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The Impacts of Climate Change on Communities of Fungi in Boreal Peatlands

Asma Asemaninejad Hassankiadeh The University of Western Ontario

Supervisor Dr. R Greg Thorn The University of Western Ontario Joint Supervisor Dr. Zoë Lindo The University of Western Ontario

Graduate Program in Biology A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Asma Asemaninejad Hassankiadeh 2016

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Recommended Citation Asemaninejad Hassankiadeh, Asma, "The Impacts of Climate Change on Communities of Fungi in Boreal Peatlands" (2016). Electronic Thesis and Dissertation Repository. 4273. https://ir.lib.uwo.ca/etd/4273

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Peatlands have an important role in global climate change through sequestration of atmospheric CO2. However, climate change is already affecting these ecosystems, including both above- and below- ground communities and their functions, such as decomposition. Fungi have a greater biomass than other soil organisms and play a central role in these communities. As a result, there is concern that altered fungal community function may turn peatlands from carbon sinks to carbon sources, greatly exacerbating the impacts of climate change. In order to gain a better insight into the effects of climate change on the structure and function of these crucial carbon sequestrating ecosystems, the studies presented in this thesis focus on the diversity and structure of fungal communities in the natural environment in boreal peatlands and in mesocosm experiments to better understand the main and interactive effects of multiple drivers of climate change (increased temperature, elevated atmospheric CO2 , lowered water table conditions) on fungal communities, and their function in boreal peatlands. The results of these studies suggest that moisture content of the peat is probably one of the key factors driving changes in community composition of fungi in boreal peatlands under natural conditions. The experimental findings indicate that the effects of water table drawdown is most likely to appear as a short-term effect, intensifying the magnitude of response of fungal communities to climate change stressors by providing more habitat for decomposers that prefer aerobic conditions. Following this short-term effect of water table drawdown, fungal groups are mainly affected by increased temperatures over a longer period of time, such that increased temperatures of 4 and 8°C above ambient conditions provoke a compositional shift in communities of fungi towards different groups of decomposers, supporting the pattern of degradative succession of fungi involved in the process of decomposition. Increased temperature treatments also lead to increased abundance of vascular plant root-associates at the expense of aquatic and moss-associated fungi, probably driven by changes in aboveground communities affected by climate change stressors. These findings are valuable in providing a broader conceptual context of climate change and its consequences for carbon dynamics of boreal peatlands.

Keywords

Fungal community composition, rDNA, LSU, Illumina sequencing, hollow, hummock, decomposers, root-associated fungi, degradative succession, climate change, peatlands

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Co-Authorship Statement

A version of Chapter 2 was published in PLoS One1 with Nimalka Weerasuriya, Dr. Gregory B. Gloor (GG), Dr. Zoë Lindo (ZL) and Dr. R. Greg Thorn (RGT). The original experimental design was from RGT. Reagents, materials and analysis tools were provided by RGT, GG and ZL. I optimized PCR conditions for the newly designed primers, and performed the PCRs for two third of samples in the first round of the experiment, and all samples in the second round of the experiment. N. Weerasuriya contributed to doing PCR for one third of samples in the first round of experiment as part of her MSc project. N. Weerasuriya processed sequence data in both rounds of experiment and prepared the graphical output of the results with RGT and GG’s assistance in data analyses. The initial draft of manuscript was developed by contribution of myself and N. Weerasuriya. RGT, ZL and I made significant contributions in preparation of final draft of manuscript.

A version of Chapter 3 has been submitted to Fungal Ecology2 with RGT and ZL. I conducted the field sampling, DNA extractions, and PCRs, processed sequence data, performed data analyses, and wrote the manuscript with edits from RGT and ZL.

A version of Chapter 4 was published in Microbial Ecology3with RGT and ZL. RGT and ZL designed and developed the experiment. ZL maintained the experimental conditions. As a PhD student, I performed the experiment including litterbag sampling at three time points with significant assistance from ZL, DNA extractions, PCRs, processing sequence data, and conducting statistical analyses, and wrote the initial draft of the manuscript. ZL made significant contribution to data interpretation with edits on manuscript from both ZL and RGT.

1 Asemaninejad, A., Weerasuriya, N., Gloor, G. B., Lindo, Z., Thorn, R. G. 2016. New primers for discovering fungal diversity using nuclear large ribosomal DNA. PLoS One, 11(7): e0159043.

2 Asemaninejad, A., Thorn, R. G., Lindo, Z. Under review. Vertical distribution of fungi in hollows and hummocks of boreal peatlands. Fungal Ecology. 3 Asemaninejad, A., Thorn, R. G., Lindo, Z. 2016. Experimental climate change modifies degradative succession in boreal peatland fungal communities. Microbial Ecology. doi:10.1007/s00248-016-0875-9. ii

A version of Chapter 5 has been submitted to International Society of Microbial Ecology (ISME)4 with RGT, Dr. Brain A. Branfireun (BB) and ZL as co-authors. BB and ZL designed and developed the experiment, and maintained the experimental conditions. I collected the sub-samples at initial and final time points of the experiment with significant assistance from RGT in initial destructive sampling, and BB and ZL in final destructive sampling. I performed DNA extraction and PCRs, processed the sequence data, conducted statistical analyses, and interpreted the results. I prepared the initial draft of manuscript with editorial input on manuscript preparation from ZL, RGT and BB.

4 Asemaninejad, A., Thorn, R. G., Branfireun, B. A., Lindo, Z. Under review. Climate change exposes specific fungal communities in boreal peatlands. International Society of Microbial Ecology. iii

Acknowledgments

I thank my supervisors, Dr. R Greg Thorn and Dr. Zoë Lindo, for their patience and unconditional support throughout my PhD studies. Particulary, when I started to work with Dr. Lindo, the feeling of “you are able to do extraordinary” was seeded inside me and then turned into a strong belief gradually. Working with her, I experienced that when the best effort is put into efficient and pointful work, the impossible can become possible. During four years of studies and doing research with Zoë and Greg I learnt how to approach a problem, and how to meet my goals and objectives no matter how many ups and downs I had to endure, and I am grateful about that forever.

I am also thankful to Dr. Gregory Gloor for his precious support during the initial steps of data analyses. His role in my academic life was much more than a member of my advisory committee. Not only did he teach me many computational and statistical skills, but also he showed me how important it is to appreciate knowledge. Whenever, I had a chance to talk to him, he gave me the feeling that what I know in this field and what I learn is really enjoyable, and I should appreciate having the ability and opportunity to enhance my knowledge.

I would also like to thank all my lab members from both Zoë’s and Greg’s lab for their friendship and academic feedbacks. Their companionship has been wonderful, and I am happy to have them as part of my life.

I dedicate this thesis to my family, particularly my parents, for their wonderful and endless support in different stages of my life. Mom and dad! Your happiness and excitement about my progress have been always one of my strongest reasons not to stop and to take big steps in my life without fear; and to Morteza Heydari Araghi, the greatest life partner that I could ever have. Morteza! With you, I have always felt that I have powerful wings to surge forward catching all my dreams.

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Table of Contents

Abstract ...... i

Co-Authorship Statement...... ii

Acknowledgments...... iv

Table of Contents ...... v

List of Tables ...... ix

List of Figures ...... x

List of Appendices ...... xii

Chapter 1 ...... 1

1 General Introduction ...... 1

1.1 Assessing Fungal Diversity...... 1

1.2 Diversity of Fungi in Boreal Peatlands ...... 3

1.3 Drivers of Fungal Diversity Across Space and Time ...... 6

1.4 Climate Change Effects on Fungal Communities in Boreal Peatlands...... 8

1.5 Rationale and Objectives ...... 11

1.6 References ...... 13

Chapter 2 ...... 23

2 New primers for discovering fungal diversity using nuclear large ribosomal DNA ...... 23

2.1 Introduction ...... 23

2.2 Materials and Methods ...... 25

2.2.1 Primer Design ...... 25

2.2.2 Soil and Peat Collection ...... 29

2.2.3 DNA Extraction ...... 30

2.2.4 PCR Protocol ...... 31

2.2.5 Bioinformatic Analysis ...... 31

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2.3 Results ...... 32

2.3.1 Primer Performance: Target Sequence Yield ...... 32

2.3.2 Primer Performance: Fungal Diversity Recovered ...... 36

2.4 Discussion ...... 43

2.5 References ...... 45

Chapter 3 ...... 51

3 Vertical distribution of fungi in hollows and hummocks of boreal peatlands ...... 51

3.1 Introduction ...... 51

3.2 Materials and Methods ...... 53

3.2.1 Study Site ...... 53

3.2.2 Sampling ...... 54

3.2.3 DNA Extraction and PCR Procedures ...... 54

3.2.4 Bioinformatic Analysis ...... 56

3.2.5 Statistical Analyses ...... 57

3.3 Results ...... 58

3.3.1 Sequence Output and Target OTUs ...... 58

3.3.2 Fungal Richness and Diversity ...... 58

3.3.3 Fungal Community Composition ...... 61

3.4 Discussion ...... 70

3.5 References ...... 73

Chapter 4 ...... 82

4 Experimental climate change modifies degradative succession in boreal peatlands fungal communities ...... 82

4.1 Introduction ...... 82

4.2 Materials and Methods ...... 84

4.2.1 Experimental Design ...... 84

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4.2.2 DNA Extraction ...... 86

4.2.3 PCR Protocol ...... 86

4.2.4 Bioinformatic Analysis ...... 87

4.2.5 Statistical Analyses ...... 88

4.3 Results ...... 89

4.3.1 Initial Fungal Community Composition before Treatment ...... 89

4.3.2 Fungal Community Composition in Litterbags ...... 90

4.3.3 Treatment Effects on Fungal Community Composition in Litterbags ...... 91

4.4 Discussion ...... 99

4.5 References ...... 103

Chapter 5 ...... 109

5 Climate change exposes specific fungal communities in boreal peatlands ...... 109

5.1 Introduction ...... 109

5.2 Materials and Methods ...... 111

5.2.1 Experimental Design ...... 111

5.2.2 Initial and Final Destructive Sampling ...... 112

5.2.3 DNA Extraction and PCR Protocols ...... 113

5.2.4 Bioinformatic Analysis ...... 114

5.2.5 Statistical Analyses ...... 114

5.3 Results ...... 116

5.3.1 Fungal Community Composition before Treatment ...... 116

5.3.2 Fungal Community ...... 119

5.4 Discussion ...... 129

5.5 References ...... 133

Chapter 6 ...... 139

6 General Discussion...... 139 vii

6.1 Fungal Species Richness in Boreal Peatlands ...... 140

6.1.1 Metabarcoding Studies of Fungi ...... 140

6.1.2 Biodiversity Assessment of Peat-inhabitting Fungi ...... 141

6.2 Climate Change Impacts on the Structure of Fungal Communities in Boreal Peatlands ...... 142

6.2.1 Degradative Sucession of Fungi Involved in the Process of Decomposition 142

6.2.2 Functional Diversity and Community Composition of Fungi undr Climate Change ...... 144

6.3 Limitations of Experimental Design ...... 146

6.4 General Conclusions and Future Research ...... 149

6.5 Concluding Remarks ...... 150

6.6 References ...... 151

Appendices ...... 156

Appendix A: Chapter 2, 3, 4 and 5 Supplementary Material...... 156

Appendix B: Permission to Reproduce Published Material ...... 188

Curriculum Vitae ...... 189

viii

List of Tables

Table 2.1 Fungal nuclear large ribosomal primer targets (nu-LSU), with positions numbered relative to Saccharomyces cerevisiae (GenBank Accession #: J01355)...... 28

Table 2.2 Summary data for all sample sets using LSU200-F/LSU481-R, and LSU200A- F/LSU476A-R (Ascomycota) primers developed in this study, and ITS3_KYO2/ITS4_KYO3 primers developed byToju et al. (2012)...... 34

Table 2.3 List of genera and species recovered by LSU200-F/LSU481-R, LSU200A- F/LSU476A-R and ITS3_KYO2/ITS4_KYO3 primers...... 37

Table 3.1 Mean and standard error of fungal richness (S) and diversity (H’) for hollow and hummock samples at three different depths (upper, middle, bottom) ...... 60

Table 3.2 Indicator fungal OTUs driving variation in community composition of Ascomycota and other fungi across a depth gradient and between two micro-topographies, identified through ALDEx test...... 63

Table 4.1 Indicator taxa significantly affected by hydrology and temperature treatments based on ALDEx results...... 96

Table 5.1 Mean and standard error of fungal richness (S) for initial sampling and control samples under “ambient” condition after 18 months of experimental treatment across three depths...... 118

Table 5.2 Indicator fungal taxa significantly affected by temperature treatments based on ALDEx results...... 126

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List of Figures

Figure 2.1 Ribosomal RNA primer map and alignment of LSU200-F/LSU481-R and LSU200A-F/LSU476A-R primers developed in this study...... 27

Figure 3.1 Conceptual diagram of the physicochemical driver of fungal communities in hollows and hummocks...... 55

Figure 3.2 NMDS plot of the variation in community composition of Ascomycota (a) and other fungi (b) between hollows and hummocks of boreal peatlands. Black and gray circles represent samples from hollow and hummock, respectively...... 68

Figure 3.3 NMDS plot of the variations in community composition of Ascomycota (a, c) and other fungal communities (b, d) across three depths of hollows (a, b) and hummocks (c, d). Squares, triangles and circles represent samples from upper, middle and bottom horizons of hollows and hummocks...... 69

Figure 4.1 Changes in the richness (number of observed OTUs) of Ascomycota (a) and other fungi (c); and Shannon’s diversity (H’) of Ascomycota (b) and other fungi (d) under elevated temperature treatments from April 2013 (4 months) to December 2014 (12 months) under experimental climate change...... 93

Figure 4.2 PCA biplot of Ascomycota (a) and other fungi (b) under increased temperature, elevated atmospheric CO2 and altered water table conditions...... 95

Figure 5.1 The effect of different temperature treatments on richness (number of observed OTUs) of Ascomycota (a) and other fungi (c), and Shannon’s diversity of Ascomycota (b) and other fungi (d) after 18 months of experimental warming treatment...... 121

Figure 5.2 PCA biplot of Ascomycota (a) and other fungi (b) under experimental climate change condition (altered temperature, atmospheric CO2 and water table position)...... 122

Figure 5.3 Nonmetric multidimensional scaling (NMDS) plot of Ascomycota community (“a” upper, “b” middle, “c” bottom layer of peat samples) and other fungal community (“d” upper, “e” middle, “f” Bottom layer of peat samples) in initial destructive samples before

x

treatment (T0) shown by black dots and under temperature treatments after 18 months (ambient, + 4 °C and + 8 °C shown, respectively, by grey circles, squares and triangles). . 124

xi

List of Appendices

Appendix A: Chapter 2, 3, 4 and 5 Supplementary Material...... 156

Appendix B: Permission to Reproduce Published Material ...... 188

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Chapter 1 1 General Introduction

Fungi are a large group of eukaryotic microorganisms, including plant and animal pathogens, symbionts, and decomposers, which play pivotal roles in many ecosystem processes such as carbon and nutrient cycling (Tedersoo et al., 2014). Approximately 100,000 fungal species have been described, but estimates of the actual global fungal diversity are in the range of 0.8-5.1 million species (Blackwell, 2011).

1.1 Assessing Fungal Diversity

The diversity of soil and marine fungi has been studied by various classical techniques involving culturing and studying morphological features of fungal reproductive or vegetative structures such as fruiting bodies (macroscopic features), spores, or hyphae (microscopic characteristics). However, slow growing species that do not produce such reproductive structures in culture can be missed by these approaches, potentially biasing the results of traditional fungal diversity assessments (Schmit & Lodge, 2005). Molecular-based techniques such as denaturing gradient gel electrophoresis, rDNA cloning, or single strand conformation polymorphism can overcome this problem and provide a fairly independent assessment of fungal diversity in different types of habitats (Pang & Mitchell, 2005). However, all these techniques are PCR-based with a fairly low- throughput (tens or at most hundreds of bands or sequences per sample) that can potentially under-represent the species with low abundance in favour of more abundant taxa present in the samples (Gonzalez et al., 2012). Additionally, if the number of samples being processed is high, these methods can be very expensive and time consuming (Hert et al., 2008). Recently, this limitation has been partially addressed by the advent of high-throughput screening of fungal diversity using next generation sequencing (NGS) (Pang & Mitchell, 2005) that can yield thousands to millions of observations (sequences) per sample, can process large numbers of environmental samples, and render identification of a much greater diversity of fungi in complex substrates (Gonzalez et al., 2012; Lindahl et al., 2013). Of particular interest in the recent

1 molecular techniques of fungal biodiversity discovery is the use of short diagnostic DNA sequences found in all fungal taxa that could in theory provide identification of the majority of fungal groups in a particular sample if an adequate reference database was available for comparison. Such analyses can be used to infer phylogenetic information about the taxa recovered from the sample (Liew et al., 1998; Atkins et al., 2004).

Components of ribosomal DNA (rDNA), which are remarkably conserved among a wide variety of organisms, are undoubtedly the most universally used genetic markers for taxonomic identification and phylogenetic analyses of microorganisms, including fungi (Lindahl et al., 2013). Specifically genes associated with the small subunit (SSU: 16S/18S) and large subunit (LSU: 23S/25S/28S), the highly variable internal transcribed spacer (ITS) regions (ITS1, ITS2), and the highly conserved 5.8S gene between the ITS1 and ITS2 (Lindahl et al., 2013) have been primary targets for assessing fungal diversity and establishing phylogenetic relationships among them. Often species are distinguished by LSU sequences alone or in combination with ITS sequences (Scorzetti et al., 2002). The ITS region of fungal rDNA is often used as a universal barcode target gene as it is generally highly variable among fungal taxa ( Schoch et al., 2012), and useful for identification of many fungi at the lower taxonomic levels (Toju et al., 2012). However, in some cases the ITS region shows no variation among closely related species of certain fungi such as Penicillium, Fusarium, or Aspergillus (O'Donnell & Cigelnik, 1997; Skouboe et al., 1999; Geiser et al., 2007), and in many cases the ITS region is too variable to classify sequences even at higher taxonomic ranks (Pařenicová et al., 2001; Gazis et al., 2011; Wang et al., 2011). The nuclear ribosomal SSU has fewer hypervariable domains in fungi than in prokaryotes (Scorzetti et al., 2002), making its lower taxonomic-level resolution in fungi poor (Lindahl et al., 2013). LSU, however, has superior species resolution than SSU for many fungal groups, and is more conserved than the ITS region, making it a popular phylogenetic marker to assign sequences to the matching reference sequences at both higher and lower taxonomic levels (Vilgalys & Hester, 1990; Lindahl et al., 2013). In addition to the type of molecular identification technique used, the scope of fungal diversity discovery is highly dependent on the choice of primers and the target region of DNA (Lindahl et al., 2013).

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Until recently, the majority of the studies describing fungal communities have detailed the structure of specific functional groups (Thormann et al., 2004; Peltoniemi et al., 2009) probably due to limitations of traditional methods of identification, or challenges of the known PCR-based molecular techniques as described above (Atkins & Clark, 2004; Sun & Guo, 2012). The limitations of currently used genetic markers, the intricacy of PCR-based analysis, and the time needed to sequence and identify a huge number of DNA molecules present in environmental samples accentuates the need to develop primer sets that can cover a broad range of fungal taxa, and are compatible with the chemistry of recent high-throughput sequencing techniques enabling us to simultaneously sequence millions of strands of DNA in the large sample sets, and to correlate the community composition of target organisms present in environmental samples to ecological processes (Pang & Mitchell, 2005).

1.2 Diversity of Fungi in Boreal Peatlands

Boreal peatland ecosystems, constituting 25-30 % of the total boreal zone (Gorham, 1991), are wetlands with a common ability to form peat (organic soil produced by accretion of herbaceous material, mostly Sphagnum) (Gower et al., 2001). The accumulated remains of hundreds or thousands of years of Sphagnum growth form the majority of the decaying peat beneath these ecosystems, and can extend to depths of 10 meters or more (Crum & Buck, 1988). A huge amount of carbon, as much as 10-20% of global terrestrial carbon is stored in this area because of disparity between the rate of biological inputs into peat soil through photosynthesis by plants and the rate of biological outputs through microbial decomposition (Allison & Treseder, 2011). Knowing that there is a positive relationship between soil fungal diversity and soil carbon source (Louis et al., 2016), fungi in boreal peatlands are relatively understudied especially with respect to global fungal diversity. Our knowledge of two main groups of fungi, the Ascomycota and Basidiomycota, in peatlands mostly comes from morphological studies of fruiting bodies and cultures (Redhead, 1989; Thormann et al., 2001; Wurtzburger et al., 2004; Stasinska, 2011; Filippova & Thormann, 2014). Although there has been a recent interest in studying fungal species richness of boreal peatlands (Filippova & Thormann, 2014; Elliott et al., 2015; Grum-Grzhimaylo et al., 2016), fungal community composition of

3 boreal peatlands as a whole have recived less attention. A literature review by Thormann (2006b) found that 656 different species of microfungi have been reported from boreal peatlands on the basis of culturing, of which 430 were classified as Ascomycota, 25 species as Basidiomycota, and 95 species as other important fungal groups such as Zygomycetes (67 species), and Chytridiomycota (28 species), while 106 species were listed as unidentified (Thormann, 2006b). A laboratory cultivation study by Thormann et al. (1999) suggested that Basidiomycetes are scant in peat soils, but this is not supported by collections (e.g. Redhead, 1981) or molecular analyses (e.g. Tedersoo et al., 2014), and it is also known that Basidiomycetes (even the saprotrophic species) are difficult to culture from soil or other complex substrates (Thorn et al., 1996). In a morphological study of macrofungi (i.e. mushrooms and other fruiting bodied fungi) in two bogs of West Siberia, 59 distinct fungal taxa were reported, two third of which were saprotrophic fungi belonging to Basidiomycota, and the rest were mycorrhizal fungi in association with Pinus spp. (Filippova & Thormann, 2014). Additionally, using direct observational methods, 51 taxa of Ascomycota (belonging to the orders Helotiales, Ostropales, Rhytismatales, and Pezzizales) were reported from boreal zone of West Siberia (Filippova, 2012). A rare sphagnicolous fungus called Pholiota henningsii belonging to Basidiomycota (order Agaricales) has been reported in Czech and French bogs (Holec et al., 2014). Recently nine species of bryophilous fungi (Galerina) were reported from boreal peatlands of central Poland (Grzesiak & Wolski, 2015), while 191 fungal species of different functional groups such as mycorrhizal root-associates (54 species), saprotrophic fungi (125 species) and pathogens (12 species) were found in traditional bogs of Pomerania in Poland (Stasinska, 2011).

From a functional perspective, boreal peatlands are known to possess functionally diverse fungal communities (Waksman & Purvis, 1932; Williams & Crawford, 1983). Some examples are ericoid mycorrhizal fungi from Ascomycota and Basidiomycota, in associations with Ericaceous shrubs such as cranberry, blueberry and rhododendron (Smith & Read, 1997; Selosse et al., 2007), or ectomycorrhizal fungi (ECM) that form symbiotic associations with trees belonging to Pinaceae, Salicaceae and Betulaceae in boreal peatlands (Thormann et al., 1999). Pseudomycorrhizal fungi are a group of fungal endophytes known as “Mycelium radicus astrovirens” that belong to the Ascomycota and

4 generally co-exist with ECM and other symbiotic fungi (Rodriguez et al., 2009). These phylogenetically and functionally overlap with another group of fungi known as dark septate endophytes (DSE) that are also associated with plant roots, however, their host or habitat specificity is very little compared to other root-associated fungi (Grunig & Sieber, 2005). All these symbiotic fungi have an important role in facilitating nutrient uptake by their hosts, especially in ecosystems with low fertility soils (Ramos-Zapata et al., 2009; Mayerhofer et al., 2013), such as peatlands. It has been also demonstrated that dark septate endophytes can secrete extracellular enzymes that are capable of breaking down phenolic compounds, and thus play a central role in carbon cycling of boreal peatlands (Caldwell et al., 2000; Grunig & Sieber, 2005). However, they have been rarely studied in these ecosystems.

Among fungal communities of boreal peatlands, the primary agents of the initial stages of decomposition are pathogens and weak parasites such as Cladosporium, Botrytis, and Alternaria that can tolerate defense mechanisms of host plants and are able to use simple sugars and amino acids leached from plant tissues (Lumely et al., 2001). After the death of plants, the first groups of fungi colonizing organic substrates are usually labile carbon decomposers including “sugar fungi” such as Mortierella and Mucor (i.e. members of Zygomycetes) and some groups of yeasts. Yeasts belonging to Ascomycota (Taphrinomycotina and Saccharomycotina) mainly can utilize simple sugars leached from litter or secreted from plants roots as they are generally incapable of breaking down complex organic compounds with the exception of a few taxa that are good lignocellulose degraders (Kurtzman, 2011; Treseder & Lennon, 2015). Yeasts belonging to Basidiomycota (mostly Pucciniomycotina, Agaricomycotina) also mostly utilize simple sugars from litter or plants roots, but some have partial capacities to degrade cellulose and hemicellulose (Boekhout et al., 2011; Treseder & Lennon, 2015). Both groups of yeasts have been poorly described in boreal peatlands (Thormann et al., 2007). Several other groups of fungi involved in the process of decomposition that are found in boreal peatlands include prolific sporulators, such as the molds Penicillium and Aspergillus, that can break down simple polymers. The last group, known as “late colonizers”, are mainly members of Basidiomycetes and some members of Ascomycetes, and are decomposers of complex organic substrates including Sphagnum and vascular plants debris (comprising

5 cellulose, hemicellulose and lignin). These late colonizers appear in later stages of decomposition (Thormann & Rice, 2007; Stasinska, 2011).

This degradative succession of fungal communities, known as “temporal niche partitioning”, is caused by an ongoing change in the quality of soil organic matter (Schoener, 1974) interacting with the enzymatic capacities of particular fungi. However, this successional pattern of fungal communities has not been consistently reported in boreal peatlands. Several studies have demonstrated the co-existence of different decomposers with various enzymatic activities in boreal peatlands (Thormann et al., 2003; Thormann, 2006a; Artz et al., 2007), which has been attributed to a mosaic, or restricted hyphal growth of fungi caused by chemical and physical constraints of the peat, potentially masking temporal niche partitioning in these ecosystems. Additionally, a full characterization of fungal community turnover associated with degradative succession has been rarely performed in boreal peatlands. One limitation to this has been the ability to observe whole communities (species and their relative abundances) across a multitude of samples using traditional culturing techniques. The development of molecular biodiversity discovery methods, particularly next generation sequencing of environmental samples, has revealed significantly greater diversity of decomposer communities in peatland habitats (Beulig et al., 2016) and has enabled us to ask important questions about the consequences of a functional shift in fungal communities towards different groups of decomposers for the pattern of carbon storage in boreal peatlands.

1.3 Drivers of Fungal Diversity Across Space and Time

Generally, fungal diversity is governed by heterogeneous environmental conditions, disturbances and succession (Fierer, 2008). Primary abiotic factors affecting fungal diversity include temperature and soil moisture regimes (Bissett & Bissett, 1979; Tedersoo et al., 2014). At global and regional scales, elevated temperatures typically have a direct positive effect on fungal metabolism (Allison & Treseder, 2011; Myers et al., 2011) consequently affecting fungal diversity and the structure of fungal communities in many ecosystems (Buée et al., 2005; Buée et al., 2007). At the local scale, however, soil moisture (specifically water table level, with its effects on oxygen availability, in

6 boreal peatlands) and litter quality are considered as dominant controls on fungal communities (Myers et al., 2011). Depending on the type of ecosystem, even a short-term water table drawdown can alter vertical distribution and community composition of fungi in the soil (Kim et al., 2008; Peltoniemi et al., 2009). Additionally, in ecosystems such as boreal peatlands, increased temperatures can interact with soil moisture lowering water table position, and creating more aerobic conditions that benefit decomposer fungi (Hobbie et al., 2000; Allison & Treseder, 2011; Myers et al., 2011).

Many fungi are also intricately linked with aboveground (plant) communities through mycorrhizal associations, as plant pathogens or as decomposers, utilizing root exudates as labile carbon sources in the rhizosphere, and plant litter inputs as substrate for degradation (Hooper et al., 2000; Pistón et al., 2016). Changes in aboveground plant community composition will likely have cascading effects on belowground soil structure, and processes such as decomposition (Hooper et al., 2000). Mechanistically, this aboveground-belowground influence may be through changes in mycorrhizal associations following changes in the structure of aboveground communities, changes associated with alterations in plant litter quantity or quality (Hooper et al., 2000; Rodriguez-Echeverria et al., 2015), or alterations in the vertical distribution of carbon into the soil through changing plant root depth and root exudates (Hooper et al., 2000; Jobbagy & Jackson, 2000). For instance, ectomycorrhizal fungi (ECM) and ericoid mycorrhizal fungi generally show higher abundance in soils where there are a larger number of their host plants (Smith & Read, 1997; Thormann et al., 1999; Selosse et al., 2007). That said, the variation in fungal communities of any type of ecosystem can be observed either by temperature, litter type, vegetation and geochemical properties of the soil (Trinder et al., 2008, 2009), or vertically in soil layers driven by resource availability and oxygen limitation that would affect their cellular energy metabolism (Thormann & Rice, 2007; Kurakov et al., 2008).

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1.4 Climate Change Effects on Fungal Communities in Boreal Peatlands

Global climate change, including climate warming and alterations in soil moisture regimes concomitant with anthropogenic increases in the atmospheric concentration of carbon dioxide (CO2), is probably the largest environmental issue facing the world today

(Christidis et al., 2011; Dell et al., 2012). Atmospheric CO2 is expected to double in the next 100 years (IPCC, 2014), which may potentially affect many biogeochemical processes as well as causing global climate warming through the greenhouse gas effect (Schlesinger, 1997). Increases in global mean temperatures that are anticipated to co- occur with elevated CO2 conditions may lead to a positive feedback of increased production of CO2 through ecosystem level processes such as decomposition (IPCC, 2014; Xu et al., 2014). Warming of air temperatures is expected to increase evapotranspiration rates, potentially causing lower water table position (Gorham, 1991).

While elevated atmospheric CO2 are expected to increase plant growth, the effects of increased temperature on plant growth under climate change conditions might vary depending on the water availability and thermal tolerances of different plant species (Blankinship et al., 2011).

The effects of climate change are not anticipated to occur in a homogeneous fashion around the globe. Rather, high latitude areas such as boreal, alpine, and arctic areas are expected to experience greater warming and more variable precipitation than equatorial or temperate regions (Sala et al., 2000; IPCC, 2014; Flombaum et al., 2016). Large areas of boreal peatlands of Canada, forming the second largest part of northern peatlands worldwide (McLaughlin, 2004), are dominated by Sphagnum mosses (“peatmoss”) (Kuhry et al., 1993; Quinton et al., 2007) that play an important role in regulation of moisture, air, temperature and nutrients around plant roots (Turetsky et al., 2012), retarding microbial activities by keeping soil temperature low and water table high as well as via low quality litter, low pH, and secretion of anti-microbial compounds (Glime, 2007; Hájek et al., 2011). As such a huge amount of carbon is stored in this area due to the historically greater photosynthesis than decomposition rates (Gorham, 1991; Gower

8 et al., 2001). However, warming in these areas is expected to be two to four times greater than the predicted global average of 2 °C (IPCC, 2014), which could potentially alter many ecosystem processes and influence the pattern of carbon cycling in these crucial carbon sequestering ecosystems (McGuire et al., 2009).

Many studies have shown that shifts in plant communities are anticipated to happen under climate change scenarios through both latitudinal migrations of plants to new areas as temperatures change, and through altering competitive outcomes within plant communities (Kelly & Goulden, 2008; Walther, 2010; Chen et al., 2011). Shifts of plant communities towards more vascular plant species, and at the expense of Sphagnum mosses have been observed in boreal peatlands (Bokhorst et al., 2007; Hill et al., 2011; Dieleman et al., 2015). This shift will reduce the controlling effects of Sphagnum on temperature and peat pH, and consequently will feedback and counteract ecosystem-level Sphagnum effects on maintaining the slow rate of decomposition through increased temperature and peat pH. These shifts also have been linked to altered biogeochemical processes in these ecosystems such as increased dissolved organic carbon (DOC) concentration and decomposibility in peat soil (Hribljan et al., 2014; Dieleman et al., 2016) that must be related to shifts in fungal community composition. However, direct and indirect effects of climate change factors on soil fungi in these ecosystems are less clear. But, fungal communities of northern peatlands are likely currently undergoing a rapid change in their environment through the multiple drivers of climate change (i.e. increased temperature, elevated atmospheric CO2 and lowered water table conditions) as well as through variations of plant inputs into peat matrix caused by shifts in aboveground communities under climate change scenarios (Allison & Treseder, 2011; Andersen et al., 2012; Dieleman et al., 2015).

