0.019) –0.23 (p = 0.105) –0.08 (p = 0.628) Spring Temp Live 0.14 (p = 0.187) 0.24 (p = 0.089) 0.12 (p = 0.447) Dead 0.11 (p = 0.294) 0.21 (p = 0.136) –0.05 (p = 0.73) Spring z–PDSI Live 0.01 (p = 0.928) –0.24 (p = 0.089) 0.09 (p = 0.54) Dead –0.25 (p = 0.017) –0.26 (p = 0.072) –0.07 (p = 0.631) Summer Precip Live 0.04 (p = 0.717) –0.02 (p = 0.88) –0.01 (p = 0.938) Dead –0.19 (p = 0.063) –0.2 (p = 0.159) –0.05 (p = 0.754) Summer z–PDSI Live 0.01 (p = 0.894) –0.08 (p = 0.586) –0.02 (p = 0.902) Dead –0.24 (p = 0.023) –0.27 (p = 0.06) –0.08 (p = 0.59) Duct Area Winter Precip Live –0.4 (p < 0.0001) –0.23 (p = 0.103) –0.52 (p = 0.0003) Dead –0.06 (p = 0.546) –0.17 (p = 0.251) 0.14 (p = 0.368) Winter z–PDSI Live –0.45 (p < 0.0001) –0.39 (p = 0.005) –0.49 (p = 0.0008) Dead –0.19 (p = 0.068) –0.38 (p = 0.007) 0.05 (p = 0.771) Spring Precip Live –0.17 (p = 0.11) –0.37 (p = 0.009) 0.07 (p = 0.661) Dead –0.34 (p = 0.0008) –0.51 (p = 0.0002) –0.09 (p = 0.542) Spring Temp Live 0.24 (p = 0.019) 0.23 (p = 0.115) 0.27 (p = 0.08) Dead 0.14 (p = 0.175) 0.31 (p = 0.027) –0.04 (p = 0.781) Spring z–PDSI Live –0.22 (p = 0.037) –0.37 (p = 0.008) –0.02 (p = 0.88) Dead –0.34 (p = 0.0007) –0.54 (p < 0.0001) –0.09 (p = 0.541) Summer Precip Live –0.16 (p = 0.123) –0.19 (p = 0.166) –0.09 (p = 0.575) Dead –0.23 (p = 0.025) –0.34 (p = 0.017) –0.002 (p = 0.985) Summer z–PDSI Live –0.2 (p = 0.052) –0.29 (p = 0.036) –0.08 (p = 0.584) Dead –0.27 (p = 0.008) –0.44 (p = 0.001) –0.04 (p = 0.817) 107 Figure S3. 30–year moving correlations with a two-year time-step for growth and defense metrics, winter precipitation (left), and winter z-PDSI (right) for live (red) and dead (black) whitebark pine. 108 Figure S4. 30–year moving correlations with a two-year time-step for growth and defense metrics, spring precipitation (left), spring z-PDSI (right), and spring temperature (right) for live (red) and dead (black) whitebark pine. 109 APPENDIX B SUPPLEMENTARY DATA FOR CHAPTER 4 “GROWTH AND DEFENSE CHARACTERISTICS OF WHITEBARK PINE (PINUS ALBICAULIS) AND LODGEPOLE PINE (PINUS CONTORTA VAR LATIFOLIA) IN A HIGH-ELEVATION, DISTURBANCE- PRONE MIXED-CONIFER FOREST IN NORTHWESTERN MONTANA, USA” 110 Figure S1. Conceptual diagram of the plot sampling protocol. From plot center, four 10m wide belt transects were extended in each cardinal direction and the closest 10 trees (> 15 cm DBH) in each transect were sampled (to a maximum distance of 100 m). A 5.6 m diameter plot was established around plot center to measure understory vegetation. Coloring: Live trees (green); dead trees (grey); sampled trees for fire history study (purple highlighting). From Kichas et al. (2020). Figure S2. Conceptual diagram of sampling design. Individual whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia) trees (blue outline) were located and increment cores and phloem tissues obtained for growth and defense analyses. Overstory competition around 111 each subject tree was also recorded (species and DBH information) for all live / dead trees (≥ 1 cm DBH) within 5 m and all trees (≥ 5 cm DBH) to a distance of 10 m. Understory species and percent cover estimates were also made within a smaller 5.6 m diameter plot around each subject tree. Table S1. Spearman correlation coefficients of mono- and sesqui- terpene concentrations (µg / g fresh weight) with tree ring growth and resin duct anatomical