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Going underground : patterns of fine-root and mycorrhizal fungal trait variation across a biogeographic… Defrenne, Camille Emilie 2019

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Going underground: patterns of fine-root and mycorrhizal fungal trait variation across a biogeographic gradient in western Canada  by  Camille Emilie Defrenne   Agronomist, Agrosup Dijon, Institut national supérieur des sciences agronomiques, de l’alimentation et de l’environnement, 2014    A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in   THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Forestry)   THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)  April 2019   © Camille Emilie Defrenne, 2019ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a dissertation entitled: “Going underground: Patterns of fine-root and mycorrhizal fungal trait variation across a biogeographic gradient in western Canada” submitted by Camille Emilie Defrenne in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Forestry.  Examining Committee: Prof. Suzanne W. Simard, Forest and Conservation Sciences Supervisor Prof. Lori D. Daniels, Forest and Conservation Sciences Supervisory Committee Member Prof. Robert D. Guy, Forest and Conservation Sciences Supervisory Committee Member Prof. Sue Grayston, Forest and Conservation Sciences University Examiner Prof. Sean Smukler, Applied biology and soil science University Examiner Prof. Ivano Brunner, Forest Soils and Biogeochemistry External Examiner  iii  Abstract  Understanding fine-root adjustments to the environment and identifying factors that shape mycorrhizal fungal communities is a prerequisite for predicting the response and feedbacks of plants to global changes. As a consequence, trait-based plant ecology, which has mostly focused on above-ground traits, is increasingly placing the emphasis below-ground.  To improve our functional understanding of fine roots, we first quantified root morphological, chemical and architectural trait variation in interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) forests across a biogeographic gradient in Western Canada. We found substantial within-population root trait variation, which may enable acclimation of trees to future environmental conditions. Yet, we also identified moderate but consistent trait-environment linkages across populations of Douglas-fir. We provided evidence for decoupled variation in fine-root morphological and chemical traits. Our results highlight the existence of multiple axes of within-species fine-root adjustments that were consistent with a potential increase in fine-root acquisitive capacity with environmental limitations.  Next, to better integrate mycorrhizal symbiosis into trait-based plant ecology, we combined trait measurements of fine roots and ectomycorrhizal fungi with next-generation sequencing. We found temperature, precipitation and soil C:N ratio affected ectomycorrhizal community similarities and exploration type abundance but had no effect on fungal richness and diversity. We did not provide evidence for a functional connection between root traits and fungal exploration types within Douglas-fir populations. Our study clarifies ectomycorrhizal taxonomic and functional responses to environmental factors but warrants further research to broaden root trait frameworks and evaluate the role of mycorrhizal fungi in mediating ecosystem responses to environmental changes. This line of inquiry will be particularly important to better manage existing forests and to ensure that well-adapted forest tree populations are regenerated in the future.    iv  Lay summary  Plant fine roots form intimate relationships with symbiotic root fungi called mycorrhizas. Traits of fine roots and mycorrhizal fungi can inform the way plants capture soil resources and play important roles in regulating the air we breathe and the water we drink. However, our understanding of the world below our feet lags behind our knowledge of above-ground plant organs, such as leaves. In this thesis, we thus quantified the variation in interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) fine-root and mycorrhizal fungal traits across environments in western Canada. We found that fine roots adjust to harsh environments by modifying their morphology. In turn, this may increase their capacity to acquire soil resources. In addition, our research clarifies responses of mycorrhizal fungal species and traits to the environment, which has implications for better management of existing forests and regeneration of well-adapted tree populations in the future.    v  Preface This thesis is original work completed by Camille E. Defrenne and guidance was given by the supervisory committee and the collaborators listed below for each chapter.  Chapter 1 was invited for submission in a special issue. The co-authors are M. Luke McCormack, W. Jean Roach, Shalom D. Addo-Danso and Suzanne W. Simard. I am responsible for initiating the study, developing its design, collecting the fine-root trait data with W. Jean Roach, analysing all data presented and writing the manuscript. Suzanne W. Simard provided feedback on the conception and design of the study and on data collection. M. Luke McCormack provided extensive feedback on data interpretation. M. Luke McCormack and Shalom D. Addo-Danso critically edited the manuscript. All co-authors provided editorial review of the manuscript prior to submission. Camille E. Defrenne, M. Luke McCormack, W. Jean Roach, Shalom D. Addo-Danso, and Suzanne W. Simard. Intraspecific fine-root trait variation across a climate gradient in western Canada. Submitted.  Chapter 2 is published in the journal Frontiers in Plant Science. The co-authors are Timothy J. Philpott, Shannon H. A. Guichon, W. Jean Roach, Brian J. Pickles and Suzanne W. Simard. I am responsible for initiating the study and developing its design with Brian J. Pickles, collecting data with W. Jean Roach and writing the manuscript. Suzanne W. Simard provided feedback on the conception, design of the study and data collection. Camille E. Defrenne and Shannon H. A. Guichon performed the molecular analyses of ectomycorrhizas. Camille E. Defrenne and Timothy J. Philpott analysed the data. All co-authors provided editorial reviews of the manuscript. Camille E. Defrenne, Timothy J. Philpott, Shannon H. A. Guichon, W. Jean Roach, Brian J. Pickles, and Suzanne W. Simard. (2019). Environment and fine-root traits relate to shifts in ectomycorrhizal fungal communities across interior Douglas-fir forests of western Canada. Frontiers in Plant Science. doi: 10.3389/fpls.2019.00643    vi   Table of Contents  Abstract .......................................................................................................................................... ii Lay summary ................................................................................................................................ iv Preface ............................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................. x List of Figures ............................................................................................................................... xi List of Acronyms and Abbreviations ....................................................................................... xiv Acknowledgements ..................................................................................................................... xv Dedication ................................................................................................................................... xvi Chapter 1: Introduction ............................................................................................................... 1 1.1. Fine roots and mycorrhizas: regulators of ecosystem functions .................................................... 1 1.2. The use of plant functional traits below-ground ............................................................................ 4 1.3. The relevance of below-ground ecology in interior Douglas-fir forests ........................................ 6 1.4. Objectives and overview ................................................................................................................ 8 Chapter 2: Intraspecific fine-root trait variation across a biogeographic gradient in western Canada ........................................................................................................................... 10 2.1. Introduction .................................................................................................................................... 10 2.2. Materials and Methods .................................................................................................................. 13 2.2.1. Study sites ................................................................................................................................. 13 2.2.2. Fine-root sampling and processing ........................................................................................... 14 2.2.3. Fine-root traits measurements ................................................................................................... 15  vii  2.2.4. Climate and soil data ................................................................................................................. 16 2.2.5. Fine-Root Ecology Database .................................................................................................... 17 2.2.6. Statistical analyses .................................................................................................................... 17 2.3. Results ............................................................................................................................................. 19 2.3.1. Response to abiotic filters ......................................................................................................... 19 2.3.2. Ordination and trait correlation ................................................................................................. 20 2.3.3. Variance partitioning................................................................................................................. 20 2.3.4. Intra- and interspecific root trait variation in Pinaceae ............................................................. 21 2.4. Discussion........................................................................................................................................ 21 2.4.1. Douglas-fir root trait adjustment to the environment ................................................................ 22 2.4.2. Dimensionality in root trait variation ........................................................................................ 24 2.4.3. Intraspecific root trait variation ................................................................................................. 25 2.5. Conclusion ...................................................................................................................................... 27 Chapter 3: Environment and fine-root traits relate to shifts in ectomycorrhizal fungal communities across interior Douglas-fir forests of western Canada ..................................... 35 3.1. Introduction .................................................................................................................................... 35 3.2. Materials and Methods .................................................................................................................. 38 3.2.1. Study sites ................................................................................................................................. 38 3.2.2. Sampling and sample processing .............................................................................................. 39 3.2.3. Molecular analyses of ectomycorrhizas .................................................................................... 40 3.2.4. Data analyses ............................................................................................................................ 42 3.3. Results ............................................................................................................................................. 44 3.3.1. Identification and taxonomic diversity...................................................................................... 44  viii  3.3.2. Taxonomic composition ............................................................................................................ 45 3.3.3. Exploration types ...................................................................................................................... 47 3.3.4. Fine-root traits and fungi ........................................................................................................... 47 3.4. Discussion........................................................................................................................................ 47 3.4.1. Ectomycorrhizal fungal richness and diversity ......................................................................... 48 3.4.2. Abiotic drivers at the regional scale .......................................................................................... 49 3.4.3. Taxonomic and morphological responses ................................................................................. 50 3.4.4. Association between fine-root and mycorrhizal traits............................................................... 52 3.5. Conclusion ...................................................................................................................................... 53 Chapter 4: Conclusions and Outlook ........................................................................................ 61 4.1. Intraspecific fine-root trait variation ............................................................................................ 61 4.2. Taxonomical and functional structure of ectomycorrhizal fungal communities .......................... 62 Bibliography ................................................................................................................................ 64 Appendices ................................................................................................................................... 87 Appendices A. Supporting information for Chapter 2 ...................................................................... 87 Appendix A.1. Properties of the 15 sites selected across five regions in western Canada ................. 87 Appendix A.2. Description of morphological attributes of fine roots for three coniferous tree species encountered in this study..................................................................................................................... 88 Appendix A.3. Means and standard error (SE) of fine-root morphological, chemical and architectural traits of interior Douglas-fir ................................................................................................................ 89 Appendix A.4. Distribution of branching intensity and dichotomous branching index values .......... 90 Appendix A.5. Effect of climate (1980 – 2010) and soil variables on fine-root morphological, chemical and architectural traits of interior Douglas-fir ..................................................................... 92  ix  Appendix A.6. Ordination plot and associated scores of samples across an environmental gradient based on principal component analysis of fine-root traits................................................................... 94 Appendix A.7. Spearman’s correlation coefficient for pairwise root order relationships .................. 96 Appendix A.8. Comparison of fine-root trait variation in our study (Pseudotsuga menziesii) and in other tree species from the Pinaceae family for second and third-order roots. ................................... 97 Appendices B. Supporting information for Chapter 3 ...................................................................... 98 Appendix B.1. Identity, relative frequency and exploration type of ectomycorrhizal fungi on interior Douglas-fir roots ................................................................................................................................. 98 Appendix B.2. Species accumulation curve using the Coleman method (a) and rarefaction curve (b) of ectomycorrhizal species richness .................................................................................................. 100 Appendix B.3. Multivariate homogeneity of regions dispersion ...................................................... 101 Appendix B.4. Richness estimators and diversity indices ................................................................ 102 Appendix B.5. Effect of sites nested within regions (a) and regions (b) on interior Douglas-fir ectomycorrhizas ................................................................................................................................ 103 Appendix B.6. Effect of environmental variables on ectomycorrhizal fungal species community composition assessed by multivariate generalized linear models. .................................................... 104     x   List of Tables  Table 1 Location, climatic and edaphic properties of the 15 sites from five regions used in this study. Soil properties reported as mean ± standard error, n =5. .................................................................................... 28  Table 2 Effect of climatic and edaphic conditions on Douglas-fir ectomycorrhizal fungal species community composition across five regions assessed by PERMANOVA (a) and assessed by multivariate generalized linear model (b). Effect of climatic and edaphic conditions on exploration types (c) assessed by PERMANOVA. Significant P- values (<0.05) are shown in bold. A negative binomial distribution was assumed for the multivariate model and explained deviance was tested after 999 permutations.  .................................................................................................................................................................... 54  Table 3 Effect of fine-root (first-order) morphological and chemical traits on interior Douglas-fir ectomycorrhizal fungal species community composition across five regions assessed by PERMANOVA. Significant P- value (<0.05) effects are shown in bold.  .................................................................................................................................................................... 55          xi  List of Figures   Figure 1 schematic representation of an absorptive fine-root branch. The absorptive pool of fine roots represents the most distal roots, generally the first three root orders, that are primarily involved in the acquisition and uptake of soil resources……………………………………………………………………9  Figure 2 Geographical distribution of study regions (rectangles) and study sites (triangles) across the current natural range of interior Douglas-fir (Pseudotsuga menziesii var. glauca; green shading) in British Columbia, Canada. ...................................................................................................................................... 29  Figure 3 Distribution of root morphological trait and root C:N values across sites along a biogeographic gradient. WL, Williams Lake; R, Revelstoke; K, Kamloops; SA, Salmon Arm; N, Nelson. Each data point represents one measurement for each root order of one root branch of Douglas-fir. The first and third quartiles are indicated by the bottom and top lines of the boxes, respectively, while the two whiskers represent the 10th and 90th percentile and the horizontal line within the boxes represents the median value. For each root order, n = 25/site except for N2 where n = 15 per site………………………………30  Figure 4 Variance partitioning of fine-root traits at different hierarchically structured ecological scales along a biogeographic gradient. RTD, root tissues density; SRA, specific root area; SRL, specific root length. ......................................................................................................................................................... 