As atmospheric CO2 increases under future climate change scenarios (IPCC, 2014), belowground fungal communities are expected to indirectly respond to elevated atmospheric CO2 (Korner, 2000; Norby et al., 2001) mediated by changes in the aboveground community composition, and the subsequent changes in plant root exudates. Changes in aboveground communities will also alter the soil temperature and moisture potentially affecting the metabolic rates of microbial decomposers in different layers of

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soil (Ostle et al., 2009). While elevated CO2 is unlikely to affect soil fungal communities because CO2 concentrations in soil are already an order of magnitude greater than in the atmosphere (Schwartz & Bazzaz, 1973), the positive effect of increased temperature on soil microbial respiration and decomposition of recalcitrant material has been well represented by many global carbon models (Bosatta & Agren, 1999; Exbrayat et al., 2013; Todd-Brown et al., 2014). Such positive effects suggest a range of relationships between carbon quality and temperature sensitivity in the process of decomposition (Davidson & Janssens, 2006; Tang & Riley, 2014). As mentioned above, warming can also accelerate the rate of decomposition through its effects on lowering water table position and thus providing more suitable habitat for aerobic decomposers (Allison & Treseder, 2011). Even in peat layers with permanently water-logged conditions, increased temperature can stimulate anaerobic microbial decomposition, leading to emission of methane. Although methane is a short-lived greenhouse gas, it has a global warming potential of twenty times more than CO2 due to its potential to confine substantial amount of heat compared to CO2 (Frolking & Roulet, 2007; Godin et al., 2012). In peatlands, although most accumulated organic matter is in the lower peat layers under anoxic conditions, the majority of carbon flux occurs from the upper peat horizon above the water table where the majority of decomposers reside (Thormann, 2006a, b). Pavelka et al. (2016) recently demonstrated that an increased rate of CO2 flux to the atmosphere happens coincidently with lowering the water table in peatlands, and that an increased rate of evapotranspiration by plants (as would occur with increased air temperatures) can further reduce soil moisture, potentially affecting the rate carbon turn over in lower peat horizons (Ostle et al., 2009). Also, the input of more labile carbon from vascular plant roots into the peat matrix can influence fungal growth and activities, and potentially stimulate microbial decomposition and prime the process of succession of fungal communities involved in nutrient mobilization not only in the upper horizon, but also lower in the peat column (Kuzyakov et al., 2000; Fontaine et al., 2007; Allison & Treseder, 2011).

All potential direct and indirect effects of climate change are expected to drive microbial shifts and push the system towards more active and richer decomposer communities

10 under climate change conditions, potentially impacting the pattern of carbon storage in boreal peatlands, and affecting both plant productivity and overall ecosystem processes (Andersen et al., 2012). Hence there have been some studies looking at possible changes in the structure of some fungal groups and cause and effect relationships between the variations observed in composition and function of these fungal communities and changes in their environments according to climate change predictions made by IPCC for northern peatlands (Gleason et al., 2004; Liaho, 2006; Morgado et al., 2015). Despite all these research efforts, there is a clear need to more conceptually comprehend the mechanistic and reciprocal relationships between climate-driven variations in the structure of soil microbial communities and re-organization of plant communities and ecosystem-scale processes (van der Heijden et al., 2008; Bardgett & van der Putten, 2014). Such studies would help to thoroughly evaluate the role of northern peatlands in global carbon dynamics, and to more accurately predict future climate change.

1.5 Rationale and Objectives

In order to better understand the impacts of future climate change on peatland fungal communities and the potential consequences for long term carbon storage in boreal peatlands, I studied total fungal communities of boreal peatlands and the inferred feedbacks among multiple drivers of climate change (i.e. increased temperature, elevated atmospheric CO2, and lowered water table condition) and different groups of fungi that have been long recognized by ecologists to have crucial roles in the functioning of terrestrial ecosystems. I performed four studies that are fundamentally associated by their theme of fungal diversity discovery and the consequences of environmental changes on fungal community composition in boreal peatlands. The objective of my first study (chapter 2) was to develop new sets of primers to better detect and identify all important groups of fungi inhabiting peatlands, to overcome known limitations of existing primer sets. The main aim of chapter 3 was to obtain more detail about fungal communities in intact peatlands, and how they are shaped by the physical factors within a peatland. Using the newly developed primer sets from my first experiment, I specifically looked at variations in fungal communities between hollow and hummock micro-topographies of

11 boreal peatlands in relation to depth. This study thus provided a better understand the mechanistic relationships between the structure of fungal communities and their environment; this chapter also provides baseline community data for the experimental chapters that follow.

The second experiment (chapter 4) was conducted to assess temporal dynamics of fungal communities colonizing peat substrates in interaction with climate change. I used peat litter bags to study the main and interactive effects of climate change drivers (increased temperature, elevated atmospheric CO2 and lowered water table, in a full factorial design) on temporal succession of fungal communities occupying peat litters. Fungal communities were assessed by next-generation sequencing of a portion of ribosomal DNA using the new sets of primers developed in the first experiment. In the next experiment (chapter 5) my aim was to focus on the magnitude of net effects of multiple drivers of climate change on diversity, functional richness and community composition of fungi across a depth gradient within peat monoliths under simulated climate change condition. Again I used a full factorial design to determine the extent of the main and combined effects of increased temperatures, elevated atmospheric CO2 and water table drawdown on fungal diversity and community composition from a functional perspective at three different depths.

The general discussion in chapter 6 places the main findings of chapters 3, 4 and 5 and previous literature in a conceptual framework, highlighting the implications of shifts in the fungal communities of boreal peatlands for carbon dynamics, and its long term consequences for future climate change. Here, I also point out other potential direct and indirect effects of climate change on soil biodiversity and soil function under future climate change scenarios. Ultimately, I recommend some other potential paths of research, compatible with my novel findings, which might be valuable for further comprehending the impacts of climate change on belowground communities in general and their consequences for ecosystem-scale functions.

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Chapter 2 2 New primers for discovering fungal diversity using nuclear large ribosomal DNA 2.1 Introduction

Identification and classification of a wide variety of organisms using DNA sequencing has overcome many limitations of traditional morphological approaches, including providing faster analyses, resolving convergent morphologies, recognition of closely related or sister species, and the ability to identify cryptic organisms from complex or opaque substrates (Atkins & Clark, 2004; Sun & Guo, 2012). This has been especially true for the assessment of the fungi for which, as the second most speciose eukaryotic group with complex and often cryptic life histories and convergent morphological characteristics, traditional taxonomic approaches have long been problematic (Shenoy et al., 2007; Taylor et al., 2014). Alongside a growing recognition that fungi have an important role in nutrient mineralization and uptake affecting plant productivity and overall ecosystem process (Clemmensen et al., 2013; Treseder & Lennon, 2015), there has been growing application of genetic markers for the purpose of identification and community composition analyses (Fierer et al., 2013; Tedersoo et al., 2014).

However, the utility of DNA as a tool to catalogue biodiversity, resolve phylogenies, or explore patterns in ecological communities depends strongly on choice or design of primers for selecting the appropriate genetic markers (Tedersoo et al., 2015). Primer sets need to be general enough to match across all members of a broad taxonomic group, while containing mismatches to non-target taxa, and yet yield a gene product variable enough to distinguish taxa at narrow, preferably species-level, resolution (Seifert et al., 2007; Watanabe et al., 2011). Using DNA for species-level identifications (DNA barcoding sensu Hebert et al., 2003) typically relies on short, diagnostic DNA sequences, such as the mitochondrial COI gene for metazoan animals (Ratnasingham & Hebert, 2007), the rbcl+matk combination of protein coding genes for plants and green algae (Chase & Fay, 2009), or the ribosomal internal transcribed spacer (ITS) region in fungi (Schoch et al., 2012). However, ongoing examination of the ITS for fungal diversity has

23 revealed that the ITS region is too variable to align over distantly related taxa, and therefore unable to confidently place sequences at the level of family, order or class for which no closely matching reference sequences exist (Lindahl et al., 2013). Moreover, in some genera of filamentous Ascomycetes there is little variation in the ITS region, making this domain undesirable for identification or taxonomic analyses at the species level (Skouboe et al., 1999; Varga et al., 2000; Pařenicová et al., 2001; Lindner & Banik, 2011; Wang et al., 2011; Jang et al., 2014). Overall, fungal identification and taxonomic analyses using ITS remains problematic. The D1 variable region of the large ribosomal subunit (Hassouna, 1984) is an attractive alternative, since it (often together with D2 region) has proven useful in species-level identification and phylogenetic reconstruction in various fungal groups (Kurtzman & Robnett, 1998; Moncalvo et al., 2002).

An ongoing challenge in diversity studies is the pairing of appropriate genetic markers and targeted primer development that works with contemporary technologies. Next Generation Sequencing (NGS) can capture low abundance DNA and provide detailed analyses of relative abundances in microbial communities (Hert et al., 2008) at a relatively low cost. Among NGS methods, Illumina MiSeq sequencing is the most effective and extensively used technology globally (Lindahl et al., 2013) due to its low rate of error and the lowest cost per million bases (Luo et al., 2012), but requires short diagnostic regions of 300 base pairs to be effective (http://www.illumina.com/systems/sequencing.html). Unfortunately, no primers have been developed to target the D1 region across a wide diversity of fungi with a short amplicon suitable for the Illumina MiSeq platform. Here we introduce and evaluate two primer sets targeting the D1 region of the large subunit (LSU) of ribosomal DNA to evaluate fungal diversity. We demonstrate how these primers can be applied for discovery of major fungal groups to recover a broad range of fungal taxa with confident higher-level placement (family, order, etc.) and potential species or species-group identification.

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2.2 Materials and Methods

2.2.1 Primer Design

Invariate regions representing potential primer sites were first identified in an alignment of 591 sequences of Basidiomycota isolated from soil supplemented with 225 reference sequences from GenBank (National Center for Biotechnology Information [NCBI]) and anchored with the sequence of Saccharomyces cerevisiae (Accession #: J01355; Georgiev et al., 1981). This alignment, from the 5’ end of LSU to position 650 (primer site LR3, Vilgalys & Hester, 1990) was created in two halves using SINA v1.2.9 (Pruesse et al., 2007) and the two files merged using profile: profile alignment in MUSCLE (Edgar, 2004). The alignment was trimmed to the potential primer sites and amplicon region (bases 200-500), and a neighbor-joining tree recovered essentially the same topology and nearly all species-level OTUs obtained using the full alignment (223 of 382 aligned sites were parsimony informative; data not shown).

The region of LSU between bases 150 and 550 from different fungal groups was used to query GenBank using BLAST (Altschul et al., 1997), separately specifying all orders of Agaricomycetes (Hibbett et al., 2014) and other Basidiomycota known from soil. The potential 5’ primer at bases 200-220 was recognized as the reverse complement of ITS6- R, previously recommended for amplification of diverse fungi (Dentinger et al., 2010), and was modified slightly for greater coverage of fungi and renamed LSU200-F. At the 3’ end, positions 458-481, which did not represent any known primers, were selected for further investigation (Figure 2.1). These short sequences were used to query GenBank using BLAST, directed to target taxa (Fungi) and non-target taxa (Eukarya, not Fungi).

The primers referred to as LSU200-F and LSU481-R (Table 2.1) showed nearly complete identity, particularly at their 3’ ends, towards all Basidiomycota, Glomeromycota, Chytridiomycota, Blastocladiomycota, Kickxellomycotina, Mortierellomycotina, Mucoromycotina, and Zoopagomycotina, but not to Ascomycota, and mismatches to other Eukarya (non-target taxa) were numerous. To optimize Ascomycota primers, a separate alignment of Ascomycota was created by querying Entrez for representatives of all major Ascomycota lineages (Schoch et al., 2009) then using BLAST to obtain similar

25 sequences, resulting in an alignment of 131 sequences. Primer LSU200-F had six sites of mismatch with various Ascomycota which were disregarded as they represented either a single base mismatch in the middle of the sequence (e.g., 208A in Saitoella, 209C in Taphrina) or were mismatches at the 3’ end that occurred only rarely (e.g., 217G in Neolecta and 218C in Taphrina; but see footnotes to Table 2.1). For other mismatches, bases 203K, 214C, 215R, and 217Y were altered to match the majority of Ascomycota for the new primer LSU200A-F (Table 2.1).

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Figure 2.1 Ribosomal RNA primer map and alignment of LSU200-F/LSU481-R and LSU200A-F/LSU476A-R primers developed in this study.

(a) Approximate location of LSU200-F/LSU481-R and LSU200A-F/LSU476A-R primers in relation to the D1/D2 variable domains within the LSU of Saccharomyces cerevisiae J01355 in relation to ITS1, ITS2, ITS4 from White et al. (1990), NS31 from Simon et al. (1992), AM1 from Helgason et al. (1998), AMV4.5N-F and AMDG-R from Sato et al. (2005), AML1 and AML2 from Lee et al. (2008), WANDA from Dumbrell et al. (2011), ITS3_KYO2 from Toju et al. (2012), and ITS7o from Kohout et al. (2014). (b) LSU200-F/LSU481-R and LSU200A-F/LSU476A-R alignments made using a custom reference sequence set, aligned using Muscle v 3.8.31 (Edgar, 2004) and visualized using CLC Sequence Viewer (http://www.clcbio.com/), with S. cerevisiae J01355 included as positional reference.

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Table 2.1 Fungal nuclear large ribosomal primer targets (nu-LSU), with positions numbered relative to Saccharomyces cerevisiae (GenBank Accession #: J01355).

Name Position Sequence (5’ to 3’) Nomenclature Tm Target Taxa Fungi minus LSU200-F 200-218 AACKGCGAGTGAAGMGGGA nu-LSU-200-5′ 64 Ascomycota Fungi minus LSU481-R 462-481 TCTTTCCCTCACGGTACTTG nu-LSU-481-3′ 59 Ascomycota LSU200A-Fa 200-218 AACKGCGAGTGAAGCRGYA nu-LSU-200-5′ 63 Ascomycota LSU476A-Rb 457-476 CSATCACTSTACTTGTKCGC nu-LSU-476-3′ 59 Ascomycota a A slight modification (AACKGCGAGTGAAGCRGBM) is recommended to recover all known Ascomycota including Archaeorhizomycetes. b LSU476A-R has a perfect match with sequences of known Archaeorhizomycete.

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The reverse primer LSU481-R had two pairs of mismatches to most Ascomycota at bases 468-469 and 474-475, which were corrected, and the revised primer LSU476A-R was moved 5 bases 3’-ward for greater specificity against plants and other non-fungal eukaryotes. Both forward and reverse primers were modified on their 5’ ends to include the following: forward and reverse Illumina MiSeq adaptors to allow sequences to bind to the flow cells, a 4 bp random linker (NNNN) to increase the diversity of the first positions sequenced and so enable the instrument to more easily separate clusters and set appropriate fluorescence levels, and unique 8 nt barcode sequences with an edit distance of at least 4 (https://github.com/ggloor/miseq_bin). Primer layout was as follows:

5′–[Illumina Forward Adaptor][NNNN][Barcode][PCR Primer]–3′.

The unique forward and reverse barcode sequences are a paired-end multiplexing approach that allowed us to combine samples within a single Illumina run and bioinformatically separate them back into samples afterwards (Gloor et al., 2010).

We compared our developed primer sets against another primer set currently used in Illumina sequencing (the ITS3_KYO2/ITS4_KYO3 primers, hereafter referred to as “ITS2 primers”) described by Toju et al. (2012), where the forward primer is ITS3_KYO2 (5′–GAT GAA GAA CGY AGY RAA–3′), and the reverse primer is ITS4_KYO3 (5′– CTB TTV CCK CTT CAC TCG –3′). These primers target diverse taxonomic groups within Ascomycota and Basidiomycota. Primers were ordered with an Illumina Adaptor, linker, and unique barcode as described above.

2.2.2 Soil and Peat Collection

Subarctic soil samples were taken from eight sites within 500 m of the Torngat Mountains Base Camp and Research Station, Kangidluasuk, Labrador, Canada (58.454° N; 62.802° W, elevation 30 – 200 m a.s.l.). Samples were collected under Parks Canada Research permit TMNP-2012-11533. Soils ranged from mostly loamy (samples 1 to 7) to sandy (sample 8). Boreal peat samples were collected from a poor nutrient Sphagnum- dominated fen at the White River Experimental Research sites near White River, Ontario,

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Canada (48.354° N; 85.338° W, elevation 450 m a.s.l.), managed by the Ontario Forest Research Institute and the Ontario Ministry of Natural Resources, with permission. Eight large, intact peat cores complete with aboveground herbaceous and shrubby vegetation were collected (Dieleman et al., 2015). Sub-samples of peat (approximately 10 g fresh weight) were obtained from the top (0 – 5 cm), and bottom (~35 cm) horizon from each core. A list of associated aboveground vegetation for both sampling locations can be found in Table A.1 (Appendix A).

2.2.3 DNA Extraction

Soil samples (approximately 10 g wwt) were shaken in 100 mL of 1M sodium pyrophosphate (Anachemia) to disrupt soil colloids. Samples were washed through three stacked sieves (1.18 mm, 250 um, 53 um) for 1 – 2 minutes with dH2O. Organic matter, including mycelia and spores, was collected from the finest sieve and lyophilised for 24 h (Virtis Bench Top 3.5 Freeze Dryer), then ground in liquid nitrogen using a mortar and pestle. DNA was isolated from 1 g of ground sample following the high molecular weight DNA soil protocol (http://www.borealgenomics.com/assets/Aurora-HMW-Soil-App- Note-13-01-09.pdf) using an Aurora electrophoretic nucleic acid separator (Boreal Genomics) (Zhou et al., 1996). Cartridges were washed and soaked in 10% bleach for at least 30 minutes between each sample.

Peat samples (approximately 25 g wwt) were lyophilised for 48 h, an aliquot ground in liquid nitrogen, and kept frozen until DNA extraction. Total genomic DNA was extracted from 25 mg of each sub-sample of peat moss from each horizon using a Zymo DNA isolation kit (Zymo Research Corporation) to obtain genomic DNA free of PCR- inhibitory phenolic compounds. Genomic DNA was quantified and tested for extraction quality using a nanodrop (Nanodrop 2000, Thermo Scientific) (please see Appendix A, Figure A.1). DNA extracts from eight soil samples and 16 peat samples were used for sequencing using newly designed LSU primers. Then three random high quality DNA extracts (determined by Nanodrop absorbance ratios) for both subarctic soil and upper horizon peat were chosen to be re-sequenced using ITS2 primers to allow for direct

30 comparison of the quality and quantity of sequence data obtained by primers targeting two different regions of the rDNA gene.

2.2.4 PCR Protocol

Optimal PCR conditions were determined from DNA extracts of Agaricus bisporus and CIL Plus MycoactiveTM Potting Soil (for LSU200-F/LSU481-R), and of a Harposporium sp. isolate for LSU200A-F /LSU476A-R. LSU PCR reactions were carried out in a total volume of 25 µL, with 0.5–4 µL template DNA, 12.5 µL of Toughmix (Quanta Biosciences), and 1.25 µL each of forward and reverse primers (5 µM). Optimal PCR conditions were 2 min at 94 °C, followed by 29 cycles for 30 s at 94 °C, 30 s at 62 °C (55 °C in first cycle followed by the remainder at 62 °C for Ascomycota primers), with a final elongation temperature of 72 °C for 18 s. ITS2 PCR reactions followed the protocol outlined by Toju et al. (2012), scaled to a total volume of 25 µL, using 1 µL template DNA (20 ng/µL for peat and soil samples). Optimal PCR conditions were 3 min at 94 °C, followed by 35 cycles for 20 s at 94 °C, 30 s at 47 °C, 20 s at 72 °C, with a final elongation temperature of 72 °C for 7 min (Toju et al., 2012). PCR products were normalized with a Qubit fluorimeter with the dsDNA HS kit (Life Technologies) and submitted for paired-end MiSeq Illumina sequencing (2 x 300 bp with V3 chemistry) at the London Regional Genomics Centre (Robarts Research Institute, London, Canada).

2.2.5 Bioinformatic Analysis

Two MiSeq runs were done for each LSU primer set, and for each soil type, resulting in four multiplexed runs, and separate bioinformatic analyses for each run. All ITS2 samples were run together on a separate multiplexed MiSeq run. Raw FASTQ data was sent through a custom MiSeq data processing pipeline (https://github.com/ggloor/miseq_bin/tree/master).

PANDAseq (https://github.com/neufeld/pandaseq) (Masella et al., 2012) was used to overlap reads with a minimum overlap length of 30 nt. Any sequences containing ambiguous base calls were removed, as well as any reads that did not perfectly match the primer sequence. Barcode and primer sequences were trimmed prior to clustering. The

31 pipeline grouped the overlapped FASTQ output into identical sequences (ISUs) by identity. The ISUs were checked for chimeras using the UCHIME algorithm (Edgar et al., 2011) and grouped into operational taxonomic units (OTUs) at 97% similarity, using the UCLUST clustering algorithm within USEARCH v 7.0.1090. We chose 97% similarity based on a study that established this as the optimal threshold to translate OTUs into taxonomic species when using single-linkage clustering and the V6 variable region of SSU (16S) rDNA, a region of comparable variability to the D1 divergent domain of LSU rDNA (Huse et al., 2010). Therefore, each OTU consists of a centroid sequence—a representative sequence from the most common sequence type within each OTU—around which are clustered reads that are ≥97% similar. Sequence reads that were less than 0.1% of the total abundance in any sample were automatically removed from the sample-mapped OTU table.

Because of the large dataset obtained by NGS, more precise identification of each OTU using a phylogenetic approach and post-hoc analysis is not practical, nor required for ecological comparisons of fungal communities. Instead, general taxonomic classifications were done via the online RDP Classifier (Wang et al., 2007) using the Fungal LSU Training set 11 as a reference database (https://rdp.cme.msu.edu/classifier/classifier.jsp) for the two LSU primer sets, and the Warcup Fungal ITS Trainset 1 for the ITS2 data (https://rdp.cme.msu.edu/classifier/classifier.jsp). Centroid sequences with confidence scores lower than 90% were queried using a nucleotide BLAST (Edgar, 2010) and re- classified using taxa that scored >80% query coverage and identity within the distance tree of results (since top BLAST hits are not always the closest phylogenetic neighbor, Koski & Golding, 2001). Raw data can be found at the ENA website under the accession number PRJEB11433. Sample IDs and their ENA accession numbers, are available as Table A.2 (Appendix A).

2.3 Results

2.3.1 Primer Performance: Target Sequence Yield

In total, 1 435 400 reads representing 68 670 ISUs were amplified from eight subarctic soil samples using the LSU200-F/LSU481-R primers (Table 2.2). Of these, 4 827 were

32 flagged as chimeric, leaving 63 843 clusters that were grouped into 2 991 OTUs at 97% similarity. Prior to analysis, 2 681 OTUs with singleton reads were discarded, leaving 310 OTUs (877 244 total reads). There were 188 fungal (target) OTUs identified (60.6% of total retained OTUs; 79.6% of final reads). From peat, many more raw reads were obtained (4 777 383); after quality filters, these clustered to 403 OTUs, of which 122 (30.3%) were identified as fungal, but the fraction of final reads on-target was similar (71.7%). Using the LSU200A-F/LSU476A-R primers, 212 target (Ascomycota) OTUs were recovered from subarctic soils (87.6% of final OTUs, 97.9% of final retained reads), and 89 Ascomycota OTUs from peat (63.5% of final OTUs, 95.3% of final retained reads). The ITS2 primers (ITS3_KYO2/ITS4_KYO3) were used on a subset of soil and peat samples, and yielded 554 358 reads, which after quality filtering, clustered to 73 OTUs, of which 72 were fungal (98.6% of final OTUs, 99.5% of final reads). The number of raw reads and of on-target reads per sample is dependent in part on the yield of the Illumina run and the number of samples multiplexed per run, but the latter number is useful as a judge of sequencing depth for each primer-sample pairing. The mean number of on-target reads per sample ranged from 38 543 to 167 685; values for the reactions using ITS2 primers were intermediate (Table 2.2). An informative measure of primer performance is the OT Score, the percent of raw reads per sample retained after quality filtering, and on target. The mean OT Scores for the three primer sets in our tests ranged from 42.3 for the ITS2 primers to 72.9 for the LSU200A-F/LSU476A-R primers with peat samples.

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Table 2.2 Summary data for all sample sets using LSU200-F/LSU481-R, and LSU200A-F/LSU476A-R (Ascomycota) primers developed in this study, and ITS3_KYO2/ITS4_KYO3 primers developed byToju et al. (2012). ITS3_KYO2/ Primers LSU200-F/LSU481-R LSU200A-F/LSU476A-R ITS4_KYO3 No. samples 8 soil 16 peat 8 soil 16 peat 3 soil; 3 peat Total reads 1 435 400 4 777 383 466 613 1 048 373 554 358 ISUs 68 670 179 317 24 862 40 705 27 116 Chimeras (ISUs) 4 827 8 151 1 223 3361 262 Clustered OTUs 2 991 2 296 772 851 1 421 Singleton OTUs 2 681 1 893 530 711 1 348

Final OTUs 310 403 242 140 73 Final reads 877 244 3 741 924 314 962 802 078 235 801 Target (fungal) OTUs 188 122 212 89 72 Percent target OTUs 60.60% 30.30% 87.60% 63.50% 98.60% Target reads as % of final reads 79.60% 71.70% 97.90% 95.30% 99.45% Mean target reads per sample 87 286 167 685 38 543 47 774 39 084 Mean OT Scorea, % 48.6 56.2 66.1 72.9 42.3

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Percent non-target OTUs 39.40% 69.70% 12.40% 36.40% 1.40% Percent non-target reads 20.40% 28.3 2.10% 4.60% 0.55% a The percentage of raw reads per sample retained after quality filtering and on-target

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2.3.2 Primer Performance: Fungal Diversity Recovered

The diversity of target and non-target OTUs recovered using the new the LSU200- F/LSU481-R and LSU200A-F/LSU476A-R primers from the full set of eight soil and 16 peat samples is available through the European Nucleotide Archive (ENA) under the accession number PRJEB11433. Here, we present the diversity recovered from the subset of samples that were sequenced using both these primers and the ITS2 primers. Using ITS2 primers, the 72 fungal OTUs included 19 of Basidiomycota and 53 of Ascomycota; 34 of these 72 OTUs could be placed with confidence in 29 genera, and 19 OTUs could be identified to species (Table 2.3). Using the LSU200-F/LSU481-R primers, 158 OTUs could be placed in 79 genera and 27 identified to species, and using LSU200A- F/LSU476A-R primers, 90 OTUs could be identified to 64 genera, and 21 identified to species. Together, the LSU primers recovered 127 genera and 28 species that were not obtained using the ITS2 primers, but the ITS2 primers recovered 10 unique genera and 16 species that were not obtained using either of the LSU primers. There was broad agreement at higher taxonomic levels between the fungal communities detected using the LSU primers and ITS2 primers (please see Appendix A, Table A.3). The predominant fungi recovered from subarctic soils using LSU200-F/LSU481-R primers included Mortierellomycetes, Agaricales, Thelephorales, and Sebacinales; OTUs recovered using LSU200A-F/LSU476A-R primers were dominated by members of Helotiales, Leotiomycetes, and Chaetothyriales; OTUs recovered using ITS2 primers by Helotiales, unknown Pezizomycotina, Chaetothyriales, Agaricales, Russulales, Sebacinales and Thelephorales. From peat, the OTUs recovered using LSU200-F/LSU481-R primers were dominated by Mortierellomycetes, Agaricales, Atheliales, Cantharellales, and Sebacinales; those recovered using LSU200A-F/LSU476A-R primers by Helotiales, unknown Ascomycota, and Mytilinidiales; and those recovered using ITS2 primers by Helotiales, unknown Leotiomycetes, Agaricales, Chaetothyriales and Sebacinales.

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Table 2.3 List of genera and species recovered by LSU200-F/LSU481-R, LSU200A-F/LSU476A-R and ITS3_KYO2/ITS4_KYO3 primers.

Taxa were included only if the total read count across the three subsamples were >3, and are listed alphabetically by higher taxa (A = Ascomycota, B = Basidiomycota, C = Chytridiomycota, E = Entomophthoromycota, G = Glomeromycota, K = Kickxellomycotina, M = Mucoromycotina, Mi = Microsporidia, Z = Zoopagomycotina). (#) = Number of OTUs identified to that taxon. LSU200-F/ LSU481-R LSU200A-F/ LSU476A-R ITS3_KYO2/ ITS4_KYO3 B Agaricus (2) A Acephala applanata A Acephala sp. B Amanita sp. A Alternaria sp. A Cenococcum geophilum (2) B Amphinema sp. A Ascocoryne turficola A Cladonia sp. B Arcangeliella sp. A Babjevia anomalus A *Cladophialophora (2) B Asterostroma laxum A Byssonectria sp. A *Colpoma sp. B Athelia epiphylla A Capronia sp. A *Hyaloscypha albohyalina B Athelia sp. A Caproventuria hanliniana (2) A Hymenoscyphus (2) B Basidiodendron (3) A Cenococcum (3) A *Leptodontidium elatius B Brevicellicium exile A Cladonia sp. A *Meliniomyces variabilis B Caloboletus inedulis A Cladosporium A *Phialocephala fortinii B Ceratobasidium sp. A Clonostachys rosea A *Remleria rhododendricola B Clavaria aff. fragilis A Collophora sp. A *Rhizoscyphus ericae

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B Clavaria (5) A Coniochaeta sp. A Stereocaulon tomentosum B Clavulina sp. A Cryptodiscus microstomus B Conocybe sp. A Cryptodiscus rhopaloides B Ceratobasidium sp. B Coprinellus sp. A Dactylaria (2) B Clavaria (2) B Coprinopsis (2) A Davidiella (2) B Inocybe acutoides B Cortinarius aff. fragilis A Devriesia sp. B Leccinum rotundifoliae B Cortinarius spp. (5) A Diplococcium spicatum (2) B Mycena alexandri B Craterellus sp. A Dipodascopsis sp. B Mycena galericulata B Cryptococcus gilvescens A Doratomyces sp. B Mycena sp. B Cryptococcus magnus A Elaphocordyceps (2) B *Pseudotomentella tristis B Cryptococcus musci A Elaphomyces sp. B Ramariopsis sp. B Cryptococcus sp. A Exophiala sp. B Russula aeruginea B Cryptococcus terricola (2) A Fusicladium sp. B Sebacina (2) B Cuphophyllus sp. A Gaeumannomyces sp. B Serendipita vermifera B Cymatoderma sp. A Geoglossum sphagnophilum B *Sistotrema oblongisporum B Entoloma sp. A Glonium sp. B Suillus cavipes B Entoloma strictius A Godronia sp. B Tomentella sp. B Exidia saccharina (2) A Heleiosa sp. B Tylospora fibrillosa B Exidia spp. (2) A Hemileucoglossum alveolatum (3) B Exobasidium sp. A Hymenoscyphus sp.

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B Galerina paludosa A Lachnum sp. B Galerina spp. (2) A Lecanicillium (2) B Guehomyces sp. A Leucophagus (2) B Hygrocybe (5) A Maasoglossum sp. B Hyphoderma (2) A Melastiza (3) B Hyphodontia (2) A Metarhizium sp. B Inocybe borealis A Mitrula (2) B Inocybe catalaunica A Mniaecia jungermanniae (2) B Inocybe spp. (3) A Mniaecia gloeocapsae B Inocybe splendens A Mollisia (3) B Inocybe squarrosa A Morchella sp. B Jaapia sp. A Myxotrichum sp. B Laccaria sp. A Neofabraea sp. B Lactarius sp. A Oidiodendron (4) B Leccinum sp. A Penicillium (5) B Leucosporidium sp. A Peziza sp. B Limonomyces sp. A Phialophora sp. B Lycoperdon sp. A Pochonia sp. B Mrakia sp. A Preussia sp. B Mycena (3) A Pseudoplectania episphagnum

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B Mycogloea sp. A Repetophragma sp. B Nidula sp. A Rhizosphaera sp. B Phanerochaete sp. A Sabuloglossum arenarium B Postia sp. A Sorocybe (2) B Psathyrella sp. A Sphaeropezia sp. B Ramariopsis (5) A Spirosphaera minuta B Russula sp. A Stereocaulon sp. B Sebacina (9) A Stomiopeltis sp. B Serendipita vermifera A Strelitziana cliviae B Spiculogloea sp. A Thelebolus sp. B Sporobolomyces sp. A Trichoderma sp. B Suillus cavipes A Troposporella sp. B Suillus palustris A Umbilicaria sp. B Syzygospora sp. B Tephrocybe sp. B Tilletia sp. B Tomentella (3) B Tomentellopsis sp. B Tremella sp. B Tricholoma sp.

