UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Environment is more important than ectomycorrhizal fungal identity in determining mycorrhizosphere enzyme… Nicholson, Bailey Anne 2014

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2014_november_Nicholson_Bailey.pdf [ 2.87MB ]
Metadata
JSON: 24-1.0074376.json
JSON-LD: 24-1.0074376-ld.json
RDF/XML (Pretty): 24-1.0074376-rdf.xml
RDF/JSON: 24-1.0074376-rdf.json
Turtle: 24-1.0074376-turtle.txt
N-Triples: 24-1.0074376-rdf-ntriples.txt
Original Record: 24-1.0074376-source.json
Full Text
24-1.0074376-fulltext.txt
Citation
24-1.0074376.ris

Full Text

ENVIRONMENT IS MORE IMPORTANT THAN ECTOMYCORRHIZAL FUNGAL IDENTITY IN DETERMINING MYCORRHIZOSPHERE ENZYME ACTIVITIES  by  Bailey Anne Nicholson  B.Sc., University of British Columbia Okanagan, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  THE COLLEGE OF GRADUATE STUDIES  (Biology)  THE UNIVERSITY OF BRITISH COLUMBIA  (Okanagan)  August 2014   © Bailey Anne Nicholson, 2014  ii Abstract  After clearcutting, there is typically a shift in ectomycorrhizal (ECM) fungal community structure and an increase in soil N availability.  Many studies have morphologically and molecularly documented this shift in ECM fungal community structure, but none have investigated if a shift in ECM fungal community structure equates to a shift in community function.  Extracellular enzyme activities (EAAs) are commonly investigated as a functional trait of interest as they comprise a major nutrient acquisition strategy of ECM fungi.  Studies investigating EAAs of ECM root tips have indicated that ECM fungal identity can be more important that soil nutrient status in determining EEAs.  However, in these studies, species differences were confounded with differences in environmental variables.   In our experiment, a reciprocal transplant of one-year-old, naturally-regenerated subalpine fir (Abies lasiocarpa) seedlings was performed between three pairs of clearcut and adjacent forest plots.  One growing season after transplantation, ECM fungal communities on seedlings originating from clearcuts and forests remained distinct in each destination environment.  Relative growth and seedling survival rates did not differ between transplant treatments, but seedlings originating from clearcuts generally produced larger apical buds in both destination environments.  The apical buds of these seedlings also had higher N contents than apical buds of seedlings originating from the forest.  Activities of glucuronidase, xylosidase, cellobiohydrolase, β-glucosidase, N-acetylglucosaminidase, phosphomonoesterase, leucine aminopeptidase, and laccase were measured on individual mycorrhizal tips of transplanted seedlings. On non-metric multi-dimensional scaling ordinations, enzyme profiles of mycorrhizas clustered together based on destination environment within each clearcut/forest pair.  No effect of source environment was apparent.  However, a multi-response permutation procedure showed that enzyme activities differed both between destination environments as well as between source environments within destination environments.  This strongly suggests that environment is the dominant determinant of EEAs, while fungal identity has a more minor influence on this functional trait.  Hence, ECM fungal community function has the capacity to be resilient to ecosystem disturbances at high elevation subalpine sites in southern British Columbia.   iii Preface  With the guidance of my supervisor, Dr. Melanie Jones, and supervisory committee Dr. Daniel Durall and Dr. John Klironomos, I was responsible for the design, implementation, sampling, and analysis of the studies described in this thesis.  I was assisted with sample collection in the field, morphotyping of root tips, and implementation of the growth chamber study by a field assistant and volunteers.  Foliage nitrogen content was measured at the Ministry of Forests, Lands, and Natural Resource Operations Research Analytical Laboratory in Victoria, British Columbia.  δ15N foliage contents were measured at the Pacific Centre for Isotopic and Geochemical Research, University of British Columbia, Vancouver, Canada.  The Laboratory for Advanced Genome Analysis (LAGA) at the Vancouver Prostate Centre performed the 454 sequencing of root tip samples.  With guidance from Dr. Melanie Jones, I was also responsible for the writing of this thesis.  Selected material from Chapters 2 and 3 of this thesis has been submitted as a manuscript to the journal New Phytologist.  It was submitted under the title: “Early-successional ectomycorrhizal fungi effectively support extracellular enzyme activities and seedling nitrogen accumulation in mature forests” and the authors were Bailey A. Nicholson and Melanie D. Jones.  I conducted all the testing and wrote the Methods and Results of the manuscript.  M. Jones wrote the Introduction and Discussion, based on material in this thesis, and did substantial editing of the manuscript.       iv Table of Contents  Abstract .................................................................................................................................... ii Preface ..................................................................................................................................... iii Table of Contents ................................................................................................................... iv List of Tables ......................................................................................................................... vii List of Figures ....................................................................................................................... viii List of Abbreviations ........................................................................................................... xiii Acknowledgements .............................................................................................................. xiv 1    Chapter: Introduction ...................................................................................................... 1 1.1 Forest harvesting and ectomycorrhizal fungal communities .......................................... 1 1.2 Ectomycorrhizal fungal communities and succession ...................................................... 2 1.3 Impact of clearcutting on soil nitrogen status .................................................................. 5 1.4 Ectomycorrhizal fungi and nutrient uptake ..................................................................... 6 1.5 Ectomycorrhizal fungi and community function ............................................................. 9 1.6 Study questions .................................................................................................................. 12 1.6.1 Research question 1 ...................................................................................................... 12 1.6.2 Research question 2 ...................................................................................................... 14 1.6.3 Research question 3 ...................................................................................................... 15 2    Chapter: Soil and Seedling Conditions at Time of Transplant .................................. 17 2.1 Synopsis .............................................................................................................................. 17 2.2 Methods .............................................................................................................................. 18 2.2.1 Study site ....................................................................................................................... 18 2.2.2 Soil analyses ................................................................................................................... 19 2.2.3 Sampling of seedlings ................................................................................................... 20 2.2.4 Biomass and nutrient analysis of seedlings ................................................................. 20 2.2.5 Morphotyping the ectomycorrhizal communities of pre-transplant seedlings ....... 21 2.2.6 Determining the age of pre-transplant seedlings ....................................................... 21 2.2.7 Molecular identification of ectomycorrhizal fungi on pre-transplant seedlings by Sanger Sequencing ..................................................................................................................... 21  v 2.2.8 Pyrosequencing ............................................................................................................. 23 2.2.9 Bioinformatics ............................................................................................................... 24 2.2.10 Statistical analyses .................................................................................................... 25 2.3 Results: ............................................................................................................................... 26 2.3.1 Soil analyses ................................................................................................................... 26 2.3.2 Age, biomass and nitrogen status of seedlings ........................................................... 29 2.3.3 Pyrosequencing ............................................................................................................. 30 2.4 Discussion ........................................................................................................................... 32 2.4.1 Soil analyses and seedling characteristics ................................................................... 32 2.4.2 Ectomycorrhizal fungal community structure ........................................................... 35 2.4.3 Seedling characteristics ................................................................................................ 37 2.5 Summary ............................................................................................................................ 38 3    Chapter: Seedling Conditions and Extracellular Enzyme Activities at the End of the Growing Season ..................................................................................................................... 39 3.1 Synopsis .............................................................................................................................. 39 3.2 Methods .............................................................................................................................. 40 3.2.1 Experimental design ..................................................................................................... 40 3.2.2 Enzyme assays ............................................................................................................... 42 3.2.3 Morphotyping the ectomycorrhizal communities of post-transplant seedlings ...... 44 3.2.4 Biomass and nutrient analysis of apical buds of post-transplant seedlings ............. 44 3.2.5 Molecular identification of the fungal symbiont of ectomycorrhizal root tips ........ 45 3.2.6 Bioinformatics ............................................................................................................... 46 3.2.7 Statistical analyses ........................................................................................................ 46 3.3 Results ................................................................................................................................ 48 3.3.1 Survival and growth of seedlings ................................................................................. 48 3.3.2 Ectomycorrhizal fungal colonization and community analysis ................................ 49 3.3.3 Exoenzyme profiles of ectomycorrhizal fungal communities ................................... 54 3.3.4 Biomass values of transplanted seedlings ................................................................... 59 3.3.5 N content of apical buds ............................................................................................... 61 3.4 Discussion ........................................................................................................................... 62 3.4.1 Ectomycorrhizal fungal community composition ...................................................... 62 3.4.2 Extracellular enzyme activity ...................................................................................... 66 3.4.3 Nitrogen uptake ............................................................................................................ 69  vi 3.4.4 Seedling characteristics ................................................................................................ 72 3.5 Summary ............................................................................................................................ 74 4    Chapter: Growth Chamber Reciprocal Transplant .................................................... 75 4.1 Synopsis .............................................................................................................................. 75 4.2 Methods .............................................................................................................................. 75 4.2.1 Sterilization of soil ........................................................................................................ 75 4.2.2 Growth chamber conditions ........................................................................................ 76 4.2.3 Soil nutrient analyses .................................................................................................... 77 4.2.4 Biomass measurements ................................................................................................ 77 4.2.5 Stable isotope analysis .................................................................................................. 77 4.2.6 Morphotyping of ectomycorrhizal root tips ............................................................... 78 4.2.7 Statistical analysis ......................................................................................................... 79 4.3 Results ................................................................................................................................ 80 4.3.1 Soil nutrient analyses .................................................................................................... 80 4.3.2 Seedling survival and relative growth rates ............................................................... 81 4.3.3 Biomass and N status of apical buds of growth chamber seedlings ......................... 81 4.4 Discussion ........................................................................................................................... 84 4.4.1 N status of apical buds .................................................................................................. 84 4.4.2 Seedling characteristics ................................................................................................ 85 4.5 Summary ............................................................................................................................ 87 5    Chapter: Conclusion ....................................................................................................... 88 5.1 Overall conclusions ........................................................................................................... 88 5.2 Potential applications and future directions ................................................................... 90 5.3 Strengths and limitations .................................................................................................. 92 References .............................................................................................................................. 96 Appendices ........................................................................................................................... 133 Appendix A Chapter 2 Supplemental Table .............................................................................. 133 Appendix B: List of R Code ........................................................................................................ 134 Appendix C: Complete QIIME Code ......................................................................................... 137   vii List of Tables   Table 2.1 Nutrient status of mineral soils sampled from three clearcut/forest pair sites in the Engelmann spruce – Subalpine fir biogeoclimatic zone at time of transplant (Welch’s two-sample t-test, n = 3) and the organic layer sampled from forests. ... 27 Table 2.2 Nutrient status of mineral soils and the forest floor among individual clearcut and forest plots (one-way ANOVA, nC = 3, nF = 3).  C1-3 represents clearcut plots at Site 1, 2, and 3.  F1-3 represents forest plots at Site 1, 2, and 3. ........................... 28 Table 3.1 Enzyme activities assayed on roots tips of A. lasiocarpa seedlings harvested 12 weeks after reciprocal transplant between mature forests and clearcuts. ............... 44 Table 3.2 The results of MRPP analyses on a site-by-site basis of enzyme profiles of ECM root tip communities on transplanted A. lasiocarpa seedlings.  The abbreviations are: CC; clearcut seedlings transplanted back into a clearcut, FC; seedlings from the forest transplanted into a clearcut, CF; seedlings from a clearcut transplanted into the forest, and FF: forest seedlings transplanted back into the forest. ............ 59 Table 4.1 Welch’s two sample t-test comparing irradiated soil nutrient status (mean ± SE) between clearcut and forest environments (nC = 3, nF = 3).  Despite data being ¼ power transformed before analysis, outliers were still present, so analysis was repeated on data set with outliers removed.  Values for available for NO3 were below levels of detectability and are not shown. .................................................... 80 Table A.1 A list of MID tags used in 454 sequencing of ECM root tips. ............................ 133   viii List of Figures  Figure 1.1 A theoretical ordination of ectomycorrhizal fungal communities colonizing seedlings prior to transplant between clearcuts and forests and seedlings harvested after transplantation.  Ectomycorrhizal fungal communities colonizing seedlings originating from clearcuts will group together, while communities colonizing seedlings from forests will group together.  Pre-C and pre-F treatments represent communities of seedlings from clearcuts and forests prior to transplant.  Transplant treatments are as follows: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest. .................................................................... 13 Figure 1.2 A theoretical ordination of enzyme profiles of ectomycorrhizal root tips colonizing transplanted seedlings consistent with the stated prediction.  The enzyme profiles of seedlings originating from the same environment will group together.  Transplant treatments are: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest. .................................................. 14 Figure 1.3 A theoretical clustering of enzyme profiles of ectomycorrhizal root tips communities of transplanted seedlings consistent with the stated prediction.  The enzyme profiles of seedlings originating from the same environment will cluster together.  Transplant treatments are: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest. .................................................. 15 Figure 1.4 Hypothetical averages of foliar N contents showing the predicted pattern of N acquisition in transplanted seedlings.  Transplant treatments are: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest.  The CF treatment is predicted to be slightly higher than the FC treatment due to higher organic matter in forest soils stimulating mycorrhizosphere enzyme activity and consequently N acquisition. ................................................................................... 16 Figure 2.1 Dry weights of pre-transplant subalpine fir seedlings from three high elevation clearcuts and forests.  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  The weights of apical buds are included in the total biomass weight.  Neither total biomass, nor biomass of tissue types  ix differed between seedlings originating from clearcuts and forests (t-tests, p > 0.1; nC = 3, nF=3). .......................................................................................................... 29 Figure 2.2 The percent nitrogen content of apical buds of pre-transplant seedlings.  The boxes represent the interquartile range and the dark line in the box represents the median.  The whiskers are the highest and lowest data points within the 1.5 interquartile range (p = 0.1, Welch’s two-sample t-test, nC = 3, nF = 3). ............... 30 Figure 2.3 A non-metric multidimensional scaling (NMS) ordination of ectomycorrhizal fungal communities colonizing the roots of pre-transplant seedlings from clearcuts (pre-C) and forests (pre-F).  A one-dimensional ordination resulted in the lowest stress value and was thus considered to be the best solution.  The single axis explains 61 % of the variation in community structure (n = 3 plots). .................... 31 Figure 2.4 Dominant ectomycorrhizal fungal species (> 10 % of tips in any plot) colonizing the roots of pre-transplant A. lasiocarpa seedlings harvested in July from clearcuts (pre-C) and forests (pre-F) (n=3 plots).  % occurrence is the percentage of root tips on which a fungal species was identified out of the total number of mycorrhizal tips. Uk. Helotiales refers to samples that were identified as an unknown Helotiales species. ................................................................................................................... 32 Figure 3.1 The reciprocal transplant experiment of naturally regenerated Abies lasiocarpa seedlings performed at three paired clearcut and forest plots. ............................... 41 Figure 3.2 Schematic diagram of a young A. lasiocarpa root system showing primary, secondary, and tertiary branches.  Red ovals represent tips that would have been selected for enzyme assays.  Black ovals represent tips that would have been passed over for assays. ........................................................................................... 43 Figure 3.3 The (a) proportion of transplanted seedlings that survived over a growth season of 12 weeks and (b) relative shoot growth rates (n = 3 sites).  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points. .................................................... 49 Figure 3.4 A nonmetric dimensional scaling ordination, using Sørensen (Bray-Curtis) distances based on relative abundance, of ectomycorrhizal fungal communities colonizing the root systems of A. lasiocarpa seedlings harvested 12 weeks after being transplanted between clearcut and forest environments (n = 3 sites).  The  x treatment CC represents seedlings originating from and transplanted into a clearcut, CF represents seedlings from a clearcut and transplanted into a forest, FC are seedlings from a forest transplanted into a clearcut, and FF are seedlings from and transplanted into the forest.  Axis 1 explains 49 % of the variation. ............... 50 Figure 3.5 A non-metric multidimensional scaling (NMS) of ectomycorrhizal fungal communities colonizing the root systems of pre-transplant and post-transplant seedlings.  Each point represents the communities in a treatment by site.  The treatment pre-C represents pre-transplant seedlings that originated from clearcuts and the treatment pre-F represents pre-transplant seedlings from the forest (n = 3 sites).  The post-transplant treatments are: CC, which are seedlings originating from and transplanted back into a clearcut; CF, seedlings from a clearcut transplanted into a forest; FC, forest seedlings planted to a clearcut; and FF, seedlings from and transplanted into the forest.  Axis 1 explained 28 % of the variation and Axis 2 explained 25 % of the variation. Together axes 1 and 2 explain 54 % of the variation. ................................................................................ 52 Figure 3.6 Dominant (> 10 % of tips in any plot) ectomycorrhizal fungal species colonizing A. lasiocarpa seedlings harvested in July (pre-C and pre-F treatments) and seedlings harvested in October.  Uk. Helotiales refers to samples that were identified as an unknown Helotiales species.  Abbreviations are defined in Figure 3.5. .......................................................................................................................... 53 Figure 3.7 The percentage of A. lasiocarpa fine root tips colonized by ectomycorrhizal fungi 12 weeks after reciprocal transplant (n = 3 sites).  Different letters above the boxplots indicate differences between treatments within each destination environment, as detected by planned contrasts.  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range. ................................................................................................................................ 54 Figure 3.8 Non-metric dimensional scaling ordination of ectomycorrhizal root tip enzyme activity of eight enzymes; (a) all three study sites, (b) site 1, (c) site 2, and (d) site 3. The open circles represent the overall activities of transplant treatments.  Red letters represent the scores of each enzyme.  Percentage values beside the axes  xi represent the amount of variation in enzyme activity explained by each axis.  C = cellobiohydrolase, G = β-glucosidase, Gu = glucosronidase, Lacc = Laccase, Leu = leucine aminopeptidase, NAG = N-acetylglucosaminidase, P = phosphomonoesterase, X = xylosidase. .................................................................. 55 Figure 3.9 Overall enzyme profiles of transplanted seedlings (a) across all three sites, (b) Site 1, (c) Site 2, (d) Site 3.  The polar graphs were drawn from the relative enzyme activities calculated as the percent of all ectomycorrhizal types at that site.  Treatments are: CC; clearcut seedlings transplanted back into a clearcut, FC; seedlings from the forest transplanted into a clearcut, CF; seedlings from a clearcut transplanted into the forest, and FF: forest seedlings transplanted back into the forest. ...................................................................................................................... 56 Figure 3.10 Enzyme profiles (the overall activities of eight extracellular enzymes) of root tip communities analyzed by k-means clustering directed to form three clusters (a) or five clusters (b)., Each colour represents a cluster.  Enzyme profiles within a cluster are more similar to each other than profiles in another cluster.   The ssi index of k-means clustering indicated that the optimum solution was when enzyme profiles formed five clusters (b). Abbreviations are defined in the same manner as in Figure 3.9. .......................................................................................................... 58 Figure 3.11 Dry weights of seedling tissues at the end of a growing season after transplant. Different letters above boxplots indicate significantly differences according to planned comparisons of CC vs. FC and CF vs. FF (n = 3 sites). The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range. ...................................................................................................................... 60 Figure 3.12 (a) Nitrogen content and (b) δ 15N values of apical buds of A. lasiocarpa seedlings 12 weeks after transplanting (n = 3 sites).  δ 15N data were power transformed.  Planned contrasts were CC vs. FC and CF vs. FF.  Different letters over boxplots indicate differences in treatments at p< 0.1. The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range. ................................................................................................................................ 61  xii Figure 4.1 The (a) proportional survival rates and (b) relative growth rates based on height of A. lasiocarpa seedlings transplanted into sterilized soil from Engelmann spruce – Subalpine fir clearcuts or adjacent forests and grown in a growth chamber for 20 weeks (nCC, FC, CF, FF = 3 sites).  Different letters represent significant effects of seedling source within each soil type according to (planned contrasts).  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  CC are seedlings from the clearcut and transplanted back into a clearcut, FC are seedlings from the forest transplanted into a clearcut, CF are seedlings from a clearcut and transplanted into the forest, and FF are seedlings from the forest transplanted back into the forest. ............................................................................ 81 Figure 4.2 Dry biomass of seedling parts.  Units were in grams but data was log transformed.  Different letters above the bars indicate significant differences between treatments according to planned comparisons of CC vs. FC and CF vs. FF (n = 3) at p < 0.1.  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  Transplant treatments are defined in Figure 4.1. ................... 83 Figure 4.3 The (a) relative N contents and (b) δ15N values of apical buds from naturally regenerated A. lasiocarpa seedlings harvested from three Engelmann spruce – Subalpine fir clearcut/forest pairs and grown in sterilized clearcut and forest soils in a growth chamber for 20 weeks (n=3 sites).  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  Abbreviations are defined in Figure 4.1. ................................................................ 84   xiii List of Abbreviations  ANOVA:  Analysis of Variance  BLAST:  Basic Local Alignment Search Tool   CC: Clearcut to Clearcut (in reference to seedlings that originated from a clearcut and were transplanted back into a clearcut)  CF: Clearcut to Forest (in reference to seedlings that originated from a clearcut and were transplanted into a forest)  ECM:  Ectomycorrhizal  EEA: Extracellular enzyme activities  ESSF:  Engelmann spruce - Subalpine fir (one of British Columbia’s biogeoclimatic zones)  FC: Forest to Clearcut (in reference to seedlings that originated from a forest and were transplanted into a clearcut)  FF: Forest to Forest (in reference to seedlings that originated from a forest and were transplanted back into a forest)  OTU:  Operational Taxonomic Unit  QIIME: Quantitative Insights Into Microbial Ecology    xiv Acknowledgements  I thank the members of my committee for their insights and expertise.  I would especially like to extend my immense gratitude to my supervisor Dr. Melanie Jones for her never-ending support and guidance.  I also thank my field assistant Logan Markaroff and volunteers Enav Shalev and Shayle Weibe for their invaluable help in the field and lab.  I also thank Dr. Melanie Jones for help in the field and Valerie Ward for her advice and support in the lab.  Jean Roach for helped me locate field sites and Carrie Van Dorp help me investigate more field sites; for that, thank-you.  I would also like to thank Sheri Maxwell for her recommendation of trying 454 sequencing when all other molecular techniques failed and Monika Gorzelak and Stéphane LeBihan for their guidance and patience in answering my numerous questions about this method.  I also thank the Hart lab for the donation of MID tags and a huge thank-you to Aaron Godin for his help with QIIME.    My thesis research was funded by an NSERC Discovery grant to Melanie Jones.  I also extend my gratitude to my fellow graduate students for their advice and support.    For their moral support and pep talks I thank my friends and family, especially my parents who have always unconditionally supported me.     1 1    Chapter: Introduction 1.1 Forest harvesting and ectomycorrhizal fungal communities Between 2000 and 2010, 5.2 million ha of forests were lost worldwide (FAO 2010).  Much of this loss is due to clearcutting; in Canada, clearcutting accounts for 89 % of the one million hectares of forests harvested annually (Marshall 2000, Durall et al. 2006).  There is concern about the impact of clearcutting on the forest ecosystem because it is so widely used and removes almost all the trees and vegetation in a stand (Cashore and Auld 2003).  Clearcutting also greatly impacts soil microbial communities and processes (Baar 1996, Prescott 1997, Setala et al. 2000).  Establishment and survival of regenerating and planted vegetation is dependent on soil quality and health, so it is therefore important to understand how soil ecosystems are impacted by forest harvesting. Tree removal from clearcut logging can cause warmer and moister soil conditions (Prescott 1997), changes in soil organic matter inputs, (Kranabetter and Wylie 1998, Setala et al. 2000), and a loss of soil nutrients via leaching (Feller 1999, Hannam and Prescott 2003).  Depending on the timing and type of method used to harvest the trees, soil compaction can also occur (Kozlowski 1999, Kranabetter et al. 2006), crushing aggregates and altering soil hydrology (Chen and Shrestha 2012).  Changes in soil physical and chemical properties caused by clearcutting can influence the soil microbial community structure (Baar 1996, Jurgensen et al. 1997, Kozlowski 1999, Setala et al. 2000).  For instance, immediately after clearcutting, carbon inputs occur in the form of dead roots and woody debris (Jurgensen et al. 1997).  These inputs are significantly reduced in the following years, which can lead to a decrease in soil microbial activity (Finzi and Canham 2000, Hassett and Zak 2005).   Shifts in ectomycorrhizal (ECM) fungal community structure after clearcutting have also been repeatedly observed (Kranabetter and Wylie 1998, Hagerman et al. 1999a, Jones et al. 2003, Barker et al. 2013).  As ectomycorrhizas influence nutrient uptake and plant growth, ECM fungi play an important role in tree growth and establishment and are an essential component of forest ecosystems (Dickie et al. 2009).  Ectomycorrhizal fungal species differ in their abilities to take up specific nutrients depending on the extent of their extramatrical hyphae (Agerer 2001) and the range of extracellular enzymes they produce (Jones et al. 2010, Taylor et al. 2010, Courty et al. 2011, Burke et al. 2014).  A change in ECM fungal community composition after clearcutting could influence seedling survival and  2 establishment rates.  Although a shift in ECM fungal community structure after clearcutting has been frequently observed (Hagerman et al. 1999a, Kranabetter and Friesen 2002, Walker and Jones 2013), no one has compared the physiological attributes of clearcut and forest ECM fungi.  Because of the influence of ECM fungi on seedling growth and survival, it is important to determine whether ECM fungi that dominate communities in clearcuts differ in important functional traits from those that dominate the nearby forest.  Also, succession of ECM fungi back to climax communities can take some time (Twieg et al. 2007, LeDuc et al. 2013), so it important to know if a loss in nutrient cycling occurs after clearcutting, corresponding with changes in ECM community structure.     1.2 Ectomycorrhizal fungal communities and succession Generally, ECM fungal communities are made up of a few dominant species, yet are very species rich (Peay et al. 2008, Taylor et al. 2010).  In samples as small as 0.25 g of soil Taylor et al. (2010) obtained over 5000 high quality sequences of soil fungi and found 218 operational taxonomic units (OTUs).  In spite of this massive cloning effort, not all ECM fungal species in this small soil sample were amplified; the rarefaction curves for these samples did not reach an asymptote.  In 4.0 g of forest soil, Buée et al. (2009) amplified fungal DNA via 454 sequencing and obtained 30 000 sequences, corresponding to 1000 soil fungal OTUs, and also found that rarefaction curves did not reach an asymptote.  The classic explanation for the high diversity of ECM fungal communities is based on niche theory, including the competitive exclusion principle and niche partitioning (Bruns 1995, Aarssen and Schamp 2002).  The competitive exclusion principle states that no two species can occupy the same niche; instead one species will outcompete and exclude the other (Lekberg et al. 2007).  