Next-Generation Approaches to Understanding the Diversity and Evolution of Marine Fungi

Home , Agaricostilbomycetes, Archaeorhizomycetes, Balamuthia infection, Basidiobolaceae, Beenakia, Calostoma cinnabarinum

Kathryn Therese Picard

Department of Biology Duke University

Date: Approved:

Kathleen Pryer, Supervisor

Daniele Armaleo

Timothy Y. James

Jason E. Stajich

Jennifer J. Wernegreen

Dissertation submitted in partial fulﬁllment of the requirements for the degree of Doctor of Philosophy in the Department of Biology in the Graduate School of Duke University 2017 Abstract Next-Generation Approaches to Understanding the Diversity and Evolution of Marine Fungi

Kathryn Therese Picard

Department of Biology Duke University

Date: Approved:

Kathleen Pryer, Supervisor

Daniele Armaleo

Timothy Y. James

Jason E. Stajich

Jennifer J. Wernegreen

An abstract of a dissertation submitted in partial fulﬁllment of the requirements for the degree of Doctor of Philosophy in the Department of Biology in the Graduate School of Duke University 2017 Copyright c 2017 by Kathryn Therese Picard All rights by Kathryn Therese Picard 2017 Abstract

Fungi are among the most diverse extant eukaryotic lineages, with estimates of total global diversity projecting millions of species that have yet to be cataloged. Though fungi from all phyla and habitats await discovery, marine fungi are particularly poorly understood. Historical surveys of fungi in marine habitats, which relied primarily on direct culturing or observation of fruiting bodies and other structures on incubated shore detritus, suggest that marine fungi are unexceptional in their diversity and frequency, and therefore unimportant, one might be tempted to conclude. However, with the increasing adoption of environmental sequencing as a primary tool for exploring fungal diversity and ecology across disparate habitats, the discovery of novel phylotypes representing new species—and in some cases, even new phyla—demands a reappraisal of fungal diversity in marine habitats using modern molecular methods. This dissertation represents an attempt to advance our understanding of the breadth of fungal diversity, to establish a broader evolutionary context for marine fungi, and to provide some molecular tools to better study marine fungi that have, until now, eluded our detection. In Chapter1, I investigate the diversity and spatio-temporal distribution of unicellular eukaryotes in the surface waters of the English Channel using high- throughput sequencing of 18S ribosomal DNA. In addition to characterizing the taxonomic and phylogenetic diversity of planktonic protists, I also estimate the niche breadth of the taxa observed and infer ecological roles and trophic modes to examine

iv if and how functional guilds change through space and time. I find that while the community observed at any given time is likely to be dominated by only a handful of species, many of them members of the Stramenopiles, Alveolates, and Rhizaria, most protistan taxa are rare specialists. I also find that the relative abundance of an individual taxon is not indicative of a specialist or generalist habit. Interestingly, I also find that fungi comprise a significant fraction of the microbial community, but the most prominent fungal taxa observed do not closely resemble phylogenetically any circumscribed species of marine fungi. Rather, they are nested within the enigmatic Cryptomycota, a recently described phylum at the base of the fungal tree. Thus, while this is the only chapter of my dissertation which does not focus primarily on fungal diversity or evolution, the results in many ways set the stage for my subsequent work. In Chapter2, I turn my sails back toward land to challenge long-standing mycological lore by re-examining the diversity of coastal marine fungi. I focus on four marine habitats in coastal North Carolina (surface water, persistent wetlands, intertidal sand flats, and marine benthos) from which I sample water and sediments over the course of a year. Using primers designed to amplify across the entire fungal kingdom, I use the Ion Torrent platform to sequence amplicons from 28S ribosomal DNA and evaluate how successfully extant reference databases identify novel fungal sequences to both high and low taxonomic ranks. I find that marine fungi are far more varied than previously thought, with some early diverging fungi, and the Chytridiomycota in particular, proving to be diverse and ubiquitous. I also find that curated reference databases struggle to assign robust taxonomic identities to novel sequences across all fungal phyla, but are particularly ill-equipped to identify the marine representatives of non-Dikarya fungi. Finally, in Chapter3 I aim to address the deficiencies of curated reference databases used in taxonomic assignment by generating high-quality reference sequences from

v complex environmental samples. Using a third-generation sequencing technology, PacBio circular consensus sequencing, which trades the high coverage of other sequencing platforms for much longer read lengths, I target an approximately 2kb fragment of the ribosomal DNA operon that contains both the full fungal internal transcribed spacer (ITS) region and over 1kb of the 28S ribosomal subunit. Using a mock community approach and successive rounds of filtering, I calculate the average sequencing error for PacBio amplicons by comparing them to known sequences from axenically cultured, circumscribed species. I then revisit my samples from Chapter 2, generate amplicon sequences to be used for phylogenetic inference, and compare the accuracy of taxonomic assignments made by reference databases curated for individual loci (i.e., ITS vs. 28S) for different rDNA regions from the same operational taxonomic unit. I find that stringent quality filtering of PacBio sequence data produces consensus sequences approaching the quality of MiSeq and 454, which can be used in phylogenetic analyses to provide improved taxonomic assignments. Furthermore, many of the fungal taxa observed belong to known marine lineages, while others are only distantly related to reference accessions and may represent new lineages.

vi For my parents, Henry and Jossie, my husband, Christopher, and my Paw-Paw.

But not for Tildepants, who’s a shiftless layabout.

vii Contents

Abstract iv

List of Tables xi

List of Figures xiii

Acknowledgements xv

Introduction1

1 Seasonal Diversity, Distribution, and Ecological Role of Protists in the Western English Channel5

1.1 Introduction...... 5

1.2 Materials and Methods...... 8

1.2.1 Sampling...... 8

1.2.2 DNA extraction and sequencing...... 8

1.2.3 Sequence processing...... 9

1.2.4 Relaxed ﬁltering datasets...... 12

1.2.5 Statistics and community analyses...... 12

1.2.6 Estimating niche breadth and functional roles...... 13

1.3 Results...... 14

1.3.1 Summary of sequence ﬁltering and overall OTU composition. 14

1.3.2 Eukaryotic alpha and beta diversity...... 17

1.3.3 Inference of niche breadth and functional roles...... 20

1.4 Discussion...... 23

viii 1.4.1 Taxonomic composition of plankton communities...... 24

1.4.2 Plankton communities through space and time...... 25

1.4.3 Methodological considerations...... 26

1.5 Conclusions...... 27

2 Coastal Marine Habitats Harbor Novel Early-Diverging Fungal Di- versity 28

2.1 Introduction...... 28

2.2 Materials and Methods...... 31

2.2.1 Study sites and sampling regime...... 31

2.2.2 DNA extraction and sequence data generation...... 33

2.2.3 Sequence data processing...... 35

2.2.4 Taxonomic assignment...... 35

2.2.5 Phylogenetic placement of most abundant OTUs...... 37

2.2.6 Diversity analyses...... 37

2.3 Results...... 37

2.3.1 Sequence ﬁltering and OTU clustering...... 37

2.3.2 Taxonomic assignment...... 42

2.3.3 Per-site diversity...... 43

2.4 Discussion...... 50

2.4.1 Plankton sampling (Piver’s Island)...... 51

2.4.2 Persistent wetland sediments (Town Marsh)...... 52

2.4.3 Intertidal sand (Bird Shoal)...... 53

2.4.4 Benthic marine sediments (Cape Lookout Bight)...... 54

2.4.5 Methodological considerations...... 56

3 Generating Reference Sequences for Molecular Operational Taxo- nomic Units (MOTUs) with PacBio: a Case Study with the Dark

ix Matter Fungi 58

3.1 Introduction...... 58

3.2 Materials and Methods...... 61

3.2.1 Community DNA samples...... 61

3.2.2 Library preparation and PacBio sequencing...... 62

3.2.3 Sequence processing of mock community data...... 65

3.2.4 Phylogenetic analyses...... 66

3.2.5 Comparison of fungal reference databases...... 68

3.3 Results...... 69

3.3.1 Eﬀect of sequence processing on observed error rate...... 69

3.3.2 Phylogenetic assignment of fungal MOTUs from marine habitats 71

3.3.3 Distribution of marine MOTUs...... 77

3.3.4 Taxonomic assignment across target loci and reference databases 77

3.4 Discussion...... 79

3.5 Conclusions...... 83

4 Further Insights into the Diversity and Evolution of Early-Diverging and Marine Fungi 85

Conclusions 87

A Supplementary Information for Chapter1 92

B Supplementary Information for Chapter2 98

C Supplementary Information for Chapter3 118

Bibliography 130

Biography 151

x List of Tables

1.1 Sample collection metadata and diversity metrics...... 10

1.2 Average rareﬁed OTU diversity at each sampling site...... 17

1.3 ANOVA of OTU distribution across sampling locations...... 18

1.4 Average rareﬁed diversity for each sampling cruise...... 18

1.5 ANOVA of OTU distribution across sampling cruises...... 18

2.1 Total number of ﬁltered sequence reads, eukaryotic OTUs, fungal sequence reads, and fungal OTUs from samples collected seasonally from four coastal North Carolina sites...... 39

2.2 Diversity metrics for coastal marine samples...... 40

3.1 Strain/voucher information for reference taxa used in mock community analyses...... 64

3.2 Sequence lengths of individual rDNA regions for reference taxa used to create a mock community for PacBio sequencing...... 67

3.3 Error rates, retained reads, and non-singleton MOTUs recovered from PacBio sequencing of mock community samples when implementing various quality ﬁltering steps...... 71

A.1 Sequence statistics for 30 water samples collected in the English Chan- nel under diﬀerent ﬁltering stringencies...... 93

A.2 Taxonomy, OTU diversity, read abundance, and functional annotation of OTUs in ecological inference dataset...... 95

B.1 Taxonomic assignments made using the RDP Classiﬁer with bootstrap support...... 99

xi B.2 Per-site relative abundances of the most abundant fungal classes observed in plankton (PT), estuarine wetland sediments (WS), intertidal sand (IS), and sediment core (SC) samples...... 113

B.3 Per-site relative abundances of the most abundant fungal orders observed in plankton (PT), estuarine wetland sediments (WS), intertidal sand (IS), and sediment core (SC) samples...... 114

B.4 Phylum-level taxonomic composition of the total fungal community observed in plankton (PT), estuarine wetland sediments (WS), intertidal sand (IS), and sediment core (SC) samples...... 115

B.5 Phylum-level taxonomic composition of the total fungal community observed in coastal marine sites across seasons...... 115

C.1 Statistics for chimeric sequences observed for data ﬁltering steps... 119

C.2 Taxonomy and GenBank accessions for 28S rDNA sequences for reference taxa used in phylogenetic analyses in Chapter3...... 120

C.3 Comparison of taxonomic assignments made with three fungal reference databases interrogating three target rDNA loci from marine MOTU sequences...... 126

C.4 Marine MOTUs recovered in both PacBio and Ion Torrent surveys.. 129

xii List of Figures

1.1 WaMS sampling positions within the English Channel...... 7

1.2 Taxonomic composition of English Channel water sample for datasets produced by diﬀerent levels of quality ﬁltering...... 16

1.3 Taxonomic composition of eukaryotic microplankton in the subsurface waters of the English Channel...... 19

1.4 Rarefaction curves for individual sampling cruises...... 20

1.5 Ordination plots of water samples from stringent dataset sub-sampled to 591 reads per sample...... 21

1.6 Inference of ecological role and niche breadth...... 22

2.1 Map of coastal North Carolina sampling sites...... 31

2.2 Contribution of each fungal phylum to total observed diversity.... 41

2.3 Proportions of reads from each fungal phylum assigned to each taxonomic level with bootstrap support ě 50% using the RDP Classiﬁer. 43 2.4 Relative abundances of fungal sequences from seasonal sampling of four coastal marine sites...... 45

2.5 Percentage of unassigned fungal sequences for each taxonomic rank when implementing a 50% bootstrap cutoﬀ using the RDP fungal database for taxonomic classiﬁcation...... 47

2.6 Maximum likelihood tree of 50 most abundant OTUs...... 49

3.1 Primer map indicating the relative positions of primers used in this study to generate full ITS1-5.8S-ITS2 and partial LSU sequences from mock communities and marine samples...... 63

xiii 3.2 Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the class Sordariomycetes (phylum Ascomycota) based on a 28S/LSU rDNA maximum likelihood phylogram...... 72

3.3 Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the class Dothideomycetes (phylum Ascomycota) based on a 28S/LSU rDNA maximum likelihood phylogram...... 73

3.4 Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the class Eurotiomycetes (phylum Ascomycota) based on a 28S/LSU rDNA maximum likelihood phylogram 74

3.5 Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the phylum Basidiomycota based on a 28S/LSU rDNA maximum likelihood phylogram...... 75

3.6 Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the phyla Chytridiomycota and Cryptomycota based on a 28S/LSU rDNA maximum likelihood phylogram...... 76

A.1 Taxonomic composition of total community observed across all water samples...... 94

B.1 Rarefaction curves for all eukaryotic sequences...... 116

B.2 Edwards Venn diagrams of shared fungal OTUs across coastal marine sites and seasons...... 117

xiv Acknowledgements

Over the last several years, I have been extremely fortunate to be surrounded by an incredible group of colleagues, mentors, and collaborators whose contributions, both academic and personal, have helped see this project from inception to completion. First and foremost, I am indebted to my advisor, Kathleen Pryer, who embraced the classical definition of ‘botany’1 and helped this marine mycologist find a home among (mostly) land-lubbing pteridologists. Without her wisdom, guidance, and encouragement, none of this work would have been possible. I am especially grateful for the mentorship of my co-advisor, Daniele Armaleo, in both the laboratory and the classroom, and cherish the time we spent discussing symbioses, exploring peda- gogical techniques, and disabusing undergraduates of the notion that Saccharomyces cerevisiae is but a ‘simple eukaryote’. My committee members, past and present— Tim James, Jason Stajich, Jen Wernegreen, and Greg Wray—have lent invaluable expertise and insight to the forging of my project, and I have benefitted enormously from their counsel and support. In my time at Duke, I have worked with several collaborators here and abroad who have been giving of their time, resources, and data. Many thanks to Rowena Stern of the Sir Alister Hardy Foundation for Ocean Science in Plymouth, UK for graciously allowing me to explore her 454 data in the early days of my doctoral studies and for providing the data presented in Chapter 1. Her work on microplankton