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B Tulasnella (2) B Tylospora (3) B Udeniomyces sp. B Xerocomus sp. B Xylodon nespori C Betamyces sp. C Chytridium (3) C Fayochytriomyces spinosus C Gonapodya sp. C Irineochytrium annulatum (2) C Monoblepharis sp. C Olpidium sp. E Basidiobolus sp. G Archaeospora (2) G Glomus (3) K Spiromyces (2) K Stachylina sp. M Endogone (4) M Mortierella globulifera (5) M Mortierella spp. (12)

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M Umbelopsis (6) Mi Mitosporidium daphniae Z Rhopalomyces sp. * Unique genera identified by ITS2 primers (ITS3_KYO2/ITS4_KYO3)

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2.4 Discussion

The primer sets presented here were designed to provide high efficiency sequence tag information data on fungal diversity in Next Generation Sequencing studies using the Illumina platform. Both primer sets (LSU200-F/LSU481-R and LSU200A-F/LSU476A- R) amplify a ~300 bp region of the rDNA LSU, which provides reliable placements of sequence clusters within the Fungi and, together they yield a broad array of Fungi from chytrids to mushrooms. In addition, the non-target reads obtained using the LSU200- F/LSU481-R primers (approximately 20-30% of retained reads) provide sequence data on a number of important eukaryotic groups that are poorly known in soil (Lawley et al., 2004): Amoebozoa, Centroheliozoa, Choanoflagellida, Metazoa, Rhizaria and unicellular Streptophyta. I establish the validity and reproducibility of these primers for high- throughput sequencing of environmental samples using replicates of DNA extracted from two types, one of which (peat) was particularly high in humic acid and phenolic compounds.

Including both soil and peat samples, data clean-up resulted in the loss of 25.7% of reads, on average, for primers LSU200-F/LSU481-R, and 26.3%, on average, using the LSU200A-F/LSU476A-R primers. The complement of the data lost to poor quality sequences, chimeras, singleton OTUs, and non-target sequences is the OT Score, the percent of raw sequence reads retained and on-target. The OT Scores for LSU200- F/LSU481-R primers averaged 53.7 across the samples in our study, and 70.6 for LSU200A-F/LSU476A-R primers. These values are higher than the OT Score for the ITS2 primers ITS3_KYO2/ITS4_KYO3 of 42.3 (57.5% data loss to clean-up) in this study, and considerably better than the 78.6% data loss reported for those primers in (Schmidt et al., 2013). In this study, the greatest loss of reads occurred through elimination of singleton OTUs, discarded from our data prior to analysis. Singleton reads are often removed to avoid introducing biases in OTU computation, since poor sequences—reads with greater numbers of errors—are likely to inflate the number of OTUs recovered (Valverde & Mellado, 2013). In contrast, the inclusion of singletons is unlikely to change observed data trends in ecological comparisons even if they include true observations of rare species (Brown et al., 2015). While my data had high loss of 43

OTUs after the removal of singletons, read loss—which is arguably a more important limitation in metagenomic studies (Smith & Peay, 2014)—was minimal.

Although the ITS2 primers recovered as many on-target sequences per sample as our new LSU primers in our tests, they yielded far fewer fungal OTUs, representing a much smaller range of higher taxa of fungi than were obtained using our new primers LSU200- F/LSU481-R and LSU200A-F/LSU476A-R. The only other published study using these ITS2 primers for the purpose of biodiversity discovery on DNA extracted from soil samples (Balint et al., 2014) recovered considerably more total fungal OTUs (3208 OTUs) than our data (72 OTUs). However, Balint et al. (2014) analysed considerably more samples (96 versus 6), used two different annealing temperatures (51 and 55 °C), and ran three PCR replications for each annealing temperature. This suggests that three replicates of two different annealing temperatures and a larger number of target reads may be required for the ITS2 primers to obtain a similar number OTUs as our new LSU primers (Schmidt et al., 2013). That said, the ITS2 primers, which were designed to recover Ascomycota and Basidiomycota (Toju et al., 2012), will not yield sequences of chytrids, Glomeromycota, or the Zygomycete groups obtained using LSU200-F/LSU481- R, and in our tests they also failed recover important groups of Ascomycota (e.g., Eurotiales, Mytilinidiales, Pezizales, Saccharomycetales, Venturiales) and Basidiomycota (e.g., Exobasidiomycetes, Microbotryomycetes, Pucciniomycetes, and Tremellomycetes) that were recovered by our primers. In contrast, the ITS2 primers did recover two OTUs tentatively identified as Archaeorhizomycetes (Ascomycota), an important group in boreal/montane soils and peat (Rosling et al., 2011) not recovered using LSU200A- F/LSU476A-R. However, as noted in Table 1, improving the match of primer LSU200A- F to Archaeorhizomycetes may be achieved by using the modified forward primer AACKGCGAGTGAAGCRGBM; the reverse primer LSU476A-R already has a perfect match to known Archaeorhizomycetes sequences.

Primer specificity, or the lack of primer universality in recovering broad groups such as the entire kingdom Fungi, has been a persistent issue that has not previously been addressed for biodiversity studies using the Illumina platform. With the exception of LR3/TW13 and LR5/TW14 primers that have been designed based on a highly conserved 44

region within the LSU, all other fungal specific genetic markers and ‘universal’ primers discriminate against various groups of fungi (Bellemain et al., 2010). The ITS1F and other ITS primers (ITS1 and ITS5) have shown biases towards the amplification of fungal groups within the Basidiomycota, while others, particularly ITS2, ITS3 and ITS4, favour Ascomycota (Bellemain et al., 2010). More specific primers such as the ITS4-B, designed to capture Basidiomycetes, can amplify the ITS region of a very limited group within the target phylum (O'Brien et al., 2005). Since our new LSU primers provide ample read counts in environmental samples, have low rates of data loss, and cover a breadth of fungal taxa, we suggest that they provide a promising addition to complement and counterpart other universal primers in metagenomic studies.

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Dumbrell AJ, Ashton PD, Aziz N et al. (2011). Distinct seasonal assemblages of arbuscular mycorrhizal fungi revealed by massively parallel pyrosequencing. New Phytologist, 190, 794–804. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32, 1792–1797. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27, 2194–2200. Fierer N, Ladau J, Clemente JC et al. (2013) Reconstructing the microbial diversity and function of pre-agricultural tallgrass prairie soils in the United States. Science, 342, 621–624. Georgiev OI, Nikolaev N, Hadjiolov AA, Skryabin KG, Zakharyev VM, Bayev AA (1981) The structure of the yeast ribosomal RNA genes. 4. Complete sequence of the 25 S rRNA gene from Saccharomyces cerevisae. Nucleic Acids Research, 9, 6953–6958. Gloor GB, Hummelen R, Macklaim JM, Dickson RJ, Fernandes AD, MacPhee R, Reid G (2010) Microbiome profiling by illumina sequencing of combinatorial sequence- tagged PCR products. PloS One, 5, p.e15406. Hassouna N, Michot B, Bachellerie JP (1984) The complete nucleotide sequence of mouse 28S rRNA gene. Implications for the process of size increase of the large subunit rRNA in higher eukaryotes. Nucleic Acids Research, 12, 3563–3583. Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proceedings of the Royal Society of London Series B: Biological Sciences, 270, 313–321. Helgason T, Daniell TJ, Husband R, Fitter AH, Young JPW (1998) Ploughing up the wood-wide web? Nature, 394, 431–431. Hert DG, Fredlake CP, Barron AE (2008) Advantages and limitations of next-generation sequencing technologies: a comparison of electrophoresis and non-electrophoresis methods. Electrophoresis, 29, 4618–4626.

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Hibbett D, Bauer R, Binder M et al. (2014). Agaricomycetes. In: The Mycota, Systematics and Evolution (eds McLachlin DJ, Spatafora JW), 2nd ed, pp. 373– 428, Springer, Berlin. Huse SM, Welch DM, Morrison HG, Sogin ML (2010) Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environmental Microbiology, 12, 1889–1898. Jang J-H, Bae S-E, Jung S, Kim H-Y, Ahn I-S, Son H-S (2014) FGMD: database of fungal genetic markers and multigene analysis in fungal classification. The Korean Journal of Public Health, 2, 81–89. Kohout P, Sudová R, Janoušková M et al. (2014) Comparison of commonly used primer sets for evaluating arbuscular mycorrhizal fungal communities: Is there a universal solution? Soil Biology and Biochemistry, 68, 482–493. Koski LB, Golding GB (2001) The closest BLAST hit is often not the nearest neighbor. Journal of Molecular Evolution, 52, 540–542. Kurtzman CP, Robnett CJ (1998) Identification and phylogeny of ascomycetous yeasts from analysis of nuclear large subunit (26S) ribosomal DNA partial sequences. Antonie Van Leeuwenhoek, 73, 331–371. Lawley B, Ripley S, Bridge P, Convey P (2004) Molecular analysis of geographic patterns of eukaryotic diversity in Antarctic soils. Applied and Environmental Microbiology, 70, 5963–5972. Lee J, Lee S, Young JPW (2008) Improved PCR primers for the detection and identification of arbuscular mycorrhizal fungi. FEMS Microbiology Ecology, 65, 339–349. Lindahl BD, Nilsson RH, Tedersoo L et al. (2013) Fungal community analysis by high- throughput sequencing of amplified markers–a user's guide. New Phytologist, 199, 288–299. Lindner DL, Banik MT (2011) Intragenomic variation in the ITS rDNA region obscures phylogenetic relationships and inflates estimates of operational taxonomic units in genus Laetiporus. Mycologia, 103, 731–740.

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Luo C, Tsementzi D, Kyrpides N, Read T, Konstantinidis KT (2012) Direct comparisons of Illumina vs. Roche 454 sequencing technologies on the same microbial community DNA sample. PloS One, 7, e30087. Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD (2012) PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics, 13, 31. Moncalvo JM, Vilgalys R, Redhead SA et al. (2002) One hundred and seventeen clades of euagarics. Molecular Phylogenetics and Evolution, 23, 357–400. O'Brien HE, Parrent JL, Jackson JA, Moncalvo JM, Vilgalys R (2005) Fungal community analysis by large-scale sequencing of environmental samples. Applied and Environmental Microbiology, 71, 5544–5550. Pařenicová L, Skouboe P, Frisvad J, Samson RA, Rossen L, ten Hoor-Suykerbuyk M, Visser J (2001) Combined molecular and biochemical approach identifies Aspergillus japonicus and Aspergillus aculeatus as two species. Applied and Environmental Microbiology, 67, 521–527. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Research, 35, 7188–7196. Ratnasingham S, Hebert PDN (2007) BOLD: The Barcode of Life Data System (http://www.barcodinglife.org). Molecular Ecology Notes, 7, 355–364. Rosling A, Cox F, Cruz-Martinez K et al. (2011) Archaeorhizomycetes: unearthing an ancient class of ubiquitous soil fungi. Science, 333, 876–879. Sato K, Suyama Y, Saito M, Sugawara K (2005) A new primer for discrimination of arbuscular mycorrhizal fungi with polymerase chain reaction-denature gradient gel electrophoresis. Grassland Science, 51, 179–181. Schoch CL, Sung GH, López-Giráldez F et al. (2009) The Ascomycota tree of life: a phylum-wide phylogeny clarifies the origin and evolution of fundamental reproductive and ecological traits. Systematic Biology, 58, 224–239. Schoch CL, Seifert KA, Huhndorf S et al. (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proceedings of the National Academy of Sciences, 109, 6241–6246.

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Schmidt PA, Bálint M, Greshake B, Bandow C, Römbke J, Schmitt I (2013) Illumina metabarcoding of a soil fungal community. Soil Biology and Biochemistry, 65, 128–132. Seifert KA, Samson RA, Houbraken J, Lévesque CA, Moncalvo JM, Louis-Seize G, Hebert PD (2007) Prospects for fungus identification using CO1 DNA barcodes, with Penicillium as a test case. Proceedings of the National Academy of Sciences, 104, 3901–3906. Shenoy BD, Jeewon R, Hyde KD (2007) Impact of DNA sequence-data on the taxonomy of anamorphic fungi. Fungal Diversity, 26, 1–54. Simon L, Lalonde M, Bruns T (1992) Specific amplification of 18S fungal ribosomal genes from vesicular-arbuscular endomycorrhizal fungi colonizing roots. Applied and Environmental Microbiology, 58, 291–295. Skouboe P, Frisvad JC, Taylor JW, Lauritsen D, Boysen M, Rossen R (1999) Phylogenetic analysis of nucleotide sequences from the ITS region of terverticillate Penicillium species. Mycological Research, 103, 873–881. Smith DP, Peay KG (2014) Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS One, 9, e90234. Sun X, Guo LD (2012) Endophytic fungal diversity: review of traditional and molecular techniques. Mycology, 3, 65–76. Taylor DL, Hollingsworth TN, McFarland JW, Lennon NJ, Nusbaum C, Ruess RW (2014) A first comprehensive census of fungi in soil reveals both hyperdiversity and fine-scale niche partitioning. Ecological Monographs, 84, 3–20. Tedersoo L, Bahram M, Põlme S et al. (2014) Global diversity and geography of soil fungi. Science, 346, 1–11. Tedersoo L, Bahram M, Põlme S et al. (2015) Response to Comment on “Global diversity and geography of soil fungi”. Science, 349, 936–936. Toju H, Tanabe AS, Yamamoto S, Sato H (2012) High-coverage ITS primers for the DNA-based identification of Ascomycetes and Basidiomycetes in environmental samples. PloS One, 7, e40863. Treseder KK, Lennon JT (2015) Fungal traits that drive ecosystem dynamics on land. Microbiology and Molecular Biology Reviews, 79, 243–262.

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Varga J, Tóth B, Rigó K, Téren J, Hoekstra RF, Kozakiewicz Z (2000) Phylogenetic analysis of Aspergillus section Circumdati based on sequences of the internal transcribed spacer regions and the 5.8 S rRNA gene. Fungal Genetics and Biology, 30, 71–80. Vilgalys R, Hester M (1990) Rapid genetic identification and mapping of enzymatically amplified ribosomal DNA from several Cryptococcus species. Journal of Bacteriology, 172, 4238–4246. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73, 5261–5267. Wang H, Xiao M, Kong F et al. (2011) Accurate and practical identification of 20 Fusarium species by seven-locus sequence analysis and reverse line blot hybridization, and an in vitro antifungal susceptibility study. Journal of Clinical Microbiology, 49, 1890–1898. Watanabe M, Yonezawa T, Lee KI, Kumagai S, Sugita-Konishi Y, Goto K, Hara‐ Kudo Y (2011) Evaluation of genetic markers for identifying isolates of the species of the genus Fusarium. Journal of the Science of Food and Agriculture, 91, 2500-2504. White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: PCR Protocols: a Guide to Methods and Applications. New York (eds Innis M, Gelfand D, Sninsky J, White T), pp. 315–322, Academic Press, Inc. Zhou J, Bruns MA, Tiedje JM (1996) DNA recovery from soils of diverse composition. Applied and Environmental Microbiology, 62, 316–322.

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Chapter 3 3 Vertical distribution of fungi in hollows and hummocks of boreal peatlands

3.1 Introduction

Hollows (wet depressions) and hummocks (drier raised areas) are common features of northern peatlands that develop through a variety of feedbacks arising from different Sphagnum species associated with this micro-topography (Belyea & Clymo, 2001). These dissimilarities include differences in moss shoot growth and height (i.e. acrotelm thickness) (Belyea & Clymo, 2001) and facilitation of vascular plants (Wallën et al., 1988), which interacts with water table position (Rydin, 1985; Rydin & McDonald, 2013; Weltzin et al., 2000), nutrient availability, and pH (Clymo, 1986). In addition to these micro-environmental conditions, hummock and hollow formations can be characterized by differences in key ecosystem processes driven by their plant species composition (Glime, 2007; Wallën & Smalmer, 1992). In particular, rates of decomposition have been cited as key controls and indicators of hummock-hollow formation (Clymo, 1965; Rudolph & Johnk, 1982; Turetsky et al., 2008; Wallën et al., 1988), where decomposition is found to be lower on raised hummock areas than in hollow depressions (Rochefort et al., 1990), which is attributable to inherent decomposability of the different constituent species (Clymo, 1965; Rudolph & Johnk, 1982; Turetsky et al., 2008), and the emergent microclimate (moisture, temperature) of the micro-topographies themselves. Underlying this process are the microbial (fungal and bacterial) communities that perform decomposition, that are also influenced by the physical micro-environmental conditions of the hummock-hollow (e.g. peat wetness, oxygen availability, plant litter deposition etc.). For example, fen hollows that are often submerged in through-flow do not experience the strong acidifying effect of Sphagnum seen in hummocks, leading to higher pH in hollows, and consequently higher microbial activities compared to hummocks (Clymo, 1986).

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Among microbial communities of boreal peatlands, fungi with heterogeneous physiology, metabolic activities, and ecological functions are recognized as key decomposers of complex carbon polymers in these ecosystems (Myers et al., 2012). Although the majority of fungi isolated and identified from peatlands are aerobic (Andersen et al., 2012), isolation of many of fungi in the anaerobic, lower peat layers suggests a range of tolerances to anoxic conditions (Thormann et al., 2004). Microbial activities can differ by depth and among different peatland types (Fisk et al., 2003; Myers et al., 2012). Community composition of different groups of bacteria changes along depth and micro- topographical gradients of boreal peatlands (Deng et al., 2014; Kotiaho et al., 2013). However, except for a few studies comparing the community composition of specific fungal groups in boreal peatland hollows and hummocks (Peltoniemi et al., 2009; Preston et al., 2012), to the best of our knowledge, there are no comprehensive studies of fungal communities describing their dissimilarities across a depth gradient in the different micro-topographies within a peatland.

Dissimilarities in fungal community composition between these two micro-topographies are expected as oxygen availability and pH varies from hollows to hummocks, as does vegetation — hollows have more easily decomposable mosses compared to hummocks, which also have an abundance of vascular plants (Clymo, 1965; Rudolph & Johnk, 1982; Turetsky et al., 2008; Wallën et al., 1988). In this study, I applied different approaches to describe microscale- and depth-dependent variations of fungal community composition within a well-characterized nutrient-poor fen of northern Ontario, Canada. I specifically tested the hypotheses that 1) fungal community composition between hollows and hummocks will differ, in general, 2) hummock communities will exhibit a less diverse and more homogeneous fungal community than hollows due to lower pH and more decay resistant mosses, and 3) fungal richness within each micro-topography will decrease with depth, probably due to oxygen limitation associated with the water table.

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3.2 Materials and Methods

3.2.1 Study Site

The study site, located at White River, northern Ontario, Canada (48.21°N, 85.21°W), is characterized as a nutrient-poor forested fen occupying approximately 4.5 hectares of the area, and covered by Sphagnum mosses (S. angustifolium (C.E.O. Jensen ex Russow) C.E.O. Jensen, S. cuspidatum Ehrh. ex Hoffm., S. fallax (H. Klinggr.) H. Klinggr., S. fuscum (Schimp.) H. Klinggr., and S. magellanicum Brid.). Other vegetation identified in the site includes black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.), and tamarack (Larix laricina (Du Roi) K. Koch) (Pinaceae), leatherleaf (Chamaedaphne calyculata (L.) Moench), Labrador tea (Rhododendron groenlandicum (Oeder) Kron & Judd), and small cranberry (Vaccinium oxycoccos L.) (Ericaceae), and Carex magellanica Lam. and Carex disperma Dewey (Cyperaceae). The study area has obvious micro- topographical variations of hollows and hummocks, each with different vegetation and hydrological regime (McLaughlin & Webster, 2013). Hollows are covered mostly by S. magellanicum, whereas hummocks are covered mostly by S. fuscum, but also have significantly higher abundances of leatherleaf, cranberry, and sedges. Trees and tall shrubs such as Picea mariana are more common in the lawn area between hollow and hummock (McLaughlin & Webster, 2013). Water table fluctuations in the study site occur following changes in weather conditions or stream flow (Webster & McLaughlin, 2010). Hollows can range from a moist condition at the top (0-5 cm) to fully saturated at the lowest peat layer that I sampled (30-35 cm below the surface), while hummocks are always dry at the top (0-5 cm), often dry in the middle (15-20 cm) depending on water fluctuations, and moist in the bottom layer (30-35 cm), but not necessarily saturated (Figure 3.1). Note that peat can extend to depths of 104-127 cm (McLaughlin & Webster, 2013), however, throughout this report I refer to the lowest zone sampled, at 30-35 cm depth, as the bottom layer. For a more complete description of the site, see McLaughlin & Webster (2013) and Webster & McLaughlin (2010).

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3.2.2 Sampling

In August 2013, 36 field samples (each approximately 25 g fresh weight) were collected at three different depths (upper 0-5 cm, middle 15-20 cm, bottom 30-35 cm) from 12 peat monoliths (each 40 cm deep and 25 kg) harvested from two different micro-topographies (six monoliths from each of hollows and hummocks) in the study site (Figure 3.1). The samples were transferred into air-tight plastic bags, and frozen in the field using dry ice to capture the fungal community under natural state as much as possible. The frozen samples were taken to -80 °C until they were processed.

3.2.3 DNA Extraction and PCR Procedures

Frozen peat samples were lyophilized for 48 hours, homogenized, and roughly 0.5 g (dry weight) of each sample was ground in liquid nitrogen to a fine powder from which approximately 25 mg (dry weight) was used for DNA extraction using Zymo Soil DNA isolation kit (Zymo Research Corporation). The concentration of extracted DNA was assessed using nanodrop (Nanodrop 2000, Thermo Scientific), and normalized to 20 ng/ μL, then kept at -20 °C until PCR amplifications.

PCR amplifications were done using two sets of primers, LSU200A-F (AACKGCGAGTGAAGCRGYA)/LSU476A-R (CSATCACTSTACTTGTKCGC) to discover Ascomycota diversity and LSU200-F

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Figure 3.1 Conceptual diagram of the physicochemical driver of fungal communities in hollows and hummocks.

The dashed box represents the area with maximum fungal richness, and where the heterogeneity of fungal community composition is similar between hollows and hummocks. A low fungal richness is observed in the upper horizon of hummocks where pH is highly acidic and lower horizon of hollows where peat is under constant anoxic condition, and the majority of peat accumulated in this layer is at the most advanced stage of decomposition.

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(AACKGCGAGTGAAGMGGGA)/LSU481-R (TCTTTCCCTCACGGTACTTG) to capture all other major groups of fungi. The size of amplicons generated by these primers is approximately 300 bp (Asemaninejad et al., 2016). Illumina MiSeq adapters, and unique barcode sequences of 8-nucleotides were inserted to the 5’ ends of each of forward and reverse primers matching Illumina chemistry (for full details see Asemaninejad et al., 2016). PCR reactions were performed in a total volume of 25 μL, with 20 ng of template DNA, 5 μM each of forward and reverse primers, and 12.5 μL of Toughmix (Quanta Biosciences), and run in a “T300” Thermocycler (Biometra). Optimal PCR conditions for other fungi included initial denaturation for two minutes at 94 °C, followed by 29 cycles of denaturation for 30 s at 94 °C, annealing for 30 s at 62 °C and elongation for 18 sec at 72 °C. For Ascomycota primers the optimal PCR condition was 94 °C for two minutes, followed by 29 cycles of 94 °C for 30 s, 55 °C for 30 s in first cycle, and 62 °C for the rest of cycles, elongation for 18 sec at 72 °C (Asemaninejad et al., 2016). The concentration of PCR products were measured using a Qubit fluorimeter with the dsDNA HS kit (Life Technologies), and then normalized. The normalized PCR products were sent to London Regional Genomics Centre (Robarts Research Institute, London, Canada) for paired-end sequencing in an Illumina MiSeq sequencer (2 x 300 bp V3 chemistry).

3.2.4 Bioinformatic Analysis

For each primer set, two Miseq runs were carried out producing four multiplexed runs. A custom MiSeq pipeline (https://github.com/ggloor/miseq_bin/tree/master) was used for Bioinformatics analysis of the FASTQ data produced from each run (for full description of methods see Asemaninejad et al., 2016). PANDAseq (https://github.com/neufeld/pandaseq) was used to align sequences (pairwise alignment) with a minimum overlap length of 30 nucleotides (Masella et al., 2012). The overlapped FASTQ outputs containing the same sequences were grouped into identical sequence units (ISUs). UCHIME algorithm was used to remove chimeras from ISUs (Edgar et al., 2011). A 3% dissimilarity value was applied as the threshold to cluster ISUs, meaning that ISUs with at least 97% similarity were grouped into OTUs (operational taxonomic

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units). The classification of OTU centroid sequences was done through the online RDP Classifier (Wang et al., 2007) using the Fungal LSU Training set 11 as a reference database (https://rdp.cme.msu.edu/classifier/classifier.jsp). Following statistical analyses, re-identification of indicator taxa correlated to a particular topography was executed using BLAST to further clarify their identity or function. Sequences were submitted to ENA (European Nucleotide Archive) under accession number PRJEB15408.

3.2.5 Statistical Analyses

To estimate species richness data were filtered by removing non-target OTUs. The final data set containing only fungal OTUs was rarified (Kenneth et al., 1975), and then used to determine fungal diversity (Shannon index, H’) and fungal richness (S = the number of target OTUs observed) in each peat sample from each micro-topography. Fungal richness and diversity were assessed through a factorial, spatial repeated measures of variance

(RM-ANOVA) with a Tukey post hoc test in Statistica 7 (StatSoft. Inc., 2004) to study the effect of micro-topography (hollows and hummocks) using depth as a repeated spatial measure due to their non-independence within a peat monolith.

At the community level, the data set was further filtered to remove target OTUs that were less than 1% abundant in the samples. To elucidate the heterogeneity in fungal community composition among depths in the two different micro-topographies, the filtered data set was analyzed using NMDS (nonmetric multidimensional scaling) in the “vegan” package and clustering analysis using the Ward.D2 method in the “compositions” package in R (version 3.2.3). For these analyses, the raw OTU sequence data was subjected to centered log ratio transformation to account for a higher rate of randomness than is expected in OTU-based multivariate community-level analyses (see Aitchison & Egozcue, 2005). This transformation also preserves the 1:1 relationship between primary OTU counts, and the data are analyzed on a common scale (Lovel et al., 2015). A Microbiome Regression-based Kernel Association Test (MiRKAT) was performed on final data set using the “ALDEx2” and “MiRKAT” packages in R (version 3.2.3) to evaluate whether topography or depth were the significant drivers of the dissimilarity observed in fungal community composition in peat samples (Zhao et al.,

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2015). Finally, to identify the important fungal taxa in each topography driving the dissimilarity in fungal community composition, an ANOVA-like differential expression procedure (ALDEx) (Fernandes et al., 2013) was executed in R (version 3.2.3) using the ALDEx2 package. In the final ALDEx test, raw p-values coming from multiple comparisons of data set were subjected to Benjamini-Hochberg False Discovery Rate (FDR) correction procedure as recommended for a large data set (Benjamini & Hochberg, 1995).

3.3 Results

3.3.1 Sequence Output and Target OTUs

A total of 3 962 117 sequences with length of approximately 300 bp, forming 265 OTUs, were recovered from 36 sub-samples (collected from three different depths of 12 monoliths, six from hollows and six from hummocks) using LSU200A-F/LSU476A-R primers targeting the Ascomycota. From this dataset, 54 non-target OTUs were removed and the 211 Ascomycota OTUs identified to 111 genera, 60 families, 34 orders, and 10 classes. LSU200-F/LSU481-R primers targeting other fungi yielded a total of 13 159 316 reads and 703 OTUs of which 365 were removed as non-target OTUs. The 338 fungal OTUs retained represented 143 genera, 75 families, 37 orders, 13 classes and 4 phyla. The asymptotic curves obtained through data rarefaction indicated a high probability of sequence coverage of majority of fungal OTUs present in peat samples (data not shown).

3.3.2 Fungal Richness and Diversity

Micro-topography (hummock – hollow) had no overall effect on Ascomycota richness

(F1,20 = 0.030, P = 0.855), although there was a significant interactive effect of micro- topography with depth (F2,20 = 5.230, P = 0.014), whereby in the upper horizon, Ascomycota richness was significantly higher in hollows than hummocks, while the opposite was observed in the bottom horizon (Table 3.1). There were no obvious differences in Ascomycota richness between hollows and hummocks in the middle peat layer. Ascomycota diversity was significantly higher in hollow than hummock samples overall and across all three depths (F1,20 = 9.510, P = 0.011). Neither richness nor 58

diversity of the other fungal group showed any significant difference between the two micro-topographies (richness: F1,20 = 0.003, P = 0.950; diversity: F1,20 = 0.226, P =

0.644), or depths (richness: F2,20 = 0.925, P = 0.412; diversity: F2,20 = 0.387, P = 0.683), but showed a similar trend to the Ascomycota where fungal richness in the upper and middle horizons was higher in hollows than hummocks while in the bottom horizon, the opposite was observed.

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Table 3.1 Mean and standard error of fungal richness (S) and diversity (H’) for hollow and hummock samples at three different depths (upper, middle, bottom) Hollow Hummock Ascomycota richness (S) Upper 174.16 ± 7.25a 153.83 ± 7.60b Middle 155.00 ± 3.89b 154.50 ± 12.81b Bottom 152.00 ± 6.41b 171.50 ± 5.85a Ascomycota diversity (H') Upper 2.90 ± 0.34a 2.46 ± 0.41b Middle 2.92 ± 0.17a 2.46 ± 0.44b Bottom 2.89 ± 0.25a 2.56 ± 0.17b Other fungi richness (S) Upper 189.50 ± 36.58a 183.16 ± 26.31a Middle 196.00 ± 22.06a 189.33 ± 28.14a Bottom 191.83 ± 17.31a 197.16 ± 21.88a Other fungi diversity (H') Upper 2.40 ± 0.82a 2.42 ± 0.38a Middle 2.49 ± 0.42a 2.36 ± 0.45a Bottom 2.34 ± 0.41a 2.41 ± 0.40a Different letters following values denote significant differences.

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3.3.3 Fungal Community Composition

A Kernel Association Test (MiRKAT) of centred log- ratio transformed sequence data suggested that both Ascomycota and other fungal communities in hollows differed significantly from hummocks (Ascomycota: P = 0.001; other fungi: P = 0.005). Based on ALDEx results, the significant differences in Ascomycota community composition between hollows and hummocks was driven by Diaportales across all depths, with significantly higher ratios in samples from hollows compared to hummocks, and by Saccharomycetales, which were abundant everywhere but the upper zone of hummocks (Table 3.2, Figure A.2). Unclassified Ascomycota and some members of Helotiales, Capnodiales, and Geoglossales appeared highly abundant, specifically in the bottom layer of hollows (e.g. OTUs 153, 35, 69 and 6). For the other fungi, the ALDEx test revealed that Thelephorales had significantly higher abundance in hollows across all depths than hummocks. Also, Russulales and some members of Polyporales and Agaricales were among the highly abundant taxa that specifically appeared in samples from hollows (in the upper and middle-bottom horizons, respectively) (Table 3.2, Figure A.3). In the NMDS plot, the amount of spread among data points representing the samples is an indication of the amount of heterogeneity in sample composition. In general, for Ascomycota, hummocks and hollows had equal heterogeneity but were generally divergent in community composition. However for other fungal groups, this trend was not observed, rather the hummock community appeared as a subset of the community found within hollows (Figure 3.2a, b). Considering the composition of both the Ascomycota and the other fungi from a functional perspective (Table 3.2), we find greater prevalence of aquatic and anaerobic species in hollows alongside mycorrhizal associates of trees (e.g. OTUs 961, 338 and 28, respectively), and greater prevalence of ericoid and graminoid root-associates in hummocks (e.g. OTUs 1187 and 984, respectively).

Trends among micro-topographies are further revealed when we consider community composition among depth profiles within hummocks and hollows separately (Figure 3.3). Similar to patterns in richness and diversity, depth trends were stronger in the samples

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from hollows than hummocks, where both Ascomycota (hollows: P = 0.001; hummocks: P = 0.084) and the other fungi (hollows: P = 0.043; hummocks: P = 0.939) had upper peat horizons differentiated from lower horizons (Figure 3.3a, b). For the Ascomycota, this was seen as a loss of members of Chaetothyriales, Hypocreales and some members of Helotiales with depth, and an increase in the relative abundances of some members of Saccharomycetales (e.g. OTUs 128 and 16), and general decomposers (e.g. OTUs 45 and 33) at depth in hollows (Table 3.2, also see Appendix A, Figure A.2). For the other fungi, while the upper horizon of hollows demonstrates significantly higher relative frequencies of potential ectomycorrhizal fungi, and some aquatic fungi (e.g. members of Boletales, Chytridiales and Zoopagales) and the bottom horizon is dominated by decomposers and some ectomycorrhizal root-associates (e.g. members of Polyporales, Mortierellales and Agaricales), the middle horizon hosted a combination of the fungi found in the upper and bottom peat layers (Table 3.2, Figure 3.3b).