Niche partitioning is based on the idea that it is the number of available niches that determines community structure (Lekberg et al. 2007).  Alternatively, the neutral theory states that the observed diversity is due to chance alone and is influenced by spatial processes, such as dispersal ability, rather than species/environment interactions (Lekberg et al. 2007, Messier et al. 2010).  However, it has been found that both soil characteristics and spatial structure influence the composition of the mycorrhizal fungal community (Lekberg et al. 2007, Taylor et al. 2010) suggesting that both niche and neutral theories play a role in ECM fungal diversity.  As environmental conditions  3 appear to play a role in shaping ECM fungal community structure and aboveground disturbances alter environmental conditions, it is not surprising that disturbances such as clearcutting are associated with altered ECM fungal communities.  In addition to clearcutting, other forest disturbances that result in changes in ECM fungal community composition include forest fires (Twieg et al. 2007, Rincón et al. 2014) and severe windthrow events (Cowden and Peterson 2013).  Disturbed areas are more frequently colonized by so-called early-stage fungi such as Amphinema and Wilcoxina species, which are characterized by possessing inoculum types able to persist after disturbances (Walker and Jones 2013) or by being able to acclimate to new soil conditions (Barker et al. 2013).  Undisturbed sites seem to be more dominated by late stage fungi such as Piloderma, Russula, and Cortinarius species that could more readily colonize sites from living hyphae (Tedersoo et al. 2008) or could have a higher degree of host specificity (Cairney and Chambers 1999).  For example, species of ECM fungi commonly found colonizing clearcuts in British Columbia’s Engelmann spruce – Subalpine fir (ESSF) biogeoclimatic zone include Thelephora terrestris, Amphinema byssoides, and Wilcoxina spp., whereas Piloderma spp., Tylospora spp., Russula spp., and Cortinarius spp. dominate undisturbed forests (Hagerman et al. 1999a, Walker and Jones 2013).  Kranabetter and Friesen (2002) observed similar ECM community compositions colonizing gaps and the mature forest in the Interior Cedar-Hemlock biogeoclimatic zone.  Along an age chronosequence of Douglas-fir and paper birch stands, including stands disturbed by clearcuts and fires, Twieg et al. (2007) observed that the occurrence of Piloderma and Russula species was positively correlated with stand age.  This suggests these species more readily colonize undisturbed forest stands.  Furthermore, in recently burned Mediterranean pine sites, Rincón et al. (2014) found an over-representation of Rhizopogonaceae and Atheliaceae (which includes Amphinema spp.) species and an under representation of Cortinariaceae species.  Compared to sites impacted by severe windthrow, undisturbed oak-pine forests were more colonized by Russula ochrospora and Boletus pinophilus.  Therefore, similar ECM fungal species across different forest types have been observed to colonize areas after various types of disturbance.    Succession of ECM fungal communities back to climax conditions can take several decades (Twieg et al. 2007, Fichtner et al. 2014).  For example in an age chronosequence  4 study, Twieg et al. (2007) saw fungal diversity plateau in 26-year-old stands, but did not see community composition stabilize until stand age reached 65 to 100 years.  In addition, Fichtner et al. (2014) observed that past agricultural practices still influenced development of soil microbial communities 100 years after practices were halted.  In contrast, it has been observed that ECM fungal communities can return to pre-fire conditions in only 15 to 18 years after fire in Alaskan boreal forests (Treseder et al. 2004) and in pine forests in the Central Alps (Kipfer et al. 2011). It has been suggested that rate of recovery of ECM fungal communities is driven by the rate of stand regeneration after disturbance (Kranabetter and Friesen 2002, Cowden and Peterson 2013).  Kranabetter and Friesen (2002) proposed that ECM fungal communities with lower diversity and dominated by early stage fungi were unavoidable in gap openings and would persist until further stand development.  Cowden and Peterson (2013) furthermore suggested that the shift in ECM fungal species composition after severe windthrow was due to the death of the host plant community and lack of ECM host regeneration.  In addition, three-year-old post-fire boreal forest sites were observed to have fewer ECM host plants than older post-fire sites (Treseder et al. 2004).  These studies indicate that the recovery rate of ECM fungi may be host limited.  Given that recovery of ECM fungal communities can take several decades, it is important to determine if the change in fungal community structure results in changes in ecosystem function that impact the establishment of regenerating and planted seedlings.  Although the recovery rate of ECM fungal communities is linked to stand regeneration, it is unknown what causes the initial change in ECM fungal community structure after clearcutting.  Jones et al. (2003) proposed two mechanisms.  The first is that the shift is due to the change in fungal inoculum type from active hyphae to spores and sclerotia, meaning that dispersal ability and persistence of ECM fungi is important in disturbed areas.  Second, it is possible that the altered edaphic conditions favour a different set of fungi; those better able to compete for colonization sites and obtain nutrients in the new soil environment (Setala et al. 2000, Hannam and Prescott 2003, Kranabetter et al. 2006).  Other possible mechanisms responsible for this shift could include release from competition in clearcut sites (Peay et al. 2009) and priority effects (Kennedy et al. 2009).   5 However, if a change in community structure does not alter ecosystem function, then an ECM fungal community shift may not be cause for concern. Many studies have documented a shift in ECM community structure after disturbances using molecular and morphotyping techniques to characterize the community (Kranabetter and Friesen 2002, Lilleskov et al. 2011, Rincón et al. 2014), but few have looked into the effect on community functioning.  This thesis examines community functioning of clearcut-dominant and forest-dominant ECM fungi by measuring mycorrhizosphere enzyme activities on naturally regenerated seedlings in clearcuts and forests.  From this point on, ECM fungi that dominate clearcut areas will be referred to as clearcut fungi and EMF that dominate the mature forest will be referred to as forest fungi.    1.3 Impact of clearcutting on soil nitrogen status Nitrogen is one of the most limiting nutrients in terrestrial ecosystems (Hobbie 1992).  In soils, nitrogen is present as ammonium and nitrate from mineralization and nitrification processes, and as organic N from the breakdown of plant litter and microbial biomass.  Simple forms of organic nitrogen include urea, amides, and amino acids; complex forms include peptides, proteins, and chitin.  In general, organic N content tends to be higher in soils than ammonium and nitrate (Aerts and Chapin 2000) and the ability of ECM fungi to obtain N from these organic sources could give plants an advantage in nutrient-limiting environments (Tibbett and Sanders 2002).      Generally, clearcut soils have higher amounts of nitrate and ammonium than unlogged forest soils (Hannam and Prescott 2003, Jones et al. 2009, Hope 2009, Jerabkova et al. 2011). In high-elevation spruce soils specifically, Hannam and Prescott (2003) and Jones et al. (2009) concluded that soluble organic nitrogen was lower in clearcuts than in the mature forest.  The increase in nitrate and ammonium in clearcuts could be explained by greater decomposition and mineralization rates caused by moister and warmer soil conditions after harvesting (Prescott 1997, Hope 2001), or could be due to reduced uptake by plants as harvesting often destroys vegetation in the area (Kranabetter and Coates 2004).  After one or two years, nitrate and ammonium are leached away or become fixed by soil minerals, and consequently, concentrations decrease.  Reduced soluble organic nitrogen (SON) observed in clearcuts compared to the mature forest is likely the result of the removal of trees as a source  6 of  SON and a reduced ability of the soil to abiotically hold organic matter (Hannam and Prescott 2003).  After a few years, soil nitrogen levels in clearcuts return to post-harvest conditions (Dahlgren and Driscoll 1994).  There is evidence that ECM fungal species differ in ability to take up and transfer nitrogen to the host plant.  For example, some species tend to take up ammonium at a faster rate than nitrate (Hobbie and Högberg 2012), but seem better able to transfer nitrate to the host plant, while ammonium is retained within fungal tissue (Hobbie et al. 2008).  Elevated levels of DIN in clearcut soils could favour colonization by ECM fungal species better able to take up ammonium or nitrate and thereby influencing the ECM fungal community observed after clearcutting.      1.4 Ectomycorrhizal fungi and nutrient uptake Compared to non-mycorrhizal plants, plants associated with ECM fungi have higher nutrient contents, especially in soils with limited nutrient availability (Smith and Read 2008). Three factors limit nutrient uptake in plants: the total surface area available for absorption, the distribution of roots in the soil column, and nutrient concentration at the root surface (Lambers et al. 2008).  Fungal nutrient transporters can also influence plant nutrient uptake.   Ectomycorrhizal fungi influence these factors directly or indirectly.  They can increase the surface area available for absorption of nutrients, grow beyond root-associated nutrient depletion zones, explore soil micropores, alter the surrounding microbial community (Simard et al. 2002), and release insoluble nutrients into solution via the secretion of organic acids and hydrolytic enzymes (Simard et al. 2002, Jones et al. 2010, Taylor et al. 2010, Courty et al. 2011).  As a result, nutrient supply to host roots is increased.   Hyphae increase the surface area available for absorption because they have higher surface area to volume ratios than roots (Simard et al. 2002, Courty et al. 2011).  Hyphae also extend beyond nutrient depleted zones within the rhizosphere.  Extension beyond nutrient depleted zones is especially important for those nutrients with limited mobility, such as P, organic N, and micronutrients, but is not as important for inorganic N, Ca and Mg, which are more mobile (Simard et al. 2002, Courty et al. 2010).  However, extension beyond the depletion zones is only effective when the hyphae transport nutrients to the plant faster than the nutrients would otherwise move in soil and if the root densities are low enough that  7 adjacent depletion zones do not overlap each other (Simard et al. 2002).  Also, fine roots of plants are restricted to soil macropores.  Hyphae of ECM fungi can access smaller micropores and can penetrate soil aggregates (Simard et al. 2002).  Access to soil aggregates is important because the water film inside these aggregates contains a higher concentration of water-soluble nutrients than external water films (Hildebrand 1994).  Hyphae therefore provide access to nutrients that would otherwise be physically unavailable to plants. Fungal transporters involved in nutrient acquisition can also increase the rate of nutrient uptake in plants.  Fungal transporters may be important for more mobile forms of soil nutrients, such as nitrate (Clarkson 1985), but are less important for nutrient forms that move slower in soils, such as inorganic phosphorus (Pi) (Silberbush and Barber 1983).  This is because roots can take up Pi at a much faster rate than this nutrient can move in the soil, resulting in a depletion of Pi at the absorbing surface (Nye and Tinker 1969).  Therefore, increasing the surface area available for absorption is more important than the properties of fungal transporters involved in the uptake of nutrients (Lambers et al. 2008).  More mobile nutrients have smaller depletion zones around roots, thereby increasing the importance of the kinetic properties of transports for such nutrients (Clarkson 1985).  For more mobile nutrients, fungal transporters can increase the rate of nutrient uptake to plants.    ECM fungi can alter the surrounding soil microbial community, which augments nutrient uptake in plants (Simard et al. 2002).  For example, some ECM fungal species appear to attract bacteria as these organisms are found on and within ECM mantles and Hartig nets (Nurmiaho-Lassila et al. 1997).  This is significant because although bacteria can compete with roots for nutrients, they also chelate nutrients, making them available to plants or associated ECM fungi (Paul and Clark 1989, Frey-Klett et al. 2005, van der Heijden et al. 2008).  In addition, some microbes that benefit plants have increased functioning in the presence of ECM fungi.  For example, some plant growth-promoting rhizobacteria (PGPRs) further enhance plant growth in the presence of ECM fungi (Shishido et al. 1996, Ahangar et al. 2012, Hrynkiewicz et al. 2012), and some N-fixing bacteria are only found in association with ECM fungi (Li et al. 1992).  Therefore, the presence of ECM fungi can benefit nutrient uptake in plants by altering the mycorrhizosphere microbial community.  In many soil types, only a small proportion of the nutrient content is readily available to plants.   For example, weathering of soil minerals and translocation of the released  8 minerals to the plant can be influenced by ECM fungi (Simard et al. 2002, Adeleke et al. 2012, Koele et al. 2014).  For example, ECM fungi release Fe from biotite (Watteau and Berthelin 1994, Balogh-Brunstad et al. 2008, Bonneville et al. 2011), P from apatite (Wallander et al. 1997, Koele et al. 2014), and Ca from calcium phosphates (Lapeyrie et al. 1990, Rosling et al. 2004).  Besides releasing nutrients, it has been found that chlorite is released at higher rates in the mycorrhizosphere of Abies lasiocarpa (Arocena et al. 1999, Tuason and Arocena 2009), which can result in higher soil cation concentrations (Landeweert et al. 2001), therefore increasing the ability of soil to supply nutrients (Simard et al. 2002).  The weathering of soil minerals is due, in part, to the secretion of organic acids by ECM fungi (van Breemen et al. 2000, Rosling 2009).  For instance, ECM fungi produce oxalic acid that precipitates Ca in calcium oxalate crystals (Cromack et al. 1979, Griffiths et al. 1994, Rineau and Garbaye 2010).  Organic acids lower the pH of soils (Arocena et al. 1999), which, in turn, can have implications for P solubilisation (Casarin et al. 2003) and subsequent uptake.  Through the secretion of organic acids, ECM fungi release soil minerals and transport them back to the plant.  Ectomycorrhizal fungi also secrete enzymes that break down complex forms of organic compounds that the plant would otherwise not be able to take up (Bending and Read 1995, Courty et al. 2010a, Pritsch and Garbaye 2011).  Extracellular enzymes are particularly important for the degradation of organic forms of N and P and possibly organic S (Simard et al. 2002, Koide et al. 2007, Burke et al. 2011).  In forest soils where most ECM fungi are found, most of the N content is in complex organic forms (Qualls et al. 1991, Michelsen et al. 1998), which ECM fungi can access more easily than plants.  Although plants can absorb simple organic forms of N such as amino acids (Kielland 1994, Nasholm et al. 1998), ECM plants can take up amino acids at a faster rate than non-mycorrhizal plants of the same species (Chalot and Bruns 1998).  Ectomycorrhizal fungi can also absorb N from more complex forms such as proteins (Abuzinadah and Read 1989, Chalot and Bruns 1998, Shah et al. 2013).   In addition, ECM fungi also produce hydrolytic and oxidative enzymes that break down N and P compounds in soil organic matter and lingo-cellulose and polyphenol complexes (Courty et al. 2010, Read and Perez-Moreno 2003).  For example, even though both ECM and non-ECM roots produce phosphatases, higher enzyme activity exists around mycorrhizal roots, possibly due to the higher surface area for secretion provided by hyphae  9 (Marschner 1996).  Some ECM fungi even produce phosphomonoesterase (and other enzymes) at a level equal to or comparable to saprotrophic fungi (Gramss et al. 1999, Simard et al. 2002, Rineau et al. 2012).   This could be responsible for the increased absorption of P by ECM plants (Jones et al. 1990).  The secretion of enzymes by ECM fungi is important as enzymes release nutrients from a source that is abundant (organic matter) but cannot be utilized by plants.  Evidence exists that species of ECM fungi play different roles in nutrient cycling (Kranabetter et al. 2006, Lilleskov et al. 2011, Jones et al. 2012).  Some ECM fungi are better at mobilizing recalcitrant forms of organic N, whereas others can directly use simple forms of organic N, rather than solely relying on inorganic N (Lipson and Nasholm 2001, Read and Perez-Moreno 2003).   In addition, some species assimilate ammonium faster than nitrate (Brown et al. 2010), but other species, in pure culture conditions, prefer nitrate (Smith and Read 2008).  The variability in the enzyme profiles of different fungi may indicate adaptations to high or low nutrient status.  For example, ECM fungi located in sites with a large amount of available inorganic N are less likely to use more complex N forms (proteins) and are less likely to produce extracellular proteases (Courty et al. 2010).  Different ECM fungal species could therefore better acclimate to soil conditions than other species.  Although inadequate investigational methods limit our knowledge of fungal role in biogeochemical cycles and ecosystem processes (Courty et al. 2010), understanding the role of specific ECM fungal species in nutrient cycling would improve our understanding of the forest ecosystem as a whole (Mayor et al. 2009).  Furthermore, it is important to understand if shifts in ECM fungal community structure after disturbances causes a change ecosystem functioning to further our understanding on how disturbance impacts the forest ecosystem (Kivlin and Treseder 2014, Koide et al. 2014, Talbot et al. 2014).  1.5 Ectomycorrhizal fungi and community function  Factors driving the composition of ECM fungal communities are becoming clearer, but the impact of composition on ecosystem function remains poorly understood (Koide et al. 2007, Courty et al. 2010, Koide et al. 2014).  This is because, until recently, methods have not allowed researchers to investigate functions of ECM fungi at a community level (Koide et al. 2007, Pritsch and Garbaye 2011).  In measuring community function, interest should be  10 focused on traits possessed by members of ECM fungal communities that determine suitability to a habitat, as well as those that influence community function (Koide et al. 2014).  In ECM fungal communities, one such trait could be mycorrhizosphere enzyme activity. A high throughput method for measuring enzyme activities of excised mycorrhizal roots tips via enzyme assays (Pritsch et al. 2004, Courty et al. 2005) has allowed researchers to investigate the effects of biotic and abiotic factors on mycorrhizosphere enzyme activities and consequently the ecosystem services ECM fungi provide in terms of nutrient uptake (Courty et al. 2010, Jones et al. 2010, Courty 2011, Jones et al. 2012, Walker et al. 2014).  Understanding the link between community structure and function is key to predicting how environmental change will affect plants and their fungal symbionts (Deslippe et al. 2011, Pickles et al. 2012). ECM fungal community composition appears to play an important role in influencing mycorrhizosphere enzyme activities (Lilleskov et al. 2011, Jones et al. 2012, Welc et al. 2014).  Lilleskov et al. (2011) observed a ECM fungal community shift over a N deposition gradient and speculated that as N availability increased, the community shifted from taxa specialized in N uptake under low N conditions (ex. Cortinarius, Piloderma) to taxa adapted to higher nutrient availability (ex. Tomentella sublilacina, Thelephora terrestris).  Furthermore, Jones et al. (2012) demonstrated that enzyme profiles of root tips were determined by their fungal symbiont.  If different ECM fungal species exhibit different enzyme activities, fungal identity may play a strong role in determining community functioning.    Edaphic conditions may also play a role in determining activities of extracellular enzymes. For example, Buée et al. (2007) observed that enzyme activities of ectomycorrhizas formed by the same ECM fungal species varied between soil niches.  In addition, Rineau and Courty (2011) determined functional groups of ECM fungal species based on enzyme activities and observed that an individual species could have enzyme activities distributed throughout more than one functional group.  Walker (2012) saw chitinase and phosphatase activities of Tylospora mycorrhizas differ among microsites at an Engelmann spruce – Subalpine fir site.  Therefore, the environment in which a fungal species occurs could also play a role in determining community function, at least in terms of enzyme activity.    11  It has been suggested that ECM fungal communities contain species that are functionally redundant (Rineau and Courty 2011).  Functional redundancy is defined as when species in an ecosystem perform similar roles and may be substitutable with little impact on ecosystem function (Lawton and Brown 1993).  It ensures that ecological services are maintained when conditions are disturbed, and communities are altered (Díaz and Cabido 2001, Loreau et al. 2001, Elmqvist et al. 2003).  Ectomycorrhizal fungal communities have demonstrated some degree of functional redundancy in terms of mycorrhizosphere enzyme activities (Courty et al. 2010, Rineau and Courty 2011).  When two ECM fungal species that exhibit similar enzyme activities are found in the same community, this may help maintain ecosystem services under changing conditions, if the species are differentially affected by the altered environment (Walker et al. 1999, Chapin et al. 2000).  However, little is known about the degree to which the environment selects for ECM fungi performing certain functions (Koide et al. 2007) and, consequently, if environmental changes alter ecosystem services provided by ECM fungi.  Mycorrhizosphere enzyme activities, which contribute to nutrient cycling and host nutrition, could be influenced through fungal identity or directly by the environment; however, it is unknown which has the greater effect (Kivlin and Treseder 2014). The studies mentioned above measured enzyme activities of different ECM fungal communities in different environments, meaning that community structure and environment were confounded.  Studies that have observed a change in ECM fungal communities with abiotic factors often but not always, record a corresponding shift in extracellular enzyme activity (Buée et al. 2007, Lucas et al. 2007, Courty et al. 2010, Rineau and Garbaye 2011, Jones et al. 2012). In a previous study in our lab, Walker (2012) observed that ECM fungal communities colonizing spruce seedlings in clearcuts had different mycorrhizosphere enzyme profiles from those in forests.  However, this is an example of where both fungal communities, and the conditions in which they developed, differed.  To overcome the limitations seen in previous research, my study consisted of a reciprocal transplant in which naturally regenerated seedlings were transplanted between forest and clearcut environments. Consequently, I could separate the effect of community composition and environment on mycorrhizosphere enzyme activities (Kivlin and Treseder 2014).  This is important if we are to understand the effect of disturbance on services supplied by ECM fungi.       12  1.6 Study questions The overall hypothesis being tested in my thesis was that ECM fungi, that are observed to be more abundant on clearcut seedlings, will be better able to acquire nutrients from clearcut soils than ECM fungi on the roots of forest seedlings.  A corollary is that ECM fungi, that are observed to be more abundant on forest seedlings, will be more adept at acquiring nutrients in forest environments than clearcut ECM fungi. As seedling growth and nutrient content can be related to ECM fungal nutrient uptake (Yamashita et al. 2007), I measured seedling growth and nitrogen content when colonized by forest or clearcut ECM fungi at the time of transplant (Chapter 2) and three months after transplant (Chapter 3). Chapter 2 also compares soil nitrogen status between clearcuts and forests at time of transplant.  I also investigated if ECM fungal communities differed between clearcut and forest environments and if the ECM fungal communities stayed the same for the duration of the experiment.  In addition, a reciprocal transplant tested whether ectomycorrhizas of subalpine fir seedlings in ESSF clearcuts differed from those in the mature forest with respect to mycorrhizosphere enzyme activities when grown in the same environment (Chapter 3).I also performed a reciprocal transplant in a growth chamber to further investigate foliar nitrogen content of seedlings when colonized by different ECM fungal communities (Chapter 4).  The objective of this thesis was to address the three questions described below.  1.6.1 Research question 1 Does the composition of ECM fungal communities colonizing seedlings in clearcuts differ from the ECM fungal communities colonizing seedlings in the mature forest?  Do seedlings retain their original ECM community structure when transplanted between clearcut and forest environments for the duration of one growing season (about four months)?  Chapter 2 will address the first part of this question and Chapter 3 will address the second part of this question.        13 Specific hypotheses:  Clearcut seedlings will have a different ECM fungal community structure than forest seedlings.   Seedlings will retain their original ECM fungal communities for the duration of the study.  Prediction:  Seedlings originating from the same environment will have more similar ECM fungal communities than seedlings from the other environment, regardless of the environment into which they were transplanted (Figure 1.1).   Figure 1.1 A theoretical ordination of ectomycorrhizal fungal communities colonizing seedlings prior to transplant between clearcuts and forests and seedlings harvested after transplantation.  Ectomycorrhizal fungal communities colonizing seedlings originating from clearcuts will group together, while communities colonizing seedlings from forests will group together.  Pre-C and pre-F treatments represent communities of seedlings from clearcuts and forests prior to transplant.  Transplant treatments are as follows: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest.   14 1.6.2 Research question 2 Do root tip communities of clearcut and mature forest seedlings differ in mycorrhizosphere enzyme activities when grown in the same environment?  Chapter 3 will address this question.   Hypothesis:  ECM fungal community will be a more important influence than growing environment on mycorrhizosphere enzyme activity.   Prediction:  Seedlings originating from the same environment will have similar enzyme profiles (the combination of eight enzyme activities), even when grown in different environments (Figures 1.2 and 1.3).    Figure 1.2 A theoretical ordination of enzyme profiles of ectomycorrhizal root tips colonizing transplanted seedlings consistent with the stated prediction.  The enzyme profiles of seedlings originating from the same environment will group together.  Transplant treatments are: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest.   15  Figure 1.3 A theoretical clustering of enzyme profiles of ectomycorrhizal root tips communities of transplanted seedlings consistent with the stated prediction.  The enzyme profiles of seedlings originating from the same environment will cluster together.  Transplant treatments are: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest.  1.6.3 Research question 3 Do fungi that colonize young seedlings in a particular environment also provide seedlings with more nitrogen in that environment than fungi from another environment?  Chapters 3 and 4 will address this question.  Chapter 3 will investigate this question in the field, while Chapter 4 will examine this question under more controlled growth chamber settings. Hypothesis:  ECM fungi from one environment are better able to supply their hosts with N in their ‘home’ environment than ECM fungi from the other environment. Prediction:  In the clearcut environment, clearcut seedlings will acquire more N than forest seedlings and in the forest, forest seedlings will acquire more N than clearcut seedlings (Figure 1.4).    16  Figure 1.4 Hypothetical averages of foliar N contents showing the predicted pattern of N acquisition in transplanted seedlings.  Transplant treatments are: CC: clearcut to clearcut, CF: clearcut to forest. FC: forest to clearcut, FF: forest to forest.  The CF treatment is predicted to be slightly higher than the FC treatment due to higher organic matter in forest soils stimulating mycorrhizosphere enzyme activity and consequently N acquisition.           17 2    Chapter: Soil and Seedling Conditions at Time of Transplant 2.1 Synopsis It is obvious how clearcutting affects above ground communities, but it is not as evident how clearcutting impacts communities belowground.  Because soil is the building block on which ecosystems form, and cutblocks must be returned to a free-growing system within seven years (B.C. Ministry of Forests, 2000), it is important to understand the impact clearcutting has on the soil ecosystem.  Investigating the effect of harvesting on ectomycorrhizal (ECM) fungal communities is especially important because these fungi aid in the establishment and growth of seedlings (Dickie et al. 2009). In the Engelmann spruce – Subalpine fir (ESSF) biogeoclimatic zone of British Columbia, different species of ECM fungi colonize the roots of conifer seedlings in recent clearcuts than in adjacent mature forest (Hagerman et al. 1999a, Hagerman et al. 1999b, Kranabetter and Friesen 2002, Jones et al. 2003, Walker and Jones 2013). For example, Thelephora terrestris, Amphinema byssoides, Tylospora spp., and Wilcoxina spp. dominate the root systems of spruce seedlings in clearcuts, whereas Piloderma spp., Russula spp., Cortinarius spp. and Lactarius spp. dominate root systems of spruce seedlings in the mature forest (Walker and Jones 2013).  Other studies that assessed ectomycorrhizas of seedlings in clearcuts and forests (Hagerman et al. 1999a, Hagerman et al. 1999b), in burns and forests (Khetmalas et al. 2002), or in forests only (Kranabetter et al. 2009), found similar species colonizing disturbed sites.   The process of clearcutting impacts both physical and chemical properties of the soil environment.  When compared to undisturbed mature forest soils, clearcut soils are warmer and moister (Prescott 1997), have had most of the organic matter removed (Kranabetter and Wylie 1998, Setala et al. 2000), and can experience leaching of nutrients (Feller 1999, Hannam and Prescott 2003).   Soluble organic N content tends to be higher in temperate mature forest soils than ammonium and nitrate (Aerts and Chapin 2000); however, recent ESSF clearcuts tend to have higher amounts of nitrate and ammonium and lower soluble organic nitrogen than in unlogged forests (Hope 2001, Hannam and Prescott 2003, Jones et al. 2009).   Engelmann spruce (Picea engelmannii (Parry ex Engelm.)) and subalpine fir (Abies lasiocarpa ((Hook.) Nutt.)) occur as codominants in ESSF sites.   A. lasiocarpa can grow in  18 all light intensities, but will survive better under closed-canopy conditions than P. engelmannii (Franklin and Dyrness 1973).  Although P. engelmannii is more shade intolerant than A. lasiocarpa, fir seedlings appear to have higher incidence of establishment in open clearcuts.  This could be due to the fact that fir seeds germinate and survive better on exposed mineral soil and moist humus than spruce seedlings, and fir in general has less strict seedbed requirements than P. engelmannii (Burns and Honkala 1990).  A. lasiocarpa also establishes well in severely disturbed areas such as burns, lava flows, and avalanche tracks, as well as in climatically harsh regions near the timberline (Franklin and Dyrness 1973).  It is thought that A. lasiocarpa seedlings are able to better establish in these harsh and open sites because of their ability to develop deeper root systems than P. engelmannii (Cui and Smith 1990).  In addition, A. lasiocarpa can also reproduce via layering, a technique that enables them to not only colonize an area faster, but also provides protection to the developing seedlings (Franklin and Dyrness 1973).  Therefore, although A. lasiocarpa is more shade tolerant, they can more successfully establish and occur more frequently in clearcuts than P. engelmannii. For these reasons, A. lasiocarpa was chosen for this study.   In this chapter I compare A. lasiocarpa seedlings sampled from three pairs of clearcuts and adjacent forests in terms of their age, size, N status and ectomycorrhizas.  In addition, I compare the soil nutrient status of the two environments.  The purpose of this chapter was to address Research Question 1 of this thesis and to document any differences in soil N status between the two environments.   2.2 Methods 2.2.1 Study site This study took place at three study sites in the Engelmann spruce - Subalpine fir wet-cold4 (ESSF wc4) biogeoclimatic zone of British Columbia.  A description of this zone can be found at http://www.for.gov.bc.ca/hre/becweb/index.html.  This area ranges in 1500 m to 2300 m in elevation and has long, cold winters and short, cool summers.  The growing season in this zone is short, with winters lasing up to 7 months and the snow pack can be up to 3 m deep.  Mean annual temperature ranges from -2 °C to +2 °C, with temperatures averaging -13 °C in the winter and 10 °C in the summer.  The ESSF biogeoclimatic zone receives 1200 – 1500 mm of precipitation each year, with 50 - 70 % falling as snow.  The  19 canopy is dominated by Engelmann spruce (Picea engelmannii (Parry ex Engelm.)) and less so by subalpine fir (Abies lasiocarpa (Hook.) Nutt.).  The forest understory is dominated by Rhododendron albiflorum, Clintonia uniflora, Rubus pedatus, Tiarella trifoliata var. unifoliata, Ribes lacustre, Viola sp., Veratrum viride, Senecio triangularis, Maianthemum racemosum, Pachistima myrsinites and moss.  Clearcuts are dominated by Lupinus sp., Clintonia uniflora, T. trifoliata var. unifoliata, M. racemosum, Chamerion angustifolium, Cornus canadensis, Amelanchier alnifolia, Rubus parviflorus, Arnica latifolia, Vaccinium membranaceum, and Streptopus lanceolatus. Each study site consisted of a clearcut plot (10 m x 10 m) paired with an adjacent mature forest plot.  Site 1 was west facing and was at 1512 m in elevation (N 118° 27’ 25.5”, W118° 39’ 24.6”).  Site 2 was 1.3 km away and was south facing at an elevation of 1657 m (N 50° 27’ 41.8”, W 118° 40’ 04.8”).  Site 3 was 8.2 km away from Site 1 and was northwest facing at an elevation of 1726 m (N 50° 30’ 06.3”, W 118° 39’ 44.8”).  Harvesting of the clearcut blocks in Sites 1 and 2 was completed in December 2008; cutblock 3 was completed in January 2009.  Clearcut sites were just over two years old at the start of the study in July 2011.  2.2.2 Soil analyses On July 20, 2011, ten 7 cm x 7 cm x 15 cm soil samples were randomly collected from each plot.  Soil samples from forest plots were separated into the organic and mineral layers.  The organic layer was only 1 cm thick and no fine roots were found in this layer.  Clearcut soils lacked a distinct organic horizon.  Samples from the same plot were consolidated into plastic bags and brought back to the lab where they were mixed more thoroughly.  Samples were then sieved through a 2 mm mesh before being dried at room temperature for at least 48 hours.  Dried soil was then consolidated and mixed again by plot. Three sub-samples were taken from each consolidated sample; 9 samples each of clearcut and forest mineral soil (3 samples x 3 sites), and 9 samples from the forest organic layer.  