31  Figure 5 Effects of environmental variables on fine-root order morphological and chemical traits of Douglas-fir. Standardized beta coefficients for linear mixed models (Cf. Appendix A.5) illustrate the effect of each environmental factor on a given fine-root trait in terms of its standardized effect size. CEC,  xii  cation exchange capacity; MAT, mean annual temperature; MAP, mean annual precipitation; soil C:N, soil carbon-to-nitrogen ratio; soil avail. P, soil available phosphorus. Circles (1st order roots, ●), triangles (2nd order roots, ∆) and squares (3rd order roots, □) indicate average estimates and lines indicate 95% confidence intervals. filled circles indicate a significant effect (P-value<0.05) of a given environmental variable on a trait…………………………………………………………………………………………..32  Figure 6 Ordination plot of samples across a biogeographic gradient based on principal component analysis of fine root traits for first-order roots of Douglas-fir. C, root carbon concentration; N, root nitrogen concentration; SRA, specific root area; SRL, specific root length; RTD, root tissue density; BrIntensity, branching intensity; DBI, dichotomus branching index………………………………. …….33  Figure 7 Comparison of fine-root trait variation in our study (Pseudotsuga menziesii) and in other tree species from the Pinaceae family for first-order roots. For the genera Pinus and Larix, data were extracted from the FRED 2.0 database. Different letters denote significant differences among tree species (Tukey HSD test, P < 0.05)…………………………………………………………………………………. …….34  Figure 8  Relative abundance (%) of ectomycorrhizal taxa on interior Douglas-fir among five regions in western Canada. Only the species/genera representing >2% of root tip abundance were included.  For a given ectomycorrhizal species, numbers represent the percentage of root tips colonized by this species in each region………………………………………………………………………………………….. …….56  Figure 9 Distance-based redundancy analysis (db-RDA) sample (a) and species (b) ordinations based on ectomycorrhizal fungal abundance on interior Douglas-fir roots across five regions in western Canada. Only the variation explained by environmental variables is visualized. The ectomycorrhizal species are color-coded by fungal exploration type and are sized according to their relative abundance. The species  xiii  epithet (when known) was removed to improve readability. MAP, mean annual precipitation (mm); MAT, mean annual temperature (ºC); CNs, soil carbon-to-nitrogen ratio………………………………… …….57  Figure 10 Ectomycorrhizal fungal species-specific response to the environment based on multivariate generalized linear models. Only species that were responsive were added to the model (coefficient > |5|).  Circles (●) represent species coefficients and lines, 95% confidence intervals. Species were grouped by exploration type indicated at the right-hand side of the plot, MAP, mean annual precipitation (mm); MAT, mean annual temperature (ºC); CNs, soil carbon-to-nitrogen ratio………………………………… …….58  Figure 11 Distance-based redundancy analysis (db-RDA) sample (a) and exploration type (b) ordinations based ectomycorrhizal fungal exploration type on interior Douglas-fir across five regions in western Canada. The ectomycorrhizal fungal exploration types are sized according to their relative abundance. MAP, mean annual precipitation (mm); CNs, soil carbon-to-nitrogen ratio……………………….. …….59  Figure 12 Distance-based redundancy analysis (db-RDA) sample (a) and species (b) ordinations based on ectomycorrhizal fungal abundance on interior Douglas-fir across five regions. Only variation explained by Douglas-fir fine-root traits is visualized. The ectomycorrhizal species are color coded by fungal exploration type and are sized according to their relative abundance. The species epithet (when known) was removed to improve readability. CN, fine-root carbon-to-nitrogen ratio; RTD, fine-root tissue density (mg cm-3)…………………………………………………………………………………………………..61      xiv   List of Acronyms and Abbreviations  BrIntensity  Branching intensity  CEC   Cation exchange capacity C:N   Carbon-to-nitrogen ratio DBI   Dichotomous branching index db-RDA  Distance-based redundancy analysis EMF   Ectomycorrhizal fungi FRED    Fine-Root Ecology Database ICH   Interior Cedar-Hemlock biogeoclimatic zone  IDF   Interior Douglas-fir biogeoclimatic zone  ITS   Internal transcribed spacer MAP   Mean annual precipitation MAT   Mean annual temperature OTU   Operational taxonomic unit PCA   Principal component analysis  PERMANOVA  Permutational multivariate analysis of variance RTD   Root tissue density SRA   Specific root area SRL   Specific root length VIF   Variance inflation factor    xv  Acknowledgements   Merci to Suzanne Simard and Les Lavkulich who have changed my life and believed that I could be useful to science. I have grown up by your sides and learned science, life and (a bit of) English with you. To my supervisory committee, Suzanne Simard, Lori Daniels and Rob Guy whose guidance was instrumental in going through this PhD journey and who gave me the freedom to explore ideas and made of me an independent scientist. I thank NSERC, the Stuntz memorial and Peter Rennie foundations and the British Columbia Ministry of Forests, Lands and Natural Resource Operations for funding and facilitating this research. To Jean Roach who started this journey with me and was the best model I could have hoped for. I hope I have learned from your patience and kindness. Heartfelt thanks to Shannon Guichon who is one of the most brilliant scientists and human beings I know, when I grow up, I want to be this woman! I am eternally grateful to M. Luke McCormack who put his trust and faith in me and has always been enthusiastic about what I was doing, but also always pushed me further. To Brian Pickles and Tim Philpott who I admire and for whom I have deep respect. Brian kept me dreaming with his insightful and fascinating research. I hope our paths will cross in the future. Tim is a model for me and collaborating with him was extremely elevating. A deep thanks to Shalom Addo-Danso who has been a light in the dark and I look forward to keep collaborating on these amazing tropical roots! To the world-known BEG group, Julia Amerongen-Maddison, Amanda Asay, Alice Chang, Monika Gorzelak, Allen Laroque, Katie McMahen, Mina Momayyezi, Gabriel Orrego, Teresa Ryan, Laura Super, and special thanks to Morgane Maillard, Lena, Nuria, Carlos, Audrey, Baba, Chloe, Simon and all. To our wonderful research assistants, Jaylen Bastos, Charles Cohen, Supreet Dhillon, Marissa Hooi, Caitlin Low, Esmee MacDonald, Daniel Malvin, Hannah Sachs, Yifan Sun, Bailey Williams, Tong Ye, and Zuofang Zhang.     xvi   Dedication To my love, Thomas, To my family,     To the little girl who was dreaming and still is. 1  Chapter 1: Introduction  1.1. Fine roots and mycorrhizas: regulators of ecosystem functions The ability to acquire soil nutrients and water is a fundamental requirement that allows plants to carry out functions such as photosynthesis, growth and maintenance. At the timescale of plant evolution, the innovation and development of an efficient root system for soil resource acquisition have prepared plants to thrive on land (Ma et al., 2018; do Nascimento et al., 2019). This turning point has led to an increase in atmospheric oxygen level, carbon (C) fixation and biotic chemical rock weathering, which in turn reshaped the global climate and biosphere (Selosse et al., 2015; Feijen et al., 2017). The root system has differentiated and increased in complexity through evolution of woody plants on land. It now comprises coarse roots, which mainly serve to anchor the plant, and fine, lateral roots that play active roles in water and nutrient uptake (Pregitzer, 2008; Bardgett et al., 2014).  Fine roots of woody plants have historically been defined as a single pool of roots ≤ 2mm in diameter. However, fine roots are highly diverse in form and function both spatially and temporally because of their complex branching architecture. Fine roots can be classified into orders according to their branch position with the most distal, unbranched roots as first order. When moving up the fine-root branching hierarchy, fine-root diameter and secondary development increase, whereas uptake capacity, turnover, respiration rate, and nitrogen (N) content tend to decrease (Pregitzer et al., 2002; Guo et al., 2008).  In view of this complexity, the historical definition based upon a single pool of fine roots has recently been reconsidered. McCormack et al. (2015) proposed to split the traditional single pool of fine roots into two functionally different pools: (1) the absorptive pools, representing the most distal roots, generally the first three root orders, that are primarily involved in the acquisition and uptake of soil resources (Figure 1), and (2) the transport pool, encompassing roots that serve structural, transport and storage functions.  2  In forest ecosystems, these two pools of fine roots represent a negligible portion of the total forest biomass, yet, trees invest a relatively large amount of photosynthates to their production and maintenance, accounting for 22% of the global terrestrial net primary productivity (McCormack et al., 2015). Therefore, fine roots play a disproportionately large role in terrestrial biogeochemical cycles (e.g., water, nutrients and C). For example, fine-root lifespan controls an important part of the C cycle through root tissue production, death and decomposition and, via exudation of labile C compounds, fuel soil microbial activity and mediate the dynamics of soil organic C pools (McCormack et al., 2012; Clemmensen et al., 2013; Churchland and Grayston, 2014).  As fine roots extract resources from the soil and drive biogeochemical cycles, they act as a key driver of plant evapotranspiration, which in turn regulates the land surface energy and water balance (Warren et al., 2015). Yet, terrestrial biosphere models currently function with little or no representation of roots (Warren et al., 2015  but see Meyer et al., 2012; Deckmyn et al., 2014). In the future, models may thus fail to predict plant responses to global changes because they do not include root structure and functions (Smithwick et al., 2014; Warren et al., 2015). Root data, in particular at broad spatial scales, are required to parametrize and validate models. Roots must also be better integrated in the simulations of the land surface, energy, water and C balances, which are often run at the km-scale (Warren et al., 2015). Integrating the fungal symbionts of roots into these models is also challenging but urgent as the mycorrhizal symbiosis is vital for the health and productivity of the world’s forest ecosystems. Association with symbiotic mycorrhizal fungi enabled the emergence of the first vascular plants on land (Berbee et al., 2017; Strullu‐Derrien et al., 2018). Early symbiotic fungi, including taxa in the Mucoromycotina, may have facilitated plant colonization of land by enabling root acquisition of limited water and bioavailable nutrients (Selosse et al., 2015; Feijen et al., 2017). Since the Devonian, about 400 million years ago, plant roots have formed mutualistic symbioses with fungi (i.e., mycorrhizal fungi), where the fungus extracts and transfers soil nutrients to the plant while being supported by the plant photosynthates and root habitat (van der Heijden et al., 2015; Strullu‐Derrien et al., 2018). It is estimated  3  that currently 86% of terrestrial plant species are engaged in one of the four types of mycorrhizal symbiosis (arbuscular, ecto, orchid and ericoid mycorrhizas; Brundrett, 2009). Contrary to the more primitive arbuscular mycorrhizal fungal lifestyle, the ectomycorrhizal fungal (EMF) habit, distinguishable by a fungal sheath that covers the root surface, emanating mycelium and intercellular mycelial network (i.e., Hartig net), arose more recently (about 150 million years ago) and arose multiple times through the evolution of both plants and fungi. Interestingly, this process may have selected for different strengths of the EMF mutualism (Balestrini and Kottke, 2016; Maherali et al., 2016; Wurzburger et al., 2017; Hoeksema et al., 2018).  In the early Cenozoic, climate cooling may have favored the diversification of EMF, which probably amplified the decline in global temperatures because they have enhanced capacity to weather calcium-magnesium silicates and thus draw CO2 out of the atmosphere (Berbee et al., 2017; Strullu‐Derrien et al., 2018). Similarly, the emergence of the EMF habit has favoured colonisation of regions where organic matter accumulates as EMF evolved from ancestral wood decaying and saprotrophic fungi and thus may possess abilities to decompose lignocellulose (Kohler et al., 2015). In turn, EMF likely contributed to the successful northward range expansion of the Pinaceae following climate cooling; thus, EMF currently dominate temperate and boreal ecosystems (LePage, 2003; Pither et al., 2018; Strullu‐Derrien et al., 2018). Geophysical feedbacks between mycorrhizas, C cycling and climate make mycorrhizal fungi key regulators of ecosystem functions (Simard and Austin, 2010; Clemmensen et al., 2013). Yet, the potential response and feedback of mycorrhizal fungal functioning to global changes remain uncertain and the role of the fungi in mediating ecosystem responses to environmental changes has been underexplored (Pickles et al., 2012; Mohan et al., 2014; Wurzburger et al., 2017).   4  1.2. The use of plant functional traits below-ground Trait-based plant ecology promises to help address the challenge of incorporating complex below-ground ecology into terrestrial biosphere models, improving our ability to predict and understand the responses and feedbacks of plants to global changes (Comas and Eissenstat, 2004; Bardgett et al., 2014; Iversen, 2014; Koide et al., 2014; McCormack et al., 2017). The trait-based approach emphasizes functional traits, commonly defined as measurable aspects that characterize a species of interest and that impact growth, reproduction or survival (Violle et al., 2007; Kattge et al., 2011).  Below-ground, plants can be characterized by, among others, traits of fine root morphology (e.g., diameter), chemistry (e.g., C:N ratio), architecture (e.g., branching pattern), physiology (e.g., respiration rate) or anatomy (e.g., stele diameter; McCormack et al., 2017). Mycorrhizal fungi can also be described by traits such as enzymatic activity, N tolerance or the differentiation of extraradical hyphae (Koide et al., 2014). Compared to leaves, these below-ground traits have been largely overlooked until recent years, owing to the difficulty of their study and to the lack of standardized protocols (Bardgett et al., 2014; Iversen, 2014; Laliberté, 2017; McCormack et al., 2017; Erktan et al., 2018a). Yet, new research reveals key roles of the evolution of below-ground traits in preparing early plants to colonize new habitats on land (Ma et al., 2018). Studies have also highlighted the role of below-ground traits in capturing plant and soil functions such as soil exploitation and aggregation, structuring of plant communities and mediation of ecosystem processes (Comas, 2017; Freschet and Roumet, 2017; Wurzburger et al., 2017). Therefore, integrating below-ground with other plant traits in examining responses to environmental changes may significantly alter our predictions. For example, conclusions about how plant trait response to climate change will affect future global productivity may need to be modified (Madani et al., 2018). Below-ground trait variation has been increasingly studied across species and environments and, with global meta-analyses, has started to generate general principals. For example, our understanding of the constraints underlying below-ground trait variation has advanced, with phylogeny, climate and growth form, as the main drivers at the global scale (Freschet et al., 2017; Simpson et al., 2016; Valverde- 5  Barrantes et al., 2017). Second, studies across plant species have found inconsistent evidence for a single root economic spectrum in a manner analogous to the leaf economic spectrum. The leaf economic spectrum recognizes a single acquisition–conservation axis, from conservative leaves with high structural investment to more cheaply constructed, acquisitive leaf types (Reich, 2014). As a result and as opposed to leaves, a multidimensional root trait framework has begun to emerge (Weemstra et al., 2016; Ma et al., 2018; de la Riva et al., 2018; Wang et al., 2018). This framework appears as a tool to establish and define the different axes that capture the variety of below-ground mechanisms and trade-offs and to accommodate and explain the variation in fine-root traits that is often multidimensional. To date, studies on below-ground trait variation have mainly focused on variation across plant or fungal species, whereas within-species variation has received less attention. This is an important shortcoming when considering the potential benefits for our understanding of environmental change effects on plant populations and associated fungal symbionts. Intraspecific variation of both above- and below-ground traits may be substantial and may strongly affect ecological processes and ecosystem services. For example, intraspecific variation in plant traits across environments can have stronger effects than variation across species and can mask interspecific trait correlations (Siefert et al., 2015; Laughlin et al., 2017; Des Roches et al., 2018; Hazard and Johnson, 2018).     6  1.3. The relevance of below-ground ecology in interior Douglas-fir forests Interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) appears to be an ideal model species to study intraspecific below-ground trait variation. Interior Douglas-fir is one of two varieties of Douglas-fir (interior and coastal) native to western North America. Paleogeographic studies on interior Douglas-fir (hereafter Douglas-fir) revealed that this variety evolved from several refugia that have migrated northward at about 60-165 m/year at the end of the last glaciation (about 11,700 years ago; Gugger et al., 2010). The northward rate of expansion of Douglas-fir might have been positively affected by the high receptivity toward EMF and/or was facilitated by mycorrhizal symbionts that survived glaciation (Pither and Pickles, 2017; Pither et al., 2018). Accordingly, Douglas-fir can associate with over 2000 EMF species including many host generalist taxa and taxa forming long-lived resistant propagules with rapid dispersal capabilities, including Rhizopogon, Suillus and Wilcoxina (Simard, 2009; Pither and Pickles, 2017).  Douglas-fir populations occupy a natural range spanning from central British Columbia into central Mexico (Lavender and Hermann, 2014). Genetic variation among populations is substantial and likely results from natural selection, especially for traits controlling growth, phenology and cold hardiness (Rehfeldt et al., 2014a). Population adaptation to the local climate and in particular to temperature, may stem from a trade-off between selection for high growth in mild climates and cold hardiness in harsh climates (Rehfeldt et al., 2014a). Because populations are locally adapted, Douglas-fir productivity may decrease throughout its entire geographical range as a consequence of the projected increase in temperature and drought stress (Chen et al., 2010; Coops et al., 2010; Leites et al., 2012). In turn, a decrease in tree productivity could negatively impact Douglas-fir fitness and resilience to global change.  Maladaptation may become widespread, especially because the expected rate of climatic change exceeds the rates at which tree populations are capable of migrating (Hamann and Wang, 2006; Courty et al., 2010; Rehfeldt et al., 2014a).  