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Table 3.2 Indicator fungal OTUs driving variation in community composition of Ascomycota and other fungi across a depth gradient and between two micro-topographies, identified through ALDEx test.

“Ho” is referred to “Hollow” and Hu” is referred to “Hummock”. OTUs marked by “*” are the unique OTUs found specifically in a particular micro-topography. Functional designations are based on literature cited.

Ascomycetous P- BLAST match Taxonomic group Function Where highly abundant References for function OTUs value No match at genus OTU_4 Diaporthales Unknown Ho/all depths 0.001 ˗ level OTU_22 Penicillium Eurotiales Saprotroph Ho/Upper Hu/Middle 0.016 Domsch et al., 2007

OTU_1174 Penicillium Eurotiales Saprotroph Ho/Upper Hu/Middle 0.020 Domsch et al., 2007 Hu/Middle, OTU_13 Sorocybe Chaetothyriales Resin fungus Ho/Upper 0.021 Seifert et al., 2007 Bottom Hu/Middle, OTU_57 Sorocybe Chaetothyriales Resin fungus Ho/Upper 0.030 Seifert et al., 2007 Bottom Hu/Middle, OTU_852 Sorocybe Chaetothyriales Resin fungus Ho/Upper 0.026 Seifert et al., 2007 Bottom Endophyte; OTU_9 Trichoderma sp. Hypocreales Ho/Upper Hu/Middle 0.015 Domsch et al., 2007 Saprotroph Parasite of lichens or Ho/Upper, OTU_90 Nectriopsis Hypocreales Hu/Bottom Domsch et al., 2007 slime moulds Middle Entomoptaogen; OTU_138 Bionectria Hypocreales Ho/Upper Hu/Bottom Guesmi-Jouini et al., 2014 endophyte Endophyte; OTU_1087 Trichoderma Hypocreales Ho/Upper Hu/Middle Domsch et al., 2007 Saprotroph Moss-associate; Han et al., 2014; Stenroos et OTU_35* Hyalocypha sp. Helotiales Ho/Bottom 0.002 Cellulose decomposer al., 2010 Root associate; Hu/Middle, Smith & Read, 2008; Gross et OTU_38 Hymenoscyphus sp. Helotiales Ho/Upper 0.025 multiple functions Bottom al., 2012 Hu/Middle, OTU_515 Naemacyclus minor Helotiales Pathogen on pines Ho/Upper 0.015 Kistler & Merril, 1978 Bottom No match at genus OTU_1141* Helotiales Unknown Hu/Upper 0.001 ˗ level 63

Ericoid Hu/Upper, OTU_1187* mycorrhizal root- Helotiales Root-associate 0.001 Vohnik et al., 2012 Middle associate OTU_1286 Hyalopeziza sp. Helotiales Cellulose decomposer Ho/Middle Hu/Bottom 0.026 Han et al., 2014 Ericoid OTU_1330* mycorrhizal root- Helotiales Root-associate Hu/Upper 0.001 Vohnik et al., 2012 associate Ericoid OTU_20* mycorrhizal root- Helotiales Root-associate Hu/Upper 0.002 Vohnik et al., 2012 associate Alternaria OTU_45* Pleosporales Saprotroph Ho/Bottom 0.004 Domsch et al., 2007 alternata OTU_69* Cladosporium sp. Capnodiales Saprotroph Ho/Bottom 0.002 Domsch et al., 2007 Hu/Middle, OTU_1175* Cistella sp. Helotiales Cellulose decomposer 0.001 Han et al., 2014 Bottom Sarcoleotia OTU_6* Geoglossales Cellulose decomposer Ho/Bottom 0.002 Dennis, 1971 turficola Hu/All OTU_14* Oidiodendron sp. Onygenales Cellulose decomposer 0.001 Rice & Currah, 2005 depths Sarcoleotia OTU_1465* Geoglossales Cellulose decomposer Ho/Bottom 0.003 Dennis, 1971 turficola Leotiomycetes incertae Ho/Upper, OTU_77 Collophora Root pathogen Hu/Bottom 0.040 Kreyling et al., 2012 sedis Middle No match at genus Leotiomycetes incertae Ho/Upper, OTU_208 Unknown Hu/Bottom 0.038 Damm et al., 2010 level sedis Middle OTU_153* Troposporella Ascomycota incertae sedis Cellulose decomposer Ho/Bottom 0.005 Tsui & Berbee, 2010

OTU_132* Troposporella Ascomycota incertae sedis Cellulose decomposer Ho/Bottom 0.003 Tsui & Berbee, 2010 Parasitoid of OTU_33* Metapochonia sp. Ascomycota incertae sedis Ho/Bottom 0.001 Kepler et al., 2007 invertebrates No match at genus OTU_131* Ascomycota incertae sedis Unknown Ho/Bottom 0.004 ˗ level No match at genus OTU_72* Ascomycota incertae sedis Unknown Ho/Bottom 0.001 ˗ level No match at genus OTU_92* Ascomycota incertae sedis Unknown Ho/Bottom 0.004 ˗ level

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Yeast; labile carbon Ho/All Hu/Middle, OTU_128 Saccharomyces Saccharomycetales 0.047 Martini & Martini, 2011 consumer depths Bottom Yeast; labile carbon Ho/All Hu/Middle, OTU_16 Dipodascopsis Saccharomycetales 0.035 De Hoog & Smith, 2011 consumer depths Bottom Other fungal OTUs Hu/ Ectomycorrhizal root- OTU_7 Suillus Boletales Ho/Upper Middle, 0.030 Palm & Stewart, 1986 associate Bottom Hu/ Lignocellulose OTU_298 Jaapia Jaapiales Ho/Upper Middle, 0.028 Telleria et al., 2015 decomposer Bottom Hu/ Ectomycorrhizal root- OTU_349 Boletus Boletales Ho/Upper Middle, 0.042 Grand & Smith, 1971 associate Bottom Hu/ Ho/Upper, Gleason et al., 2004; Vélez et OTU_27 Chytrid Chytridiales Parasite; Saprotroph Middle, 0.014 Middle al., 2011 Bottom Hu/ OTU_85 Chytridium Chytridiales Algal parasite Ho/ Middle Middle, 0.010 Vélez et al., 2011 Bottom Hu/ OTU_249 Chytriomyces Chytridiales Chitin decomposer Ho/ Middle Middle, 0.030 Karling, 1946 Bottom Hu/ OTU_1466 Chytridium Chytridiales Algal parasite Ho/ Middle Middle, 0.043 Vélez et al., 2011 Bottom Ho/Upper, OTU_961 Rhopalomyces Zoopagales Nematode parasite Hu/Bottom 0.029 Barron, 1973 Middle Ho/Upper, OTU_14 Rhopalomyces Zoopagales Nematode parasite Hu/Bottom 0.040 Barron, 1973 Middle Ho/Middle, OTU_4* Antrodia Polyporales Brown rotter 0.002 Ortiz-Santana et al., 2013 Bottom Lignocellulose Ho/Middle, OTU_188* Hypochnicium sp. Polyporales 0.002 Eriksson & Ryvarden, 1976 decomposer Bottom Lignocellulose OTU_17* Hyphoderma sp. Polyporales Hu/Middle 0.016 Eriksson & Ryvarden, 1976 decomposer No match at genus Ho/All OTU_338 Neocallimastigomycota Unknown Hu/Bottom 0.040 ˗ level depths No match at genus Ho/All OTU_358 Neocallimastigomycota Unknown Hu/Bottom 0.020 ˗ level depths 65

Ectomycorrhizal root- OTU_28* Russula Russulales Ho/Upper 0.004 Fatto, 1999 associate Ectomycorrhizal root- OTU_2443* Lactarius Russulales Ho/Upper 0.002 Montoya et al., 1990 associate Graminoid root- Hu/Middle, OTU_984* Sebacina sp. Sebacinales 0.022 Kühdorf et al., 2014 associate Bottom Graminoid root- OTU_19* Sebacina sp. Sebacinales Hu/Middle 0.015 Kühdorf et al., 2014 associate Ectomycorrhizal root- OTU_1977* Cortinarius sp. Agaricales Ho/Middle 0.003 Peintner et al., 2002 associate Ectomycorrhizal Ectomycorrhizal root- OTU_161* Agaricales Ho/Middle 0.011 Peintner et al., 2002 root-associate associate Ectomycorrhizal root- Ho/Middle, OTU_4075* Cortinarius sp. Agaricales 0.013 Peintner et al., 2002 associate Bottom Ectomycorrhizal root- OTU_4323* Cortinarius sp. Agaricales Ho/Middle 0.025 Peintner et al., 2002 associate Root associate; Hu/Upper, Mosquera-Espinosa et al., OTU_1 Ceratobasidium sp. Cantharellales Ho/Upper 0.035 multiple functions Middle 2013 Ectomycorrhizal root- OTU_6 Craterellus Cantharellales Ho/Upper Hu/Upper 0.020 Matheny et al., 2010 associate No match at genus OTU_24 Cantharellales Unknown Ho/Upper Hu/Upper 0.034 ˗ level Lignocellulose Hu/Middle, OTU_3 Mycena Agaricales Ho/Upper 0.049 Worrall et al., 1997 decomposer Bottom Saprotroph; ericoid Clavaria OTU_16* Agaricales mycorrhizal root- Hu/Middle 0.014 Olariaga et al., 2015 sphagnicola associate Lycoperdon Lignocellulose OTU_128* Agaricales Hu/Middle 0.020 Jeppson et al., 2012 pyriforme decomposer Lignocellulose OTU_1440* Xeromphalina sp. Agaricales Ho/Bottom 0.023 Thormann & Rice, 2007 decomposer Labile carbon Hu/Middle, OTU_18 Mortierella Mortierellales Ho/upper 0.025 Domsch et al., 2007 decomposer Bottom Labile carbon Ho/Middle, OTU_43 Mortierella Mortierellales Hu/Middle 0.043 Domsch et al., 2007 decomposer Bottom Labile carbon Ho/Middle, OTU_48 Mortierella Mortierellales Hu/Middle 0.036 Domsch et al., 2007 decomposer Bottom Labile carbon OTU_4057 Mortierella Mortierellales Ho/Middle Hu/Middle 0.014 Domsch et al., 2007 decomposer

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No match at genus Ectomycorrhizal root- OTU_15* Thelephorales Ho/All depths 0.003 ˗ level associate Ectomycorrhizal root- OTU_45* Polyozellus Thelephorales Ho/All depths 0.018 Lee et al., 2000 associate Ectomycorrhizal root- Ho/Upper, OTU_546* Tomentellopsis Thelephorales 0.001 Kõljalg et al., 2002 associate Middle

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Figure 3.2 NMDS plot of the variation in community composition of Ascomycota (a) and other fungi (b) between hollows and hummocks of boreal peatlands.

Black and gray circles represent samples from hollow and hummock, respectively.

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Figure 3.3 NMDS plot of the variations in community composition of Ascomycota (a, c) and other fungal communities (b, d) across three depths of hollows (a, b) and hummocks (c, d).

Squares, triangles and circles represent samples from upper, middle and bottom horizons of hollows and hummocks.

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Within hummocks, the Ascomycota have large overlap in community composition in middle and bottom horizons (Figure 3.3c), where members of Chaetothyriales and yeasts are dominant (e.g. OTUs 13, 57 and 128), while the upper layer contains moderate relative abundances of ericoid mycorrhizal root-associated fungi (Table 3.2, Figure A.2). For other fungal communities, members of Cantharellales were significantly more abundant in the upper horizon (e.g. OTUs 1 and 6), while the middle and bottom horizons were distinguished by significantly higher ratios of fungi associated with graminoids and ectomycorrhizal root-associates (e.g. OTUs 984, 7, and 349), aquatic fungi belonging to Chytridiales and different groups of decomposers (OTUs 27, 298, 18). The bottom horizon also hosted high frequencies of fungi from Neocallimastigomycota (known members are anaerobic and flagellate) and Zoopagales (predators of invertebrates) (Table 3.2, Figure A.3).

3.4 Discussion

Samples from hollows contained more diverse and heterogeneous fungal communities in general than hummocks, which support my first hypotheis of a difference between hummock and hollow fungal communities, and the second hypothesis of a greater homogeneity in community composition of fungi in hummock microscale. However, the results of this metabarcoding study do not completely support my third hypothesis of reduced fungal richness and diversity with depth within each area. Rather, I identified an area within each of hollow and hummock micro-topology where fungal richness and diversity peak — the absolute depth of this optimal area differs between hollows and hummocks, but occurs at approximately the same position relative to the water table. The middle horizon in hollows might have been an area of overlap, bearing species from both upper and lower peat layers, and consequently expressing the highest richness and diversity as well as the most variable fungal community composition. In hummocks, however, the lowest peat layer sampled showed the highest richness, diversity and the most variable community composition of fungi. For instance, the abundances of aquatic and anaerobic fungi (e.g. OTUs 128, 1466, and 358) increased with depth within hummocks. The overall differences in richness, diversity, and community composition in

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hollows compared to hummocks is likely associated with a suite of variables such as water table position, but also vegetation community and litter quality (Clymo, 1986; Peltoniemi et al., 2009). Hollows contain much more saturated conditions closer to the surface than hummocks that would favour anaerobic and flagellated fungi (e.g. Neocallimastigomycota) as well as other species that normally grow under moist environments (e.g. Saccharomycetales) (Gruninger et al., 2014; Kurtzman et al., 2011). The presence of aquatic fungi such as members of Chytridiales or Zoopagales, whose prey may include rotifers (Barron, 1973), in the middle horizon of hollows with fairly high abundances, comparable to those in middle and bottom horizon of hummocks might suggest a fluctuating water table in these layers (McLaughlin & Webster, 2013) and a limited range of tolerances of these fungi to anaerobic conditions (Kirk et al., 2008). Together this suggests the potential effect of water table shaping the structure of fungal communities at the microscale of boreal peatlands.

Although the overall variation observed in the structure of both Ascomycota and other fungal communities between hollows and hummocks were more obvious at the order level, from a functional perspective, I observed ectomycorrhizal root-associates of trees (e.g. OTUs 28, 1977, 15) were more abundant in hollows, and ericoid and graminoid root-associates (e.g. OTUs 1187 and 984, respectively) had greater prevalence in hummocks. The higher frequency of ericoid and graminoid root-associates in hummocks is attributed to higher abundance of their hosts (e.g. sedges and ericaceous shrubs) in this area (McLaughlin & Webster, 2013). However, hollows are known to have higher concentrations of porewater phosphate and ammonium than hummocks (Courtwright & Findlay, 2011), which might explain higher frequencies of ectomycorrhizal root- associates in hollows, as the positive effects of soil phosphorus and nitrogen on the distribution and function of ectomycorrhizal root-associated fungi have been previously reported (Rineaua et al., 2015; Rosling & Rosenstock, 2008).

In hollows, except for Ascomycota whose richness reduced by depth, the overall richness and diversity of fungi were higher in the middle horizon compared to other layers. In boreal peatlands, peat stratifications are created according to the water table and oxygen availability as well as peat accumulation across a depth gradient (Frolking et al., 2001; 71

Koretsky et al., 2006) giving rise to dominance of fungi that can tolerate anoxic conditions (e.g. OTUs 128, 338 and 43) and are adapted to limited sources of energy in lower peat layers (Domsch et al., 2007; Kurtzman, 2011). In contrast, in the upper horizon, the majority of fungi are those that prefer aerobic conditions (e.g. OTUs 349, 6 and 28) (Boucher & Malajczuk, 1990). In addition to the demonstrated significant effects of water table on the structure of soil fungal communities (Peltoniemi et al., 2009; Trinder et al., 2008), there seem to be other factors creating this pattern. The more diverse and species-rich decomposer community in hollows than hummocks is possibly driven by litter quality as well. Usually, microbial activities are higher in hollows due to their more decomposable Sphagnum mosses compared to hummocks (Clymo, 1986; Courtwright & Findlay, 2011). The low moisture normally observed in the upper peat layer of hummock (Clymo, 1986; McLaughlin & Webster, 2013) could be limiting fungal diversity in this habitat. In addition, the appearance of fungal root-associates of graminoids and trees (e.g. OTUs 7 and 984) in the middle and bottom horizons suggests that plants and the depth of their roots are probably other important drivers in the organization of fungal communities in boreal peatlands (Bonfante & Genre, 2010; Natel & Neumann, 1992).

Overall a wide range of taxa belonging to most major fungal groups was detected in both hollows and hummocks. However, no OTUs related to Glomeromycota were identified from either hollows or hummocks, despite the fact that especially hummocks have sedges; these have been reported to have symbiotic associations with arbuscular mycorrhizal (AM) fungi of the Glomeromycota (Miller et al., 1997; Turner et al., 2000), but are also reported to be frequently non-mycorrhizal (Smith & Read, 2008). Seven of twenty-three species of Carex examined by Miler et al. (1997) were found to be non- mycorrhizal and, in mycorrhizal species, infection rates were lowest in nutrient-poor and acidic conditions. The absence of this group of fungi in my metabarcoding results could be due to their low abundance in the low nutrient, acidic (pH~ 4.4) sampling site (Webster & McLaughlin, 2010). Arbuscular mycorrhizal fungi are known to be more abundant in nutrient-rich fens of boreal peatlands (Wolfe et al., 2007).

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Although hollows contained more diverse, and variable fungal communities than hummocks in general, the richness, diversity and heterogeneity of fungal community in the optimal zones of these two micro-topographies were comparable and many fungi detected in the middle horizon of hollows were also identified in the bottom layer of hummocks. While the effect of water table on the distribution of fungi in hollows and hummocks has been previously reported (Peltoniemi et al., 2009; Trinder et al., 2008), this study is the first to characterize the zone of high fungal diversity in hollows and hummocks of boreal peatlands. I suggest that the different location of this area of high fungal diversity in hollows compared to hummocks is a driving factor of variation in fungal community composition between these two microscales. It is expected that following water table drawdown under future climate change scenarios, these optimum zones would be most affected in boreal peatlands. It is possible that under future climate scenarios and water table drawdown, these high diversity regions will shrink, or shift downwards. However, in either circumstance, other factors such as peat compaction or resource limitations due to more decomposed material in lower peat layers (Frolking et al., 2001) might additionally affect fungal diversity. Regardless, a better understanding of community composition and functional diversity of fungi will help us to improve models and the accuracy of predictions about boreal carbon sinks, the diversity and distribution of decomposer fungal communities in boreal peatlands, and the response of these ecosystems to climate change.

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Chapter 4 4 Experimental climate change modifies degradative succession in boreal peatlands fungal communities

4.1 Introduction

Boreal peatlands are wetland ecosystems that contain 10-20% of the global terrestrial carbon (Clymo et al., 1998) despite representing just 3% of the earth surface (Gorham, 1991). This huge amount of carbon is stored due to the imbalance between a low rate of photosynthesis (uptake of CO2) by plants and an even lower rate of decomposition

(release of CO2) through microorganisms (Moore & Basiliko, 2006). Northern peatlands are at latitudes where the greatest global warming is predicted to happen (IPCC, 2014), making them highly susceptible to climate change that is subject to anthropogenic and natural forcing (Frolking & Roulet, 2007). Increases in air and soil temperature, elevated atmospheric CO2 levels, and lowered water table conditions are all environmental factors expected to be experienced by boreal peatlands in the future (IPCC, 2014). Whether and how these changes will affect the balance between ecosystem productivity and carbon sequestration, and ecosystem respiration and carbon release remains unknown, but a key player in the overall equation will be the response of biotic communities that regulate these processes.

Peatlands are renowned for highly diverse microbial communities of broad metabolic diversity and enzymatic activities (Williams & Crawford, 1983) that can influence each other through competitive interactions, and also be affected by environmental conditions (Andersen et al., 2013). Decomposition of organic matter and nutrient cycling processes in peatlands are performed by different groups of Fungi (such as saprotrophs, ectomycorrhizal fungi, endomycorrhizal fungi and ericoid mycorrhizal fungi), Archaea and Bacteria (Williams & Crawford, 1983; Andersen et al., 2013). Possible changes in fungal community composition due to climate change that favour saprotrophic decomposers at the expense of other microbial groups could have a dramatic and crucial impact on the carbon sequestration pattern in boreal peatlands, potentially releasing

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previously sequestered carbon, and exacerbating climate change (Treseder et al., 2016). Increases in temperature could directly increase the abundance and activity of competitive fungal species favoured by warm conditions, and indirectly influence fungal communities through a lowering of the water table providing more habitat for aerobic fungi, in particular saprotrophs, that can accelerate the rate of decomposition especially decomposition of deep peat. Changes in climate may also indirectly influence fungal communities via changes in plant community structure through mycorrhizal associations and changes in the chemical and physical environment for decomposition processes

(Allison & Treseder, 2011). For instance, plant growth under increased CO2 concentrations may lead to increased lignin: nitrogen ratios in litter, which could consequently shift fungal community composition, and potentially slow rates of decomposition (Kasurinen et al., 2007).

The significance of fungal communities has been repeatedly pointed out in many ecosystem processes such as carbon turnover and sequestration in peatlands that can significantly affect global climate change (Trinder et al., 2008; Yuste et al., 2011). Several studies have addressed the individual effects of climate change factors (mainly increased temperature, elevated atmospheric CO2 and lowered water table position associated with predicted climate change) on fungal communities and fungal activities (Bradford et al., 2008; Drigo et al., 2008; Peltoniemi et al., 2009). However, to the best of my knowledge, no studies focused on combined effect of these three factors on the composition of fungal communities inhabiting boreal peatlands, suggesting that our knowledge of the interactions among these multiple drivers that are expected to play simultaneously under climate change scenarios is very limited. Furthermore, previous studies based on laboratory cultivation (e.g. Thormann et al., 1999), or DNA sequencing methods (Thormann et al., 2004; Peltoniemi et al., 2009), likely underrepresent many fungal groups as they are difficult to culture from soil or other complex substrates (Thorn et al., 1996), or suffered from problems of poor sequence coverage or lacked details about the structure of fungal communities in these important ecosystems. As such, the results of many previous studies of fungal communities in boreal peatlands suggest that various groups of fungi and their functions have been under-represented, possibly due to

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the choice of inappropriate genetic markers or improper data analyses (Elliott et al., 2015; Tedersoo et al., 2016).

Here, I take advantage of a manipulative experimental design in which intact peat cores were subjected to artificially altered temperature, carbon dioxide concentration, and hydrology in a full factorial design in a state-of-the-art greenhouse at Western (the Biotron). My study was performed as part of a larger experimental project described by Dieleman et al. (2015, 2016) and Lindo (2015). Work by Dieleman et al. focused mainly on the climate-driven changes in aboveground plant communities (Dieleman et al., 2015), rate of decomposition, and porewater dissolved organic carbon (Dieleman et al., 2016). Lindo (2015) focused on changes in the body size of soil microarthropods under warming treatments. Results from these studies demonstrate a shift in aboveground and belowground communities in favour of graminoids and ericaceous shrubs, and small- bodied faunal species, alongside increased rates of decomposition and carbon loss through dissolved organic carbon under warming treatments. None of these studies focused on the important changes in fungal communities. I use two newly developed rDNA primer sets, one that directly targets the phylum Ascomycota and another that recovers all other fungal phyla (Asemaninejad et al., 2016), to examine the effects of experimental climate change on fungal communities from a well-characterized nutrient- poor boreal peatland. Specifically I evaluated the hypothesis that changes in temperature, atmospheric CO2 concentration, and water table position associated with future climate change will result in a shift in fungal community composition in the favour of saprotrophs, and correspondingly, the pattern of degradative succession.

4.2 Materials and Methods

4.2.1 Experimental Design

In August 2012, 100 cylindrical intact peat cores (each approximately 25 liters in volume and 40 cm depth) complete with aboveground moss, herbaceous and shrubby vegetation were collected from a well-characterized nutrient poor fen near White River, Ontario, Canada (for site description, see Webster & McLaughlin, 2010). Each mesocosm (peat core) was transferred into a five gallon pail with a drainage port near the base fitted with 84

a clear hose held vertically to assess the internal water level. All mesocosms were transported to the Western University’s Biotron Environmental Climate Change Research

Centre in London, Ontario, where they acclimated at ambient temperature and CO2 conditions for three months, during which the aboveground vegetation was characterized (Dieleman et al., 2015). Prior to experimental deployment, 12 mesocosms were destructively sampled to obtain peat from the top 5 cm of each core. This peat was homogenized, subsampled for moisture content, and subsequently used to fill 252 nylon mesh litterbags (10 cm × 7 cm with 1 mm mesh) each with approximately 25 g (fresh weight) of composited and root-free peat. Litterbags were placed in triplicate 10-15 cm deep in each mesocosm for removal of one litterbag every 4 months (at 4, 8, and 12 months) for fungal DNA extraction.

Experimental deployment (Dec. 2012) placed the 84 intact peat mesocosms into full factorial treatments of water table (lowered, saturated), atmospheric CO2 (ambient, ~430 ppm; elevated, 750 ppm), and temperature (ambient, ambient + 4 °C and ambient + 8 °C) across six experimental chambers as described in Dieleman et al. (2015). Experimental chambers are state-of-the-art, environmentally controlled, Biosafety Level 2 greenhouses; combinations of each temperature and CO2 conditions were controlled and regulated for each chamber using a fully automated system (ARGUS Control Systems Ltd., White Rock, BC, Canada). Winter and summer growing conditions differed with respect to temperature regimes (see Lindo (2015) for full details). Briefly, since temperatures below 10 °C could not be reliably managed by the infrastructure in the experimental chambers, from November to April, the ambient temperature was static at the average temperature of the growing season in the sample location (11.5 °C); elevated temperature treatments were set at 15.5 °C and 19.5 °C. From May to October the ambient temperature was set to a fluctuating average daily temperature (per hour) based on past five years in London, Ontario; the average summer temperature for the ambient treatment was 16.9 °C. Elevated temperature offsets of + 4 °C and + 8 °C followed accordingly. Temperatures chosen are considered reasonable for high latitudes and boreal regions of Canada as future elevated temperatures of + 8 - +10 °C have been estimated by the end of 21st century (IPCC, 2014). Fourteen mesocosms were placed within each of six experimental

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greenhouse chambers; seven replicates under a saturated water table condition where the water table was 5 cm below the peat surface, and seven replicates under a lowered water table condition with the water table 25 cm below the peat surface. Simulated rain events were applied twice a week using 750 ml of pH adjusted Rudolph’s solution as appropriate for Sphagnum mosses (Faubert & Rochefort, 2002). Water (reverse osmosis) was also added daily below the surface moss layer of each mesocosm to keep the water table condition constant over the year.

Due to logistical reasons, replication at the chamber level was not possible, although every consideration to minimize within or across chamber effects was taken. Specifically, the six experimental chambers were flanked by non-experimental greenhouse units to prevent any climate or light edge effect. During the experiment, every six months, the specific experimental conditions of a chamber were changed and all mesocosms moved to new replicate conditions to remove any potential chamber effects. Every month, within each chamber, all mesocosms were randomly relocated to prevent any possible within- chamber microclimate effects.

4.2.2 DNA Extraction

Subsamples of peat used to create litterbags (time point T0 litters), and from litterbags collected after 4, 8 and 12 months of experimental treatment were freeze dried for 48 hours, homogenized, and an aliquot immersed in liquid nitrogen and ground to a fine powder with mortar and pestle. Total genomic DNA was extracted from 25 mg (dwt) of each sample of peat moss from each sample using a Zymo Soil DNA isolation kit according to manufacturer’s instructions (Zymo Research Corporation). To measure the quality of DNA extraction, genomic DNA was quantified by nanodrop (Nanodrop 2000, Thermo Scientific), then to confirm DNA isolation was successful, 2 μL of DNA extract was run on a 1.5 % agarose gel in 1X TAE buffer and then stored at -20 °C in aliquots until PCR amplification.

4.2.3 PCR Protocol

PCR was performed using two sets of primers, LSU200A-F (AACKGCGAGTGAAGCRGYA)/LSU476A-R (CSATCACTSTACTTGTKCGC) to 86

capture Ascomycota and LSU200-F (AACKGCGAGTGAAGMGGGA)/LSU481-R (TCTTTCCCTCACGGTACTTG) to amplify all other fungi (mostly consisting of Basidiomycota, Chytridiomycota and Zygomycota). These primers amplify an approximately 300 bp region of the rDNA LSU, and outperform some of the existing ITS primers by providing a broader range of fungal biodiversity, but at the expense of a higher range of non-target taxa (Asemaninejad et al., 2016). Illumina sequencing adapters and distinctive 8-base barcode sequences were attached to the 5’ end of forward and reverse primers, as fully described in Asemaninejad et al. (2016). PCR reactions were prepared in a total volume of 25 μL, with 1 μL of template DNA (20 ng), 12.5 μL of Toughmix (Quanta Biosciences), and 1.25 μL each of forward and reverse primers (5 μM). Reactions were run in a thermocycler (T300 Thermocycler, Biometra) with initial denaturation of 94 °C for 120 s, followed by 29 cycles of denaturation at 94 °C for 30 s, annealing at 62 °C for 30 s (for Ascomycota primers, annealing temperature was 55 °C in first cycle, and 62 °C for the rest of cycles), elongation at 72 °C for 18 sec (Asemaninejad et al., 2016). A Qubit fluorimeter with the dsDNA HS kit (Life Technologies) was used to evaluate the concentration of DNA that can be used as a basis to normalize PCR products. The normalized PCR products were submitted to the London Regional Genomics Centre (Robarts Research Institute, London, Canada) for paired-end sequencing in an Illumina MiSeq sequencer using the 2 x 300 bp V3 chemistry.

4.2.4 Bioinformatic Analysis

For each primer set, two MiSeq runs were performed generating four multiplexed runs. Bioinformatic analyses were done separately for each run and raw FASTQ data were processed through a custom MiSeq pipeline (https://github.com/ggloor/miseq_bin/tree/master). Before running the analysis, the sequences of LSU200-F/LSU481-R and LSU200A-F/LSU476A-R primers were manually inserted in the primer_sequence.txt file. Sequenced amplicons were checked for quality using a log-odds scale. All sequences with errors including incomplete match in primer or barcode sequence, ambiguous bases in forward or reverse read, and ambiguous bases in overlapped region (Q score <10) were discarded. The rest of the sequences were distinguished by the unique combination of forward and reverse barcodes in each 87

sequence. These sequences were pairwise aligned using PANDAseq (https://github.com/neufeld/pandaseq) (Masella et al., 2012) with a minimum overlap length of 30 nucleotides. Pre-clustering was performed to merge sequences containing identical reads to get ISUs (identical sequence units) and the UCHIME algorithm was used to check for chimeras (Edgar et al., 2011). Chimeras were removed and then ISUs were clustered into OTUs (operational taxonomic groups) at 97% similarity using the UCLUST clustering algorithm within USEARCH v7.0.1090 (Edgar, 2010). Only sequence reads with a total abundance of more than 0.1% in each sample were kept in the sample-mapped OTU table. The seed sequences were subjected to online RDP Classifier (Wang et al., 2007) using the Fungal LSU Training set 11 as a reference database (https://rdp.cme.msu.edu/classifier/classifier.jsp). Since many real fungi cannot be confidently identified by RDP classifier due to the lack of reference sequence data (Lan et al., 2012), all the sequences identified with 50% confidence score at the order level were kept for downstream statistical analyses. Identification was re-performed for the indicator OTUs, identified with confidence score of less than 80% at the genus level, in each sampling point using BLAST in order to attain clearer identification of the under- represented taxa within the RDP database. Sequences have been deposited in the ENA website (European Nucleotide Archive) under accession number PRJEB14309.

4.2.5 Statistical Analyses

The data were rarified (Kenneth et al., 1975) and richness (S = number of observed OTUs) and diversity (H’ = Shannon index) based on final target OTUs were calculated for each sample and analysed using a three-way, repeated measures analysis of variance in Statistica 7. Since high-throughput sequencing DNA is extracted from a small proportion of an environmental sample and the amplified DNA is also only a portion of DNA present in the sample, OTU-based multivariate community-level analyses need to account for a higher than expected rate of randomness compared to other types of community data (Aitchison & Egozcue, 2005). We transformed the raw OTU sequence data using a centered log ratio where the log2 of each OTU value is subtracted from the log2 of the geometric mean of all OTUs present in the sample (Aitchison & Egozcue,

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2005). This transformation maintains the 1:1 conformity between the original OTU counts and keeps the data on a common scale (Lovell et al., 2015).