1 Which includes fungi and algae under the umbrella of ‘plants’.

xv diversity directly inspired much of the research presented here. Megumi Shimizu at Duke University Marine Lab was kind enough to allow me to accompany her on her research cruises to collect sediments at Cape Lookout Bight, providing a critical component of this thesis. Steven Haddock and Shannon Johnson Williams at the Monterey Bay Aquarium Research Institute were generous and enthusiastic contributors of deep-sea water samples that broadened the scope of my PacBio research. I would also like to thank Amy Grunden and Heike Sederoff of North Carolina State University for welcoming me into their labs while I was working on side projects related to the Rhizidium-Bracteacoccus symbiosis, providing the occasional much- needed distraction from endless high-throughput sequence analysis. I am grateful to members of the Duke Biology community who helped make this experience, though daunting at times, a net positive one. I owe a particular debt of gratitude to Sönke Johnsen and Mohamed Noor for their guidance as Director of Graduate Studies and Department Chair, respectively. Anne Lacey, Jo Bernhardt, Randy Smith, Jim Tunney, Andrew Turnier, and Jill Foster have all ensured that I had the academic, financial, and computing resources I needed. (Anne, Randy, and Jim especially deserve recognition, as they’ve each saved the day for me at least twice thrice a bunch’a times.) Olivier Fedrigo and Nicolas Devos of the Duke University Genome Sequencing Center worked with me to perform R&D on Ion Torrent and to generate PacBio data, respectively. I am lucky to be surrounded by an amazing lab family, past and present—Bernie Ball, James Beck, Ko-Hsuan Chen, Ariana Eily, Ester Gaya, Amanda Grusz, Bren- dan Hodkinson, Layne Huiet, Suzanne Joneson, Tzu-Tong Kao, Emilie Lefèvre,Fay- Wei Li, Tami McDonald, Edgar Medina, Ryoko Oono, Eimy Rivas-Plata, Carl Roth- fels, Erin Sigel, Karla Sosa, Camille Truong, and Michael Windham—who have made Duke a fun place to work, and whose questions and suggestions throughout the years have improved this thesis tremendously. I have also made many lifelong friends and

xvi colleagues, and am particularly thankful for the camaraderie of Cathy Rushworth, Paul Durst, Amanda Lea, Kendra Mojica, Jessie Uehling, and my dissertation fairy godparent, Deb Greene. Finally, I would be remiss if I failed to acknowledge my husband, Christopher Duryee, who braved frigid waters when sampling plankton, ﬁelded late-night Python questions, and continues to believe in me more than I do. This research was funded by a National Science Foundation Doctoral Disserta- tion Improvement Grant (DEB-1311540), a Duke Sigma Xi Mini-Grant-in-Aid, a Mycological Society of America Graduate Fellowship, and two Grants-in-Aid from the Duke University Biology Department Keever Fund.

xvii Introduction

Mycology has a diversity problem. To clarify, I am not referring to the diversity problem that plagues STEM fields generally.2 Rather, mycology’s diversity problem centers on the enormous disparity between the number of circumscribed fungal taxa—approximately 130,000 (Hibbett et al., 2016)—and various projections of total extant diversity, which range widely from approximately 600,000 (Mora et al., 2011) to upwards of 3 to 6 million species globally (Hawksworth, 2001; Blackwell, 2011; Hawksworth and Lucking, 2017). By the rosiest of estimates, which used mathematical models to extrapolate true fungal species richness, just 20% of fungi have been described. However, surveys incorporating molecular data (O’Brien et al., 2005; Taylor et al., 2014) consistently point to a more sobering scenario: despite our best efforts and three centuries of fungal classification (Ainsworth, 1976), we have characterized fewer than 5% of all fungal species. Mycology’s diversity problem is compounded by the fact that known fungi are not equally distributed across established phyla. The overwhelming majority of circumscribed taxa, nearly 98%, belong to the Ascomycota or Basidiomycota (collectively referred to as the Dikarya) (Blackwell, 2011), with the remaining 2% of species divided among the zoosporic lineages (Blastocladiomycota, Chytridiomycota, Cryptomycota, and Neocallimastigomycota) and the recently revised “Zygomycete” lineages (Zoopagomycota and Mucoromycota). Characterized fungi are often macro-

2 Mycology has that diversity problem, too, but we are working on it.

1 scopic with large or showy fruiting bodies or thalli (the“charismatic megamycota”), or important agriculturally, economically, or medically. Fungi that await discovery, however, are more likely to be microscopic (often unicellular), have a cryptic lifecycle (whether as symbionts, parasite, or endophytes), or hail from undersampled habitats. Thus, the relative scarcity of described species among the non-Dikarya phyla likely belies overall species richness within these lineages. As molecular sequencing methods continue to evolve, they are increasingly used alongside or in place of the morphological and culturing tools traditionally used to assess fungal diversity. High-throughput sequencing methods, in particular, have helped to usher in a new age of mycological exploration. The improved sensitivity of these culture-independent methods has revealed not only new fungal species, but entire phyla and classes. For example, the phylum Cryptomycota was recognized almost exclusively from environmental sequences originating from diverse soil, freshwater, and marine habitats (Jones et al., 2011a). How is it that the earliest branch of the fungal tree managed to elude discovery for so long? Estimated to be as diverse as the rest of the fungi (Jones et al., 2011b), the Cryptomycota likely went unnoticed due to their roles as obligate intracellular parasites of microscopic hosts, which are themselves often poorly understood. However, major discoveries are being made among even well-characterized fungal groups. Environmental sequencing has also helped lead to the circumscription of the Archaeorhizomycetes, an ancient class of globally distributed soil fungi in the Ascomycota, overlooked due to their minute size, absence of macroscopic fruiting bodies, and obligate association with plant roots (Rosling et al., 2011). While environmental sequencing surveys have helped to unearth new fungal groups, they should not be considered a panacea for mycology’s diversity problem. In the course of analyzing community sequence data, raw sequencing reads must be quality ﬁltered, clustered into Molecular Operational Taxonomic Units (MOTUs),

2 and then assigned a provisional identity from which biological interpretations can be made (Lindahl et al., 2013). This penultimate step—taxonomic identification—is critically important for teasing out biological and ecological patterns observed in an individual data set (Nguyen et al., 2015; Truong et al., 2017). Unfortunately, the quality of these taxonomic identifications, which are made by comparing MOTUs to curated databases populated with reference sequences from known taxa, is influenced by existing disparities in described fungi. Simply put, well-sampled groups are more likely to have genetic data incorporated into reference databases, and those groups are more likely to be identified from environmental surveys with high support (Yahr et al., 2016). Increasingly, efforts are being made to generate reference data from specimens in herbaria and culture collections (Brock et al., 2009; Dentinger et al., 2010; Osmund- son et al., 2013; Hibbett et al., 2016). However, for many fungal groups, particularly those outside of the Dikarya or from neglected habitats, collections may be scant, if not altogether non-existent. Therefore, generating reference data for uncultured or unsampled taxa will prove challenging, but perhaps not impossible. This dissertation represents an attempt to ameliorate mycology’s diversity problem, if ever so slightly, by using modern molecular sequencing tools to investigate the scale of fungal diversity—both known and as yet unrealized—from the marine realm, generate reference data from previously unknown marine fungi, and to put marine taxa into a broader evolutionary context. In Chapter 1, I combine pyrosequencing data, niche breadth estimation, and trophic mode inference to characterize the protistan community (including microscopic fungi) in surface waters of the English Channel taxonomically and ecologically. In Chapter 2, I narrow my focus to fungi and target four different habitats in coastal North Carolina for which I estimate total fungal diversity and identify putatively novel lineages. In Chapter 3, I develop an analysis pipeline to generate reference sequences for uncultured fungi, from which I

3 infer the phylogenetic relationships of marine fungi from North Carolina samples. Fi- nally, Chapter 4 summarizes additional contributions I have made during my tenure as a student at Duke.

4 1

Seasonal Diversity, Distribution, and Ecological Role of Protists in the Western English Channel

1.1 Introduction

Microbial communities are the foundation of marine ecosystems, driving primary production and nutrient cycling (Falkowski et al., 2008; DeLong, 2009). The introduction of molecular methods to marine microbiology, only a few decades ago, has dramatically altered our understanding of ecosystem structure and functioning in the ocean, revealing both novel lineages across all domains of the tree of life, as well as complex associations among disparate taxa (Krabberød et al., 2017). Over the last decade, many studies employing culture-independent methods either alongside or, increasingly, in place of traditional morphological classiﬁcation have shown that unicellular eukaryotes in the pico- (0.2 µm to 2 µm), nano- (2 µm to 20 µm), and micro- (20 µm to 200 µm) fractions are particularly diverse and abundant components of plankton communities across various marine habitats (Massana et al., 2014, 2015; Not et al., 2009; Richards et al., 2015; Zhu et al., 2005; de Vargas et al., 2015; Bazin et al., 2013). These tiny denizens of the water column occupy diverse roles as

5 phototrophs, heterotrophs, saprotrophs, and symbiotrophs (Sieburth et al., 1978). Despite mounting interest in marine plankton assemblages and improved methods for characterizing microbial consortia quantitatively and qualitatively, the diversity and biogeography of most marine microbiota remain a black box. Because the cost of marine research can be prohibitive, most marine habitats remain under-sampled and very few have been sampled repeatedly to assess how microbial communities change in response to environmental factors over time (Fuhrman et al., 2015; Karl and Church, 2014). Addressing this knowledge gap is of critical importance. Human activity threatens to alter microbial community composition through pollution and the introduction of invasive species (Drake et al., 2007), and looming climatic changes pose a serious threat to microbial taxa unable to withstand even narrow fluctuations in ocean temperatures or pH (Doney et al., 2012; Riebesell et al., 2000) An earlier study (Stern et al., 2015) describing the development and inaugu- ral deployment of the Water and Microplankton Sampler (WaMS) used Roche 454 GS-FLX pyrosequencing to investigate the diversity of eukaryotes in the English Channel from five different locations (Fig. 1). Beyond capturing a broad swath of taxa representing all major eukaryotic lineages, the use of the WaMS demonstrated that long-term monitoring of plankton communities in the English Channel could be executed in concert with existent sampling programs, thereby reducing sampling costs. In that study, however, the sampling regime was limited to four consecutive months (February - May) in 2011, precluding investigations into the spatio-temporal distribution of the communities observed. Here, in this companion study to Stern, Picard et al. (2015), surface-water samples were collected monthly from the same five localities in the English Channel (Fig. 1) between June 2011 and February 2012 using the WaMS deployed within the Continuous Plankton Recorder. Microplankton diversity was assessed using high- throughput sequencing of the V4 region of the nuclear small subunit (nuSSU/18S)

6 51.0

N Plymouth, UK 50.5

E5 50.0

E4 Lat 49.5 E3

E2 49.0 E1

Roscoff, FR 48.5

0 50 100 km 48.0 −6 −5 −4 −3 −2 −1 Lon

Figure 1.1: WaMS sampling positions within the English Channel. of ribosomal DNA. To build on the ﬁndings of our previous study, which served chieﬂy as a “proof of concept”, our primary objective in this study was to address the following questions: (1) What is the taxonomic composition of the pico- and microplankton communities across the English Channel? (2) Do species richness and composition vary across seasons? (3) Do functional guilds exhibit seasonality? The results of this study aim to expand our knowledge of the breadth of extant microbial diversity in the English Channel and how microbial communities in marine waters change through time and space.

7 1.2 Materials and Methods

1.2.1 Sampling

Water samples were collected as described by Stern et al.(2015) with the automated WaMS carried within a Continuous Plankton Recorder (CPR) towed across the En- glish Channel by the MV Armorique, a ferry ship. The WaMS is equipped with a sensor that detects immersion and initiates sample collection on a user-defined sched- ule. The sampler can accommodate up to 10 samples with a maximum volume of 150 ml per sample. To minimize bacterial contamination, sampling bags were bleach- sterilized overnight and rinsed with sterile DDI water just prior to deployment of the CPR/WaMS. Deployed at a depth of 10 m, the WaMS was programmed to collect duplicate 120 ml to 150 ml water samples at 70-minute intervals—corresponding to five sampling locations (Figure 1.1)—during its voyage across the Channel. Collected samples were housed underwater in the WaMS during the course of the ferry route and processed within 1.5 hours of landing. On shore, the two per-site samples were pooled, and 2 ml was collected and preserved for flow cytometry to estimate cell counts for individual taxa (analysis not included here). The remaining pooled sample was passed through a sterile 0.2 µm SUPOR membrane filter (Pall Corporation, USA). Tissue- laden filters were stored in 95% ethanol or RNAlater (QIAGEN, Germantown, MD, USA) until DNA extraction. Sampling was conducted monthly between June 2011 and February 2012, save for August and November 2011 and January 2012 when sampling was not possible.