7  A key factor for Douglas-fir persistence as climate changes will be the response of their associated ectomycorrhizal symbionts. Both partners are adapted to the local environment, with EMF community composition following host population genetic gradients. Therefore, host populations display local optimization when matched with local symbiont populations (Kranabetter et al., 2015; Pickles et al., 2015a; Pither et al., 2018). The extensive interconnections between plant roots and EMF mycelium below-ground (i.e., common mycorrhizal network) in Douglas-fir forests may mitigate the effect of temperature and drought stress on tree populations through the transfer of carbon, nutrients and water among trees (Simard, 2009; Bingham and Simard, 2012; Gehring et al., 2017). Alternatively, EMF symbiosis could express maladaptation to predicted changes in climate, which may lead to a decline in EMF effectiveness and, in turn, may reduce host fitness (Kranabetter et al., 2015). Exploring current host/mycorrhizal symbiont interactions thus appears critical for predicting the response of Douglas-fir to global change, and to design appropriate management and reforestation practices.    8  1.4. Objectives and overview Determining within-species fine-root trait-environment linkages has motivated my first research questions, using Douglas-fir as a model tree species: 1. What is the extent of intraspecific fine-root trait variation across a biogeographic gradient? 2. How do climatic and edaphic factors shape regional patterns in fine-root trait variation? After gaining insights on these first questions, I have integrated responses of fine roots and ectomycorrhizal symbionts in an effort towards clarifying the relative importance of environmental factors on mycorrhizal fungal community shifts and linking fine-root and mycorrhizal fungal functional traits. I use the same study design and leverage fine-root trait data to address the following research questions: 3. At the regional scale, what is the strength of abiotic environmental filtering on EMF community taxonomic and functional structure? 4. Within a plant species, what are the links between mycorrhizal taxonomy and/or functional structure and fine-root functional traits? Chapter 2 of my thesis addresses the first two questions. I used precipitation, temperature and fertility gradients in British Columbia to explore variation in morphological, chemical and architectural traits of the first three fine-root orders, corresponding to the absorptive pool of fine roots. I also compared patterns of variation observed in Douglas-fir with other species in the Pinaceae using the Fine-Root Ecology Database in order to better assess the relative importance of intra- vs. interspecific root trait variation at the family level. Chapter 3 of my thesis addresses the last two research questions. I present patterns in Douglas-fir ectomycorrhizal diversity, richness and composition as well as exploration type abundance. I also investigated relationships between fine-root traits and mycorrhizal fungal community composition and exploration types.    9   Figure 1 schematic representation of an absorptive fine-root branch. The absorptive pool of fine roots represents the most distal roots, generally the first three root orders, that are primarily involved in the acquisition and uptake of soil resources.     10  Chapter 2: Intraspecific fine-root trait variation across a biogeographic gradient in western Canada  2.1. Introduction Functional traits of fine roots control plant resource uptake and mediate ecosystem processes, such as nutrient cycling and soil carbon sequestration (Bardgett et al., 2014; Roumet et al., 2016; McCormack et al., 2017). Consequently, traits of fine roots and of their mycorrhizal symbionts have become central to understanding plant responses to environmental changes from local to global scales (Laliberté, 2017; McCormack et al., 2017; Ostonen et al., 2017; Ma et al., 2018). Recent syntheses of large-scale datasets have facilitated exploration of interspecific (i.e., among-species) fine-root functional trait variation. These studies have notably advanced our understanding of the fundamental constraints underlying fine-root trait variation (e.g., phylogeny, climate and growth form; Simpson et al., 2016; Freschet et al., 2017; Li et al., 2017; Valverde-Barrantes et al., 2017; Ma et al., 2018). Importantly, studies across plant species have reported inconsistent evidence for a single root economic spectrum varying from more conservative roots with high structural investment to more cheaply constructed, acquisitive root types (McCormack et al., 2012; Reich, 2014; Roumet et al., 2016). As a result, a multidimensional root trait framework has begun to emerge (Weemstra et al., 2016; Erktan et al., 2018b; de la Riva et al., 2018; Wang et al., 2018; Zhou et al., 2018). Furthermore, several reports have suggested that fine-root architectural and mycorrhizal traits might form additional, independent axes of variation from those formed by morphological and chemical traits (Chen et al., 2013; Kong et al., 2014; Liese et al., 2017; Weemstra et al., 2016). Thus far, most studies have focused on assessing fine-root functional trait variation among species, following the assumption that among species differences in root trait values are greater than those within species, similar to studies on aboveground traits (McGill et al., 2006; Siefert et al., 2015; Fajardo and Siefert, 2018). However, within-species variation in fine-root functional traits can also be important, reflecting both heritable genetic variation (Zadworny et al. 2016, 2017) and phenotypic plasticity (Doi et al., 2017; Li et al., 2017; Ostonen et al., 2017). Intraspecific variation in fine-root trait expression is  11  therefore likely to be an important driver of plant community assembly and may enable populations of plants to adjust to environmental conditions and changing climate (Zadworny et al., 2016; Li et al., 2017). Despite recent studies stressing the need for empirical research that links intraspecific plant trait variation to the environment (Johnson et al., 2012; Des Roches et al., 2018; Fajardo and Siefert, 2018), such investigations among fine roots have only been conducted on relatively few species and in limited environmental contexts. This is an important shortcoming when considering the potential outcomes of environmental change on plant populations. Furthermore, identifying meaningful patterns of intraspecific fine-root trait variation may give insights into structural investments in fine roots in relation to their local environment. For instance, across an environmental gradient, Zadworny et al. (2016, 2017) found that fine-root traits of Scots pine (Pinus sylvestris) were related to mean annual temperature (MAT) as roots were thicker with lower specific root length (SRL) and lower root tissue density (RTD) in colder environments. The larger diameters were associated with a greater cortex area, which may indicate an overall shift among Scot pine trees from northern populations to adapt to colder environments by building cheaper fine roots (low RTD), with potentially higher absorptive capacity. Alternatively, across a similar temperate to boreal transect, Ostonen et al. (2017) showed that Norway spruce (Picea abies) and Scots pine trees, at higher latitudes, had longer and thinner fine roots with higher RTD, compared to trees in warmer, lower latitude forests. According to Ostonen et al. (2017), these adjustments were also closely related to an overall increase in absorptive root biomass. Despite some similarities, notable contradictions in the aforementioned studies demonstrate the need to better understand intraspecific below-ground trait-environment linkages (Freschet et al., 2017; Laliberté, 2017; McCormack et al., 2017). To investigate intraspecific fine-root trait variation and trait-environment linkages, we quantified root trait variation in interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn). Franco) across a biogeographic gradient in western Canada. Aboveground traits (e.g., bud phenology and growth) of interior Douglas-fir (hereafter Douglas-fir) are known to be locally adapted to climatic conditions in this region (Rehfeldt et al., 2014b), yet it is currently unknown if the observed phenotypic variability  12  aboveground is mirrored below-ground. Here, we measured fine-root morphological (diameter, RTD, SRL, and specific root area -SRA), chemical (root N concentration and root C:N ratio) and architectural traits (branching intensity and branching pattern). These traits were selected due to their expected connections to plant resource investment into root construction and function, and their benefits for soil resource foraging and acquisition.  Variation in fine-root traits was related to climate (MAT and mean annual precipitation - MAP) and edaphic variables (soil C:N, cation exchange capacity – CEC, and available phosphorus) and we also explored pairwise trait correlations. Our first hypothesis was that temperature would be an important driver of root trait variation such that trade-offs among traits favor greater root acquisitive capacity in colder than warmer climates. These trade-offs should translate into higher SRL, SRA, lower root diameter, RTD and root C:N. Increased root branching intensity, particularly within resource patches, is generally associated with greater root metabolism and enhanced root proliferation as it results in a higher density of more metabolically active, and more absorptive first- and second-order roots (Kong et al., 2014; Zadworny et al., 2016; Liese et al., 2017). Therefore, we also expected higher root branching intensity in harsher environments. We then assessed variation in fine-root traits at different ecological scales starting with the regional scale and then to the site, tree, and individual root branch levels. Our second hypothesis was that, consistent with observations aboveground, much of the variation in fine-root traits of Douglas-fir would occur at the regional scale. In addition, to better assess the relative importance of intra- vs. interspecific root trait variation at the family level, we compared the amount of variation observed within Douglas-fir in this study to the range of variation expressed across multiple, widely distributed species in the Pinaceae family, extracted from the Fine-Root Ecology Database 2.0 (FRED; McCormack et al., 2018).   13  2.2. Materials and Methods 2.2.1. Study sites A biogeographic gradient including five study regions ranging in latitude from 49.6 to 51.7º N was located within the natural range of Douglas-fir in British Columbia (Figure. 2). Two regions (Kamloops and Williams Lake) were in the Interior Douglas-fir biogeoclimatic zone (IDF) and three regions (Salmon Arm, Nelson and Revelstoke) occurred in the Interior Cedar-Hemlock biogeoclimatic zone (ICH) (Meidinger and Pojar, 1991). Regions were distributed along substantial precipitation and temperature gradients (Table 1). Sites in Williams Lake had the lowest MAT (on average 3.4°C) followed by Revelstoke, Kamloops, Salmon Arm and Nelson (on average 7.3°C). The driest region was Kamloops (average MAP, 441 mm) and the wettest region was Revelstoke (average MAP 1200 mm). Unlike other large environmental gradients that often correspond to wide ranges in latitude and daylength (e.g., Ostonen et al. 2017), the limited latitudinal range encompassed here leads to minimal differences in day length among study regions.  In each region, three replicated study sites separated by at least 400 m were selected in naturally regenerated, mature, closed-canopy forest stands on ecosystems that best reflect the regional climate (namely, zonal ecosystem; Meidinger and Pojar, 1991). Average stand age at each region ranged from 98-years-old (Revelstoke) to 143-years-old (Salmon Arm; Appendix A.1). The northern most stands in the IDF were growing on Luvisolic soils and the southern most stands in the ICH occurred predominantly on Brunisolic soils (Appendix A.1; Soil Classification Working Group, 1998; BC Ministry of Forests and Range, 2010). The southern most regions (Nelson, Revelstoke and Salmon Arm) were N-limited but mineral soil available P concentration was up to five times higher than the northern regions. The mineral soils in Revelstoke and Nelson were also characterized by low CEC values and low soil pH compared to the northern regions (Table 1). In the semi-arid regions of interior British Columbia, Douglas-fir occurs in pure stands (in the IDF), while in the wetter regions, Douglas-fir trees grow in mixed species forests (in the ICH; Meidinger and Pojar, 1991). Consequently, six sites were pure Douglas-fir forest stands and nine  14  sites were mixed stands (Appendix A.1). The proportion of Douglas-fir by basal area ranged from 49% in the mixed, even-aged forest stands of Salmon Arm to 100% in the pure, uneven-aged forest stands of Kamloops (Appendix A.1). In summary, the characteristics of the gradient were: southern regions had the wettest and warmest climate but were less fertile with regards to N, CEC and pH and had more available phosphorus compared with the northern regions, which were on average colder and drier. Thus, tree growth in the southern regions may mostly be limited by soil factors that limit decomposition, whereas, temperature rather than soil properties may limit tree growth in the northern regions. 2.2.2. Fine-root sampling and processing In 2016, we sampled sites near Kamloops and Williams Lake (drier) in mid-July and sites near Salmon Arm, Nelson, Revelstoke (wetter) in August. We used a nested sampling strategy to sample fine roots: a single sample plot of 30 x 30 m containing at least ten dominant or co-dominant Douglas-fir trees was established at each site. We selected five healthy Douglas-fir trees per plot in a manner to avoid clumping of sampling regions. For each selected tree, a coarse root originating from the target tree was identified and traced 200 cm out from the trunk. At that point, a single soil block (20 × 20 × 20 cm) was extracted as close as possible to the coarse root. Soil blocks were collected by hammering a steel frame into the soil and extracted using a flat shovel. In addition, one organic (L, F and H) and one mineral soil sample (upper mineral horizons A and B, 0 - 10 cm depth) were collected from each selected tree using a trowel near the location of the soil block. Collected soil blocks and soil samples were stored individually in plastic bags, transported on ice to the laboratory within 1 to 4 days, and stored at 4 °C until processing to avoid alteration of fine-root traits that can occur with freezing.  A total of 75 sample sets were collected in this study (5 regions, 3 sites per regions, 5 trees per site). To extract fine roots, each soil block was soaked in water overnight, and washed over a 4 mm screen. All fine-root branches (diameter < 2 mm) and fragments > 3 cm in length were recovered from the sieve and sorted by tree species. To do this, we developed a morphological key from root samples of known species identity collected from our study sites (Appendix A.2). The proportion of roots belonging  15  to other tree species was not estimated in this study. Douglas-fir roots that were turgescent with visible, intact periderm and that had (if present) colourful, swollen ectomycorrhizal tips were considered live roots. For trait measurements, we selected intact root branches containing at least three root orders, live ectomycorrhizal tips and minimal breakage. Whenever possible, selected branches were carefully cleaned with a soft brush and tweezers and analysed immediately after extraction; otherwise, branches were stored in a plastic bag with a damp paper towel (changed daily) at 4°C for no more than 3 days until further analysis.  2.2.3. Fine-root traits measurements We selected five, live and intact Douglas-fir fine-root branches from each of 73 soil blocks (five blocks/site, but only three blocks for Nelson - site N2). A total of 365 root branches were scanned on a desktop scanner (400 dpi, 165 -level gray scale, EPSON Perfection V800 Photo, STD 4800) and analysed with WinRHIZO pro 2016 software (Regent Instruments Inc., Quebec City, Canada). Branches were analysed for topology (magnitude, altitude, external path length). We acknowledge the possible limitations of scanning roots at 400 dpi for root length measurements, particularly for very fine roots (Delory et al., 2017). However, in our study system, this resolution was a good trade-off between speed and accuracy as we avoided scanning overlapping roots (i.e., root length density < 1 cmcm-2). Furthermore, our scans had a good contrast between the roots and the background as Douglas-fir is a relatively thick-rooted tree species (first-order root diameter > 0.40 mm).  Following initial scans of intact branches, each branch was divided into individual root orders using a scalpel under a stereomicroscope following the morphometric classification approach (Pregitzer et al., 2002). In our system, typical first-order roots were either comprised of ectomycorrhizal tips or displayed unbranched and uncolonized root tips and we avoided thicker, longer pioneer first-order roots (Zadworny and Eissenstat, 2011). Each root order group was scanned separately and analysed for morphology (total length, total surface area, average diameter and total volume). For the measure of root volume and area, we used the total value rather than the sum of values provided for each diameter class  16  (Rose, 2017), because in our study system, these two methods of estimation led to similar results (R2 = 0.99 for length and R2 = 0.97 for volume; data not shown). Root orders were then stored in envelopes, dried at 65°C for 48 hours and weighed. For each root order group, SRL (m g-1) was calculated as the ratio of root length to root dry mass; SRA (cm2 g-1) as the ratio of root surface area to dry mass; and RTD (mg cm-3) as the ratio of root dry mass to root volume. To determine C and N concentration (%) in each of the three root orders, we randomly selected samples for each of the 15 sites as follows: two soil blocks were sampled out of five, and two root branches were selected out of five originally sampled per block for a total of 180 root samples (Thermo Scientific Flash 2000 NC analyzer). The number of first-order roots for each branch was determined with the ImageJ software (National Institute of Health, USA). Root BrIntensity was calculated as the number of first-order roots per length of second-order roots. The dichotomous branching index (DBI) was calculated as DBI=[Pe-min(Pe)]/[max(Pe)-min(Pe)], with Pe, the external path length, defined as the sum of the number of root segments from the most distal root segment to the most basal root segment (i.e., third-order roots). Values of DBI range from 0, a fully dichotomous branching pattern to 1, a fully herringbone branching pattern (Šmilauerová and Šmilauer, 2002; Beidler et al., 2015). 2.2.4. Climate and soil data We obtained long-term averages for climatic variables (MAT and MAP) over the period 1981-2010 from ClimateNA (Wang et al., 2016). To obtain soil properties, organic and mineral soil samples were air-dried and sieved to 2 mm. Samples were then sent to the analytical laboratory of the B.C. Ministry of Environment (Victoria, British Columbia). Total soil C and N concentration (%) were measured using a combustion elemental analyser (Thermo Scientific Flash 2000 NC analyzer). For available phosphorus (PO4-P; orthophosphate as phosphorus), samples were prepared with the Bray P-1 method (dilute acid fluoride; Kalra and Maynard, 1991) and analysed with an UV/visible Spectrophotometer (Agilent Cary 60). To estimate the effective CEC, cations were extracted from the soil samples with 0.1 M barium  17  chloride (Hendershot and Duquette, 1986) and analysed with an ICP spectrometer (Teledyne Leeman, Prodigy Dual view).  