measurements for whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia) during the 20-year period prior to sampling (1998–2018). Correlations were conducted on metrics averaged over the 20-year period. Monoterpenes Sesquiterpenes Pinus albicaulis Pinus contorta Pinus albicaulis Pinus contorta Growth (BAI) 0.16 0.22 0.27 0.22 Duct Production 0.13 –0.08 0.22 –0.12 Duct Size 0.21 0.17 0.45 0.14 Duct Area 0.27 0.11 0.48 0.05 Duct Density 0.32 –0.09 –0.08 –0.18 Relative Duct Area 0.32 –0.08 –0.003 –0.25 Note: Bold values indicate significant correlations (p < 0.025). DBH = diameter at breast height; BAI = basal area increment. Figure S3. Constitutive monoterpene diversity (calculated as Shannon’s H’, a measure of within- sample diversity; A) and constitutive sesquiterpene diversity (B) for whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia). 112 Table S2. Spearman correlation coefficients of mono- and sesqui- terpene diversity (calculated as Shannon’s H’, a measure of within-sample diversity) with tree ring growth and resin duct anatomical measurements for whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia). Correlations were conducted on metrics averaged over the full record (1914–2018). Monoterpene Diversity Sesquiterpene Diversity Pinus albicaulis Pinus contorta Pinus albicaulis Pinus contorta Growth (BAI) 0.33 0.06 0.16 0.22 Duct Production 0.43 -0.18 0.12 0.08 Duct Size 0.17 -0.03 0.17 0.09 Duct Area 0.37 -0.12 0.22 0.11 Duct Density 0.12 0.02 0.07 < 0.01 Relative Duct Area 0.08 -0.06 0.22 -0.14 Note: Bold values indicate significant correlations (p < 0.05). DBH = diameter at breast height; BAI = basal area increment. Table S3. Major monoterpenes and sesquiterpenes (μg / g fresh weight) collected from phloem tissues of whitebark (P. albicaulis) and lodgepole pine (P. contorta var. latifolia) sampled from a high elevation mixed-conifer forest stand on the Flathead Indian Reservation. Shading of individual compounds reflects significant difference (α = 0.05) between the two species (light grey = greater concentration in whitebark pine / dark grey = greater concentration in lodgepole pine). Contribution refers to the relative percent contribution of each compound to the total monoterpene or sesquiterpene profile for each species. No. Compound ID⁎ Pinus albicaulis Contri Pinus contorta Contri Kruskal- bution bution Wallis Test Mean S.E. Mean S.E. p-value Monoterpenes (Hydrocarbons) 1 α-thujene NIST>80 21.32 4.85 0.25 9.43 1.09 0.16 36.183 2 (–)-α-pinene SS 676.31 141.41 7.99 113.46 12.49 1.93 < 0.001 3 (+)-α-pinene SS 399.72 86.57 4.72 48.51 4.81 0.83 0.017 4 MTP 1† NIST n.d. n.d. n.d. 1.97 0.59 0.03 0.026 5 MTP 2† NIST 29.69 10.92 0.35 10.65 1.00 0.18 18.848 6 MTP 3† NIST 3.73 0.88 0.04 6.18 0.63 0.11 < 0.001 7 MTP 4† NIST 3.46 1.62 0.04 4.95 1.63 0.08 22.284 8 (–)-sabinene NIST>80 2.25 0.47 0.03 10.87 1.51 0.18 < 0.001 9 (+)-sabinene NIST>80 332.22 111.56 3.93 21.15 2.79 0.36 < 0.001 10 ꞵ-myrcene SS 407.72 69.53 4.82 90.28 13.44 1.54 0.004 11 (+)-ꞵ-pinene SS 10.01 2.05 0.12 3.99 0.54 0.07 0.979 12 (–)-ꞵ-pinene SS 656.45 97.16 7.76 412.79 76.55 7.02 14.104 13 3-carene SS 724.58 158.63 8.56 243.68 73.62 4.15 27.835 14 α-phellandrene NIST>80 7.62 1.85 0.09 51.53 5.20 0.88 < 0.001 15 α-terpinene NIST>80 12.89 2.87 0.15 6.35 0.97 0.11 9.589 16 (–)-δ-limonene SS 1413.29 235.07 16.70 293.93 101.08 5.00 < 0.001 