Samples were sent to the Ministry of Forests, Lands, and Natural Resource Operations Research Analytical Laboratory in Victoria, British Columbia to be analyzed for total C and N, mineralizable N, and available nitrate and ammonium.  Total C and N were analyzed by combustion in a Fisons NA-1500 (Carlo-Erba, Italy) elemental analyzer (Carter 1993, Kalra  20 1998).  The colourimetry for mineralizable N was conducted in an Alpkem FSIV Segmented Flow Analyzer (Oregon, USA), after an anaerobic incubation in a KCl extraction (Waring and Bremner 1964a, Waring and Bremner 1964b, Bremner 1996, Keeney and Bremner 1966,).  Available NO3 and NH4 were also measured in an Alpkem FSIV Segmented Flow Analyzer after aerobic extraction with 2 N KCl. (Bremner 1965, Carter and Gregorich 2008).     2.2.3 Sampling of seedlings Within each clearcut and forest site, 10 naturally regenerated subalpine fir seedlings were located and carefully excavated to keep the root systems intact (10 seedlings x 3 sites x 2 environments = 60 seedlings in total).  These pre-transplant seedlings were placed in individual plastic bags, transported back to the lab, and stored temporarily in a walk-in cooler until further analysis.  The next day, main shoot length was measured.  Main shoot length was used to calculate relative growth rates of seedlings at harvest (Chapter 3).  2.2.4 Biomass and nutrient analysis of seedlings After measuring pre-transplant seedlings, each seedling was dissected into roots, stem, old foliage, new foliage, and the apical bud.  The fresh weight of each seedling part was measured before being dried in an oven for at least 48 hours at 105 °C, after which the dry weight of each seedling part was measured.   To determine the nutrient status of the seedlings at the beginning of the reciprocal transplant, the nitrogen contents of the apical buds were measured.  Apical buds were selected because A. lasiocarpa trees form these buds at the end of the growing season and N is allocated to the youngest foliage age class (Millard and Grelet 2010).  The apical buds are, therefore, representative of the N acquired by the seedlings during the course of the growing season.  Other studies have used apical buds as an indicator of the current season’s nutrient supply (Kummel and Lostroh 2011).  The dry weights of the buds were so small (less than 0.01g) that only one type of nutrient analysis could be performed.  Nitrogen was chosen as it tends to be the more limiting nutrient in this type of ecosystem.  In order to have enough biomass, the apical buds of seedlings originating from each environment were combined by site.  Buds were then divided based on their state of development (open or closed).  This resulted in a total of ten foliage samples, as seedlings originating from the forest plots at Sites  21 1 and 2 all had closed buds.  Samples were sent to the Ministry of Forests, Lands, and Natural Resource Operations Research Analytical Laboratory in Victoria, British Columbia for analysis.  Total C and N were measured by combustion in an elemental analyzer.  2.2.5 Morphotyping the ectomycorrhizal communities of pre-transplant seedlings  Root systems were washed in water.  Every ECM root tip on each pre-transplant seedling was morphotyped (approximately 20 root tips per seedling).  Mycorrhizal tips were examined under a dissecting scope at total magnifications of 10X to 40X as required to examine external features.  Mycorrhizas were examined for mantle colour and texture, the presence of emanating hyphae and/or cystidia, and the pattern of any emanating hyphae according to Goodman et al. (1996).  Mantle peels were examined under a compound microscope at a total magnification of 100X when ECM status was uncertain due to the presence of thin mantles.  Ectomycorrhizas were placed into morphotype groups according to these anatomical features.  Two tips per morphotype per seedling were selected for molecular identification and were placed in in strip tubes, with one tip per tube.  A drop of distilled water was added to each tube and samples were stored at -80 °C.   2.2.6 Determining the age of pre-transplant seedlings On young fir seedlings, visible horizontal dark lines on the exterior of the stems of seedlings mark each year’s growth (Trudy Kavanagh, pers. comm.).  Therefore, seedlings were aged by observing the stems underneath a dissecting microscope and counting these lines.  A cross section of the stems also confirmed seedling age.    2.2.7 Molecular identification of ectomycorrhizal fungi on pre-transplant seedlings by Sanger Sequencing DNA from at least one tip per morphotype per seedling was extracted using the Extract-N-AMP Plant PCR Kit (Sigma-Aldrich, St. Louis, MO) following the manufacturer’s instructions with the exception that 25 µL of extraction and dilution solutions were added to the root tips instead of the recommended 100 µL.  The internal transcribe spacer (ITS) region of fungal ribosomal DNA was amplified using the forward primer ITS 1F (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and the reverse primer ITS 4 (5’- 22 TCCTCCGCTTATTGATATGC-3’) (Gardes and Bruns 1993).  Each 25 µL reaction consisted of 9 µL H20, 12.5 µL GoTaq Green Master Mix, 1.25 µL of each primer, and 1.0 µL of template DNA.  Thermocycler settings were 94 °C for 3 minutes, followed by 40 cycles of 94 °C for 1 min, 51 °C for 1 min, 72 °C for 1 min and finally 72 °C for 10 min followed by cooling to 4 °C.  Reactions were visualized on 1 % agarose gels.  Experience in the Jones lab is that amplification of fungal DNA from ectomycorrhizas using these methods typically results in a single band of DNA in approximately 60 % of samples.  In this case, greater than 90 % of samples showed multiple bands.  Samples with multiple bands were run on a 1 % agarose gel for at least one hour at a low voltage to separate the multiple bands.  The target band was extracted and then purified using an E.Z.N.A. Gel Extraction Kit (Omega Bio-Tek, Norcross, GA).  Gel-extracted samples were amplified using the ITS-1F primer by the Big Dye Terminator Cycle Sequencing Kit (Applied Biosystems Inc., Foster City, CA, U.S.A.) at the University of British Columbia, Okanagan campus.  Sequences were visualized and corrected using FinchTV (Geospiza Inc., Seattle, WA).  However, in most cases, sequencing was unsuccessful, with the chromatogram indicative of several sequences in the samples.  The few successful Sanger sequences were BLASTed (Basic Local Alignment Search Tool) against the National Center for Biotechnology Information (NCBI) GenBank database and the User Friendly Nordic ITS Ectomycorrhizas (UNITE) database.  The name of the accessioned sequence was applied to the sample sequence if there was a greater than 97 % match to greater than 400 base pairs (bp).  If the degree of similarity was less than 97 % of the matched sequence length was shorter than 400 bp, than samples were named based on the taxonomic affiliations of the BLAST hits and morphotyping observations.  However, due to the high incidence of double bands, only 10 to 20 % of sequences were successfully amplified with this technique.   Several approaches were used in an attempt to increase the proportion of samples with single bands and/or to identify the source of the major band.  Over several months, a dilution series, range of annealing temperatures (gradient PCR using 48 °C, 49 °C, 50 °C, 51 °C, 52 °C, and 53 °C) and several changes in PCR master mixes were used, without success. In addition, the basidiomycete-specific primer pair ITS 1F (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS 4B (5’-CAGGAGACTTGTACACGGTCCAG-3’) was used to amplify the ITS region to try and  23 exclude bands that were caused by ascomycetes. It is common that multiple bands are from endophytic fungi and these are frequently ascomycetes (Gardes and Bruns 1993). This approach has been used successfully by some researchers (Twieg et al. 2007, Kranabetter et al. 2009, Jones et al. 2012).  I also tried using the universal fungal primer pairs NSI 1 (5’-GATTGAATGGCTTAGTAGG-3’) and NLC 2 (5’-GAGCTGCATTCCCAAACAACTC-3’), ITS 5 (5’-GGAAGTAAAAGTCGTAACAAGG-3’) and ITS 4 (5’-TCCTCCGCTTATTGATATGC-3’).  Thermocycler conditions were as described above, with variation in the number of cycles ranging from 30 cycles to 40 cycles. Also, several different methods were used to clean the PCR products.  After the gel extraction, which also cleans the PCR product, the product was cleaned again with Exo-Sap-It (Affymetrix, Santa Clara, CA).  Another approach I tried was to run an additional PCR on the gel extraction product before cleaning with Exo-Sap-It.  A Promega SV Wizard PCR (Madison, WI, USA) clean-up kit was tested on some samples to clean the initial PCR product, and was also used to perform gel extractions.   These efforts were unsuccessful, however, in reducing the number of multiple bands.  Hence it was decided to use pyrosequencing to characterize the fungal communities, including the ECM fungi, of the sampled seedlings.  2.2.8 Pyrosequencing The ITS 1 region from DNA previously extracted from mycorrhizal root tips (see section 2.2.7) was amplified using the forward primer ITS 1F (5’ – CCATCTCATCCCTGCGTGTCTCCGACTCAG (Lib-L Primer A)XXXXXXXXXXCTTGGTCATTTAGAGGAAGTAA (ITS 1F) – 3’ (Gardes and Bruns 1993), where ‘XXX…’ is one of 101 MID tags (see Appendix A for complete list of MID tags) and the reverse primer ITS 2(5’ – CCTATCCCCTGTGTGCCTTGGCAGTC (Lib-L Primer B) TCAGGCTGCGTTCTTCATCGATGC (ITS 2) – 3’) (White et al. 1990).  Samples were amplified only once because ectomycorrhizas were expected to be dominated by one EMF species.  Each 25 µL PCR reaction contained 14.75 µL H2O, 5 µL 5X colorless GoTaq Reaction Buffer (Promega, Wisconsin, USA), 0.5 µL dNTPs, 2.5 µL MgCl2, 0.25 µL GoTaq DNA polymerase (Promega, Wisconsin, USA) and 0.5 µL of each primer.  Thermocycler settings were 94 °C for 85 seconds, 13 cycles of 95 °C for 35 sec, 55°C for 55  24 sec, and 72°C for 45 sec, 13 cycles of  95 °C for 35 sec, 55°C for 55 sec, , and 72°C for 120 sec, and 9 cycles of 95 °C for 35 sec, 55°C for 55 sec, , and 72°C for 180 sec, and finally, 72 °C for 10 min, following the procedure of Gardes and Bruns 1993.  Reactions were visualized on 1 % agarose gels.   Successful amplifications were cleaned and normalized (1 to 2 ng/µL) with SequalPrep Normalization Plate (96) kit (Invitrogen, Burlington, ON).  Samples were randomly assigned to a library and were pooled in an equimolar mixture by combining 5 µL of PCR amplicon to create a total of eight amplicon libraries.  Each library contained 101 unique MID tags, which represented one morphotype per seedling.  Each library was amplified in a 1/16 plate Next Generation pyrosequencing reaction on a Roche-GS-FLX at the Laboratory for Advanced Genome Analysis (LAGA) at the Vancouver Prostate Centre.  2.2.9 Bioinformatics   Pyrosequencing data was imported into the QIIME 1.7.0 (Quantitative Insights Into Microbial Ecology; Caporaso et al. 2010) software for sequence quality control. Low quality sequences were filtered out using split_libraries.py (see Appendix C for complete QIIME code).  Sequences with primer mismatches, homopolymers > 6 bp, and length < 100 bp and > 1000 bp were removed.  The ITS 1 region of sequences was isolated using the Fungal ITS Extractor (Nilsson et al. 2010).  Sequences were clustered into operational taxonomic units (OTUs) using the function pick_otus.py with the cd-hit clustering method selected (A.D. Godin pers. comm.).  Sequences were clustered into OTUs at a threshold of 95 % similarity (Jumponnen and Jones 2009).  The most abundant sequence for each OTU was selected by QIIME as a representative sequence using pick_rep_set.py. Taxonomy was assigned to these representative sequences via QIIME using the BLAST method in assign_taxonomy.py, which compared sequences to a database of reference sequences compiled from the GenBank and User-friendly Nordic ITS Ectomycorrhiza Database (UNITE) databases.  Taxonomy was assigned based on 97 % similarity of query sequence to reference sequence.  OTUs were then grouped according to sample ID (in this case the individual root tip) in an OTU table via make_otu_table.py.   As I was only interested in the ECM fungal species, I manually inspected the sequences of each root tip sample and excluded all OTUs that were not ECM.  I then  25 assigned a single ECM OTU to the root tip sample based on (i) the morphological description of root tip sample, and (ii) the number of sequences matched to an OTU (i.e., the OTU with the largest number of reads in the root tip sample was considered first).  Assigned sequences were then manually BLASTed in GenBank to confirm that the query OTU sequence had > 100 bp coverage and 97 % similarity to the BLAST hit.  If these criteria were not met, the next OTU that best matched the morphotype description and had the next highest abundance of sequences was chosen and BLASTed. This process was repeated until the criteria were met.  For example, a root tip sample could have 50 OTUs, with 10 of these being ECM fungi.  OTU1 (an ECM fungus) could contain 400 sequences, with the remaining OTUs containing <100.  As OTU1 appeared to be the most abundant species in the root tip sample, the ID of OTU1 was assigned to the root tip sample, as long as the sequence was consistent with the morphological description and the BLAST results matched the above criteria.   2.2.10 Statistical analyses  Due to unequal variances even after data was log transformed, Welch’s two sample t-tests performed in R 3.0.0 (R Core Team 2013; see Appendix B for R code) were used to test for differences between clearcut and forest environments in soil N and C content, seedling biomass, height, and RCD, and foliar N content of apical buds.  For soil chemistry, Kruskal-Wallis tests in R 3.0.0 were used to determine if soil chemical differences existed among clearcut sites and then among forest sites.  When a significant difference between sites was found, a post-hoc Tukey’s HSD test determined specifically which sites were different.  Non-metric multidimensional scaling (NMS) ordination with Sørensen distances based on relative abundance in PC-ORD 6 (McCune and Grace 2002) was used to visualize ECM fungal community data of pre-transplant seedlings.  Sørensen distance is also known as the Bray-Curtis coefficient when applied to quantitative data, as in this case, and is based on relative abundance (McCune and Grace 2002).  An initial run of the NMS ordination was used to optimize the results and started with a random configuration of the data points in the ordination space.  The parameters set to optimize the NMS output were (B. Pickles, pers. comm.): number of axes = 6, number of runs with real data = 50, stability criterion = 0.0005, iterations to evaluate stability = 200, and maximum number of iterations = 250.  The number of dimensions that had the least stress was concluded to be the best solution for the NMS  26 ordination.  Once this was determined, another run of the NMS ordination was performed, this time using the starting configuration supplied by the best solution, to ensure that the best solution was not sub-optimal by chance.   The parameters were the same as above with the exception that the number of axes was set to the number of dimensions in the best solution (ex. if a 2D NMS ordination was the best solution, the number of axes was set to 2), and number of runs with real data was set to 1.   Differences between clearcut and forest communities were assessed using ranked multi-response permutation procedures (MRPP). MRRP gives A, T and p values.  The test statistic A describes within-group heterogeneity and ranges from 0 to 1.   When A is close to 0, this indicates that groups are homogenous.  When A is close to one, this indicates that groups are exhibiting a higher amount of heterogeneity than is expected by chance.  In community ecology, a value of A > 0.3 in considered high (McCune and Grace 2002).  The test statistic, T, indicates the separation between groups; the more negative T is, the stronger the separation is between groups (McCune and Grace 2002). P-values indicated which pairwise comparisons were significantly different.  2.3 Results: 2.3.1 Soil analyses Mineral soils sampled from forests and clearcuts differed considerably.  Soil from clearcut sites had at least 50 % higher available nitrate, mineralizable nitrogen, total nitrogen, and total carbon than soil from forest sites (Table 2.1).  Extractable ammonium did not differ.  In forest plots, organic soils had at least 60 % higher nutrient contents for all nutrient analyses than mineral soils.  Among forest plots, Site 3 had very different soil chemistry than the other two sites.  It generally had the highest nitrogen contents (Table 2.2) in mineral soil samples, but the lowest mineralizable nitrogen in the organic soil samples.  Site 1 had the highest C content in mineral soils.  By contrast, when comparing clearcut samples, Site 3 had the lowest C content in mineral soils.      27 Table 2.1 Nutrient status of mineral soils sampled from three clearcut/forest pair sites in the Engelmann spruce – Subalpine fir biogeoclimatic zone at time of transplant (Welch’s two-sample t-test, n = 3) and the organic layer sampled from forests.    Mineral soil  Organic soil Analysis Clearcut Forest p-value Forest Available NO3 (mg kg-1) 26 ± 6.4 18 ± 12 0.01 56 ± 36 Available NH4 (mg kg-1) 7.2 ± 1.3 7.4 ± 1.9 0.7 44 ± 3.8 Total DIN (mg kg-1) 34 ± 5.4 26 ± 12 0.06 99 ± 37 Mineralizable N (mg kg-1) 55 ± 4.7 31 ± 6.6 <0.01 307 ± 18 Total N (%) 0.24 ± 0.01 0.17 ± 0.01 <0.01 0.77 ± 0.05 Total C (%) 6.9 ± 0.8 4.4 ± 0.21 <0.01 23 ± 3.2 Data are provided as means ± one standard error. P – values are shown only for comparisons between clearcut and forest mineral soils (d.f. = 16) as fine lateral roots were only found in mineral soil (the organic layer in forests was only 1 cm thick). Analyses were performed on three mineral and organic soil samples for each plot, for a total of 9 mineral samples from clearcuts and 9 mineral and 9 organic samples from forests.   28 Table 2.2 Nutrient status of mineral soils and the forest floor among individual clearcut and forest plots (one-way ANOVA, nC = 3, nF = 3).  C1-3 represents clearcut plots at Site 1, 2, and 3.  F1-3 represents forest plots at Site 1, 2, and 3.  Clearcut Forest       Mineral Soil  Forest Floor Analysis C1 C2 C3 p-value F1 F2 F3 p-value F1 F2 F3 p-value Available NO3 (mg kg-1) 33.73 ± 12.98 19.13 ± 0.27 28.03 ± 27.70 0.3 0.37 ± 25.82 0.30 ±  4.25 54.30 ± 17.99 0.05 0.60 ± 0.45 0.83 ± 0.19 165.40 ± 81.28 0.06 Available NH4 (mg kg-1) 8.30 ± 1.58 8.63 ± 2.61 4.93 ± 1.23 0.4 5.67 ± 6.33 5.97 ± 1.18 10.70 ± 1.63 0.4 41.83 ± 8.38 46.23 ± 8.96 43.33 ± 4.34 0.6 Total DIN (mg kg-1) 42.03 ± 12.60 27.77 ± 2.87 32.97 ± 27.30 0.4 6.03 ± 24.95 6.27 ± 4.17 65.00 ± 19.62 0.06 42.43 ± 7.93 47.00 ± 8.81 208.77 ± 84.29 0.3 Mineralizable N (mg kg-1) 68.33 ± 12.36 48.37 ± 2.51 47.93 ± 10.84 0.1 16.40 ± 20.19 20.20  ±6.73 56.33 ± 1.73 0.05 314.83 ± 34.50 339.67 ± 28.80 265.77 ± 24.20 0.3 Total N (%) 0.25 ± 0.02 0.24 ± 0.01 0.20 ± 0.03 0.1 0.16 ± 0.08 0.14 ± 0.02 0.23 ± 0.03 0.02 0.75 ± 0.01 0.93 ± 0.04 0.63 ± 0.06 0.03 Total C (%) 9.04 ± 0.34 7.47 ± 0.56 4.26 ± 0.17 0.02 5.09 ± 1.41 3.79 ± 1.41 4.25 ± 0.38 0.03 23.04 ± 0.24 34.00 ± 1.35 11.82 ± 1.27 0.03 Data are provided as means ± one standard error. Ten soil samples were randomly taken from each clearcut and forest plot and were consolidated and mixed by plot.  Three sub-samples from each clearcut and forest plot were drawn from dried composited soils and sent away for analyses.   29  2.3.2 Age, biomass and nitrogen status of seedlings All seedlings sampled from both clearcut and forest plots were 2 or 3 years old.  The individual weights of seedling parts and total biomass did not differ between seedlings that originated from the two environments (Figure 2.1).  Buds weights are included in total seedling weights in Figure 2.1, but are not plotted separately because seedlings differed in stage of bud development between the two environments.  Of clearcut seedlings, 93 % had buds that were flushed or partially flushed into new shoots, whereas only 47 % of seedling from the forest had buds that were partially flushed and none that were fully flushed. Because they were in a later stage of flush, buds from clearcut seedlings were significantly larger than buds of forest seedlings (Welch’s two-sample t-test, p<0.01).  Seedlings that originated from clearcuts tended to have higher percent N in their apical buds than seedlings sampled from the forest (Welch’s two-sample t-test, p=0.1) (Figure 2.2).  Open and closed buds were combined as the initial weight of the buds was accounted for during this analysis.  Figure 2.1 Dry weights of pre-transplant subalpine fir seedlings from three high elevation clearcuts and forests.  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  The weights of apical buds are included in the total biomass weight.  Neither total biomass, nor biomass of tissue types differed between seedlings originating from clearcuts and forests (t-tests, p > 0.1; nC = 3, nF=3).  30   Figure 2.2 The percent nitrogen content of apical buds of pre-transplant seedlings.  The boxes represent the interquartile range and the dark line in the box represents the median.  The whiskers are the highest and lowest data points within the 1.5 interquartile range (p = 0.1, Welch’s two-sample t-test, nC = 3, nF = 3).   2.3.3 Pyrosequencing A non-metric multidimensional scaling (NMS) ordination showed that ECM fungal communities colonizing pre-transplant seedlings from clearcuts were distinct from fungal communities colonizing forest pre-transplant seedlings.  A one-dimensional ordination had the lowest stress value (p = 0.04).  As the stress value reflects how well the ordination summarizes the observed distances between samples, the one-dimensional ordination was therefore the optimial solution.  Clearcut ECM fungal communities clustered to the right of the ordination space while forest ECM fungal communities clustered to the left (Figure 2.3).  Multi-response permutation procedures confirmed that the separation observed in the ordination was statistically significant (MRPP, A= 0.05, T = -2.6, p = 0.02).      31  Figure 2.3 A non-metric multidimensional scaling (NMS) ordination of ectomycorrhizal fungal communities colonizing the roots of pre-transplant seedlings from clearcuts (pre-C) and forests (pre-F).  A one-dimensional ordination resulted in the lowest stress value and was thus considered to be the best solution.  The single axis explains 61 % of the variation in community structure (n = 3 plots).    Amphinema spp. colonized at least 20 % of roots in clearcuts vs. 3 % in forests (Figure 2.4).  Wilcoxina spp. colonized 35 % of roots in clearcuts vs. 5 % in forests. Piloderma spp. colonized 20 % of roots in forests vs. 0 % occurrence in clearcuts and Tylospora spp. occurred on 13 % of root tips forests vs. 5 % occurrence in clearcuts.  None of these differences in abundance were statistically significant.   32  Figure 2.4 Dominant ectomycorrhizal fungal species (> 10 % of tips in any plot) colonizing the roots of pre-transplant A. lasiocarpa seedlings harvested in July from clearcuts (pre-C) and forests (pre-F) (n=3 plots).  % occurrence is the percentage of root tips on which a fungal species was identified out of the total number of mycorrhizal tips. Uk. Helotiales refers to samples that were identified as an unknown Helotiales species.  2.4 Discussion 2.4.1 Soil analyses and seedling characteristics  Prior to reciprocally transplanting naturally regenerated A. lasiocarpa seedlings between clearcuts and forests, it was important to determine whether the soil chemistry differed between clearcut and forest soils.  Differences in edaphic conditions influence ECM fungal community structure and mycorrhizosphere enzyme activities (Buée et al. 2007, Courty et al. 2010, Rineau and Courty 2011). Soil nitrogen content was examined because N is typically the limiting mineral nutrient for tree growth in northern temperate and boreal forests (Smith and Read 2008, Vitousek et al. 2010).  Effects of clearcutting on soil properties can be highly varied (Johnson 1992, Johnson and Curtis 2001, Grenon et al. 2004, Nave et al. 2010), but generally soils experience an increase in dissolved inorganic nitrogen (DIN) concentration compared to soils in the undisturbed forest (Dahlgren and Driscoll 1994, Hope 2009, Jerabkova et al. 2011).  These elevated nitrogen levels persist for a few years after harvest before declining to pre-harvest levels (Dahlgren and Driscoll 1994).  The response of soil inorganic N to clearcutting in this study was broadly consistent with other 0% 20% 40% 60% 80%ClearcutForest% Occurrence Amphinema spp.Uk. Helotiales spp.Piloderma spp.Tylospora spp.Wilcoxina spp. 33 studies that reported an increase in nitrate (NO3) contents after clearcutting (Fisk and Fahey 1990, Hope 2009).  This study detected higher NO3 content, mineralizable N, and total N in clearcut than forest soils, but detected no difference in NH4 between the environments.  The increase in DIN after clearcutting can be attributed to both abiotic and biotic influences.  Often, the increase in NO3 in clearcuts is attributed to warmer and moister soil conditions, which cause an increase in decomposition and mineralization rates and thus higher levels of NO3 (Bormann et al. 1974, Edmonds and McColl 1989, Prescott et al. 2000, Hope 2001).  Also, changes in plant cover on harvested sites (Marshall 2000) impacts the quality and quantity of litter fall inputs, which influences decomposition rates as clearcut sites have reduced inputs and accumulate more recalcitrant material (Yin et al. 1989).  Although faster decomposition rates in clearcuts have been thought to be a major factor increasing DIN levels after clearcutting (Bormann et al. 1974, Dahlgren and Driscoll 1994), post-harvest decomposition rates can be highly variable (Yin et al. 1989) and may not be the main influence on increased N levels after clearcutting (Prescott et al. 2000).  Instead, Prescott et al. (2000) suggested that lower C levels after harvesting cause a reduction in microbial immobilization of NO3, contributing to increased abundance and subsequent leaching of NO3 often observed after harvesting (Stark and Hart 1997).  Therefore, edaphic conditions after clearcutting can influence abiotic and biotic factors in nutrient cycling, affecting soil NO3 and potentially NH4 levels.   Although soil NO3 and NH4 levels can increase after harvest, the impact of clearcutting on NH4 tends to be more variable.  The results of this study concur with the findings of a meta-analysis of N fluxes in clearcut environments that concluded that generally, NH4 levels do not increase after clearcutting (Jerabkova et al. 2011).  In addition, due to varying responses of net mineralization to clearcutting, Grenon et al. (2004) suggested that generalizations should not be made about N availability after harvest.  This suggests that although other studies observed a temporary increase in NH4 after clearcutting (Fisk and Fahey 1990, Hope 2009), this should not be considered the norm. Nitrogen contents in the forest Site 3 plot were considerably higher than the other two plots, and were several orders of magnitude higher in NO3 content and consequently total DIN.  It is not clear why this difference was so extreme, but high N levels have been observed in other studies (Hope et al. 2003, Lajzerowicz et al. 2004).  In a study examining  34 available soil N in harvested openings in the ESSF, (Hope et al. 2003) also observed large NO3 (≤ 260 mg kg-1), NH4 (≤ 210 mg kg-1), and total inorganic N (≤ 470 mg kg-1) levels in the forest floor layer of uncut forests.  Levels could reach below 100 mg kg-1 for these three nitrogen pools.  Lajzerowicz et al. (2004) also observed order of magnitude differences in NO3, NH4, and dissolved organic nitrogen pools between timber harvest treatments in an ESSF site.  In addition, the high N levels observed in this study are roughly on par with N levels observed in forest stands that received fertilization.  For example, in a study of long term fertilization of Pinus contorta stands, the fermentation-humic layer in plots that received annual fertilization were noted to have levels of NO3 reaching as high as 104.6 mg kg-1 compared to 1.0 mg kg-1 in control plots that received no fertilization (Jones et al. 2012).  Therefore, although it is unusual for the N levels (especially NO3 and DIN contents), of Site 3 to be so different from the other two sites, the high contents observed are not entirely uncommon.  The results of this study broadly concur with the findings of others in terms of soil N levels, and confirm that clearcut and forest soils in our study differed in N content, and also somewhat in C content.   A significant amount of C is stored in forest floors (Nave et al. 2010) and as harvesting disturbs the forest floor, it was expected that total carbon in clearcut sites would be reduced compared to the mature forest.  However, this study detected higher levels of C in clearcut mineral soils compared to forest mineral soils. Clearcut soils lacked an organic layer, but this layer in forests had higher levels of C than mineral soils in either environment.  Fine roots of forest seedlings did not occur in the forest floor layer however, but were instead found in the mineral soil.  Therefore, root tips in both environments were found in mineral soil, so mycorrhizal root tips of clearcut seedlings experience higher levels of C than mycorrhizal root tips of forest seedlings.      Responses of soil C to harvesting can be variable with some studies reporting reduced C levels in soil following harvest (Nave et al. 2010, Chen and Shrestha 2012) and others reporting an abundance of C following harvest (Sanscrainte et al. 2003, Grand and Lavkulich 2012).  Reduction in soil C post-harvest can be attributed to warmer soil temperatures increasing microbial activity (Chen and Shrestha 2012) and mechanical operations during the harvest process incorporating the previous forest floor into underlying mineral soil (Mroz et al. 1985, Ryan et al. 1992), or changing soil physical properties like soil hydrology and  35 crushing aggregates (Chen and Shrestha 2012).  Increases in soil C can be due to translocation of C from decomposition of logging residues to early successional plants, which decompose faster than conifer needles (Covington 1981, Entry and Emmingham 1998, Prescott et al. 2000) and logging inputs (Grand and Lavkulich 2012).  The response of soil C to harvesting is highly variable and is dependent on site conditions such as dominant vegetation type, soil taxonomic order, climatic conditions (Borchers and Perry 1992, Prescott 1997, Nave et al. 2010), and study design, especially time after harvest and sampling depth (Covington 1981, Black and Harden 1995, Sanscrainte et al. 2003).  Nonetheless, in the context of this study, clearcut and forest soils have differing levels of C in mineral soils.   2.4.2 Ectomycorrhizal fungal community structure Ectomycorrhizal fungal communities colonizing clearcut seedlings in the early summer at the time of transplant were distinct from fungal communities colonizing forest seedlings of the same age.  It is important to note that although ECM fungal communities colonizing seedlings and mature trees have been observed to be similar (Jonsson et al. 1999), differences between fungal communities colonizing seedlings and mature trees have also been noted (Reverchon et al. 2012).  Consequently, ECM fungi colonizing one-year-old naturally regenerated A. lasiocarpa seedlings in my study may not be representative of the whole ECM fungal community at these ESSF sites.  Nonetheless, in regards to very young A. lasiocarpa seedlings, ECM fungal communities colonizing seedlings from clearcuts differed from communities colonizing seedlings in clearcuts.   Ectomycorrhizal fungal species that dominated roots of clearcut seedlings included species of genera commonly found in recently disturbed areas:  Amphinema spp. and Wilcoxina spp.  Species of these genera frequently occur in areas after fire or clearcutting, and in nursery soils (Cairney and Chamber 1999, Barroetaveña et al. 2010, Barker et al. 2013).  Peay et al. (2009) speculated that after disturbance, these species experience a release from competition, which could play a major role in determining ECM fungal community structure post-disturbance.  In some forest systems, it has been observed that Wilcoxina and Amphinema spp. may be more effective at colonizing root tips of young seedlings than other ECM fungal species (Rosling et al. 2003, Walker and Jones 2013).  It is also possible that dispersal ability (i.e., fungal inoculum type) and/or adaptations to post-disturbance edaphic  36 conditions drive community composition in disturbed areas (Jones et al. 2003).  The ability of Amphinema spp.to easily colonize disturbed environments indicates that species of this genera are easily dispersed and are capable of establishing from spores (Kernaghan et al. 2003, Rudawska et al. 2006, Walker and Jones 2013).  Based on rhizosphere enzymatic activities, Wilcoxina spp. seem to acclimate to a range of disturbance types (Jones et al. 2010), including fires (Smith et al. 2005, Barker et al. 2013) and clearcutting (Lazaruk et al. 2005, Walker and Jones 2013).  Also, compared to other fungi, Wilcoxina spp. seem to be more effective at colonizing spruce seedlings inoculated with a mixed ECM fungal species, including later successional species (Velmala et al. 2014).  In addition to dispersal ability, Wilcoxina spp. appear to be more efficient at taking up nitrogen in clearcuts (Jones et al. 2009) and have also been associated with high chitinase activities (Velmala et al. 2014). Due to dispersal ability and potential ability to acclimate disturbed environments it is not surprising that Amphinema and Wilcoxina spp. are the dominant colonizers on clearcut pre-transplant seedlings. The ECM fungal species dominating the root systems of forest seedlings in my study were species often associated with mature forest stands (Erland 1995, Visser 1995) and included Piloderma spp. and Tylospora spp.  Species of these genera are often found in decomposing buried woody debris, (Smith et al. 2000, Tedersoo et al. 2003), as hyphae of Piloderma fallax are commonly found in decomposing and buried wood (Smith et al. 2000) and some Piloderma species can be found almost exclusively colonizing root tips of all tree ages in the forest floor (Goodman and Trofymow 1998).  Walker (2012) found a Tylospora species colonizing fine root tips of young spruce saplings to be correlated with coarse woody debris in forest environments, compared to clearcuts, but, surprisingly, Piloderma spp. were absent from coarse woody debris microsites.  Studies have noted an abundance of Tylospora spp. colonizing spruce forests in Europe (Erland 1995, Eberhardt et al. 1999, Wallander et al. 2010) and studies examining ECM fungal community structure along forest successional chronosquences have noted the prevalence of these genera in older stands (Visser 1995, Smith et al. 2000, Twieg et al. 2007).   The predominance of these genera in mature forest stands could be due to dispersal ability.  As Piloderma occurred so infrequently in 5-year old stands, it has been suggested that this could be due to low spore dispersal and germination (Twieg et al. 2007).   Little is  37 known about the dispersal ability of Tylospora spp. but is has been suggested that as species of this genus are mainly found on seedlings with access to mature trees (Tedersoo et al. 2008), root-to-root spread may be the main form of dispersal. Competition can also play a role as Tylospora spp. are not usually found in recently disturbed sites and appear to be poor competitors of fungi commonly found in disturbed sites (like Thelephora terrestris and Amphinema byssoides) for root space of young seedlings (Walker and Jones 2013).  Piloderma and Tylospora spp. are commonly found in mature forest stands, so it is not surprising that these species dominate A. lasiocarpa seedlings originating from the forest.     