2.2.5. Fine-Root Ecology Database To compare the extent of Douglas-fir fine-root trait variation in our study to that of other tree species, data were extracted from FRED 2.0 (Iversen et al., 2018). To facilitate comparison with Douglas-fir root trait variation from our study, we restricted our analysis to the following groups: (i) gymnosperm (ii) ectomycorrhizal tree species; which resulted in selecting tree species from the Pinaceae family only, thus removing the families Araucariaceae, Cupressaceae, Podocarpaceae, Sciadopityaceae and Taxaceae; (iii) fine roots, (iv) centripetal classification (i.e., morphometric classification) (v) first-, second- and third-order roots, (vi) living roots and (vii) in situ experiment (i.e. not hydroponic or pot grown). After applying these filters, we removed the genera Abies, Picea and Pseudotsuga and the species European larch (Larix decidua), slash pine (Pinus elliottii), Korean pine (P. koraiensis), black pine (P. nigra), longleaf pine (P. palustris), Manchurian red pine (P. tabuliformis) and Chinese red pine (P. massoniana), as data were too limited for the root traits considered. Similarly, SRA was not analysed as this trait has not been reported as frequently in FRED. Because most data reported in FRED are taken from site-level means, Douglas-fir trait values in this study were averaged within sites for comparisons with FRED. We then compared the variation in root diameter, RTD, SRL and root C:N of Douglas-fir to that of Dahurian larch (except for C:N as data were lacking), eastern white pine (P. strobus), Scots pine (P. sylvestris) and Virginia pine (P. virginiana) for each of the first three root orders.  2.2.6. Statistical analyses Statistical analyses were conducted in R version 3.5.1 (R Core Development Team 2018). To quantify fine-root trait variation within sites, among sites and across regions along the biogeographic gradient, we performed a variance partitioning analysis. For each root trait (i.e., SRL, SRA, RTD, root diameter, root C:N, BrIntensity and DBI), we fitted linear models (ANOVA) with nested random effects in this  18  increasing order: region, site, tree. To partition variance among these hierarchically structured ecological scales, we used the function ‘varcomp’ from the package ape (Venables and Ripley, 2002). To investigate how root traits respond to abiotic factors, we fitted linear mixed-effects models.  For each root order separately, we considered SRL, SRA, RTD, root diameter and root C:N as response variables. For third-order root C:N ratio, we used a linear model instead, as random effects were not significant. Models for BrIntensity and DBI were fitted considering the whole absorptive root branch. Before analyses, all the response variables were log10-transformed to meet the assumptions of the models. Data points that were > 3 standard deviations from the region median were considered as statistical outliers and removed. Removing the outliers did not change the outcome of the models. Climate (i.e., MAT and MAP) and soil variables (i.e., C:N ratio, available phosphorus, CEC) were added as fixed factors while region, site and tree were added as nested random factors. We also added stand age, Douglas-fir basal area, soil type and stand composition (mixed vs pure) as fixed effects. However, to avoid multicollinearity among predictors, these variables were removed from models as they all had a variance inflation factor >3 (Zuur et al., 2010). We used the ‘lmer’ function in the package lme4 (Bates et al., 2018). Models were fitted by maximum likelihood and had this general form: log10(root trait) ~ MAP + MAT + CEC + Soil C:N + Soil available phosphorus+ (1|region/site/tree) We used the function ‘step’ from the lmerTest package to eliminate non-significant effects of models based on the Akaike information criterion (Venables and Ripley 2002). We tested model significance with log likelihood ratio and significance of fixed effects with type II Wald χ2 test. Conditional and marginal R2 values were estimated following Nakagawa and Schielzeth (2013). Standardized beta coefficients and their 95% confidence intervals were extracted with the ‘beta.coef’ function from the sjstats package. Pairwise trait relationships were assessed using Spearman’s correlation and trait coordination was explored using Principal Component Analysis (PCA). We acknowledge the mathematical dependence among root morphological traits and discuss the results accordingly. To test for species differences within  19  our dataset and FRED, a two-way ANOVA with subsequent Tukey HSD test was performed. Results were considered statistically significant at P<0.05 and marginally statistically significant at P<0.1.  2.3. Results 2.3.1. Response to abiotic filters  Responses of fine-root traits to abiotic factors were mixed and these factors explained c. 10 % of the variation in fine-root traits as most of the variation occurred at small ecological scales (e.g., root branch, tree; Figure 3; Figure 4).With the exception of SRL, most traits were responsive to at least one abiotic factor, yet the direction and strength of these responses were often dependant on root order (Figure. 5; Appendix A.5). Root diameter, SRA and RTD each responded significantly to different aspects of climate. However, the effect sizes were relatively small. In other words, the values of the standardized regression coefficients, which refer to how many standard deviations a dependent variable will change, per standard deviation increase in the predictor variable were small (Figure. 5). Still, most first- and second-order root morphological traits were significantly related to MAT, with root diameter and RTD increasing with increasing MAT, while SRA decreased with MAT (P<0.05). In contrast, to first- and second-order roots, diameter of third-order roots decreased with MAP as well as CEC (marginal R2 = 0.03), while SRA of third-order roots increased with soil available phosphorus (marginal R2 = 0.03). Root tissue density of third-order roots was unrelated to any abiotic factor (Figure. 5). The C:N ratios of first- and third-order roots were most responsive to soil properties, with C:N of first- and third-order roots being positively related to soil C:N, while that of third-order roots was also positively related to MAT, but negatively related to CEC and soil available phosphorus (Figure. 5; Appendix A.5). The positive relationship between BrIntensity and soil available phosphorus was only marginally significant (marginal R2= 0.02, P-value = 0.05; Appendix A.5), while DBI was not related to any of the environmental factors considered in this study.  20  2.3.2. Ordination and trait correlation Consistent among the three root orders, root trait variation was two-dimensional (Figure. 6; Appendix A.6, Appendix A.7). Root morphological traits, except RTD, were well correlated with the first dimensional axis which explained c. 30% of the variation for the three root orders and represented a gradient from thinner, high SRL roots to thicker, low SRL roots. The five regions considered were not well separated along this axis. The second axis of variation accounted for c. 21% of the total variation and was best represented by chemical traits (root C and N; root C:N was not included to avoid redundancy) and RTD, especially for second-order roots. Regions were well separated across this axis which represented a gradient of higher root N and RTD (Kamloops, Revelstoke) to lower root N and RTD (Nelson, Williams Lake). Variation in RTD, compared to that of root C and N, changed markedly from first- and second-order (positive) to third order (negative). As a result, for third-order roots, samples from northern most regions (Williams Lake and Kamloops) clustered together and were opposite of those from southern most regions (Revelstoke and Nelson). Root architectural traits were not well represented along any of the axes (scores <0.15, for each root order) nor were they well captured by the third PCA axis, which accounted for <20% of the variation in each root order (Figure. 6; Appendix A.6). 2.3.3. Variance partitioning More than half of the root morphological trait variance in Douglas-fir was expressed at small ecological scales (tree and branch levels; Figure. 3; Figure. 4; Appendix A.2). In other words, differences among fine-root branches within soil blocks from individual trees explained most of the variation in root diameter, RTD, SRL and SRA. Consistent among morphological traits and root orders, the tree level (among trees within sites) accounted for, on average, 20% of the total variation. This pattern of high variance at small scales was even stronger for the architectural traits, BrIntensity and DBI, which accounted for over 90% of the variation among branches and individual trees (Appendix A.4). Alternatively, the proportion of the root trait variance at the cross-site level was negligible for root diameter, SRL and SRA (Figure. 4). At the regional scale, intraspecific variation was the highest for root  21  C:N and RTD. On average for the three orders, the regional scale accounted for roughly a quarter and 8% of the total variation in root C:N and RTD, respectively.  2.3.4. Intra- and interspecific root trait variation in Pinaceae Comparisons between our observations of Douglas-fir fine-root trait variation and species in FRED highlighted that intraspecific variation in fine-root traits for all the species was large, especially for RTD and C:N ratio (Figure.7; Appendix A.8). Douglas-fir and Scots pine expressed the highest intraspecific variability in RTD and C:N ratio compared to other species, while the variation in SRL and diameter was the most extensive for white pine. The analysis also showed that interspecific species effects were significant for most of the traits. Generally, RTD increased and C:N ratio decreased from European larch to Virginia pine. Conversely, with the exception of eastern white pine, root diameter and SRL did not vary much among- and within-species. Mean trait values for Douglas-fir fine-root traits were consistently significantly different from those of the other tree species considered (P<0.05 for all traits). In general, Douglas-fir averaged larger diameters with intermediate RTD, lower SRL as well as higher C:N than the other species in FRED. 2.4. Discussion Moderate but consistent trait-environment linkages occurred across populations of Douglas-fir that were distributed across climatic and edaphic gradients within a constrained geographic range in western Canada, despite high levels of within-site variation. Our first hypothesis was partly confirmed as mean annual temperature was the environmental variable that correlates the most highly with morphological traits, however, root C:N was primarily responsive to soil properties (soil C:N, soil available phosphorus and CEC). Generally, colder and/or drier climates were characterized by fine roots with potentially higher acquisitive capacities and variation in morphological and chemical traits represented two separate axes for fine-root adjustments. We rejected our second hypothesis as root trait variance was unevenly distributed across ecological scales, with over 50% of the variation in morphological traits occurring within individual trees of a single site. Furthermore, comparisons between Douglas-fir in our study and data for  22  other species obtained from FRED, indicated that for all the species, within- and among-species root trait variations are comparably high and, SRL was the least variable trait compared to RTD and C:N ratio. 2.4.1. Douglas-fir root trait adjustment to the environment Across the gradient, first- and second-order roots of Douglas-fir trees tended to increase in diameter and SRA and decrease in RTD with decreasing MAT. This result partly agrees with our hypothesis because, with the exception of increasing root diameter, these trait adjustments were expected to increase root resource acquisition potential, which was expected under colder climatic conditions. The responses of Douglas-fir morphological and chemical traits to MAT were largely consistent with that of Scots pine absorptive fine roots reported by Zadworny et al. (2017).  However, these results are in opposition to those reported by Ostonen et al. (2017), despite similar temperature ranges used in both previous studies. To adjust to colder conditions, fine roots may increase their potential for soil resource acquisition to compensate for more limited resource availability and a shorter window for growth and acquisition (Kramer-Walter et al., 2016; Liese et al., 2017; Erktan et al., 2018b; de la Riva et al., 2018; Wang et al., 2018). Alternatively, trees in colder environments may build fine roots with higher tissue protection and persistence (e.g., increased number of phellem layers, increased phenolic compound content; Zadworny et al. 2017). Our results provide evidence that in environments where temperature limits the availability of soil resources, fine roots increase their potential to acquire resources via morphological adjustments manifesting greater surface of roots deployed per unit biomass invested (i.e. higher SRA). These adjustments make roots less costly to construct while potentially maximizing habitat for ectomycorrhizal fungi as roots are thicker with lower density.  The greater root diameters in colder climates are incongruous with our expectations, as larger diameter roots are generally associated with a more resource-conservative strategy. In theory, plants with a conservative strategy have fine roots with low SRL, low N concentration, low uptake capacity, low respiration rate and long life spans (Roumet et al., 2016). The same conservative plants would be expected to have large root diameters, high RTD and high C:N ratios. Nonetheless, the increase in  23  Douglas-fir root diameter could be associated with enhanced root absorptive capacity if it maximizes habitat for ectomycorrhizal fungi. Accordingly, studies have reported a strong positive correlation between mycorrhizal colonization and root diameter in woody species (Comas et al., 2014; Eissenstat et al., 2015). Zadworny et al. (2016) also reported increased ectomycorrhizal mantle area on roots of Scot pine growing in colder compared to warmer locations. However, in our study system, we did not observe significant changes in colonization rate across the gradient as it averaged c. 95% for all the sites (data not shown), consistent with other studies in this region (Beiler et al., 2010; Barker et al., 2013). The limited responses observed in our study may be because Douglas-fir has relatively thick fine roots which are generally associated with higher, and sometimes more constant levels of fungal colonization (Kong et al., 2014; Eissenstat et al., 2015; Cheng et al., 2016). However, a single measure of colonization rate may be more relevant in arbuscular mycorrhizal plant species. Additional assessments of fungal hyphal densities in soils are also needed to better assess variation in associations with, and potential reliance of Douglas-fir trees on their mycorrhizal partners across environmental gradients. Fine-root C:N ratio was primarily responsive to soil properties (soil C:N, soil available phosphorus and CEC). The increase of root N concentration with increasing soil pH and CEC and decreasing soil C:N ratio was counterintuitive because elevated soil nutrient availability (high pH and CEC) tend to favour fine roots with low metabolism and hence, in theory, with low N concentration (Roumet et al., 2016). This is because plants in fertile environments can invest less energy to acquire soil resources than plants in less fertile environments. In our study, the N-rich soils were in the colder regions, but low temperature and drier conditions, likely limit the diffusion rate of soil resources. Therefore, in the colder/drier regions of our study area, even where nitrogen availability is higher, high root N concentrations with higher SRL/SRA and lower RTD, that are generally associated with shorter root lifespan, may represent a strategy to thrive where nutrient availability is heterogeneous and intermittent due to seasonality and soil freezing (Ma et al., 2018). In the less fertile soils of our study area, the growth of Douglas-fir trees may not be limited by the low nutrient availability because of the high MAT and  24  MAP. Accordingly, in these environments, we observed a more resource-conservative root strategy (higher RTD, C:N ratio and lower SRA). As suggested by Freschet et al. (2017), in wetter environments with low nutrient availability, investments in higher branching intensity and/or mycorrhizal hyphae may be more beneficial to capture available N prior to leaching rather than investing in high metabolism (higher root N concentration). Developing along an axis independent of morphological traits, root architectural traits may facilitate greater proliferation and exploitation of the soil environment at both the patch level and entire root-system level (Kong et al. 2014; Zadworny et al. 2016; Liese et al. 2017). Yet, our results do not provide strong evidence that Douglas-fir fine roots rely heavily on adjustments in architectural traits. The relatively low values and narrow range of variation of root branching intensity are consistent with that of other ectomycorrhizal gymnosperm species considered by Tobner et al. (2013) and Liese et al. (2017). These low values could be related to the consistently high rate of ectomycorrhizal colonization across our gradient, which suggests that local proliferation of fungal hyphae instead of increased fine-root branching may be the primary pathway to facilitate greater proliferation and exploitation of the soil environment and as mentioned earlier, especially in the southern regions. 2.4.2. Dimensionality in root trait variation Variation in fine-root chemical traits was orthogonal to the variation in root morphological traits. This provides evidence for the multidimensionality of fine-root trait variation, even within a single species as observed here in Douglas-fir, and across species as demonstrated by recent studies on interspecific root trait variation (Kramer-Walter et al. 2016; Liese et al. 2017; Erktan et al. 2018; Wang et al. 2018; de la Riva et al. 2018). Among morphological traits and across all root orders, the variation in RTD was negatively related to the variation in root diameter but was not well correlated with the variation in SRL. This implies that in fine roots with a larger diameter, there is no concomitant increase in structural tissue and tissue density as expected from the root economic spectrum. This may again result from alternative adjustments for resource acquisition, whereby Douglas-fir trees could increase root diameter to maximize  25  habitat for ectomycorrhizal fungi (Valverde-Barrantes and Blackwood, 2016), if increased diameters are associated with enlarged cortical parenchyma (Brundrett, 2002; Li et al., 2017). Consequently, thick roots may be associated with a low RTD as cortical parenchyma density is lower than the tissue density of the stele (Gu et al., 2014; Zadworny et al., 2016).  2.4.3. Intraspecific root trait variation  Though aspects of fine-root trait variation were significantly related to abiotic factors across regions, morphological, architectural and to a lesser extent, chemical traits, expressed the majority of their variation among root branches obtained from soil blocks at individual trees. We thus rejected our second hypothesis stating that most of the variation in fine-root traits of Douglas-fir would occur at the regional scale. The high degree of within-site variation observed here indicates substantial within-population root trait variation, which may enable acclimation of trees to future environmental conditions. Although our study was primarily designed to investigate fine-root trait-environment linkages across regions, these findings demonstrate that processes at lower ecological scales are also important in determining root trait variation. It is not always feasible to intensively sample and quantify root trait variation at such small scales (i.e. within plot or even within tree variation), but in light of this result, care should be taken when interpreting and extrapolating a single mean value for a stand-level functional trait or for an individual species (Messier et al., 2010). Thus, while environmental filters operate on the overall distribution of trait values within a region, their effects are lessened due to local variation among trees and root branches.  The high variation in root traits observed among branches within a single sampling location could be explained by differences in resource allocation or by differences in ectomycorrhizal symbiont identity (see Chapter 3). This may in turn affect carbon allocation to each root branch and the distinct morphology and chemistry expressed by individual roots (Agerer, 2001; McCormack et al., 2017).  