17 (+)-δ-limonene SS 3.08 1.40 0.04 17.44 1.98 0.30 < 0.001 18 ꞵ-phellandrene NIST>80 3059.84 355.55 36.16 4411.69 398.14 75.05 < 0.001 19 ꞵ-ocimene SS 423.81 49.17 5.01 4.57 1.86 0.08 < 0.001 20 γ-terpinene SS 16.01 3.59 0.19 4.69 0.69 0.08 0.038 21 MTP 5† NIST 0.42 0.19 < 0.00 0.32 0.26 0.01 10.422 22 terpinolene SS 176.65 41.16 2.09 72.67 10.74 1.24 2.562 23 ρ-cymenene NIST>80 1.81 0.91 0.02 0.05 0.05 0.001 0.232 113 Table S3 Continued Monoterpenes (Oxygenated Monoterpenes) 24 MTP 6† NIST 0.006 0.002 < 0.00 0.001 0.001 < 0.00 1.248 25 cis-ꞵ-terpineol NIST>80 0.01 0.003 < 0.00 0.002 0.001 < 0.00 1.303 26 (–)-linalool SS 5.76 2.09 0.07 6.29 2.91 0.11 39.413 27 (+)-linalool NIST>80 1.84 0.51 0.02 3.26 0.69 0.06 1.735 28 citronellal NIST>80 0.83 0.36 0.01 8.67 3.92 0.15 2.58 29 trans-2-menthenol NIST>80 1.12 0.46 0.01 1.12 0.34 0.02 10.619 30 dill ether NIST>80 0.09 0.09 < 0.00 2.89 0.53 0.05 < 0.001 31 MTP 7† NIST 0.69 0.34 0.01 0.41 0.18 0.01 38.397 32 MTP 8† NIST 1.00 0.47 0.01 0.17 0.12 < 0.00 9.123 33 Anisole NIST>80 22.65 3.62 0.27 2.29 1.01 0.04 < 0.001 34 (–)-terpinene-4-ol NIST>80 0.87 0.32 0.01 0.45 0.19 0.01 21.455 35 (+)-terpinene-4-ol NIST>80 2.93 1.27 0.03 1.13 0.43 0.02 38.073 36 MTP 9† NIST 4.71 1.04 0.06 1.10 0.36 0.02 0.059 37 MTP 10† NIST 0.29 0.17 < 0.00 0.94 0.53 0.02 16.478 38 (–)-α-terpineol NIST>80 0.93 0.47 0.01 0.75 0.45 0.01 33.194 39 (+)-α-terpineol NIST>80 0.45 0.25 0.01 0.26 0.17 < 0.00 27.523 40 α-longipinene NIST>80 30.58 5.36 0.36 3.96 0.99 0.07 0.003 41 bornyl acetate SS 5.18 1.15 0.06 3.15 0.88 0.05 6.302 Phenolics 42 4-allylanisole NIST>80 4.52 1.01 0.05 45.74 8.27 0.78 < 0.001 Sesquiterpenes 43 α-cubebene NIST>80 58.54 7.69 2.81 5.69 1.27 0.7 < 0.001 44 α-copaene NIST>80 152.85 22.89 6.71 7.11 1.59 0.82 < 0.001 45 SQT 1† NIST 10.27 7.11 0.38 0.28 0.28 0.04 0.004 46 SQT 2† NIST 4.8 0.95 0.18 2.93 0.93 0.19 19.909 47 longifolene NIST>80 46.53 11.66 2.64 0 0 0 < 0.001 48 SQT 3† NIST 66.49 10.23 2.85 0.48 0.23 0.02 < 0.001 49 caryophellene SS 50.27 6.534 2.3 2.56 0.68 0.58 < 0.001 50 α-guaiene NIST>80 4.06 2.286 0.16 0 0 0 0.012 51 SQT 4† NIST 0.15 0.147 0.01 3.34 1.05 0.41 0.084 52 cis-ꞵ-farnesene NIST>80 27.94 3.667 1.39 0.08 0.08 < 0.01 < 0.001 53 humulene NIST>80 18.88 2.491 0.84 1.16 0.53 0.09 < 0.001 54 (-)-γ-murrolene NIST>80 16.18 2.666 0.81 2.88 0.91 0.18 < 0.001 55 (+)-γ-murrolene NIST>80 66.15 9.5 2.86 10.98 2.47 1.06 < 0.001 56 SQT 5† NIST 5.12 0.889 0.2 0 0 0 < 0.001 57 α-muurolene NIST>80 890.25 118.417 40.18 51.03 10.42 6.56 < 0.001 58 γ-elemene NIST>80 51.61 7.971 2.47 6.94 2.16 0.63 < 0.001 59 SQT 6† NIST 0 0 0 7.07 1.93 7.44 0.001 60 SQT 7† NIST 8.47 1.613 0.46 0 0 0 < 0.001 61 SQT 8† NIST 33.41 5.185 1.43 81.06 17.81 9.66 < 0.001 62 SQT 9† NIST 53.17 8.489 2.33 103.51 22.95 14.89 < 0.001 63 SQT 10† NIST 6.78 3.069 0.28 6.67 2.08 1.46 0.926 64 SQT 11† NIST 2.95 2.951 0.13 5.84 3.01 0.68 0.04 65 SQT 12† NIST 9.57 3.461 0.37 5.02 1.47 0.37 17.23 66 cubedol NIST>80 134.93 18.563 6.46 25.08 5.36 2.76 < 0.001 67 SQT 13† NIST 2.95 2.951 0.13 1.79 0.57 0.11 0.332 68 SQT 14† NIST 0.18 0.176 0.01 1.30 0.47 0.07 0.805 69 nerolidol NIST>80 12.55 1.659 0.53 0 0 0 < 0.001 70 cubebol NIST>80 12.82 1.923 0.55 7.11 1.76 0.6 31.383 114 Table S3 Continued 71 SQT 15† NIST 217.89 37.567 9.73 294.31 64.39 37.97 < 0.001 72 SQT 16† NIST 3.25 0.892 0.11 0.09 0.09 0.01 0.022 73 SQT 17† NIST 