At the community level, a compositional PCA biplot analysis was run on the reduced data set (containing OTUs with ratio of at least 1% in the samples) using the “compositions” package (van den Boogaart & Tolosana-Delgado, 2008) in R (version 3.2.3). To detect the drivers of the variation explained by the first two PCA components, PC axis scores for the first two principal components were analysed through ANOVA for the primary effect of temperature, water table position and atmospheric CO2 concentration. To measure the similarity of fungal community composition within and between treatments, and to further interpret and clarify the compositional biplot, clustering analysis was done using Ward.D2 method. To test the effect of each of three environmental factors (temperature, CO2, water table) on the variation observed in fungal community composition under different treatments, a Microbiome Regression-based Kernel Association Test (MiRKAT) was applied using the “ALDEx2” and “MiRKAT” packages in R (version 3.2.3). ALDEx2 was used to transform the data (centered log- ratio), while MiRKAT was used to elucidate which variable is the main driver of change in fungal community composition (Zhao et al., 2015). A subsequent ANOVA-like differential expression procedure (ALDEx) developed by Fernandes et al. (2013) was performed in R (version 3.2.3) using the ALDEx2 package to identify the OTUs with greater variance between treatments than within treatments (i.e. the OTUs that are truly responding to the treatment not to the sample variation). A Benjamini-Hochberg False Discovery Rate (FDR) correction was used to correct raw p-values under multiple comparisons as recommended for large datasets (Benjamini & Hochberg, 1995).

4.3 Results

4.3.1 Initial Fungal Community Composition before Treatment

A total of 4 258 834 sequences were produced using LSU200A-F/LSU476A-R primers targeting the Ascomycota (1 704 160 sequences) and LSU200-F/LSU481-R targeting the remaining fungal community (2 554 674 sequences) in 12 replicates of initial (T0) litter used to make the litterbags. After removal of non-target reads in each data set (85 208 89

and 740 855 reads, respectively), the remaining target sequences generated 79 genera in 47 families, 27 orders, and 7 classes from Ascomycota, and 101 genera in 58 families, 32 orders, and 11 classes from other fungal community. Among Ascomycota members of Leotiomycetes, Sordariomycetes and Eurotiomycetes showed the highest relative frequencies (see Appendix A, Figure A.4a) while looking at other fungal community, members of Agaricomycetes, unclassified fungi belonging to Zygomycota, and Chytridiomycetes had the most reads compared to other taxa (See Appendix A, Figure A.4b). The initial richness (the number of observed OTUs) and OTU diversity in 12 replicates of starting material (T0) showed no obvious differences for either Ascomycota (average S = 171.7 ± 2.87 SE; average H’ = 3.24 ± 0.06 SE) or other fungal group (average S = 452.35 ± 2.59 SE; average H’ = 3.59 ± 0.10 SE).

4.3.2 Fungal Community Composition in Litterbags

Primers targeting Ascomycota (LSU200A-F/LSU476A-R) generated a total of 7 913 141 sequences and primers targeting other fungi (LSU200-F/LSU481-R) produced a total of 14 998 839 sequences from 108 litterbags resulting in 7 359 221 and 10 499 187 target reads, respectively, that represented 93 genera in 51 families, 27 orders and 7 classes of Ascomycota, and 94 genera in 58 families, 32 orders and 11 classes of other fungi. Among all OTUs identified with at least 50% confidence score at the order level, the most frequent Ascomycetous OTUs identified in litterbags collected after 4 months of treatment were still members of Leotiomycetes, Sordariomycetes and Eurotiomycetes. However, there was a noticeable increase in the relative frequency of Sordariomycetes after 8 and 12 months. Among the sub-dominant groups, noticeable increases in Saccharomycetes and Pezizomycetes were observed after 8 and12 months, respectively (see Appendix A, Figure A.4a). Among the other fungi, the members of Agaricomycetes continued to be the most dominant group, increasing in relative frequency with time. Zygomycota generally declined in relative frequency with time, while the Chytridiomycota showed a noticeable increase after 4 months, but declined after 8 and 12 months (see Appendix A, Figure A.4b). Monoblepharidomycetes, which was a minor group at the beginning of the experiment, increased in relative frequency over the course

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of the experiment. Microbotryomycetes were also observed as a sub-dominant taxon after 4 and 8 months experimental treatment.

4.3.3 Treatment Effects on Fungal Community Composition in Litterbags

While time was the main factor dictating changes in the fungal communities over the experiment, several analyses revealed the fungal community responded to the experimental treatments. Ascomycota richness demonstrated an overall increase over time, but was significantly decreased under elevated temperature treatments such that the lowest Ascomycota richness was observed under the + 8 °C treatments after 12 months

(Time × Temperature F4,48 = 4.00, P = 0.007) (Figure 4.1a). The effect of CO2 treatment or hydrological changes on Ascomycota richness was not significant. Ascomycota diversity (Shannon’s H’) was not significantly affected by any of the aforementioned environmental factors but increased over time (Time F2,48 = 6.29, P = 0.004) (Figure

4.1b). OTU richness of the other fungi decreased strongly over time (Time F2,48 = 47.62, P < 0.001), with the largest decrease occurring in the + 8 °C treatments after 12 months

(Time × Temperature F4,48 = 5.53, P = 0.001) (Figure 4.1c). Fungal diversity was also significantly reduced through time (Time F2,48 = 19.93, P < 0.001) and highly affected by temperature increases of + 8 °C (Temperature F2,48 = 7.26, P = 0.001) (Figure 4.1d), however, there was no significant effect of lowered water table condition or elevated atmospheric CO2 on fungal diversity.

A compositional biplot PCA for the Ascomycota dataset explained 35.4% of the variance in the first two axes (PC1 = 20.9%, PC2 = 14.5%), while 39.6% of the variance in the other fungi was explained by the the first two axes (PC1 = 23.3%, PC2 = 16.3%) in the second compositional biplot PCA (Fig. 2a and 2b). The first PC axis for the Ascomycota was significantly related to sampling time (F2,102 = 412.6, P < 0.001) and temperature

(F2,102 = 16.1, P < 0.001), while the second axis was significantly related to water table

(F1,102 = 29.4, P < 0.001) and temperature (F2,102 = 7.1, P = 0.001); positive PC1 scores are related to litterbags collected from + 8 °C , and positive PC2 scores are associated with litterbags from saturated treatments (Figure 4.2a). Similarly, the first axis of the

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PCA biplot for other fungi was significantly related to sampling time (F2,102 = 926.3, P <

0.001) and temperature (F2,102 = 108.58, P < 0.001), while the second axis was significantly related to water table treatments (F1,102 = 129.1, P < 0.001); positive PC1 scores are related to litterbags collected from + 8 °C, and positive PC2 scores are associated with litterbags collected from saturated treatments (Figure 4.2b). The results of clustering analysis for both taxonomic groups showed similar patterns of community compositional changes through time where litterbag samples collected after 4 months were similar to each other and dissimilar to litterbag samples collected after 8 and 12 month. The results of the Microbiome Regression-based Kernel Association Test (MiRKAT) were similar for both the Ascomycota and the other fungal group.

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Figure 4.1 Changes in the richness (number of observed OTUs) of Ascomycota (a) and other fungi (c); and Shannon’s diversity (H’) of Ascomycota (b) and other fungi (d) under elevated temperature treatments from April 2013 (4 months) to December 2014 (12 months) under experimental climate change.

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While there were no significant effects of CO2 concentration (P = 0.07 Ascomycota; P = 0.09 other fungi) on the variation in genus-level fungal community composition, both water table and temperature treatments led to compositional changes through time (all P- values < 0.001).

Among the Ascomycetous OTUs identified with fairly high confidence at the genus level (with coverage score of 100% and identity score of at least 96%), ALDEx revealed six OTUs that were significantly affected by hydrology, and six OTUs significantly affected by temperature treatments (Table 4.1). Specifically, four OTUs (OTU 1 closely related to Barrenia panicia, OTU 412 identified as Penicillium, OTU 64 closely related to an endophyte, and OTU 157 closely related to Dactylaria sp.) increased greatly under drier, low water table conditions, while two OTUs (OTU 19 closely related to Dipodascopsis, and OTU 49 closely related to an endophyte) had noticeably higher proportional abundance under saturated conditions. Examining the temperature treatment effects closer, OTUs 22, 13 and 33 related to Neofabraea, an endophyte and Mitrula, respectively, increased in proportional abundance with increased temperature treatment of + 8 °C, while OTUs 48 and 63 (closely related to an ericoid mycorrhizal root fungus and an entomopathogen, respectively) demonstrated increased ratios under + 4 °C temperatures. OTU 5, closely related to Hyaloscypha, was more abundant under ambient treatments. One OTU (OTU 196), identified as an ericoid mycorrhizal root fungus, responded to both hydrology and temperature; it greatly increased under lowered water table and + 8 °C conditions.

For the other fungi, five OTUs were significantly affected by hydrology and three OTUs were significantly affected by temperature treatments (Table 4.1). OTU 0 (closely related to Mycena), OTU 159 (identified as Rhizophlyctis rosea), and OTU 153 (closely related to Phaffia) increased under lower water table conditions, while OTU 1 (related to Hypochnicium) and OTU 241 (closely related to Mesochytrium) increased in abundance under saturated water table conditions.

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Figure 4.2 PCA biplot of Ascomycota (a) and other fungi (b) under increased temperature, elevated atmospheric CO2 and altered water table conditions.

In both (a) and (b), PCA axis 1 is driven by time and temperature treatments. PCA axis 2 in (a) is driven by temperature and water table, while water table only is the driver of

PCA axis 2 in (b). The effects of CO2 treatments on fungal communities were not significant. The OTUs in both biplots are representatives of the group of OTUs, identified through ALDEx tests that were identified as responding to experimental climate change treatments. The black dots in both (a) and (b), represent other OTUs present in peat samples.

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Table 4.1 Indicator taxa significantly affected by hydrology and temperature treatments based on ALDEx results.

Effect size for water table treatments are positive values when indicating a positive effect of increased water table position, and negative when indicating a positive effect of lowered water table. Effect size for temperature treatments shows where a particular temperature treatment had a positive effect (increased ratio) of a particular fungus. The main or interactive effect of CO2 treatment with other factors on fungal community composition was not significant. OTU numbers are shown with their closest known identity based on BLAST and RDP classifier; functional designations are based on the literature where known.

Hydrology effect Temperature effect

Ascomycetous Closest known identity Function* Effect size P-value Effect size/ temp P-value OTUs Ericoid mycorrhizal root OTU 196 Root-associate -0.60 0.023 0.65/ +8°C 0.003 fungus OTU 1 Barrenia panicia Root-associate -0.50 0.019 - Not sig OTU 412 Penicillium sp Saprotroph -0.50 0.012 - Not sig OTU 22 Neofabraea Pathogen - Not sig 0.59/ +8°C 0.001 Yeast; labile carbon OTU 19 Dipodascopsis 0.56 0.039 - Not sig consumer OTU 64 Fungal endophyte Endophyte -0.54 0.046 - Not sig OTU 13 Fungal endophyte Endophyte - Not sig 0.76/ +8°C <0.001 OTU 63 Pochonia bulbillosa Entomopathogen - Not sig 0.50/+4°C 0.010

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Ericoid mycorrhizal root OTU 48 Root-associate - Not sig 0.63/+4°C 0.001 fungus OTU 49 Fungal endophyte Endophyte 0.56 0.002 - Not sig OTU 33 Mitrula Cellulose decomposer - Not sig 0.59/ +8°C 0.037 OTU 157 Dactylaria sp Cellulose decomposer -0.50 0.002 - Not sig Moss-associate; OTU 5 Hyaloscypha - Not sig 0.66/ ambient 0.013 saprotroph Other fungal

OTUs Lignocellulose OTU 0 Mycena -0.63 <0.001 - Not sig decomposer OTU 159 Rhizophlyctis rosea Cellulose decomposer -0.59 <0.001 - Not sig Yeast; labile carbon OTU 153 Phaffia -0.61 0.004 - Not sig decomposer Lignocellulose OTU 1 Hypochnicium 0.57 0.039 - Not sig decomposer Lignocellulose OTU 143 Globulicium -0.50 0.001 0.61/ +8°C 0.003 decomposer OTU 241 Mesochytrium Parasite on green alga 0.69 <0.001 - Not sig Lignocellulose OTU 873 Polyporus 0.51 0.011 0.59/ +8°C 0.019 decomposer Lignocellulose OTU 3173 Mycena -0.50 0.002 0.55 / +8°C 0.013 decomposer OTU 81 Galerina Cellulose decomposer -0.50 0.020 0.57/ +8°C 0.021 97

Root associate; OTU 3764 Ceratobasidium - Not sig 0.51/ +8°C 0.002 multiple functions Pollen and chitin OTU 3552 Rhizidium phycophilum - Not sig 0.57/ ambient 0.001 decomposer Labile carbon OTU 106 Mortierella - Not sig 0.55/ ambient <0.001 decomposer *References for the functions can be found in Appendix A, Table A.4.

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Examining the temperature treatment effects closer for this group, OTU 3764 (related to Ceratobasidium) increased under temperatures elevated by + 8 °C, while OTUs 3552 and 106 (related to Rhizidium phycophilum and Mortierella, respectively) were more abundant in ambient treatments. Four OTUs responded to both hydrology and temperature; OTU 143, 3173, and 81 (identified as Globulicium, and as close to Mycena and Galerina, respectively) demonstrated noticeable increases under lowered water table and + 8 °C treatments. Increased temperature of + 8 °C was also favourable for OTU 873 (closely related to Polyporus) but only under saturated water table conditions, however, it was more strongly affected by temperature than hydrology. The overall effect of hydrology was stronger at 4 months, more powerfully affecting other fungi than Ascomycota, while the temperature effect noticeably increased over time.

4.4 Discussion

Temperature, hydrology and litter quality are usually considered as dominant controls on fungal communities in peatlands that can significantly alter carbon dynamics (Belyea, 1996; Janssens & Davidson, 2006; Wang et al., 2015). At the same time, temperature, moisture availability, and litter quality associated with plant community shifts are anticipated to change under future climate change. Here I show that both temperature and hydrological changes are drivers of the shift in fungal community composition but act in conjunction with a temporal sequence of functional groups and the degradative succession observed.

There is an established relationship between chemical changes in substrate, and fungal community composition and degradative succession (Osono, 2005). Degradative succession describes the sequence of change in detrital communities as organic matter is broken down; a progressive change in soil organic carbon quality associated with physical, chemical and biological processing. Easily accessible, soluble carbons (organic acids, sugars), and some proteins are the first products of decomposition made available to microbes from litter leaching, followed by a proportional increase in the availability of cellulose and hemicellulose. As detrital substrates continue to be broken down, persistent, recalcitrant, and structural compounds such as lignins and polyphenols become dominant 99

(Adair et al., 2008). During this ‘ontogeny’ of detritus (Moore et al., 2004) low molecular weight compounds become more recalcitrant over degradative succession as labile components are released back into the pool. My study demonstrates two main successional patterns over time. First, there was an increase in Ascomycota coinciding with a general decrease in other fungal richness and diversity, and second, there was a functional group change in community composition matching degradative succession. Part of the decline in other fungal richness and diversity is undoubtedly due to exclusion of immigration of new taxa to mesocosms incubated in the Biotron, which is a biohazard containment level 2 facility, and/or possibly due to unfavourable environmental conditions during our experiment for particular fungi.

In contrast, Ascomycota did not show the same trend, suggesting experimental conditions were not detrimental to all fungi. Also this pattern of decline is not observed after 4 months where both richness and diversity of the other fungal group were higher under increased temperature treatments than under ambient conditions, and are comparable to those at the beginning of the experiment (T0). I believe that the overall decline in other fungal richness and diversity after 4 months is more in line with resource exploitation (Dieleman et al., 2016) in the litterbags by the decomposer community, and that this limitation is mostly placed on the other fungal group compared to the Ascomycota. For instance, while potential labile carbon decomposers (e.g. OTU 106 and OTU 153) were highly abundant at the starting point of the experiment, the majority of cellulose decomposers (e.g. OTUs classified as Dactylaria, Mycena and Galerina, etc.), noticeably increased at 8 months, followed by potential late colonizers and lignin decomposers at 12 months (e.g. OTUs classified as Polyporus and Globulicium).

My study further demonstrates how this successional change in the fungal species composition during decomposition of Sphagnum litter is modified by climate change conditions. Overall fungal richness (both Ascomycota and other fungi) was negatively affected by elevated temperatures of +8 °C, an observation consistent with several other studies (Talley et al., 2002; Blankinship et al., 2011; Morgado et al., 2015). However, several groups benefitted from elevated temperatures over the short-term, in particular some endophytes and mycorrhizal root-associated fungi in Ascomycetous group (e.g. 100

OTU 13 and OTU 196), and saprotrophs (e.g. OTU 33, OTU 3173, etc.) in both fungal groups. At the community level, temperature was identified as the bigger driver of the shift in fungal community composition, mostly emerging at 8 and 12 months. Often the direct effects of fungi on ecosystem processes under increased temperature are elusive; primary responses to warming can be variable as different functional groups of microbial communities may respond physiologically to temperature differently (Allison & Treseder, 2008; Allison et al., 2010). The short-term positive response of saprotrophs under warmer conditions could be due to an initial release from cold limitation in boreal systems (Blankinship et al., 2011). In particular, the increased ratio of cellulose and lignin decomposers between three temperature treatments is obvious, possibly reflecting optimal condition for the growth of saprotrophic fungi (Rousk & Bååth, 2011). While labile carbon decomposers were highly abundant under ambient conditions at the starting point of the experiment, temperature enhancement of +8 °C expanded the proportional abundances of the majority of cellulose decomposers at 8 months followed by increased ratios of lignin decomposers at 12 months.

The occurrence of compositional and functional shifts within the saprotrophic community was also correlated to changes in hydrological status, in particular lowered water table condition. Despite the fact that I did not see the significant effect of hydrological changes on fungal richness or diversity in either fungal group, hydrology drove the secondary PCA axis for both groups with the largest effect at 4 months (e.g. OTUs 159, 153 and 241). Both positive (Jaatinen et al., 2008) and negative effects (Peltoniemi et al., 2009; Philben et al., 2015) of water table drawdown on peatland fungal communities have been previously observed; short-term water table drawdown can increase oxygen availability and facilitate the cellular energy metabolism in fungi, especially saprotrophs, while long- term water table drawdown can impact the overall peat-soil environment. In my experiment, lowered water table did not fully dry the peat-soil matrix enough to negatively affect fungi, with the possible exception of some Chytridiomycota – typically common under saturated conditions. However, even within this group, responses were not unilateral to climate change, for example, OTU 241 classified as Mesochytrium, was highly abundant under saturated condition, while OTU 159, closely related to a weak

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cellulose decomposer Rhizophlyctis rosea, was more abundant under lowered water table conditions. Many chytrids such as Rhizophlyctis rosea can endure short-term desiccation and heat in the soil by formation of resistant sporangia (Gleason et al., 2004) suggesting that intermediate water table drawdown might not detrimentally affect the survival of all members in this group.

I cannot fully discuss the temporal degradative successional process without discussing climate change effects on the aboveground plant community as observed by Dieleman et al. (2015) in this study. Increased temperatures increased the biomass of graminoids and ericaceous shrubs, while reducing Sphagnum mosses (Dieleman et al., 2015), which may explain the increase in some Ascomycetous fungi, in particular vascular plant endophytes (e.g. OTUs 13 and 64) and vascular plant root-associated fungi (e.g. OTUs 196 and 1) under the same experimental conditions. At the same time, alterations in carbon substrate quantity and quality in peat porewater (Dieleman et al., 2016) may have interacted with the degradative successional process in the litterbags. For instance, differences in carbon root exudates among different types of plants (Broeckling et al., 2008) and also host- specific immune responses (Bever et al., 2012) may have had an interactive effect on fungal communities. However, Dean et al. (2015) found that under stressful environmental conditions such as increased temperature or low soil moisture, abiotic factors are more important in structuring community composition of mycorrhizal root- associated fungi than changes in the host or neighbouring plants. While I did not explicitly link changes in plant communities with changes in belowground fungal communities, I suggest that early changes in fungal communities, particularly endophytes and mycorrhizal root-associated fungi, with their role in facilitating the development of their hosts (Ramos-Zapata et al., 2009; Mayerhofer et al., 2013), may signal impending changes in aboveground plant communities under climate change scenarios.

Both increased temperature and lowered water table position drove shifts in fungal community composition overlying the shifts associated with degradative succession; the community response to changes in water table appeared more strongly during early stage decomposition (4 months) when labile carbon decomposers were prominent, while elevated temperature treatments altered the cellulose and lignin decomposer fungal 102

community at 8 and 12 months, respectively. Increases in Ascomycetous root-associated fungi and endophytes may have helped facilitate the establishment of vascular plant species observed under climate warming (Dieleman et al., 2015), but further study is warranted to explore the feedbacks between plant and fungal communities. Overall, I show that a reduced fungal richness under experimental climate change conditions was accompanied by increased dominance of saprotrophic fungi that supported my hypothesis. In this study, the ambient temperature treatment from May to October was almost 5 °C higher than average growing season temperature in the sampling site. Yet the experimental condition applied in this study can be applicable as an analogy to future climate change condition in Ontario as currently there are some extreme events such as summer temperatures of 27 °C and higher (https://www.canada.ca/en/services/environment/weather/index.html) that might happen as longer term events under future climate change scenarios. I demonstrate that increased temperatures of +8 °C are the limits needed for compositional shifts in the community of fungi in Boreal peatlands. These results suggest a potential for accelerated rate of decomposition following increased temperatures of +8 °C and higher, and subsequently increased rate of carbon loss through dissolved organic carbon as observed in both field (Hribljan et al., 2014; Kane et al., 2014), and laboratory (Dieleman et al., 2016) studies, critically affecting long-term stored carbon in these ecosystems.

4.5 References

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Allison SD, Treseder KK (2011) Climate change feedbacks to microbial decomposition in boreal soils. Fungal Ecology, 4, 362–374. Andersen R, Chapman SS, Artez RRE (2013) Microbial communities in natural and disturbed peatlands: A review. Soil Biology and Biochemistry, 57, 979–994. Asemaninejad A, Weerasuriya N, Gloor GB, Lindo Z, Thorn RG (2016) New primers for discovering fungal diversity using nuclear large ribosomal DNA. PLoS One, 11, e0159043. Belyea LR (1996) Separating the effects of litter quality and microenvironment on decomposition rates in a patterned peatland. Oikos, 77, 529–539. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of Royal Statistical Society Series B, 57, 289–300. Bever JD, Platt TG, Morton ER (2012) Microbial population and community dynamics on plant roots and their feedbacks on plant communities. Annual Review of Microbiology, 66, 265–283. Blankinship JC, Niklaus PA, Hungate BA (2011) A meta-analysis of responses of soil biota to global change. Oecologia, 165, 553–565. Bradford MA, Davies CA, Frey SD et al. (2008) Thermal adaptation of soil microbial respiration to elevated temperature. Ecological Letters, 11, 1316–1327. Broeckling CD, Broz AK, Bergelson J, Manter DK, Vivanco JM (2008) Root exudates regulate soil fungal community composition and diversity. Applied Environmental Microbiology, 74, 738–744. Clymo RS, Turunen J, Tolonen K (1998) Carbon accumulation in peatland. Oikos, 81, 368–388. Dean SL, Warnock DD, Litvak ME, Porras-Alfaro A, Sinsabaugh R (2015) Root- associated fungal community response to drought-associated changes in vegetation community. Mycologia, 107, 1089–1104. Dieleman CM, Branfireun BA, McLaughlin JW, Lindo Z (2015) Climate change drives a shift in peatland ecosystem plant community: Implications for ecosystem function and stability. Global Change Biology, 21, 388–395.

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Thormann MN, Currah RS, Bayley SE (2004) Patterns of distribution of microfungi in decomposing bog and fen plants. Canadian Journal of Botany, 82, 710–710. Thorn RG, Reddy CA, Harris D, Paul EA (1996) Isolation of saprophytic basidiomycetes from soil. Applied Environmental Microbiology, 62, 4288–4292. Treseder KK, Marusenko Y, Romero-Olivares AL, Maltz MR (2016) Experimental warming alters potential function of the fungal community in boreal forest. Global Change Biology, 22, 3395–3404. Trinder CJ, Artz RRE, Johnson D (2008) Interactions between fungal community structure, litter decomposition and depth of water-table in a cutover peatland. FEMS Microbiology Ecology, 64, 433–448. van den Boogaart KG, Tolosana-Delgado R (2008) “compositions”: a unified r package to analyze compositional data. Computational Geoscience, 34, 320–338. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied Environmental Microbiology, 73, 5261–5267. Wang H, Richardson CJ, Ho M (2015) Dual controls on carbon loss during drought in peatlands. Nature Climate Change, 5, 584–587. Webster KL, McLaughlin JW (2010) Importance of the water table in controlling dissolved carbon along a fen nutrient gradient. Soil Science Society of American Journal, 74, 2254–2266. Williams RT, Crawford RL (1983) Microbial diversity of Minnesota peatlands. Microbial Ecology, 9, 201–214. Yuste JC, Penuelas J, Estiarte M et al. (2011) Drought‐resistant fungi control soil organic matter decomposition and its response to temperature. Global Change Biology, 17, 1475–1486. Zhao N, Chen J, Carroll IM et al. (2015) Testing in microbiome-profiling studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test. American Journal of Human Genetics, 96, 797–807.

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Chapter 5 5 Climate change exposes specific fungal communities in boreal peatlands

5.1 Introduction

Soil biodiversity contributes significantly to biogeochemical processes and ecosystem- level functioning such that the maintenance of ecosystem function is highly dependent on a functionally rich biodiversity in that ecosystem (Copley, 2000). Among the soil microbial communities inhabiting boreal peatlands, fungi, with their broad enzymatic activities and competitive advantages over bacteria and archaea in decomposing low quality materials with high C:N ratios (Myers et al., 2012), play a significant role in mineralization of recalcitrant compounds (Thormann, 2006) that constitute more than 50% of the carbon polymers in these habitats (Turetsky et al., 2000). Currently, belowground microbial diversity is under threat of anthropogenic disturbances such as climate change, which raises a critical demand for ecologists to elucidate how the structure of soil microbial communities, in particular fungi, will be affected by changes in their environment (Wolters et al., 2000). Understanding the mechanisms of climate- driven variations in soil microbial community composition is the key, but a challenging step is predicting how these changes will influence the re-organizations of aboveground communities and ecosystem-level processes (van der Heijden et al., 2008; Bardgett & van der Putten, 2014).

Boreal peatlands of the northern hemisphere are expected to experience an increase in air and soil temperature, atmospheric CO2 concentrations, and a reduction in water table position under future climate change scenarios (IPCC, 2014). The direct, short-term positive effects of increased temperature on the diversity and activity of some fungal groups such as saprotrophs (generalist decomposers) has been well documented (Rippon & Anderson, 1970; Maheshwari et al., 2000). Elevated temperatures can stimulate metabolic rates directly, as well as reduce soil moisture, promoting cellular energy metabolism, particularly in fungal species that benefit from aerobic conditions (Allison &

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Treseder, 2011). Except for a few studies demonstrating the positive effects of elevated atmospheric CO2 on fungal richness and biomass (Lipson et al., 2005; Janus et al., 2005), the direct effect of CO2 enrichment on soil fungal communities is not well studied. It is usually hypothesized that elevated atmospheric CO2 will have a weak or neutral direct effect on soil fungal communities as soil CO2 concentrations are greater than atmospheric

CO2 levels (Schwartz & Bazzaz, 1973). However, several recent studies have demonstrated changes in aboveground community composition under climate change conditions, in particular under elevated atmospheric CO2 and increased temperature. In boreal peatlands, laboratory (Dieleman et al., 2015) and field (Weltzin et al., 2000; Buttler et al., 2015) studies have found communities dominated by non-vascular plants (e.g., mosses) are replaced by vascular plants under experimental climate change, altering litter inputs and increasing root exudates that could potentially affect fungal communities belowground (Korner, 2000; Norby et al., 2001) through inputs of more labile carbon sources (Dieleman et al., 2016a). Soil fungal communities are highly influenced with temporal resource pulses in the soil driven by plant root exudates (Placella & Firestone, 2013). These resource-driven changes in fungal communities can affect both nutrient cycling and plant nutrient supply (Bardgett & van der Putten, 2014).

Several studies have also shown an absolute turnover in fungal communities caused by seasonal changes in air temperature (Schadt et al., 2003; Lauber et al., 2013). This sensitivity of fungal communities to their environment suggests that climate-driven changes should favour general saprotrophs that benefit from warmer and drier conditions. At the same time, shifts to vascular plant associated mycorrhizal species could potentially drive co-evolutionary changes in aboveground communities through facilitating the establishment of their hosts (Bardgett & van der Putten, 2014), which might consequently affect resistance and resilience of the ecosystems to disturbances (de Vries et al., 2012). Both scenarios have unclear consequences for the pattern of carbon dynamics over the next decades. Further to this, the majority of research on the impacts of climate change factors, such as increased temperature, elevated atmospheric CO2 and lowered water table levels, on fungal richness, activity and community composition in Northern peatlands has typically focused on fungal communities of upper layer of peat (Lipson et al., 2005;

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Bradford et al., 2008; Peltoniemi et al., 2009), while very little is known about changes in the structure of fungal communities in lower peat layers (beyond 15 cm depth) under climate change conditions. Although carbon dynamics in the lower horizons are less affected by climate change compared to the upper soil layer (Mathieu et al., 2015), examining the combined effects of multiple drivers of climate change (i.e. increased temperature, elevated atmospheric CO2 and lowered water table position) on the structure of the fungal communities in lower peat layers will give greater insight into carbon dynamics in boreal peatlands. In addition, changes in fungal species richness and community composition, alongside any change in the relative abundances of functional groups that represent different substrate utilization, may affect different layers of peat soil differently. Accordingly, the general objective of this study was to assess the magnitude of combined climate change factors (increased temperature, elevated atmospheric CO2 and lowered water table) on fungal richness, diversity and community composition across a depth gradient of peat soil in boreal peatlands. I specifically examined 1) whether fungal richness and diversity in the surface peat and peat fractions up to a depth of 35 cm will be similarly affected by climate change factors, 2) the functional turnover in the fungal community across this depth gradient, and 3) whether climate-driven changes in fungal community composition can be attributed to changes in aboveground functional groups.

5.2 Materials and Methods

5.2.1 Experimental Design

In August 2012, one hundred cylindrical intact peat monoliths (each 40 cm deep and 25 kg) covered by Sphagnum mosses and with aboveground herbaceous and shrubby vegetation, were collected from a nutrient poor fen site in northern Ontario, Canada (48°21′N, 85°21′W). Each monolith was transferred to a 19 L pail with an ABS fitting port attached to a French drain system and a clear plastic hose held vertically to maintain the water table treatment (see Dieleman et al., 2015 for full mesocosm design). All the mesocosms were transported to the Western University’s Biotron Environmental Climate Change Research Centre in London, Ontario, and kept under seasonal temperatures and

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light conditions for three months to recover. Following three months of acclimation, four mesocosms did not recover and were discarded, 12 mesocosms were destructively sampled, and 84 mesocosms went into the experimental treatments. Fourteen mesocosms were positioned randomly in each of six custom-designed, state-of-the-art, environmentally controlled chambers set at three temperature treatments (‘ambient’, ambient + 4 °C and ambient + 8 °C), crossed with two atmospheric CO2 regimes (430 ppm, 750 ppm), with seven replicates in each chamber under saturated conditions (water table 5 cm from the peat surface) and seven replicates under lowered water table conditions (water table 20 cm from the peat surface). As it is predicted that high latitudes and boreal regions of Canada will experience temperature increases of more than + 8 °C by 2100 (IPCC, 2014), we chose corresponding experimental conditions across this range of temperature (see Lindo (2015) for full temperature regime description), with ‘ambient’ temperature conditions matching the 5 year average daily temperature between April and October for London, Ontario. Full simulation of winter conditions was not logistically possible as temperatures below 10 °C could not be reliably maintained, so between November and March, ‘ambient’ conditions were set at 11.5 °C, with + 4 and + 8 offsets in other chambers. The specific environmental conditions for the 84 mesocosms placed in experimental chambers was regulated and maintained using a fully automated system (ARGUS Control Systems Ltd., White Rock, BC, Canada). Every six months, all the mesocosms within each chamber and their related experimental treatment were randomly moved to another chamber to prevent any potential chamber effects.