For instance, the concentration of primary photosynthates in ectomycorrhizal root tips such as starch, glucose and non-structural carbohydrates, can change substantially among ectomycorrhizal symbionts (Trocha et al., 2010). Similarly, Pickles et al. (2010) demonstrated that the distribution of many ectomycorrhizal  26  individuals is often patchy. This leads to the possibility that different soil blocks from within the same site may be dominated by morphologically distinct ectomycorrhizas, contributing to the high variation in root traits at small spatial scales. In addition to testing for intraspecific differences within our dataset, further comparison with FRED revealed that within-species variation in Douglas-fir was similar to variation observed within other Pinaceae species. Trait values of each species overlapped for most of the traits we considered for Douglas-fir and in the case of fine-root C:N, variation within Douglas-fir largely eclipsed variation among species. Additionally, finer-scale variation observed in our study highlights the wide breadth of intraspecific variation possible within sites (i.e. among tree and branch level variation, Figures 3 and 4). This indicates that relatively modest trait adjustments within a species could result in similar or larger differences than are found across other dominant species in the Pinaceae. These results support those presented by Laughlin et al. (2017) and indicate that high intraspecific variability in root traits seems to be common, at least in the Pinaceae family. Therefore, intraspecific trait variation below-ground can play an important role in driving community-level shifts in root traits across environment, similar to what has been observed in aboveground traits (Laughlin et al., 2017; Fajardo and Siefert, 2018).  Despite substantial intraspecific variation, interspecies effects were still significant for most of the traits, except SRL. This suggest that SRL is a conserved trait, while shifts in RTD and C:N may express root plasticity or genetic differences in trait values within and among species in the Pinaceae, which is in accordance with findings on angiosperms (Valverde-Barrantes et al., 2015). However, the lack of variation in SRL here was likely due to contrasting patterns of variation in diameter and RTD among species, as species with larger diameter roots but lower RTD, expressed similar SRL to roots with smaller diameters and higher RTD.  Our analysis encompassed Pinaceae species whose natural ranges differed in latitude, from Virginia pine growing in lower latitude forests (c. 34°N) to European larch and Scots pine that are found at higher latitudes (c. 58°N). Patterns in RTD and root C:N variation followed this latitudinal gradient, as  27  RTD decreased whereas root C:N increased from Virginia pine to Scots pine. This suggests that higher latitude Pinaceae tree species (e.g., Scots pine and European Larch) may express a more resource-acquisitive root strategy (lower RTD and root C:N) than lower latitude species. Regardless, studies encompassing more families are needed to corroborate this statement and place within-species root trait variation in a larger environmental context.  2.5. Conclusion Across gradients of climate and edaphic factors in western Canada, we identified moderate but consistent trait-environment linkages across populations of Douglas-fir, despite high levels of within-site variation. Colder/drier climates were characterized by fine roots with lower RTD, higher SRA, higher diameter, and lower C:N ratio. We also provided evidence for decoupled variation in fine-root morphological and chemical traits. These findings highlight the existence of multiple axes of within-species fine-root adjustments that were consistent with a potential increase in fine roots acquisitive capacity in harsher environments. Therefore, as climate warms, fine roots may adopt a more resource-conservative strategy and the substantial within-population root trait variation may enable further acclimation at the stand level. Further studies measuring both root form and function are required to better predict the fate of plant resource strategies with global change. Intraspecific root trait variation was comparable to, or in some cases exceeded the variation observed across multiple species of Pinaceae species. This further highlights the need to better quantify within-species variation in fine-root traits, certainly within the Pinaceae, but also among a wider range of plant families.      28  Table 1 Location, climatic and edaphic properties of the 15 sites from five regions used in this study. Soil properties are reported as mean ± standard error, n =5.   Table 1 (continued)   Region Site within region Latitude (°N) Longitude (°W) Elevation (m) MAT (°C)aMAP (mm)bWL1 51.73 123.01 1149 3.1 485WL2 51.74 122.78 1087 3.5 477WL3 51.74 122.76 1080 3.6 476R1 50.80 118.00 762 5.3 1216R2 50.79 117.98 706 5.6 1212R3 50.83 118.02 726 5.7 1167K1 50.89 120.33 945 5.4 448K2 50.92 120.28 895 5.8 434K3 50.92 120.28 939 5.6 442SA1 50.76 119.19 712 6.4 688SA2 50.65 119.05 721 6.1 701SA3 50.79 119.06 703 6.3 696N1 49.55 117.72 671 7.0 886N2 49.60 117.75 679 7.5 860N3 49.61 117.77 754 7.3 856Salmon ArmNelsonLocation Climatic properties (1981-2010)Williams LakeRevelstokeKamloopsRegion Site within regionSoil avail. P (ppm) CEC (cmol(+)kg-1)c Total soil N (%) Total soil C (%)  Soil C:N pH Bulk density (gcm-3)WL1 25.7 ± 8.6 11.3 ± 2.4 0.08 ± 0.02 1.83 ± 0.60 23.2 6.5 0.78 ± 0.14WL2 59.2 ± 24.6 15.6 ± 4.6 0.09 ± 0.02 1.95 ± 0.40 21.7 6.5 0.67 ± 0.10WL3 62.9 ± 9.3 21.2 ± 5.3 0.13 ± 0.04 2.57 ± 0.97 20.4 6.8 0.75 ± 0.10R1 117.5 ± 68.4 4.8 ± 1.7 0.08 ± 0.03 1.96 ± 0.43 24.4 5.8 0.67 ± 0.16R2 33.8 ± 9.8 4.1 ± 3.0 0.12 ± 0.03 3.74 ± 1.02 33.5 6.0 0.43 ± 0.11R3 232.4 ± 79.5 8.3 ± 2.0 0.14 ± 0.05 4.33 ± 1.62 30.1 5.6 0.37 ± 0.08K1 81.2 ± 18.3 21.5 ± 14.2 0.18 ± 0.12 3.42 ± 2.54 18.5 6.6 0.47 ± 0.08K2 47.8 ± 17.0 21.9 ± 7.3 0.22 ± 0.10 3.83 ± 2.10 17.3 6.4 0.48 ± 0.15K3 46.4 ± 19.3 15.5 ± 4.3 0.16 ± 0.02 2.90 ± 0.75 17.8 5.9 0.45 ± 0.10SA1 3.6 ± 1.5 34.8 ± 11 0.18 ± 0.11 5.84 ± 3.65 32.4 7.6 0.49 ± 0.09SA2 161.8 ± 41.5 10.8 ± 6.1 0.07 ± 0.03 1.86 ± 0.74 28.5 6.5 0.60 ± 0.14SA3 225.6 ± 95.3 12.3 ± 5.7 0.09 ± 0.05 2.94 ± 1.74 33.9 6.6 0.56 ± 0.23N1 124.1 ± 34.1 3.8 ± 2.3 0.10 ± 0.03 3.37 ± 0.86 34.3 5.9 0.50 ± 0.13N2 328.9 ± 77.8 5.7 ± 3.1 0.09 ± 0.04 2.66 ± 1.43 28.2 5.9 0.47 ± 0.19N3 379.3 ± 22.1 5.9 ± 2.5 0.09 ± 0.03 2.37 ± 0.83 27.0 6.1 0.52 ± 0.04Mineral soil propertiesWilliams LakeRevelstokeKamloopsSalmon ArmNelson 29   Figure 2 Geographical distribution of study regions (rectangles, □) and study sites (triangles, ▲) across the current natural range of interior Douglas-fir (Pseudotsuga menziesii var. glauca; green shading) in British Columbia, Canada.   30   Figure 3 Distribution of root morphological trait and root C:N values across sites along a biogeographic gradient. WL, Williams Lake; R, Revelstoke; K, Kamloops; SA, Salmon Arm; N, Nelson. Each data point represents one measurement for each root order of one root branch of Douglas-fir. The first and third quartiles are indicated by the bottom and top lines of the boxes, respectively, while the two whiskers represent the 10th and 90th percentile and the horizontal line within the boxes represents the median value. For each root order, n = 25/site except for N2 where n = 15 per site.  31    Figure 4 Variance partitioning of fine-root traits at different hierarchically structured ecological scales along a biogeographic gradient. RTD, root tissues density; SRA, specific root area; SRL, specific root length.  32   Figure 5 Effects of environmental variables on fine-root order morphological and chemical traits of Douglas-fir. Standardized beta coefficients for linear mixed models (Cf. Appendix A.5) illustrate the effect of each environmental factor on a given fine-root trait in terms of its standardized effect size. CEC, cation exchange capacity; MAT, mean annual temperature; MAP, mean annual precipitation; soil C:N, soil carbon-to-nitrogen ratio; soil avail. P, soil available phosphorus. Circles (1st order roots, ●), triangles (2nd order roots, ∆) and squares (3rd order roots, □) indicate average estimates and lines indicate 95% confidence intervals. Filled circles indicate a significant effect (P-value<0.05) of a given environmental variable on a trait.   33    Figure 6 Ordination plot of samples across a biogeographic gradient based on principal component analysis of fine root traits for first-order roots of Douglas-fir. C, root carbon concentration; N, root nitrogen concentration; SRA, specific root area; SRL, specific root length; RTD, root tissue density; BrIntensity, branching intensity; DBI, dichotomus branching index.  34   Figure 7 Comparison of fine-root trait variation in our study (Pseudotsuga menziesii) and in other tree species from the Pinaceae family for first-order roots. For the genera Pinus and Larix, data were extracted from the FRED 2.0 database. Different letters denote significant differences among tree species (Tukey HSD test, P < 0.05).  35  Chapter 3: Environment and fine-root traits relate to shifts in ectomycorrhizal fungal communities across interior Douglas-fir forests of western Canada  3.1. Introduction Shifts in the taxonomic and functional structure of mycorrhizal communities across plant host distributions underpin changes in biogeochemical processes, such as modification of carbon (C) and nitrogen (N) cycles (Clemmensen et al. 2013; Koide et al. 2014; Cheeke et al. 2017; Wurzburger and Clemmensen 2018; Jassey et al. 2018). Therefore, identifying the biotic and abiotic factors that shape mycorrhizal fungal communities is a prerequisite for understanding terrestrial ecosystem processes and predicting the impacts of global change on plant communities (Hazard and Johnson, 2018; Hoeksema et al., 2018; van der Linde et al., 2018). Mycorrhizal fungi will likely respond to a range of environmental factors, and not necessarily the same factors as their hosts (Pickles et al., 2012), placing a premium on large-scale studies that examine communities across multiple environmental gradients (Lilleskov and Parrent 2007; van der Linde et al. 2018). Ectomycorrhizal fungi (EMF) play a dominant role in temperate and boreal forest ecosystems, where they control plant acquisition of soil resources (e.g., inorganic and organic forms of N and phosphorus, P; Read and Perez-Moreno, 2003; Heijden and Horton, 2009) and soil C dynamics (Simard and Austin 2010). Biogeographic patterns in EMF diversity are now being studied (Tedersoo, 2017; Hu et al., 2019). However, there is a lack of baseline information on patterns of EMF community composition and functional trait distribution, especially at large spatial scales such as the regional (i.e., scale of a country), continental or global scales (Tedersoo, 2017; van der Linde et al., 2018; Hu et al., 2019).Yet, biogeographic data on EMF community structure are necessary to assess their role in mediating current and predicted alterations in the C cycle (Jassey et al., 2018), the hydrologic cycle (Bjorkman et al., 2018) or plant production (Coops et al., 2010; Richardson et al., 2018).   36  At the continental scale, patterns of EMF community composition have been investigated in Europe on Scots pine, Norway spruce and European beech, where host plant family and N- deposition have the predominant filtering effect (Tedersoo et al., 2012; Põlme et al., 2013; Suz et al., 2014; Rosinger et al., 2018; van der Linde et al., 2018). Within Europe, at the regional scale, N-deposition, rainfall and soil moisture were also found to drive shifts in Scots pine EMF community structure (Jarvis et al., 2013), whereas other European studies highlighted the filtering effect of temperature and soil fertility on EMF communities (Sterkenburg et al., 2015; Pena et al., 2017). In Western North America, Pickles et al. (2015b) have also inferred from a common-garden greenhouse study on interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco; hereafter Douglas-fir) seedlings that temperature and soil fertility may drive habitat filtering in EMF communities.  Across environments, variation in EMF functional traits may relate better to ecosystem processes than variation in species composition because it informs how groups of species function and the extent that there is functional redundancy in species diversity (Koide et al., 2014; Hazard and Johnson 2018). For instance, EMF functional traits such as enzymatic activity (Courty et al., 2016), N preference (Haas et al., 2018; Leberecht et al., 2015) or the differentiation of extraradical hyphae (i.e., exploration type; Agerer, 2001; Jarvis et al., 2013; Pickles et al., 2015b; Fernandez et al., 2017; Ostonen et al., 2017; Pena et al., 2017; Köhler et al., 2018; Rosinger et al., 2018) have been shown to impact ecosystem processes (Koide et al., 2014). Exploration type is a functional trait that connects the morphology and differentiation of EMF hyphae to differences in nutrient acquisition strategies. From a functional perspective, exploration type determines the ability of EMF to colonize new roots, form common mycorrhizal networks, or forage, acquire and transport resources. EMF with contact, short- and medium-distance exploration types, for example, may preferentially use labile, inorganic N forms (Lilleskov et al., 2002; Hobbie and Agerer, 2010). Alternatively, long-distance explorers may be more effective in capturing patchily disturbed organic N (Koide et al., 2014), and are likely to be more resistant to decay due to their hydrophobicity. Hence, EMF fungi with long-distance exploration types may drive soil C storage and  37  C:N ratio (Suz et al., 2014), although some short-range EMF including Cenococcum geophilum and Cadophora finlandia are also resistant to decay (Agerer, 2001; Fernandez et al., 2016).  Shifts in EMF exploration type may compensate for changes in fine-root structure. For instance, across 13 temperate tree species, the abundance of larger absorptive fine-roots, whose large diameter and associated high construction costs limits efficient resource foraging, was positively correlated with the proportion of longer distance exploration types, thus resulting in functional complementarity between fine roots and EMF with respect to soil resource capture (Chen et al. 2018a; Chen et al. 2018b). This is because plants with coarser roots are less able to forage for and absorb soil resources, thus they should benefit the most from medium- or long-distance explorers that can acquire and transport resources well beyond root depletion zones (Chen et al. 2018a; Chen et al. 2018b). Similarly, thick-rooted tree species tend to preferentially proliferate mycorrhizal hyphae compared to fine roots when foraging within nutrient patches (Cheng et al., 2016). To the best of our knowledge, only three studies have linked root and EMF functional traits (Ostonen et al., 2011; Cheng et al., 2016; Chen et al., 2018a). Yet, studies connecting fine-root and mycorrhizal functional traits are essential for broadening root trait frameworks (McCormack et al., 2017). To assess the extent of abiotic environmental filtering on EMF community taxonomic and functional structure, and to examine relationships between fine-root and EMF exploration type, we investigated patterns of belowground trait variation across five regions that differed in precipitation, temperature and soil fertility in an area of c. 160, 000 km2 (49.6 to 51.7º N) in British Columbia, Canada. We focused on Douglas-fir in interior Douglas-fir dominated forests which are widely distributed from the Rocky Mountains of Canada and the United States to the mountains of central Mexico (Lavender and Hermann, 2014). In Chapter 2, we explored the variation in morphological, chemical and architectural traits among fine roots across the same biogeographic gradient and revealed that Douglas-fir trees from colder/drier climates had fine roots with higher diameter, lower root tissue density, and lower carbon-to-nitrogen ratio, compared to trees from milder climates. In this study, we first hypothesized that  38  temperature and soil fertility would be the main drivers of EMF diversity and community composition. Our second hypothesis was that medium and/or long-distance explorers would be more abundant in colder climates and/or in soils with a high C:N ratio. Building on the results of Chapter 2, our third hypothesis was that EMF traits compensate for changes in fine-root structure, and especially root diameter, where colder/drier climates with larger diameter roots are dominated by EMF with medium and/or long-distance exploration types.  3.2. Materials and Methods 3.2.1. Study sites Fine roots and EMF root tips were collected from five regions in three naturally regenerated, mature, closed-canopy forest stands per region (Table 1; Appendix A.1). We selected the five regions to obtain a biogeographic gradient with substantial precipitation and temperature ranges. Mean annual temperature (MAT) ranged from 3.4 to 7.3 °C and was lowest in Williams Lake, followed by Revelstoke, Kamloops, Salmon Arm and Nelson. The driest region was Kamloops (average mean annual precipitation- MAP, 441 mm) and the wettest was Revelstoke (average MAP 1200 mm). We picked stands that were at least 400 m apart and were ecosystems which best reflected the regional climate (namely, zonal ecosystem; Meidinger and Pojar, 1991). Average stand age ranged from 98-years-old (Revelstoke) to 143-years-old (Salmon Arm), and the proportion of Douglas-fir by basal area ranged from 49% in the mixed, even-aged forest stands of Salmon Arm to 100% in the pure, uneven-aged forest stands of Kamloops (for further details on site and stand characteristics, see Chapter 2, Materials and Methods and Appendix A.1). The southern-most stands (Nelson, Revelstoke and Salmon Arm) occurred predominantly on Brunisolic soils that were characterized by lower cation exchange capacity (CEC), soil pH and soil N compared to the northern-most stands (in Williams Lake and Kamloops) which occurred on Luvisolic soils (British Columbia Ministry of Forests and Range and British Columbia Ministry of Environment, 2010). Climatic variables for the period 1981-2010 were obtained from ClimateNA (Wang et al., 2016) and soil samples were  39  analyzed for total soil carbon (C) and N concentration, available P (PO4-P; orthophosphate as phosphorus) and CEC (for further details on soil sample analyses Cf. Chapter 2, Material and Methods). 