2.4.3 Seedling characteristics  With the exception of apical buds, which had started to flush in seedlings from clearcuts, no differences in biomass were detected between clearcut and forest seedlings.  Other studies have detected size differences between clearcut and forest seedlings, with forest seedlings being larger (Parish and Antos 2005, Welke et al. 2003).  For example, Parish and Antos (2005) measured the heights of advanced regeneration in an ESSF site up to 5 years after timber harvest.  They found that prior to harvest, 2-yr old A. lasiocarpa seedlings were 2 to 3 cm from root collar diameter to shoot height and that after harvest, height growth of fir seedlings in the forest edge averaged 3.0 cm over 5 years, with seedlings in the clearcut only averaging 1.2 cm over 5 years.  Also, Welke et al. (2003) sampled fine roots of ESSF conifer species by taking cores in clearcuts and in the mature forest.  They saw that fine root biomass was generally reduced in 1.0-ha and 0.1-ha cutblocks compared to the forest and was significantly reduced in 10-ha cutblocks.  In my study, root biomass did not differ between clearcut seedlings and forest seedlings.  However, at an ESSF site, Lajzerowicz et al. (2004) observed that 9-year-old seedlings in clearcuts were taller and had larger total mass than seedlings in the forest.  This increased growth of clearcut seedlings was determined to be more correlated with open-sky light than soil N availability or temperature.  Although other studies have observed size differences between seedlings growing in clearcuts and forests, my study observed no differences in size, with the exception of larger buds in clearcut seedlings.    More open-sky light availability in clearcuts is most likely responsible for apical buds of seedlings from clearcuts being in a more advanced state of bud flush and having  38 consequently larger buds than seedlings from forests.  Kummel and Lostroh (2011) observed that the length of apical buds of A. balsamea increased with increasing light availability.  This is not surprising as bud burst in the spring is linked to temperature and light availability.  Therefore, more light in clearcut environments triggered bud burst and clearcut seedlings consequently had larger apical buds than forest seedlings.         Clearcut and forest seedlings had similar apical bud N contents.  This result is consistent with the findings of Lajzerowicz et al. (2004) who found that timber harvesting treatments did not affect foliage N contents of 9-year-old planted fir seedlings.   Light availability (Kranabetter and Simard 2008, Kranabetter et al. 2010) has been shown to influence N contents of young seedlings.  Foliar N contents have been shown to increase with increasing light availability (Niinemets 1997, Grassi and Bagnaresi 2001) and conifer seedlings accumulate more N in their foliage when grown in nitrate vs. ammonium   As clearcuts had higher open sky light availability than forests, it is surprising that the buds of clearcut seedlings did not have higher N contents than the buds of forest seedlings.   Nonetheless, prior to the field reciprocal transplant, characteristics of seedlings excavated from forests and clearcuts were roughly similar.   2.5 Summary         Clearcut and forest soils differed in terms of N and C content, presenting different environmental conditions to the distinct clearcut and forest ECM fungal communities colonizing A. lasiocarpa seedlings in these environments.  In addition, no statistical differences were detected between clearcut and forest seedlings in terms of age, biomass or foliar N.  These factors allowed us to test how clearcutting influences ECM fungal community functioning via a reciprocal transplant of seedlings between clearcut and mature forests.             39 3    Chapter: Seedling Conditions and Extracellular Enzyme Activities at the End of the Growing Season 3.1 Synopsis Chapter 2 confirmed that composition of ectomycorrhizal (ECM) fungal communities differed between clearcut and forest environments in our ESSF sites.  Because young trees in the Pinaceae do not grow and develop optimally in the field without their ECM symbionts, it is important to determine whether the shift in ECM fungal community after clearcutting equates to a change in function. In particular, the ability of the fungi to break down soil organic matter and provide nutrients to the seedlings is an important functional attribute of the EMF community. Extracellular enzymes excreted by ECM fungi release nutrients from complex organic compounds, which plants would otherwise be unable to utilize (Bending and Read 1995, Conn and Dighton 2000, Koide et al. 2007).   In forest soils, most of the N content is in complex organic forms (Qualls et al. 1991, Michelsen et al. 1998) and extracellular enzymes are particularly important in degrading organic N (Simard et al. 2002).  Although plants are capable of acquiring some form of organic N like amino acids, ECM plants take up amino acids at a faster rate than non-mycorrhizal plants of the same species (Chalot and Bruns 1998). Ectomycorrhizal fungi are also capable of acquiring N from more complex N forms otherwise inaccessible to plants, such as proteins (Abuzinadah and Read 1989, Chalot and Bruns 1998).  The secretion of enzymes by ECM fungi is therefore a key functional trait related to their mutualistic relationship with trees.  Different ECM fungal species are thought to play distinct roles in nutrient cycling in forest ecosystems (Kranabetter et al. 2006, Lilleskov et al. 2011, Jones et al. 2012).  For instance, some ECM fungi are better at mobilizing recalcitrant forms of organic N using extracellular enzymes, whereas others use simple forms of organic N (Lipson and Nasholm 2001, Read and Perez-Moreno 2003).  Furthermore, enzyme activities of clearcut and forest ECM fungal communities colonizing spruce seedlings at an Engelmann spruce – Subalpine fir (ESSF) site have been found to differ (Walker 2012). Specifically, laccase and phosphatase activities were higher in forest than in clearcut plots and aminopeptidase, cellobiohydrolase, and xylosidase activities were lower in clearcuts that retained coarse woody debris than in plots where woody debris was removed, or in forest plots.  However,  40 this difference was observed by comparing roots that not only had ectomycorrhizas formed by different fungi, but that were also growing in different environments (clearcut vs. forest).  Because species differences were confounded with environment differences, Walker (2012) could not definitively test for genetically-based differences in physiology between the two ECM fungal communities.  The foliage of ECM plants is frequently depleted in 15N compared to non-mycorrhizal plants (Schmidt and Stewart 1997, Michelson et al. 1998, Hobbie and Högberg 2012). This has been attributed to fractionation of N by ECM fungi during the synthesis of compounds that are subsequently translocated to their host plants (Michelson et al. 1998, Högberg 1990, Hobbie et al. 1999). ECM plants enriched in δ15N are thought to obtain a higher percentage of N from fungal symbionts than from non-mycorrhizal roots (Hobbie et al. 2008).  In order to more effectively attribute the differences in community function to either fungal species or environment, I conducted a reciprocal transplant experiment of young Abies lasiocarpa seedlings between clearcut and forest environments.  Seedlings were harvested and characterized for ECM fungal communities four months later. Extracellular enzyme activities, seedling biomass, and N and δ15N contents of apical buds were also measured to determine if clearcut and forest ECM fungal communities differed in term of nutrient acquisition.  3.2 Methods 3.2.1 Experimental design In July, 2011, 50 naturally regenerated A. lasiocarpa seedlings were excavated in each of the three clearcuts and forests.  To minimize edge effects (Hagerman et al. 1999b, Kranabetter and Friesen 2002), only seedlings located 25 m from the clearcut/forest edge were selected.  Seedlings were also at least 1.2 m apart to avoid spatial auto-correlation effects (Pickles et al. 2010).  Root systems were kept intact and were gently rinsed with water in the field to remove excess soil, but to leave ECM root tips undamaged.  Length of shoot (from base of shoot to tip of shoot) was measured.  Twenty-five of the excavated seedlings from each clearcut (CC treatment) and 25 seedlings from each forest (FC treatment) were then randomly selected and transplanted at 1-m spacing into a 10 m x 10 m plot in each clearcut.  To randomly intersperse clearcut and forest seedlings in the grid, a coin  41 toss determined if a seedling from the clearcut or forest was planted.  If an unsuitable planting location was encountered, the next suitable location in the grid was used.  Shade tents using 1 mm screen material were erected over forest seedlings planted into a clearcut to prevent rapid bleaching of foliage.  The remaining 25 seedlings harvested from each clearcut were transplanted into a 10 m x 10 m plot in the forest (CF treatment), interspersed with the remaining seedlings harvested from the forest (FF treatment), following the same guidelines (see Figure 3.1). Seedlings were watered once only, immediately after transplantation, to aid in survival.   After one growing season, (12 weeks, early July 2011 to early October 2011) seedlings were excavated and transported back to the lab.  Roots were kept in contact with soil (Courty et al. 2005) and were stored in a walk-in cooler at 4 °C until further analysis (up to 13 days for enzyme assays; up to 4 weeks for fungal identification).  Instead of harvesting seedlings one site at time and then performing assays in a staggered manner, seedlings from all sites were harvested in one day as snow was starting to fall at the field sites.     Figure 3.1 The reciprocal transplant experiment of naturally regenerated Abies lasiocarpa seedlings performed at three paired clearcut and forest plots.    42 3.2.2 Enzyme assays On the morning of the assay, root systems of transplanted seedlings were randomly selected from each plot, gently washed in water, and examined under a stereomicroscope at 10 X to 40 X magnification as needed.  Five root tips of approximately 2 mm in length were selected from throughout the root system of all surviving seedlings; starting at the root collar, every second primary branch off the main root was examined (if a tip occurred on the main root before the primary branch it was ignored).  If tips occurred along the primary branch the first tip was chosen but the rest ignored until a secondary branch (Figure 3.2).  Every other secondary branch was examined.  The third tip along a secondary branch was selected.  If only one tip was present, that tip was selected.  If a tertiary branch occurred, the first tip along that branch was selected and other branching was ignored.  The next primary branch was then examined.  This procedure was repeated until five tips were selected.  If not enough tips were selected using this method, every other tip along the main root was selected until the five tips were obtained. Dead tips were avoided, but active non-mycorrhizal root tips were included.  If a tip appeared shrivelled, it was not selected in order to avoid selecting a dead tip. If five tips were not available, then all available tips on the seedling were selected.  A brief description of root tip colour and texture was recorded when each tip was selected.  Tips were placed into individual micro-sieves constructed from 200 µl PCR strip tubes (Fisher Scientific, Ontario, Canada ) with the bottoms removed and replaced with 0.25 mm Nylon mesh (see Pritsch et al. 2004) arranged in a 96-well microtitration plate (Costar®, Cole-Parmer, Quebec, Canada).  Tips were stored in Tris-buffer (Pritsch et al. 2004) until the start of enzyme assays. One or two 96-well flat-bottom microtitration plates were filled per day.  Root systems were separated from the shoot and seedling parts were returned to a walk-in cooler.    43  Figure 3.2 Schematic diagram of a young A. lasiocarpa root system showing primary, secondary, and tertiary branches.  Red ovals represent tips that would have been selected for enzyme assays.  Black ovals represent tips that would have been passed over for assays.    Enzyme assays on individual root tips were performed following the procedures of Pritsch et al. (2004) and Courty et al. (2005).  The activities of seven enzymes were measured using fluorogenic substrates (either methylumbelliferone (MU) or aminomethylcoumarin (AMC); Table 3.1): β-glucuronidase (EC 3.2.1.31), β-1,4-xylosidase (EC 3.2.1.37), cellobiohydrolase (EC 3.2.1.91), β-1,4-glucosidase (EC 3.2.1.21), β-1,4-N-acetylglucosaminidase (EC 3.2.1.52), acid phosphatase (EC 3.1.3.2), and leucine aminopeptidase (EC 3.4.11.1).  Laccase (EC 1.10.3.2) activity was measured by colorimetry using the substrate diammonium 2,2’-azinobis-3-ethylbenzothiazoline-6-sulfonate (ABTS).  Working stock solutions of fluorimetric and colorimetric substrates were made up weekly and were stored in the dark until use.  Root tips were kept in a plate of rinse buffer for a least 5 min before being immersed in a substrate incubation plate.  Plates were incubated in the dark (Pritsch et al. 2004) at 21 °C and 100 rpm (MaxQ 4000 E-Class shaker, Thermo Fisher Scientific, Ontario, Canada).  After each incubation, tips were placed back into the rinse plate and then stop buffer was added to the incubation plate.  Sieve strip tubes containing tips were removed from the rinse plate, blotted on paper towel to remove rinse buffer, and placed in the next substrate.  This technique allowed the activities of eight enzymes to be measured in succession on the same tip.  A FLUOstar Galaxy fluorescent microplate reader (BMG Lab Technologies, Ortenberg, Germany) was used to read the incubation plates at 364 ± 10 nm excitation and 445 ± 15 nm emission.  The colorimetric  44 assay (laccase) was read at 420 ± 5 nm.  After assays, root tips were scanned using a Canon CanoScan 9000F scanner; WinRHIZO (Regent Instruments, CANADA) was then used to calculate projected surface area of root tips.  Tips were then transferred to a 96-well PCR plate and were stored in a drop of reverse osmosis water at -80 °C for possible future molecular identification.    Table 3.1 Enzyme activities assayed on roots tips of A. lasiocarpa seedlings harvested 12 weeks after reciprocal transplant between mature forests and clearcuts. Enzyme Substrate Activity Glucuronidase 4-MU1-β-D D-glucuronide hydrate  carbohydrate breakdown Xylosidase 4-MU β-D-xylopyranoside hemicellulose degradation Cellobiohydrolase 4-MU β-D-cellobioside cellulose degradation β-glucosidase 4-MU β-D-glucopyranoside cellulose degradation N-acetylglucosaminidase 4-MU N-acetyl-β-glucosaminide chitin degradation Phosphomonoesterase 4-MU phosphate free acid hydrolyses phosphate esters Leucine aminopeptidase L-leucine 7-AMC2 protein degradation Laccase ABTS3 lignin degradation 14-methylumbelliferone   27-aminomethylcoumarin   3Diammonium 2,2’-azinobis-3-ethylbenzothiazoline-6-sulfonate   3.2.3 Morphotyping the ectomycorrhizal communities of post-transplant seedlings  After completion of the enzyme assays, the remaining mycorrhizal root tips on the root systems of post-transplant seedlings were placed into morphotypes using procedure described in section 2.2.5.  Two tips per morphotype per seedling were placed in PCR strip tubes with a drop of reverse osmosis water and were stored at -80 °C until subsequent molecular identification.  The total number of tips on a root system was also counted, and ECM fungal percent colonization on a seedling was calculated from this.  3.2.4 Biomass and nutrient analysis of apical buds of post-transplant seedlings  Directly after completion of enzyme assays, the shoot lengths of seedlings (to be used to calculate relative growth rates) were measured.  Wet weights and dry weights (at least 48 hours at 105 °C) of pre-2011 foliage, 2011 foliage, stem, apical bud and root systems were then determined.  15N and total N of dried apical buds (one composite sample per treatment per site) were analyzed at the Pacific Centre for Isotopic and Geochemical Research,  45 University of British Columbia, Vancouver, Canada.  A Thermoquest NC 2500 (Bath, UK) elemental analyzer was used to combust lab standards and samples.  The sample gas was taken up by a Thermo Finnigan Conflo III (Bath, UK), and transferred to a Finnigan DELTAplus XP (Bath, UK) mass spectrometer.  The operating system Windows NT 4.0 and ISODAT software (Bath, UK) were used to find the isotopic value of the sample from the 15N/14N ratio of the sample (Rs) relative to the 15N/14N ratio of the reference gas (Rr).  Stable isotope abundances are reported as δ15N in parts per mil (‰), expressed as:  δ15N (‰) of sample = (Rs / Rr - 1) * 1000 ‰  Where: Rs  = 15N/14N ratio of the sample Rr = 15N/14N ratio of the reference gas  A calibration curve of expected lab standard isotopic values vs. measured lab standard isotopic values was used to correct the δ15N samples values in Excel.  Lab standards were from the international standards IAEA-N1 (+0.4 per mil) and IAEA-N2 (+20.3 per mil).  Nitrogen content was determined from the total area under the peaks formed by the two nitrogen isotopes, which allowed me to make comparisons within the study, but was not useful for absolute estimates.  3.2.5 Molecular identification of the fungal symbiont of ectomycorrhizal root tips  The procedures described in section 2.2.7 were followed to extract, amplify and identify fungal DNA from mycorrhizal root tips of post-transplant seedlings using Sanger sequencing.  However, 80 to 90 % of samples contained multiple bands of fungal DNA and significant efforts were unsuccessful in securing successful Sanger sequences. These efforts included attempts to optimize the amplification step by diluting the DNA template, and trying different annealing temperatures, PCR master mixes, and primer sets.  Different methods were used to clean the PCR product including Exo-Sap-It, various gel extraction kits, and some combinations therein.  See section 2.2.7 for details on methods that were tried.  46 Consequently, 454 pyrosequencing was used to identify the fungal communities of the post-transplant seedlings (refer to section 2.2.8 for procedure).      3.2.6 Bioinformatics    Pyrosequencing reads were imported into QIIME 1.7.0 (Quantitative Insights Into Microbial Ecology; Caporaso et al. 2010) software for quality control and analysis.  The same bioinformatics procedures described in section 2.2.9 of this thesis were followed to assign ECM fungal identities to root tip samples.    3.2.7 Statistical analyses  Seedling growth and N status - Two-way ANOVAs performed in R 3.0.0 were used to determine interactions between source and destination environment on seedling survival and relative growth rates, seedling biomass and on relative N and δ15N content of apical buds, and % colonization of roots. Treatment effects on seedling survival, relative growth rates based on length of shoot, biomass, N content and δ15N of apical buds, and % colonization of roots were assessed using defined planned treatment contrasts fitted to a one-way ANOVA (Logan 2010) in R 3.0.0 (R Core Team 2000; see Appendix B for R code). The defined treatment contrasts were CC vs. FC and CF vs. FF, which were set up to test our hypothesis that seedlings originating from different environments would differ from each other when grown in the same environment.  Data were averaged by plot prior to analysis.  Dry biomass data were log transformed and δ15N data were power transformed, in order to satisfy assumptions of normality and homogeneity of variance.  Relative growth rate of seedlings was calculated using the formula: RGR = (ln W2 – ln W1)/(t2-t1)  Where;  W1= length of shoot (cm) at time of transplant  W2= length of shoot (cm) at final harvest in October T1= initial time (weeks) T2= final time (weeks)    47 Analyses of covariance (ANCOVA) was performed in R 3.0.0 to determine if differences in seedling size and bud N contents were due to pre-existing differences in seedling size and N contents. Analysis of ECM community – ECM fungal community data were assessed visually using a non-metric dimensional scaling (NMS) ordination with Sørensen distances in PC-ORD (McCune and Grace 2002).  Refer to section 2.2.10 for details on NMS optimization procedures.  Pairwise comparisons between the four treatments were assessed using a ranked MRPP.  MRPP was used instead of perMANOVA because MRPP calculates the mean of within group distances and then compares among groups (McCune and Grace 2002).  perMANOVA calculates the mean of across group distances and then compares among groups.  As I was interested in how tightly data points clustered within a treatment and then wanted to compare means among treatments, an MRPP was more appropriate.  Refer to section 2.2.10 for details on MRPP output.  When pairwise comparisons were determined to be statistically significant, an indicator species analysis was used to determine whether some fungal species occurred more commonly in a specific treatment.  Indicator species analyses were conducted in PCORD, with source environment as the grouping factor.  A Monte Carlo test was used to test indicator values for significance (McCune and Grace 2002).   As replication among treatments was low (n = 3 for each treatment), indicator species with a p-value of less than 0.1 were considered significant (Root et al. 2007). This is because, when making pairwise comparisons, there are only 10 possible combinations that can be made with 6 points (3 per treatment); therefore, the p value of 1/10=0.1 can be considered to be statistically significant.  The ordinations and NMS analyses were first run on community data of post-transplant seedlings only and then on community data including both pre- and post-transplant seedlings.    Exoenzyme activity – As the scales of different exoenzyme activities varied (for example, glucuronidase activities ranged from 0 to 15 pmol mm-2 min-1 and the scale for phosphomonoesterase was from -20 to 80 pmol mm-2 min-1), data were standardized to zero mean and unit variance prior to multivariate analysis, as recommended by Borcard et al. (2011). Furthermore, as data of individual enzymes were non-normal, enzyme activities were visualized via an NMS ordination performed in R using the using the Bray-Curtis  48 dissimilarity index in the metaMDS function of the vegan package.  Each point on the NMS ordination represented the combination of the eight enzyme activities, averaged across all tips for each treatment and plot.  To create a data set containing all positive numbers, the lowest value for each enzyme was added to each value.  For pairwise comparisons of enzyme activity between treatments, the distance matrix underwent a rank transformation, and multivariate response permutation procedures (MRPP) were performed in PCORD (McCune and Grace 2002).   Cluster analysis of enzyme activities – Cluster analyses can bring out trends that are not otherwise apparent in data, but do not statistically test a hypothesis. Objects within a cluster are more similar to each other than objects in another cluster.  To visualize the relative importance of factors such as site, source environment and destination environment on patterns of enzyme activities, k-means partitioning, a type of cluster analysis (Borcard et al. 2011), was performed in R 3.0.0 using the kmeans function of the stats package.  This cluster analysis was chosen because it uses the local structure of the data to define clusters (Borcard et al. 2011) and allows the user to set the number of groups objects will be clustered into.  In this case, the analysis was run four times, with the number of groups set at 3 (number of sites), 4, 5 and 6 (2 environments x 3 sites).   3.3 Results 3.3.1 Survival and growth of seedlings   Overall, 57 % of transplanted seedlings survived the 12-week growing season, although this ranged from 20 % to 88 %, depending on the treatment and site. There was no difference in seedling survival among treatments in either destination environment (pCC vs. FC = 0.2; pCF vs. FF =0.9; Figure 3.3a).  On average, seedlings grew 1.19 ± 0.03 cm in height over the summer with no difference in relative growth rates (RGR) within a destination environment (pCC vs. FC = 0.7; pCF vs. FF =0.7; Figure 3.3b).   A two-way ANOVA detected no interaction between source and destination environment for both survival and relative growth rates.   49   Figure 3.3 The (a) proportion of transplanted seedlings that survived over a growth season of 12 weeks and (b) relative shoot growth rates (n = 3 sites).  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points.  3.3.2 Ectomycorrhizal fungal colonization and community analysis  Pyrosequencing resulted in 741/808 samples being assigned an ECM fungal identity.  The results of Chapter 2 confirmed the prediction that prior to transplantation, the ECM fungal community colonizing seedlings originating from clearcuts was different from the fungal community colonizing seedlings from the forest.  Given that the overall goal of my thesis research was to compare the physiology of mycorrhizas formed in clearcuts from those formed in forests, it was then important to establish that the differences in ECM fungal communities on seedlings initially transplanted from clearcuts and forests were retained over the course of the experiment.  As is clear from the ordination (Figure 3.4), the community composition of ECM fungi did not differ among seedlings originating from the same environment, regardless of the environment they were transplanted into. A one-dimensional ordination had the lowest stress value (p = 0.02).  As the stress value measures how well the ordination summarazes the observed distances among samples, the one-dimensional ordination was the best solution.  The single axis explained 49 % of the variation. Communities on seedlings originating from the forest clustered to the left of the ordination a)  b)  Source:          Clearcut       Forest       Clearcut      Forest Destination:              Clearcut                        Forest Source:          Clearcut       Forest       Clearcut      Forest Destination:              Clearcut                        Forest  50 space and communities from the clearcut clustered to the right (Figure 3.4).  MRPP analysis confirmed that differences in ECM fungal communities between clearcut and forest seedlings were retained throughout the experiment. Specifically, the ECM communities on CC seedlings differed from those of FC seedlings (MRPP, A = 0.2, T = -1.7, p = 0.05) and the communities of FF seedlings differed from those of CF seedlings (MRPP, A = 0.2, T = -2.4, p = 0.03).   The overall A-value for the MRPP was 0.3 and the overall T-value was -2.2. Furthermore, post-transplant seedlings originating from forests clustered with pre-transplant seedlings from forests to the left of the ordination space, while post-transplant seedlings from clearcut clustered with pre-transplant clearcut seedlings to the right (Figure 3.5). This strongly indicates that the ECM fungal community composition at the time of harvest was still primarily determined by source environment.   Figure 3.4 A nonmetric dimensional scaling ordination, using Sørensen (Bray-Curtis) distances based on relative abundance, of ectomycorrhizal fungal communities colonizing the root systems of A. lasiocarpa seedlings harvested 12 weeks after being transplanted between clearcut and forest environments (n = 3 sites).  The treatment CC represents seedlings originating from and transplanted into a clearcut, CF represents seedlings from a clearcut and transplanted into a forest, FC are seedlings from a forest transplanted into a clearcut, and FF are seedlings from and transplanted into the forest.  Axis 1 explains 49 % of the variation.   51 The dominant ectomycorrhizas (i.e., those with a frequency of 10 % or greater in at least one treatment) were formed by Amphinema spp., Cenococcum spp., Unknown Helotiales spp., Piloderma spp., Tylospora spp., and Wilcoxina spp. (Figure 3.6).  Wilcoxina spp., Unknown Helotiales spp., and Amphinema spp. mycorrhizas were primarily found on seedlings originating from clearcuts.  Helotiales spp. mycorrhizas were abundant on seedlings originating from clearcuts, but also on FC seedlings.  Tylospora spp. and Piloderma spp. mainly colonized seedlings from the forest.  Cenococcum spp. occurred in similar frequencies between seedlings originating from both environments, but were almost negligible in the CF treatment.  Rare species were responsible for just under half of the community composition of FF and pre-F seedlings (Figure 3.6).   Indicator species analysis of ECM fungal communities of post-transplant seedlings showed the presence of Amphinema sp. 1 (Monte Carlo test, p = 0.06), Helotiales sp. 7 (p = 0.06), Helotiales sp. 14 (p = 0.01), Helotiales sp. 22 (p = 0.06), and Wilcoxina sp. 6 (p = 0.1) to be indicative of clearcut environments.  The presence of Cenococcum sp. 1 (p =0.1), Cenococcum sp. 2 (p = 0.06), Piloderma sp. 10 (p = 0.06), Piloderma sp. 15 (p = 0.04), and Tylospora sp. 3 (p =0.04) were characteristic of the forest environment.    52  Figure 3.5 A non-metric multidimensional scaling (NMS) of ectomycorrhizal fungal communities colonizing the root systems of pre-transplant and post-transplant seedlings.  Each point represents the communities in a treatment by site.  The treatment pre-C represents pre-transplant seedlings that originated from clearcuts and the treatment pre-F represents pre-transplant seedlings from the forest (n = 3 sites).  The post-transplant treatments are: CC, which are seedlings originating from and transplanted back into a clearcut; CF, seedlings from a clearcut transplanted into a forest; FC, forest seedlings planted to a clearcut; and FF, seedlings from and transplanted into the forest.  Axis 1 explained 28 % of the variation and Axis 2 explained 25 % of the variation. Together axes 1 and 2 explain 54 % of the variation.  Although the ECM fungal communities of seedlings originating from clearcuts and forests still clearly differed from each other within each destination environment at the end of the growing season, there appeared to have been a slight shift in the communities over the course of the season in the clearcuts.  The ECM fungal communities of CC seedlings appeared to shift from the communities of seedlings harvested in July (pre-transplant clearcut vs. CC seedlings MRPP, A = 0.2, T = -1.9, p = 0.04; Figure 3.5), and the communities of CF seedlings were different (pre-transplant clearcut vs. CF seedlings MRPP, A = 0.2, T = -1.6, p = 0.07; Figure 3.5).  The original ECM community composition from forests was not altered one growing season after transplant (pre-transplant Forest vs. FF MRPP, A = 0.2, T = -0.12,  53 p = 0.5; pre F vs. FC MRPP, A = -1.0 x 10-8, T = 4.0 x 10-8, p = 0.5). This indicates that although clearcut and forest ECM fungal communities were still distinct from each other, slight changes in community structure occurred over the growing season.     Figure 3.6 Dominant (> 10 % of tips in any plot) ectomycorrhizal fungal species colonizing A. lasiocarpa seedlings harvested in July (pre-C and pre-F treatments) and seedlings harvested in October.  Uk. Helotiales refers to samples that were identified as an unknown Helotiales species.  Abbreviations are defined in Figure 3.5.   In addition to differences in the fungal community colonizing the seedlings, root systems also differed in the extent to which they were colonized at the end of the growing season (Figure 3.7).  A two-way ANOVA indicated that an interaction existed between source and destination environment on colonization rates (p < 0.01).  Planned contrasts showed that the root systems of seedlings originating from forests were more highly colonized by ECM fungi than those originating from clearcuts (planned contrasts: p = 0.02 for clearcut destinations; p < 0.001 for forest destination environments).       0% 20% 40% 60% 80% 100%pre-CCCCFFCFFpre-F% Occurrence Amphinema spp.Cenococcum spp.UK. Helotiales spp.Piloderma spp.Tylospora spp.Wilcoxina spp. 54  Figure 3.7 The percentage of A. lasiocarpa fine root tips colonized by ectomycorrhizal fungi 12 weeks after reciprocal transplant (n = 3 sites).  Different letters above the boxplots indicate differences between treatments within each destination environment, as detected by planned contrasts.  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.     3.3.3 Exoenzyme profiles of ectomycorrhizal fungal communities Enzyme profiles (the combined activities of all eight enzymes) of root tip communities were highly influenced by site. When ordinated by NMS, site 1 separated from the other two sites along Axis 1, and Sites 2 and 3 separated from each other along Axis 2 (Figure 3.8a).  When NMS ordination was performed on individual sites, enzyme profiles of root communities in forest destinations tended to co-locate with high enzyme activities for most enzymes. They grouped at the right of Axis 1 for Site 1 (Figure 3.8b), the bottom right of the ordination for Site 2 (Figure 3.8c), and the lower left of the ordination for Site 3(Figure 3.8d). A notable exception was laccase, which was associated with the clearcut destinations for Site 1 (Figure 3.8b) and the CC treatment for Site 2.  The generally higher enzyme activities in forest destinations are also illustrated in Figure 3.9, which displays enzyme activity as a percentage of the average of all ECM types in a site and shows that activity was highest, and also more variable in the forest environment.     55        Figure 3.8 Non-metric dimensional scaling ordination of ectomycorrhizal root tip enzyme activity of eight enzymes; (a) all three study sites, (b) site 1, (c) site 2, and (d) site 3. The open circles represent the overall activities of transplant treatments.  Red letters represent the scores of each enzyme.  Percentage values beside the axes represent the amount of variation in enzyme activity explained by each axis.  C = cellobiohydrolase, G = β-glucosidase, Gu = glucosronidase, Lacc = Laccase, Leu = leucine aminopeptidase, NAG = N-acetylglucosaminidase, P = phosphomonoesterase, X = xylosidase.        c) d) a) b)  56      Figure 3.9 Overall enzyme profiles of transplanted seedlings (a) across all three sites, (b) Site 1, (c) Site 2, (d) Site 3.  The polar graphs were drawn from the relative enzyme activities calculated as the percent of all ectomycorrhizal types at that site.  Treatments are: CC; clearcut seedlings transplanted back into a clearcut, FC; seedlings from the forest transplanted into a clearcut, CF; seedlings from a clearcut transplanted into the forest, and FF: forest seedlings transplanted back into the forest.     The results of the cluster analysis supported the findings of the NMS ordinations.  When the k-means analysis was directed to cluster the enzyme profiles into three groups, the groups were highly influenced by site (Figure 3.10a).  Enzyme profiles of samples from both environments at Site 3 grouped into one cluster, while enzyme profiles of Site 2 and clearcut destinations of Site 1 grouped into another cluster.  Enzyme profiles of Site 1 forest destination seedlings formed their own cluster (Figure 3.10a).   The simple structure index (ssi) criterion determines the best solution of the k-means cluster analysis, which is indicated by the highest ssi criterion value.  For this data set, the ssi criterion was highest when enzyme profiles formed five clusters (Figure 3.10b). In that optimum clustering, the enzyme profiles clustered almost perfectly by destination a) b) c) d)  57 environment within sites.  The only exception was for Site 2 samples where the profiles of clearcut seedlings transplanted into the forest clustered with the forest destination samples of Site 3 (Figure 10b).  Enzyme profiles from the Site 2 clearcut destination clustered together with forest seedlings transplanted back into the forest.  Overall, both the NMS ordinations and cluster analysis demonstrated that mycorrhizosphere enzyme activities was influenced more by the environment a seedling was transplanted into, not the environment it came from.  