9.56 1.696 0.42 0 0 0 < 0.001 74 SQT 18† NIST 4.02 1.452 0.15 1.45 0.52 0.09 12.488 75 SQT 19† NIST 14.83 1.707 0.71 0 0 0 < 0.001 76 SQT 20† NIST 6.76 1.834 0.32 0.36 0.36 0.02 0.026 77 (–)-copaene NIST>80 88.65 12.188 3.87 17.04 3.61 2.19 < 0.001 78 (+)-copaene NIST>80 34.15 4.345 1.83 19.47 3.64 3.58 1.136 79 α-cadinol NIST>80 14.83 2.425 0.68 21.69 4.80 2.88 0.348 80 α-bisabolo NIST>80 46.05 9.861 2.3 5.37 1.23 3.95 15.417 81 SQT 21† NIST 7.31 1.379 0.44 0 0 0 < 0.001 ⁎ Identification (ID) of compounds based upon comparison of retention time and mass spectra with synthetic standards (SS) or comparison of mass spectra using NIST Mass Spectral Search Program (NIST); NIST>80 = Denotes NIST match quality greater than 80 †MTP = unidentified monoterpene; SQT = unidentified sesquiterpene Figure S4. Relationship of radial growth (mm) to resin duct metrics for whitebark (light grey) and lodgepole (black) pines. All metrics were log-transformed to meet the assumptions of linear regression. Only statistically significant models (α = 0.05) are depicted. 115 Table S4. Analysis of variance comparing constitutive mono- and sesqui- terpenes and near-tree competition (by species as well as total competition) at varying densities (high and low basal area estimates; “High Comp” and “Low Comp”) for all whitebark and lodgepole pine. Competition Competitive Load Monoterpenes Sesquiterpenes (Species) (BA estimates) Engelmann Spruce PIAL & PICO Interaction: F = 5.14 Interaction: F = 6.31 p = 0.0273 p = 0.0149 High Comp. n.s. n.s. Low Comp. n.s. Interaction: F = 4.5 p = 0.0499 PIAL F = 5.818 n.s. p = 0.0227 High Comp. n.s. n.s. Low Comp. F = 6.219 n.s. p = 0.0373 PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. Lodgepole Pine PIAL & PICO n.s. F = 6.57 p = 0.0131 High Comp. n.s. n.s. Low Comp. n.s. n.s. PIAL n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. F = 9.27 n.s. p = 0.0159 Whitebark Pine PIAL & PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PIAL n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. Douglas–fir PIAL & PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PIAL n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. Subalpine Fir PIAL & PICO n.s. n.s. High Comp. n.s. F = 0.0165 p = 0.0165 Low Comp. n.s. n.s. PIAL n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PICO F = 4.02 n.s. p = 0.0546 High Comp. n.s. n.s. 116 Table S4 Continued Low Comp. n.s. n.s. All Live Comp. PIAL & PICO Interaction: F = 4.78 F = 9.69 p = 0.033 p = 0.00292 High Comp. n.s. n.s. Low Comp. n.s. n.s. PIAL F = 4.61 n.s. p = 0.0406 High Comp. n.s. n.s. Low Comp. n.s. n.s. PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. All Live & Dead Comp. PIAL & PICO n.s. n.s. High Comp. n.s. F = 7.07 p = 0.0171 Low Comp. n.s. n.s. PIAL n.s. n.s. High Comp. n.s. n.s. Low Comp. n.s. n.s. PICO n.s. n.s. High Comp. n.s. n.s. Low Comp. F = 5.55 n.s. p = 0.0462 Note: High Comp = subset of 10 lodgepole and whitebark pines with highest basal area estimates for neighboring competition. Low Comp = subset of 10 lodgepole and whitebark pines with lowest basal area estimates for neighboring competition. BA = basal area; PIAL = whitebark pine; PICO = lodgepole pine. 117 APPENDIX C SUPPLEMENTARY DATA FOR CHAPTER 5 “INCREASED WHITEBARK PINE (PINUS ALBICAULIS) GROWTH AND DEFENSE UNDER A WARMER AND REGIONALLY DRIER CLIMATE” 118 119 120 121 122 123