1.2.2 DNA extraction and sequencing

Ethanol-stored filters were dried prior to sectioning with a sterile scalpel. Plank- tonic tissue was washed from the filter sections using AP1 buffer and then ex-

8 tracted using the DNeasy Plant Mini Kit (QIAGEN) according to manufacturer’s protocol. Thirty samples of genomic DNA were shipped to Molecular Research LP (http://www.mrdnalab.com, Shallowater, TX, USA) where a 500-bp portion of the variable V4 region of the nuSSU was amplified by PCR using the adaptor-linked barcoded eukaryotic primer pair euk516F (5’-GGAGGGCAAGTCTGGT-3’) and euk1055R (5’-CGGCCATGCACCACC-3’). Amplification was performed using HotStarTaq Plus Master Mix Kit (QIAGEN) under the following conditions: initial denaturation at 94˝C for 3 minutes, followed by 30 cycles of 94˝C for 30 seconds, 53˝C for 40 seconds, and 72˝C for 1 minute, concluding with a final elongation step at 72˝C for 5 minutes. Amplicons were pooled in equimolar concentrations and purified using Agencourt AMPure beads (Agencourt Bioscience, MA, USA). Amplicon libraries were pyrosequenced using Roche 454 GS-FLX Titanium chemistry.

1.2.3 Sequence processing

Raw sequence data were processed using QIIME v1.9.1 (Caporaso et al., 2010). In the first round of screening (“first pass filtering”), reads were checked for the presence of the forward sequencing primer (euk516F) and a valid barcode. Reads were discarded if they failed to meet the following criteria: mean Phred score ě25, zero ambiguous bases, homopolymer length ď6, and 1 or fewer primer mismatches. Using a 50-bp sliding window, reads that passed initial filters were screened for low-quality regions; if truncation at a low-quality region resulted in a read shorter than 200 bp, the read was discarded. Reads that met length requirements (200–700 bp) were then denoised to reduce OTU inflation (Reeder and Knight, 2010), a well-known problem in microbiome studies using GS-FLX Titanium chemistry to sequence the V4 region of nuSSU (Behnke et al., 2011). Denoised reads were clustered at 97% sequence similarity and screened for chimeras using USEARCH (Edgar, 2010). Reads that were tagged as potential chimeras by both de novo and reference-based methods, the

9 Table 1.1: Sample collection metadata and diversity metrics. Samples for which water temperature data are unavailable are indicated by a dash (–)

Sampling Date Sample Sampling Temp OTU Good’s Chao1 Shannon (CPR Tow No.) ID Position (˝C) Count Coverage WS14 E1 13.7 162 221 2.83 0.978 WS15 E2 12.9 42 71 2.87 0.930 6/15/11 WS16 E3 13.9 169 260 3.72 0.953 (348PR) WS17 E4 13.9 222 275 3.77 0.977 WS18 E5 13.9 229 272 4.36 0.974

WS19 E1 14.5 225 268 4.82 0.972 WS20 E2 14.4 176 224 3.96 0.969 7/13/11 WS21 E3 15.3 203 268 3.35 0.982 (349PR) WS22 E4 15.7 123 188 3.67 0.941 WS23 E5 15.7 230 265 3.27 0.987

WS24 E1 – 25 28 2.53 0.995 WS25 E2 – 35 43 4.53 0.885 9/14/11 WS26 E3 – 206 266 6.13 0.961 (351PR) WS27 E4 – 185 229 5.81 0.966 WS28 E5 – 83 110 5.38 0.918

WS29 E1 14.7 111 167 5.57 0.900 WS30 E2 14.9 105 215 5.48 0.827 10/18/11 WS31 E3 15.0 522 600 4.63 0.989 (352PR) WS32 E4 14.9 80 97 4.92 0.961 WS33 E5 14.7 104 192 4.23 0.938

WS34 E1 13.0 72 101 5.13 0.879 WS35 E2 12.7 191 237 4.55 0.951 12/20/11 WS36 E3 12.5 194 296 5.22 0.936 (354PR) WS37 E4 12.3 175 224 5.62 0.936 WS38 E5 12.0 37 56 4.53 0.839

WS39 E1 10.3 251 347 6.13 0.942 WS40 E2 10.6 126 196 2.15 0.989 2/21/12 WS41 E3 10.3 198 318 4.67 0.952 (355PR) WS42 E4 10.2 38 62 4.40 0.810 WS43 E5 9.7 82 123 4.61 0.889

10 latter of which used the SILVA SSU 111 release (Quast et al., 2013) as a reference database, were removed, as were global singletons (i.e., OTU clusters containing only a single sequence across all samples). For each OTU cluster, the most abundant sequence was chosen as the representative sequence. Taxonomic assignment was carried out using both UCLUST (Edgar, 2010) with a minimum sequence similarity of 90% and the Ribosomal Database Project’s nave-Bayesian classifier (”RDP Classifier”; (Wang et al., 2007)) with a minimum bootstrap threshold of 80%. In both methods, the QIIME-compatible SILVA 111 taxonomic database was used as a reference. Assignments made by UCLUST and the RDP Classifier were examined for concordance and all non-eukaryotic and metazoan taxa were removed from the dataset for downstream analyses. UCLUST compares overall sequence similarity of a queried representative sequence against a database of reference sequences to generate a consensus taxonomy based on a user- specified similarity threshold. In contrast, the RDP Classifier compares queries to references using 8 bp-long strings across the entire length of the sequence, and estimates a confidence score for the assignment of each taxonomic level based on 100 bootstrap replicates. Because the likelihood of recovering novel or divergent protist sequences from marine habitats is high and protists are poorly represented among described species, the use of a similarity-based method alone could result in many unassigned OTUs (as was the case here). Thus, the taxonomic identities presented here are based on the RDP Classifier results. Throughout the remainder of the text, the dataset produced from the preceding analysis pipeline will be referred to as the “stringent” dataset. Similarly, the series of quality screening steps implemented to produce the stringent dataset (i.e., “first pass filtering”, denoising of 454 reads, and removal of chimeras and singletons) will be referred to as “stringent filtering”. The stringent dataset will be the basis for all downstream analyses unless otherwise specified.

11 1.2.4 Relaxed ﬁltering datasets

To examine the effects of various quality filtering steps on observed diversity, two additional datasets were generated, the first with “lax” filtering (e.g., “first pass filtering” as above, but no denoising step and retention of chimeras and singletons) and the second with “moderate” stringency level (e.g., “first pass filtering” as above, reads “pseudo-denoised” by pre-clustering at 99% to remove potential artefacts, chimeras and singletons removed). Taxonomic assignments for these datasets were carried out using the RDP Classifier with an 80% bootstrap threshold. The OTU/read counts and taxonomic composition of the three datasets were compared, but no downstream analyses were conducted on the relaxed filtering datasets.

1.2.5 Statistics and community analyses

To evaluate sampling adequacy and sequencing depth, rarefaction analysis of samples summed by cruise date was carried out in the iNEXT package (Hsieh et al., 2016) implemented in R. Alpha diversity (i.e., diversity within a sample or location) metrics (corrected Chao1, Shannon’s biodiversity index, and Good’s coverage estimator) were calculated for all water samples using the vegan package (Oksanen et al., 2017), also in R. To compare OTU richness across sampling cruises, a one-way analysis of variance (ANOVA) was performed on the OTU table after subsampling to 591 reads per sample to maintain a majority of the samples (n “ 21, of an original 30). A simple linear regression was used to examine the relationship between water temperature at time of sampling and the OTU diversity observed. Correlations between water temperature and the abundance of particular taxa (i.e., families or genera, not individual OTUs) were calculated using Spearman’s Rank Correlation (Spearman’s rho), Bonferroni-corrected for multiple comparisons. Beta diversity (i.e., diﬀerences in community composition among samples) was visualized using Principal Coor- dinates Analysis (PCoA) calculated using weighted UniFrac distances (Lozupone

12 et al., 2011) estimated from the sub-sampled dataset. Because UniFrac distances incorporate phylogenetic information, a tree was inferred in FastTree (Price et al., 2009) from representative sequences aligned using MAFFT v.7 (Katoh and Standley, 2013). Diﬀerences in taxonomic composition across sampling cruises (eﬀectively seasons) and water temperature were tested using analysis of similarity (ANOSIM), a non-parametric statistical test of spatio-temporal structure, with 999 permutations.

1.2.6 Estimating niche breadth and functional roles

Given both the genetic and ecological diversity of known marine microbes, there is high potential for niche specialization (e.g., host-parasite speciﬁcity). To examine shifts in the distribution and abundance of specialist or generalist taxa/consortia, niche breadth was calculated for each OTU in the sub-sampled dataset using Levins’ niche width (B) index (Levins, 1968):

N 2 B “ 1{ pij i“1 ÿ

where pij is the proportion of OTU j in water sample i, and N is the total number of water samples analyzed. Here, B determines the extent of niche or habitat specialization based on the distribution of OTU abundances across the entire dataset, independent of the spatial location or environmental conditions within an individual water sample. OTUs with high B (i.e., presumptive generalists) will be found across many individual samples at roughly the same abundance, whereas OTUs with low B (i.e., presumptive specialists) will be found at uneven abundances, and/or in few samples. Niche breadth was calculated using the ‘spaa’ package implemented in R (http://cran.r-project.org/; v.3.2.1). Because the range of niche breadths is relative to individual datasets, designations of specialist or generalist used here were chosen arbitrarily.

13 To infer ecological patterns among pico- and microplankton, putative trophic modes/functional roles were assigned for all OTUs that could be identiﬁed to genus level. Trophic modes for each individual genus were derived from a survey of the rele- vant literature (Table A.2) and included the following designations: (i) autotroph for all strictly photosynthetic organisms; (ii) heterotroph for known grazers and preda- tors; (iii) parasite for obligately parasitic taxa; and (iv) polymorphic for mixotrophic taxa that have been shown to employ multiple feeding strategies (e.g., autotrophy and heterotrophy among many dinoﬂagellate and ciliate taxa). A new OTU table containing only OTUs with ecological assignments was constructed and sub-sampled to a depth of 232 sequences to retain as many samples as possible (n “ 20, of an original 30). Relative abundances of each functional guild were then calculated for each sampling cruise.

1.3 Results

1.3.1 Summary of sequence ﬁltering and overall OTU composition

Sequencing generated 350,293 sequences, of which 148,361 (48.6%) passed initial quality filtering steps (Table A.1). In “first pass filtering”, most sequences were discarded due to the presence of ambiguous bases. In the OTU clustering step, an additional 19,830 chimeric and singleton sequences were identified and removed. Fil- tering non-eukaryotic and Metazoan taxa produced a final OTU table containing 55,122 reads clustered into 1,233 OTUs. Read and OTU distributions were unequal across samples and exacerbated by the removal of sequences associated with metazoan OTUs, which comprised up to 99% of filtered reads in individual samples (Table A.1). In the “moderate” and “lax” filtering datasets, 65,110 and 72,213 reads were retained, respectively. In the “moderate” dataset, reads were clustered into 4,049 non-metazoan eukaryotic OTUs; the “lax” dataset produced 7,183 non-metazoan eukaryotic OTUs. Overall taxonomic composition for the entire community observed

14 did not change markedly across the three datasets save for the relative contribution of Alveolates and Unidentified Eukaryotes (Figure 1.2). As filtering stringency increased, the number of reads that could not be identified beyond Eukarya decreased, from nearly 12% in the “lax” dataset to less than 3% in the “stringent” dataset. A concomitant increase in the proportion of Alveolates was observed when more rigor- ous filtering criteria, such as the removal of chimeras and singletons and denoising of the raw data, were implemented. Taxa from all major eukaryotic lineages were observed, though most groups were relatively rare (Figure A.1). The dominant taxonomic groups observed across the Channel were the supergroup SAR (Stramenopiles-Alveolates-Rhizaria) (80.4% filtered reads), the Chloroplastida (8.5%), and the Haptophyta (4.9%). Three additional groups comprised ą1% of the community: Unidentified Eukaryotes (2.3%), Fungi (1.3%), and Cryptophyceae (1.0%). Among both the SAR supergroup and the community as a whole, the dinoflagellates were the most diverse and abundant OTUs recovered, comprising 57.1% of all quality-filtered reads and 44.3% of all OTUs. Rare

(ă0.01% of read abundance) and abundant (ą1%) taxa had inversely proportional representation among OTU and read diversity: rare taxa contributed just 3.2% of total reads, but represented nearly half of all OTUs (541; 43.9%), while a majority of the dataset (62.6%) corresponded to reads shared among just 15 abundant OTUs (1.2%). These abundant OTUs comprised six Alveolates (three members of the core dinoﬂagellates, two ciliates from the genera Cyclidium and Colpoda, and one representative of the Syndiniales), three Chloroplastida (in the picophytoplankton genera Bathycoccus, Micromonas, and Ostreococcus), two Haptophytes (Phaeocystis and an unidentiﬁed prymnesiophyte), three Stramenopiles (two bicosoecids and one member of the MAST-3), and one Cryptomycotan fungus (LKM11). In the sub-sampled dataset, which contained 912 OTUs, overall taxonomic composition across cruises followed a similar pattern (Figure 1.3) in which communi-

15 100 Rare Groups (<0.1%) Telonema Stramenopiles 80 Rhizaria Picozoa Holozoa 60 Haptophyta Fungi Cryptophyceae 40 Conosa Chloroplastida

Relative abundance (%) Centrohelida 20 Alveolata Unidentified Eukaryotes

0 Lax Moderate Stringent Dataset Filtering

Figure 1.2: Taxonomic composition of English Channel water sample for datasets produced by different levels of quality filtering. Lax - raw reads not denoised, chimeras and global singletons retained; Moderate - raw reads pseudodenoised by clustering at 99%, chimeras and global singletons removed; Stringent - raw reads denoised with AmpliconNoise, chimeras and global singletons removed. ties were generally dominated by Alveolate taxa, particularly among the Dinoflagel- lates and Syndiniales, the former of which peaked during the summer months (June and July). Non-dinoflagellate phototrophs from the Chlorophyta and Haptophyta were most abundant in fall (September and October) and winter (December and February), as were the flagellated heterotrophs allied to the Ciliophora. While Stra- menopiles were observed during all months, their relative abundance shifted markedly over time, but without any obvious pattern. Though Fungi from the Ascomycota, Basidiomycota, and Chytridiomycota were observed at very low frequencies, the only fungal taxa contributing greater than 1% of total reads were OTUs most closely related to LKM11, a Cryptomycota genotype known only from environmental sequencing; LKM11 was most abundant in June and July.