3.2.2. Sampling and sample processing  We sampled fine root branches and EMF root tips from each of the 15 stands (30 ×30 m) in summer 2016. In each stand, single soil blocks (20 × 20 × 20 cm) were extracted from five Douglas-fir trees (200 cm out from the trunk) in a manner that avoided clumping of sampling location (i.e., trees were at least 5 m apart). The soil blocks encompassed organic layers (L, F and H) and mineral horizons (A and B) to obtain a more complete vertical representation of the EMF community (Rosling et al., 2003). In addition, one organic and one mineral soil sample (upper mineral horizons, 0 - 10 cm depth) were collected using a trowel from each target tree to evaluate relationships between EMF communities and soil properties. A total of 75 sample sets were collected (5 regions, 3 stands per region, 5 trees per stand) and stored at 4 °C until processing (up to three months). To recover Douglas-fir fine roots and ectomycorrhizas, each soil block was soaked in water overnight before being washed over a 4 mm screen. All fine-root branches (< 2 mm diameter) and fragments (> 3 cm length) were collected from the sieve and sorted by tree species (based on the morphological key described in Appendix A.2). To guarantee random selection of EMF root tips, root fragments from each block were laid out on a numbered grid, and grid cells were selected using a random number generator. We examined and cleaned root fragments with a soft brush under the microscope until c. 50 live fine root tips/block were collected. Excised fine-root tips were classified as individual morphotypes (based on the presence of fungal mantle and according to Goodman et al. 1996) or uncolonized (root hairs present, or no visible mantle, usually unbranched). All tips were frozen at -80°C but only 5-10 tips per morphotype, across all blocks for the entire study were used for later DNA analysis. To assess the effect of fine-root traits on EMF taxonomic and functional diversity (exploration type), a total of 365 Douglas-fir fine-root branches was divided into individual root orders following the morphometric classification approach of Pregitzer et al. (2002). In this classification, the most distal,  40  unbranched roots are first order and second-order roots begin at the junction of two first order roots and so on. First-order roots were either colonized by EMF or were unbranched and root tips uncolonized. We avoided thicker, longer pioneer first-order roots (Zadworny and Eissenstat, 2011). Each first-order group (i.e., all first-order roots of a given branch) was scanned separately and analysed for morphological features using WinRHIZO (total length, total surface area, average diameter and total volume; 400 dpi, 165 -level gray scale, EPSON Perfection V800 Photo, STD 4800; WinRHIZO pro 2016 software, Regent Instruments Inc., Quebec City, Canada). For each first-order group, we determined specific root length, SRL (m g-1), specific root area, SRA (cm2 g-1), and root tissue density, RTD (mg cm-3). In addition, a subsample of 180 first-order roots were randomly selected and analyzed for C and N concentration (%) (Cf. Chapter 2, Material and Methods). These traits were selected for analysis due to their expected relationships with plant investment into root construction and maintenance and their benefits for soil resource foraging and acquisition. 3.2.3. Molecular analyses of ectomycorrhizas  Five to ten frozen EMF root tips per morphotype (across all blocks for the entire study) were ground in liquid nitrogen before extracting fungal DNA using DNeasy® PowerSoil® kit, according to the manufacturer instructions (Qiagen®, 2017, ON, Canada). Fungal DNA extracts were sent to the Centre for Comparative Genomics and Evolutionary Bioinformatics (CGEB) at Dalhousie University, Halifax, Canada. High-throughput sequencing (Illumina MiSeq v3 chemistry, 600 cycles; Illumina, San Diego, CA, USA) was used to identify the target EMF OTU. For the library preparation, amplicon fragments were PCR-amplified with fungal specific primers (ITS86F, GTGAATCATCGAATCTTTGAA; ITS4R, TCCTCCGCTTATTGATATGC) targeting the internal transcribed spacer region 2 (ITS2) (variable length, avg. c. 350 bp) of ribosomal DNA (Turenne et al., 1999; Vancov and Keen, 2009; White et al., 1990). Primers contained Illumina barcodes and overhang adaptors, allowing for a one-step PCR preparation of sequence libraries.   41  DNA sequencing results were analyzed using the QIIME2TM bioinformatics platform (Caporaso et al., 2010). The software package DADA2 was used to assemble bidirectional reads while filtering for quality and dereplicating sequences (Callahan et al., 2016). Prior to taxonomic assignment, representative sequences were exported from QIIME2 into fasta format and then ITS2 regions were extracted, chimeras were detected and non-ITS2 sequences were screened out using the software tool ITSx (Bengtsson‐Palme et al., 2013). Extracted ITS2 sequence data were imported back into QIIME2 and the corresponding QIIME2 feature table was filtered to remove non-ITS sequence data. Demultiplexed, quality-controlled ITS2 sequence data were further screened for chimeras and then clustered into operational taxonomic units (OTUs) at 99% sequence similarity using a de novo clustering method with VSEARCH (Rognes et al., 2016; Westcott and Schloss, 2015).  For fungal species identification, we used the Basic Local Alignment Search Tool (BLAST) against the National Center for Biotechnology Information (NCBI) GenBank and UNITe public sequence databases (Abarenkov et al., 2010). We used two criteria to assign species or genus names to each morphotype: (i) Only EMF OTU were considered (no consideration of root-associated fungi such as saprotrophs, root endophytes, moulds or pathogens), and (ii) pairwise identity (i.e., the amount of nucleotide that matches exactly between two different sequences) corresponding to the indicated EMF species were higher than 97%. In addition, morphotype characteristics were compared to reference photos from the Ectomycorrhizae Descriptions Database (http://forestrydev.org/biodiversity/bcern)  and the DEEMY database (www.deemy.de/). Using this method, 82% of the morphotypes were identified to the species or genus level. For all but five morphotypes, the assigned EMF species or genus corresponded to the EMF OTU with the highest number of reads; for the remaining five morphotypes (morphotype IDs 81, 50, 36, 31 and 25, see Appendix B.1), the EMF OTU with the highest number of reads had a low pairwise identity (< 94). In this case, the EMF OTU with the second highest number of reads was chosen, but only if the morphology of the morphotype corresponded to the photos from the databases. For each species or genus, exploration types (contact, short-distance, medium-distance, medium-distance fringe  42  and long-distance) were assigned after Agerer (2001) and compared to the published data from Ostonen et al. (2017) Pena et al. (2017) and Fernandez et al. (2017). We assumed that exploration type was conserved within a genus as we did not study EMF genotypic trait variation. This assumption has been made in many other studies although plasticity of EMF mycelium can be substantial and should be taken into account more consistently (Hazard and Johnson, 2018; van der Linde et al., 2018). 3.2.4. Data analyses All statistical analyses were conducted in R version 3.5.1 (R Core Development Team, 2018). Fungal richness was examined across regions by estimating components of α-diversity: (i) observed species richness in each soil block and (ii) richness estimators: Chao1 (Chao, 1984),  first- and second-order Jackknife (Burnham and Overton, 1979). Diversity patterns were examined by calculating the following diversity indices: Shannon-Wiener Diversity index (H’ = - Σ pi ln pi), Shannon’s Evenness (E), and Simpson's Index of Diversity (1 – D; D = 1 - Σ pi2), where pi is the proportion of species i relative to the total number of species in a sample. We assessed the effect of region on EMF richness, evenness and diversity using a nested ANOVA (linear mixed effect model) with region as a fixed effect and site nested within region as a random effect using the function ‘lmer’ from the lme4 package. We did not rarefy to the lowest sampling depth (i.e., we only collected 12 root tips in a soil block from Revelstoke) because rarefaction curves indicated that our sampling effort was sufficient as it resulted in EMF species saturation (Appendix B.2).  To investigate the effects of environment and fine-root traits on the taxonomic and functional structure of EMF communities, we first calculated the variance inflation factor (VIF) for each environmental (MAT, MAP, soil C:N, CEC, pH, soil available P) and root trait (SRL, SRA, diameter, RTD, root CN) predictor, to avoid multicollinearity among predictive variables, using the ‘vif’ function from the usdm package (Naimi et al., 2014) . Predictors with the highest VIF were sequentially dropped until all VIF values were below three (Zuur et al., 2010). This process removed CEC and soil N from the environmental predictors and SRL from the root trait predictors. Second, unidentified morphotypes (from  43  which DNA was not extracted and for which a sequence was not found) were removed before all analyses on the basis that reconsideration of photographic evidence suggested that they were most likely to have been dead root tips. A Hellinger transformation was applied to species and exploration type data matrices. We used a distance-based redundancy analysis (db-RDA; Legendre and Anderson, 1999) to examine β-diversity based on Bray-Curtis dissimilarities using the ‘capscale’ function in vegan. The best model was chosen utilising forward model selection with permutation tests (P-value for variable retention = 0.05). The general form of the models was: EMF species or exploration type ~ environmental factors (e.g., MAT, MAP) or root traits (e.g., SRA, RTD).  Models were tested for significance using permutational multivariate analysis of variance (PERMANOVA, function ‘adonis’ in vegan, 999 permutations), after assessing the multivariate homogeneity of regions dispersions (function ‘betadisper’ in vegan; Oksanen et al. 2018; Appendix B.3). Significant PERMANOVA effects were assessed using post-hoc pairwise contrasts (function ‘multiconstrained’ from the package BiodiversityR; Kindt, 2018).  The db-RDA /PERMANOVA approach was not developed to account for nested data (here, sites are nested within regions). Thus, to complement these analyses, we used a two-way approach, that only worked with two nested factors: (i) For the effect of sites within regions, we used PERMANOVA with permutations constrained within sites (strata=site in ‘adonis’) and (ii) for the effect of regions, we ran a nested analysis of variance with the function ‘nested.npmanova’ from the package BiodiversityR. These two complementary analyses were run on the models:  EMF species or EMF exploration types ~ Regions/Sites.    In addition, to account for the presence of mean-variance relationships in multivariate community analyses, we built multivariate generalized linear models using the package ‘MVABUND’ (Wang et al. 2012). An offset (log of row sums) was added to the models to standardize the response variables and account for the unequal sample size. Models were run twice: with all the species and without the  44  unresponsive species (coefficient < |5|).  Model significance was tested with a likelihood-ratio test and univariate P-values were adjusted for multiple testing using a step-down resampling procedure. Results were considered statistically significant at P<0.05.  3.3. Results 3.3.1. Identification and taxonomic diversity  In this study, 3914 fine-root tips were extracted from 75 soil blocks and sorted into 97 putative EMF morphotypes (on average, 4.0±0.2 morphotypes per soil block), of which 82 EMF morphotypes were successfully sequenced. The sequencing of the 82 morphotypes resulted in 6,322,065 sequences that were clustered into 1,901 OTUs. These OTUs comprised the following guilds: EMF, saprotrophs, root endophytes, moulds or pathogens. Considering all the guilds, the average number of OTUs per morphotype was 69.0 ± 3.6. Out of these 69 fungal OTUs per morphotype, on average, 10 OTUs were EMF. After using the criteria described in section 3.2.3, we assigned a unique EMF OTU to each of the 82 morphotypes. We then obtained 54 unique EMF OTUs because some morphotypes were assigned the same OTU (Appendix B.1). Of these 54 EMF OTU, 46% and 54% were identified to genus and species, respectively. Of the 54 EMF taxa, 91% were Basidiomycota and 9% were Ascomycota. In addition, 33% were resupinate fungi, 13% were hypogeous and 54% were mushroom-forming fungi. Tips from the 15 EMF morphotypes for which no sequences were obtained (most likely representing dead root tips) represented 10% of all the mycorrhizal root tips. Species accumulation curves showed that almost the entirety of species richness was recovered for Kamloops. Alternatively, we recovered c. 80% of the estimated species richness for the remaining regions (Appendix B.2). Richness estimators (Chao1, Jack1, Jack2) were similar to the observed species richness, which confirm that only a small number of species were not accounted for with our sampling scheme (Appendix B.4). We did not detect any differences in α-diversity among the five regions, where species richness averaged four EMF species/soil block for each region. Similarly, species evenness and diversity as estimated with Shannon and Simpson’s diversity indexes were low, averaging 0.8 and 0.5,  45  respectively, and did not vary by region (Appendix B.4). The species richness per region was 20 species for Kamloops, 23 for Nelson, 24 for Salmon Arm and Williams Lake and 25 species for Revelstoke. 3.3.2. Taxonomic composition  Across the environmental gradients, the most abundant OTUs identified at the species level with >2% root tip abundance were Cenococcum geophilum (8.3%), Lactarius rubrilacteus (8%), Russula mordax (4%), Lactarius cf. resimus (2.5%), and Cortinarius cedriolens (2.1%), and the most abundant OTUs identified at the genus-only level were Russula sp. (8.4%), Rhizopogon sp. (5.6%), Piloderma cf. (3.3%), Tomentella sp. (3.3%), Wilcoxina sp. (3.4%) and Suillus sp. (2.5%; Figure.8; Appendix B.1). The five regions had five species/genera in common: C. geophilum, Rhizopogon sp., Wilcoxina sp., Piloderma sp. and Russula sp. The vast majority of fine-root tips colonized by Russula sp. and Lactarius resimus, were found in the wettest region (Revelstoke; Figure. 8), whereas the majority of root tips colonized by Cenococcum geophilum, Tomentella sp., Russula mordax and Cortnarius cedriolens were found in the driest region. Almost half (43%) of the occurrence of Wilcoxina sp. and half of the occurrence of Suillus sp. were in the coldest region (Williams Lake). Alternatively, 24% and 11% of the occurrence of Wilcoxina sp. were in the warmest regions of Kamloops and Nelson respectively, while the other half of Suillus sp. was also in warm regions (Salmon Arm and Kamloops). 75% of the occurrence of Lactarius rubrilactus was in the warmest region (Nelson). Differences in climatic and edaphic conditions among the regions explained 12.2% (adjusted R2 = 0.07) of the variation in the Douglas-fir EMF community composition (Figure. 9a, Table 2a). The first axis of the db-RDA was mostly explained by differences in MAT (score= -0.89) and soil C:N ratio (score=-0.55) and separated the EMF communities of the warmest region (Nelson, MAT = 7.3ºC) from those in colder regions (William’s Lake, MAT = 3.4 ºC; Kamloops, c. 5.6 ºC; Revelstoke, MAT=5.5 ºC). The second axis of the analysis was best explained by the gradients in precipitation (score=0.53) and soil acidity (score = -0.79) and separated fungal communities from drier (Williams lake) and weakly calcareous soils (Salmon Arm) from those in wetter, strongly acidic Brunisolic soils (Revelstoke; Figure.  46  9a). Genera such as Tomentella, Cenococcum and Sebacina, classified as the short-distance exploration type, were commonly associated with low MAT, soils richer in N and mid to low MAP (Figure 9b) and more generally, the short-distance exploration type taxa clustered in colder/drier regions. Fungal species such as Rhizopogon sp. and Suillus sp. with the long-distance type were exclusively found in drier climates while medium and medium-fringe explorers such as Hydnum sp., Cortinarius sp. and Piloderma sp. clustered in wet climates. Contact explorers such as Russula sp. and Lactarius sp. had a broader environmental range compared to the other exploration types but tended to be more abundant in wet climates. Uncolonized root tips occurred exclusively in regions with low MAT, low MAP and rich soils. Sites nested within regions did not have a significant effect on the EMF community composition, as revealed by the PERMANOVA with permutation restricted within sites. However, the nested PERMANOVA confirmed the significant effect of regions (P-value =0.001; Appendix B.5).  Additionally, species-specific responses to the environmental gradients were obtained using multivariate generalized linear models (Figure 10; only the most responsive species, with a coefficient> |5| were included in this model, Cf. Appendix B.6 for results with all the species). In agreement with the PERMANOVA model (except for soil pH), MAT, MAP and soil C:N ratio were all significantly related to shifts in EMF species across regions (Table 2b), yet EMF species were more responsive to MAP and MAT than soil C:N ratio (larger effect size; Figure 10). Generally, responses to climate were phylogenetically conserved in the Russulaceae as most taxa responded in a similar fashion, with species increasing in abundance with MAT and MAP (except for Russula benwooii). Response to climate was not conserved in the Cortinariaceae and Sebacinaceae. For instance, Cortinarius renidens and C. decipiens expressed opposite responses to MAT, positive and negative, respectively, but had matching responses to MAP. Similarly, the genera Sebacina responded moderately but positively to MAP and negatively to MAT which was opposite to the response of the related genus Helvellosebacina.  47  3.3.3. Exploration types  Considering all morphotypes, 36.4% of mycorrhizal root tips were contact-distance type, 25.5% were short-distance type, 20.0 % medium-fringe-distance type, 7.3%, medium-distance type, and 7.3% long-distance type (Appendix B.1). Precipitation and temperature explained 14% (adjusted R2 =0.14; Table 2c; Figure 11a) of the variation in the dominant exploration types across the gradient. Only the first axis of the db-RDA was significant and represented the variation in MAP (score= -0.83; Figure 11a) and to a lesser extent MAT (score=-0.56). This axis separated long- and short-distance exploration types occurring in drier/colder regions from contact and medium exploration types in wetter regions (Figure. 11b). We found no significant effect of site on exploration type abundance but found an effect of region (P-value= 0.02; data not shown), whereas multivariate generalized linear models did not yield significant results. 3.3.4. Fine-root traits and fungi Douglas-fir fine-root morphological and chemical traits explained 5% (adjusted R2 = 0.02; Table 3; Figure 12a) of shifts in EMF species community structure. Only the first axis of the db-RDA was significant and was represented by the variation in first-order root C:N ratio (score = -0.72) and RTD (score= 0.83; Figure 12a). This axis separated the symbionts associated with fine roots of low RTD (in Williams Lake and Nelson) from those associated with fine roots of low C:N ratio (mainly in Kamloops). Fungal taxa such as Wilcoxina, Tomentella and Sebacina that were classified as contact and short exploration types tended to cluster together and were related to fine-roots with low RTD (Figure 12b). Similarly, uncolonized root tips were all associated with fine-roots of low RTD. Alternatively, medium-fringe explorers such as Cortinarius, Piloderma and Amphinema, as well as the short distance explorer, Cenococcum tended to be more abundant on fine roots with high RTD. The multivariate generalized linear model did not yield significant results. 3.4. Discussion The wide gradient in climate and soil fertility across southern British Columbia was ideal for investigating the extent of environmental filtering on EMF community taxonomic and functional structure  48  (exploration type) across populations of Douglas-fir. Our first hypothesis was partly rejected because climate and soil fertility were not related to either EMF species richness or diversity. However, abiotic factors (MAT, MAP and soil C:N) did filter EMF community composition and the abundance of exploration types. As predicted, medium-fringe, and also contact explorers, were more abundant in less fertile environments, however, our second hypothesis was only partly confirmed because these exploration types were also more abundant in warmer and/or wetter environments. We did not find evidence for a functional connection between root diameter and EMF exploration type within Douglas-fir populations, which contradicts our third hypothesis. 