5.2.2 Initial and Final Destructive Sampling

At the starting point of the experiment (T0), in December 2012, 12 mesocosms were destructively sampled by splitting the monolith vertically and collecting 125 cm3 of wet peat from the centre of each monolith at three different depths (upper 0-5 cm, middle 15- 20 cm, and bottom 30-35 cm) to measure fungal communities and biochemical properties of the peat prior to treatment. After 18 months (T18), at the end of second growing season, three mesocosms from each treatment were destructively harvested using the same protocols as in the initial sampling. All samples were put into air-tight plastic bags and transferred to -80 °C until processed. 112

5.2.3 DNA Extraction and PCR Protocols

The initial and final destructively harvested peat samples were freeze dried for 48 hours, homogenized and approximately 0.5 g (dwt) submerged in liquid nitrogen and ground using mortar and pestle. A Zymo Soil DNA isolation kit was used to extract total genomic DNA from 25 mg (dwt) of each peat sample from each horizon according to manufacturer’s instructions (Zymo Research Corporation). The extracted DNAs were normalized to 20 ng/ μL, then stored at -20 °C for downstream PCR amplifications.

Two sets of primers were used to capture major fungal groups present in peat samples: LSU200A-F (AACKGCGAGTGAAGCRGYA)/LSU476A-R (CSATCACTSTACTTGTKCGC) to amplify Ascomycota and LSU200-F (AACKGCGAGTGAAGMGGGA)/LSU481-R (TCTTTCCCTCACGGTACTTG) to capture all other fungi. These primers target the D1 variable region of large subunit of ribosomal DNA, and produce amplicons of ~ 300 bp (Asemaninejad et al., 2016a). The 5’ ends of both forward and reverse primers were modified by insertion of unique barcode sequences of 8-nucleotides to separate sequences coming from each sample and Illumina sequencing adapters (for full details see Asemaninejad et al., 2016a). PCR reactions were conducted in a total volume of 25 μL, with 20 ng of template DNA (1 μL), 1.25 μL each of forward and reverse primers (5 μM), and 12.5 μL of Toughmix (Quanta Biosciences). PCR amplifications were performed in a “T300” Thermocycler (Biometra), using a program of initial denaturation for 120 s at 94 °C, followed by 29 cycles of denaturation for 30 s at 94 °C, annealing for 30 s, and elongation for 18 sec at 72 °C. For the Ascomycota primers (LSU200A-F/LSU476A-R) the optimal annealing temperature was 62 °C, whereas for other fungi (primers LSU200-F/LSU481-R) the annealing temperature was 55 °C in the first cycle and 62 °C for the rest of cycles. PCR product normalization was carried out using a Qubit fluorimeter with the dsDNA HS kit (Life Technologies). The normalized PCR products were submitted for paired-end sequencing in an Illumina MiSeq sequencer (2 x 300 bp V3 chemistry) at London Regional Genomics Centre (Robarts Research Institute, London, Canada).

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5.2.4 Bioinformatic Analysis

Two MiSeq runs were executed for each primer set, creating four multiplexed runs. The raw FASTQ data for each run were separately subjected to bioinformatics analyses using a custom Miseq pipeline (https://github.com/ggloor/miseq_bin/tree/master) (for full description of methods see Asemaninejad et al., 2016a). Assembly of sequences from forward and reverse reads, with a minimum overlap length of 30 nucleotides, was performed using PANDAseq (https://github.com/neufeld/pandaseq; Masella et al., 2012). The overlapped FASTQ outputs were clustered into ISUs (identical sequence units). Suspected chimeras were removed via UCHIME (Edgar et al., 2011), then ISUs were grouped into OTUs (operational taxonomic units) using a 97% threshold value, such that ISUs with at least 97% similarity were clustered together to generate an OTU. The OTU seed sequences were classified through online RDP Classifier (Wang et al., 2007) using the Fungal LSU Training set 11 as a reference database (https://rdp.cme.msu.edu/classifier/classifier.jsp). For the purpose of statistical analyses, the dataset was filtered to contain only OTUs identified with a confidence score of at least 50% at the order level. Then, for the significant taxa that were truly responding to experimental treatment, but which were identified with a confidence score of less than 80% at the genus level, re-identification was performed using BLAST to get a better indication of their identity and function. Sequences can be found at ENA (European Nucleotide Archive) under accession number PRJEB14310. The environmental data containing sample IDs with ENA sample accession number can be found in Appendix A, Table A.5.

5.2.5 Statistical Analyses

The filtered data containing only fungal OTUs were rarified (Kenneth et al., 1975) and used to calculate fungal richness (S = the number of observed OTUs) and diversity (H’ = Shannon index) in each peat sample. Fungal richness and diversity in peat samples collected before treatment (T0) and the samples under ambient experimental conditions after 18 months were analyzed using a one-way, repeated measures of variance with time as the fixed factor, and depth as the spatial repeating (non-independent) factor with a

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Tukey post hoc test in Statistica 7 (StatSoft. Inc., 2004). Additionally, a three-way, repeated measures of variance was carried out in Statistica 7 (StatSoft. Inc., 2004) to analyze differences in fungal richness and diversity of the peat samples under different environmental conditions after 18 months of the experiment.

Since in high-throughput sequencing methods, only a small fraction of an environmental sample is used for DNA extraction, and only a small portion of the amplified DNA is used for sequencing, a higher rate of stochasticity is expected in OTU-based multivariate community-level analyses than other types of community data (Aitchison & Egozcue, 2005). To account for this problem, the raw OTU sequence data were transformed to centered log ratio by subtracting the log2 of each OTU read count from the log2 of the geometric mean of all OTU reads present in the sample (Aitchison & Egozcue, 2005). The 1:1 conformity between original OTU counts and standardised OTU counts are preserved by this transformation, and the data is visualised and compared on a common scale (Lovell et al., 2015). The data transformation (centered log-ratio) was done using ALDEx2 package in R (version 3.2.3).

At the community level, the target OTUs with ratios of 1% or higher in all samples were subjected to a compositional PCA biplot analysis using the “compositions” package in R (version 3.2.3). An ANOVA was run on the PC axis scores for the first two principal components to determine the drivers of the variation explained by each axis for the main effects of the treatment. To measure the magnitude of the effect of each of the three environmental factors (temperature, water table, CO2) on changes in the structure of fungal community under experimental climate change condition, a Microbiome Regression-based Kernel Association Test (MiRKAT) was performed on the filtered dataset using the “MiRKAT” package in R (version 3.2.3). The MiRKAT was used to determine the principal environmental driver of the variation observed in fungal community composition (Zhao et al., 2015). To illustrate changes in community composition of fungi under different treatments over the course of the experiment, communities in samples from 18 months were assessed by nonmetric multidimensional scaling (NMDS) test using the “vegan” package in R (version 3.2.3).

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Subsequently, to characterize the OTUs responding to specific environmental treatments, we used an a-posteriori ANOVA-like differential expression procedure (ALDEx) (Fernandes et al., 2013) conducted in R (version 3.2.3) using the ALDEx2 package. This analysis considers OTUs that have lower variance in their abundances within treatments than between treatments, as OTUs that are being affected by the treatment, versus responding to sample variation. As recommended for multiple comparisons, the raw p- values obtained through ALDEx test were corrected using a Benjamini-Hochberg False Discovery Rate (FDR) correction procedure (Benjamini & Hochberg, 1995).

5.3 Results

5.3.1 Fungal Community Composition before Treatment

In the destructively harvested peat samples before treatment, there was no obvious differences in the initial Ascomycota richness across the three depths of peat monoliths

(F1,40 = 1.044, P = 0.361). While richness of other fungal group was significantly higher in the middle horizon compared to other peat layers (F1,44 = 14.736, P = 0.001) (Table 5.1). In the reduced sequence datasets encompassing only target OTUs classified with at least 50% confidence value at the order level, Helotiales and Dothideales were the most frequent taxa at all three depths; OTUs belonging to Pezizales, Chaetothyriales and Hypocreales appeared as more abundant taxa, respectively, in the upper, middle and bottom horizons of the destructively harvested peat samples before treatment. For other fungi, the highest numbers of reads in the upper and middle horizons were members of Agaricales, Polyporales, and Sebacinales, whereas in the bottom horizon, the most frequent taxa were, in order, members of Chytridiales, Agaricales and Polyporales. Comparing Ascomycota richness between initial samples and control samples (samples under ambient conditions) after 18 months, other than a slight decline in Ascomycota richness in the upper horizon, richness of Ascomycota was unchanged in control samples

(F1,40 = 0.050, P = 0.824) (Table 5.1). For other fungi, richness (except in the middle horizon) declined over the experiment (F1,44 = 49.407, P < 0.001). At the community level, MiRKAT revealed both Ascomycota and other fungi community composition differed between initial samples before treatment and control samples after 18 months

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(all P-values at each depth < 0.001). Fungal orders driving this change were increases in the Eurotiales, Dothideales, Xylariales (Ascomycota) and Platygloeales, Monoblepharidales, and Kickxellales (other fungal group) while Chaetothyriales, Agyriales (Ascomycota) and Thelephorales, Boletales and Russulales (other fungal group) decreased under ambient conditions over time (See Appendix A, Figure A.5 and A.6).

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Table 5.1 Mean and standard error of fungal richness (S) for initial sampling and control samples under “ambient” condition after 18 months of experimental treatment across three depths.

“T0” represents before treatment, “T18” represents after 18 months of experimental treatment.

Initial conditions (T0) Ambient treatment (T18) Ascomycota richness (S) Upper 162.25 ± 11.0a 153.41 ± 17.2a Middle 158.00 ± 7.2a 159.80 ± 17.3a Bottom 158.75 ± 10.9a 160.60 ± 16.7a Other fungi richness (S) Upper 244.58 ± 17.0b 239.83 ± 26.9b Middle 258.83 ± 14.9a 258.41 ± 18.0a Bottom 242.33 ± 17.0b 233.58 ± 29.7b

Different letters following values denote significant differences.

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5.3.2 Fungal Community Composition after Treatment

After 18 months, both Ascomycota and other fungal group richness were significantly greater under + 8 °C temperature treatment compared to other temperatures (Ascomycota richness F2,34 = 8.510, P = 0.002; other fungi richness F2,48 = 8.130, P = 0.002) (Figure 5.1). For other fungal richness, these temperature effects were more pronounced in the bottom horizon, such that richness was 21.7% greater under + 8 °C temperature compared to ambient conditions in the bottom peat layer as opposed to 12.1% and 10.3% higher in the upper and middle horizons, respectively. Ascomycota richness showed an interactive effect from both increased temperature and elevated atmospheric CO2 (F2,34 = 12.250, P <0.001), by which Ascomycota richness increased by 12.1% under + 8 °C temperature and elevated atmospheric CO2 treatments compared to ambient temperature and ambient CO2 regime. Additionally, hydrological changes did not demonstrate any main effect on other fungal richness (Appendix A, Table A.6).

The compositional biplot PCA analysis for the Ascomycota dataset (Figure 5.2a) indicated that temperature was the main factor explaining 26.9% of the variation observed in community composition across the three different depths at 18 months (PC1:

F2,91 = 23.21, P < 0.001). The second axis (PC2) was also significantly related to temperature (PC2: F2,91 = 11.36, P = 0.010), explaining an additional 12.5% of the variation in the Ascomycota community after 18 months of experimental treatment. Positive PC1 scores were related to ambient temperature treatments, and negative PC1 scores were related to peat samples from + 8 °C treatment. The positive PC2 scores were associated with peat samples from + 4 °C treatments (Figure 5.2a). Similarly, the compositional biplot PCA analysis for other fungi (Figure 5.2b) suggested that temperature was the primary factor explaining 16.6% of the variation observed in fungal community composition across three different depths (F2,98 = 12.83, P < 0.001), while

PC2 was significantly related to both temperature (F2,98 = 16.36, P < 0.001) and water table treatments (F2,98 = 9.79, P = 0.002), explaining an additional 16.4% of the variation in the other fungal group composition after 18 months of experimental treatment. Positive PC1 scores are related to peat samples collected from + 4 °C treatment, and negative PC1

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scores are related to peat samples from + 8 °C treatment. Positive PC2 scores are associated with peat samples from lowered water table treatments, and negative PC2 scores are related to peat samples from ambient treatments (Figure 5.2b).

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Figure 5.1 The effect of different temperature treatments on richness (number of observed OTUs) of Ascomycota (a) and other fungi (b) after 18 months of experimental warming treatment.

Temperature treatments differing significantly from ambient condition at 18 months are indicated by different letters (lowercase letteres for Ascomycota and uppercase letters for other fungi).

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Figure 5.2 PCA biplot of Ascomycota (a) and other fungi (b) under experimental climate change condition (altered temperature, atmospheric CO2 and water table position).

In (a), temperature is the driver of both PCA axes 1 and 2. In (b), PCA axis 1 is driven by fungal species favoured by increased temperature treatments, and PCA axis 2 is driven by species associated with both ambient temperature and lowered water table conditions.

There were no significant effects of CO2 treatments on fungal communities. The labelled OTUs in both biplots are examples of indicator taxa responding to experimental climate change treatments, identified through ALDEx test (Table 5.2). In both (a) and (b), other OTUs present are shown by black dots.

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Similarly, MiRKAT results demonstrated that for both the Ascomycota and other fungal groups, temperature was the key factor dictating changes in the genus-level community composition at all three depths after 18 month of experimental treatments (all P-values < 0.001), with no significant effects of water table position (P = 0.55 Ascomycota; P = 0.21 other fungi) or CO2 concentration (P = 0.08 Ascomycota; P = 0.06 other fungi) on the changes observed in the structure of fungal community. The NMDS plots showed that for both Ascomycota and other fungal data sets, community composition across all depths in warmed treatments became more homogenous, particularly in + 8 °C (Figure 5.3). ANOVA-like differential expression procedure (ALDEx) was used to further understand which OTUs were responding to temperature and driving homogenization within communities. Among the Ascomycota OTUs confidently identified at the genus level, 19 OTUs were significantly affected by temperature treatments in the upper horizons, eight OTUs in the middle horizon, and two OTUs in the bottom horizon (Table 5.2). Within the upper peat layer, one OTU (i.e. OTU 1426, a potential moss-associated fungus) was negatively affected by increased temperatures of + 4 °C and + 8 °C, while the majority of fungi that benefitted from + 4 °C treatments were closely related to saprotrophs and cellulose decomposers (e.g. OTUs 19, 14 and 152). Potential mycorrhizal root-associated and endophytic species (e.g. OTUs 3, 347 and 309) showed the highest relative frequencies in the upper peat layer under + 8 °C treatments; in the middle and bottom horizons, the highly abundant taxa under + 8 °C treatments consisted mainly of OTUs with affinities to cellulose decomposers. Here there were several OTUs, mainly identified as root-associates and cellulose decomposers that had not been detected in initial samples, and yet appeared with fairly high abundances under warming treatments at the end of the experiment (e.g. OTUs 14, 1999 and 1321). Among other fungi there were losses relative to the initial sampling point of many OTUs with affinities to aquatic fungi (e.g. Chytridiales and Blastocladiales) and some OTUs related to Harpellales and Mucorales, but such losses could not be detected by ALDEx due to the absence of these OTUs in samples from 18 months. In addition to these losses, there was a significant reduction in OTU 13 (identified as a moss-associated fungus) in the upper peat layer under warming treatments.

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Figure 5.3 Nonmetric multidimensional scaling (NMDS) plot of Ascomycota community (“a” upper, “b” middle, “c” bottom layer of peat samples) and other fungal community (“d” upper, “e” middle, “f” Bottom layer of peat samples) in

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initial destructive samples before treatment (T0) shown by black dots and under temperature treatments after 18 months (ambient, + 4 °C and + 8 °C shown, respectively, by grey circles, squares and triangles).

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Table 5.2 Indicator fungal taxa significantly affected by temperature treatments based on ALDEx results.

“Effect size/ temp” indicates the positive effect of a particular temperature treatment on a particular fungus causing a significant increase in their ratios compared to other treatments. The closest known identities and functional designations assigned to OTU numbers are, respectively, based on BLAST search and the literature where known.

Ascomycetous Closest known identity Function** Depth Effect size/ temp P-value OTUs OTU_3* Ericoid mycorrhizal root fungus Mycorrhizal root-associate Upper 1.04/ +8°C 0.043 OTU_14 Sarcoleotia turficola Cellulose decomposer Upper 1.12/ +4°C 0.015 OTU_19* Alternaria Saprotroph Upper 1.03/ +4°C 0.017 OTU_24* Cladosporium Saprotroph Upper 1.31/ +4°C 0.019 Mycorrhizal root associate; multiple OTU_86* Hymenoscyphus Upper 0.89/ +4°C 0.021 functions OTU_152 Hyalopeziza Cellulose decomposer Upper, Middle 0.99/ +4°C, +8°C 0.031 Mycorrhizal root associate; multiple OTU_208 Hymenoscyphus Upper 1.30/ +4°C 0.013 functions OTU_697* Unclassified Hyaloscyphaceae Endophyte Upper 1.32/ +4°C 0.014 OTU_1202 Lachnellula Stem pathogens of conifers Upper 1.08/ +4°C 0.019 OTU_1426 Hyaloscypha Moss-associate Upper 1.30/ ambient 0.012 Mycorrhizal root associate; multiple OTU_1073 Hymenoscyphus Upper, Middle 1.27/ +4°C, +8°C 0.001 functions Mycorrhizal root associate; multiple OTU_1964 Hymenoscyphus Upper, Middle 0.84/ +4°C, +8°C 0.025 functions OTU_2* Chaetomium thermophilum Cellulose decomposer Upper 1.24/ +8°C 0.023 OTU_13 Microthyrium Saprotroph Upper 0.99/ +4°C 0.021

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OTU_309 Helotiales sp. Endophyte Upper 1.38/ +8°C 0.012 OTU_1825 Helotiales sp. Endophyte Middle 1.13/ +8°C 0.008 OTU_347* Meliniomyces variabilis Mycorrhizal root-associate Upper 1.40/ +8°C 0.012 OTU_461 Unclassified Helotiales Mycorrhizal root-associate Upper 0.98/ +8°C 0.015 OTU_938* Xenopolyscytalum Cellulose decomposer Upper, Middle 0.67/ +4°C, +8°C 0.047 OTU_1999 Barrenia Mycorrhizal root-associate Upper 0.98/ +8°C 0.024 OTU_29* Penicillium sp. Saprotroph Middle 1.06/ ambient 0.027 OTU_761 Unclassified Helotiales. endophyte Middle 1.84/ +8°C 0.012 OTU_1337* Arachnopeziza Cellulose decomposer Middle 1.88/ +8°C 0.003 OTU_58* Phialocephala sp. Dark septate root endophyte Bottom 2.40/ +8°C 0.021 OTU_1321 Arachnopeziza Cellulose decomposer Bottom 1.84/ +8°C 0.032 Other fungal OTUs OTU_0* Mycena Lignocellulose decomposer Upper 0.80/ +8°C 0.035 OTU_1* Ceratobasidium sp. Root associate; multiple functions Upper, Middle 0.90/ +4°C 0.008 OTU_9* Sebacina sp. Mycorrhizal root-associate Middle 0.76/ +4°C 0.044 OTU_13* Tephrocybe palustris Moss-associate Upper 0.64/ ambient 0.045 OTU_218* Galerina Cellulose decomposer Bottom 1.01/ +8°C 0.005 OTU_2175* Kuehneromyces Lignocellulose decomposer Upper 1.08/ +8°C 0.003 OTU_4532 Cortinarius Mycorrhizal root-associate Upper, Middle 0.92/ +8°C 0.004 OTU_7* Sebacina sp. Mycorrhizal root-associate Middle 0.92/ +4°C 0.021 OTU_266* Antrodia sp. Brown rotter Middle 1.00/ +8°C 0.013 OTU_801 Tricholoma Mycorrhizal root-associate Middle 1.17/ +4°C 0.005 OTU_2481* Sebacina sp. Mycorrhizal root-associate Middle 0.94/ +4°C 0.034 OTU_72 Basidiomycetous yeast Labile carbon decomposer Bottom 0.97/ +4°C 0.011

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OTU_2486 Mycena Lignocellulose decomposer Bottom 0.75/ +8°C 0.03 OTU_4900* Cymatoderma dendriticum Lignocellulose decomposer Bottom 1.08/ ambient 0.011 **The references for the functional designations of indicator taxa can be found in Appendix A, Table A.7. *OTUs also detected in initial samples before treatment.

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Similar to species in the Ascomycota, many other fungi benefitted from increased temperatures of + 4 °C and + 8 °C. For instance, across all horizons potential cellulose and lignocellulose decomposers (e.g. OTUs 0, 266 and 2486) increased with + 8 °C temperature treatments, while in the middle peat layer some vascular plant root- associated fungi (e.g. OTUs 9, 7 and 801) increased under + 4 °C treatments (Table 5.2). Among other fungi there were also some OTUs closely related to root-associates and lignocellulose decomposers (e.g. OTUs 801 and 2486) that emerged as dominant groups under higher temperature treatments although they had not been detected in the initial samples before experimental treatments. The sequence output obtained from initial samples before treatment, and final destructive samples after 18 months of experimental treatment can be found in Appendix A, Table A.8.

5.4 Discussion

I found that among three co-occurring environmental factors (CO2, temperature, and water table), temperature was the main driver of change in the structure of fungal communities across a depth gradient, with two main patterns emerging. First, was the overall transition in fungal communities in favour of saprotrophs, endophytic and mycorrhizal root-associated fungi under increased temperature treatments at the expense of moss-associated and aquatic fungi. This was seen in both Ascomycota and other fungal groups, and this was a consistent pattern across all three depths observed. Second, there were shifts within functional groups under warmer conditions leading to the homogenisation of communities and the dominance of specific fungal groups under warming conditions. The increased dominance of decomposers and root-associated fungi over moss-associated and aquatic fungi is likely due to both direct (abiotic) and indirect (biotic) environmental conditions. A warmer, drier environment induced by our experimental treatments would favour decomposers and disadvantage aquatic fungi (Xiong et al., 2014; Treseder et al., 2016), while increases in vascular plants alongside decreases in moss cover that took place in the same system under the same experimental conditions (Dieleman et al., 2015) explains the increase in root-associated fungi over moss-associated fungi (Morgado et al., 2015; Semenova et al., 2015).

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Among the decomposers, specific functional groups appeared highly abundant under different temperature treatments leading to greater similarity (homogenisation) of fungal communities among replicate mesocosms with increasing temperature. For instance, potential cellulose decomposers dominated under + 4 °C treatments, while under elevated temperatures of + 8 °C, lignocellulose decomposers were dominant, especially in the upper peat horizon. Cellulose decomposers are able to colonize their substrates more rapidly and extract cellulose selectively from their substrates under more favourable warm conditions than lignocellulose decomposers are (Bagley & Richter, 2002). However, high cellulase activity among cellulose decomposers can over-exploit resources, leading to high nitrogen loss from litter (Talbot & Treseder, 2012) and can generate a nitrogen-limited system (Sheppard et al., 2013) that might subsequently drive the decomposer community in favour of lignocellulose decomposers. It is also possible that increasingly warm conditions saw faster resource and therefore species turnover times that led to lignocellulose decomposers dominating at + 8 °C, while cellulose decomposers were being maintained at + 4 °C. However, our observed pattern of cellulose versus lignocellulose decomposers under warming was dependent on depth within the peat profile. Cellulose decomposers were more persistent under + 8 °C treatment in the bottom peat horizon, suggesting that depth may buffer or moderate warming effects that favour cellulose decomposers, so that lignocellulose decomposers were unable to dominate despite the higher temperatures. The low levels of oxygen in the bottom horizon could be another factor producing unfavourable conditions for lignocellulose decomposers by limiting their ligninolytic oxidase activities (Arantes et al., 2011).

Similar shifts within functional groups were observed in the community of root- associated fungi. In the upper and middle peat horizons, intermediate increases in temperature favoured OTUs identified as endophytic and mycorrhizal root-associates belonging to Basidiomycota (e.g. OTUs 9 and 801), while increased temperatures of + 8 °C yielded less, but more mycorrhizal root-associated fungi from Ascomycota (e.g. OTUs 3 and 347). The shift observed within root-associated fungi under warming treatments might be due to competitive interactions caused by different quantities of

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phenolic exudates from plants roots into the soil matrix under different temperature treatments (Dieleman et al., 2016b), which can affect carbon utilization efficiency of some fungi (Badri & Vivanco, 2009). The facilitating effects of symbiotic root-associated fungi on the development and root-biomass of their hosts have been previously reported (van der Heijden et al., 1998; Mayerhofer et al., 2013). This role of root-associated fungi can have some ramifications for the pattern of long-term carbon storage in boreal peatlands, as fresh inputs of labile carbon from plant roots can stimulate microbial decomposition of old carbon in deeper layers of soil (Fontaine et al., 2007; de Graaff et al., 2010). This increased rate of litter degradation can further foster the establishment of vascular plants through higher input of nutrients into the soil matrix, and hence higher quantities of labile carbon secreted by plants roots will stimulate higher fungal growth and activity, causing further decomposition of deep peat (Natali & Mack, 2011; Bragazza et al., 2013; Jassey et al., 2013).

Several aspects of the experimental system need to be considered when interpreting the results. First, while the mesocosms were large enough to contain a wide array of aboveground plant biodiversity, mesocosms did not include trees that are present in the natural environment from which the peat was extracted, other than an occasional black spruce seedling (< 5 cm tall). As such, differences between pre-treatment (T0) and post- treatment (T18) ambient conditions were driven mostly by losses in ectomycorrhizal root- associates (e.g. members of Boletales, Russulales and Thelephorales) of conifers. Also, a few fungal groups appeared at the end of the experiment that were not detected initially; these likely benefitted from the generally favourable conditions under ambient temperature treatments such that they rose above the detection threshold. Despite these shifts, the distinct trends under warming conditions are obvious. Temperature as the main driver on the richness, diversity and structure of fungal communities has been previously reported (Morgado et al., 2015; Newsham et al., 2016). However, perhaps because of the short-term nature of the experiment, I did not observe the previously reported positive, albeit minor, effects of elevated atmospheric CO2 on fungal richness that were associated with increased root biomass and root exudates, or changes in the physiology of plants and chemistry of plant material (Strain et al., 1983; Zak et al., 1993; Sadowsky &

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Shortemever, 1997; Janus et al., 2005). In a recent meta-analysis, Veresoglou et al. (2016) found a positive linear relationship between the time scale of the experiment and the effect size of CO2 enrichment on fungal community composition. Similarly, in my study water table was not a main factor affecting fungal communities, with the exception of aquatic fungal groups that disappeared under warm, dry conditions. However, the lowered water table increased the abundance of some cellulose and lignocellulose decomposers in combination with increased temperatures. The observed effect of water table drawdown on fungal diversity has been contradictory (Jaatinen et al., 2008; Peltoniemi et al., 2009; Philben et al., 2015), but water table drawdown could increase oxygen availability and provide more habitat for aerobic fungi, and therefore play an important role in the magnitude of the response by fungal communities to climate change factors (Asemaninejad et al., 2016b).

Fungal communities in boreal peatlands are anticipated to be greatly affected by future climate conditions, fostering the expansion of decomposer species (Trinder et al., 2008; Yuste et al., 2011). Of particular concern are changes in fungal communities in the subsurface layer of peat that has an important role in buffering climate change impacts through the accumulation of a vast amount of carbon (Charman, 2002). Very few studies measured the net effect of all three environmental factors associated with climate change on different functional groups of fungi in boreal peatlands. A field study by Treseder et al. (2016) is one of the few that showed how warming-induced changes in the growth and physiology of fungi belowground might modify fungal functional responses leading to different community composition with higher abilities to break down complex organic compounds in Boreal systems. Here, I not only confirm the shift under climate change conditions in fungal community composition in favour of decomposers of recalcitrant carbon compounds, but also I clearly show that this shift co-occurs with increased root- associates of vascular plants (endophytes and mycorrhizal fungi) that coincides with the increased dominance of vascular plants over Sphagnum mosses occurring aboveground (Dieleman et al., 2015). This transition in fungal communities away from aquatic and moss-associated fungi to thermophilic and drought-resistant decomposers and root-

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associates is important for understanding long-term carbon storage in boreal peatlands under future climate change.

5.5 References Aitchison J, Egozcue JJ (2005) Compositional data analysis: Where are we and where should we be heading? Mathematical Geology, 37, 829–850. Allison SD, Treseder KK (2011) Climate change feedbacks to microbial decomposition in boreal soils. Fungal Ecology, 4, 362–374. Arantes V, Milagres AM, Filley TR, Goodell B (2011) Lignocellulosic polysaccharides and lignin degradation by wood decay fungi: the relevance of nonenzymatic Fenton-based reactions. Journal of Industrial Microbiology and Biotechnology, 38, 541–555. Asemaninejad A, Weerasuriya N, Gloor GB, Lindo Z, Thorn RG (2016a) New primers for discovering fungal diversity using nuclear large ribosomal DNA. PLoS One, 11, e0159043. Asemaninejad A, Thorn RG, Lindo Z (2016b) Experimental climate change modifies degradative succession in boreal peatland fungal communities. Microbial Ecology. In press. Badri DV, Vivanco JM (2009) Regulation and function of root exudates. Plant, Cell and Environment, 32, 666–681. Bagley ST, Richter DL (2002) Biodegradation by brown-rot fungi. In: The Mycota (eds Osiewacz HD), pp. 327–341, Industrial Applications, Springer-Verlag, Berlin. Bardgett RD, van der Putten WH (2014) Belowground biodiversity and ecosystem functioning. Nature, 515, 505–511. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57, 289–300. Bradford MA, Davies CA, Frey SD et al. (2008) Thermal adaptation of soil microbial respiration to elevated temperature. Ecology Letters, 11, 1316–1327.

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Bragazza L, Parisod J, Buttler A, Bardgett RD (2013) Biogeochemical plant-soil microbe feedback in response to climate warming in peatlands. Nature Climate Change, 3, 273–277. Buttler A, Robroek BJM, Laggoun-Défarge F et al. (2015) Experimental warming interacts with soil moisture to discriminate plant responses in an ombrotrophic peatland. Journal of Vegetation Science, 26, 964–974. Charman D (2002). Peatlands and Environmental Change. John Wiley, New York. Copley J (2000) Ecology goes underground. Nature, 406, 452–454. de Graaff MA, Classen AT, Castro HF, Schadt CW (2010) Labile soil carbon inputs mediate the soil microbial community composition and plant residue decomposition rates. New Phytologist, 188, 1055–1064. de Vries FT, Liiri ME, Bjornlund L, Bowker MA, Christensen S, Setälä HM, Bardgett RD (2012). Land use alters the resistance and resilience of soil food webs to drought. Nature Climate Change, 2, 276–280. Dieleman CM, Branfireun BA, McLaughlin JW, Lindo Z (2015). Climate change drives a shift in peatland ecosystem plant community: Implications for ecosystem function and stability. Global Change Biology, 21, 388–395. Dieleman CM, Lindo Z, McLaughlin JW, Craig AE, Branfireun BA (2016a) Climate change effects on peatland decomposition and porewater dissolved organic carbon biogeochemistry. Biogeochemistry, 128, 385–396. Dieleman CM, Branfireun BA, McLaughlin JW et al. (2016b) Enhanced carbon release under future climate conditions in a peatland mesocosm experiment: the role of phenolic compounds. Plant and Soil, 400, 81–91. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27, 2194–2200. Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB (2013) ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-seq. PLoS One, 8, e67019. Fontaine S, Barot S, Barre P, Bdioui N, Mary B, Rumpel C (2007) Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature, 450, 277–280.

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IPCC (2014) Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the International Panel on Climate Change. Cambridge University Press, London. Jaatinen K, Laiho R, Vuorenmaa A et al. (2008) Microbial communities and soil respiration along a water-level gradient in a northern boreal fen. Environmental Microbiology, 10, 339–353. Janus LR, Angeloni NL, McCormack J, Rier ST, Tuchman NC, Kelly JJ (2005) Elevated

atmospheric CO2 alters soil microbial communities associated with trembling aspen (Populus tremuloides) roots. Microbial Ecology, 50, 102–109. Jassey VEJ, Chiapusio G, Binet P et al. (2013) Above- and belowground linkages in Sphagnum peatland: climate warming affects plant-microbial interactions. Global Change Biology, 19, 811–823. Lauber CL, Ramirez KS, Aanderud Z, Lennon J, Fierer N (2013) Temporal variability in soil microbial communities across land-use types. International Society for Microbial Ecology, 7, 1641–1650. Lindo Z (2015) Warming favours small-bodied organisms through enhanced reproduction and compositional shifts in belowground systems. Soil Biology and Biochemistry, 91, 271–278.