16 Table 1.2: Average rareﬁed OTU diversity at each sampling site (cf. Fig. 1.1)

Sampling Site n Avg. No. OTUs σ E1 4 93.8 64.0 E2 3 93.3 40.4 E3 6 116.8 20.4 E4 5 107.8 24.2 E5 3 105.0 20.0

1.3.2 Eukaryotic alpha and beta diversity

Measures of alpha diversity suggest the planktonic community was incompletely sampled (Table 1.1) and the calculated rarefaction curves for each cruise failed to reach plateaus (Figure 1.4). Per-sample OTU diversity exhibited only a weak negative cor-

relation with water temperature (Pearson’s r “ ´0.29) and there was no detectable relationship between water temperature at time of sampling and Shannon Index

(r “ ´0.14, p “ 0.50). Furthermore, OTU diversity was not influenced significantly by sampling location within the Channel or sampling run (Table 2-5). Abundance of only one taxonomic group, the enigmatic MAST-9 (MArine STramenopile) lineage, was found to correlate significantly with temperature (Spearman’s rho “ ´0.5592, Bonferroni-corrected p “ 0.008). However, this group was represented by only 60 sequences clustered into three OTUs at 97% similarity. While sampling date and water temperature had no appreciable effect on the number of OTUs observed, overall community composition varied significantly across the date of sampling (ANOSIM R “ 0.30, p “ 0.002) (Figure 1.5a) and with water temperature (ANOSIM R “ 0.56, p “ 0.006) (Figure 1.5b). Principal Coordinates Analysis showed some clustering of samples collected during cruises in the warmer months, most notably among June and July samples, but less structure was observed among samples collected in cooler months (Figure 1.5).

17 Table 1.3: ANOVA of OTU distribution across sampling locations. df - Degrees of Freedom; SS - Sum of Squares; MS - Mean Squares

Source df SS MS F P Between-sites 4 1792.7595 448.1899 0.34534 0.84 Within-sites 16 20765.05 1297.8156 Total 20 22557.8095

Table 1.4: Average rareﬁed diversity for each sampling cruise.

Sampling Cruise n Avg. No. OTUs σ 348PR (Jun) 4 97.3 24.5 349PR (Jul) 5 102.0 12.0 351PR (Sep) 3 95.0 63.6 352PR (Oct) 3 99.3 26.9 354PR (Dec) 3 127.7 10.7 355PR (Feb) 3 115.0 62.9

Table 1.5: ANOVA of OTU distribution across sampling cruises. df - Degrees of Freedom; SS - Sum of Squares; MS - Mean Squares

Source df SS MS F P Between-cruises 5 2521.72619 504.3452 0.37758 0.87 Within-cruises 15 20036.08333 1335.7389 Total 20 22557.80952

18 100 Rare Groups (<1%) Unidentified Eukaryotes Telonema MAST-3 Stramenopiles 80 Bicosoecida; Cafeteriidae Prymnesiophyceae;Other Prymnesiophyceae; Prymnesiales Haptophyta Prymnesiophyceae; Phaeocystis 60 Cryptomycota; LKM11; Fungi Prasinophytae; Pyramimonas Mamiellophyceae; Ostreococcus Chloroplastida Mamiellophyceae; Micromonas 40 Mamiellophyceae; Bathycoccus Other Protalveolata; Syndiniales 19 Relative abundance (%) Dinoflagellata; Dinophyceae Alveolata 20 Ciliophora; Spirotrichea Ciliophora; ConThreeP

0 348PR 349PR 351PR 352PR 354PR 355PR Jun Jul Sep Oct Dec Feb Figure 1.3: Taxonomic composition of eukaryotic microplankton in the subsurface waters of the English Channel calculated from dataset sub-sampled to 591 reads/sample. 600

400

348PR (Jun) 349PR (Jul) 351PR (Sep) OTU Richness 352PR (Oct) 200 354PR (Dec) 355PR (Feb) interpolation extrapolation

0 10000 20000 Sequences per sample

Figure 1.4: Rarefaction curves for individual sampling cruises. Shaded grey regions indicate 95% conﬁdence intervals for OTU richness.

1.3.3 Inference of niche breadth and functional roles

Of the 1,233 OTUs observed in the full dataset (stringent ﬁltering, but not sub- sampled), only 258 (20.9%) were assigned to the genus level; collectively, these taxa represented 15,579 reads (28.3% of ﬁltered reads). Autotrophs and heterotrophs comprised the largest fraction of OTUs (35.4% and 31.2%, respectively) and reads (40.6% and 41.5%, respectively) in the ecological dataset. After subsampling to an even 232 sequences and excluding low-abundance samples, the ecological dataset contained 226 OTUs. Autotrophs were the dominant fraction of the community in July, September, and December, and were nearly equal to heterotrophs in Octo- ber. Heterotrophs were least abundant in the early summer and reached their peak in February (Figure 1.6a). Parasites and polymorphic taxa were most commonly observed during the warmer sampling months (June and July).

20 a b PCoA - PC1 vs PC2 PCoA - PC1 vs PC2 0.3 0.3 0.2 0.2

16.0 C 0.1 0.1

14.0 C 0.0 0.0 12.0 C

10.0 C No data -0.1 -0.1 21 -0.2 -0.2 PC2 - Percent variation explained 19.99% PC2 - Percent variation explained -0.3 PC2 - Percent variation explained 19.99% 348PR (Jun) -0.3 348PR (Jun) 349PR (Jul) 349PR (Jul) 351PR (Sep) 351PR (Sep) 352PR (Oct) 352PR (Oct) -0.4 354PR (Dec) -0.4 354PR (Dec) 355PR (Feb) 355PR (Feb)

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 PC1 - Percent variation explained 33.43% PC1 - Percent variation explained 33.43% Figure 1.5: Ordination plots of water samples from stringent dataset sub-sampled to 591 reads per sample. (a) Inferred ecological role for OTUs identiﬁed to genus level. Dataset was sub-sampled to 232 reads per sample and then summed for each cruise. (b) Per-cruise community composition based on estimated niche breadth (Levins’ B). B was calculated for each OTU in the rareﬁed dataset (591 reads/sample). a. b. 1.0 Polymorphic 100 Specialist Parasite No Designation Heterotroph Generalist Autotroph 0.8 80

0.6 60

0.4 40 Relative abundance (%) Relative abundance (%)

0.2 20 22

0.0 0 348PR 349PR 351PR 352PR 354PR 355PR 348PR 349PR 351PR 352PR 354PR 355PR (Jun) (Jul) (Sep) (Oct) (Dec) (Feb) (Jun) (Jul) (Sep) (Oct) (Dec) (Feb) Figure 1.6: Inference of ecological role and niche breadth. (a) Inferred ecological role for OTUs identified to genus level. Dataset was sub-sampled to 232 reads per sampleand then summed for each cruise. (b) Per-cruise community composition based on estimated niche breadth (Levins’ B). B was calculated for each OTU in the rarefied dataset (591 reads/sample). The relative abundances of individual functional groups (i.e., autotroph, heterotroph, parasite, polymorphic) were not significantly correlated with temperature across individual samples. Overall abundance in either the complete or rarefied dataset did not predict a specialist or generalist designation. Among the 15 most abundant OTUs, three were generalists, eight were specialists, and four were not classified. All three abundant generalists were autotrophic taxa representing known cosmopolitan phytoplankters: the green alga Micromonas (Chloroplastida); Phaeocystis (Haptophyta); and a dinoflagellate in the Gymnodiniphycidae whose nearest circumscribed hit in GenBank is the naked, photosynthetic Gymnodinium aureolum (100% query coverage; 99% sequence identity). Abundant specialist OTUs were either parasites (Syndiniales), bloom-forming haptophyte algae (unidentified Prymnesiales), or heterotrophic flagellates known to graze on bacteria and picoplankton (Cyclidium, Colpoda, MAST-3, and bicosoecids).

1.4 Discussion

The oceans are vast reservoirs of microbial diversity—both taxonomic and functional— that remain largely unexplored (Sogin et al., 2006). As traditional morphological characterization of marine microbes has largely given way to molecular methods, and high-throughput sequencing in particular, we have discovered novel lineages among the prokaryotes, archaea, and eukaryotes that, despite playing fundamental roles in the microbial loop and broader biogeochemical processes, had previously eluded detection (Krabberød et al., 2017). For the overwhelming majority of these newly recognized taxa, however, little is known about their spatio-temporal distribution. In this study, small-fraction (nano-, pico-, and micro-) planktonic communities in the English Channel were repeatedly sampled at multiple points within the channel over a period of nine months. Pyrosequencing of the V4 variable region of 18S was

23 used to characterize microbial consortia and examine how their composition varied through space and time.

1.4.1 Taxonomic composition of plankton communities

This study identified 1,233 OTUs in the small-fraction plankton community in the western English Channel, representing all major eukaryotic lineages and a variety of trophic modes. Communities were composed primarily of many rare taxa, with only a few individual OTUs accounting for the majority of sequenced reads. Alveolates and Stramenopiles proved to be phylogenetically diverse and abundant, with the dinoflagellates and haptophytes contributing over half of all clustered OTUs collectively. Several studies employing culture-independent methods have also found that marine plankton communities are hotbeds of SAR diversity at both global (de Vargas et al., 2015) and regional scales (Cury et al., 2011; Genitsaris et al., 2016; Lee et al., 2012), with dinoflagellates (Le Bescot et al., 2016) and haptophytes (Liu et al., 2009) being notable for their hyperdiversity. Because this study sequenced DNA rather than RNA, it is not clear whether rare OTUs represent metabolically active taxa, or if seemingly abundant taxa are actually as predominant as they appear. Studies comparing differences in the relative abundance of rDNA sequences and RNA tran- scripts for various eukaryotic groups have found inconsistencies across taxa, with some lineages that appear abundant in DNA studies comprising significantly less of the metabolically active community (Not et al., 2009), whereas others exhibit a near 1:1 correspondence between the two measures (Logares et al., 2014). The dominance of SAR groups observed in this study is in stark contrast to the predominance of fungal sequences recovered in the previous WaMS study (Stern et al., 2015). While fungi constituted the largest overall fraction of the community in the first survey, here they represented just over 1% of all reads. However, in both cases, fungal abundance was driven almost entirely by LKM11, an uncultured

24 member of the Cryptomycota known from environmental surveys (Lara et al., 2010). One possible explanation for this discrepancy is the difference in average sampling depth between surveys, which increased from 7,913 raw reads per sample in the previous study to 11,676 reads here. Furthermore, a majority of the filtered dataset in the previous study was discarded because the sequences belonged to zooplankton, primarily copepods, whose peak abundance in the English Channel in April and May (Eloire et al., 2010) coincided with sampling. Similarly high levels of copepod reads were observed in samples from summer (July and August) and early fall (September) cruises in this study (data not shown), when zooplankton populations are still high in response to blooms of various phototrophs. These findings validate the necessity of deep sequencing when attempting to tease apart spatial and temporal shifts in community composition.

1.4.2 Plankton communities through space and time

Though there was no evidence that microbial communities were significantly different across the breadth of the English Channel, overall community composition did shift in response to seasonal variations in temperature. Additional environmental factors, both biotic and abiotic, which were not measured in this study, undoubtedly contribute to community dynamics in the WEC, but it is unlikely that physico- chemical characteristics alone can explain the observed shifts in protist diversity and abundance. A three-year survey of microbial communities in the eastern English Channel (EEC) (Genitsaris et al., 2015) found patterns of taxonomic diversity, OTU rarity/abundance, and temporal variation similar to those presented here, but determined that the variation across protistan assemblages was only partially explained by environmental parameters (e.g., inorganic nutrient supply, temperature, physical mixing). Instead, relationships among individual taxonomic or functional groups— that is to say, trophic interactions—appear to have a greater effect in determining

25 community composition at any given time, even at smaller geographic scales and particularly when environmental conditions at a given site are more variable (Genit- saris et al., 2016). In our survey, no clear temporal patterns could be distinguished from the taxa whose trophic mode could be inferred. However, as many of the OTUs observed could not be identiﬁed to genus, they were not included in the ecological inference estimations. Thus, any patterns observed across cruises is unlikely to accurately represent the true shifts in functional guilds over time.

1.4.3 Methodological considerations

With increasing sampling, the exceptional diversity of marine microbes and their importance in marine ecosystems are being elucidated. In our study, protists from the SAR supergroup dominated not only OTU diversity, but total read abundance. However, caution should be exercised when extrapolating the diversity and frequency of many protistan taxa from their observed OTU and read abundances, particularly when sequencing rDNA, as the number of rDNA copies can vary widely across species and broader groups. Some dinoflagellates have rDNA copy numbers that rival those observed in plants (Zhu et al., 2005), though most species examined have been shown to possess more modest numbers of repeats. Moreover, in many taxa with extremely high rDNA copy numbers, such as the ciliates, polymorphisms across individual copies may artificially inflate OTU diversity estimates (Gong et al., 2013). In parasitic taxa, which are prevalent across the alveolates, variations in rDNA copy number and sequence divergence within an individual genome might even be further exacerbated by accelerated evolution (Guillou et al., 2008). Here, efforts were made to reduce the likelihood of overestimating the contribution that individual taxa made toward observed OTU diversity by comparing the taxonomic composition recovered using differing levels of quality filtering (Figure 1.2). Decreases in both read abundances and OTU numbers were observed across

26 nearly all taxonomic groups with more stringent filtering. The only notable exception was the Alveolata, which saw a 64% decrease in OTU diversity between the moderate and stringent analyses, yet comprised a greater fraction of the total community observed in the stringent dataset. One possible explanation for this observation is that existing polymorphisms between rDNA copy numbers coupled with platform-specific sequencing errors combined to create sufficient variation to force the formation of a new OTU when clustering at a 97% similarity threshold. By denoising the dataset and removing artificial variation caused by sequencing errors, polymorphic rDNA copies within the same individual genome were then clustered into a single OTU.