Even though enzyme profiles clustered together based on destination environment, an MRPP analysis showed that, within each destination environment, enzyme profiles of seedlings originating from the clearcut and forest differed significantly from each other (see Table 3.2).  The only exception to this was that the enzyme profiles of seedlings from the clearcut did not differ from seedlings from the forest when grown in clearcuts at Site 1 (MRPP, p = 0.6).  This indicates that although environment plays a major role in determining mycorrhizosphere enzyme activity, fungal community also influences this functional trait.     58  Figure 3.10 Enzyme profiles (the overall activities of eight extracellular enzymes) of root tip communities analyzed by k-means clustering directed to form three clusters (a) or five clusters (b)., Each colour represents a cluster.  Enzyme profiles within a cluster are more similar to each other than profiles in another cluster.   The ssi index of k-means clustering indicated that the optimum solution was when enzyme profiles formed five clusters (b). Abbreviations are defined in the same manner as in Figure 3.9.  59  Table 3.2 The results of MRPP analyses on a site-by-site basis of enzyme profiles of ECM root tip communities on transplanted A. lasiocarpa seedlings.  The abbreviations are: CC; clearcut seedlings transplanted back into a clearcut, FC; seedlings from the forest transplanted into a clearcut, CF; seedlings from a clearcut transplanted into the forest, and FF: forest seedlings transplanted back into the forest. Seedlings from different source environments transplanted into the same destination environment: Site Pair-wise Comparison A-value p-value Site 1 CC  vs.   FC -0.004 0.6  FF   vs.   CF 0.001 0.007 Site 2 CC  vs.   FC 0.04 <0.001  FF   vs.   CF 0.04 <0.001 Site 3 CC  vs.   FC 0.008 0.01  FF  vs.   CF 0.02 0.004 Seedlings from same source environment transplanted into different destination environment: Site 1 CC  vs.  CF 0.09 <0.001  FF  vs.  FC 0.1 <0.001 Site 2 CC  vs.  CF 0.3 <0.001  FF  vs.  FC 0.05 <0.001 Site 3 CC  vs.  CF 0.09 <0.001  FF  vs.  FC 0.07 <0.001  3.3.4 Biomass values of transplanted seedlings Seedlings originally sampled from clearcuts produced more new biomass than seedlings from forests when transplanted into either environment.  By the end of a growing season in the clearcut environments, seedlings originating from clearcuts had produced larger buds (planned contrasts, p < 0.01; Figure 3.11a) and more new foliage (p < 0.01; Figure 3.11b) than seedlings originally from forests. Interestingly, when transplanted into the forest, seedlings transplanted from clearcuts also produced more new foliage (p < 0.01; Figure 3.11b), but not larger apical buds (planned contrasts, p = 0.2) than seedlings transplanted within forests.  Not surprisingly, treatment effects were not observed within destinations for tissues that had been present prior to transplant: older foliage, stem, roots, nor for total biomass, which was primarily comprised of pre-existing tissues (Figure 3.11c-f).  No interactions between source and destination environment on seedlings biomass were observed (two way ANOVA, p = 0.9).  When sizes of seedlings at time of transplant were taken into consideration via and ANCOVA, these results were confirmed.   60       Figure 3.11 Dry weights of seedling tissues at the end of a growing season after transplant. Different letters above boxplots indicate significantly differences according to planned comparisons of CC vs. FC and CF vs. FF (n = 3 sites). The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.     61 3.3.5 N content of apical buds Supporting the above observation, the buds of seedlings originally sampled from clearcuts also generally had a higher N content relative to the buds of seedlings that originated from the forest (planned contrasts of CC vs. FC, p = 0.02; CF vs. FF, p = 0.08; Figure 3.12a).  An ANCOVA confirmed these results even when seedling N contents at time of transplant were taken into consideration.  No interaction of source between source and destination environments on relative N contents of apical buds was observed (two-way ANOVA, p = 0.5).  δ 15N values of apical buds did not differ between treatments within clearcut destinations, but in the forest destinations, seedlings that originated from the forest produced apical buds that tended to be enriched in 15N (p = 0.08; Figure 3.12b).  An interaction between source and destination environments on δ 15N did exist (two-way ANOVA, p = 0.03).   Figure 3.12 (a) Nitrogen content and (b) δ 15N values of apical buds of A. lasiocarpa seedlings 12 weeks after transplanting (n = 3 sites).  δ 15N data were power transformed.  Planned contrasts were CC vs. FC and CF vs. FF.  Different letters over boxplots indicate differences in treatments at p< 0.1. The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.      a) b)  62 3.4 Discussion 3.4.1 Ectomycorrhizal fungal community composition  As expected, seedlings originating from the same environment had similar ECM fungal community structures, regardless of the environment seedlings were transplanted into.  ECM fungal communities were distinct between clearcut and forest seedlings at time of harvest.  Species characteristic of clearcut ECM fungal communities were Amphinema, Wilcoxina, and Helotiales spp., which are more commonly found in early successional environments than in undisturbed sites (Kranabetter and Friesen 2002, Walker and Jones 2013, Rincón 2014).  For example, Lilleskov et al. (2002) observed a higher incidence of A. byssoides colonizing Picea glauca on low N sites than high N sites and Amphinema is also commonly found colonizing seedlings in nursery soils (Cairney and Chambers 1999, Kataja-aho et al. 2012).  This indicates that Amphinema species can successfully colonize soils that are nutrient limited or have experienced some sort of perturbation.  In addition, Amphinema seems to possess a dispersal ability that allows species to readily colonize seedlings in disturbed areas as Amphinema spp. can easily colonize young seedlings when a forest inoculum source is present (Danielson et al. 1984, Shishido et al. 1996).  In clearcut environments, this could be leftover organic matter, tree roots, etc. from the pre-existing forest.  These species are also commonly found, along with Wilcoxina spp., on seedlings planted into reclaimed sites (Danielson 1991) again indicating an ability to colonize disturbed areas.  Moreover, Amphinema spp., along with Wilcoxina spp. and Helotiales fungi, seem to be prominently found in disturbed areas such as along roadsides (Kranabetter 2004), in areas that have been mined (Mϋnzenberger et al. 2003), and in areas that have been clearcut (Hagerman et al. 1999a).  They are less commonly found in undisturbed forests, even on seedlings (Walker and Jones 2013).  Therefore, it is not surprising that these three groups of fungi dominated the mycorrhizas on the roots of seedlings originating from clearcut environments. In this study, fungi identified as being characteristic of forest environments are species more commonly found in older, undisturbed forests than areas that have experienced a disturbance.  For instance, Visser (1995) examined ECM fungal succession in Pinus banksiana stands and found that Piloderma byssinium was absent from six year old stands, colonized 0.2 % of root tips in 41 years old stands, and colonized 10 % of root tips in 65 and  63 122 year old stands.  Consistent with this, Twieg et al. (2007) found that a Piloderma complex (consisting of two morphotypes possessing P. fallax like features) increased in abundance with increasing age of mixed Pseudotsuga menziesii and Betula papyrifera stands.  Smith et al. (2000) observed that Piloderma fallax occurred in 57 % of plots in old growth Pseudotsuga menziesii stands and in only 1 % of plots in young stands.  In addition, P. fallax failed to colonize hosts in greenhouse studies despite inoculum sources being taken from areas that have P. fallax (Danielson and Visser 1989).  This, together with the observation that P. fallax hyphae grow much faster when associated with a host than in pure culture (Erland et al. 1990), suggests that when a disturbance such as clearcutting severs mycelia connection to a host, colonization ability of Piloderma may be inhibited.  Thus, it is not surprising that Piloderma spp. were better able to colonize seedlings in the forest sites than in the clearcuts in the current study.  Species of Lactarius, Tylospora, and Cenococcum are also more commonly found in mature forests than in younger sites.  Compared to species more commonly found in disturbed areas, Lactarius species have poor root colonization ability. For example, Lactarius pubescens failed to colonize Betula pendula seedlings from basidiospore inocula (Fox 1983).  Lactarius species tend to have a fairly high degree of host specificity (Hutchison and Piché 1995, Cairney and Chambers 1999) and in aseptic conditions, Lactarius subpurpureus colonized twelve tree species to a lesser extent than fungi with a broader host range (Hutchison and Piché 1995).  Lactarius species also typically fruit in older forest stands (Visser 1995).  Sporocarps of Tylospora species are typically found on well-rotted wood, possibly indicating a better ability to colonize older forests than new clearcuts.  Cenococcum is typically found at all stand ages (Visser 1995, Twieg 2007), but is generally not dominant.    Colonization ability could influence ECM fungal community structure in forests and genera indicative of forest ECM fungal communities in this study are better able to colonize mature sites.   ECM fungal communities colonizing the roots of transplanted seedlings were maintained for the duration of the transplant, thus ECM community structure was still determined by a seedling’s environment of origin.  Colonization of root systems by new ECM fungi may have shifted the community slightly, but this did not significantly alter the overall community composition.  Root systems experiencing new conditions have been  64 shown to maintain their original mycorrhizal status, at least for the first two to three years in the new environment (Hagerman 1999b, Kranabetter and Friesen 2002).  For instance, coring of fine roots in a harvested gap in a Pinus contorta stand revealed that fine roots remained non-mycorrhizal for up to three years (Parsons et al. 1994).  In addition, about 23 % to 35 % of roots of Picea engelmannii × Picea glauca seedling outplanted into clearcuts remained non-mycorrhizal for one year (Hagerman et al. 1999b).   This demonstrates that the mycorrhizal status of root systems can be maintained for some time.  However, a shift in community structure can happen eventually.  Kranabetter and Friesen (2002) compared ECM fungal community structures of Tsuga heterophylla seedlings colonized with either pioneer or forest ECM fungi and transplanted into openings.  After two years, the forest ECM fungal community structures appeared to change as the pioneer fungi, Thelephora terrestris, increased in abundance and forest fungi were unable to maintain root colonization in the clearcut.  Although a community shift can happen over time, a growing season of four months, as in this study, appears to be too short to allow for colonization of roots by new ECM fungal species. Another important consideration is that ECM fungi demonstrate priority effects in that fungi already colonizing roots have an advantage in colonizing new roots over other fungi (Kennedy et al. 2009).  For example, Kennedy et al. (2009) observed that for three out of four Rhizopogon species colonizing Pinus muricata seedlings, the dominant competitor was always the first colonizing species.  This demonstrated that timing of colonization strongly determined the outcome of competition for root colonization space.  Therefore, as seedlings in this study were already mycorrhizal when transplanted, priority effects could have maintained the original ECM fungal community structures.  Although the overall species composition of ECM communities originating from clearcuts remained distinct from those originating from forests one growing season after transplant, they may have changed slightly over that time (Figure 3.5).  This slight shift could be due to colonization of new species during the growing season, or could be an artefact of seasonal (Courty et al. 2010, Burke et al. 2011) or transplant effects (Romell 1939, Danielson and Visser 1989).  Ectomycorrhizal fungal communities were measured at the start of the transplant in the spring and then again at the end of the experiment in the fall and it is possible that seasonal effects could have influenced ECM fungal community structure. This  65 idea is supported by the fact that the ECM fungal communities of clearcut seedlings harvested in July differed from the communities of CC seedlings harvested in October.  In addition, although ECM fungal communities were not significantly different, the communities of FC and FF seedlings harvested in October seemed to shift from the communities of forest seedlings harvested in July, but in opposite directions.  This could indicate a seasonal shift in ECM community structure.  Over a period of fifteen months, Courty et al. (2010) saw ECM fungal community structure of an oak forest change with the season.  During the course of one growing season of three months, Burke et al. (2011) also saw ECM and saprotrophic fungal communities change.  Seasonal shifts in community structure could be due to the accumulation and decay of herbaceous plant litter (Burke et al. 2011) or to rainfall variability (Querejeta et al. 2009).    In addition, transplant influences could also cause a shift in ECM fungal community structure.  The community structure of FC seedlings harvested in October appeared to shift slightly towards CF and CC seedlings, indicating a change from a forest to a clearcut ECM fungal community structure.  Ectomycorrhizal fungal communities of CF seedlings also appeared to move away from CC seedlings and slightly towards the communities of forest seedlings, further suggesting a transplant effect on community structure.  It is possible that the act of excavating and transplanting seedlings disrupted the ECM fungal communities as the severing of hyphae has been shown to have a negative impact on the colonization ability of some ECM fungal species (Romell 1939, Danielson and Visser 1989).  Therefore, the slight shift in community structure, seen particularly in seedlings originally harvested from clearcuts and planted in either destination environment (i.e. the shift of CC and CF from clearcut seedlings harvested in July), could be due seasonal or transplant influences.    I used the novel approach of pyrosequencing individual root tip communities to identify ECM mycobionts.  Having 101 unique MID tags per 1/16th plate raises concerns about insufficient sampling depth because higher numbered MID tags result in fewer sequences.  This is because higher numbered MID tags require more reagent flows to be sequenced, leaving fewer reagents for sequencing of the amplicon fragment (Roche 2009).  To account for the bias, root tip samples were randomly assigned an MID tag.  However, the number of MID tags per library that resulted in zero sequences was no more than ten.  All other tags had at least one associated sequence.  As the goal was to identify the ECM fungus  66 and the DNA of that species was expected to dominate the sample, one sequence could be sufficient.  In the end, 92 % of samples were assigned an ECM fungal identity, which is considerably higher than the 60-70 % success rate typically achieved for Sanger sequencing.     Despite the  sequencing bias against certain MID tags and the fact that sequences obtained from this approach were fairly short (typically between 100 and 200 bp), I have confidence that they accurately detected differences between forest and clearcut communities because the ECM fungi identified as indicator species of clearcut and forest communities in this study were consistent with the findings of others (Kranabetter and Friesen 2002, Walker and Jones 2013, Rincón et al. 2014 and refs cited above).            3.4.2 Extracellular enzyme activity Previous studies of extracellular enzymes activities (EEA) of ECM fungi were unable to determine if differences in EEA was due to different fungal communities or the different environments in which the communities existed (Buée et al. 2007, Courty et al. 2010, Lilleskov et al. 2011, Rineau and Courty 2011, Jones et al. 2012, Walker 2012, Welc et al. 2014). The reciprocal transplant design of this study allowed me to separate the effects of ECM fungal communities and environment. Since differences in fungal communities were maintained between clearcut and forest ECM communities, this allowed me to determine if differences in EEAs existed between clearcut and forest ECM fungal communities.  If ECM fungal community composition influenced EEA, then it would be expected that seedlings originating from the same environment would exhibit similar enzyme profiles regardless of the environment they were transplanted into.  A previous study in our research group found that ECM fungal communities colonizing Picea engelmannii seedlings differed between clearcut and forest environments and that mycorrhizosphere enzyme profiles also differed between these two communities (Walker 2012), suggesting that fungal identity was important in determining enzyme activity.  Furthermore, ECM fungal species have been shown to vary in extracellular enzyme activity (Courty et al. 2010, Jones et al. 2010, Jones et al. 2012, Burke et al. 2014).  For example, Courty et al. (2010) examined ECM fungal communities in an oak forest and found that Tomentella lilacinogrisea, the most abundant species in the A1 soil horizon, was responsible for 30 % of eight enzyme activities, with the exception of laccase, for which Xerocomus chrysenteron and Russula ochroleuca were the  67 most active.  Jones et al. (2010) found that ectomycorrhizas formed by different fungal species on the same P. menziesii seedling exhibited up to a six-fold difference in enzyme activity and that differences in enzyme profiles of certain ECM fungi were consistent among different disturbance treatments.  Across hundreds of kilometers, Jones et al. (2012) observed mycorrhizosphere enzyme activities grouping together based on fungal species rather than fertilizer treatment or site, indicating that the fungal symbiont had a strong influence over EEA.  In addition, Burke et al. (2014) measured enzyme activity in response to leaf litter and found that enzyme activity varied greatly among ECM fungal species.  Therefore, there is strong evidence that fungal identity plays an important role in determining enzyme activity in mycorrhizospheres.  However, many of the above studies did not control for differences in environment and could therefore not distinguish if differences in EEA were due to different fungal species or different environmental conditions. Furthermore, fungi in different environments could be from the same species, but still be genetically distinct. It is important to consider both community composition and environment because abiotic conditions can also influence EEA (Conn and Dighton 2000, van Aarle and Plassard 2010, Burke et al. 2012).    Contrary to what was expected, enzyme profiles were most similar between seedlings transplanted into the same environment and therefore it appears that environment was more important than ECM fungal identity in determining EEA.  As mentioned above, soil edaphic conditions can influence enzyme activity.  For example, litter quality and chemical environment can influence mycorrhizosphere acid phosphatase activity of dominant ECM colonizing Pinus rigida (Conn and Dighton 2000).  Furthermore, in a growth chamber study, van Aarle and Plassard (2010) examined Hebeloma cylindrosporum on the host Pinus pinaster and found that phosphatase activity was linked to soil type in that high phosphatase activity was observed in soil types with low amounts of P and high phosphatase activity was observed in soil types with high amounts of P.  Also, Burke et al. (2012) observed that in an old growth hardwood forest, soil enzymes were positively correlated with soil C and N content.  It has also been observed that enzyme activity of Cenoccocum mycorrhizas on Pinus contorta was positively correlated with total soil N, while enzyme activity of Piloderma mycorrhizas was negatively correlated with soil pH (Jones et al.2012).  Therefore, edaphic conditions can affect the EEA of root tip communities.    68 Although both edaphic conditions and ECM fungal identity can influence enzyme activity, my study is one of the first to carefully control for both.  My results strongly support that environment is the dominant determinant of mycorrhizosphere enzyme activities on young seedlings. However, the influence of fungal identity on EEA cannot be discounted completely, as enzyme activities differed between ECM communities transplanted into the same destination environment. Therefore, both environment and fungal identity play a role in influencing ecosystem services in terms of mycorrhizosphere enzyme activities.  This conclusion is supported by the findings of Kivlin and Treseder (2014) who collected soil samples from sites in southern California during wet and dry seasons and measured soil enzyme activity.  Data was also collected on climate, soil nutrients, edaphic factors, and fungal community composition.  They concluded that abiotic factors explained 35.3 % of the variation in enzyme activity while community composition explained only 24.7 % of the variability.  Therefore, both environment and fungal identity can influence extracellular enzyme activity of ectomycorrhizas. As enzyme activities of root tip communities were determined primarily by destination environment after transplanting, this suggests a degree of plasticity of physiological function in terms of enzyme activity of mycorrhizal root tips.  Such plasticity has been observed for soil microbial communities in varying soil conditions (Bueé et al. 2007, Rineau and Courty 2011, Kotroczó et al. 2014).  In a temperate oak forest, Bueé et al. (2007) found that different ECM fungal species had different enzyme activity patterns when in the same niche, but also observed that catabolic activities changed within species between niches. In addition, Rineau and Courty (2011) evaluated the functional diversity of ECM between two oak forest stands and observed that ECM fungal species could belong to more than one ‘functional group’ (defined as a collection of species performing similar ecological roles) and could shift between groups.  As EEA in this study shifted when seedlings were planted in a different environment, this indicates the capacity of a single ECM fungal community to exhibit a range mycorrhizosphere enzyme activities.   Extracellular enzyme activities were higher in forests, suggesting the ability of ECM fungi to down-regulate or up-regulate enzyme production.  Higher enzyme activities could be due to more organic matter in the forest floor as soil organic matter availability can influence EEA activity (Brzostek and Finzi 2011).  For example, ECM fungi play an essential role in  69 nitrogen cycling by releasing nitrogen from older leaf litter (Colpaert and van Tichelen 1996).  Burke et al. (2011) speculated that seasonal changes in EEA were linked to litter fall from herbaceous plants and that the presence of herbaceous litter in late summer could stimulate enzyme activity.  However, litter additions have can have negative or neutral, as well as positive effects, on extracellular enzyme activities (Conn and Dighton 2000, Burke et al. 2014).  Nonetheless, forest environments appeared to stimulate enzyme production of root tip communities.      It should be noted that the ECM root tips used in the enzyme assays were randomly selected, so some non-mycorrhizal tips were chosen.  This means that the enzyme activity of mycorrhizal vs. non-mycorrhizal roots tips could account for some of the variability in enzyme activity.  However, although clearcut and forest seedlings exhibited different colonization rates, seedling enzyme profiles still clustered together when in the same environment, which still suggests an influence of environment of mycorrhizosphere enzyme activities.  In addition enzyme activities should not be thought of solely due to the ECM fungi, but rather as the activity of the entire root tip community, which includes the ECM fungus and associated bacteria and fungi.  3.4.3 Nitrogen uptake  If clearcut and forest ECM fungal communities had been adapted to take up and translocate N from the soil in which they originated, then it was expected that the apical buds of CC seedlings would have higher N contents than CF seedlings, and the buds of FF seedlings would have higher contents than CF seedlings.  Apical buds were selected as the tissue that would be most informative regarding the influence of ECM fungal community on N uptake because they are formed at the end of the growing season and have been shown to contain N primarily taken up over the summer (Millard and Grelet 2010).  No differences in foliar N were present between forest and clearcut seedlings at time of transplant, so any distinctions in bud N among transplant treatments at final harvest would be expected to reflect variations in uptake over the growing season.  At time of harvest, apical buds of clearcut seedlings in both environments had higher N contents than forest seedlings, which could suggest some advantage of clearcut ECM fungal communities in terms of nitrogen acquisition.  It is possible that differences in enzyme activities between seedlings in the same  70 environment could account for differences in bud N at the end of the study.  Wilcoxina, one of the dominant genera of clearcut ECM fungal communities, has been linked to higher foliar N status.  Velmala et al. (2014) observed that needle N content of Picea abies was positively correlated with high chitinase activities and that a Wilcoxina sp. had the highest chitinase activity.  In addition, in a subalpine clearcut similar to the ones in this study, a Wilcoxina sp. was found to translocate the largest amount of N to spruce seedlings from several N sources (Jones et al. 2009).   In this study, however, it is unlikely that EEAs of clearcut fungi were responsible for the higher apical bud N contents because EEAs, including chitinase, were highest in the forest.  Instead, other attributes of clearcut fungi may have contributed to higher bud N contents.    Other factors could have resulted in the higher N contents of buds in clearcut destinations.  These include the ability of pioneer fungi to take up and pass on N (Hobbie et al. 2008), the form of N available (Finlay et al. 1992, Keller 1996, Bown et al. 2010), and the activities of bacteria associated with ECM fungi (Olsson and Wallander 1998, Timonen et al. 1998, Frey-Klett et al. 2005).  For example, in pure culture conditions, some pioneer fungi have been shown to prefer nitrate over ammonium (Smith and Read 2008), while others take up ammonium at a much faster rate (Finlay et al. 1992, Keller 1996, Brown et al. 2010).  Most fungi will also retain ammonium and may be better at transferring nitrate to the host (Hobbie et al. 2008).  In high nitrate conditions, such as in clearcut soils, selection may still favour colonization by ECM fungal species that are more capable of using nitrate (Smith and Read 2008).  Forest mineral soils contained more nitrate than ammonium, although nitrate levels were still below those of clearcut mineral soils.  Therefore, it is possible that clearcut fungi are more suited to take up and translocate the more available N form (nitrate) to their host.  In addition, the bacteria and microfungi associated with different ECM fungal species also vary in their extracellular enzyme activities (Olsson and Wallander 1998, Timonen et al. 1998, Frey-Klett et al. 2005), which could influence the amount of N a fungus can acquire and contribute to the higher apical bud N contents of clearcut seedlings.  Although there is evidence that fungi exploration type can influence N uptake (Corrêa et al. 2012), it is unlikely that differences in N uptake were due to exploration type in this case.  This is because the dominant clearcut fungi (Wilcoxina and Amphinema) and the dominant forest  71 fungi (Tylospora and Piloderma) are all categorized by Agerer (2001) as short-distance exploration types.       Root systems of seedlings originating from clearcuts were less colonized than seedlings originating from the forests, so another explanation for the higher N content in apical buds of these seedlings is that non-mycorrhizal roots tips were more efficient at taking up N compared to mycorrhizal roots.  Velmala et al. (2013) observed that foliar N of Norway spruce seedlings increased with less ECM colonization of roots in a greenhouse study, indicating and advantage of non-mycorrhizal roots in nitrogen acquisition.  If non-mycorrhizal roots were more efficient at N uptake than mycorrhizal roots, then it would be expected that the foliage of clearcut seedlings would be enriched in 15N, compared to forest seedlings.  This is because when ECM fungi take up N and transfers it to the host, 14N is preferentially transferred, while 15N is retained (Högberg 1990, Hobbie et al. 1998).  This causes foliage of mycorrhizal plants to be depleted in 15N and mycorrhizal roots to be enriched in 15N.  If seedlings were obtaining N without going through a fungal symbiont, their foliage would be more enriched in 15N than if N was acquired through a fungus.  In the clearcut environment, there was no significant difference in 15N between seedlings originating from forests and those from clearcuts (Figure 12b).  Furthermore, in the forest environment, seedlings originating from clearcuts were significantly depleted in 15N compared to forest seedlings.  Thus, in neither destination environment did clearcut seedlings appear to be acquiring more N through non-mycorrhizal roots.  This supports the idea that differences in N uptake can be attributed to uptake through the ectomycorrhizas.  Different 15N signatures observed between transplant treatments in the forest could be due to the different fungal communities.  Tedersoo et al. (2012) also concluded that 15N signatures in sporocarps were determined by fungal lineage.  In addition, it has been observed that with only one N source and roughly equal colonization rates, Laccaria colonized root tips were depleted in 15N compared to Suillus colonized root tips, indicating that Laccaria either transferred less N than Suillus or had less fractionation against 15N (Hobbie et al. 2008).  Therefore, fungal identity could be potentially influencing 15N signatures as species could differ in ability to fractionate and transfer N isotopes.  In addition, there appeared to be an interaction between seedling source and destination with respect to 15N in the buds.  δ15N values did not differ in clearcuts, but did differ in the forest.   72 Generally, ECM fungi assimilate nitrate more slowly than ammonium (Finlay et al. 1992, Keller 1996) and transfer some unreduced nitrate to the host (Hobbie et al. 2008), causing foliage to become enriched in 15N.  As nitrate levels were higher in clearcut mineral soils it is possible clearcut and forest fungi were assimilating and translocating N to their host at a more equal rate, accounting for the more equal bud δ15N values observed in clearcuts.  Also, δ15N values varied in different directions depending on the destination environment.  When transplanted into their non-native environment, seedlings became depleted in δ15N, further suggesting some interaction between source and destination environments on δ15N in apical buds.  I cannot comment on what this interaction is, but it is possibly due to different abilities of ECM fungi to fractionate ammonium vs. nitrate (Hobbie and Colpaert 2003) or use organic vs. inorganic N (Keller 1996).  Another factor that could explain higher N levels in clearcut seedling foliage is light availability, which has been correlated with foliar N allocation (Schoettle & Smith, 1999).  Apical bud N contents of seedlings in clearcuts were higher than the contents of seedlings from the same environments transplanted into forests.  This is likely due to light availability as seedlings in clearcut environments received more light than seedlings in the forest environment.  Foliar N contents and N concentrations, expressed on an area basis, increased with higher levels of relative irradiance (Niinemets 1997, Grassi and Bagnaresi 2001). Others have observed foliar N values declining with relative irradiance (Zhang et al. 1997) or photosynthetically active radiation (Le Roux et al. 2001).  In contrast, however, foliar N has been observed to decrease at higher light levels (Fownes and Harrington 2004), indicating that the effect of light on foliar N can be variable.  Nonetheless, bud N contents seemed to be affected by both EMF community and environment.  3.4.4 Seedling characteristics  At the time of transplant, clearcut and forest seedlings did not differ in total biomass or biomass distribution.  This was not expected to change over the course of the growing season.  Although enzyme activities differed between transplant destination environments, this did not seem to influence establishment of seedlings, as there were no treatment differences in seedling survival and relative growth rates. With respect to biomass production however, it appears that seedlings originating from clearcuts produced more new foliage than  73 seedlings from the forests.  They also produced larger buds, but in clearcut destinations only, potentially due to more available sunlight in the clearcuts.  Newsome et al. (2010) found that naturally regenerating species in the Interior Cedar – Hemlock (ICH) and Montane Spruce zones increased in growth with decreasing canopy cover, ten years after harvesting.  Another study in the ICH found similar results, with higher conifer seedling growth in larger than smaller forest gaps (Coates 2000).  Although light availability is thought to be a primary determinant in seedling growth (Wright et al. 1998), other factors are also important.  For example, the presence of an understory may restrict seedling performance not only by reducing sunlight, but also through competition for soil resources (Wohlgemuth et al. 2002), as well as increased rodent consumption (Caccia and Ballaré 1998).  These factors may explain why buds produced in the forest environment were smaller than buds formed in the clearcut environment.          In both destination environments, seedlings originating from the clearcuts produced more new foliage than seedlings from the forest.  This is likely because the amount of new growth in a fir seedling is pre-determined by growing conditions experienced the previous growing season; the apical bud contains all the leaf primordial cells that will elongate in the spring (Wilson 1970, Owens and Singh 1982).  Therefore, clearcut seedlings, having experienced higher light levels than seedlings in the forest, likely set buds at the end of the 2009 growing season with greater potential for new growth in 2011.  However, forest seedlings transplanted into the clearcut (FC) also produced significantly more new foliage than when transplanted back into the forest (FF) and destination environment had a significant effect on the amount of new foliage produced (two-way ANOVA, p <0.01) and on the size of apical buds (two-way ANOVA, p <0.01).  This suggests that although the amount of new growth is partially pre-determined when the bud is set, the environmental conditions experienced by a seedling during bud burst can also influence the amount of new foliage produced.  This is supported by the study done by Coates (2000), investigating the response of seedlings (Thuja plicata, Tsuga heterophylla, A. lasiocarpa, Picea glauca x Picea sitchensis, and P. contorta) to gaps in the ICH zone.  That study observed seedlings that expected to have considerable light advantage did not experience corresponding increased growth and thus concluded that belowground effects also have a significant influence on  74 seedling growth.  Therefore both aboveground and belowground effects could influence seedlings biomass values.    