3.4.1. Ectomycorrhizal fungal richness and diversity  Across populations of Douglas-fir, we found a total of 54 EMF taxa, consistent with previous studies that used molecular techniques to study belowground diversity on Douglas-fir trees (72 taxa, Twieg et al. 2007; 12 taxa; Bingham and Simard 2012b). Contrary to our first hypothesis, we found no evidence that EMF diversity and richness varied across environmental gradients. However, most of the studies that found temperature and/or soil fertility to have an impact on EMF diversity used experimental treatments or covered continental gradients (Deslippe et al., 2011; Suz et al., 2014; Haas et al., 2018; Köhler et al., 2018; Rosinger et al., 2018). It is then possible that results from our regional-scale biogeographic gradient may not directly compare to these studies with regard to EMF diversity. Nonetheless, EMF richness was not affected by climatic transfer in a genecological study in boreal forests of British Columbia (Kranabetter et al. 2015) or by experimental warming in boreal forests of Minnesota (Fernandez et al. 2017, Mucha et al. 2018).  In our study, EMF communities were dominated by host-generalist taxa such as C. geophilum, L. rubrilactus and Russula, whereas taxa in Rhizopogon and Suillus lakei that are Pinaceae specific and Douglas-fir specific, respectively, represented only 8% of the total colonized root tips. This pattern could explain the lack of changes in richness and diversity because the host-generalist taxa tend to be less sensitive to environmental changes (Bahram et al., 2012; Mucha et al. 2018). Alternatively, if rare or  49  specialist taxa were to dominate EMF communities in our study, we could have observed a change in richness and diversity (Bahram et al., 2012; Mucha et al. 2018). In addition, shifts in Douglas-fir rooting depth across regions may impact EMF diversity estimates (Appendix A.1; Pickles and Pither, 2014). However, our sampling scheme was similar along the gradient which may have hindered the detection of changes in EMF richness and diversity deeper in the soil profile. 3.4.2. Abiotic drivers at the regional scale In our study, temperature, precipitation and soil C:N ratio moderately, but significantly, explained some of the changes in EMF assemblages, despite the relatively small range of temperature and soil fertility encompassed here. Hence, our first hypothesis was partly confirmed. We found that EMF communities varied from communities dominated by Tomentella and Sebacina in the colder, more fertile regions of Douglas-fir’s natural range to communities dominated by Hydnum sp., Cortinarius sp. or Russulaceae members in the warmer, less fertile regions of the range. These results add to Pickles et al. (2015), who reported variation in EMF community primarily between inside and outside the range of Douglas-fir when studying EMF communities on Douglas-fir seedlings. Our finding that temperature, precipitation and soil C:N ratio appear to act as filters that explain part of the regional variation in EMF assemblages was similar to that of Pena et al. (2017). However, large-scale studies in Europe have shown different responses. For example, EMF community composition varied with temperature, pH and soil nutrients but not with precipitation in some European forests (Rosinger et al. 2018; van der Linde et al. 2018), whereas elsewhere precipitation, but not temperature, influenced EMF community structure (Jarvis et al. 2013; Suz et al. 2014). In our study, the effect of temperature and soil fertility on EMF community structure could be related to co-evolutionary history between Douglas-fir populations and fungal symbionts (Gehring et al., 2017; Pither et al., 2018; Strullu-Derrien et al., 2018) because local adaptation of Douglas-fir populations is driven by temperature and soil N availability but can also be mediated by EMF (Rehfeldt et al., 2014a; Kranabetter et al., 2015; Pickles et al., 2015a). Temperature directly influences tree growth potential and  50  may thus impact host C supply to fungal taxa. In turn this could induce a shift in EMF community structure across our study regions as EMF taxa differ in their C cost. Alternatively, temperature may have indirectly affected EMF assemblage through its impact on soil fertility such as availability of NO3- and NH4+ (Kranabetter et al. 2015). In addition to temperature, fitness and growth of Douglas-fir populations have been shown to relate to soil N availability (Kranabetter et al., 2015), and close affiliation of Douglas-fir populations with local EMF symbionts may maximize tree nutritional adaptations (Kranabetter et al., 2015; Leberecht et al., 2015). In turn this may reinforce the filtering effect of soil C:N ratio on EMF assemblage observed in our study. 3.4.3. Taxonomic and morphological responses We hypothesized that medium and/or long-distance explorers would be more abundant in colder climates and/or in soils with a high C:N ratio. Our results partly confirm this hypothesis as in the warmer, less fertile environments of our study area, the medium-fringe explorers Cortinarius sp., Piloderma sp., or Amphinema sp. and taxa in the Russulaceae classified as contact explorers, were more abundant compared to short- and medium-distance types, which were more frequent and abundant in colder and less fertile conditions. In our study system, this pattern of longer distance explorers associated with warmer climates can been linked to higher host photosynthetic capacity that can sustain more C demanding mycorrhizal symbionts (Jarvis et al., 2013; Fernandez et al., 2017; Köhler et al., 2018; Mucha et al., 2018; Rosinger et al., 2018). Furthermore, the positive response to temperature of the genera Cortinarius (except C. decipiens) and Lactarius are potentially related to the increased genetic capacity within these taxa for mobilization of N from organic matter (Bödeker et al. 2014; Kyaschenko et al. 2017). This may also hold true for the genus Russula as Jones et al. (2010) and Kyaschenko et al. (2017) highlighted the positive correlation between Russula taxa and enzymes mobilising N and P from organic matter. Lilleskov et al. (2002, 2018) further classified Cortinarius and Russula as “nitrophobic” taxa. However, Looney et al.  51  (2018) suggested that some members in the Russulaceae have lost the capacity to access C from organic matter.  The supposition that taxa associated with warmer climates tend to have competitive advantages in low N environments is supported by our data. Russulaceae and Cortinariaceae (with the exception of C. decipiens) were positively related to both C:N ratio and temperature. Consequently, in less fertile environments, fungi with proteolytic abilities such as Lactarius or Cortinarius and/or with rhizomorphs such as Piloderma may be more competitive because they preferentially use organic N. The latter fungi are likely less beneficial in richer soils where extensive exploration is not required (Ekblad et al. 2013; Suz et al. 2014; Koide et al. 2014). Similarly, Douglas-fir trees in the colder, more fertile environments of our study area may favor more C efficient symbionts such as EMF with short emanating hyphae or “nitrophilic” EMF such as Tomentella (Nilsson and Wallander 2003; Tedersoo and Smith 2013; Haas et al. 2018).  Ascomycetes such as Wilcoxina sp., Tuber sp. and the drought-tolerant C. geophilum, exclusively occurred in the drier environments of our study area. This is in agreement with studies showing positive shifts in Ascomycetes abundance from mesic to xeric conditions (Allison and Treseder, 2008; Fernandez et al., 2017). It has been hypothesized that Ascomycetes have a lower C cost to their host due to their relatively thin mantles and contact or short-distance exploration types (Fernandez et al. 2017). This may be beneficial in the drier regions of southern British Columbia where water and carbon availability for growth is reduced and where lower basal area increment of Douglas-fir is accompanied by lower fine-root carbohydrate reserve concentration (Wiley et al. 2018). The long-distance explorer Rhizopogon also exclusively occurred in the drier climates of our study area. This likely represents host preference for drought-tolerant EMF as this taxon can transport water over long distances (e.g., Parke et al., 1983). As drier soils limit the diffusion rate of resources, this pattern of spatial niche separation (i.e., short-distance hyphae close to the roots and long-distance hyphae further away) could be an adaptation to stressful conditions (Pickles et al., 2015b). In addition, regions with drier soils were also phosphorus-limited, yet  52  C. geophilum and Rhizopogon, both have a competitive advantage for plant nutrition in these conditions because the former possess acid phosphatase for P hydrolysis and mobilization, while the latter can forage for P more efficiently (Kyaschenko et al. 2017; Khöler et al. 2018). 3.4.4. Association between fine-root and mycorrhizal traits We expected fine-root diameter to be correlated with abundance of exploration types along the biogeographic gradient, yet we found RTD and fine-root C:N, but not diameter, to be significantly related to EMF community structure and patterns of exploration type frequency. Fine roots with lower tissue density occurred predominantly in colder regions (Cf. Chapter 2.) and were more frequently uncolonized or colonized by EMF with many short emanating hyphae. As we do not provide evidence for a functional connection between root diameter and mycorrhizal exploration types, EMF traits might not compensate for changes in fine-root structure. Fungi with short hyphae in colder conditions may instead serve a function to protect roots from environmental stresses (e.g., frost, pathogens). This would increase root persistence without investing as much in short hyphae construction and maintenance as in hyphae for long-distance exploration (Zadworny et al., 2016). In our study area, colder environments (excluding Revelstoke) were also poorer in available P, therefore, resistance to root pathogens, potentially conferred by short-distance type EMF, could be at the expense of efficient P-acquisition, for which long-distance explorers are thought to be better adapted (Köhler et al., 2018). Alternatively, Zadworny et al. (2017) argue that lower RTD in absorptive roots could be due to increased percentage of mycorrhizal mantle area in the root, which would then relate to enhanced capacity for resource uptake. In addition, the cost of producing new root tips with low RTD is lower than producing roots with high RTD, this potentially leads to increased efficiency in nutrient acquisition and thus to a more precise foraging strategy.  In any case, selection for complementarity in foraging strategy was not a major mechanism within tree species in a study by Chen et al. (2018a). Chen and colleagues proposed bet hedging as a potential explanation because EMF traits selected for root pathogen protection may be at odds with those selected for resource foraging. Finally, the absence of a relationship between root diameter and  53  exploration type abundance could be associated with the design of our study compared to that of Chen et al. (2018a). We used a regional scale biogeographic gradient and selected a tree host that further expressed moderate intraspecific variation in root diameter whereas Chen et al. (2018b) surveyed several tree species with large differences in mean root diameter and investigated links between roots and mycorrhizal traits at the level of the nutrient patch. 3.5. Conclusion Here, we combined fine-root and EMF trait measurements with next-generation sequencing across a biogeographic gradient. Douglas-fir EMF communities were dominated by host-generalist taxa which potentially explains the low variation in EMF richness and diversity across environments. We did find, however, that temperature, precipitation and soil C:N ratio affected EMF community similarities and exploration type abundance. Fungi with rhizomorphs (e.g., Piloderma sp.) and/or proteolytic abilities (e.g., Cortinarius sp.) dominated EMF communities in warmer and less fertile environments, whereas Ascomycetes (e.g., C. geophilum) and/or shorter distance explorers, that are potentially more C-efficient, were favored in colder/drier climates and richer soils. This pattern might be associated with co-evolutionary history between Douglas-fir populations and fungal symbionts, suggesting that the success of Douglas-fir as climate changes and stress increases may be dependent on maintaining strong associations with local communities of mycorrhizal fungi. At the regional scale, we did not find evidence for a functional connection between root diameter and EMF exploration types within Douglas-fir populations. Whether this implies no complementarity in resource foraging between fine roots and EMF is difficult to say, but this suggests that simply incorporating mycorrhizal symbioses, or at least EMF symbionts, into broader root trait frameworks may not be a suitable option if we are to represent the diversity of below-ground resource strategies. We thus encourage future research to simultaneously examine both root and fungal functional traits as separate entities and to consider other fungal traits such as phylogenetic traits     54  Table 2 Effect of climatic and edaphic conditions on interior Douglas-fir ectomycorrhizal fungal species community composition across five regions assessed by (a) PERMANOVA and (b) assessed by multivariate generalized linear model. Effect of climatic and edaphic conditions on (c) exploration types assessed by PERMANOVA. Significant P- values (<0.05) are shown in bold. A negative binomial distribution was assumed for the multivariate model and explained deviance was tested after 999 permutations. (a) Ectomycorrhizal fungal community composition, PERMANOVA model Abiotic factors Df Pseudo F Pseudo R2 P-value MAPa 1 2.38 0.03 <0.01 MATb 1 2.94 0.04 <0.01 soil CNc 1 2.48 0.03 <0.01 pH 1 1.79 0.02 0.02 Residuals 69   0.88   Total 73   1.00    (b) Ectomycorrhizal fungal community composition, multivariate generalized model Abiotic factors Res.Df Df.diff Deviance P-value     (Intercept) 61           MAP 59 1 121.37 0.01     MAT 60 1 61.90 0.01     Soil CN 58 1 98.22 0.04     pH 57 1 102.44 0.05      (c) Ectomycorrhizal fungal exploration type, PERMANOVA model Abiotic factors Df Pseudo F Pseudo R2 P-value MAP 1 5.4 0.07 <0.01 MAT 1 4.5 0.05 <0.01 soil CN 1 3.94 0.05 0.01 Residuals 69   0.83   Total 72   1.00   aMAP, mean annual precipitation bMAT, mean annual temperature csoil CN, soil carbon-to-nitrogen ratio   55  Table 3 Effect of fine-root (first-order) morphological and chemical traits on interior Douglas-fir ectomycorrhizal fungal species community composition across five regions assessed by PERMANOVA. Significant P- value (<0.05) effects are shown in bold. Ectomycorrhizal fungal community composition, PERMANOVA model Fine-root traits Df Pseudo F Pseudo R2 P-value   RTDa 1 1.88 0.03 0.01   Fine-root CNb 1 1.93 0.03 0.01   Residuals 69   0.95     Total 71   1.00      aRTD, root tissue density  bFine-root CN, fine-root carbon-to-nitrogen ratio    56    Figure 8 Relative abundance (%) of ectomycorrhizal taxa on interior Douglas-fir among five regions in western Canada. Only the species/genera representing >2% of root tip abundance were included.  For a given ectomycorrhizal species, numbers represent the percentage of root tips colonized by this species in each region.    57  (a)       (b)  Figure 9 Distance-based redundancy analysis (db-RDA) sample (a) and species (b) ordinations based on ectomycorrhizal fungal abundance on interior Douglas-fir roots across five regions in western Canada. Only the variation explained by environmental variables is visualized. The ectomycorrhizal species are color-coded by fungal exploration type and are sized according to their relative abundance. The species epithet (when known) was removed to improve readability. MAP, mean annual precipitation (mm); MAT, mean annual temperature (ºC); CNs, soil carbon-to-nitrogen ratio.   58   Figure 10 Ectomycorrhizal fungal species-specific response to the environment based on multivariate generalized linear models. Only species that were responsive were added to the model (coefficient > |5|).  Circles (●) represent species coefficients and lines, 95% confidence intervals. Species were grouped by exploration type indicated at the right-hand side of the plot, MAP, mean annual precipitation (mm); MAT, mean annual temperature (ºC); CNs, soil carbon-to-nitrogen ratio.  59   (a)       (b)  Figure 11 Distance-based redundancy analysis (db-RDA) sample (a) and exploration type (b) ordinations based ectomycorrhizal fungal exploration type on interior Douglas-fir across five regions in western Canada. The ectomycorrhizal fungal exploration types are sized according to their relative abundance. MAP, mean annual precipitation (mm); CNs, soil carbon-to-nitrogen ratio.        60  (a)       (b)  Figure 12 Distance-based redundancy analysis (db-RDA) sample (a) and species (b) ordinations based on ectomycorrhizal fungal abundance on interior Douglas-fir across five regions. Only variation explained by Douglas-fir fine-root traits is visualized. The ectomycorrhizal species are color coded by fungal exploration type and are sized according to their relative abundance. The species epithet (when known) was removed to improve readability. CN, fine-root carbon-to-nitrogen ratio; RTD, fine-root tissue density (mg cm-3).     61  Chapter 4: Conclusions and Outlook  My thesis was motivated by the need to improve our functional understanding of fine roots and their associated mycorrhizal symbionts. I used a biogeographic gradient in western Canada and combined morphological with molecular analyses of Douglas-fir fine roots and ectomycorrhizal fungi to address two research gaps: (i) determining within-species fine-root and mycorrhizal fungal trait-environment linkages and (ii) linking fine-root and mycorrhizal fungal functional traits. My thesis advances our knowledge of plant-mediated below-ground processes and should foster incorporation of below-ground ecology into terrestrial biosphere models which is key to predict plant persistence and resilience in the face of environmental change. In the next sections, I summarize the main findings of the two research chapters, then I discuss limitations and future directions. 4.1. Intraspecific fine-root trait variation Chapter 2 focuses on fine-root trait variation across environments and was initiated further to a call for below-ground trait focused studies. This call stemmed from the fact that much of the effort linking plant traits to plant functions and ecosystem processes has focused above ground. Therefore, several studies have drawn attention towards fine-root and mycorrhizal traits. Building on these researches, the main objective of this Chapter was to investigate intraspecific fine-root trait variation and trait-environment linkages. Using gradients in temperature, precipitation and soil fertility within a constrained geographic range, we tested that temperature would be an important driver of root morphological, chemical and architectural trait variation such that trade-offs among traits favor greater root acquisitive capacity in colder climates.  Our main hypothesis was partly confirmed as we showed that fine-root adjustments at the regional scale are multidimensional and concordant with a potential increase in root acquisitive ability in harsher environments (colder/drier), despite high levels of within site variation. This chapter makes an important contribution to unravel intraspecific fine-root adjustments to climatic and edaphic conditions.  62  Yet, this work would have been improved if the effect of belowground interactions (e.g., fine-root competition) on fine-root traits was assessed (Xiang et al., 2015). Combining results from our observational gradient with those from experiments simulating climate change (e.g., warming above- and below-ground and raising atmospheric CO2) is also required to clarify the role of fine-root traits in mediation of plant responses to climate changes. We measured key traits that were selected due to their expected connections to plant resource investment into root construction and functions, and their benefits for soil resource foraging and acquisition. However, to better assess plant resource strategies below-ground, it is necessary to combine fine-root structural traits with fine-root dynamic (e.g., decomposition rate) and physiology (e.g., respiration rate), and thus to link root form and function across broad spatial scales. In an effort to integrate our results in a global context, we have initiated assessment of the relative importance of intra- vs. interspecific fine-root trait variation at the family level. Further work connecting root traits and environmental variation is needed, certainly within the Pinaceae, but also among a wider range of plant families.  