1.5 Conclusions

The ﬁndings of this study corroborate those of previous surveys showing that marine protist communities are diverse taxonomically, phylogenetically, and ecologically. Most taxa observed in this study were rare specialists, though the appearance of abundant specialists throughout the year points to the importance of trophic interactions in shaping overall community composition through time. As sequencing technologies improve, particularly in per-sample sequencing depth, future studies can employ long-term repeated sampling within a single site or habitat to build a more complete picture of the ecological dynamics governing complex microbial assemblages.

27 2

Coastal Marine Habitats Harbor Novel Early-Diverging Fungal Diversity1

2.1 Introduction

Fungi are among the most diverse groups in Eukarya with estimates of total global diversity projecting upwards of 5.1 million species (Blackwell, 2011; O’Brien et al.,

2005; Taylor et al., 2014). However, with only „100,000 circumscribed taxa (Kirk et al., 2008), the overwhelming majority of which belong to the Ascomycota and

Basidiomycota („96,000 species), our current understanding of fungal diversity remains incomplete. As a consequence, eﬀorts to reconstruct evolutionary relationships within and among major fungal lineages that lie outside of the crown groups have been stymied by limited taxon sampling. Further, the potential ecological roles that these poorly known taxa may play in diﬀerent environments, and how important they might be in ecosystem functioning, largely remain a mystery. Marine fungi, which represent less than 1% of described fungal species (Kis-Papo, 2005; Richards et al., 2012), are particularly poorly characterized, despite a century of

1 This chapter was published as: Picard, K.T., 2017. Coastal marine habitats harbor novel early- diverging fungal diversity. Fungal Ecology 25, 1-13.

28 study (Jones, 2011). Historically, marine fungi were either isolated from or observed on substrata such as vegetation, macroalgae, and driftwood, reported as parasites of animal, plant, and algal hosts, or cultured from water, sediments, and sea foam (Kohlmeyer and Kohlmeyer, 1979). The taxa recovered from these marine surveys were predominantly Dikarya and localized to coastal habitats, where organic matter was readily available. The relative paucity of marine taxa from other fungal lineages (especially the zoosporic groups) or taxa from surface waters, led Kohlmeyer and Kohlmeyer(1979) to conclude that marine fungi were relatively species-poor and that the open oceans were largely a “fungal desert”. These observations, coupled with phylogenetic studies showing that many marine ascomycetes are secondarily derived from terrestrial groups (Schoch et al., 2007; Spatafora et al., 1998; Suetrong et al., 2009) rather than descended from an ancient obligately marine lineage, in many ways cemented the view that the marine realm, though a vast reservoir of microbial diversity (Sogin et al., 2006), was home to only a few fungi outside of the Ascomycota. To wit, when discussing habitats that might harbor as-yet undiscovered fungi, (Hawksworth and Rossman, 1997) mention marine environments only brieﬂy, and with regard to endophytes of marine plants. Over the past two decades, culture-independent methods, including environmental cloning and, increasingly, next-generation sequencing, have begun to reveal substantial fungal diversity from previously un- and under-sampled habitats across the globe, including soils (Tedersoo et al., 2014; Penton et al., 2013), freshwater lakes (Monchy et al., 2011; Ishida et al., 2015; Lef`evre et al., 2008), and glacial snowpack (Brown et al., 2015). Taxa recovered in these studies can and do belong to well- characterized fungal lineages, but many others represent entirely novel clades that have previously eluded detection. Though there are undescribed taxa across the fungal tree—recently termed the “dark matter fungi” (DMF) by Grossart et al.(2016)— they are especially common among the zoosporic fungi (Blastocladiomycota, Chytrid-

29 iomycota, Cryptomycota, Neocallimastigomycota, and the genus Olpidium) and former Zygomycotan (Entomophthoromycota, Kickxellomycotina, Mortierellomycotina, Mucoromycotina, Zoopagomycotina) lineages. The nameless, faceless members of these early-diverging groups are often microscopic and may have very specific nutritional requirements [e.g., obligate endoparasites in the Cryptomycota, putative symbionts in the Chytridiomycota (Picard et al., 2013; Nyvall et al., 1999; Newell, 1981)] making them difficult to isolate into culture. Most notably, the recently described phylum Cryptomycota was established using phylotypes recovered almost exclusively from environmental surveys (Jones et al., 2011a). Taxa in this group have subsequently been shown to be not only ubiquitous in their distribution (Liver- more and Mattes, 2013; Matsunaga et al., 2014; Lazarus and James, 2015), but also diverse—and often abundant— relative to other microbial eukaryotes (Taib et al., 2013; Debroas et al., 2015; Capo et al., 2015). In addition to revealing new taxa among better characterized terrestrial and freshwater habitats, culture-independent methods have increasingly reported novel clades from marine environments, many of which are allied to the early-diverging branches of the fungal tree (Richards et al., 2012; Bass and Richards, 2011; Richards et al., 2015). Recent culture-independent studies describing fungi from marine environments have investigated deep-sea and benthic sediments (Nagahama et al., 2011; Nagano et al., 2010; Tisthammer et al., 2016; Pachiadaki et al., 2016; Richards et al., 2015; Thaler et al., 2012; Edgcomb et al., 2011), hydrothermal vents (Burgaud et al., 2015), oxygen-deficient environments (Stoeck et al., 2006; Stock et al., 2009; Jebaraj et al., 2012; Wang et al., 2014b), and global surface waters (Stern et al., 2015; de Var- gas et al., 2015; Tisthammer et al., 2016; Richards et al., 2015; Wang et al., 2014a). Comparatively fewer studies have focused on marine fungi in coastal habitats (Arfi et al., 2012; Jeffries et al., 2016), which have historically been the best studied. In this study, I used ion semiconductor sequencing of the nuclear large subunit

30 34.66 34.73

34.72 34.64 Piver's Island

Town Marsh Cape Lookout (A1) 34.71 34.62 Latitude lat lat

Bird Shoal

34.70 34.60

34.69

34.58 a. b. −76.68 −76.67 −76.66 −76.65 −76.64 −76.58 −76.56 −76.54 −76.52 −76.50 lon Longitude lon

Figure 2.1: Map of coastal North Carolina sampling sites. (a) Collection sites within Beaufort Inlet, near Beaufort, NC. - Piver’s Island, plankton; - Town Marsh, persistent wetland sediments; N - Bird Shoal, intertidal sand ﬂat sediments. (b) Collection site in Cape Lookout Bight, NC. • - Station A-1 (Martens and Klump, 1980), shallow marine sediments.

(LSU, 28S) to investigate the taxonomic richness and diversity of marine and estuarine fungi from four disparate habitats in coastal North Carolina over the course of a year. My primary objectives were: (i) to characterize the fungal communities in coastal habitats and compare community composition across sites; (ii) assess the diﬃculty in classifying putative marine taxa across fungal lineages; and (iii) elucidate potential ecological roles for marine fungi as suggested by spatio-temporal distribution of taxa in coastal sites.

2.2 Materials and Methods

2.2.1 Study sites and sampling regime

A total of four sampling sites located in coastal Carteret County, North Carolina, USA were sampled quarter-annually between April 2011 and May 2012. For the

31 first two sites, sediments were collected from persistent intertidal wetlands (Town Marsh; 34˝42145.58322N 76˝40117.74922W) and intertidal sand flats (Bird Shoal; 34˝42128.79282N 76˝39142.87962W)—part of the Rachel Carson site within the North Carolina National Estuarine Research Reserve (NCERR) (Figure 2.1A). Town Marsh is a sandy island whose interior is dominated by supratidal grasslands and scrub- shrub vegetation such as southern redcedar (Juniperus virginiana var. silicicola), yaupon (Ilex vomitoria), loblolly pine (Pinus taeda), and Hercules’ club (Zanthoxy- lum clava-herculis). The periphery of the island comprises intertidal persistent wetlands that support oyster beds and avian rookeries. Adjacent to Town Marsh, Bird Shoal primarily comprises intertidal sand- and mud-flats dominated by dwarf glass- wort (Salicornia bigelovi) and smooth cordgrass (Spartina alterniflora). Town Marsh and Bird Shoal are subject to diurnal tides. Sediments from both sites were collected at low tide, using sterile 50 ml centrifuge tubes, up to a depth of 5 cm. Piver’s Island (34˝43112.47822N 76˝40122.73882W), home to the National Oceanic and Atmospheric Administration (NOAA) Fisheries Lab and the Duke University Marine Lab, is situated in the lower Newport River estuary less than 1 km west of Bird Shoal and Town Marsh, and approximately 2 km from the Beaufort Inlet (Figure 2.1A). This site experiences semi-diurnal tides of approximately 1 m (NOAA, 2012). A thorough description of the tidal and climatic variables at this site can be found in DeVries et al.(1994). To facilitate surveying surface water fungi, especially potential phytopathogens, plankton tows were performed from a platform under the Piver’s

Island bridge using a 0.5 m diameter 80 µm plankton net. The net was deployed for 15 min and a total of 200 ml of surface water was collected in sterile 50 ml centrifuge tubes.

Finally, marine sediments were collected from the shallow waters („9 m) at Sta- tion A-1 (34˝3717.04222N 76˝32143.11602W) in Cape Lookout Bight, located at the southern tip of the Outer Banks (Martens and Val Klump, 1984) (Figure 2.1B). This

32 small marine basin is rich in organic detritus originating from barrier islands upstream, with sediments containing 3-5% organic C (Martens and Val Klump, 1984). Sampling was performed seasonally over the course of a year (July and October 2011, February and May 2012). Sediments were collected using a piston core deployed from the research vessel Susan Hudson; collected sediment cores measuring 100 cm to 120 cm in total length were divided into 2 cm strata. Due to high activity of sulfate-reducing and methanogenic bacteria in the spring and summer months, respectively (Alperin et al., 1994), and limited penetration of dissolved oxygen from overlying water in the winter (Martens and Val Klump, 1984), surface sediments in the bight quickly become anoxic. Therefore, only the upper 2 cm of the core was included in this study. The upper core sediments were subsampled with sterilized, ethanol-rinsed spatulas and placed into sterile 15 ml centrifuge tubes. All samples taken from Town Marsh, Bird Shoal, Piver’s Island, and Cape Look- out Bight were sealed with paraﬁlm, transported to Duke University on ice, and stored at ´80˝C until the extraction of genomic DNA.

2.2.2 DNA extraction and sequence data generation

Collected sediments (Town Marsh, Bird Shoal, and Cape Lookout Bight) were thawed at room temperature and homogenized by hand. Large pieces of plant matter and other detritus were removed manually, if present. For the plankton tow site (Piver’s Island), tissue from thawed samples was collected through centrifugation (4000x g for 15 min at 4˝ C) in volumes of 100 ml, and dried at 30˝C in a Vacu-fuge R con- centrator (Eppendorf, Hamburg, Germany) for 15-30 min. Following mixing and/or drying steps, approximately 1 g of sediment or mixed planktonic tissue was used for total genomic DNA extraction using the PowerSoil R DNA Isolation Kit (MO BIO, Carlsbad, CA) according to the manufacturer’s protocol. Extracted DNA was eluted

in 100 µl of Solution C6 (10 mmol tris) that had been heated to 55˝C.

33 Amplicon libraries were generated using nuclear LSU primers LR0R [5’-ACCCGCTGAACTTAAGC-3’;(Moncalvo et al., 2000)] and EDF360R (5’-TACTTGTICGCTATCGGTCTC-3’; designed here for this study to accommodate the 400-bp read length of Ion Torrent), with Ion Torrent sequencing adaptors A (forward) and trP1 (reverse) (Life Technologies, Carlsbad, CA) and sample-specific DNA tags attached. For each sample, PCR reactions were performed in triplicate and pooled following purification to reduce bias. Conditions for each 25 µl reaction were: 20 ng to 100 ng template DNA per sample, 200 µmol Invitrogen mixed dNTPs (Life Tech- nologies, Carlsbad, CA), 10 µmol forward (LR0R) and reverse (EDF360R) primers, 2.5 µl 10x Master Taq Buffer with 1.5 mmol Mg2+ (5Prime, Hamburg, Germany), 5 µl 5x TaqMaster PCR Enhancer (5Prime), 0.5 U Taq DNA Polymerase (5Prime), and 6 µl molecular biology grade water (Fisher Scientific). PCR reactions were carried out using a Veriti R thermal cycler (Applied Biosystems, Foster City, CA) with the following specifications: initial denaturation at 94˝C for 2 min, followed by 30 cycles of denaturing at 94˝C for 1 min, annealing at 48˝C for 30 sec, and extension at 72˝C for 1.5 min, concluding with a final extension at 72˝C for 7 min. Negative controls containing only molecular biology grade water showed no amplification, indicating amplicon libraries were free from contamination. PCR replicates for each sample were pooled and then purified on a 0.8% high-melt agarose gel. Excised bands were cleaned using the illustra GFXTM PCR DNA and Gel Band Purifica- tion Kit (GE Healthcare, Piscataway, NJ) according to manufacturer’s protocol for maximum product recovery. After quantification using a Qubit R fluorometer (Life Technologies), samples were pooled in an equimolar solution and submitted to the Duke University Genome Sequencing and Analysis Core (Durham, NC). Following assessment for DNA concentration and size distribution on a BioAnalyzer 2100 (Agi- lent, Santa Clara, CA), amplicons were sequenced using the Ion Torrent PGM 400bp sequencing kit (Life Technologies) and one Ion 314TM chip. Raw sequence data have

34 been submitted to the National Center for Biotechnology Information Sequence Read Archive under accession number SRP091681.