3.5 Summary In conclusion, when A. lasiocarpa seedlings were transplanted for four months, the original ECM fungal community structures colonizing clearcut and forest seedlings were maintained.  Although mycorrhizosphere enzyme activities, an important community-level functional trait of ECM fungi, differed between clearcut and forest environments (Walker 2012, this study), this difference does not appear to be due to ECM fungal identity.  Instead, edaphic conditions seemed to have a more dominant influence on this functional trait and fungal identity had a lesser influence on mycorrhizosphere enzyme activities.   In addition, fungi did not seem to be better adapted to providing more N to their hosts from their ‘home’ environment than fungi from the other environment.  Instead, clearcut fungi seemed to possess some advantage over forest fungi in N acquisition in both environments.  Abiotic factors also played a role in seedling N acquisition.  It is also interesting to note that despite having different enzyme profiles between clearcut and forest environment biomass values did not differ between treatments, with the exception of new foliage and buds.  This suggests that community functioning in terms of extracellular enzymes in both environments is sufficient for seedling development.  This study also demonstrates that environment influences mycorrhizosphere enzyme activities and that ECM fungal communities have the potential to maintain mycorrhizosphere enzyme activities after clearcutting.            75 4    Chapter: Growth Chamber Reciprocal Transplant 4.1 Synopsis Field experiments are essential for understanding processes in forest soils.  Nevertheless, it is often difficult to attribute any findings to the variable of interest because there are often confounding environmental factors (Smith and Smith 2006).  Some confounding factors in the reciprocal transplant experiment reported in Chapter 3 include light seedlings received, soil and air temperatures, and precipitation.  Soil biological activity was another confounding factor, especially as many soil bacteria also produce exoenzymes (Filip 1998), which could influence observed enzyme activities.  In addition to affecting ECM fungal communities, disturbances such as clearcutting and fire can also change soil bacterial communities (Kennedy and Egger 2010, Mummey et al. 2010) and can alter nutrient cycling (Goodale and Aber 2001, Jiménez Esquilín et al. 2008).  A reciprocal transplant experiment was therefore implemented in a growth chamber in which confounding above-ground environmental factors could be eliminated.  This experiment was also planned as a ‘back-up’ to the field experiment of Chapter 3. One of the premises of this field study was that, after transplanting, seedlings would maintain their original ectomycorrhizal (ECM) fungal community structure over the course of one growing season.  In order to eliminate any additional colonization by new ECM fungi in the growth chamber study, forest and clearcut soils were sterilized.  Here I present only the growth responses of the seedlings.  Ectomycorrhizal community data are not presented because the field experiment was successful; hence, no molecular confirmation of fungal identity was performed on the growth cabinet samples.  4.2 Methods 4.2.1 Sterilization of soil In July 2011, approximately 40 L of soil was excavated from each clearcut and forest site.  These were the same sites at which the reciprocal transplant experiment in Chapter 3 was carried out.  Fifteen 20 cm x 20 cm x 15 cm samples were taken randomly at each site.  Samples for each plot were consolidated in individual Rubbermaid bins (6 bins total).  In forest plots, the 1 cm deep LFH and moss layer was separated from the mineral soil and was discarded as the root systems of one-year-old Abies lasiocarpa seedlings were only found in  76 the mineral soil to a depth of 10 cm.  Once transported back to the lab, soil was thoroughly mixed in the bins.  Soil was stored at -4 °C in a walk-in cooler for a maximum of one week until it was packaged for sterilization by γ-irradiation.  In preparation for γ-irradiation, soil was sealed into plastic bags, for a total of 20 bags of 2 L each per forest or clearcut plot.  A total of 120 bags (20 bags x 2 plot types x 3 replicates) of soil were sent to Iotron Industries Canada (Port Coquitlam, BC, Canada) for electron beam sterilization at a dosage of 25 KGy, which typically eliminates soil invertebrates, fungi, and most bacteria (McNamara 2003).   For each plot, 100 g of unsterilized soil was added to 500 mL of sterile water and was shaken at 75 rpm for 10 minutes and decanted.  The liquid was filtered through a # 1 Whatman filter paper (Leigh et al. 2011).  This filtrate would later be added to sterilized soil at the start of the transplant in order to re-establish natural bacterial populations that were removed via irradiation.  Filtrate was stored at 4 °C until use (approximately one week).  4.2.2 Growth chamber conditions  In September 2011, 20 seedlings from each clearcut and forest plot (20 seedlings x 2 plot types x 3 replicates = 120 seedlings total) were excavated and placed in bags with roots still in soil. At this point, the seedlings had set buds and the buds were well developed. Seedlings were transported back to the lab and were immediately processed.  Shoot length and root collar diameter were measured.  Root systems were then gently rinsed and cleaned of excess soil, being careful to keep the ECM fungal community intact. Seedlings were transplanted into sterile soil mixed with equal amounts of autoclaved (1 hr) silica sand in 9 cm sterile pots. Ten seedlings originating from each clearcut and forest plot were planted into pots of clearcut soil, and ten were planted in pots of forest soil, from the same site.  An aliquot of the corresponding filtrate was added to each pot (i.e., 2 mL of filtrate from clearcut Site 1 soil was added to each pot of clearcut Site 1 soil).   Seedlings were then watered and randomly placed in a plant growth chamber (BioChamber SPC-56, Winnipeg, MB, Canada) at 15 °C for 11 hours (day) and 10 °C for 13 hours (night), to simulate natural day lengths and temperatures at the time of harvest. Seedling placement within the chamber was re-randomized every two weeks.  To encourage bud burst, the cycle was adjusted two weeks later to simulate conditions experienced by seedlings during the summer 2011 growing season, according to  77 Environment Canada.  The new cycle was: 20 °C/12 °C, 13 hrs/11 hrs, day/night. After bud burst (about 3 months), the cycle was adjusted to 16 °C/10 °C, 11 hrs/13 hrs, day/night to encourage seedlings to set new buds.  Again the cycle was set according to data from Environment Canada.  As this decrease in temperature and day length did not seem to be sufficient to encourage bud set, the cycle was further adjusted to 14 °C/7 °C, 12 hrs/12 hrs, day/night two weeks later.  Humidity was maintained at 70 % to simulate natural humidity levels.  Seedlings were harvested after new buds were set in February 2012.     4.2.3 Soil nutrient analyses Irradiated soil not used to fill pots was re-mixed, consolidated by plot and mixed again.  Soil was sieved through a 2 mm sieve and was divided into three samples per plot per site (18 samples total) and was dried at room temperature for at least 48 hours.  These samples were sent to the Ministry of Forests, Lands, and Natural Resource Operations Research Analytical Laboratory in Victoria, British Columbia and the same analyses discussed in Section 2.2.2 were performed.     4.2.4 Biomass measurements After harvest, seedlings were measured for shoot length and growth increment (length of new growth shoot produced during the experiment).  Seedlings were then dismantled into old foliage, new foliage, bud, stem, and roots.  The root systems were placed in a bag with soil and were stored -4 °C for later morphotyping.  The remaining seedling parts were weighed and then dried at 105 °C for at least 48 hours, after which the dry weights were determined.  4.2.5 Stable isotope analysis δ15N and N content of dried apical buds were measured at the UBC-EOS-PCIGR-Deltaplus Lab, Vancouver, British Columbia.  A Thermoquest NC 2500 (Bath, UK) elemental analyzer was used to combust lab standards and samples.  The sample gas was taken up by a Thermo Finnigan Conflo III (Bath, UK), and transferred to a Finnigan DELTAplus XP (Bath, UK) mass spectrometer.  The operating system Windows NT 4.0 and ISODAT software (Bath, UK) were used to find the isotopic value of the sample from the  78 15N/14N ratio of the sample (Rs) relative to the 15N/14N ratio of the reference gas (Rr).  Stable isotope abundances are reported as δ15N in parts per mil (‰), expressed as:  δ15N (‰) of sample = (Rs / Rr - 1) * 1000 ‰  Samples that contained more 15N are described as ‘enriched’; samples with more 14N are described as ‘depleted’ (Hobbie et al. 2000).  A calibration curve of expected lab standard isotopic values vs. measured lab standard isotopic values was used to correct the δ15N samples values in Excel.  Lab standards were calibrated against the international standard IAEA-N1 (+0.4 per mil) and IAEA-N2 (+20.3 per mil).  Nitrogen content was determined from the total area under the peaks formed by the two nitrogen isotopes.  This approach allowed us to make comparisons within the study, but was not useful for absolute estimates.  4.2.6 Morphotyping of ectomycorrhizal root tips Root systems of growth chamber seedlings were rinsed in water.  As root systems had around 70 tips, roots were cut into 1 to 2 cm pieces and were placed in a petri dish with water.  One piece was randomly picked from this dish and was examined under a stereo microscope at a total of 10 X to 40 X magnification as required.  To randomly select tips, every other secondary branch off of main root was examined.  If a tip occurred on the main root before this branch, it was ignored.  If a tip occurred along the secondary branch, it was selected.  The first tertiary branch was examined and the third tip along this branch was selected.  If the tertiary branch only had one tip, this tip was selected.  If a quarterly branch occurred, the first tip along this branch was chosen.  All other branching was ignored.   Selected root tips were examined for mantle colour and texture, the presence of emanating hyphae and/or cystidia, and the pattern of any emanating hyphae as per Goodman et al. (1996).  The root segment was placed back in the Petri dish and another segment was randomly selected.  This procedure was repeated until ten ECM root tips were obtained.  Ectomycorrhizal root tips were placed into morphotype groups according to their anatomical features.  Although two tips per morphotype per seedling were frozen for potential future molecular identification, molecular identification was not done on these samples because of time constraints and because thorough molecular identification was conducted on other  79 seedlings sampled from the field at the same time. Ectomycorrhizal fungal community data is not presented here because the field experiment was successful; hence these data were not required. Root segments were placed in envelopes to and were dried at 105 °C for 48 hours and the dry weight recorded.   4.2.7 Statistical analysis Soil nutrient analyses – Values for available NO3 were below levels of detectability and are thus not included in these analyses.  Data for NH4 and mineralizable N analyses did not follow a normal distribution and transformation did not correct the situation.  Therefore, a Kruskal-Wallis test with treatment nested in site in R 3.0.0 (R Core Team 2013; R code available in Appendix B) was used to test the null hypothesis that treatment means did not differ between sites or between treatments within sites.  To test the null hypothesis that treatment means were equal regardless of site, a Welch’s two-sample t-test was used. Seedling growth and N content – Defined planned contrasts fitted to a one-way ANOVA (Logan 2010) in R 3.0.0 were used to test treatment effects on seedling survival, relative growth rates based on height, dry biomass values, and N content of apical buds.  Prior to analysis, data was averaged by plot.  Data for the dry biomass of all seedling parts were log transformed to achieve a normal distribution and homogeneity of variance.  The planned comparisons were CC vs. FC and CF vs. FF to reflect the original hypothesis that when in the same environment, seedlings originating from different environments would vary from each other.   Relative growth rate of seedlings was calculated using the formula:  RGR = (ln W2 – ln W1)/(t2-t1)  Where;  W1= length of shoot (cm) before experiment W2= length of shoot (cm) after experiment T1= initial time (weeks) T2= final time (weeks)   80 4.3  Results 4.3.1 Soil nutrient analyses It was expected that soil from clearcut environments would have higher N levels than soil from forest environments.  However, the only difference in nutrient status of irradiated soils was higher amounts of available NH4 in forest soils than clearcut soils (Welch’s two sample-test, p=0.04, see Table 4.1).  It was possible that this difference was due to the three outliers present in NH4 and mineralizable N, despite data being transformed.  These outliers represented samples from Site 3 and when present, data did not follow a normal distribution even after transformation.  The data was therefore reanalyzed with these outliers removed and no treatment differences were found.  No treatment differences with respect to mineralizable N were found with or without outliers.  A Kruskal-Wallis rank sum test concluded that no differences existed among or treatments within sites.  It should be noted the values for available NO3 were below levels of detectability and thus were not analyzed.      Table 4.1 Welch’s two sample t-test comparing irradiated soil nutrient status (mean ± SE) between clearcut and forest environments (nC = 3, nF = 3).  Despite data being ¼ power transformed before analysis, outliers were still present, so analysis was repeated on data set with outliers removed.  Values for available for NO3 were below levels of detectability and are not shown.       Dataset including outliers Dataset excluding outliers Analysis Clearcut soil Forest soil p-value Clearcut soil Forest soil p-value Available NH4 (mg/Kg) 42.4 ± 2.6 84.3 ± 19.3 0.04 42.4 ± 2.6 45.8 ± 1.0 0.2 Mineralizable N (mg/Kg) 44.9 ±3.9 88.1 ± 23.7 0.1 44.9 ± 3.9 40.8 ± 1.7 0.5 Total C (%) 4.7 ± 4.7 3.7 ± 0.2 0.2 N/A* N/A* N/A* Total N (%) 0.2 ± 0.2 0.2 ± 0 0.6 N/A* N/A* N/A* *Since the total C and N datasets did not contain outliers, these values did not change when outliers are removed. Outliers were determined in R 3.0.0 using the outlier function of the outliers package.        81 4.3.2 Seedling survival and relative growth rates It was expected that when seedlings were planted back into their original soil type, they would exceed seedlings not from that soil type in terms of survivorship, growth rates, and in biomass.  Seedlings survival rates did not differ among treatments (Figure 4.1a). Seedlings from clearcuts tended to have higher relative growth rates (Figure 4.1b) than seedlings from forest in clearcut soils (planned contrasts, p =0.07), with no difference in forest soils.         Figure 4.1 The (a) proportional survival rates and (b) relative growth rates based on height of A. lasiocarpa seedlings transplanted into sterilized soil from Engelmann spruce – Subalpine fir clearcuts or adjacent forests and grown in a growth chamber for 20 weeks (nCC, FC, CF, FF = 3 sites).  Different letters represent significant effects of seedling source within each soil type according to (planned contrasts).  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  CC are seedlings from the clearcut and transplanted back into a clearcut, FC are seedlings from the forest transplanted into a clearcut, CF are seedlings from a clearcut and transplanted into the forest, and FF are seedlings from the forest transplanted back into the forest.   4.3.3 Biomass and N status of apical buds of growth chamber seedlings At time of harvest, clearcut seedlings were generally heavier than forest seedlings when transplanted into either soil type (Figure 4.2).  In clearcut soils, clearcut seedlings had significantly more new foliage (Figure 4.2b, planned contrasts of CC vs. FC, p = 0.003) and older foliage (Figure 4.2c, p = 0.04), larger stems (Figure 4.2d, p = 0.01), and larger total biomass (Figure 4.2f, p = 0.02).  Clearcut seedlings also had marginally larger root systems a) b)  82 (Figure 4.2e, p = 0.06) in clearcut soils.  After growing in forest soils for 18 weeks, seedlings from clearcuts produced more new foliage (Figure 4.2b, planned contrasts of CF vs. FF, p =0.03) and marginally larger stems (Figure 4.2d, p =0.07) than their forest counterparts, but with no differences in new foliage or root biomass.  No differences between seedlings from clearcuts and forests were detected in apical bud size (Figure 4.2a), N contents or δ15N (Figure 4.3) in either soil type.                   83         Figure 4.2 Dry biomass of seedling parts.  Units were in grams but data was log transformed.  Different letters above the bars indicate significant differences between treatments according to planned comparisons of CC vs. FC and CF vs. FF (n = 3) at p < 0.1.  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  Transplant treatments are defined in Figure 4.1.  84    Figure 4.3 The (a) relative N contents and (b) δ15N values of apical buds from naturally regenerated A. lasiocarpa seedlings harvested from three Engelmann spruce – Subalpine fir clearcut/forest pairs and grown in sterilized clearcut and forest soils in a growth chamber for 20 weeks (n=3 sites).  The boxes represent the interquartile range and the dark line in the box represents the median. The whiskers are the highest and lowest data points within 1.5 the interquartile range.  Abbreviations are defined in Figure 4.1.  4.4 Discussion 4.4.1 N status of apical buds  No differences in apical bud N and δ15N contents were observed, which was surprising given that ECM fungal species have been shown to differ in ability to acquire N (Smith and Read 2008).  It is possible that this similarity among treatments is due to seedlings from both environments experiencing equal light and soil N conditions.  As light availability has been correlated with foliar N allocation (Schoettle and Smith 1999) and this correlation can be positive (Kranabetter et al. 2010) or negative (Kranabetter and Simard 2008), similar light conditions across treatments could result in similar apical bud N and δ15N contents.  Also, quantity (Kranabetter and MacKenzie 2010) and form of soil N available (Öhlund and Näsholm) has also been shown to influence foliar N values.  Foliar N content can increase with increasing soil N content (Kranabetter and MacKenzie 2010) and many conifer seedlings have been shown to preferentially acquire NH4 over NO3 when both forms are available (Lavoie et al. 1992, Kronzucker et al. 1997, Gessler et al. 1998).  Clearcut and forest soils were similar in terms of N content, which could have been responsible for the similar foliar nutrient values.     a) b)  85  4.4.2 Seedling characteristics When transplanted into clearcut soil, clearcut seedlings exhibited faster relative growth rates, and were generally larger than forest seedlings in both soil types.  Faster growth rates and larger biomass values of clearcut seedlings could be due to advantages presented by pioneer ECM fungal communities. For example, despite being slightly smaller at the start of the study, Kranabetter (2004) observed that, when transplanted into clearcuts, seedlings colonized by pioneer fungi had a 25 % greater increase in height than seedlings colonized by forest fungi.  As no differences in foliar N were observed, Kranabetter speculated that some pioneer fungi might increase in proliferation of fine roots to fully utilize resources and contributing to plant growth.  Pena et al. (2013) also demonstrated that ECM fungi can influence root architecture by increasing fine root proliferation and root length.  It is thus interesting to note that in clearcut soil, clearcut seedlings had larger root systems than forest seedlings at the end to the experiment, which could be indicative of an advantage of pioneer fungi causing proliferation of fine roots. Corresponding with the faster growth rates observed for seedlings originating from clearcuts, these seedlings also produced more new foliage than seedlings from the forest.  As previously discussed in Chapter 3, this is mostly likely due to the fact that the amount of new foliage a seedling produces is pre-determined from the previous growing season (Wilson 1970, Owens and Singh 1982).  As clearcut seedlings experienced more light than forest seedlings in the previous growing season, the apical buds set by clearcut seedlings at the end of that season could have had more potential for new growth than buds set by seedlings in the forest.   This is supported further by data from the end of the experiment: after experiencing the same light availability, clearcut and forest seedlings set apical buds of similar size regardless of the soil type they were transplanted into.  Also, when compared to CC and FF seedlings harvested in July (Chapter 3), seedlings harvested from clearcuts in September for the growth chamber study had larger buds than seedlings harvested from the forest at the start of the growth chamber experiment.  The flushing of these buds during the course of the experiment translated into the new growth observed at time of harvest, and consequently seedlings from clearcut had produced more new foliage.  86 It is interesting to note that in both soil types, clearcut seedlings had larger stems than forest seedlings at time of harvest.  Seedlings transplanted in the growth chamber were excavated from the field September 2011, and thus had already experienced a growing season.  As discussed previously in Chapter 3, seedlings from clearcuts experienced more growth than seedlings from forests in the 2011growing season, which may explain why clearcut seedlings had larger stems   Clearcut soil conditions could also be responsible for larger biomasses in clearcuts.  If soil conditions instead of light played a more important role in biomass allocation, then in the growth chamber study, it would be expected that seedlings transplanted into clearcut soil would be larger than seedlings transplanted into forest soil.  Instead, it was observed that generally, seedlings from clearcuts had larger biomass values than forest seedlings in both types of soil (Figure 4.3).  As light and moisture conditions were equal across treatments, results are consistent with an advantage presented by clearcut ECM fungal over forest communities.  These advantages could include N preference (Smith and Read 2008) or fine root proliferation (Kranabetter 2004).   Thus, biomass allocation seems to be influenced by ECM fungal community structure. A resource allocation strategy that may be optimal in one environment may not be ideal in another environment (Tilman 1990).  This could explain the trend that seedlings from clearcuts had higher growth rates and larger biomass values for some parts than seedlings from forests when transplanted back into clearcut soil; it could be that ECM fungal communities of forest seedlings were less beneficial to seedling growth in clearcut environments than ECM fungal communities of clearcut seedlings.  For example, Alster et al. (2013) saw that enzyme activities of microbial communities were higher in their original environment, suggesting that microbial communities may be adapted to their local environment.  However, if ECM fungal communities were indeed optimally adapted to their original environment, then it would be expected that in forest soil, forest seedlings would have higher growth rates and larger biomass values than clearcut seedlings.  As there were no treatment differences in forest soil except for clearcut seedlings producing more new growth, it appears that other factors than ECM fungal communities are influencing seedling growth.  Because soil was γ-irradiated, the influence of other fungal species can be eliminated, however, the interaction of bacterial communities associated with ECM fungi can have a  87 positive effect on plant growth (Chanway et al. 1991, Frey-Klett et al. 2005).  Soil moisture (Ettl and Peterson 1995, Peterson et al. 2002) and light availability (Wright et al. 1998, Kranabetter and Coates 2004) also influence seedling growth.  Although the ECM fungal community appears to have an important role in seedlings growth and development, abiotic factors can also play an important role.  4.5 Summary In conclusion, seedlings originating from clearcuts were generally larger than seedlings harvested from forests.  As light availability and soil moisture and nutrients status were equal across treatments, this seems to indicate some advantage of clearcut ECM fungal and associated microbial communities over forest communities in terms of seedlings growth.  No differences in foliar nitrogen and δ15N contents were detected among transplant treatments, possibly due to similar abiotic conditions shared by the treatments.       88 5    Chapter: Conclusion 5.1 Overall conclusions Understanding how forest ecosystems respond to disturbance is essential for sustainable forest management.  New environmental conditions caused by disturbances could affect community function by influencing soil enzyme activities, which in turn affect organic matter turnover, or by selecting for fungal communities possessing traits better suited for the new conditions (Koide et al. 2014).  Ecosystem function could therefore be influenced by both fungal community composition and abiotic environment, but the influence of each factor on mycorrhizosphere enzyme activity remains poorly understood (Koide et al. 2014, Kivlin and Treseder 2014).  The results of this thesis research suggest that both ECM fungal community and environment influenced nutrient acquisition and growth of naturally regenerated seedlings, as well as the activities of extracellular enzymes.   The results of this study strongly indicated that edaphic conditions were more important than fungal community composition in determining mycorrhizosphere enzyme activity.  Other studies have observed similar results.  In old growth forest soils, Burke et al. (2012) found that enzyme activities of soil microbial communities were affected by a variety of environmental conditions, including soil nutrient status.  Kivlin and Treseder (2014) concluded that abiotic factors accounted for more variation in soil enzyme activities than biotic factors.  Also, Moeller et al. (2014) saw nutrient acquisition strategies of ECM fungal species reflect changes in an environmental gradient.  In this study, regardless of ECM fungal community, when transplanted into the same environment, seedlings exhibited similar enzyme profiles.  This indicated that root tip communities are capable of a range of physiological attributes that are differentially expressed in various edaphic conditions, suggesting ECM fungal communities have the potential to be resilient to disturbances. Resilience has been defined as the time it takes for a system to return to a new steady state following a perturbation (Mittelbach et al. 1995, Gunderson 2000).  As clearcut and forest ECM fungal communities exhibited a range of extracellular activities in different environments, these communities have the potential to be resilient and recover pre-disturbance physiological functions over time.  Others have observed apparent physiological plasticity.  For example, enzyme activities of specific ECM fungal species differed between niches (Buée et al. 2007).  Rineau and Courty (2011) classified ECM fungal species to  89 several functional groups as defined by species’ enzyme profiles, and Courty et al. (2010) saw enzyme activities of individual species vary with time and with soil horizon. This suggests a capacity of fungi to functionally acclimatize to limiting conditions. However, the results of these previous studies could also have been influenced by intraspecific variation, which was accounted for in the experimental design used in this thesis. Nevertheless, my data adds to the evidence that ECM fungal communities have the ability to become acclimated to disturbances (Díaz and Cabido 2001, Loreau et al. 2001, Elmqvist et al. 2003) and contribute to resilience.   In this study, I found that enzyme activities also differed within a destination environment, demonstrating that ECM fungal identity played a detectable role in determining mycorrhizosphere enzyme profiles.  Some of the studies mentioned above found that enzyme activities were influenced by abiotic factors but also observed some biotic influences on function (Buée et al. 2007, Courty et al. 2010, Rineau and Courty 2011). For example, Burke et al. (2012) observed that while edaphic factors determined soil enzyme activities, the activities of certain enzymes were correlated with specific ECM fungal species.  In the study by Kivlin and Treseder (2014), soil communities also influenced soil enzyme activity, but to a lesser degree than environmental conditions.  I had predicted that ECM fungal community composition would be highly influential in determining exoenzyme activities, based on previous evidence that ECM fungi vary in ability to break down and acquire nutrients (Jones et al. 2010, Taylor et al. 2010, Courty et al. 2011).  However, edaphic conditions were the main drivers behind mycorrhizosphere enzyme activities in this study, with fungal identity playing a smaller role. It should be noted that in some cases, microbial communities may be best adapted to their environment of origin.  Alster et al. (2013) measured microbial biomass and activities of nine extracellular enzymes in a California grassland and found that efficiencies of β-glucosidase, β-xylosidase, and polyphenol oxidase were greater when microbes were transplanted into environment from which they originated.  In addition, microbial communities seem to be distinct in different litter types (Leckie et al. 2004, Grayston and Prescott 2005) and may better decompose these litter types.  Also, an analysis of the phylogenetic structure of ECM fungal communities after fire revealed that some ECM groups are fire-adapted (Rincón et al. 2014).    90 It is possible that the design of this study made detection of fungal species effects more difficult.  Ectomycorrhizal fungal species exhibit complementarity of enzyme profiles (Courty et al. 2006, Buée et al. 2007, Jones et al. 2010).  For example, dominant ECM fungal species have exhibit peak laccase activities at different times of the year, indicating a seasonal functional complementarity (Courty et al. 2006).  Different enzyme profiles were observed for different ECM fungal species occurring in the same niche (Buée et al. 2007), although profiles of the same species also differed among niches.  Jones et al. (2010) observed no difference in activities of three exoenzymes among ECM fungal communities in different disturbance treatments, but saw different ECM fungal species colonizing the same seedling exhibit up to a six-fold difference in enzyme activity.  In addition, closely related fungi have been shown to occupy different niches, a pattern which was observed in major functional guilds (Taylor et al. 2014).  Even though clearcut and forest ECM fungal communities differed from each other, these communities may have consisted of species whose functions complemented each other.  As enzyme activities were measured at the community level and enzyme profiles of fungal species can be complementary, any species differences would not have been detected. Edaphic conditions, and to a smaller degree, ECM fungal species determined ecosystem function.  Clearcut and forest ECM fungal communities displayed a range of physiological attributes when transplanted into different environmental conditions.  No loss of function was observed after clearcutting and the fungal communities have the capacity for resilience after disturbance in regards to nutrient cycling.  5.2 Potential applications and future directions A major finding of this thesis is that a shift in ECM fungal community does not necessarily result in loss of ecosystem function.  Forest management must consider the impact of disturbance on soil edaphic conditions and the consequences this will have on soil microbial communities, but a shift in ECM fungal community structure may not be cause for concern.  Ectomycorrhizal fungal communities exhibited a range of physiological activities in differing edaphic conditions which could contribute to ecosystem resilience.  This has implications for management and reclamation practices.  Some reforestation efforts attempt to colonize seedlings in the nursery with ECM fungi that are adapted to edaphic conditions in  91 which the seedlings will be planted (Sousa et al. 2011, Kᶅaviᶇa et al. 2013, Dulmer et al. 2014).  Containerized seedling can also become naturally colonized by nursery fungi (Perry et al. 1987).  However, as ECM fungal communities seem to have the capacity to be physiologically plastic, regardless of the species seedlings are colonized with.  Also, ECM fungi used as inoculants do not always persist once transplanted into the field (Chu-Chou and Grace 1990).  Therefore, it may be economically efficient to allow colonization of transplanted seedlings to occur naturally the field as clearcut ECM fungal communities seem fully capable of high levels of extracellular enzyme activities, at least in Engelmann spruce – Subalpine fir sites. It would be interesting to relate enzyme activities of clearcut and forest ECM communities to specific fungal roots tips by determining the fungal identities of the excised root tips used for the enzyme assays in Chapter 3 of this thesis (these tips were not molecularly identified due to time and budget constraints).  This would allow me to investigate if the differences and similarities observed in enzymatic profiles are due to a few individual species or to the overall community.  It is possible that one or two species are responsible for the similarities of enzyme profiles observed when clearcut and forest seedlings were transplanted into the same environment.  This would allow insight into whether clearcut and forest ECM fungal species exhibit functional complementarity. This study used the unique approach of applying 454 sequencing to individual root tips, thus amplifying the entire root tip community.  For this thesis, I was only interested in the ECM fungal identity of the individual tip, but it would be interesting to analyze the entire root tip fungal and bacterial community and compare the communities between clearcut and forest environments.  For example, bacteria associated with ECM fungi have been shown to influence enzyme activity (Paul and Clark 1989, Olsson and Wallander 1998, Timonen et al. 1998) and it would be informative to see what differences exist between clearcut and forest bacterial communities.  It was also interesting to note that amplicon libraries sent for 454 sequencing showed two bands when visualized on a gel; one at 300 bp, which was amplified, and one at 700 bp.  It is likely that the long fragment was due to an intron between the ITS1f and ITS1 primer sites that is found in many ascomycetes (Bjorn Lindahl pers. comm.).  As this intron is mainly found in ascomycetes (not including Wilcoxina), the decision was made to risk losing some ECM species (such as Cenoccocum, which was easily identified via  92 morphotyping) and sequence the shorter amplicon.   It would be interesting to sequence the larger amplicon to determine if it contained ECM species not otherwise sequenced.    5.3 Strengths and limitations  Previous studies investigating the effect of ECM fungal community structure on ecosystem functioning have measured enzyme activities of different communities existing in different environments (Buée et al. 2007, Rineau and Courty 2011, Jones et al. 2012, Walker and Jones 2013).  This makes it difficult to determine if any difference in enzyme activities were due to the different fungal community or to varying environmental conditions.  