Lipson DA, Wilson RF, Oechel W (2005) Effects of elevated atmospheric CO2 on soil microbial biomass, activity, and diversity in a Chaparral ecosystem. Applied Environmental Microbiology, 71, 8573–8580. Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bahler J (2015) Proportionality: a valid alternative to correlation for relative data. PLoS Computational Biology, 11, e1004075. Maheshwari R, Bharadwaj G, Bhat MK (2000) Thermophilic fungi: their physiology and enzymes. Microbiology and Molecular Biology Reviews, 64, 461–488. Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD (2012) PANDAseq: paired-end assembler for illumina sequences. BMC bioinformatics, 13, 31. Mathieu JA, Hatté C, Balesdent J, Parent E (2015) Deep soil carbon dynamics are driven more by soil type than by climate: a worldwide meta-analysis of radiocarbon profiles. Global Change Biology, 21, 4278–4292.

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Mayerhofer MS, Kernaghan G, Harper KA (2013) The effects of fungal root endophytes on plant growth: a meta-analysis. Mycorrhiza, 23, 119–128. Morgado LN, Semenova TA, Welker JM, Walker MD, Smets E, Geml J (2015) Summer temperature increase has distinct effect on the ectomycorrhizal fungal communities of moist tussock and dry tundra in Arctic Alaska. Global Change Biology, 21, 959– 972. Myers B, Webster KL, Mclaughlin JW, Basiliko N (2012). Microbial activity across a boreal peatland nutrient gradient: The role of fungi and bacteria. Wetlands Ecology and Management, 20, 77–88. Natali SM, Mack MC (2011) Fungal feedbacks to climate change. Nature Climate Change 1, 192–193. Newsham KK, Hopkins DW, Carvalhais LC, Fretwell PT, Rushton SP, O’Donnell AG, Dennis PG (2016) Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nature Climate Change, 6, 182–186.

Norby RJ, Cotrufo MF, Ineson P, O’niell EG, Canadell JG (2001) Elevated CO2, litter chemistry, and decomposition: a synthesis, Oecologia, 127, 153–165. Peltoniemi K, Fritze H, Laiho R (2009) Response of fungal and actinobacterial communities to water-level drawdown in boreal peatland sites. Soil Biology and Biochemistry, 41, 1902–1914. Philben M, Holmquist J, MacDonald G, Duan D, Kaiser K, Benner R (2015) Temperature, oxygen, and vegetation controls on decomposition in a James Bay peatland. Global Biogeochemical Cycles, 29, 729–743. Placella SA, Firestone MK (2013) Transcriptional response of nitrifying communities to wetting of dry soil. Applied and Environmental Microbiology, 79, 3294–3302. R Development Core Team (2011) R: a Language and Environment for Statistical Computing. ISBN 3-900051-07-02011. R Foundation for Statistical Computing,Vienna, Austria. Rippon JW, Anderson DN (1970) Metabolic rate of fungi as a function of temperature and oxidation-reduction potential (Eh). Mycopathologia et Mycologia Applicata, 40, 349–352.

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Sadowsky MJ, Shortemeyer M (1997) Soil microbial responses to increased

concentrations of atmospheric CO2. Global Change Biology, 3, 217–224. Schadt CW, Martin AP, Lipson DA, Schmidt SK (2003) Seasonal dynamics of previously unknown fungal lineages in tundra soils. Science, 301, 1359–1361. Schwartz DM, Bazzaz FA (1973) In situ measurements of carbon dioxide gradients in a soil-plant-atmosphere system. Oecologia, 12, 161–167. Semenova TA, Morgado LN, Welker JM, Walker MD, Smets E, Geml J (2015) Long‐ term experimental warming alters community composition of ascomycetes in Alaskan moist and dry arctic tundra. Molecular Ecology, 24, 424–437. Sheppard LJ, Leith ID, Leeson SR, Dijk NV, Field C, Levy P (2013) Fate of N in a peatland, Whim bog: immobilisation in the vegetation and peat, leakage into pore

water and losses as N2O depend on the form of N. Biogeosciences, 10, 149–160. StatSoft Inc (2004) Statistica (Data Analysis Software System), Version 7.0 (Tulsa, USA). Strain, BR, Bazaaz, FA (1983) Terrestrial plant communities. In: The Response of Plants to Rising Levels of Atmospheric Carbon Dioxide (eds Lemon, EH), pp. 117–222, AAAS Selected Symposium 8, Washington, DC, United States. Talbot JM, Treseder KK (2012) Interactions among lignin, cellulose, and nitrogen drive litter chemistry–decay relationships. Ecology, 93, 345–354. Thormann MN (2006) Diversity and function of fungi in peatlands: A carbon cycling perspective. Canadian Journal of Soil Science, 86, 281–293. Treseder KK, Marusenko Y, Romero-Olivares AL, Maltz MR (2016) Experimental warming alters potential function of the fungal community in boreal forest. Global Change Biology, 22, 3395–3404. Trinder CJ, Artz RRE, Johnson D (2008) Interactions between fungal community structure, litter decomposition and depth of water-table in a cutover peatland. FEMS Microbiology Ecology, 64, 433–448. Turetsky MR, Wieder RK, Williams CJ, Vitt DH (2000) Organic matter accumulation, peat chemistry, and per-mafrost melting in peatlands of boreal Alberta. Écoscience, 7, 379–392.

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van der Heijden MGA, Klironomos N, Ursie M et al. (1998) Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity, Nature, 396, 69–72. van der Heijden MGA, Bardgett RD, van Straalen NM (2008) The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters, 11, 296–310. Veresoglou SD, Anderson IC, de Sousa NM, Hempel S, Rillig MC (2016) Resilience of

fungal communities to elevated CO2. Microbial Ecology, 6, 1–3. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73, 5261–5267. Weltzin JF, Pastor J, Harth C, Bridgham SD, Updegraff K, Chapin CT (2000) Response of bog and fen plant communities to warming and water-table manipulations. Ecology, 81, 3464–3478. Wolters v, Silver WL, Bignell DE et al. (2000) Effects of global changes on above- and belowground biodiversity in terrestrial ecosystems: implications for ecosystem functioning. BioScience, 50, 1089–1098. Xiong J, Peng F, Sun H, Xue X, Chu H (2014) Divergent responses of soil fungi functional groups to short-term warming. Microbial Ecology, 68, 708–715. Yuste JC, Penuelas J, Estiarte M et al. (2011) Drought-resistant fungi control soil organic matter decomposition and its response to temperature. Global Change Biology, 17, 1475–1486. Zak DR, Pregitzer KS, Curtis PS, Teeri JA, Fogel R, Randlett DL (1993) Elevated

atmospheric CO2 and feedbacks between carbon and nitrogen cycles. Plant and Soil, 151, 105–117. Zhao N, Chen J, Carroll IM et al. (2015) Testing in microbiome-profiling studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test. The American Journal of Human Genetics, 96, 797–807.

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Chapter 6 6 General Discussion

It is predicted that increases, induced by climate change, in the respiration of carbon from organic soils will be widespread across high latitude ecosystems such as boreal peatlands (IPCC, 2014). Accordingly, over several decades, there has been a growing interest in studying carbon cycling, the implications of long-term carbon dynamics in these ecosystems, and their vulnerability to future climate change (Gorham, 1991; McLaughlin, 2004; Hajek et al., 2011; Garneau et al., 2014). The controls exerted by multiple drivers of climate change, including increased temperature, elevated atmospheric CO2 and water table drawdown, on nutrient cycling and the community structure of different groups of above- and belowground organisms in boreal peatlands have been well studied and documented (Hobbie, 1996; Peltoniemi et al., 2009; Blankinship et al., 2011; Lindo, 2015). The results of these studies have raised several concerns about direct and indirect effects of climate change factors on the pattern of carbon dynamics in boreal peatlands potentially turning these ecosystems from carbon sinks into carbon sources, that might subsequently compound the negative effects of climate change worldwide (Limpens et al., 2008; Dise, 2009). Of exceptional concern are the largely unknown potential interactions among these multiple drivers of climate change and thus their possible combined effects on the structure and function of boreal peatlands (Jonsson et al., 2015).

Understanding the combined impacts of multiple climate change stressors on peatlands carbon dynamics is elaborately linked to their soil biodiversity, particularly the fungi, as the dominant group of decomposers and key players of the ecological functioning of these ecosystems. Because of this, my PhD research was planned to advance our

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knowledge on the biodiversity and community composition of fungi in boreal peatlands, and subsequently to study the combined effects of multiple drivers of climate change on the community composition of fungi, while inferring the potential consequences of these changes for long term carbon storage in boreal peatlands. Within this context, my thesis 1) outlines new methodological techniques to quantify and characterize fungal communities more accurately, 2) describes patterns of diversity in variable habitats of natural peatlands, and 3) presents results of a large-scale experimental climate change project examining multiple climate factors on boreal peatland fungal communities at two different methodological scales.

6.1 Fungal Species Richness in Boreal Peatlands

6.1.1 Metabarcoding Studies of Fungi Fungal diversity discovery using traditional molecular and culture-based techniques have several limitations to identifying many fungal groups present in environmental substrates (Gleason et al., 2004; Peltoniemi et al., 2009; Filippova & Thormann, 2014; Grum- Grzhimaylo et al., 2016). Although recently, our knowledge of species richness of soil- inhabiting fungi has improved substantially by the advent of Next Generation Sequencing (NGS) (Hert et al., 2008), the diversity of fungi recovered from environmental samples is also highly affected by the choice of genetic marker and primers (Beeck et al., 2014). Accordingly, the aim of the second chapter of my thesis was to develop new sets of primers based on the D1 variable region of large subunit (LSU) rDNA that is compatible with chemistry of current NGS platform (Illumina MiSeq) to provide a broader insight into fungal species richness in soils. The reproducibility of these primers was empirically tested using two different types of soil, and their performance was validated by comparing to other established primers (ITS3_KYO2/ITS4_KYO3). The majority of fungal taxa belonging to major fungal groups were recovered from both soil types using these newly designed primers, and despite the fact that there were some trade-offs in fungal biodiversity discovery, the newly designed LSU primers outperformed other existing primers in terms of the range of fungal taxa recovered. Therefore, they can be used as complementary primers sets to improve the efficiency of other universal primers in metabarcoding studies. 140

6.1.2 Biodiversity Assessment of Peat-inhabitting Fungi

Fungi are known to play major roles in decomposition of recalcitrant organic compounds in acidic environments such as boreal peatlands (Williams & Crawford, 1983; Thormann, 2006b). It is generally predicted that, given an expected lowering of the water table under future climate change scenarios, the rate of decomposition may increase in boreal peatlands not only at the surface, but also lower in the peat column as microbial activities are less restricted by anoxic conditions (Aerts & Ludwig, 1997; Blodau, 2002; Kotowska, 2013). This release from anoxia and concomitant increase in microbial activity and heterotrophic respiration may potentially accelerate the impacts of climate change through additional CO2 release (Freeman et al., 2001). Yet, neither does this prediction account for the physical conditions of peatlands at the microscale (i.e. hummocks and hollows), nor does it include the effects of microscale on the structure of fungal communities that might affect the ecosystem level responses to future climate change. Therefore, the objective of my third chapter was to use the newly designed primers to elucidate the dissimilarities in fungal community composition driven by different microscales of boreal peatlands as well as depth under a natural state.

Field sampling of hummock and hollow from a boreal nutrient-poor fen area revealed the wide variety of fungal taxa belonging to different functional groups that occurred in each microscale. I found that there were higher frequencies of aquatic and anaerobic species, as well as root-associates of trees, in hollows and a greater abundance of root-associates of graminoids and ericaceous shrubs in hummocks. While the hollow microscale revealed more diverse and heterogeneous fungal communities than hummocks in general, the bottom layer of hummocks contained comparable fungal species richness to the middle horizon of hollows. This zone possessed the highest fungal richness and the most variable community composition among all depths examined in this study, most likely propelled by peat moisture and the relative position of the water table in each microscale. Although the effect of water table on the structure of fungal communities in peatlands has been demonstrated previously (Peltoniemi et al., 2009; Trinder et al., 2008), my study is one of the first to identify a limited area in each microscale as the zone of high fungal diversity hosting the most varied community composition of fungi in boreal peatlands.

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Taken together, when the interactive effects of depth and microscales are considered, fungi from the high diversity zone might be most affected by changes in their environmental conditions, in particular water table drawdown, under future climate change scenarios.

6.2 Climate Change Impacts on the Structure of Fungal Communities in Boreal Peatlands

6.2.1 Degradative Sucession of Fungi Involved in the Process of Decomposition

The process of decomposition generally comprises several steps during which complex organic matter gradually turns into assimilable energy and nutrient sources for microbes and plants (Brinson et al., 1981; Thormann, 2006b). Each step of the process of decomposition is usually accomplished by a particular group of microorganisms that have appropriate enzymatic capacities to process the available substrate. Normally in the initial stages of decomposition when there are labile resources, fungi that use these labile carbon sources appear in high abundances, and in the later stages of decomposition when soluble resources are used up and complex organic compounds are left, recalcitrant carbon decomposers form the main colonizers of the substrate (Thormann, 2006b). However, this succession of microbes, in particular fungi, involved in the process of decomposition is not often seen in boreal peatlands, perhaps due to chemical and physical constraints of peatlands (such as low temperatures, or high levels of antimicrobial compounds secreted by Sphagnum mosses) that would affect fungal growth and metabolism, slowing the process of decomposition (Thormann et al., 2004; Thormann, 2006a; Artz et al., 2007). Several studies have demonstrated the sensitivity of decomposition rate of different types of substrata (organic layers beneath the surface) as well as decomposer communities to changes in moisture, temperature and litter quality (Bartsch & Moore, 1985; Gorham, 1991; Thormann et al., 2001; Preston et al., 2012). Therefore in my fourth chapter I specifically analysed the main and interactive effects of multiples drivers of climate change including increased temperature, elevated atmospheric CO2 and water table drawdown on the degradative succession of fungi in decomposing peat in litterbags. Here, I specifically demonstrated that warmer and drier conditions pushed fungal

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communities in decomposing peat towards the successional expansion of different saprotrophic groups that corresponded with the rate of resource utilization in litterbags observed by Dieleman et al. (2016).

This trend of succession of saprotrophic groups observed in litterbags under experimental climate change conditions is not surprising from a metabolic perspective. I attribute this successional pattern in fungal community to changes in the quality and water potential of Sphagnum litter, and specifically, the exploitation of resources within the litterbags under the experimental conditions. However, within litterbags, where resources were the same to start, and limited in supply, labile resources were used quickly, such that after four months, recalcitrant materials were left, leading to shifts in fungal communities towards more recalcitrant decomposers. A recent study has demonstrated that using labile carbon is not limited to a particular group of fungi; even recalcitrant carbon decomposers exploit a huge amount of labile carbon pool while also utilizing complex carbon pools (de Vries & Caruso, 2016) that might explain the fast consumption of labile resources in this experiment.

Under natural environmental conditions following changes in weather conditions such as increased temperature, there will be fluctuations of labile carbon input into soil matrix through changes in the rate of photosynthesis and plant root exudates (Bardgett et al., 2005). Such dynamic of carbon input into the soil and high exploitation rate of labile resources by different groups of fungi as their main and the most efficient carbon sources will increase the competition among them (de Vries & Caruso, 2016). Particularly at higher temperatures, recalcitrant carbon decomposers will proliferate faster due to their ability to use broader sources of energy leading to appearance of temporal niche partitioning or degradative succession of fungi in boreal peatlands (Treseder et al., 2016). Therefore even under natural soil conditions where resources are much bigger and are always being replenished by plant root exudates or mycorrhizal hyphae (Scharlemann et al., 2014), the dynamics of labile carbon input into peat matrix might create hotspots for the populations of particular groups of fungi under climate change conditions (de Vries & Caruso, 2016). The priming effects of these labile carbon sources on microbial activities and their metabolic rates might increase the rates of CO2 release from boreal peatlands 143

under future climate scenarios. The effects of warmer and drier conditions on fungal community composition as observed in this study, correspond well with the results of previous studies demonstrating increased temperature, and lower water table as drivers of the variations in the structure of soil fungal communities (Janssens & Davidson, 2006; Wang et al., 2015). I also found that endophytic and mycorrhizal root-associated fungi considerably benefited from warmer and drier conditions most likely due to an indirect effect of increased abundances of their host plants (i.e. graminoids and ericaceous shrubs) in the system under the same experimental conditions (Dieleman et al., 2015). Taken together, this data chapter indicates that following lowered water table position and increased temperatures under future climate change scenarios, a re-organization of belowground fungal communities are likely to happen with a potential to accelerate the rate of decomposition and CO2 release at different stages of decomposition. However, the degree and pattern of community turnover and its effects on carbon dynamics and CO2 release may vary depending on resources available and the peatland type.

6.2.2 Functional Diversity and Community Composition of Fungi undr Climate Change

Species diversity is usually considered as the more important factor in soil microbial communities than species richness in nutrient mineralization (Heemsbergen et al., 2004; Setälä et al., 2005; Bardgett & van der Putten, 2014); however, changes in belowground community composition and the high redundancy of species within functional groups are of the most significance for ecosystem-level services (Bardgett & van der Putten, 2014). Particularly in boreal peatlands, carbon dynamics are considerably affected by community composition of fungi (Thormann, 2006a) that are known to be very responsive to different climate change stressors (Blankinship et al., 2011). It is unclear how the interaction among these multiple drivers of climate change might contribute to a compositional or a functional shift in overall fungal community of these crucial habitats. Hence, my fifth chapter develops based on the results reported in previous chapters to evaluate the combined effects of multiple climate change stressors (tested in chapter four) on fungal communities in different fractions of peat column over two growing seasons. I

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clearly reveal that increased temperature is the key factor that leads to specific changes in the structure of fungal community across a depth gradient in boreal peatlands.

Despite the observed importance of water table and soil moisture in structuring fungal communities and influencing fungal richness and diversity under natural (field) conditions (chapter three), and the short-term effects of water table drawdown on degradative succession of fungi observed in chapter four, the results of my experimental work in chapters four and five show the importance of temperature as a driver in fungal community composition. Both experiments demonstrated a fairly similar pattern of temperature effects on fungal community composition whereby relative frequencies of recalcitrant compound decomposers and mycorrhizal root-associated fungi were significantly more abundant under increased temperatures of + 4 °C and + 8 °C than ambient conditions. These changes suggest that depending on the depth of peat fraction, increased temperatures of 4 °C to 8 °C above ambient might be the threshold for peatland fungal communities to undergo a compositional shift under future climate change scenarios. The positive effects of increased temperatures on different groups of decomposers and root-associated fungi of boreal ecosystems have been previously reported in different studies (Morgado et al., 2015; Treseder et al., 2016). However, this study is one of the few studies that empirically show the co-occurrence of the shift in fungal community composition in the favour of both recalcitrant compound decomposers and vascular plant root-associated fungi at the expense of aquatic and moss-associated fungi across a depth gradient. Comparing the pattern of fungal richness and community composition exposed to experimental climate change conditions to those studied in different microscales of boreal peatlands under natural state (chapter three), suggests that the pattern of fungal richness in peat samples before treatment was most similar to the pattern observed in hollows with highest richness in the middle horizon of the peat monoliths. This pattern is not surprising as the peat monoliths used in chapter five had been collected from the “lawn area” between hollow and hummock that has relatively similar moisture conditions to hollows (McLaughlin & Webster, 2013). However, the experimental climate change conditions changed this pattern to the trend observed in hummocks, with highest richness in the bottom horizon. These results strengthen my

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prediction in chapter three that the area of high fungal diversity would be most affected by future climate change. These results show that the area of high fungal diversity will most likely shift downward under climate change conditions.

The increased dominance of graminoids and ericaceous shrubs at the expense of Sphagnum mosses that were seen in peat monoliths after 18 months of experimental warming treatments (Dieleman et al., 2015) will probably occur in boreal peatlands under future climate change conditions. This increase will co-occur with an increase in the abundances of root-associated fungi as observed in this experiment. These together will increase labile carbon input into peat matrix through root and hyphal exudates providing more resources for different groups of decomposers expanding the richness of decomposer comunity and priming the process of decomposition of more complex organic compounds (de Vries & Caruso, 2016). As a result, a combination of the fungal community compositions from both microscales (i.e. higher abundances of root- associates of graminoids and ericaceous shrubs, observed in hummocks, and consequently higher richness and diversity of the decomposer community, observed in hollows) might be seen throughout boreal peatlands under future climate change scenarios. These scenarios might accelerate the rate of carbon release from these significant global stores, escalating climate change impacts around the world.

6.3 Limitations of Experimental Design

In each of the two main parts of my PhD research including discovery of fungal biodiversity in boreal peatlands (chapter two and chapter three) and climate change impacts on fungal community of boreal peatlands (chapter four and chapter five), some caveats might have limited the interpretation of my results. For example, in chapters two and three, although the primers that we developed resolve a fairly wide range of fungal taxa compared to some other existing ITS primers in peat samples, they did not recover any members of Archaeorhizomycetes belonging to Ascomycota that are known to be common taxa in boreal peatlands (Rosling et al., 2011). This could be due to mismatches between forward primer (LSU200A) and the target region in this group of fungi. Therefore the fungal diversity in different microscales of natural peatland or in peat

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samples under experimental conditions was definitely higher than what I reported in this study. It is hard to predict how this omission would have affected the interpretation of my results as members of Archaeorhizomycetes have been reported from different soil types, and their specific ecological niches are still uncertain (Rosling et al., 2011). Additionally, in the development of the LSU primers, it was necessary to create two sets of primers to capture all important groups of fungi in peat samples (i.e. one set to capture Ascomycota and another set to recover all other fungi). The consequence of this, which became pronounced in chapter four and five, was that I was not able to comprehensively describe the climate change impacts on fungal communities in their totality. Although it was possible to combine analyses, doing so would increase the computation time and the probability of exaggerating the fungal diversity discovered (Gobet et al., 2010). Therefore, I decided to analyze the datasets for each fungal group separately to get a more accurate estimate of fungal diversity in peat samples. As such I could not see the whole community changing at once, which prevented getting a full assessment of the dynamics of fungal community under experimental climate change conditions. Obviously, the overall trend of compositional shifts in decomposer communities under different experimental treatments remain solid as different taxonomic groups with similar functions belonging to either Ascomycota or other fungal groups showed similar trends under different warming treatments. However, it is uncertain that the compositional shifts within root-associate communities with dominance of root-associated fungi from Basidiomycota under + 4 °C treatments, and prevalence of root-associated fungi from Ascomycota under + 8 °C treatments would have been still observed if I could simultaneously look at total fungal communities, under experimental climate change conditions.

One major factor that requires consideration of my results, is the condition associated with the environmentally controlled greenhouses used in chapters four and five to identify the principal climate change drivers re-structuring community composition of fungi in boreal peatlands under highly standardized conditions. There are several considerations here. First, the infrastructure in these greenhouses could not maintain temperatures below 10 °C, precluding simulation of winter conditions of boreal

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peatlands. As such, from November to April, the ambient temperature was set to 11.5 °C (the average growing season temperature of the sampling site). Such temperature conditions would have precluded temporal fluctuations of different fungal species that normally happen under natural state through the year by providing constantly favourable environment for particular groups of fungi. The second consideration is the growing season temperatures used from May to October. Here the ambient temperature was programmed to match the mean daily temperature of London, Ontario in the past five years (with + 4 °C and + 8 °C offsets in other treatments). These temperature conditions were about 5 °C higher than average daily temperature of the growing season in the past 30 years in the original sampling site. As a result, our warming treatments of + 4 °C and + 8 °C were in fact greater temperature treatments than anticipated, and represent extreme warming scenarios, rather than average warming scenarios predicted for the next 50- 100 years for boreal peatlands (IPCC, 2014). That said, the patterns observed in the experimental research presented here, which occurred under warming treatment in 1-2 years, I would expect to take several decades under natural environmental warming. In addition to the temperature regimes, all mesocosms were isothermal by depth due to the ‘pot-type’ nature of the experimental set-up, consequently affecting the pattern of soil temperature decline normally seen under natural environments (Nichols, 1998). Based on the climate change predictions, an increase in surface soil temperature is anticipated following an increase in surface air temperature (IPCC, 2014), with a buffered effect in lower peat profiles. Although the buffering effect of depth on the magnitude of temperature impacts on fungal community composition was observed to some extent in my study as discussed in chapter 5, this artefact of the experiment might have overstressed the potential changes in community composition of fungi in lower peat profile (below 5 cm depth).

Lastly, the exclusion of trees in mesocosms used in this study, as discussed in chapter five, obviously influenced and precluded the inclusion of important groups of mycorrhizal root-associated fungi, in particular ectomycorrhizal root-associates in association with trees. This limitation definitely influenced the fungal diversity observed, and possibly affected the pattern of restructuring of fungal communities that I observed

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under simulated climate change conditions. For example, having trees could have caused ectomycorrhizal fungi to have competitive advantages in the dynamics of the fungal community. Additionally, ectomycorrhizal root-associates are known not only to facilitate nutrient uptake for their host plants, but also to provide favourable environment for some particular groups of fungi with limiting functional characteristics in the soil (mainly labile carbon consumers such as some groups of yeasts) through secretion of simple sugars and proteins in the soil (Fransson & Rosling, 2014). Therefore with trees and their associated ectomycorrhizal fungi I might have detected greater richness or diversity of fungi developing under experimental climate change.

6.4 General Conclusions and Future Research

Collectively, my PhD research elucidates the fungal biodiversity as well as interactive and cascading effects of multiple drivers of climate change on community composition of these fungi in a portion of boreal peatlands. Particularly, this work points out that climate change can trigger a shift in fungal community composition of Boreal peatlands, interactively affecting re-organization of aboveground communities and modifying the pattern of carbon dynamics in this ecosystem. The shifts in the structure of fungal communities demonstrated here, alongside examples from other studies, substantiate a fairly stable transition in some Boreal peatlands under climate change scenarios that could accentuate CO2 emission over longer period of time.

While my research provides insights into fungal ecology and climate change biology, further research can build on the results demonstrated here. As a case in point, although a wide range of fungal taxa were recovered in my metabarcoding studies of Boreal peatlands, there are certainly other additional fungi that were not detected probably due to their very low abundances in the substrate or mismatches to our primers. Perhaps the third generation sequencing techniques such as “Single Molecule Sequencing” that eliminates the requirement for doing a PCR (Lavezzo et al., 2016), and provides sequences directly from single molecules of nucleic acid present in the sample, can increase the probability of detection of even fungal species with very low abundances (Lavezzo et al., 2016). Likewise, improving primer matches (developed here) to the fungi 149

not intended as targets by these primers as discussed earlier will unravel further fungal assemblages in Boreal peatlands.

My research also focuses on the impacts of climate change on fungal communities; although the mesocosms that I used here were environmentally controlled greenhouses, which partially simulated the realistic climate change condition, they had some limitations as discussed above that hindered them ideal representations (Stewart et al., 2013). Therefore, more comprehensive simulations of climate change conducted in the fields that could include different aspects of naturally oscillating environmental conditions such as solar irradiation, permafrost degradation or precipitation are required for Boreal peatlands to determine the impacts of ideally realistic climate change on these ecosystems structures and functions. Also, the long-term experiments that are empowered in the field would allow fungal communities to adapt to new environmental conditions enabling us to detect the response of all important groups of fungi even those with very slow growth rate such as lichens to climate change under natural environment (Green et al., 2012).

6.5 Concluding Remarks

Boreal peatlands are globally significant ecosystems that cover approximately 17% of the boreal zone, and sequestrate a large fraction of terrestrial carbon. If the pattern of carbon accumulation changes in these ecosystems (i.e., the rate of sequestration lowers) under climate change conditions, boreal peatlands will have the potential to exacerbate the impacts of climate change worldwide. Comprehending how these ecosystems will respond to future climate change is convolutedly linked to their soil biodiversity, meaning that soil biodiversity discovery is regarded as the first step to understand the impacts of climate change (Wolters et al., 2000). Following this step are research efforts considering the interactive and cascading effects of multiple drivers of climate change on ecosystem structure and function that have come into effect. Such studies provide a conceptual framework for understanding the potential shifts in ecosystem structure and their consequences on ecosystem function that can eventually enable us to accurately

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predict if boreal peatlands will continue to sequestrate carbon or they will turn into carbon sources in the near future.

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de Vries FT, Caruso T (2016) Eating from the same plate? Revisiting the role of labile carbon inputs in the soil food web. Soil Biology and Biochemistry, 102, 4–9. Dieleman CM, Branfireun BA, McLaughlin JW, Lindo Z (2015) Climate change drives a shift in peatland ecosystem plant community: Implications for ecosystem function and stability. Global Change Biology, 21, 388–395. Dise NB (2009) Peatland response to global change. Science, 326, 810–811. Filippova NV, Thormann MN (2014) Communities of larger fungi of ombrotrophic bogs in West Siberia. Mires and Peat, 14, 1–22. Fransson P, Rosling A (2014) Fungal and bacterial community responses to Suillus variegtus extraradical mycelia and soil profile in Scots pine microcosms. Plant and Soil, 385, 255–272. Freeman C, Ostle N, Kang H (2001) Peatlands: an enzyme ‘latch’ on a global carbon store. Nature, 409, 149. Garneau M, van Bellen S, Magnan G, Beaulieu-Audy V, Lamarre A, Asnong H (2014) Holocene carbon dynamics of boreal and subarctic peatlands from Québec, Canada. The Holocene, 24, 1043–1053. Gleason FH, Letcher PM, McGee PA (2004) Some Chytridiomycota in soil recover from drying and high temperatures. Mycological Research, 108, 583–589. Gobet A, Quince C, Ramette A (2010) Multivariate Cutoff Level Analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38, e155. Gorham E (1991) Northern peatlands: Role in the carbon cycle and probable responses to Climatic Warming. Ecological Applications, 1, 182–195. Green TG, Brabyn L, Beard C, Sancho LG (2012) Extremely low lichen growth rates in Taylor Valley, Dry Valleys, continental Antarctica. Polar Biology, 35, 535–541. Grum-Grzhimaylo OA, Debets AJ, Bilanenko EN (2016) The diversity of microfungi in peatlands originated from the White Sea. Mycologia, 108, 233–254. Heemsbergen DA, Berg MP, Loreau M, van Hal JR, Faber JH, Verhoef HA (2004) Biodiversity effects on soil processes explained by interspecific functional dissimilarity. Science, 306, 1019–1020.

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Hert DG, Fredlake CP, Barron AE (2008) Advantages and limitations of next-generation sequencing technologies: a comparison of electrophoresis and non-electrophoresis methods. Electrophoresis, 29, 4618–4626. Hobbie S (1996) Temperature and plant species control over litter decomposition in Alaskan tundra. Ecological Monographs, 66, 503–522. IPCC (2014) Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the International Panel on Climate Change. Cambridge University Press, London. Janssens IA, Davidson EA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165–173. Jonsson M, Kardol P, Gundale MJ, Bansal S, Nilsson MC, Metcalfe DB, Wardle DA (2015) Direct and indirect drivers of moss community structure, function, and associated microfauna across a successional gradient. Ecosystems, 18, 154–169. Kotowska A (2013) The long-term effects of drainage on carbon cycling in a boreal fen. PhD Thesis. McGill University. Lavezzo E, Barzon L, Toppo S, Palù G (2016) Third generation sequencing technologies applied to diagnostic microbiology: benefits and challenges in applications and data analysis. Expert Review of Molecular Diagnostics, 16, 1011–1023. Limpens, J., Berendse, F., Blodau, C et al. (2008) Peatlands and the carbon cycle: from local processes to global implications–a synthesis. Biogeosciences, 5, 1475–1491. Lindo Z (2015) Warming favour small-bodied organisms through enhanced reproduction and compositional shifts in belowground systems. Soil Biology and Biochemistry, 91, 271–278. McLaughlin JW, Ontario Forest Research I (2004) Carbon assessment in boreal wetlands of Ontario, project no. CC-167. Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste. Marie, ON. McLaughlin J, Webster K (2013) Effects of a changing climate on peatlands in permafrost zones: a literature review and application to Ontario's Far North, project no. CCRR-34. Ontario Forest Research Institute, Sault Ste. Marie, ON. Morgado LN, Semenova TA, Welker JM, Walker MD, Smets E, Geml J (2015) Summer temperature increase has distinct effect on the ectomycorrhizal fungal communities

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of moist tussock and dry tundra in Arctic Alaska. Global Change Biology, 21, 959– 972. Nichols DS (1998) Temperature of upland and peatland soils in a north central Minnesota forest. Canadian Journal of Soil Science, 78, 493–509. Peltoniemi K, Fritze H, Laiho R (2009) Response of fungal and actinobacterial communities to water-level drawdown in boreal peatland sites. Soil Biology and Biochemistry, 41, 1902–1914. Preston MD, Smemo KA, McLaughlin JW, Basiliko N (2012) Peatland microbial communities and decomposition processes in the James Bay Lowlands, Canada. Frontiers in Microbiology, 3, 70–76. Rosling A, Cox F, Cruz-Martinez K et al. (2011) Archaeorhizomycetes: unearthing an ancient class of ubiquitous soil fungi. Science, 333, 876–879. Scharlemann JP, Tanner EV, Hiederer R, Kapos V (2014) Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Management, 5, 81–91. Setälä H, Berg MP, Jones TH (2005) Trophic structure and functional redundancy in soil communities. In: Biological Diversity and Function in Soils (eds Bardgett RD, Usher MB, Hopkins DW), pp. 236–249, Cambridge University Press, Oxford. Stewart RI, Dossena M, Bohan DA et al. (2013) Mesocosm experiments as a tool for ecological climate-change research. Advances in Ecological Research, 48, 71–181. Thormann MN, Bayley SE, Currah RS (200l) Comparison of decomposition of belowground and aboveground plant litters in peatlands of boreal Alberta, Canada. Canadian Journal of Botany, 79, 9–22. Thormann MN, Currah RS, Bayley SE (2004) Patterns of distribution of microfungi in decomposing bog and fen plants. Canadian Journal of Botany, 82, 710–720. Thormann MN (2006a) Diversity and function of fungi in peatlands: a carbon cycling perspective. Canadian Journal of Microbiology, 86, 281–293. Thormann MN (2006b) The role of fungi in boreal peatlands. In: Boreal Peatland Ecosystems, pp. 101–123, Springer, Berlin, Heidelberg.