2.2.3 Sequence data processing

Sequence data were processed using the QIIME 1.9.1 framework (Caporaso et al., 2010). In the initial quality control ﬁltering, reads were screened for the presence of the forward sequencing primer (LR0R) and a valid barcode, and discarded if they

failed to meet the following criteria: average Phred quality score ě25, no ambiguous bases, and homopolymer length ď6. Reads were then screened for low-quality regions with 50 bp sliding window and removed if truncation at a low-quality region resulted in a sequence shorter than 200 bp. After quality filtering, reads shorter than 200 bp and longer than 400 bp were discarded. Using the USEARCH quality-filtering pipeline (Edgar, 2010) as implemented in QIIME, noisy sequences were filtered at 99% similarity before de novo and reference-based chimera checks were performed, the latter using the SILVA LSU 119 release as a reference (Quast et al., 2013; Yil- maz et al., 2014). Sequences tagged as potential chimeras by both de novo and reference-based analyses were discarded. Retained sequences were then clustered into operational taxonomic units (OTUs) at a similarity threshold of 95%. Clusters containing only one sequence across all samples (i.e., global singletons) represent likely sequencing artifacts (Tedersoo et al., 2010) and were removed to reduce OTU inflation. The most abundant sequence from each remaining cluster was selected as a representative sequence for that OTU.

2.2.4 Taxonomic assignment

Taxonomic assignment of the representative sequences was carried out using two methods: (1) BLAST + MEGAN v. 5.8.4 (MEtaGenome ANalyzer, Center for Bioinformatics, T¨ubingen,Germany) (Huson et al., 2011); and (2) the Ribosomal

35 Database Project’s (RDP) nave Bayesian classifier (NBC) (Wang et al., 2007). In the first method, which is similarity-based, representative sequences were queried against a local installation of the GenBank nonredundant database using BLAST 2.2.30+ and the blastn algorithm (Altschul et al., 1997) with an e-value threshold of 10´10. BLAST results were imported into MEGAN with the following parameters: minimum support = 1, minimum score = 100, top percent = 1.0, and winscore = 0.0. Using a Lowest Common Ancestor (LCA) algorithm (Huson et al., 2007, 2011) and the established NCBI taxonomy, MEGAN parses BLAST hits for a query and assigns the queried sequence to the lowest taxonomic rank supported. Though this method has been shown to be accurate in placing short fungal LSU reads even at lower taxonomic levels (Porter and Golding, 2012), novel sequences are often placed only to high-level classifications (e.g., ‘Fungi’) or not classified at all (Kunin et al., 2008). The second taxonomic assignment method used, the RDP Classifier, compares 8 bp fragments of the queried sequence against reference sequences in a curated training set and calculates a score at genus level. Statistical support for the placement of a query sequence in a given genus is then estimated from 100 bootstrap replicates. Representative sequences from this study were classified using the RDP classifier v2.10 trained with LSU fungal training set 11 both with and without a bootstrap threshold of 50% (referred to as ‘50% cutoff’ and ‘best-match’ analyses, respectively).

For partial short reads ď250 bp, a threshold of 50% bootstrap support has been shown to be accurate at placing fungal LSU sequences to genus level (Porras-Alfaro et al., 2014; Liu et al., 2012), but the ‘best-match’ analysis allows for provisional identification for groups that are poorly represented in databases, such as aquatic and early-diverging fungi. Taxonomic assignments made by the RDP Classifier were manually edited to reflect current accepted taxonomies [e.g., Rozella assigned to Cryptomycota instead of Chytridiomycota (Jones et al., 2011b); recently described

36 phyla and sub-phyla within the former ‘Zygomycota’ (Hibbett et al., 2007)]. Results from the ‘best-match’ RDP classiﬁcation were compared to those from the BLAST + MEGAN analysis and examined for concordance. When taxonomic assignments between the two methods diﬀered, the RDP assignment was chosen.

2.2.5 Phylogenetic placement of most abundant OTUs

Sequences from the 50 most abundant fungal OTUs across all sites were aligned to the kingdom-wide nucLSU dataset from James et al.(2006b) using the --add fragments function in maﬀt v.7 (Katoh and Standley, 2013). Alignments were then reﬁned by eye and ambiguously aligned regions were excluded. Maximum likelihood (ML) trees were inferred using RAxML v.8.0.0 (Stamatakis, 2014) under the GTRCAT model of nucleotide substitution with 1000 rapid bootstrapping replicates.

2.2.6 Diversity analyses

Because per-sample read totals varied signiﬁcantly after the removal of non-fungal taxa, diversity metrics were assessed using the full eukaryotic dataset subsampled to 10,781 sequence reads, the lowest number of reads across samples. Alpha-diversity measures (corrected Chao index, and Shannon and Simpson biodiversity indices) for each sample were calculated in QIIME. To assess sampling completeness, rarefaction curves were generated for each sample using the complete eukaryotic dataset, also in QIIME. The distribution of OTUs across both habitats and seasons was visualized through Edwards’ Venn diagrams generated using jvenn (Bardou et al., 2014).

2.3 Results

2.3.1 Sequence ﬁltering and OTU clustering

Of the 654,728 raw input sequences, 355,102 (54.2%) were retained for OTU clustering and downstream analysis with most discarded sequences failing to meet the

37 200-400 bp length requirements. Previous microbial diversity surveys using the Ion Torrent sequencing platform have reported similarly high rates of low-quality sequences (Brown et al., 2013; Kemler et al., 2013). During OTU clustering, an additional 3,701 chimeric or singleton sequences were identified and removed, resulting in a filtered dataset of 351,366 sequences. Per-sample read counts ranged from a minimum of 10,781 to a maximum of 36,653, with a mean of 21,960. Clustering of filtered sequences at 95% similarity generated 4,379 non-singleton eukaryotic OTUs, 770 of which (17.6%) were assigned to ‘Fungi’ by both taxonomic assignment methods (BLAST + MEGAN and RDP without a bootstrap cutoff, or ‘best match’). These 770 OTUs encompassed 56,005 reads (15.9% of total filtered reads), which were unequally distributed among samples (Table 2.1), with the highest read values generated from plankton samples taken at Piver’s Island and intertidal sand samples collected from Bird Shoal. For the complete eukaryotic dataset, the number of OTUs was weakly positively correlated with sample read count (Table 2.2; Pearson’s r “ 0.35). For all but one sample, corrected Chao1 OTU estimates were higher than observed OTU counts, but per-sample OTU estimates recapitulated observed richness (e.g., the highest Chao1 estimates corresponded to the samples with the highest observed OTU richness). Rarefaction curves for each sample failed to reach plateaus, suggesting that the communities from each site were incompletely sampled (Figure B.1).

38 Table 2.1: Total number of ﬁltered sequence reads, eukaryotic OTUs, fungal sequence reads, and fungal OTUs from samples collected seasonally from four coastal North Carolina sites. Values in parentheses indicate percentage of total reads/OTUs observed.

Filtered Total Site Season Fungal Reads Fungal OTUs reads OTUs Piver’s Island Winter 32715 707 14473 (44.2%) 246 (34.8%) plankton (PT) Spring 23032 443 465 (2.0%) 63 (14.2%) Summer 29742 1155 4159 (14.0%) 240 (20.8%) Fall 21139 523 2854 (13.5%) 151 (28.9%) Town Marsh Winter 36653 1248 6300 (17.2%) 318 (25.5%) wetland sediments (WS) Spring 28098 635 261 (0.9%) 72 (11.3%) Summer 22034 258 351 (1.6%) 58 (22.5%) Fall 12891 391 6796 (52.7%) 142 (36.3%) Bird Shoal Winter 10781 371 4319 (40.1%) 141 (38.0%) intertidal sand (IS) Spring 22306 864 1011 (4.5%) 85 (9.8%) Summer 15958 792 437 (2.7%) 62 (7.8%) Fall 31188 1016 10550 (33.8%) 318 (31.3%) Cape Lookout Bight Winter 14747 931 1010 (6.8%) 176 (18.9%) sediment core (SC) Spring 13684 978 775 (5.7%) 162 (16.6%) Summer 20522 1143 944 (4.6%) 175 (15.3%) Fall 15876 1097 1300 (8.2%) 209 (19.1%)

39 Table 2.2: Diversity metrics for coastal marine samples.

Site Season Total OTUs Chao1 Shannon Simpson Pivers Island Winter 707 782 4.988 0.904 plankton (PT) Spring 443 497 3.804 0.811 Summer 1155 1119 6.470 0.962 Fall 523 538 4.821 0.917 Town Marsh Winter 1248 1267 7.016 0.964 wetland sediments (WS) Spring 635 700 4.926 0.848 Summer 258 359 0.793 0.138 Fall 391 572 4.438 0.903 Bird Shoal Winter 371 450 4.782 0.900 intertidal sand (IS) Spring 864 941 7.326 0.985 Summer 792 1001 6.724 0.957 Fall 1016 1054 7.536 0.981 Cape Lookout Bight Winter 931 1168 6.405 0.947 sediment core (SC) Spring 978 1236 6.813 0.964 Summer 1143 1241 6.907 0.972 Fall 1097 1279 6.520 0.912

40 0 10 20 30 40 50 60 70 a. b. Plankton (PT) Wetland Sediment (WS) Ascomycota

Basidiomycota

Blastocladiomycota

Ascomycota Chytridiomycota Basidiomycota n=21,951 n=13,708 Blastocladiomycota 399 OTUs 400 OTUs Chytridiomycota Cryptomycota Cryptomycota Entomophthoromycota Intertidal Sand (IS) Sediment Core (SC) Glomeromycota Neocallimastigomycota Entomophthoromycota “Zygomycota”

Glomeromycota

Neocallimastigomycota % Fungal OTUs % Fungal Reads 41 “Zygomycota” n=16,317 n=4,029 440 OTUs 376 OTUs Figure 2.2: (a) Contribution of each fungal phylum to total observed diversity, as both proportion of fungal OTUs and proportion of fungal sequences. (b) “Best-match” taxonomic composition of fungal sequences observed at four sites in coastal North Carolina. 2.3.2 Taxonomic assignment

‘Best-match’ analysis placed reads to all eight phyla (Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Cryptomycota, Entomophthoromycota, Glom- eromycota, Neocallimastigomycota) and four ‘Zygomycete’ sub-phyla (Kickxellomy- cotina, Mortierellomycotina, Mucoromycotina and Zoopagomycotina) (Figure 2.2a; Table B.1). Reads were binned to 33 classes, 89 orders, 174 families, and 318 genera. The dominant groups observed were the Ascomycota (66.8% fungal reads), Chytrid- iomycota (19.4%), and Basidiomycota (7.0%). When employing a 50% bootstrap confidence level, the proportions of unclassified sequences across all sites ranged from 10.9% (phylum) to 31.5% (genus). Sequences were binned to only 173 genera from 108 families, 65 orders, 24 classes, and 5 phyla (in addition to two sub-phyla from the ‘Zygomycota’). Across all samples, the dominant fungal phyla observed were Ascomycota (66.6% fungal reads), Chytridiomycota (15.4%), and Basidiomycota (6.7%). OTUs allied to the ‘Zygomycota’ (12, 0.33%), Blastocladiomycota (1, 0.02%), and Neocallimastigomycota (1, 0.01%) were mini- mally abundant. The remaining 224 OTUs (representing 10.9% of fungal reads) could not be identified beyond ‘Fungi’. Notably, lineages that are better represented in the RDP’s LSU fungal training set 11 – typically the Ascomycota and Basidiomycota – were more likely to be identified to both higher and lower taxonomic levels (Figure 2.3). For example, 99.7% of all ‘best-match’ Ascomycota sequences and 96.9% of all ‘best-match’ Basidiomy- cota sequences could be assigned to their respective phyla under the 50% confidence threshold. At the genus level, 62.4% of ascomycete reads and 76.9% of basidiomycete reads could be binned. By comparison, only 79.5% of ‘best-match’ Chytridiomycota sequences could be binned at the phylum level, and just 5.7% could be binned to genus. Among the other zoosporic lineages, ă 2% of Blastocladiomycota sequences

42 Ascomycota

Basidiomycota

Blastocladiomycota

Chytridiomycota

Cryptomycota

Entomophthoromycota

Glomeromycota Phylum Class Order Neocallimastigomycota Family Genus “Zygomycota”

0 25 50 75 100

Figure 2.3: Proportions of reads from each fungal phylum assigned to each taxonomic level with bootstrap support ě 50% using the RDP Classiﬁer. Phylum names are based on best-match assignments. and ă 0.5% of Neocallimastigomycota sequences could be binned to genus. None of the sequences binned to the Cryptomycota, Entomophthoromycota, and Glom- eromycota in the ‘best-match’ analysis could be placed to any taxonomic level with 50% bootstrap support.