The work in this thesis compared enzyme profiles of different ECM fungal communities existing in the same environment, allowing inferences to be made about the effects of community composition vs. environment on ecosystem function.  This is important when trying to predict the impact environmental changes, such as clearcutting, will have on overall ecosystem function (Kivlin and Treseder 2014).  The enzyme assay method used to measure the functional trait of ECM enzyme activity has several advantages and disadvantages.  This technique is a relatively inexpensive method to measure a specific functional trait of field ECM at a community level (Courty et al. 2005, Pritsch et al. 2004).  It is also fairly fast, allowing for high throughput analyses.  In addition, detection of enzyme activity is very sensitive, which allows analysis on small samples, like root tips. In spite of these advantages, several limitations of this method exist.  Enzyme assays are performed under standard, optimal conditions (pH and temperature) that do not reflect heterogeneous soil conditions found in the field.  Measured enzyme activities are therefore not the actual activities expressed in the field at the time of harvest, but are a measure of potential enzyme activity (Jones et al. 2011).  Also, cleaning of excised root tips removes extramatical hyphae.  These structures are important in soil exploration and nutrient uptake (Leake et al. 2004) but the activities of these hyphae are not measured in this technique.  It is difficult to extract intact field grown mycelia, but ingrowth bags could be used to collect mycelia (Phillips et al. 2014) and subsequently be analyzed via enzyme assays.  Whether measured enzyme activities are mostly correlated with activities of the root tip or if activities of extramatrical hyphae cause a difference, remains poorly understood (Courty et al. 2005).  Nonetheless, in this study  93 interpretation of the results is based on activities measured on the mycorrhizosphere (i.e., the area surrounding the mantle), not extramatrical hyphae.  There is also concern that the activities of excised root tips could differ from intact root tips receiving photosynthates from the host (Jones et al. 2011).  However, short handling and incubation times of excised root tips limit the depletion of root tip C reserves (Ritter et al. 1986).  Also, this study measured extracellular enzymes, which would have previously been excreted by the ectomycorrhizas and would therefore not be dependent on ongoing photosynthate. Finally, bacterial communities associated with ECM fungi, and the root tissue itself also secrete exoenzymes that could contribute to the measured activities (Jones et al. 2011).  Additions of antibiotics to enzyme assays however, showed little change in activity (Cullings et al. 2008), indicating that contribution of bacteria to enzyme activity may be small.  However, measured enzyme activities represent the activity of the entire root tip community, including bacteria and plant tissue.  Therefore, as mentioned previously, it would be interesting to analyze 454 sequencing data of the entire root tip community.  Due to time and budget constraints, root tips used in the enzyme assays did not receive molecular work to identify the fungal symbiont.  Instead, differences between clearcut and forest community compositions were investigated using different root tips selected from the same seedlings as the enzyme tips.  The possibility exists that the tips selected for the enzyme assays exhibited a different community structure than the tips that were selected for molecular identification.  However, as the enzyme tips were selected randomly from the same seedlings as the molecular tips, I feel the tips used in for molecular identification adequately represent the tips used in the enzyme assays.  Also, sequences obtained from pyrosequencing were fairly short, around 100 bp.  However, the ECM fungal species that were indicative of clearcut communities and species that were indicative of forest communities were consistent with the findings of others (Rincón et al. 2014, Walker and Jones 2013, Kranabetter and Friesen 2002).   Therefore, I have confidence that despite the short sequences, the pyrosequencing results accurately reflect clearcut and forest ECM fungal community composition. It should also be noted that in the field transplant, forest seedlings were covered with shade tents in the clearcut environment to improve their survival rate in a higher light environment.  Therefore, forest seedlings in clearcut environments received less sunlight than  94 clearcut seedlings, which could have repercussions on our results.  For instance, the shade tents could have affected the growth of seedlings in clearcut environments.  Forest seedlings transplanted into clearcut typically had larger biomass than corresponding seedlings planted back into the forest, but compared to clearcut seedlings planted into clearcuts, had smaller biomass values.  It is possible that if shade tents were absent, then forest seedlings could have equaled the growth of clearcut seedlings in clearcuts.  However, in the growth chamber where seedlings received the same amount of light, forest seedlings grew less than clearcut seedlings in clearcut soil, verifying the results observed in the field.  Also, light availability influences foliar N content (Schoettle and Smith 1999).  Forest seedlings in clearcuts generally accumulated more N than when in the forest, but accumulated less N than clearcut seedlings in clearcut.  It is possible that with the removal of the shade tents, forest seedlings could have accumulated as much N a clearcut seedlings, which seems possible given that in the growth chamber no differences in foliar N were observed across transplant treatments.  If forest seedlings were to acquire as much N as clearcut seedlings in clearcuts, this would indicate a light advantage in the uptake of N for A. lasiocarpa seedlings.  Erecting shade tents over forest in seedlings in clearcut improved their survival, but may have influenced the results of this study, at least in terms of foliar N contents.   δ15N values of apical buds were measured in an attempt to determine how much of the foliar N was from ECM fungi (Hobbie and Colpaert 2003).  However, it should also be noted that δ15N values could only be compared among treatments and inferences could only be made about the amount of N transferred by the fungus in one treatment relative to other treatments.  Conclusions could not be made about how much nitrogen ECM fungi were actually taking up and transferring to the seedlings. Despite these limitations, this study nonetheless allowed us to determine if a shift in ECM community structure meant a change in ecosystem function, and if a change in function was due to different communities or environmental conditions.  Previous studies were unable to determine if changes in function was due to biotic or abiotic factors.  This study concluded that new edaphic conditions after clearcutting rather than new ECM fungal community compositions determined exoenzyme activities.  Ectomycorrhizal fungal communities demonstrated the potential for functional resilience, which may ensure that clearcutting does  95 not result in a loss of community function in terms nutrient cycling in the Engelmann spruce – Subalpine fir biogeoclimatic zone in the southern interior of British Columbia.                               96 References Aarssen, L.W. and Schamp, B.S. 2002. Predicting distributions of species richness and species size in regional floras: Applying the species pool hypothesis to the habitat template model. Perspectives in Plant Ecology, Evolution and Systematics 5: 3-12. Abuzinadah, R.A. and Read, D.J. 1989. The role of proteins in the nitrogen nutrition of ECM plants. 5. Nitrogen transfer in birch (Betula pendula) grown in association with mycorrhizal and non-mycorrhizal fungi. New Phytologist 112: 61-68. Adeleke, R.A., Cloete, T.E., Bertrand, A. and Khasa, D.P. 2012. Iron ore weathering potentials of ECM plants. Mycorrhiza 22: 535-544.  Aerts, R. and Chapin, F.S. 2000. The mineral nutrition of wild plants revisited: A re-evaluation of processes and patterns. Advances in Ecological Research 30: 1-67.   Agerer, R. 2001. Exploration types of ectomycorrhizas: A proposal to classify ectomycorrhizal mycelial systems according to their patterns of differentiation and putative ecological importance. Mycorrhiza. 11: 107-114.  Ahangar, M.A., Dar, G.H. and Bhat, Z.A. 2012. Growth response and nutrient uptake of blue pine (Pinus wallichiana) seedlings inoculated with rhizosphere microorganisms under temperate nursery conditions. Annals of Forest Research 2: 217-227.  Alster, C.J., German, D.P., Lu, Y. and Allison, S.D. 2013. Microbial enzymatic responses to drought and to nitrogen addition in a southern California grassland. Soil Biology & Biochemistry 64: 68-79.  Arocena, J.M., K.R. Glowa, H.B. Massicotte and L. Lavkulich. 1999. Chemical and mineral composition of ectomycorrhizosphere soils of subalpine fir (Abies lasiocarpa (Hook.) Nutt.) in the Ae horizon of a Luvisol. Canadian Journal of Soil Science 79: 25-35.   97 Baar, J. 1996. The ECM flora of primary and secondary stands of Pinus sylvestris in relation to soil conditions and ECM succession. Journal of Vegetation Science 7: 497-504.   Balogh-Brunstad, Z., Keller, C.K., Dickinson, J.T., Stevens, F., Li, C.Y. and Bormann, B.T. 2008. Biotite weathering and nutrient uptake by ECM fungus, Suillus tomentosus, in liquid-culture experiments. Geochimica et Cosmochimica Acta 72: 2601-2618.  Barker, J.S., Simard, S.W., Jones, M.D., and Durall, D.M. 2013. Ectomycorrhizal fungal community assembly on regenerating Douglas-fir after wildfire and clearcut harvesting. Oecologia 172: 1179-1189.   Barroetaveña, C., Pildain, M.B., Salgado Salomón, M.E., and Eberhart, J.L. 2010. Molecular identification of ectomycorrhizas associated with ponderosa pine seedlings in Patagonian nurseries (Argentina). Canadian Journal of Forest Research 40: 1940-1950.  B.C. Ministry of Forests. 2000. Establishment to free growing guidebook. Kamloops Forest Region. Revised edition, Version 2.2. In: Practices Code of British Columbia Guidebook. B.C. Ministry of Forests, Victoria, B.C.   Bending, G.D. and Read, D.J. 1995. The structure and function of the vegetative mycelium of ECM plants. 5. Foraging behaviour and translocation of nutrients from exploited litter. New Phytologist 130: 401-409. Blaalid, R., Carlsen, T., Kumar, S., Halvorsen, R., Ugland, K.I., Fontanas, G. and Kauserud, H. 2012. Changes in the root-associated fungal communities along a primary succession gradient analyzed by 454 pyrosequencing. Molecular Ecology 21: 1897-1908.  Black, T.A. and Harden, J.W. 1995. Effect of timber harvest on soil carbon storage at Blodgett Experimental Forest, California. Canadian Journal of Forest Research 25: 1385-1396.  98  Bonneville, S., Morgan, D.J., Schmalenberger, A., Bray, A., Brown, A., Banwart, S.A. and Benning, L.G. 2011. Tree-mycorrhiza symbiosis accelerate mineral weathering: Evidences from nanometer-scale elemental fluxes at the hypha-mineral interface. Geochimica et Cosmochimica Acta 75: 6988-7005.   Borcard, D., Gillet, F. and Legendre, P. 2011. Numerical Ecology with R. Springer, New York, USA.  Borchers, J.G. and Perry, D.A. 1991. The influence of soil texture and aggregation on carbon and nitrogen dynamics in southwest Oregon forests and clearcuts. Canadian Journal of Forest Research 22: 298-305.  Bormann, F.H., Likens, G.E., Siccama, T.G., Pierce, R.S., and Eaton, J.S. 1974. The export of nutrients and recovery of stable conditions following deforestation at Hubbard Brook. Ecological Monographs 44: 255-277.   Bown, H.E., Watt, M.S., Clinton, P.W. and Mason, E.G. 2010. Influence of ammonium and nitrate supply on growth, dry matter partitioning, N uptake and photosynthetic capacity of Pinus radiata seedlings. Trees 24: 1097-1107.  Branco, S. 2010. Serpentine soils promote ECM fungal divsersity. Molecular Ecology 19: 5566-5576.  Bremner, J.M. 1965. Inorganic forms of nitrogen. In: C.A. Black, ed. “Methods of Soil Analysis. Part 2.” Agronomy. Wisconsin : American Society of Agronomy Pub.,  1179-1237.  Bremner, J.M. 1996. Nitrogen Availability Indexes. In: C.A. Black, ed. “Methods of Soil Analysis, Part 2,” Agronomy. Wisconsin : American Society of Agronomy Pub., 1324-1345.  99  Bruns, T.D. 1995. Thoughts on the processes that maintain local species diversity of ectomycorrhizal fungi. Plant and Soil 170: 63-73.  Buée, M., Courty, P.E., Mignot, D. and Garbaye, J. 2007. Soil niche effect on species diversity and catabolic activities in an ECM fungal community. Soil Biology & Biochemistry 39: 1947-1955.  Buée, M, Reich, M., Murat, C., Morin, E., Nilsson, R.H., Uroz, S. and Martin, F. 2009. 454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. New Phytologist 184: 449-456.  Burke, D.J., Weintraub, M.N., Hewins, C.R. and Kalisz, S. 2011. Relationship between soil enzyme activities, nutrient cycling and soil fungal communities in a northern hardwood forest. Soil Biology & Biochemistry 43: 795-803.  Burke, D.J., Smemo, K.A., Lopez-Gutierrez, J.C. and Hewins, C.R. 2012. Soil enzyme activity in an old-growth northern hardwood forest: Interactions between soil environment, ECM fungi and plant distribution. Pedobiologia 55: 357-364.  Burke, D.J., Smemo, K.A. and Hewins, C.R. 2014. ECM fungi isolated from old-growth northern hardwood forest display variability in extracellular enzyme activity in the presence of plant litter.  Soil Biology & Biochemistry 68: 219-222.   Burns, R.M., and Honkala, B.H., tech. coords. 1990. Silvics of North America: 1. Conifers; 2. Hardwoods. In: Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service, Washington, DC.   Buée, M., Courty, P.E., Mignot, D. and Garbaye, J. 2007. Soil niche effect on species diversity and catabolic activities in an ECM fungal community. Soil Biology & Biochemistry 39: 1947-1955.  100 Caccia, F.D. and Ballaré, C.L. 1998. Effects of tree cover, understory vegetation, and litter on regeneration on Douglas-fir (Pseudotsuga menziesii) in southwestern Argentina. Canadian Journal of Forest Research 28: 683-692.  Cairney, J.W.G. and Chambers, S.M. 1999. ECM fungi key genera in profile. Springer, New York, USA.  Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., and Knight, R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7: 335-336.  Carter, M.R. 1993. Soil Sampling and Methods of Analysis. CRC Press, Florida, USA.  Carter, M.R. and Gregorich, E.G., eds. 2008. Soil Sampling and Methods of Analysis, Second Edition. CRC Press, Boca Raton, USA.  Casarin, V., Plassard, C., Souchie, G. and Arvieu, J.C. 2003. Quantification of oxalate ions and protons released by ECM fungi in rhizosphere soil. Agronomie 23: 461-469.  Cashore, B. and G. Auld. 2003. British Columbia’s environmental forestry policy record in perspective. Journal of Forestry 101: 42-47.  Chalot, M. and Bruns, A. 1998. Physiology of organic nitrogen acquisition by ectomycorrhizal fungi and ectomycorrhizas. FEMS Microbiology Reviews 22: 21-44.  Chapin, F.S., Zavaleta, E.S., Eviner, V.T., Naylor, R.L., Vitousek, P.M., Reynolds, H.L., Hooper, D.U., Lavorel, S., Sala, O.E., Hobbie, S.E., Mack, M.C. and Díaz, S. 2000. Consequences of changing biodiversity. Nature 405: 234-242.   101  Chen, H. Y. H., Shrestha, B.M. 2012. Stand age, fire and clearcutting affect soil organic carbon and aggregation of mineral soils in boreal forests. Soil Biology & Biochemistry 50: 149-157.  Chu-Chou, M. and Grace, L.J. 1990. Mycorrhizal fungi of radiata pine seedlings in nurseries and trees in forests. Soil Biology & Biochemistry 22: 959-966.   Clarkson, D.T. 1985. Factors affecting mineral nutrient acquisition by plants. Annual Review of Plant Physiology 36: 77-115.  Coates, K.D. 2000. Conifer seedling response to northern temperate forest gaps. Forest Ecology and Management 127: 249-269.  Colpaert, J.V. and van Tichelen K.K. 1996. Decomposition, nitrogen and phosphorus mineralization from beech leaf litter colonized by ectomycorrhizal or litter-decomposing basidiomycetes. New Phytologist 134: 123-132.  Conn, C. and J. Dighton. 2000. Litter quality influences on decomposition, ectomycorrhizal community structure and mycorrhizal root surface acid phosphatase activity. Soil Biology & Biochemistry 32: 489-469.  Corrêa, A., Gurevitch, J., Martins-Loução, M.A. and Cruz, C. 1995. C allocation to the fungus is not a cost to the plant. Oikos 121: 449-463.  Courty, P.E., Pritsch, K., Schloter, M., Hartmann, A. and Garbaye, J. 2005. Activity profiling of ectomycorrhiza communities in two forest soils using multiple enzymatic tests. New Phytologist 167: 309-319.   102 Courty, P.E., Pouysegur, R., Buée, M. and Garbaye, J. 2006. Laccase and phosphatase activities of the dominant ECM types in a lowland oak forest. Soil Biology & Biochemistry 38: 1219-1222.  Courty, P.E., Franc, A. and Garbaye, J. 2010a. Temporal and functional pattern of secreted enzyme activities in an ECM community. Soil Biology & Biochemistry 42: 2022-2025.  Courty, P.E., Buée, M., Diedhiou, A.G., Frey-Klett, P., Le Tacon, F., Rineau, F., Turpault, M.P., Uroz, S., and Garbaye, J. 2010b. The role of ECM communities in forest ecosystem processes: New perspectives and emerging concepts. Soil Biology & Biochemistry 42: 679-698.  Courty, P.E., J. Labbé, A. Kohler, B. Marcais, C. Bastien, J.L. Churin, J. Garbaye and F. Le Tacon. 2011. Effect of poplar genotypes on mycorrhizal infection and secreted enzyme activities in mycorrhizal and non-mycorrhizal roots. Journal of Experimental Botany 62: 249-260. Covington, W.W. 1981. Changes in forest floor organic matter and nutrient content following clear cutting in northern hardwoods. Ecology 62: 41-48.  Cowden, C.C. and Peterson, C.J. 2013. Annual and seasonal dynamics of ectomycorrhizal fungi colonizing white pine (Pinus strobus) seedlings following catastrophic windthrow in northern Georgia, USA. Canadian Journal of Forest Research 43: 215-223.  Cromack, K., Sollins, P., Graustein, W.C., Speidel, K., Todd, A.W., Spycher, G., Li, C.Y., and Todd, R.L. 1979. Calcium-oxalate accumulation and soil weathering in mats of the hypogeous fungus Hysterangium crassum. Soil Biology & Biochemistry 11: 463-468.  Cui, M. and Smith, W.K. 1991. Photosynthesis, water relations and mortality in Abies lasiocarpa seedlings during natural establishment. Tree Physiology 8: 37-46.   103 Cullings, K., Ishkhanova, G. and Henson, J. 2008. Defoliation effects on enzyme activities of the ectomycorrhizal fungus Suillus granulatus in a Pinus contorta (lodgepole pine) stand in Yellowstone National Park. Ecosystem Ecology 158: 77-83.  Dahlgren, R.A. and Driscoll, C.T. 1994. The effects of whole-tree clear-cutting on soil processes at the Hubbard Brook Experimental Forest, New Hampshire, USA. Plant and Soil 158: 239-262.  Danielson, R.M., Zak, J.C. and Parkinson, D. 1984. Mycorrhizal inoculum in a peat deposit form under a white spruce stand in Alberta. Canadian Journal of Botany 62: 2557-2560.  Danielson, R.M. and Visser, S. 1989. Effects of forest soil acidification on ectomycorrhizal and vesicular – arbuscular mycorrhizal development. New Phytologist 112: 41-17.  Danielson, R.M. 1991. Temporal changes and effects of amendments on the occurrence of sheathing (ecto-) mycorrhizas of conifers growing in oil sands tailings and coal spoil. Mycorrhiza 35: 261-281.  Deslippe, J.R., Hartmann, M., Mohn, W.W. and Simard, S.W. 2011. Long-term experimental manipulation of climate alters the ECM community of Betula nana in Arctic tundra. Global Change Biology 17: 1625-1636.   Díaz, S. and Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends in Ecology & Evolution 16: 646-655.  Dickie, I.A., Richardson, S.J. and Wiser, S.K. 2009. Ectomycorrhizal fungal communities and soil chemistry in harvested and unharvested temperate Nothofagus rainforests. Canadian Journal of Forest Research 39: 1069-1079.  104 Dimitriu, P.A., Prescott, C.E., Quideau, S.A. and Grayston, S.J. 2010. Impact of reclamation on surface-mined boreal forest soils on microbial community composition and function. Soil Biology & Biochemistry 42: 2289-2297. Dulmer, K.M., LeDuc, S.D. and Horton, T.R. 2014. Ectomycorrhizal inoculum potential of northeastern US forest soil for American chestnut restoration: results from field and laboratory bioassays. Mycorrhiza 24: 65-74. Durall, D.M., Gamiet, S., Simard, S.W., Kudrna, L. and Sakakibara, S.M. 2006. Effects of clearcut logging and tree species composition on the diversity and community composition of epigeous fruit bodies formed by ECM fungi. Canadian Journal of Botany 84: 966-980. Eberhardt, U., Walter, L., and Kottke, I. 1999. Molecular and morphological discrimination between Tylospora fibrillosa and Tylospora asterophora mycorrhizae. Canadian Journal of Botany 77: 11-21.  Edmonds, R.L. and McColl, J.G. 1989. Effects for forest management on soil nitrogen in Pinus radiata stands in the Australia capital territory. Forest Ecology and Management 26: 199-212.   Elmqvist, T., Folke, C., Nyström, M., Peterson, G., Bengtsson, J., Walker, B. and Norberg, J. Response diversity, ecosystem change, and resilience. Frontiers in Ecology and the Environment 1: 488-494.  Entry, J.A. and Emmington, W.H. 1998. Influence of forest age on forms of carbon in Douglas-fir soils in the Oregon Coast Range. Canadian Journal of Forest Research 28: 390-395.  Erland, S., Soderström, B. and Solbritt, A. 1990. Effect of liming on ectomycorrhizal fungi infecting Pinus sylvestris L. II. Growth rates in pure culture at different pH values compared to growth rated in symbiosis with the plant host. New Phytologist 115: 683-688.  105  Erland, S. 1995. Abundance of Tylospora fibrillosa ectomycorrhizas in a south Swedish spruce forest measured spruce forest measured by RFLP analysis of the PCR-amplified rDNA ITS region. Mycological Research 99: 1425-1428.   FAO. 2010. Global forest resources assessment 2010 – main report. FAO Forestry Paper No. 163 [WWW document] URL http://www.fao.org/docrep/013/i1757e/i1757e00.htm [accessed 18 June 2014].  Feller, M. 1999. Sicamous Creek Silvicultural Systems Project: The response of Engelmann Spruce Subalpine Fir forest ecosystems to logging-Effects of harvesting on nutrient budgets. Forest Renewal BC. Ref TO96095-RE.  Fichtner, A., von Oheimb, G., Härdtle, W., Wilken, C. and Gutknecht, J.L.M. 2014. Effects of anthropogenic disturbances on soil microbial communities in oak forests persist for more than 100 years. Soil Biology & Biochemistry 70: 79-87.  Filip, Z., Claus, H. and Dippell, G. 1998. Degradation of humic substances by soil microorganisms – a review. Journal of Plant Nutrition and Soil Science 161: 605-612.   Finlay, R.D., Frostegård, A. and Sonnerfeldt, A.M. 1992. Utilization of organic and inorganic nitrogen sources of ectomycorrhizal fungi in pure culture and in symbiosis with Pinus contorta Dougl. ex Loud. New Phytologist 120: 105-115.  Finzi, A.C. and Canham, C.D. 2000. Sapling growth in response to light and nitrogen availability in a southern New England forest. Forest Ecology and Management 131: 153-165.  Fisk, M. and Fahey, T.J. 1990. Nitrification potential in the organic horizons following clearfelling of northern hardwood forests. Soil Biology & Biochemistry 22: 277-279.   106 Fox, F.M. 1983. Role of basidiospores as inocula of mycorrhizal fungi of birch. Plant and Soil 71: 269-273.  Franklin, J. F. and Dyrness, C.T. 1973. Natural vegetation of Oregon and Washington. Report PNW-8. USDA Forest Service, Pacific Northwest Forest and Range Experiment Station, Oregon, USA.  Frey-Klett, P., Chavatte, M., Clausse, M.L., Courrier, S., Le Roux, C., Raaijmakers, J., Martinotti, M.G., Pierrat, J.C. and Garbaye, J. 2005. Ectomycorrhizal symbiosis affects functional diversity of rhizosphere fluorescent pseudomonads New Phytologist 165: 317-328.  Gardes, M. and Bruns, T.D. 1993. ITS primers with enhanced specificity for basidiomycetes - application to the identification of mycorrhizae and rusts. Molecular Ecology 2: 113-118. Gardes, M. and Bruns T.D. 1996. Community structure of ectomycorrhizal fungi in a Pinus muricata forest: above- and below-ground views. Canadian Journal of Botany 74: 1572-1583. Gebauer, G. and Taylor A.F.S. 1999. 15N natural abundance in fruit bodies of different functional groups of fungi in relation to substrate utilization. New Phytologist 142: 93-101. Gessler, A., Schneider, S., Von Sengbusch, D., Weber, P., Hanemann, U., Huber, C., Rothe, A., Kreutzer, K. and Rennenberg, H. 1998. Field and laboratory experiments on net uptake of nitrate and ammonium by the roots of spruce (Picea abies) and beech (Fagus sylvatica) trees. New Phytologist 138: 275-285.  Glass, A.D.M. 2003. Nitrogen use efficiency of crop plants: Physiological constraints upon nitrogen absorption. Critical Reviews in Plant Sciences 22: 453-470.   107 Goodale, C.L. and Aber, J.D. 2001. The long-term effects of land-use history on nitrogen cycling in northern hardwood forests. Ecological Applications 11: 253-267.  Goodman, D.M., Durall, D.M., Trofymow, J.A. and Berch, S.M. 1996. A manual of concise descriptions of North American ectomycorrhizas.  Mycologue Publications, Victoria, BC.   Goodman, D.M. and Trofymow, J.A. 1998. Distribution of ectomycorrhizas in microhabitats in mature and old-growth stands of Douglas-fir on southeastern Vancouver Island. Soil Biology & Biochemistry 30: 2127-2138.  Gramss, G., Ziegenhagen, D., and Sorge, S. 1999. Degradation of soil humic extract by wood and soil associated fungi, bacteria, and commercial enzymes. Microbial Ecology 37: 140-151.  Grand, S. and Lavkulich, L.M. 2012. Effects of forest harvest on soil carbon and related variable in Canadian spodosols. Soil Science Society of America Journal 76: 1816-1827.   Grayston, S.J. and Prescott, C.E. 2005. Microbial communities in forest floors under four tree species in coastal British Columbia. Soil Biology & Biochemistry 37: 1157-1167.  Grenon, F., Bradley, R.L., Joanisse, G., Titus, B.D. and Prescott, C.E. 2004. Mineral N availability for conifer growth following clearcutting: responsive versus non-responsive ecosystems. Forest Ecology and Management 188: 305-316.   Griffiths, R.P., Baham, J.E., and Caldwell B.A. 1994. Soil solution chemistry of ectomycorrhizal mats in forest soil. Soil Biology & Biochemistry 26: 331-337.   108 Hagerman, S.M., Jones, M.D., Bradfield, G.E., Gillespie, M. and Durall, D.M. 1999a. Effects of clear-cut logging on the diversity and persistence of ectomycorrhizas at a subalpine forest. Canadian Journal of Forest Research 29: 124-134.  Hagerman, S.M., Jones, M.D., Bradfield, G.E. and Sakakibara, S.M. 1999b. Ectomycorrhizal colonization of Picea engelmannii x Picea glauca seedlings planted across cut blocks of different sizes. Canadian Journal of Forest Research 29: 1856-1870.    Hannam, K.D. and Prescott, C.E. 2003. Soluble organic nitrogen in forests and adjacent clearcuts in British Columbia, Canada.  Canadian Journal of Forest Research 33: 1709-1718. Hassett, J.E. and Zak, D.R. 2005. Aspen harvest intensity decreases microbial biomass, extracellular enzyme activity, and soil nitrogen cycling. Soil Science Society of America Journal 69: 227-235. Heiskanen, J. and Rikala, R. 2000. Effect of peat-based container media on establishment of scots pine, Norway spruce and silver birch seedlings after transplanting in contrasting water conditions. Scandinavian Journal of Forest Research 15: 49-57. Hildebrand, E.E. 1994. The heterogenous distribution of mobile ions in the rhizosphere of acid forest soils: facts, causes, and consequences. Journal of Environmental Science and Health Part A 29: 1973-1992.  Hobbie, S.E. 1992. Effects of plant species on nutrient cycling. Trends in Ecology & Evolution 7: 336-339.  Hobbie, E.A., Macko, S.A. and Shugart. H.H. 1998. Insights into nitrogen and carbon dynamics of ECM and saprotrophic fungi from isotopic evidence. Oecologia 118: 353-360.   109 Hobbie, E.A., Macko, S.A. and Shugart, H.H. 1999. Insights into nitrogen and carbon dynamics of ectomycorrhizal and saprotrophic fungi from isotopic evidence. Oecologia 118: 353-360. Hobbie, E.A., Macko, S.A. and Williams, M. 2000. Correlation between foliar δ15N and nitrogen concentrations may indicate plant-mycorrhizal interactions. Oecologia 122: 273-283. Hobbie, J.E. and Hobbie, E.A. 2006. 15N in symbiotic fungi and plants estimates nitrogen and carbon flux rates in Arctic tundra. Ecology 87: 816-822.  Hobbie, E.A., Colpaert, J.V., White, M.W., Ouimette, A.P. and Macko, S.A. 2008. Nitrogen form, availability, and mycorrhizal colonization affect biomass and nitrogen isotope patterns in Pinus sylvestris. Plant and Soil 310: 121-136.   Hobbie, E.A. and Högberg, P. 2012. Nitrogen isotopes link mycorrhizal fungi and plants to nitrogen dynamics. New Phytologist 196: 367-382. Högberg, P. 1990. 15N natural abundance as a possible marker of the ectomycorrhizal habit of trees in mixed African woodlands. New Phytologist 115: 483-486. Holden, S.R., Gutierrez, A. and Treseder, K.K. 2013. Changes in soil fungal communities, extracellular enzyme activities, and litter decomposition across a fire chronosequence in Alaskan boreal forests. Ecosystems 16: 34-46. Hope, G. 2001. The soil ecosystem of and ESSF forest and its response to a range of harvesting disturbances. B.C. Ministry of Forests Ext. Note 53.  Hope, G.D., Prescott, C.E., and Blevins, L.L. 2003. Responses of available soil nitrogen and litter decomposition to openings of different sizes in dry interior Douglas-fir forests in British Columbia. Forest Ecology and Management 186: 33-46.   110 Hope, G.D. 2009. Clearcut harvesting effects on soil and creek inorganic nitrogen in high elevation forests of southern interior British Columbia. Canadian Journal of Soil Science 89: 35-44.   Hrynkiewicz, K., Dabrowska, G., Baum, C., Niedojadlo, K. and Leinweber, P. 2012. Interactive and single effects of ectomycorrhiza formation and Bacillus cereus on metallothionein MT1 expression and phytoextraction of Cd and Zn by willow. Water Air Soil Pollution 223: 957-968.  Hutchison, L.J. and Piché, Y. 1995. Effect of exogenous glucose on mycorrhizal colonization in-vitro by early-stage and late-stage ectomycorrhizal fungi. Canadian Journal of Botany 73: 898-904.  Ibáñez, I. and McCarthy-Neumann, S. 2014. Integrated assessment of the direct and indirect effects of resource gradients on tree species recruitment. Ecology 95: 364-375.    Jerabkova, L., Prescott, C.E., Titus, B.D., Hope, G.D., and Walters, M.B. 2011. A meta-analysis of the effects of clearcut and variable-retention harvesting on soil nitrogen fluxes in boreal and temperate forests. Canadian Journal of Forest Research 41: 1852-1870.  Jiménez Esquilín, A.E., Stromberger, M.E. and Shepperd, W.D. 2008. Soil scarification and wildfire interactions and effects on microbial communities and carbon. Soil Science Society of America Journal 72: 111-118.  Johnson, N.C., Angelard, C., Sanders, I.R. and Kiers, E.T. 2013. Predicting community and ecosystem outcomes of mycorrhizal responses to global change. Ecology Letters 16: 140-153.   111 Jones, M.D., Durall, D.M., and Tinker, P.B. 1990. Phosphorus relationships and production of extrametrical hyphae by two types of willow ectomycorrhizas at different soil-phosphorus levels. New Phytologist 115: 259-267. Jones, M.D., Durall, D.M., and Cairney, J.W.G. 2003. Ectomycorrhizal fungal communities in young forest stands regenerating after clearcut logging. New Phytologist 157: 399-422. Jones, M.D., Grenon, F., Peat, H., Fitzgerald, M., Holt, L., Phillip, L.J. and Bradley, R. 2009. Differences in 15N uptake amongst spruce seedlings colonized by three pioneer ectomycorrhizal fungi in the field. Fungal Ecology 2: 110-120.  Jones, M.D., Twieg, B.D., Ward, V., Barker, J., Durall, D.M. and Simard, S.W. 2010. Functional complementarity of Douglas-fir ectomycorrhizas for extracellular enzyme activity after wildfire or clearcut logging. Functional Ecology 24: 1139-1151. Jones, M.D., Brooks, D.D., Courty, P.E., Garbaye, J., Grierson, P.F. and Pritsch, K. 2011. Enzyme activities of root tips and in situ profiles of soil and rhizospheres. In: Methods of Soil Enzymology, eds. Dick, R.P. Soil Science of America Inc, Madison, USA.  Jones, M.D., Phillips, L.A., Treu, R., Ward, V. and Berch, S.M. 2012. Functional responses of ectomycorrhizal fungal communities to long-term fertilization of lodgepole pine (Pinus contorta Dougl. Ex Loud. Var. latifolia Engelm.) stands in central British Columbia. Applied Soil Ecology 60: 29-40.  Johnson, D.W. 1992. Effects of forest management on soil carbon storage. Water Air and Soil Pollution 64: 83-120.  Johnson, D.W. and Curtis, P.S. 2001. Effects of forest management on soil C and N storage: meta analysis. Forest Ecology and Management 140: 227-238.     112 Jonsson, L., Dahlberg, A., Nilsson, M.C., Kårén, O., and Zackrisson, O. 1999. Continuity of ectomycorrhizal fungi in self-regenerating boreal Pinus sylvestris forests studied by comparing mycobiont diversity on seedlings and mature trees. New Phytologist 142: 151-162.   Jumpponen, A. and Jones, K.L. 2009. Massively parallel 454-sequencing indicates hyperdiverse fungal communities in temperate Quercus macrocarpa phyllosphere. New Phytologist 184: 438-448.  Jumpponen, A., Jones, K.L and Blair, J. 2010. Vertical distribution of fungal communities in tallgrass prairie soil. Mycologia 102: 1027-1041. Jurgensen, M.F., Harvey, A.E., Graham, R.T., Page-Dumroese, D.S., Tonn, J.R., Larsen, M.J., and Jain, T.B. 1997. Impacts of timber harvesting on soil organic matter, nitrogen, productivity, and health of inland Northwest forests. Forest Science 43: 234-251.  Kalra, Y. P., ed. 1998. Handbook of reference methods for plant analysis. Soil and Plant. CRC Press, Boca Raton, USA.  Kataja-aho, S., Pennanen, T., Lensu, A. and Haimi, J. 2012. Does stump removal affect early growth and mycorrhizal infection of spruce (Picea abies) seedlings in clear-cuts? Scandinavian Journal of Forest Research 27: 746-753.  Keeney, D.R. and Bremner, J.M. 1966. Comparison and evaluation of laboratory methods of obtaining an index of soil nitrogen availability. Agronomy Journal 58: 498-503.  Keller, G. 1996. Utilization of inorganic and organic nitrogen sources by high-subalpine ectomycorrhizal fungi of Pinus cembra in pure culture. Mycological Research 100: 989-998.   113 Kennedy, P.G., Peay, K.G. and Bruns, T.D. 2009. Root tip competition among ectomycorrhizal fungi: Are priority effects a rule or an exception? Ecology 90: 2098-2107. Kennedy, P. 2010. Ectomycorrhizal fungi and interspecific competition: species interactions, community structure, coexistence, mechanisms, and future research directions. New Phytologist 187: 895-910. Kennedy, N. and Egger, K.N. 2010. Impact of wildfire intensity and logging on fungal and nitrogen-cycling bacterial communities in British Columbia forest soil. Forest Ecology and Management 260: 787-794.  Kernaghan, G., Sigler, L., and Khasa, D. 2003. Mycorrhizal and root endophytic fungi of containerized Picea glauca seedlings assessed by rDNA sequence analysis. Microbial Ecology 45: 128-136.   Khetmalas, M.B., Egger, K.N., Massicotte, H.B., Tackaberry, L.E., and Clapperton, M.J. 2002. Bacterial diversity associated with subalpine fir (Abies lasiocarpa) ectomycorrhizas following wildfire and salvage-logging in central British Columbia. Canadian Journal of Microbiology. 48: 611-625.  Kielland, K. 1994. Amino acid absorption by arctic plants: Implications for plant nutrition and nitrogen cycling. Ecology 75: 2373-2383. Kipfer, T., Moser, B., Egli, S., Wohlgemuth, T. and Ghazoul, J. 2011. Ectomycorrhiza succession patterns in Pinus sylvestris forests after stand-replacing fire in the Central Alps. Oecologia 167: 219-228. Kivlin, S.N. and Treseder, K.K. 2014. Soil extracellular enzyme activities correspond with abiotic factors more than fungal community composition. Biogeochemistry 117: 23-37. Kᶅaviᶇa. D., Gaitnieks, T. and Menkis, A. 2013. Survival, growth and ectomycorrhizal community development of container- and bare-root grown Pinus sylvestris and Picea abies seedlings outplanted on a forest clear-cut. Baltic Forestry 19: 39-49.  114  Koele, N., Dickie, I.A., Blum, J.D., Gleason, J.D. and de Graaf, L. 2014. Ecological significance of mineral weathering in ectomycorrhizal and arbuscular mycorrhizal ecosystems from a field-based comparison. Soil Biology & Biochemistry 69: 63-70.  