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Treseder KK, Marusenko Y, Romero-Olivares AL, Maltz MR (2016) Experimental warming alters potential function of the fungal community in boreal forest. Global Change Biology, 22, 3395–3404. Wolters V, Silver WL, Bignell DE et al. (2000) Effects of global changes on above-and belowground biodiversity in terrestrial ecosystems: implications for ecosystem functioning. Bioscience, 50, 1089–1098. Wang H, Richardson CJ, Ho M (2015) Dual controls on carbon loss during drought in peatlands. Nature Climate Change, 5, 584–587. Williams RT, Crawford R L (1983) Microbial diversity in Minnesota peatlands. Microbial Ecology, 9, 201–214.

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Appendices

Appendix A: Chapter 2, 3, 4 and 5 Supplementary Material

Table A.1 Chapter 2 supplementary material: Partial list of vegetation found in association with samples taken from subarctic and peatland soils. Peat plant species list obtained from Dieleman et al. (2015); plants associated with subarctic soil samples identified by Michael Burzynski. Subarctic soils in Torngat, Labrador *Peatland soils in White River, ON Armeria maritima Abies balsamea Antennaria sp. Andromeda polifolia Arnica angustifolia subsp. angustifolia Campylium stellatum var. stellatum Astragalus alpinus Carex disperma Betula glanduosa Carex magellanica Calamagrostis stricta Chamaedaphne calycalata Carex scirpoidea Drosera rotundifolia Cerastium arvense Gaultheria hispidula Empetrum nigrum Kalmia polifolia Eriophorum sp. Lycopodiella inundata Pedicularis labradorica Maianthemum trifolium Poa sp. Picea mariana Potentilla crantzii Rhododendron groenlandicum Phyllodoce caerulea Sphagnum spp. Rhodiola rosea Vaccinium myrtilloides Rhododendron groenlandicum Vaccinium oxycoccos Rhododendron tomentosum subsp. subarcticum Salix sp. Solidago multiradiata Taraxacum sp. Vaccinium uliginosum * Plant species list obtained from (Dieleman et al., 2015)

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Figure A.1 Chapter 2 supplementary material: DNA concentrations and comparative absorption values for three soil types. Black lines indicate the median, red diamonds indicate the mean. (a) DNA concentrations (ng/µL) of three soil types after Aurora HMW DNA Extraction (Subarctic Soil), and Zymogen Soil DNA Extraction Kit (Lower Peat and Upper Peat). (b) 260/280 and (c) 260/230 absorption ratios for the resulting DNA extractions. A ratio value of ~1.8 for 260/280 and 2.0–2.2 for 260/230 values indicate DNA free from most proteins, phenols, or other contaminants.

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Table A.2 Chapter 2 supplementary material: Identitiy and ENA accession number of all samples (Arctic soil and peat soil), amplified using primer sets LSU200A-F/LSU476A-R (Ascomycota), LSU200-F/LSU481-R (all other fungi), and ITS3_KYO2/ITS4_KYO3.

Lat ENA Sample Accession Sample F-Primer R-Primer Type Depth Horizon (DD) Lon (DD) Number TG1 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG2 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG3 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG4 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG5 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG6 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG7 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 TG8 LSU200-F LSU481-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197777 P0_7 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_18 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_10 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_1 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_2 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_19 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_11 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_3 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197986 P0_17 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 P0_5 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 P0_6 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 P0_14 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 P0_20 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 P0_13 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 158

P0_21 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986 P0_24 LSU200-F LSU481-R Peat 0.35 m Lower 85.338 48.354 ERS1197986

TG1 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG2 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG3 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG4 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG5 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG6 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG7 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 TG8 LSU200A-F LSU476A-R Arctic Soil 0-0.1 m - 62.802 58.454 ERS1197987 P0_10 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_2 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_1 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_18 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_11 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_7 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_3 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_19 LSU200A-F LSU476A-R Peat 0-0.05 m Upper 85.338 48.354 ERS1197988 P0_21 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_20 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_14 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_6 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_5 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_17 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_24 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988 P0_13 LSU200A-F LSU476A-R Peat 0.35 m Lower 85.338 48.354 ERS1197988

TG4 ITS3_KYO2 ITS4_KYO3 Arctic Soil 0-0.1 m - 62.802 58.454 ERS1196420 159

TG5 ITS3_KYO2 ITS4_KYO3 Arctic Soil 0-0.1 m - 62.802 58.454 ERS1196420 TG7 ITS3_KYO2 ITS4_KYO3 Arctic Soil 0-0.1 m - 62.802 58.454 ERS1196420 P0_18 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1196419 P0_1 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1196419 P0_3 LSU200-F LSU481-R Peat 0-0.05 m Upper 85.338 48.354 ERS1196419

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Table A.3 Chapter 2 supplementary material: Median relative abundanc of sequences amplified by new LSU primers in comparison to Toju et al. (2012) ITS2 Fungal primers. Relative abundance (%) calculated using total number of reads within the phylum for each soil type (348 237a, 941 098b, 134 391, and 96 830 reads, respectively; n=3 for all)

LSU primers ITS2 Fungal primers Rankc Subarctic Upper peat Subarctic Upper peat soil soil Fungi: Unknown 0.22 0.33 0.23 1.66 Zygomycota: Kickxellomycotina: 0.00 — — — Kickxellales Z: K: Harpellales — 0.00 — — Z: Mucoromycotina: Unknown 0.30 — — — Z: Muc: Mucorales 0.48 0.17 — — Z: Muc: Endogonales 0.26 0.00 — — Z: Muc: Mortierellales 11.30 3.88 — — Z: Entomophthoromycotina — 0.04 — — Z: Zoopagomycotina — 0.71 — — Chytridiomycota: Unknown — 1.07 — — C: Chytridiomycetes: Unknown 0.39 1.54 — — 161

C: Chytr: Chytridiales 0.32 0.08 — — C: Chytr: Rhizophydiales 0.14 — — — C: Chytr: Spizellomycetales 0.00 0.03 — — C: Chytr: Lobulomycetales 0.15 0.02 — — C: Monoblepharidomycetes: 0.05 0.04 — — Monoblepharidales Glomeromycota: Unknown — 0.06 — — G: Glomeromycetes: 4.46 — — — Archaeosporales G: Glom: Glomerales 1.62 — — — Basidiomycota: Unknown 0.73 0.30 0.95 0.00 B: Agaricostilbomycetes: — 0.05 — — Spiculogloeales B: Pucciniomycetes: Unknown 0.08 — — — B: Microbotryomycetes incertae 0.05 0.05 — — sedis B: M: Leucosporidiales 0.06 — — — B: M: Sporidiobolales 0.00 — — — B: Exobasidiomycetes: 0.05 — — — Exobasidiales

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B: Exobasidiomycetes: Tilletiales — 0.00 — — B: Tremellomycetes: Unknown 0.07 — — — B: T: Cystofilobasidiales 0.15 — — — B: T: Filobasidiales 1.09 0.00 — — B: T: Tremellales 0.07 0.02 — — B: Agaricomycetes: Unknown 3.01 1.40 — — B: Ag: Auriculariales 0.80 0.12 — — B: Ag: Sebacinales 2.82 8.30 3.81 2.82 B: Ag: Cantharellales 2.28 5.81 0.01 1.08 B: Ag: Trechisporales 0.28 — — — B: Ag: Gomphales — 0.00 — — B: Ag: Hymenochaetales 0.02 3.12 — — B: Ag: Corticiales — 2.24 — — B: Ag: Polyporales 0.04 3.41 0.00 0.61 B: Ag: Thelephorales 7.11 0.48 5.34 0.02 B: Ag: Russulales 1.03 0.00 13.37 0.03 B: Ag: Agaricales 8.98 30.15 11.90 6.57 B: Ag: Atheliales 0.04 12.68 — — B: Ag: Boletales 1.88 2.18 0.87 0.41 Ascomycota: Unknown 2.51 2.24 — —

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Ascomycota incertae sedis 0.13 0.04 — — A: Saccharomycetes: 0.33 0.04 — — Saccharomycetales A: Orbiliomycetes: Orbiliales 0.01 0.02 — — A: Archaeorhizomycetesd — — 0.53 0.55 A: Pezizomycotina: Unknown — — 5.13 0.67 A: Pezizomycetes: Pezizales 0.07 0.08 — — A: Geoglossomycetes: 0.02 0.10 — — Geoglossales Ascomycota: Coniocybomycetes: 0.05 — — — Coniocybales Ascomycota: Dothideomycetes: 0.05 — — — Unknown A: D: incertae sedis 2.30 — 2.90 0.06 A: D: Capnodiales 0.41 0.00 — — A: D: Pleosporales 0.39 0.05 — — A: D: Mytilinidiales 0.44 1.50 — — A: D: Venturiales 3.25 0.04 — — A: Eurotiomycetes: Unknown 0.25 — 2.67 0.03 A: E: Chaetothyriales 3.40 0.19 4.13 2.92

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A: E: Eurotiales 1.16 0.20 — — A: Lecanoromycetes: Unknown 1.00 — — — A: Lecanoromycetes: Lecanorales 3.17 — 3.35 0.04 A: Lecanoromycetes: Ostropalales 0.02 — — — A: Leotiomycetes: Unknown 6.29 0.28 0.23 5.88 A: Leotiomycetes: incertae sedis 0.63 0.13 0.12 3.37 A: L: Helotiales 20.15 16.41 42.28 71.12 A: L: Rhytismatales 1.17 0.01 2.14 0.44 A: L: Thelebolales 0.45 — — — A: Sordariomycetes: Unknown 1.01 0.00 — — A: S: incertae sedis 0.30 — — — A: S: Xylariales 0.01 — 0.01 0.54 A: S: Hypocreales 0.66 0.37 0.02 1.18 A: S: Microascales 0.00 0.00 — — A: S: Coinochaetales 0.04 — — — A: S: Diaporthales 0.01 — — — A.S: Sordariales — 0.02 — — A: S: Magnaporthales 0.00 0.00 — —

165

Figure A.2 Chapter 3 supplementary material: Cluster dendrogram of samples from three depths (“upper” shown by squares, “middle” shown by triangles, “bottom” shown by circles) of hollows/ hummocks and the bar plot of relative frequencies of Ascomycota orders corresponding to each sample. Samples from hollows and hummocks are shown by black and grey symbols, respectively.

166

Figure A.3 Chapter 3 supplementary material: Cluster dendrogram of samples from three depths (“upper” shown by squares, “middle” shown by triangles, “bottom” shown by circles) of hollows/ hummocks and bar chart of relative frequencies of other fungal orders corresponding to each sample. Samples from hollows and hummocks are shown by black and grey symbols, respectively.

167

Figure A.4 Chapter 4 supplementary material: Changes in the relative frequencies of Ascomycota (a) and other fungal community (b) at higher taxonomic levels (classes) observed at different time points of the experiment.

168

Table A.4 Chapter 4 supplementary material: Indicator taxa significantly affected by hydrology and temperature treatments based on ALDEx results. Functional designations are based on the literature where known.

Ascomycetous Closest known identity Function* Reference OTUs OTU 196 Ericoid mycorrhizal root Root-associate Vohnik et al., 2012 fungus OTU 1 Barrenia panicia Root-associate Walsh et al., 2015 OTU 412 Penicillium sp Saprotroph Domsch et al., 2007 OTU 22 Neofabraea Pathogen Michalecka et al., 2015 OTU 19 Dipodascopsis Yeast; labile carbon consumer De Hoog & Smith, 2011 OTU 64 Fungal endophyte Endophyte BLAST OTU 13 Fungal endophyte Endophyte BLAST OTU 63 Pochonia bulbillosa Entomopathogen Adachi et al., 2015

OTU 48 Ericoid mycorrhizal root Root-associate Vohnik et al., 2012 fungus OTU 49 Fungal endophyte Endophyte BLAST OTU 33 Mitrula Cellulose decomposer Redhead et al., 1977 OTU 157 Dactylaria sp Cellulose decomposer Goh & Hyde, 1997 OTU 5 Hyaloscypha Moss-associate; saprotroph Han et al., 2014 Other fungal OTUs OTU 0 Mycena Lignocellulose decomposer Worrall et al., 1997 OTU 159 Rhizophlyctis rosea Cellulose decomposer Gleason et al., 2004 OTU 153 Phaffia Yeast; labile carbon Fell & Johnson, 2011 decomposer OTU 1 Hypochnicium Lignocellulose decomposer Eriksson & Ryvarden, 1976 OTU 143 Globulicium Lignocellulose decomposer Eriksson & Ryvarden, 1976 OTU 241 Mesochytrium Parasite on green alga Gromov et al., 2000 OTU 873 Polyporus Lignocellulose decomposer Núñez & Ryvarden, 1995 OTU 3173 Mycena Lignocellulose decomposer Worrall et al., 1997 OTU 81 Galerina Cellulose decomposer Gulden et al., 2005 OTU 3764 Ceratobasidium Root associate; multiple Mosquera-Espinosa functions et al., 2013 OTU 3552 Rhizidium phycophilum Pollen and chitin decomposer Picard et al., 2009 OTU 106 Mortierella Labile carbon decomposer Domsch et al., 2007

References Adachi H, Doi H, Kasahara Y et al. (2015) Asteltoxins from the entomopathogenic fungus Pochonia bulbillosa 8-H-28. Journal of Natural Products, 78, 1730–1734. De Hoog GS, Smith MT (2011) Dipodascopsis Batra & P. Millner emend. Kurtzman, Albertyn & Basehoar-Powers (2007). In: The Yeasts, A Taxonomic Study (eds 169

Domsch KH, Gams W, Anderson TH (2007) Compendium of Soil Fungi, second ed. IHW-Verlag Eching, Germany. Eriksson J, Ryvarden L (1976) The Corticiaceae of North Europe, vol 4. Fungiflora, Oslo, Norway. Fell JW, Johnson EA (2011) Phaffia M.W. Miller, Yoneyama & Soneda (1976). In: The Yeasts, A Taxonomic Study (eds Kurtzman CP, Fell JW, Boekhout T), PP. 1853– 1856, fifth ed, vol 3. Elsevier, USA. Gleason FH, Letcher PM, McGee PA (2004) Some Chytridiomycota in soil recover from drying and high temperatures. Mycological Research, 108, 583–589. Goh TK, Hyde KD (1997) A revision of Dactylaria, with description of D. tunicata sp. nov. from submerged wood in Australia. Mycological Research, 101, 1265–1272. Gromov BV, Mamkaeva KA, Pljusch AV (2000) Mesochytrium penetrans gen. et sp. nov. (Chytridiales) - a parasite of the green alga Chlorococcum minutum (Chlorococcales), with an unusual behaviour of the sporangia. Nova Hedwigia, 71, 151–160. Gulden G, Stensrud Ǿ, Shalchian-Tabrizi K, Kauserud H (2005) Galerina Earle: A polyphyletic genus in the consortium of dark-spored agarics. Mycologia, 97, 823– 837. Han JG, Hosoya T, Sung GH, Shin HD (2014) Phylogenetic reassessment of Hyaloscyphaceae sensu lato (Helotiales, Leotiomycetes) based on multigene analyses. Fungal Biology, 118, 150–167. Michalecka M, Bryk H, Poniatowska A, Puławska J (2015) Identification of Neofabraea species causing bull's eye rot of apple in Poland and their direct detection in apple fruit using multiplex PCR. Plant Pathology, 65, 643–654. Mosquera-Espinosa AT, Bayman P, Prado GA, Gómez-Carabalí A, Otero JT (2013) The double life of Ceratobasidium: orchid mycorrhizal fungi and their potential for biocontrol of Rhizoctonia solani sheath blight of rice. Mycologia, 105, 141–150. Núñez M, Ryvarden L (1995) Polyporus (Basidiomycotina) and related genera. Synopsis Fungorum, 10, 1–85. Picard KT, Letcher PM, Powell MJ (2009) Rhizidium phycophilum, a new species in Chytridiales. Mycologia, 101, 696–706. 170

Redhead SA (1977) The genus Mitrula in North America. Canadian Journal of Botany, 55, 307–325. Vohnik M, Sadowsky JJ, Kohout P, Lhotakova Z, Nestby R, Kolarik M (2012) Novel root-fungus symbiosis in Ericaceae: sheathed ericoid mycorrhiza formed by a hitherto undescribed basidiomycete with affinities to Trechisporales. PLoS One, 7, e39524. Walsh E, Luo J, Naik A, Preteroti T, Zhang N (2015) Barrenia, a new genus associated with roots of switchgrass and pine in the oligotrophic Pine Barrens. Fungal Biology, 119, 1216–1225. Worrall JJ, Anagnost SE, Zabel RA (1997) Comparison of wood decay among diverse lignicolous fungi. Mycologia, 89, 199–219.

171

Figure A.5 Chapter 5 supplementary material: The comparison of relative frequencies of fungal orders belonging to Ascomycota at initial sampling and in control samples (under ambient conditions) after 18 months of experimental treatment across three depths.

172

Figure A.6 Chapter 5 supplementary material: The comparison of relative frequencies of other fungal orders at initial sampling and control samples (under ambient conditions) after 18 months of experimental treatment across three depths.

173

Table A.5 Chapter 5 supplementary material: Sample IDs with ENA sample accession number. “A” represents samples amplified by “Ascomycota” primers, and “F” represents the same samples amplified by “other fungal” primers.

ENA Sample Sample Accession ID Depth F-primer R-primer Lat (DD) Lon (DD) Number A0_1 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_10 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_11 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_12 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_13 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_14 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_15 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_16 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_17 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_18 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_19 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_2 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_20 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_21 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_22 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_23 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_24 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_25 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_26 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_27 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_28 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_29 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_3 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_30 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_31 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_32 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_33 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_34 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_35 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_36 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_4 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_5 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_6 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_7 Top LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A0_8 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996

174

A0_9 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197996 A18_1 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_10 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_100 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_101 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_102 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_103 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_104 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_105 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_106 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_107 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_108 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_12 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_13 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_16 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_17 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_18 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_19 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_2 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_20 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_21 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_22 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_23 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_25 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_26 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_27 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_28 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_29 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_30 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_31 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_32 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_33 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_34 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_35 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_36 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_37 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_38 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_39 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_4 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_40 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_41 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_42 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_43 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_44 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 175

A18_45 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_46 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_47 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_48 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_49 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_5 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_50 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_51 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_52 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_53 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_54 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_55 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_56 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_57 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_58 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_59 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_60 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_61 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_62 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_63 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_64 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_65 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_66 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_67 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_68 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_69 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_7 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_70 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_71 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_72 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_73 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_74 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_75 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_76 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_77 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_78 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_79 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_8 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_80 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_81 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_82 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_83 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_84 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_85 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 176

A18_86 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_87 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_89 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_9 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197997 A18_90 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_91 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_92 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_93 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_94 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_95 Bottom LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_97 Middle LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_98 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 A18_99 Upper LSU200A-F LSU476A-R 48°21 85°21 ERS1197998 F0_1 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_10 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_11 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_12 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_13 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_14 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_15 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_16 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_17 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_18 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_19 Top LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_2 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_20 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_21 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_22 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_23 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_24 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_25 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_26 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_27 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_28 Top LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_29 Top LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_3 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_30 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_31 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_32 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_33 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_34 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_35 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_36 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197995 F0_4 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197994 177

F0_5 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_6 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_7 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_8 Top LSU200-F LSU481-R 48°21 85°21 ERS1197994 F0_9 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197994 F18_1 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_10 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_100 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_101 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_102 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_103 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_104 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_105 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_106 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_107 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_108 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_11 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_12 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_13 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_14 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_15 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_16 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_17 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_18 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_19 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_2 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_20 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_21 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_22 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_23 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_24 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_25 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_26 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_27 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_28 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_29 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_3 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_30 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_31 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_32 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_33 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_34 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_35 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_36 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 178

F18_37 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_38 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_39 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_4 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_40 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_41 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_42 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_43 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_44 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_45 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_46 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_47 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_48 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_49 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_5 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_50 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_51 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_52 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_53 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_54 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_55 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_56 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_57 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_58 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_59 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_6 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_60 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_61 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_62 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_63 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_64 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_65 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_66 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_67 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_68 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_69 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_7 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_70 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_71 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_72 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_73 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_74 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_75 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_76 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 179

F18_77 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_78 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_79 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_8 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_80 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_81 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_82 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_83 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_84 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_85 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_86 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_87 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_88 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_89 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_9 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197997 F18_90 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_91 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_92 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_93 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_94 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_95 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_96 Bottom LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_97 Middle LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_98 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996 F18_99 Upper LSU200-F LSU481-R 48°21 85°21 ERS1197996

180

Table A.6 Chapter 5 supplementary material: A summary of main and interactive effects (F statistics and P-value) of all the treatments on fungal richness (the number of observed OTUs “S”) and diversity (Shannon index “H”) obtained by repeated measures analysis of variance after 18 months of experimental treatment.

Ascomycota Other fungi Richness (S) Diversity (H') Richness (S) Diversity (H')

Treatment F P F P F P F P

Temperature 8.510 0.002 5.204 0.017 8.130 0.002 3.444 0.048

Atmospheric CO2 3.080 0.097 0.080 0.780 3.084 0.091 7.679 0.010 Water table 0.220 0.646 0.147 0.706 0.056 0.815 2.189 0.151

Temperature×AtmosphericCO2 12.250 <0.001 1.553 0.240 0.466 0.633 0.144 0.866 Temperature×Water table 0.340 0.717 0.684 0.517 0.434 0.652 0.050 0.951

Atmospheric CO2×Water table 0.010 0.941 0.023 0.880 0.584 0.452 0.681 0.417

Temperature×Atmospheric CO2×Water table 0.010 0.991 0.632 0.543 0.293 0.748 0.778 0.470 Depth 2.10 0.138 2.872 0.070 5.129 0.009 0.366 0.695 Depth×Temperature 0.610 0.656 1.660 0.181 4.690 0.002 1.068 0.382

Depth×Atmospheric CO2 1.890 0.166 1.109 0.341 2.343 0.063 0.295 0.745 Depth×Water table 0.040 0.957 0.331 0.720 0.615 0.544 1.123 0.333

Depth×Temperature×Atmospheric CO2 1.040 0.402 0.245 0.910 0.624 0.647 0.817 0.520 Depth×Temperature×Water table 0.10 0.981 0.210 0.931 1.219 0.315 3.167 0.021

Depth×Atmospheric CO2×Water table 0.660 0.523 0.840 0.440 0.621 0.541 0.768 0.469 Depth×Temperature×Atmospheric 0.140 0.965 2.034 0.111 1.295 0.285 1.823 0.081 CO2×Water table

181

Table A.7 Chapter 5 supplementary material: Indicator taxa significantly affected by temperature treatments based on ALDEx results. Functional designations assigned to OTU numbers are based on the literature where known.

Ascomycetous OTUs Closest known identity Function References

Ericoid mycorrhizal root OTU_3 Root-associate Vohnik et al., 2012 fungus OTU_14 Sarcoleotia turficola Cellulose decomposer Dennis, 1971 OTU_19 Alternaria Saprotroph Domsch et al., 2007 OTU_24 Cladosporium Saprotroph Domsch et al., 2007 Root associate; multiple OTU_86 Hymenoscyphus Smith & Read, 1997; Gross et al., 2012 functions OTU_152 Hyalopeziza Cellulose decomposer Han et al., 2014 Root associate; multiple OTU_208 Hymenoscyphus Smith & Read, 1997; Gross et al., 2012 functions Unclassified OTU_697 Endophyte BLAST Hyaloscyphaceae OTU_1202 Lachnellula Stem pathogens of conifers Lamarche et al., 2015 OTU_1426 Hyaloscypha Moss-associate Stenroos et al., 2010 Root associate; multiple OTU_1073 Hymenoscyphus Smith & Read, 1997; Gross et al., 2012 functions Root associate; multiple OTU_1964 Hymenoscyphus Smith & Read, 1997; Gross et al., 2012 functions OTU_2 Chaetomium thermophilum Cellulose decomposer Domsch et al., 2007 OTU_13 Microthyrium Saprotroph Ellis, 1976 OTU_309 Helotiales sp. Endophyte BLAST OTU_1825 Helotiales sp. Endophyte BLAST

182

OTU_347 Meliniomyces variabilis Root-associate Hambleton & Sigler, 2005 OTU_461 Unclassified Helotiales Root-associate BLAST OTU_938 Xenopolyscytalum Cellulose decomposer Crous & Groenewald, 2010 OTU_1999 Barrenia Root-associate Walsh et al., 2015 OTU_29 Penicillium sp. Saprotroph Domsch et al., 2007 OTU_761 Unclassified Helotiales endophyte BLAST OTU_1337 Arachnopeziza Cellulose decomposer Han et al., 2014 OTU_58 Phialocephala sp. Dark septate root endophyte Lukesova et al., 2015 OTU_1321 Arachnopeziza Cellulose decomposer Han et al., 2014

Other fungal OTUs

OTU_0 Mycena Lignocellulose decomposer Worrall et al., 1997 Root associate; multiple OTU_1 Ceratobasidium sp. Mosquera-Espinosa et al., 2013 functions

OTU_9 Sebacina sp. Root-associate Kühdorf et al., 2014

OTU_13 Tephrocybe palustris Moss-associate Hafstetter et al., 2002

OTU_218 Galerina sp. Cellulose decomposer Gulden et al., 2005

OTU_2175 Kuehneromyces Lignocellulose decomposer Bon, 1987

OTU_4532 Cortinarius sp. Root-associate Peintner et al., 2002 OTU_7 Sebacina sp. Root-associate Kühdorf et al., 2014

183

OTU_266 Antrodia sp. Brown rotter Ortiz-Santana et al., 2013 OTU_801 Tricholoma sp. Root-associate Shanks, 1996 OTU_2481 Sebacina sp. Root-associate Kühdorf et al., 2014

OTU_72 Basidiomycetous yeast Labile carbon decomposer Boekhout et al., 2011

OTU_2486 Mycena sp. Lignocellulose decomposer Worrall et al., 1997

OTU_4900 Cymatoderma dendriticum Lignocellulose decomposer Nakasone, 1990

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Table A.8 Chapter 5 supplementary material: The sequence output obtained from initial samples before treatment, and final destructive samples after 18 months of experimental treatment. Total number Initial samples Total OTUs Target OTUs Non-target OTUs of reads Ascomycota 2756903 260 164 96 Other fungi 9547664 449 260 189 Final samples Ascomycota 6988375 342 237 105 Other fungi 7021251 585 381 204

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Appendix B: Permission to Reproduce Published Material

The data from chapter two has been published in Plos One journal as of 2016. This journal is an open access peer-reviewed journal that is free of all restrictions on the access to published works. The journal specifically says:

“You are free to copy and redistribute the material in any medium or format, and remix, transform, and build upon the material for any purpose, even commercially.”

The material in Chapter four has been published in the journal of Microbial Ecology. Copyright of the article entitled “Experimental climate change modifies degradative succession in boreal peatlands fungal communities” published as of 2016 in this journal with contribution of authors (Asma Asemaninejad, R Greg Thorn, Zoë Lindo) was granted by contacting Springer. This article was included in this thesis with permission of Springer.

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Curriculum Vitae Name: Asma Asemaninejad Hassankiadeh Post-secondary University of Guilan Education and Rasht, Guilan, Iran Degrees: Sep 2000- Jun 2004 B.Sc. (Plant Protection)

University of Guilan Rasht, Guilan, Iran Sep 2005-Jan 2008 M.Sc. (Plant Pathology)

The University of Western Ontario London, Ontario, Canada Jan 2013-present Ph.D. (Biology- Molecular Fungal Ecology)

Honours and Dean’s Honor List - average 95% in 6 completed classes during MSc studies. University of Guilan, Iran. Awards: 2005-2008. Western Graduate Research Scholarship, London Ontario 2013-2016

Publications:

Asma Asemaninejad Hassankiadeh, Mostafa Niknejad Kazempour, Fereidoun padasht Dehkaei, Heshmat Rahimian. (2011). Detection of Xanthomonas oryzae pv. oryzae from rice seeds through BIO- PCR technique in paddy fields of Guilan province in northern Iran. Agricultura Tropica et Subtropica, 44(2), 71–76.

Asma Asemaninejad, Nimalka Weerasuryia, Gregory B. Gloor, Zoë Lindo, R Greg Thorn. (2016). New primers for discovering fungal diversity using nuclear large ribosomal DNA. PloS One, 11(7), e0159043.

Asma Asemaninejad, Greg Thorn, Zoë Lindo. (2016). Experimental climate change modifies degradative succession in boreal peatland fungal communities. Microbial Ecology. doi:10.1007/s00248-016-0875-9.

Asma Asemaninejad, Greg Thorn, Brian Branfireun, Zoë Lindo. Climate change exposes specific fungal communities in Boreal peatlands. Interational Society of Microbial Ecology. Under review.

Asma Asemaninejad, Greg Thorn, Zoë Lindo. Vertical distribution of fungi in hollows and hummocks of Boreal peatlands. Fungal Ecology. Under review.

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Conference Presentations:

Asma Asemaninejad Hassankiadeh, Mostafa Niknejad Kazempour, Fereidoun Padasht. Heshmat Rahimian. (2008). Isolation and identification of Pseudomonas syringae pv. syringae from rice ears in paddies of Guilan province. A poster presented in 18th Plant Protection Conference of Iran at university of Hamedan.

Asma Asemaninejad Hassankiadeh, Mostafa Niknejad Kazempour, Fereidoun Padasht. Heshmat Rahimian. (2008). Isolation and identification of Xanthomonas oryzae pv. oryzae from rice ears in paddies of Guilan province. A poster presented in 18th Plant Protection Conference of Iran at university of Hamedan.

Asma Asemaninejad Hassankiadeh, Mostafa Niknejad Kazempour, Fereidoun Padasht. Heshmat Rahimian. (2008). Study on transmission of Xanthomonas oryzae pv. oryzae via rice seeds of cv. Hashemi by BIO-PCR. A 15 minute talk in 18th Plant Protection Conference of Iran at university of Hamedan.

Asma Asemaninejad, Greg Thorn. (2014). How Basidiomycota in decomposing peat litters from boreal peatland respond to climate change? A 12 minute talk in BGRF (Biology Graduate Research Forum). Western University. London, Canada.

Asma Asemaninejad, Greg Thorn, Zoë Lindo, Brian Branfireun. (2015). The impacts of climate change on fungal communities in boreal peatlands. A 20 minute talk in MSA (Mycological Society of America). Edmonton, Canada.

Asma Asemaninejad, Greg Thorn, Zoë Lindo. (2015). The response of major fungal groups across a depth gradient of peat soil to climate change condition. A 2 minute talk in BGRF (Biology Graduate Research Forum). Western University. London, Canada.

Asma Asemaninejad, Greg Thorn, Zoë Lindo. (2016). Vertical distribution of fungi in hollows and hummocks of boreal peatlands. A 5 minute talk in BGRF (Biology Graduate Research Forum). Western University. London, Canada.

Related Work Microbiologist Experience ZargolChemi Productive Company, Tehran, Iran 2009-2012

Teaching Assistant The University of Western Ontario, London, Canada 2013-present

Research Assistant The University of Western Ontario, London, Canada 2013-present

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