2.3.3 Per-site diversity

OTU counts diﬀered across the four sites (Table 2.1; Table B.4), ranging from a high of 440 fungal OTUs at the intertidal sand ﬂats of Bird Shoal, to a low of 376 OTUs in the marine sediments from Cape Lookout Bight. Estuarine sediments on Town Marsh harbored 400 OTUs, and 399 OTUs were observed from the microplankton sampled at Piver’s Island. Seven of the eight phyla and all four ‘Zygomycete’ sub- phyla reported were found in all four sampling sites (Figure 4; Table B.4); members

43 of the Cryptomycota were observed only in samples from Bird Shoal and Cape Look- out Bight (Figure 2.2b). For all sites, the Ascomycota was the most speciose phylum reported; however, most taxa that were recovered were found at relatively low abundances. For all sites except Piver’s Island, fungi were more diverse and constituted a larger fraction of the total eukaryotic community observed in the cooler months (Table 2.1). Although the microplankton samples collected at Piver’s Island comprise the greatest number of fungal reads of all sites considered, very few taxa were major contributors to the overall fungal community. Ascomycota comprised 93.7% of all sequencing reads (Figure 2.2b), with the Dothideomycetes, Lecanoromycetes, and Eurotiomycetes alone contributing 86.3% of total reads (Table B.2). The primary Dothideomycete representatives were in the Capnodiales (Mycosphaerella) and Pleosporales (Phaeosphaeria, Preussia) (Table B.3). The Lecanoromycetes was the single most abundant class in the microplankton tows, driven largely by the presence of the lichen genus Buellia, which was the dominant taxon recovered from the Piver’s Island samples. Nearly all of the sequences belonging to Eurotiomycete-aligned OTUs were binned to a single genus, Exophiala, in the Chaetothyriales. Other groups contributing to the fungal community were the Malasseziales (Malassezia) and the Spizellomycetales (Spizellomyces). Piver’s Island samples contained the fewest reads that could not be identiﬁed to phylum or beyond using a 50% conﬁdence threshold (1.5%) (Figure 2.5).

44 100 Other Mucoromycetes “Zygomycota” Kickxellomycetes Neocallimastigomycetes Neocallimastigomycota Entomophthoromycetes Entomophthoromycota 80 Monoblepharidomycetes Chytridiomycetes Chytridiomycota Blastocladiomycetes Blastocladiomycota

Exobasidiomycetes Basidiomycota Agaricomycetes Sordariomycetes 60 Leotiomycetes Lecanoromycetes Ascomycota Eurotiomycetes Dothideomycetes Ascomycota i.s. Abundance (%)

40 Relative 45 20

0 Winter Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall

Plankton Tow Wetland Sediment Intertidal Sand Sediment Core Figure 2.4: Relative abundances of fungal sequences from seasonal sampling of four coastal marine sites. Assignments to fungal classes are based on best match taxonomic designations made using the RDP fungal database for classification. Note: these data are not normalized due to the wide variation of fungal sequences across sitesand seasons. See Table 2.2 for per-sample read counts. In the wetland sediments of Town Marsh, the most abundant fungal classes were the Dothideomycetes (Ascomycota), Exobasidiomycetes (Basidiomycota), Chytrid- iomycetes (Chytridiomycota), and Monoblepharidomycetes (Chytridiomycota) (Fig- ure 2.4; Table B.2). Collectively, these groups comprise 80.6% of all sequence reads for Town Marsh samples. The fungal community in these sediments was dominated by the Pleosporales (Phaeosphaeria, Phaeodothis), Malasseziales (Malassezia), Capn- odiales (Mycosphaerella), Chytridiales (Entophlyctis,Karlingiomyces), Spizellomyc- etales (Spizellomyces), and Monoblepharidales (Oedogoniomyces) (Table B.3). The samples collected from Town Marsh also contained considerable novel diversity, with 110 OTUs (27.5% of OTUs observed in site; 12.2% of total site reads) unidentifiable to phylum or beyond when implementing a 50% bootstrap constraint (Figure 2.5). The dominant classes in the intertidal sand flats on Bird Shoal mirrored those in the microplankton tows from Piver’s Island and the sediments from Town Marsh (Figure 2.4; Table S1). The Dothideomycetes, Chytridiomycetes, Monoblepharidomycetes, and Exobasidiomycetes, with the addition of the Neocallimastigomycetes, comprised 68.9% of the total sequencing reads from the annual sampling at the site. As on Town Marsh, the Pleosporales (Phaeosphaeria, Phaeodothis), Malasseziales (Malassezia), and the zoosporic orders Monoblepharidales (Oedogoniomyces) and Spizellomycetales (Spizellomyces) were among the dominant groups. Under a 50% confidence threshold, 16.3% of total site reads, most allied to the Chytridiomycota and Neocallimastigomy- cota in ‘best-match’ analyses, could not be identified beyond ‘Fungi’ (Figure 2.5). Finally, the fewest OTUs were reported from the oxygen-deficient marine sediments at Cape Lookout Bight (Table B.4), which also contained the fewest fungal reads (7.2% of all fungal reads) (Table 2.1). As seen in the other sampling sites, the Dothideomycetes, Sordariomycetes, and Exobasidiomycetes were among the more abundant classes recovered (Table B.2). However, unlike most sites located near the Beaufort Inlet, the overall dominant classes in the Cape Lookout sediments

46 rcino Tswr lcdtxnmclyt h soyoa(4 Ts 38.8% genera— OTUs, ten (146 only Ascomycota diversity), the site to total taxonomically placed were OTUs of fraction myces ( the included callimastigales sediments these in taxa (Figure ( dominant site Chytridiales the this scales, for taxonomic reads finer total At the of 2.4). 50.3% Chytridiomycetes, encompass from the samples) OTUs core to sediment total the binned of OTUs (34.8% 110 Neocallimastigomycetes and the Monoblepharidomycetes, lineages: zoosporic to belonged tax- for database fungal RDP the using cutoff classification. bootstrap onomic 50% a implementing when 2.5 Figure ,adEtmptoae ( Entomophthorales and ), 70 10 20 % Reads30 Unassigned40 50 60 ecnaeo nsindfna eune o ahtxnmcrank taxonomic each for sequences fungal unassigned of Percentage : Entophlyctis Cyllamyces Sediment Core Plankton Intertidal Sand Wetland Sediments Phylum , Mesochytrium ,Eoaiils( Exobasidiales ),

T ow Class Basidiobolus Aspergillus 47 ,Sielmctls( Spizellomycetales ), Order Malassezia .Atog h largest the Although B.3). (Table ) , Chaetomidium Family ,Batcails( Blastocladiales ), Spizellomyces Genus , Immersiella Cateno- ,Neo- ), , My- cosphaerella, Phaeodothis, Phaeosphaeria, Preussia, Saccharata, Cladosporium, and

Trichothecium—contributed ě1.0% each to total read abundance. No individual ascomycete genus comprised more than 2.6% of total read abundance. Finally, when employing a 50% bootstrap cutoff for taxonomic assignment, sediments from Cape Lookout harbored the highest percentage of putatively novel OTUs (Figure 2.5), which comprised 33.6% of total read abundance and are heavily weighted toward the early-diverging lineages. Although many of the dominant taxa were shared across habitats, each site harbored unique diversity (Figure B.2a). Over 40% of the fungal OTUs observed (317) were observed at a single site. The intertidal sand flats of Bird Shoal had the highest number of unique OTUs (103, comprising 6.5% of site reads), while the sediments collected from the persistent wetlands on Town Marsh contained the fewest (67, 1.9% of site reads). Unlike the Beaufort Inlet sites where unique OTUs represented only a small fraction of the communities at each site, the 73 OTUs unique to the sediments at Cape Lookout Bight comprised 12.1% of all site reads. Despite the proximity between Town Marsh, Bird Shoal, and Piver’s Island, only 49 OTUs were shared among all three sites. OTUs unique to their respective sites were typically rare or nominally abundant. By contrast, the 50 most abundant OTUs across all habitats, comprising 79.8% of total fungal reads, were widespread across sampling locations, with 38 being found in all four locales. Only 2 of the 50 most abundant OTUs were localized to a single site. Fungi in these coastal sites may also exhibit seasonality: fungal communities were more diverse and more abundant in cooler months (Figure B.2b), although the principal taxa at each site were more likely to be present year- round. Across all fungal OTUs, 37.1% (286 OTUs) were observed during a single season, with the winter and fall having the highest number of unique OTUs (105 and 107, respectively). By contrast, only 121 OTUs (15.7%) were observed throughout the year (Figure B.2b). The numbers of ascomycete and basidiomycete OTUs fluctu-

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Figure 2.6: Maximum likelihood tree of 50 most abundant OTUs. Gray and black fonts denote reference and amplicon sequences, respectively. Colors indicate major fungal phyla, clockwise from left: Ascomycota (red); Basidiomycota (orange); Glom- eromycota (light gray); ‘Zygomycota 1’ (dark gray); Blastocladiomycota + ‘Zygomy- cota 2’ (yellow); Chytridiomycota + Neocallimastigomycota (blue).

49 ated widely across seasons (Table B.5), while the species richness of early-diverging groups was less variable. Notably, the 50 most abundant OTUs were not exclusively from the Ascomycota or Basidiomycota, but rather originated from groups across the fungal tree (Figure 2.6). All major phyla were represented, although Ascomycota and Chytridiomycota were the most diverse with 28 and 10 representatives, respectively.

2.4 Discussion

Culture-dependent and molecular studies of fungal diversity and ecology have doc- umented the critical roles fungi play as primary decomposers, parasites, and symbionts in terrestrial environments. By contrast, the diversity and functional roles of fungi in aquatic environments, and especially marine habitats, are poorly understood (Wurzbacher et al., 2010). The application of next-generation sequencing technologies to microbial surveys of under-sampled aquatic habitats has revealed considerable novel diversity across the fungal kingdom, including the poorly characterized early- diverging lineages (Grossart et al., 2016). In the case of coastal marine habitats, classical culturing studies suggest that marine fungi are relatively rare, localized to the coasts, and primarily allied to the Dikarya (Hyde et al., 1998). In the present study, amplicon libraries derived from sediment and water samples collected seasonally from four coastal marine habitats in North Carolina revealed that marine fungal communities are considerably more diverse than culture-dependent studies have found, with sites harboring OTUs from all major phyla and sub-phyla. Despite high species richness, only a few taxa were consistently abundant across all sites and/or seasons. Particularly noteworthy was the ﬁnding that the zoosporic fungi are among the more abundant and species-rich taxa represented, which is contrary to historical surveys indicating that marine fungi are dominated by the Ascomycota and Basidiomycota. A signiﬁcant fraction of total fungal reads from surveyed sites represented novel lineages that are only distantly related to sequences in curated

50 reference databases. Furthermore, the vast majority of these novel phylotypes were most closely allied to taxa in the zoosporic fungi and the lineages that formerly comprised the ‘Zygomycota’.

2.4.1 Plankton sampling (Piver’s Island)

Plankton samples collectively contributed the greatest number of fungal sequences of any habitat sampled; however, those sequences were distributed across only a small fraction of site OTUs. The single most abundant OTU from this site - and across all sites surveyed - was allied to the crustose microlichen genus Buellia, whose species are largely terricolous, and can be found growing in coastal areas. Other prominent Ascomycota included parasites of Spartina spp. (Phaeosphaeria, Mycosphaerella) common to the eastern U.S. coast (Buchan et al., 2002; Lyons et al., 2010), plant pathogens (Sclerotinia, Saccharata), and animal parasites (Exophiala). Among the ten most abundant OTUs at this site, only one basidiomycete (Malassezia) and one chytridiomycete (a putative member of the order Spizellomycetales) were represented. Malassezia, which is primarily known as a human pathogen but has also been shown to be widely distributed across marine habitats (Amend, 2014), was also recovered at all sites in the present study. The relatively low abundance and diversity of the zoosporic fungi in plankton samples was particularly surprising. In freshwater habitats, chytrids play dual roles as saprobes/parasites of phytoplankton (Kagami et al., 2007; Rasconi et al., 2012) and nutrient-rich food sources for zooplankton, forming an oft-ignored component of the microbial food web called the mycoloop (Kagami et al., 2014). Although considerably less is known about the mycoloop in marine environments, recent high-throughput sequencing studies of pelagic fungal communities in Arctic (Hassett and Gradinger, 2016; Comeau et al., 2016) and temperate (Jeﬀries et al., 2016; Comeau et al., 2016; Richards et al., 2015) waters have shown a predominance of novel, chytrid-like phylotypes. Moreover, parasitization of

51 marine phytoplankton by chytrid fungi has been observed directly (Hassett et al., 2017; Hassett and Gradinger, 2016; Scholz et al., 2016), suggesting that these fungi play a similarly critical role in nutrient-cycling in the marine realm (Jephcott et al., 2016). Several factors likely contributed to the poor sampling of zoosporic fungi from Piver’s Island, with the principal one being the mesh size of the plankton net used. While zoosporic fungi are common parasites of larger desmids and diatoms in freshwater environments (Kagami et al., 2014), the mesh size used in the present study (80 µm) was too large to capture many smaller marine algal hosts (Hassett and Gradinger, 2016) and free-swimming zoospores. Deploying a plankton net with a ﬁner mesh or direct ﬁltration of unfractionated seawater may be a better strategy for sampling planktonic zoosporic fungi more thoroughly. In addition to mesh size, the relatively small amount of seawater sampled in the present study and the collection site’s proximity to shore likely account for the over-representation of largely terrestrial lichen taxa.