Koide, R.T., Courty, P.E., and Garbaye, J. 2007. Research perspectives on functional diversity in ECM fungi. New Phytologist 174: 240-243.  Koide, R.T., Fernandez, C. and Malcolm, G. 2014. Determining place and process: functional traits of ectomycorrhizal fungi that affect both community structure and ecosystem function. New Phytologist 201: 433-439.   Kotroczó, Z., Veres, Z., Fekete, I., Krakomperger, Z., Tóth, J.A., Lajtha, K. and Tóthmérész, B. 2014. Soil enzyme activity in response to long-term organic matter manipulation. Soil Biology & Biochemistry 70: 237-243.  Kozlowski, T.T. 1999. Soil compaction and growth of woody plants. Scandinavian Journal of Forest Research 14: 596-619.  Kranabetter, J.M. and Wylie, T. 1998. Ectomycorrhizal community structure across forest openings on naturally regenerated western hemlock seedlings. Canadian Journal of Botany 76: 189-196.  Kranabetter, J.M. and Friesen, J. 2002. Ectomycorrhizal community structure on western hemlock (Tsuga heterophylla) seedlings transplanted from forests into openings. Canadian Journal of Botany 80: 861-868. Kranabetter, J.M. 2004. Ectomycorrhizal community effects on hybrid spruce seedling growth and nutrition in clearcuts. Canadian Journal of Botany 82: 983-991.   115 Kranabetter, J.M. and Coates, K.D. 2004. Ten-year postharvest effects of silviculture systems on soil-resource availability and conifer nutrition in a northern temperate forest. Canadian Journal of Forest Research 34: 800-809.   Kranabetter, J.M., Sanborn, P., Chapman, B.K., and Dube, S. 2006. The contrasting response to soil disturbance between lodgepole pine and hybrid white spruce in subboreal forests. Soil Science Society of America Journal 70: 1591-1599. Kranabetter, J.M. and Simard, S.W. 2008. Inverse relationship between understory light and foliar nitrogen along productivity gradients of boreal forests. Canadian Journal of Forest Research 38: 2487-2496.  Kranabetter, J.M., Durall, D.M. and MacKenzie, W.H. 2009. Diversity and species distribution of ectomycorrhizal fungi along productivity gradients of a southern boreal forest. Mycorrhiza 19: 99-111.  Kranabetter, J.M. and MacKenzie, W.H. 2010. Contrasts among mycorrhizal plant guilds in foliar nitrogen concentration and δ15N along productivity gradients of a boreal forest. Ecosystems 13: 108-117.  Kranabetter, J.M., Simard, S.W., Guy, R.D., and Coates, K.D. 2010. Species patterns in foliar nitrogen concentration, nitrogen content and 13C abundance for understory saplings across light gradients. Plant and Soil 327: 389-401.  Kronzucker, H.J., Siddiqi, M.Y. and Glass, A.D.M. 1997. Conifer root discrimination against soil nitrate and the ecology of forest succession. Nature 385: 59-61.  Kummel, M. and Lostroh, P. 2011. Altering light availability to the plant host determined the identity of the dominant ectomycorrhizal fungal partners and mediated mycorrhizal effects on plant growth. Botany 89: 439-450.    116 Lajzerowicz, C.C., Walters, M.B., Krasowski, M., and Massicotte, H.B. 2004. Light and temperature differentially colimit subalpine fir and Engelmann spruce seedling growth in partial-cut subalpine forests. Canadian Journal of Forest Research 34: 249-260.  Lambers, H., Raven, J.A., Shaver, G.R., and Smith, S.E. 2008. Plant nutrient-acquisition strategies change with soil age. Trends in Ecology and Evolution 23: 95-103. Landeweert, R., Hoffland, E., Finlay, R.D., Kuyper, T.W. and van Breemen, N. 2001. Linking plants to rocks: ectomycorrhizal fungi mobilize nutrients from mineral. Trends in Ecology and Evolution 16: 248-254.  Lapeyrie, F., Picatto, C., Gerard, J. and Dexheimer, J. 1990. T.E.M. study of intracellular and extracellular calcium-oxalate accumulation by ectomycorrhizal fungi in pure culture or in association with Eucalyptus seedlings. Symbiosis 9: 163-166.   Lavoie, N., Vézina, L.P. and Margolis, H.A. 1992. Absorption and assimilation of nitrate and ammonium ions by jack pine seedlings. Tree Physiology 11: 171-183.  Lawton, J.H. and Brown, V.K. 1993. Redundancy in ecosystems. In: Biodiversity and Ecosystem Function, eds. Schulze, E.D and Mooney, H.A. Springer, New York, USA, 255-270.  Lazaruk, L.W., Kernaghan, G., Macdonald, S.E., and Khasa, D. 2005. Effects of partial cutting on the ectomycorrhizas of Picea glauca forests in northwestern Alberta. Canadian Journal of Forest Research 35: 1442-1454.  Leckie, S.E., Prescott, C.E., Grayston, S.J., Neufeld, J.D. and Mohn, W.W. 2004. Characterization of humus microbial communities in adjacent forest types that differ in nitrogen availability. Microbial Ecology 48: 29-40.  117 LeDuc, S.D., Rothstein, D.E., Yermakov, Z. and Spaulding, S.E. 2013. Jack pine foliar δ15N indicates shifts in plant nitrogen acquisition after severe wildfire and through forest stand development. Plant and Soil 373: 955-965.   Leigh, J., Fitter, A.H., and Hodge, A. 2011. Growth and symbiotic effectiveness of an arbuscular mycorrhizal fungus in organic matter in competition with soil bacteria. FEMS Microbiology Ecology 76: 428-438.  Lekberg, Y., Koide, R.T., Rohr, J.R., Aldrich-Wolfe, L., and Morton, J.B. 2007. Role of niche restrictions and dispersal in the composition of arbuscular mycorrhizal fungal communities. Journal of Ecology 95: 95-105.  Li, C.Y., Massicote, H.B., and Moore, L.V.H. 1992. Nitrogen-fixing Bacillus sp. associated with Douglas-fir tuberculate ectomycorrhizas. Plant and Soil 140: 35-40.  Lilleskov, E.A., Fahey, T.J., Horton, T.R. and Lovett, G.M. 2002. Belowground ectomycorrhizal fungal community change over a nitrogen deposition gradient in Alaska. Ecology 83: 104-115.  Lilleskov, E.A., Hobbie, E.A. and Horton, T.R. 2011. Conservation of ectomycorrhizal fungi: exploring the linkages between functional and taxonomic responses to anthropogenic N deposition. Fungal Ecology 4: 174-183.  Lindo, Z. and Visser, S. 2003. Microbial biomass, nitrogen and phosphorus mineralization, and mesofauna in boreal conifer and deciduous forest floors following partial and clear-cut harvesting. Canadian Journal of Forest Research 33: 1610-1620.  Lipson, D. and Nasholm, T. 2001. The unexpected versatility of plants: organic nitrogen use and availability in terrestrial ecosystems. Oecologia 128: 305-316. Logan, M. 2010. Biostatistical design and analysis using R: A practical guide. Oxford: Wiley-Blackwell.  118  Loreau, M., Naeem, S., Inchausti, P., Bengtsson, J., Grime, J.P., Hector, A., Hooper, D.U., Huston, M.A., Raffaelli, D., Schmid, B., Tilman, D. and Wardle, D.A. 2001. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294: 804-808.  Luoranen, J., Sutinen, S. and Rikala, R. 2010. Predicting spring frost sensitivity by bud development and temperature sum in Norway spruce seedlings. Trees 24: 809-817.  Mah, K., Tackaberry, L.E., Egger, K.N. and Massicotte, H.B. 2001. The impacts of broadcast burning on the diversity of ectomycorrhizal fungi associated with hybrid spruce seedlings in cental British Columbia. Canadian Journal of Forest Research 31: 224-235.  Mailly, D. and Kimmins, J.P. 1997. Growth of Psuedotsuga menziesii and Tsuga heterophylla seedlings along a light gradient: resource allocation and morphological acclimation. Canadian Journal of Botany 75: 1424-1435.  Marschner, H. 1996. Mineral nutrient acquisition in nonmycorrhizal and mycorrhizal plants. Phyton 36: 61-68. Marshall, V.G. 2000. Impacts of forest harvesting on biological processes in northern forest soils. Forest Ecology and Management 133: 43-60.  Mayor, J.R., Schuur, E.A.G. and Henkel, T.W. 2009. Elucidating the nutritional dynamics of fungi using stable isotopes. Ecology Letters 12: 171-183. McCune, B. and Grace, J.B. 2002. Analysis of ecological communities. MjM Software Design, Gleneden Beach, Oregon.   119 McNamara, N.P., Black, H.I.J., Beresford, N.A. and Parekh, N.R. 2003. Effects of acute gamma irradiation on chemical, physical and biological properties of soils. Applied Soil Ecology 24: 117-132.  Mello, A., Napoli, C., Morin, C.M.E., Marceddu, G. and Bonfante, P. 2011. ITS-1 versus ITS-2 pyrosequencing: a comparison of fungal populations in truffle grounds. Mycologia 103: 1184-1193. Messier, J., McGill, B.J. and Lechowicz, M.J. 2010. How do traits vary across ecological scales? A case for trait-based ecology. Ecology Letters 13: 838-848. Michelsen, A., Quarmby, C., Sleep, D. and Jonasson, S. 1998. Vascular plant 15N natural abundance in heath and forest tundra ecosystems in closely correlated with presence and type of mycorrhizal fungi in roots. Oecologia 115: 406-418.  Millard, P. and Grelet, G.A. 2010. Nitrogen storage and remobilization by trees: ecophysiological relevance in a changing world. Tree Physiology 30: 1083-1095.  Miller, S.L., McClean, T.M., Stanton, N.L., and Williams, S.E. 1998. Mycorrhization, physiognomy, and first-year survivability of conifer seedlings following natural fire in Grand Teton National Park. Canadian Journal of Forest Research 28: 115-122.  Moeller, H.V., Peay, K.G. and Fukami, T. 2014. Ectomycorrhizal fungal traits reflect environmental conditions along a coastal California edaphic gradient. FEMS Microbiology Ecology 87: 797-806.  Mroz, G.D., Gale, M.R., Jurgensen, M.F., Frederick, D.J., and Clark III, A. 1985. Composition, structure, and aboveground biomass of two old-growth northern hardwood stands in Upper Michigan. Canadian Journal of Forest Research 15: 78-82.      120 Mummey, D.L., Clarke, J.T., Cole, C.A., O’Connor, B.G., Gannon, J.E. and Ramsey, P.W. 2010. Spatial analysis reveals differences in soil microbial community interactions between adjacent coniferous forest and clearcut ecosystems. Soil Biology & Biochemistry 42: 1138-1147. Mϋnzenberger, B., Golldack, J., Ullrich, A., Schmincke, B. and Hϋttl R.F. 2004. Abundance, diversity, and vitality of mycorrhizae of Scots pine (Pinud sylvestris L.) in lignite recultivation sites. Mycorrhiza 14: 193-202.  Nasholm, T., Ekblad, A., Nordin, A., Giesler, R., Hogberg, M and Hogberg, P. 1998. Boreal forest plants take up organic nitrogen. Nature 392: 914-916. Nave, L.E., Vance, E.D., Swanston, C.W., and Curtis, P.S. 2010. Harvest impacts on soil carbon storage in temperate forests. Forest Ecology and Management 259: 857-866.  Newsome, T.A., Heineman, J.L., Linnell Nemec, A.F., Comeau, P.G., Arsenault, A. and Waterhouse, M. Ten-year regeneration responses to varying levels of overstory retention in two productive southern British Columbia ecosystems. Forest Ecology and Management 260: 132-145.   Nilsson, R.H., Veldre, V., Hartmann, M., Unterseher, M., Amend, A., Bergsten, J., Kristiansson, E., Ryberg, M., Jumpponen ,A., and Abarenkov, K. 2010. An open source software package for automated extraction of ITS1 and ITS2 from fungal ITS sequences for use in high-throughput community assays and molecular ecology. Fungal Ecology 3: 284-287.  Nurmiaho-Lassila, E.L., Timonen, S., Haahtela, K., and Sen, R. 1997. Bacterial colonization patterns of intact Pinus sylvestris mycorrhizospheres in dry pine forest soil: an electron microscopy study. Canadian Journal of Microbiology 43: 1017-1035.  Nye, P.H. and Tinker, P.B. 1969. The concept of a root demand coefficient. Journgal of Applied Ecology 6: 293-300.   121  Öhlund, J. and Näsholm, T. 2001. Growth of conifer seedlings on organic and inorganic nitrogen sources. Tree Physiology 21: 1319-1326.  Olsson, P.A. and Wallander, H. 1998. Interactions between ectomycorrhizal fungi and the bacterial community in soil amended with various primary minerals. FEMS Microbiology Ecology 27: 195-205.  Owens, J.N. and Singh, H. 1982. Vegetative bud development and the time and method of cone initiation in subalpine fir (Abies lasiocarpa). Canadian Journal of Botany 60: 2249-2262.   Parish, R. and Antos, J.A. Advanced regeneration and seedling establishment in small cutblocks in high-elevation spruce–fir forest at Sicamous Creek, southern British Columbia. Canadian Journal of Forest Research 35: 1877-1888.  Parsons, W.F.J., Miller, S.L., and Knight, D.H. 1994. Root-gap dynamics in a lodgepole pine forest: ectomycorrhizal and nonmycorrhizal fine root activity after experimental gap formation. Canadian Journal of Forest Research 24: 1531-1538.    Paul, E.A. and Clark, F.E. 1989. Soil microbiology and biochemistry. Academic Press Inc. San Diego, USA.  Peay, K.G., Bruns, T.D., Kennedy, P.G., Bergemann, S.E. and Gargelotto, M. 2007. A strong species-area relationship for eukaryotic soil microbes: island size matter for ectomycorrhizal fungi. Ecology Letters 10: 470-480.  Peay, K.G., Kennedy, P.G., and Bruns, T.D. 2008. Fungal community ecology: A hybrid beast with a molecular master. Bioscience 58: 799-810.   122 Peay, K.G., Garbelotto, M., and Bruns, T.D. 2009. Spore heat resistance plays an important role in disturbance-mediated assemblage shift of ectomycorrhizal fungi colonizing Pinus muricata seedlings. Journal of Ecology 97: 537-547.  Peay, K.G., Kennedy, P.G. and Bruns, T.D. 2011. Rethinking ectomycorrhizal succession: are root density and hyphal exploration types drivers of spatial and temporal zonation? Fungal Ecology 4: 233-240.   Pena, R., Simon, J., Rennenberg, H. and Polle, A. 2013. Ectomycorrhiza affect architecture and nitrogen partitioning of beech (Fagus sylvatica L.) seedlings under shade and drought. Environmental and Experimental Botany 87: 207-217.  Pena, R. and Polle, A. 2014. Attributing functions to ectomycorrhizal fungal identities in assemblages for nitrogen acquisition under stress. The ISME Journal 8: 321-330.    Perry, D.A., Molina, R. and Amaranthus, M.P. 1987. Mycorrhizae, mycorrhizospheres, and reforestation: current knowledge and research needs. Canadian Journal of Forest Research 17: 929-940.  Phillips, L.A., Ward, V. and Jones, M.D. 2014. ECM fungi contribute to soil organic matter cycling in sub-boreal forests. The ISME Journal 8: 699-713.  Pickles, B.J., Genney, D.R., Potts, J.M., Lennon, J.J., Anderson, I.C. and Alexander, I.J. 2010. Spatial and temporal ecology of Scots pine ectomycorrhizas. New Phytologist 186: 755-768. Pickles, B.J., Egger, K.N., Massicotte, H.B. and Green, D.S. 2012. Ectomycorrhizas and climate change. Fungal Ecology 5: 73-84.   Prescott, C.E. 1997. Effects of clearcutting and alternative silvicultural systems on rates of decomposition and nitrogen mineralization in a coastal montane coniferous forest. Forest Ecology and Management 95: 253-260.   123  Prescott, C.E., Blevins, L.L., and Staley, C.L. 2000. Effects of clear-cutting on decomposition rates of litter and forest floor in forests of British Columbia. Canadian Journal of Forest Research 30: 1751-1757.  Pritsch, K. and Garbaye, J. 2011. Enzyme secretion by ectomycorrhizal fungi and exploitation of mineral nutrients from soil organic matter. Annals of Forest Science 68: 25-32.  Qualls, R.G., Haines, B.L. and Swank, W.T. 1991. Fluxes of dissolved organic nutrients and humic substances in a deciduous forest. Ecology 72: 254-266. Querejeta, J.I., Egerton-Warburton, L.M. and Allen, M.F. 2009. Topographic position modulates the mycorrhizal response of oak trees to interannual rainfall variability. Ecology 93: 649-662.   R Core Team. 2013. R: A language and environment of statistical computing. R foundation for statistical computing. [WWW document] URL http://www.R-project.org/. [accessed 27 June 2014].  Read, D.J. and Perez-Moreno, J. 2003. Mycorrhizas and nutrient cycling in ecosystems – a journey towards relevance? New Phytologist 157: 475-492. Reverchon, F., del Pilar Ortega-Larrocea, M., Bonilla-Rosso, G., and Pérez-Moreno, J. 2012. Structure and species composition of ectomycorrhizal fungal communities colonizing seedlings and adult trees of Pinus montezumae in Mexican neotropical forests. FEMS Microbiology and Ecology 80: 479-487. Rincón, A., Santamaria, B.P., Ocana, L. and Verdu, M. 2014. Structure and phylogenic diversity of post-fire ECM communities of maritime pine. Mycorrhiza 24: 131-141.  Rineau, F. and Garbaye, J. 2010. Effects of liming on potential secretion and iron chelation of beech ECM root tips. Microbial Ecology 60: 331-339.  124  Rineau, F. and Courty, P.E. 2011. Secreted enzymatic activities of ectomycorrhizal funig as a case study of functional diversity and functional redundancy. Annals of Forest Science 68: 69-80.  Rineau, F., Roth, D., Shah, F., Smits, M., Johansson, T., Canbäck, B., Olsen, P.B., Persson, P., Grell, M.N., Lange, L. and Tunlid, A. 2012. The ectomycorrhizal fungus Paxillus involutus converts organic matter in plant litter using a trimmed brown-rot mechanism involving Fenton chemistry. Environmental Microbiology 14: 1477-1487.   Roche. 2009. Using multiplex identifier (MID) adaptors for the GS FLX Titanium chemistry - Extended MID set. Technical Bulletin No. 005-2009.   Romell, L.G. 1939. The ecological problem of mycotrophy. Ecology 20: 163-167.  Rosling, A., Landeweert, R., Lindahl, B.D., Larsson, K.-H., Kuyper, T.W., Taylor, A.F.S., and Finlay, R.D. 2003. Vertical distribution of ectomycorrhizal fungal taxa in a podzol soil profile. New Phytologist 159: 775-783.   Rosling, A., Lindahl, B.D., Taylor, A.F.S. and Finlay, T.R. 2004. Mycelial growth and substrate acidification of ectomycorrhizal fungi in response to different minerals. FEMS Microbiology Ecology 47: 31-37.  Rosling, A. 2009. Trees, mycorrhiza and minerals – Field relevance of in vitro experiments. Geomicrobiology Journal 26: 389-401.   Roy, M., Rochet, J., Manzi, S., Jargest, P., Gryta, H., Moreau, P.A. and Gardes M. 2013. What determines Alnus-associated ectomycorrhizal community diversity and specificity? A comparison of host and habitat effects at a regional scale. New Phytologist 198: 1228-1238.   125 Rudawska, M., Leski, T., Trocha, L.K., and Gornowicx, R. 2006. Ectomycorrhizal status of Norway spruce seedlings from bare-root forest nurseries. Forest Ecology and Management 236: 375-384.  Ryan, D.F., Huntington, T.G., and Martin, C.W. 1992. Redistribution of soil nitrogen, carbon and organic matter by mechanical disturbance during whole-tree harvesting in northern hardwoods. Forest Ecology and Management 49: 87-99.  Sanscrainte, C.L., Peterson, D.L., and McKay, S. 2003. Carbon storage and soil properties in late-successional and second-growth subalpine forests in the North Cascade Range, Washington. Northwest Science 77: 297-307.   Schmidt, M.G., Macdonald, S.E. and Rothwell, R.L. 1996. Impacts of harvesting and mechanical site preparation on soil chemical properties of mixed-wood boreal forest sites in Alberta. Canadian Journal of Soil Science 76: 531-540.  Schmidt, S. and Stewart, G.R. 1997. Waterlogging and fire impacts on nitrogen availability and utilization in a subtropical wet heathland (wallum). Plant, Cell and Environment 20: 1231-1241.  Schoenberger, M.M. and Perry, D.A. 1982. The effect of soil disturbance on growth and ectomycorrhizas of Douglas-fir and western hemlock seedlings: a greenhouse bioassay. Canadian Journal of Forest Research 12: 343-353.    Schoettle, A.W. and Smith, W.K. 1998. Interrelationships among light, photosynthesis and nitrogen in the crown of mature Pinus contorta spp. latifolia. Tree Physiology 19: 13-22.   Setala, H., Haimi, J. and Siira-Pietikainen, A. 2000. Sensitivity of soil processes in northern forest soils: are management practices a threat? Forest Ecology and Management 133: 5-11.  126  Shah, F., Rineau, F., Canbäck, B., Johansson, T. and Tunlid, A. 2013. The molecular components of the extracellular protein-degradation pathways of the ectomycorrhizal fungus Paxillus involutus. New Phytologist 200: 875-887.  Shishido, M., Petersen, D.J., Massicotte, H.B. and Chanway, C.P. 1996. Pine and spruce seedling growth and mycorrhizal infection after inoculation with plant growth promoting Pseudomonas strains. FEMS Microbiology Ecology 21: 109-119.  Silberbush, M. and Barber, S.A. 1983. Sensitivity of simulated phosphorus uptake to parameters used by a mechanistic-mathematical model. Plant and Soil 74: 93-100.  Simard, S.W., Jones, M.D. and Durall, D.M. 2002. Carbon and nutrient fluxes within and between mycorrhizal plants. In: Mycorrhizal Ecology, Ecological Studies 157. eds. M.G.A. van der Heijden and I.R. Sanders. Springer, Berlin, Germany, 33-74.  Smith, J.E., Molina, R., Huso, M.M.P. and Larsen, M.J. 2000. Occurrence of Piloderma fallax in young, rotation-age, and old-growth stands of Douglas-fir (Pseudotsuga menziesii) in the Cascade Range of Oregon, U.S.A. Canadian Journal of Botany 78: 995-1001.  Smith, J.E., McKay, D., Brenner, G., McIver, J., Spatafora, J.W. 2005.  Early impacts of forest restoration treatments on the ectomycorrhizal fungal community and fine root biomass in a mixed conifer forest. Journal of Applied Ecology 42: 526-535.   Smith, M.T. and Smith, R.L. 2006. Elements of ecology.  6th ed. Benjamin Cummings, San Francisco, USA.  Smith, S.E. and Read, D.J. 2008. Mycorrhizal Symbiosis. Academic Press: Great Britain.   127 Sousa, N.R., Franco, A.R., Ramos, M.A., Oliveira, R.S. and Castro, P.M.L. 2011. Reforestation of burned stands: The effect of ectomycorrhizal fungi on Pinus pinaster establishment. Soil Biology & Biochemistry 43: 2115-2120.  Stark, J.M. and Hart, S.C. 1997. High rates of nitrification and nitrate turnover in undisturbed coniferous forests. Nature 385: 61-64.  Sthultz, C.M., Whitham, T.G., Kennedy, K., Deckert, R. and Gehring, C.A. 2009. Genetically based susceptibility to herbivory influences the ectomycorrhizal fungal communities of a foundation tree species. New Phytologist 184: 657-667. Talbot, J.M., Allison, S.D. and Treseder, K.K. 2008. Decomposers in disguise: mycorrhizal fungi as regulators of soil C dynamics in ecosystems under global change. Functional Ecology 22: 955-963.  Talbot, J.M., Bruns, T.D., Taylor, J.W., Smith, D.P., Branco, S., Glassman, S.I., Erlandson, S., Vilgalys, R., Liao, H.L., Smith, M.E. and Peay, K.G. 2014. Endemism and functional convergence across the North American soil mycobiome. Proceedings of the National Academy of Sciences 111: 6341-6346.    Taylor, D.L. and Bruns, T.D. 1999. Community structure of ectomycorrhizal fungi in a Pinus muricata forest: minimal overlap between the mature forest and resistant propagule communities. Molecular Ecology 8: 1837-1850. Taylor, D.L., Herriott, I.C., Stone, K.E., McFarland, J.W., Booth, M.G. and Leigh, M.B. 2010. Structure and resilience of fungal communities in Alaskan boreal forest soils. Canadian Journal of Forest Research 40: 1288-1301. Taylor, D.L., Hollingsworth, T.N., McFarland, J.W., Lennon, N., Nusbaum, C. and Ruess, R.W. 2014. A first comprehensive census of fungi in soil reveals both hyperdivsersity and fine-scale niche partitioning. Ecological Monographs 84: 3-20.   128 Tedersoo, L., Kõljalg, U., Hallenberg, N., and Larsson, K.-H. 2003. Fine scale distribution of ECM fungi and roots across substrate layers including coarse woody debris in a mixed forest. New Phytologist 159: 153-165.    Tedersoo, L., Suvl, T., Jalrus, T., and Kõljalg, U. 2008. Forest microsite effects on community composition of ectomycorrhizal fungi on seedlings of Picea abies and Betula pendula. Environmental Microbiology 10: 1189-1201.     Tedersoo, L., Bahram, M., Toots, M., Diédhiou, A.G., Henkel, T.W., Kjøller, R., Morris, M.H., Nara, K., Nouhra, E., Peay, K.G., Pōlme, S., Ryberg, M., Smith, M.E. and Kōljalg, U. 2012. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Molecular Ecology 21: 4160-1170. Tibbett, M. and Sanders, F.E. 2002. Ectomycorrhizal symbiosis can enhance plant nutrition through improved access to discrete organic nutrient patches of high resource quality. Annals of Botany 89: 783-789. Timonen, S., Jorgensen, K.S., Haahtela, K. and Sen, R. 1998. Bacterial community structure at defined locations of Pinus sylvestris - Suillus bovinus and Pinus sylvestris – Paxillus involutus mycorrhizospheres in dry pine forest humus and nursery peat. Canadian Journal of Microbiology 44: 499-513. Treseder, K.K., Mack, M.C. and Cross, A. 2004. Relationships among fires, fungi, and soil dynamics in Alaskan boreal forests. Ecological Applications 14: 1826-1838.  Toljander, J.E., Eberhardt, U., Toljander, Y.K., Paul, L.R. and Taylor, A.F.S. 2006. Species composition of and ectomycorrhizal fungal community along a local nutrient gradient in a boreal forest. New Phytologist 170: 873-883.  Tuason, M.M.S. and Arocena, J.M. 2009. Root organic acid exudates and properties of rhizosphere soils of white spruce (Picea glauca) and subalpine fir (Abies lasiocarpa). Canadian Journal of Soil Science 89: 287-300.   129 Twieg, B.D., Durall, D.M. and Simard, S.W. 2007. Ectomycorrhizal fungal succession in mixed temperate forests. New Phytologist 176: 437-447.  Twieg, B.D., Durall, D.M., Simard S.W. and Jones, M.D. 2009. Influence of soil nutrients on ECM communities in a chronosequence of mixed temperate forests. Mycorrhiza 19: 305-316.  van Aarle, I.M. and Plassard, C. 2010. Spatial distribution of phosphatase activity associated with ectomycorrhizal plants in related to soil type. Soil Biology & Biochemistry  42: 324-330.  van Breemen, N., R. Finlay, U. Lundstrom, A. Jongmans, R. Giesler and M. Olsson. 2000. Mycorrhizal weathering: A true case of mineral plant nutrition? Biogeochemistry 49: 53-67.  van der Heijden, M.G.A., Bardgett, R.D. and van Straalen, N.M. 2008. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters 11: 296-310.  Velmala, S.M., Rajala, T., Haapanen, M., Taylor, A.F.S. and Pennanen, T. 2013. Genetic host-tree effect on the ectomycorrhizal community and root characterisitcs of Norway spruce. Mycorrhiza 23: 21-33.  Velmala, S.M., Rajala, T., Heinonsalo, J., Taylor, A.F.S. and Pennanen, T. 2014. Profiling functions of ectomycorrhizal diversity and root structuring in seedlings of Norway spruce (Picea abies) with fast- and slow-growing phenotypes. New Phytologist 201: 610-622.  Visser S. 1995. Ectomycorrhizal fungal succession in Jack Pine stands following wildfire. New Phytologist 129: 389-401.   130 Vitousek, P.M., Porder, S., Houlton, B.Z., and Chadwick, O.A. 2010. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen phosphorus interactions. Ecological Applications 20: 5-15.  Walker, B., Kinzig, A., Langridge, J. 1999. Plant attribute diversity, resilience, and ecosystem function: The nature and significance of dominant and minor species. Ecosystems 2: 95-113.  Walker, J.K.M. 2012. The effect of decayed or downed wood on the structure and function of ectomycorrhizal fungal communities at a high elevation forest. PhD. Dissertation U of British Columbia, Okanagan. cIRcle. https://circle.ubc.ca/handle/2429/41810  Walker, J.K.M. and Jones, M.D. 2013. Little evidence for niche partitioning among ectomycorrhizal fungi on spruce seedlings planted in decayed wood versus mineral soil microsites. Oecologia 173: 1499-1511.  Walker, J.K.M., Cohen, H., Higgins, L.M. and Kennedy, P.G. 2014. Testing the link between community structure and function for ectomycorrhizal fungi involved in a global tripartite symbiosis. New Phytologist 202: 287-296.  Wallander, H., Wickman, T. and Jacks, G. 1997. Apatite as a P source in mycorrhizal and non-mycorrhizal Pinus sylvestris seedlings. Plant and Soil 196: 123-131.  Wallander, H., Johansson, U., Sterkenburg, E., Durling, M.B., and Lindahl, B.D. 2010. Production of ectomycorrhizal mycelium peaks during canopy closure in Norway spruce forests. New Phytologist 187: 1124-1134.   Waring, S.A. and Bremner, J.M. 1964a. Ammonium production in soil under waterlogged conditions as an index of nitrogen availability. Nature 201:951-952.   131 Waring, S.A. and Bremner, J.M. 1964b. Effect of soil mesh-size on the estimation of mineralizable nitrogen in soils. Nature 202: 1141.  Watteau, F. and Berthelin, J. 1994. Microbial dissolution of iron and aluminum from soil minerals: efficiency and specificity of hydroxamate siderophores compared to aliphatic-acids. European Journal of Soil Biology 30: 1-9.  Welc, M., Frossard, E., Egli, S., Bünemann, E.K. and Jansa, J. 2014. Rhizosphere fungal assemblages and soil enzymatic activities in a 110-years alpine chronosequence. Soil Biology & Biochemistry 74: 21-30.  Welke, S.E., Hope, G.E., and Hunt, G.A. 2003. Effects of harvesting on fine root biomass and decomposition in an Engelmann spruce – subalpine fir forest. Canadian Journal of Forest Research 33: 847-853.  White, T.J., Bruns, T., Lee, S. and Taylor, J.W. 1990. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: PCR Protocols: A Guide to Methods and Applications, eds. Innis, M. A., D. H. Gelfand, J. J. Sninsky, and T. J. White. Academic Press Inc., New York, USA.  Wilson, B.F. 1970. The growing tree. University of Massachusetss Press, Amherst, USA.  Wohlgemuth, T., Kull, P. and Wϋthrich, H. 2002. Disturbance of microsites and early tree regeneration after windthrow in Swiss mountain forests due to the winter storm Vivian 1990. Forest Snow and Landscape Research 77:17-47.   Wright, E.F., Coates, K.D. and Bartemucci, P. 1998. Regeneration from seed of six tree species in the interior cedar-hemlock forests of British Columbia as affected by substrate and canopy gap position. Canadian Journal of Forest Research 28: 1352-1364.   132 Yamashita, S., Fukuda, K. and Ugawa, S. 2007. Ectomycorrhizal communities on tree roots and in soil propagule banks along a secondary successional vegetation gradient. Forest Science 53: 635-644.   Yin, X., Perry, J.A., and Dixon, R.K. 1989. Influence of canopy removal on oak forest floor decomposition. Canadian Journal of Forest Research 19: 204-214.  133 Appendices Appendix A  Chapter 2 Supplemental Table  Table A.1 A list of MID tags used in 454 sequencing of ECM root tips. MID MID Sequence MID MID Sequence MID MID Sequence MID1 ACGAGTGCGT MID36 CGACGTGACT MID69 TCTGACGTCA MID2 ACGCTCGACA MID37 TACACACACT MID70 TGAGTCAGTA MID3 AGACGCACTC MID38 TACACGTGAT MID71 TGTAGTGTGA MID4 AGCACTGTAG MID39 TACAGATCGT MID72 TGTCACAGA MID5 ATCAGACACG MID40 TACGCTGTCT MID73 TGTCGTCGCA MID6 ATATCGCGAG MID41 TAGTGTAGAT MID74 ACACATACGC MID7 CGTGTCTCTA MID42 TCGATCACGT MID75 ACAGTCGTGC MID8 CTCGCGTGTC MID43 TCGCACTAGT MID76 ACATGACGAC MID10 TCTCTATGCG MID44 TCTAGCGACT MID77 ACGACAGCTC MID11 TGATACGTCT MID45 TCTATACTAT MID78 ACGTCTCATC MID13 CATAGTAGTG MID46 TGACGTATGT MID79 ACTCATCTAC MID14 CGAGAGATAC MID47 TGTGAGTAGT MID80 ACTCGCGCAC MID15 ATACGACGTA MID48 ACAGTATATA MID81 AGAGCGTCAC MID16 TCACGTACTA MID49 ACGCGATCGA MID82 AGCGACTAGC MID17 CGTCTAGTAC MID50 ACTAGCAGTA MID83 AGTAGTGATC MID18 TCTACGTAGC MID51 AGCTCACGTA MID84 AGTGACACAC MID19 TGTACTACTC MID52 AGTATACATA MID85 AGTGTATGTC MID20 ACGACTACAG MID53 AGTCGAGAGA MID86 ATAGATAGAC MID21 CGTAGACTAG MID54 AGTGCTACGA MID87 ATATAGTCGC MID22 TACGAGTATG MID55 CGATCGTATA MID88 ATCTACTGAC MID23 TACTCTCGTG MID56 CGCAGTACGA MID89 CACGTAGATC MID24 TAGAGACGAG MID57 CGCGTATACA MID90 CACGTGTCGC MID25 TCGTCGCTCG MID58 CGTACAGTCA MID91 CATACTCTAC MID26 ACATACGCGT MID59 CGTACTCAGA MID92 CGACACTATC MID27 ACGCGAGTAT MID60 CTACGCTCTA MID93 CGAGACGCGC MID28 ACTACTATGT MID61 CTATAGCGTA MID94 CGTATGCGAC MID29 ACTGTACAGT MID62 TACGTCATCA MID95 CGTCGATCTC MID30 AGACTATACT MID63 TAGTCGCATA MID96 CTACGACTGC MID31 AGCGTCGTCT MID64 TATATATACA MID97 CTAGTCACTC MID32 AGTACGCTAT MID65 TATGCTAGTA MID98 CTCTACGCTC MID33 ATAGAGTACT MID66 TCACGCGAGA MID99 CTGTACATAC MID34 CACGCTACGT MID67 TCGATAGTGA MID100 TAGACTGCAC MID35 CAGTAGACGT MID68 TCGCTGCGTA MID101 TAGCGCGCGC   134 Appendix B: List of R Code  y = numerical response variable x = categorical independent variable (usually transplant treatment) A = factor (usually transplant treatment) B = second factor  # Normality testing normtest<-function(x){par(mfrow=c(2,2))                          hist(x);rug(x)                          boxplot(x);rug(x,side=2);title("boxplot")                          qqnorm(x);qqline(x);stripchart(x~treatment)} attach(mydata). normtest(y) # y is response variable from mydata bartlett.test(y~x, data=mydata) # Tests for homogeneity of vaiances  # Welch’s two sample t-test t.test(y ~ x, data=mydata)  # One-way ANOVA aov1 <- aov(y~A, data=mydata) summary(aov1)  # Two-way ANOVA aov2 <- aov(y~A*B, data=mydata) summary(aov2)  # Nested ANOVA aov3<-aov(y~A + Error(B/A), data=mydata) summary(aov3)  # Kruskall-Wallis test kruskal.test(y~A+ Error(B/A), data-mydata)   # Tukey’s HSD TukeyHSD(aov1)            135 # User-defined planned treatment contrasts (Borcard et al. 2011, pg. 270) A<-(mydata$x) Y<-(mydata$y) c1<-c(1,0,-1,0) c2<-c(0,1,0,-1) contrastmatrix<-cbind(c1,c2) contrasts(A)<-contrastmatrix biomass.list<-list(A=list('CC vs FC'=1, 'CF vs FF'=2)) aov4<-aov(Y~A, data=mydata) summary(aov4, split=biomass.list  # Non-metric multidimensional scaling # To standardize environmental variables (Borcard et al. 2011, pg. 29): mydata.std<-as.data.frame(scale(mydata)) apply(mydata.std, 2, mean)  #adjusts means = 0 apply(mydata.std, 2, sd)    #adjusts standard deviation = 1  #Standardization results in negative values. Add lowest value to each number to get positive values: x<-cbind(mydata.std) y<-x+2.5634749 #lowest value mydata.std2<-(as.data.frame(y))  #Need the packages vegan and MASS: library(vegan) library(MASS)  # To run NMS using metaMDS: mydata.num<- mydata.std2 mydata.mds<-metaMDS(enzymes.num) mydata.plot<-plot(mydata.mds, type="t", cex=1.15)  # Creates ordination plot  # type=t puts text on plot mydata.plot  # If labels are cluttered, the following allows user to adjust labels: mydata.plotlab<-ordipointlabel(mydata.plot)  #ordipointlabel puts both scores on plot  orditkplot(enzymes.plotlab)  # orditkplot allows user to adjust labels with mouse              # Fix labels and then dump to R.  Name object mydata.plotlabn plot(mydata.plotlabn) # Gives new plot with adjusted labels mydata.plotsites<-plot(mydata.mds, type="p", display="sites") # Plots sites only stressplot(mydata.mds) # Creates stressplot       136 # k-means clustering  kmeans.cascade<-cascadeKM(mydata, inf.gr=2, sup.gr=6, iter=100, criterion="ssi")  # mydata contains no categorical variables # inf.gr is the minimal number of groups # sup.gr is the maximum number of groups plot(enzymes.kmeans.cascade) 137 Appendix C: Complete QIIME Code For QIIME tutorials:  http://qiime.org/tutorials/index.html   For description of QIIME scripts:  http://qiime.org/scripts/index.html.  QIIME Workflow: 1. Check map file.  The map file is made in excel and contains sample ID, MID, TSS sequence, treatment, and description of sample.    $ check_id_map.py -m mapfile.txt -o outputfile.txt    Proceed when QIIME returns with no errors or warning.  2. Assign sequences to sample IDs based on MID barcodes.  $ split_libraries.py -m mapfile.txt -f 454Reads.fna –q 454Reads.qual -o splitlibrary_outputfile -b 10 -l 100 -e 0  3. Run sequences through the Fungal ITS Extractor (need to download; Nilsson et al. 2010): i. Change working directory to folder that contain the Fungal ITS Extractor.   ii. Copy ‘seqs.fna’ from ‘splitlibrary_outputfile’ to the ‘indata’ file of the Extractor.  Change name to ‘indata.fasta’. iii. Run the extractor:  $ perl FungalITSextractor.pl  A new directory named by the date it was made is created in the ‘outdata’ directory.  Rename to ‘ITSextract’ (or other appropriate name).  Use the ‘ITS1.fasta’ file in this directory for the next step.  Copy into ‘splitlibrary_outputfile’ and rename file ‘seqs_ITSextract.fna’ and.  Change working directory back to what it was in step 1.  4. Pick OTUs.  Use cd-hit, recommended by A. Godin who did some trials with the different pick OTUs functions.    $ pick_otus.py –i splitlibrary_outputfile/seqs_ITSextract.fna –o cdhit_picked_otus/ -s 0.95 -m cdhit  5. Pick a representative sequence for each OTU.    $ pick_rep_set.py -i cdhit_picked_otus/seqs_ITSextract_otus.txt -f splitlibrary_outputfile/seqs_ITSextract.fna -o rep_set.fna   The ‘rep_set.fna’ file will be used in downstream analysis.   138 6. Assign taxonomy.   Download database from UNITE (http://unite.ut.ee/repository.php).  Go to ‘QIIME release download’.  Rename files to ‘97_refs.fasta’ and ‘97_taxonomy.txt’.    $ assign_taxonomy.py -i rep_set.fna -m blast -r 97_refs.fasta -t 97_taxonomy.txt -o blast_assigned_taxonomy/  7. Make OTU table.  $make_otu_table.py  -i cdhit_picked_otus/seqs_ITSextract_otus.txt -t blast_assigned_taxonomy/rep_set_tax_assignments.txt -o otutable.biom  9. Convert .biom file to .txt file.  $convert_biom.py -i otutable.biom -o otutable.txt -b --header_key taxonomy     

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
https://iiif.library.ubc.ca/presentation/dsp.24.1-0074376/manifest

Comment

Related Items