4.2. Taxonomical and functional structure of ectomycorrhizal fungal communities Chapter 3 stems from the necessity to better integrate mycorrhizal symbiosis into trait-based plant ecology. The long-term goal of this body of research is to identify mycorrhizal fungal traits that reflect root-fungal interactions and capture the role of fungi in nutrient acquisition across and within species. Therefore, we aimed to assess the extent of abiotic environmental filtering on Douglas-fir ectomycorrhizal community taxonomic and functional structure and to examine relationships between fine-root traits and fungal exploration types.  We combined trait measurements with next-generation sequencing which allowed us to describe most of the mycorrhizal community on Douglas-fir fine roots. We first hypothesized that temperature and soil fertility would be the main drivers of EMF diversity and community composition. This hypothesis was partly confirmed as environment filtered mycorrhizal community dissimilarities and exploration type without alteration in α-diversity. Furthermore, fungi with rhizomorphs (e.g., Piloderma sp.) and/or  63  proteolytic abilities (e.g., Cortinarius sp.) dominated EMF communities in warmer and less fertile environments which was partly in accordance with our second hypothesis. This pattern might be associated with co-evolutionary history between Douglas-fir populations and fungal symbionts. Using the root data collected for Chapter 2, we rejected our third hypothesis that EMF traits compensate for changes in fine-root structure, and especially root diameter.  Moving forward, it will be important for studies across environmental gradients to account for spatial and temporal variation in below-ground traits. Host fine-root and mycorrhizal turnover rates can vary substantially between host and fungal species. In turn, this can modify conclusions drawn from a snapshot in time (Pickles et al., 2010; Pickles and Anderson, 2016; McCormack et al., 2017). Our soil samples comprised mycorrhizal communities from organic layers and mineral horizons (20 cm deep), however, it would have been interesting to adjust the sampling depth to the rooting depth of Douglas-fir at each site because the distribution of ectomycorrhizal species tends to vary throughout the soil profile (Pickles and Anderson, 2016). Finally, we have selected five regions to obtain a biogeographic gradient with substantial precipitation and temperature ranges, yet we have not covered the range boundaries of the host species and, to date, Douglas-fir mycorrhizal fungal distribution boundaries remain unexplored (Pickles and Anderson, 2016). 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Properties of the 15 sites selected across five regions in western Canada  L, loam; SiC, silty clay; SiCL, silty clay loam; SiL, silt loam; SL, sandy loam   Study region Stand within region Dominant tree speciesBasal area m2ha-1(% Douglas-fir )Stand Age (years)Soil type (texture) Humus type rooting depth (m)Williams Lake WL1 22.1 (98%) 125 luvisol (C) mor 58WL2 15.3 (96%) 141 luvisol (SiCL) mull 34WL3 18.5 (97%) 106 luvisol (SiCL) mull 37Revelstoke R1 60.6 (66%) 106 brunisol (SiCL) mor 45R2 38.4 (66%) 83 brunisol (L) moder 50R3 47.9 (72%) 104 brunisol (L) moder 50Kamloops K1 56.4 (100%) 126 luvisol (SiC) moder 40K2 37.6 (100%) 94 luvisol (L) moder 40K3 48.3 (100%) 104 luvisol (L) moder 30Salmon Arm SA1 98.5 (50%) 142.5 brunisol (SiL) mull 80SA2 81.8 (58%) 140 brunisol  (L) mor 76SA3 80.6 (39%) 147 brunisol  (L) mull 40Nelson N1 49.8 (65%) 115 brunisol  (SL) mor 60N2 65.4 (29%) 100 brunisol  (SL) moder 37N3 56.0 (55%) 104 brunisol  (SL) moder 49Pseudotsuga menziesiiPinus contortaStand propertiesPseudotsuga menziesiiTsuga heterophyllaThuja plicataPseudotsuga menziesiiLarix occidentalisTsuga heterophyllaThuja plicataPseudotsuga menziesiiLarix occidentalisTsuga heterophyllaThuja plicataPseudotsuga menziesii 88   Appendix A.2. Description of morphological attributes of fine roots for three coniferous tree species encountered in this study Description of root periderm texture and colour, root branching pattern and root tips habit are accompanied by exemplary pictures of root periderm and fine-root branching pattern. We validated the key with molecular genetic analysis of interior Douglas-fir (Pseudotsuga menziesii var. glauca) and western hemlock (Tsuga heterophylla (Raf.) Sarg.; most similar morphologically). Samples were sent to the Appalachian laboratory at the Centre for Environmental Science (University of Maryland) and the findings of BLASTing Chloroplast DNA sequences from the rpl7 locus confirmed our expectations (accession numbers GQ999630.1, Douglas-fir and HQ846196.1, hemlock; Gugger et al.,2010).    89  Appendix A.3. Means and standard error (SE) of fine-root morphological, chemical and architectural traits of interior Douglas-fir     Study location Root order N NMean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SEWilliams Lake 1 75 16.97 0.43 213.60 4.21 470.50 10.10 0.41 0.01 40.35 0.25 1.02 0.02 40.43 0.692 75 10.51 0.37 172.20 4.05 449.00 11.33 0.54 0.01 44.55 0.29 1.10 0.02 41.15 0.493 75 3.75 0.18 105.10 3.25 426.60 9.41 0.93 0.02 49.02 0.16 1.03 0.02 48.28 0.73Revelstoke 1 75 16.12 0.46 200.70 3.63 505.80 9.18 0.40 0.01 44.24 0.08 1.02 0.01 43.62 0.252 75 10.90 0.41 164.30 3.32 505.40 9.39 0.50 0.01 46.14 0.11 1.04 0.01 44.60 0.413 75 4.00 0.20 99.00 2.68 503.70 8.27 0.84 0.02 48.34 0.12 0.81 0.01 60.08 0.53Kamloops 1 75 15.51 0.39 185.30 4.04 559.60 12.95 0.39 0.00 42.54 0.23 1.25 0.01 34.23 0.272 75 10.53 0.37 157.20 3.33 523.90 10.70 0.49 0.01 45.91 0.17 1.22 0.01 37.86 0.483 75 4.07 0.22 99.71 3.60 494.50 13.44 0.86 0.02 49.82 0.10 0.94 0.01 54.16 0.91Salmon Arm 1 75 15.97 0.38 197.30 3.68 529.30 10.83 0.40 0.01 42.17 0.08 0.87 0.03 48.35 0.162 75 10.34 0.30 161.40 3.29 513.30 11.20 0.50 0.01 44.69 0.07 0.84 0.00 53.36 0.293 75 4.06 0.19 101.30 2.72 493.00 9.58 0.84 0.02 48.00 0.10 0.80 0.01 60.28 0.52Nelson 1 65 16.88 0.57 203.00 4.51 513.30 11.33 0.38 0.01 39.51 0.53 0.84 0.03 48.89 0.942 65 10.91 0.43 165.50 3.74 495.50 9.08 0.51 0.01 43.93 0.16 0.81 0.01 54.14 0.383 65 4.37 0.21 113.10 3.82 445.00 10.58 0.85 0.02 47.49 0.20 0.86 0.01 55.53 0.5775 3.32 0.24 0.28 0.0265 3.39 0.33 0.32 0.0275 2.99 0.19 0.30 0.0275 2.91 0.17 0.30 0.01BrIntensity (cm-1) DBI75 3.15 0.24 0.27 0.01Morphological root traits Chemical root traits Architectural root traitsSRL (mg-1) SRA (cm2g-1) RTD  (mgcm-3) Root diameter (mm) Root C % Root N % Root C:N 90  Appendix A.4. Distribution of branching intensity and dichotomous branching index values  (a) across an environmental gradient and variance partitioning (b) of architectural traits at different hierarchically structured ecological scales (region, site, tree and branch). WL, Williams Lake; R, Revelstoke; K, Kamloops; SA, Salmon Arm; N, Nelson.  For (a), each data point represents one measurement for one root branch, n = 25 except for N2 where n = 15. The sign ‘+’ within the boxes represents the mean value for comparison with the median value (center line).  (a)     91  Appendix A.4. (continued) (b)        92  Appendix A.5. Effect of climate (1980 – 2010) and soil variables on fine-root morphological, chemical and architectural traits of interior Douglas-fir a. Models  NA, non-applicable; linear mixed-effects models were fitted with maximum likelihood. LR, log likelihood ratio; values in bold indicate statistically significant results at P<0.05   Data transformation d.f. LR χ2 P -value Marginal R2Conditional R2Root diameter1st NA2nd Log10 1 5.43 0.02 0.03 0.193rd Log10 2 7.5 0.02 0.03 0.18Specific root length NASpecific root area1st Log10 2 9.03 0.01 0.05 0.192nd Log10 1 3.83 0.05 0.02 0.183rd Log10 1 4.81 0.03 0.02 0.22Root tissue density1st Log10 2 8.34 0.01 0.05 0.342nd Log10 1 5.61 0.02 0.04 0.23rd NARoot C:N ratio1st Log10 1 9.19 <0.01 0.28 0.62nd NA3rd Log10 4 F-value=7.17 <0.01 Multiple R2= 0.35 Ajusted R2=0.30 Root branching intensity Log10 1 3.97 0.05 0.02 0.18Dichotomous branching index NA 93  Appendix A.5. (continued) b. Fixed effects  MAT, mean annual temperature; MAP, mean annual precipitation; CEC, cation exchange capacity; Soil C:N, soil carbon-to-nitrogen ratio; Soil avail. P, soil available phosphorus. Bold values indicate statistically significant results at P<0.05   Fine-root categoryAbiotic variables Wald χ2 P -value Sign Wald χ2 P -value Sign Wald χ2 P -value Sign Wald χ2 P -value Sign Wald χ2 P -value Sign 1stMAT 5.56 0.02 - 8.44 <0.01 +MAPCEC 7.33 <0.01 -Soil C:N 13.12 <0.01 +Soil avail. P 3.98 0.04 -2nd MAT 7.16 0.01 + 6.82 <0.01 +MAPCEC 4.1 0.04 -Soil C:N Soil avail. P3rdMAT 4.72 0.03 +MAP 6.82 0.01 -CEC 5.19 0.02 - 6.3 0.02 _Soil C:N 9.66 <0.01 +Soil avail. P 5.03 0.02 + 13.2 <0.01 -1st  - 2nd MATMAPCECSoil C:N Soil avail. P 4.07 0.04 +Root diameter Specific root area Root tissue density Root C:N ratio Root branching intensity 94  Appendix A.6. Ordination plot and associated scores of samples across an environmental gradient based on principal component analysis of fine-root traits  Second-order (a) and third-order roots (b) of Douglas-fir. C, root carbon concentration; N, root nitrogen concentration; SRA, specific root area; SRL, specific root length; RTD, root tissue density; BrIntensity, branching intensity; DBI, dichotomus branching index. Branching intensity was not included on the ordination plot of third-order roots as it is calculated as the number of first- order root/ length of second- order root.     95   Appendix A.6. (continued)     1st order PC1 (30.33%) PC2 (21.93%) PC3 (15.10%)SRL 0.94 0.22 -0.18RTD -0.60 0.32 -0.66Root diameter -0.44 -0.57 0.64SRA 0.95 -0.01 0.18DBI 0.15 -0.17 -0.11Branching intensity -0.06 -0.27 -0.21Root N -0.15 0.76 0.33Root C -0.17 0.78 0.372nd order 29.10% 20.58% 17.09%SRL 0.97 0.07 -0.10RTD -0.26 0.67 -0.64Root diameter -0.69 -0.50 0.46SRA 0.89 -0.30 0.24DBI 0.12 -0.10 -0.22Branching intensity -0.14 -0.08 -0.15Root N 0.06 0.62 0.58Root C -0.01 0.67 0.523rd order 35.95% 22.03% 17.77%SRL 0.97 -0.12 0.07RTD -0.27 -0.47 0.80Root diameter -0.78 0.37 -0.43SRA 0.93 0.12 -0.26DBI 0.00 -0.10 -0.10Branching intensity NA NA NARoot N 0.16 0.81 0.26Root C 0.04 0.71 0.51 96  Appendix A.7. Spearman’s correlation coefficient for pairwise root order relationships   SRL, Specific root length; RTD, Root tissues density; SRA, specific root area; DBI, Dichotomous branching index, values closer to 0 indicate a dichotomous branching pattern and values closer to 1, a herringbone branching pattern, see Beidler et al. (2015); Branching intensity was calculated as the number of first- order root/ length of second- order root, this was not assessed for third-order roots; Root C, Root carbon concentration (%);  Root N, Root nitrogen concentration (%). Bold values indicate statistically significant correlation at P<0.05.   1st order SRL RTD Root diameter SRA DBI Branching intensity Root NRTD -0.26Root diameter -0.67 -0.46SRA 0.83 -0.73 -0.20DBI 0.05 -0.08 0.00 0.09Branching intensity 0.09 0.04 -0.11 0.04 -0.03Root N -0.01 0.08 -0.06 -0.04 -0.04 0.01Root C -0.02 0.07 -0.05 -0.03 -0.09 0.01 0.402nd orderRTD -0.07Root diameter -0.81 -0.46SRA 0.86 -0.51 -0.44DBI 0.07 -0.01 -0.06 0.07Branching intensity 0.10 -0.04 -0.10 0.10 -0.03Root N 0.02 0.00 -0.01 0.02 -0.02 0.00Root C 0.02 0.06 -0.09 -0.02 -0.06 0.03 0.443rd orderRTD -0.17Root diameter -0.88 -0.23SRA 0.92 -0.48 -0.67DBI -0.03 0.02 0.02 -0.03Branching intensity NA NA NA NA NA NARoot N 0.10 -0.27 0 0.17 0.00 NARoot C 0.01 -0.01 0 0.02 -0.02 NA 0.46 97  Appendix A.8. Comparison of fine-root trait variation in our study (Pseudotsuga menziesii) and in other tree species from the Pinaceae family for second and third-order roots.  For the genera Pinus and Larix, data were extracted from the FRED 2.0 database. Different letters denote significant differences among tree species (Tukey HSD test, P < 0.05).   98  Appendices B. Supporting information for Chapter 3 Appendix B.1. Identity, relative frequency and exploration type of ectomycorrhizal fungi on interior Douglas-fir roots Ectomycorrhizal root tips were collected across an environmental gradient in western Canada. The relative frequency corresponds to the percentage of 3914 ectomycorrhizal root tips from 75 Douglas-fir trees in five regions that were colonised by the indicated species. Pairwise identity corresponds to the amount of nucleotide which matches exactly between two different sequences. For each species or genus, exploration types were assigned after Agerer (2001).   Morphotype ID Accession number (GenBank) Best BLAST sequence matchTotal relative frequency (%)Pairwise identity (%)Fungal exploration type33; 50; 72c KP814275 Amphinema byssoides 1.4 99.7 medium-fringe 4 LC095167 Cenococcum geophilum 8.3 99 short 3 HQ650744 Cortinarius casimiri 0.5 100 medium-fringe 1b KC842393 Cortinarius cedriolens 2.1 100 medium-fringe 65 GQ159776 Cortinarius decipiens 0.2 100 medium-fringe 22 - Cortinarius cf. rubelus 0.4 - medium-fringe 74FJ717589UDB002178 (UNITE)Cortinarius  sp.Cortinarius cf. renidens (UNITE)0.199.5medium-fringe 28 AF377097 Gautieria  sp. 0.03 100 medium-fringe70 KC581306 Gomphidius oregonensis 1.5 100 medium53 HQ445589 Hebeloma  sp. 0.3 99.8 short85 MF352772 Helvellosebacina  sp. 1.1 98.9 short15.1; 45.2 DQ367902 Hydnum repandum 0.5 100 medium39KX388687 UDB024843 (UNITE) Hydnum  sp.Hydnum cf. umbilicatum (UNITE)1.2 100 medium10a EU563921 Hysterangium separabile 0.2 100 medium-fringe55 KY990545 Inocybe  aff. 0.8 99.5 contact23 HQ604639 Inocybe fuscodisca 0.4 99.5 contact83 EU525981 Inocybe geophylla 0.3 99.5 contact29, 38 HQ604168 Inocybe leiocephala 1.5 100 contact44 GU234156 Inocybe  sp. 1 0.4 98.1 contact64, 66 JQ711986 Inocybe  sp. 2 0.7 100 short48, 49, 48 MF804315 Inocybe urceolicystis 1.9 99.5 contact52 JF908320 Lactarius aquizonatus 0.4 100 contact13 EU486449 Lactarius pseudomucidus 1.3 100 contact11 HQ650764 Lactarius rubrilacteus 8.0 100 contact20, 60, 80EU222964UDB000838 (UNITE)Fungal sp.Lactarius cf. resimus (UNITE)2.5100contact54 HQ232481 Mallocybe leucoblema 0.6 99.8 contact27 KP814486 Piloderma  cf. 3.3 100 medium-fringe 24 JQ711984 Piloderma  sp.1 0.8 98.4 medium-fringe51KX168654UDB001740 (UNITE)Piloderma  sp. 2Piloderma cf. bicolor  (UNITE)1.0 100 medium-fringe2 MH277968 Rhizopogon sp. 5.6 100 long  73a; 76; 82 HQ385848 Rhizopogon vinicolor 1.0 100 long   99    Appendix B.1. (continued)    Morphotype ID Accession number (GenBank) Best BLAST sequence matchTotal relative frequency (%)Pairwise identity (%)Fungal exploration type67 KX813332 Russula benwooii 1.7 99.8 contact21; 46 HM240541 Russula cascadensis 1.8 100 contact 9 KX813095 Russula mordax 4.0 100 contact63; 68; 81 JQ711997 Russula odorata 0.8 100 contact31; 32; 34b; 35 KX813441 Russula  sp. 8.4 100 contact41; 18b DQ974767 Sebacina  sp. 1 0.7 99.5 short72a KM576586 Sebacina  sp. 2 0.6 99.5 short84 HQ215803 Sebacina  sp. 3 0.8 99.5 short15.2 KP814533 Sistotrema  aff. 1.6 99.5 medium25 KU721549 Suillus lakei 1.0 99.8 long45.3; 78; 79 KY462279 Suillus  sp. 2.5 99.1 long61 FN669269 Thelephoraceae  sp. 0.3 100 medium71 KP783477 Tomentella  cf. 1.3 98.7 short69 GQ900538 Tomentella  sp. 1 0.8 97.7 short72b KT800337 Tomentella  sp. 2 0.2 98.6 short88 HQ215824 Tomentella  sp. 3 0.1 99.1 short 43 KP814535 Tomentella  sp. 4 0.8 100 short30 - Tomentella  sp. 3.3 - short8; 87 KP814555 Tomentella sublilacina 0.2 100 short7 KT968570 Truncocolumella citrina  Zeller 0.3 100 long 14; 62 KU186938 Tuber anniae 2.0 98.5 contact34a; 36 KT275646 Tuber  sp. 1.9 100 contact19; 77; 10b; 56; 57 EF458013 Wilcoxina  sp. 3.4 100 contact37; 45.1; 73b; 75; 86; 5; 6; 12; 16; 17; 40; 42; 47; 59, 26- Unidentified 10.6 - - 100  Appendix B.2. Species accumulation curve using the Coleman method (a) and rarefaction curve (b) of ectomycorrhizal species richness Ectomycorrhizal species on Douglas-fir root tips were collected from 75 soil blocks across a biogeographic gradient. For reach region (Kamloops, Revelstoke, Salmon Arm, Williams Lake and Nelson), 15 soil blocks and 750 fine-root tips were extracted. The dotted lines correspond to the 95% confidence interval. (a)  (b)   101   Appendix B.3. Multivariate homogeneity of regions dispersion The boxplot (a) shows the distance of values of β diversity of each region in relation to their centroids, calculated with the function ‘betadisper’ in the R package vegan. Non-euclidean distances between objects and group centroids are handled by reducing the original distances to principal coordinates (b). R, Revelstoke; N, Nelson; SA, Salmon Arm; K, Kamloops; WL, Williams Lake.   (a)  (b)     102  Appendix B.4. Richness estimators and diversity indices Estimators and indices (a) were calculated for interior Douglas-fir ectomycorrhizal fungal species from five regions across a biogeographic gradient. The effect of region (b) on mycorrhizal richness, evenness and diversity was assessed by nested ANOVA with region as a fixed effect and site nested within region as a random effect. (a)  (b)    Revelstoke Nelson Salmon Arm Kamloops Williams Lakeα diversity metrics (mean ± SE) Observed mean species richness 3.6 ± 0.7 3.8 ± 0.5 4.1 ± 0.4 3.2 ± 0.3 3.7 ± 0.4Mean Chao 1 richness estimator 3.6 ± 0.7 3.9 ± 0.5 4.3 ± 0.5 3.2 ± 0.3 3.8 ± 0.5Mean Jack 1 richness estimator 3.9 ± 0.8 4.2 ± 0.6 4.4 ± 0.5 3.3 ± 0.4 3.9 ± 0.5Mean Jack 2 richness estimator 4.0 ± 0.8 3.9 ± 0.7 4.2 ± 0.6 3.3 ± 0.3 3.7 ± 0.5Mean Shannon-Wiener (H’) index 0.8 ± 0.2 0.9 ± 0.1 1.1 ± 0.1 0.9 ± 0.1 0.9 ± 0.1Mean Shannon-Wiener evenness index (E= H'/Hmax) 0.7 ± 0.1 0.7 ± 0.1 0.8 ± 0.1 0.8 ± 0.0 0.7 ± 0.1Mean Simpson's Index of Diversity (1 – D) 0.4 ± 0.1 0.5 ± 0.1 0.6 ± 0.1 0.5 ± 0.1 0.5 ± 0.1α-diversity metrics d.f. Log-likelihood ratio χ2 P -valueObserved species richness 4 2.12 0.71Chao1 richness estimator 4 2.32 0.68Jack 1 richness estimator 4 2.04 0.73Jack 2 richness estimator 4 1.29 0.86Shannon-Wiener (H') index 4 2.73 0.6Shannon-Wiener eveness index (E=H'/Hmax) 4 4.36 0.36Simpson's Index of Diversity (1-D) 4 3.95 0.41 103  Appendix B.5. Effect of sites nested within regions (a) and regions (b) on interior Douglas-fir ectomycorrhizas Hellinger transformation was applied to the species data matrix. The effect of sites within regions was assessed by PERMANOVA with permutations constrained within sites and the effect of region was evaluated with a nested analysis of variance. Significant P- values (<0.05) are shown in bold. This two-way approach was used to complement the db-RDA/PERMANOVA approach that was not developed to account for nested data (here, sites are nested within regions).   (a) Effect of sites within region Model: ectomycorrhizal fungal species ~ Region/Site  Strata = Site    The residual error term give the correct test for the Region:Site interaction, but the test for the Region main effect is wrong because Site is the correct error term for testing it. (b) Effect of regions  In this case, region is tested with site; site is tested with the residual error term but that latter test is not correct in this instance.   Df F.Model R2P-valueRegion 4 3.37 0.16 1Region:Site 10 1.38 0.16 1Residuals 59Total 73Df F.Model P-valueRegion 4 2.44 0.001Region:Site 10 1.38 0.022Residuals 59 0.34 104  Appendix B.6. Effect of environmental variables on ectomycorrhizal fungal species community composition assessed by multivariate generalized linear models.  On the figure, circles (●) represent species coefficients and lines, 95% confidence intervals. This approach was used to account for the presence of mean-variance relationships in multivariate community analyses. Model significance was tested with likelihood-ratio test and univariate P-values were adjusted for multiple testing using a step-down resampling procedure. MAT, mean annual temperature; MAP, mean annual precipitation; CNs, soil C:N ratio.   Res.Df Df.diff Dev P-value (Intercept) 73MAT 72 1 97.67 0.004MAP 71 1 151.88 0.004CNs 70 1 124.22 0.033pH 69 1 99.31 0.31

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