2.4.2 Persistent wetland sediments (Town Marsh)

In coastal wetlands along the Atlantic coast of North America, the smooth cordgrass Spartina alterniflora is the dominant vegetation and thus an abundant food source for symbiotic and saprophytic microbes (Peterson and Howarth, 1987), both above- ground on senescent plant tissue and in marsh sediments. Predictably, many of the fungi recovered from the persistent intertidal wetlands between Town Marsh and Bird Shoal islands are plant-associated. Pathogens of Spartina alterniflora and cellulose decomposing hyphomycetes were the principal ascomycetes recovered, and increased proportions of the Mucoromycotina relative to other sites were attributable chiefly to the endomycorrhizal symbiont Endogone. In Beaufort Inlet, salt marsh productivity is also fueled by benthic microalgae such as cyanobacteria and, to a lesser degree, diatoms (Currin et al., 1995), perhaps explaining the predominance

52 of algae-associated genera (e.g., Entophlyctis, Mesochytrium, Olpidium) among the zoosporic fungi. Of particular note with regard to the zoosporic fungi is the relative abundance of OTUs binned to the Monoblepharidomycetes (=monoblephs), a small class within the Chytridiomycota containing only six genera (James et al., 2006a) and 20-25 species. Monoblephs, the second most abundant class observed in wetland sediments (Figure 2.4), have been isolated solely from freshwater habitats where they degrade plant material including twigs, leaves, and fruits (Sparrow, 1933). The profusion of cellulosic material in salt marsh sediments coupled with ﬁndings that early-diverging fungi (and speciﬁcally the Monoblepharidomycetes) have long had the capacity to decompose tissues from green plants (Chang et al., 2015), suggest that the monoblephs may constitute a previously unknown, but critical microbial component governing nutrient transfer in salt marshes.

2.4.3 Intertidal sand (Bird Shoal)

Arenicolous fungi, which are generally deﬁned as fungi that live on or among sand grains, play similar roles to soil fungi in decomposing organic material (Kohlmeyer and Kohlmeyer, 1979). Relatively few arenicolous fungi have been described, nearly all of which belong to the Ascomycota and Basidiomycota, and are functionally characterized by their preferred substrata (e.g., driftwood, macroalgae, cellulose detritus, feathers). Intertidal sand samples collected from Bird Shoal harbored the most fungal species of all sites (Figure B.2a), though many taxa were shared with the wetland sediments from Town Marsh. The Ascomycota was both the most abundant and speciose phylum recovered, but the Chytridiomycota, which was the second most diverse group, had a higher per-OTU abundance. Just as observed in the wetland sediments, over half of all sequences binned to the Chytridiomycota belonged to the Monoblepharidomycetes. The abundance of zoosporic fungi within these intertidal sand samples may at ﬁrst seem surprising, but considering historical methods of

53 surveying arenicolous fungi, it is clear that sampling strategies would have largely missed interstitial chytrid fungi. Studies assessing both diversity and abundance of sand fungi have mostly relied on microscopic examination of fruit bodies on incubated detritus or ungerminated spores collected from sea foam. These sampling methods preferentially select for taxa that (1) have specialized nutritional requirements, such as those that can degrade lignin and cellulose, and (2) have spores that are resistant to drying and/or adapted to passive dispersal onto suitable substrates, thus precluding the description of many early-diverging fungi (Kohlmeyer, 1966), particularly the chytrids whose zoospores lack chitinous cell walls. In addition to zoosporic fungi, higher proportions of other early-diverging groups, such as the Entomophthoromy- cota and Mucoromycotina were also observed (Figure 2.4), suggesting that these microfungi target invertebrate hosts and particulate refractory materials embedded in sand.

2.4.4 Benthic marine sediments (Cape Lookout Bight)

Sediments collected from Cape Lookout produced the lowest number of total reads, the least fungal reads, and smallest number of fungal OTUs across all sites. The overall abundance and taxonomic composition observed in these marine sediments, however, were less variable throughout the year than at other sites, suggesting that the fungal communities are relatively stable across seasons. As seen at other sites, taxon diversity was high, but most taxa were rare, with over 80% of all OTUs from Cape Lookout being represented by ten or fewer sequences (data not shown). Because sediments act as a reservoir for spores, it is diﬃcult to determine whether these OTUs are active rare taxa or simply those whose propagules were carried downstream in detritus. Proportions of the various ‘Zygomycota’ lineages were elevated in marine sediment samples, but did not comprise a substantial fraction of the community (Figure 2.4). Zoosporic fungi were particularly abundant in these sediments (Figure

54 2.4), though few could be placed taxonomically beyond phylum or class with ě50% bootstrap support in RDP Classifier analyses. Putative representatives (or possible near-relatives) of the enigmatic zoosporic fungal genus Olpidium, whose phylogenetic placement is outside the Chytridiomycota but remains unresolved (James et al., 2006b; Sekimoto et al., 2011), were among the most abundant OTUs. The presence, and often predominance, of early-diverging ‘near-chytrid’ phylotypes in the Cape Lookout sediments reflects similar findings from other culture-independent surveys of marine sediments (Schloss et al., 2015; Richards et al., 2015; Jeffries et al., 2016; Le Calvez et al., 2009; Nagahama et al., 2011; Nagano et al., 2010). Another especially interesting result is the year-round presence of OTUs allied to the Neocallimastigomycota (i.e., the rumen fungi), which are currently understood to be obligate endosymbionts inhabiting the guts of primarily ruminant hosts (Gruninger et al., 2014). Neocallimastigomycota were also reported from plankton, estuarine sediment, and intertidal sand samples in low numbers, however, their occurrence can be attributed to the presence of feral horses in the RCERR. While the Neocallimastigomycota-like OTUs observed in the Cape Lookout sediments might also have originated from vertebrate hosts upstream, there is some evidence for symbiotic marine members of this group. Anaerobic fungal thalli and flagellated zoospores were observed in the gut and coelomic fluid of the coastal sediment-dwelling sea urchin Echinocardium cordatum (Thorsen, 1999), and also found in the guts of the algae-grazing marine iguana Amblyrhynchus cristatus (Mackie et al., 2004). Thus, sequences assigned to this phylum may have originated from resting spores awaiting ingestion by a marine invertebrate host. Alternatively, anaerobic zoosporic fungi may also be free-living in anoxic sediments and soils; molecular signatures of Neo- callimastigomycota have been detected in landfill soils (Lockhart et al., 2006) and lacustrine (Wurzbacher et al., 2016) and estuarine (Mohamed and Martiny, 2011) sediments. While the functional roles these fungi play remain unclear, it has been

55 proposed that in addition to cellulose decomposition, anaerobic fungi, including potential free-living relatives of the rumen fungi, may form symbiotic relationships with chemoautotrophic prokaryotes in deep-sea sediments, generating bioavailable hydro- gen (Ivarsson et al., 2016). None of the 25 putative Neocallimastigomycotan OTUs in Cape Lookout sediments could be assigned to the phylum with ě50% bootstrap support, suggesting that free-living anaerobic zoosporic fungi may be only distantly related to symbiotic taxa, or represent a separate clade entirely.

2.4.5 Methodological considerations

One of the primary challenges inherent in molecular surveys of broad-scale eukaryotic diversity is the selection of an appropriate DNA marker and, consequently, a corresponding primer pair. In high-throughput surveys of fungal diversity, three ribosomal loci are utilized: small subunit (SSU/18S), the internal transcribed spacers (ITS1 and ITS2), and large subunit (LSU/28S). For this study, I chose to target the D1 region of the ribosomal large subunit, which is sufficiently conserved for kingdom- wide sequence alignment (unlike the hyper-variable ITS), but also variable enough for finer scale phylogenetic delimitations (Porter and Golding, 2012). Although ITS has been adopted as the universal barcode for fungi due to its high levels of interspecific variability (Schoch et al., 2012), LSU can be used to infer deep relationships among fungi (James et al., 2006b), is a commonly employed marker in phylogenetic studies in zoosporic lineages (James et al., 2006a; Letcher et al., 2006; Porter et al., 2011), and when used in environmental sequencing surveys recovers community patterns congruent to those found by ITS (Brown et al., 2014; Porras-Alfaro et al., 2014). Neither of the primers used in this study are fungal-specific, although the reverse primer was designed to amplify taxa across the fungal tree, including the Cryptomy- cota. However, as the vast majority of fungal species remain unknown and primer pairs may only amplify a subset of a target community (Stoeck et al., 2006), it

56 is likely that the diversity reported here is but a snapshot of the fungal diversity present in the coastal sites surveyed. Furthermore, extant reference databases are biased toward terrestrial taxa – and the Dikarya in particular – confounding efforts to identify novel aquatic fungi (Panzer et al., 2015). In the first round of taxonomic identification in this study, which queried amplicons against the GenBank database and assigned taxonomy using MEGAN’s Lowest Common Ancestor algorithm, 1,273 OTUs were tentatively identified to ‘Fungi’ (data not shown), although only 770 of those were also placed to the fungal kingdom by the RDP analyses. Many of these additional 503 OTUs were assigned to early-diverging fungal lineages and may represent true fungal species, but in light of the current limitations of taxonomic databases, I adopted a conservative approach and excluded them from downstream analyses. Due to additional methodological considerations such as sample size and study design (Lindahl et al., 2013), the results presented herein cannot draw firm conclusions about the structure of marine fungal communities through space and time, nor do they provide an exhaustive catalogue of marine fungi from these sites. Rather, these data offer preliminary insight into the breadth of fungal diversity present in these historically undersampled marine habitats, and demonstrate that our current understanding of marine fungal diversity and ecology is largely incomplete. I conclude that coastal marine fungi are considerably more diverse than previously thought, especially among the poorly understood early-diverging lineages. The findings from this study will contribute to a more complete understanding of marine and estuarine fungi and help inform future studies of their occurrence and the functional roles they play in their respective ecosystems.

57 3

Generating Reference Sequences for Molecular Operational Taxonomic Units (MOTUs) with PacBio: a Case Study with the Dark Matter Fungi

3.1 Introduction

Advances in DNA sequencing technologies have ushered in a new age of exploration in microbial ecology, with environmental molecular surveys highlighting the vast diversity of hitherto unknown microorganisms in under-sampled habitats. The ﬁrst culture-independent studies of microbial diversity and community structure were performed using Sanger sequencing, which generated high-quality sequences nearing 1,000 bp per read (Shendure and Ji, 2008). However, Sanger-mediated studies of microbial communities are costly, labor-intensive, and comparatively low-throughput compared to sequencing platforms that have emerged in the last ﬁfteen years, such as 454, Illumina, and Ion Torrent. Second-generation platforms are capable of generating millions of reads from hundreds of multiplexed samples, and it is this sequencing depth that has driven the discovery of previously unrecognized clades among prokaryotes and eukaryotes alike (Massana et al., 2014; Rosling et al., 2011; Zaremba-

58 Niedzwiedzka et al., 2017). Yet, the increasing adoption of high throughput sequencing (HTS) to characterize microbial communities supplants traditional culturing and isolation studies, decreas- ing the generation and availability of reference sequences in public databases. Tax- onomic assignment of molecular operational taxonomic units (MOTUs) in surveys of microbial communities hinges upon marker-specific reference databases. These databases associate complete or near-complete reference sequences (primarily the ribosomal markers SSU/18S, ITS, and LSU/28S) that have been deposited in public databases with curated taxonomic data. Consequently, taxa that are easier to culture or sample, or for which tissue is readily available, are more likely to be in reference databases, and therefore reported from studies employing those databases. The continued shift away from culturing and the generation of reference sequences in favor of HTS platforms will only serve to reinforce these disparities. In the fungal kingdom, undescribed taxa recovered in HTS surveys have been placed to all phyla (Grossart et al., 2016), but are especially common among the microscopic and early diverging lineages, putatively symbiotic taxa, and fungi from historically neglected or undersampled habitats. Unfortunately, it is these very clades which are already poorly represented among curated reference databases used in taxonomic assignment. Moreover, there has been increasing interest in how to integrate— both phylogenetically and functionally—the thousands of MOTUs recovered in community profiling surveys with fungal taxa more conventionally circumscribed from physical specimens (Hibbett, 2016). However, many proposed strategies, such as link- ing environmental sequences to existing herbarium and culture collection accessions and inferring phenotypic or functional traits from phylogenetic similarity (Hibbett et al., 2016), are feasible only for those lineages for whom significant genetic, biochemical, and ecological data exist. In clades that are underrepresented in reference databases and for which there are few to zero vouchers or accessions, novel phylo-

59 types can be so distinct from characterized taxa that we may only speculate as to their taxonomic affinities, let alone their possible functional roles. The primary deficiencies of extant databases in assigning provisional taxonomies to MOTUs from HTS studies are readily apparent in recent surveys of fungi across aquatic and terrestrial habitats (Comeau et al., 2016; Richards et al., 2015; Wurzbacher et al., 2016; Picard, 2017; Tedersoo et al., 2017), which have identified novel taxa across the tree, but particularly among the early diverging and zoosporic lineages. Often, these MOTUs represent entirely new clades within existing phyla (Tedersoo et al., 2017). As most studies employ short-read HTS platforms (454, Illumina, Ion Torrent), including several of those mentioned above, the MOTUs recovered cannot be integrated into most manually curated reference databases, which have minimum length requirements (e.g., 1900bp for the SILVA LSURef database; 1400bp for the RDP LSU training set); these MOTUs are also of limited utility in phylogenetic analyses, as the target loci used in HTS studies have varying degrees of phylogenetic signal. Consequently, despite the detection of wholly unknown groups among the fungi, we are unable to put these taxa into a robust phylogenetic framework, nor can we add them to existing databases to inform future HTS studies. Existing disparities in the representation of individual phyla or clades within sequence databases are unlikely to be solved solely through increased culturing efforts alone; thus, there is a pressing need for the generation of high quality reference sequences for cryptic fungi known only from environmental surveys. In this study, I explore the possibility of using emerging long-read sequencing (PacBio) to generate high quality reference sequences for unculturable fungi. By first sequencing a mock community composed of known fungal strains, I calculate the error rate of the PacBio sequencing platform, which allows for read lengths up to and beyond 10,000 bp, but with a high single-pass error rate. By targeting a smaller fragment, on average 2,000 bp, I can capitalize on PacBio’s circular consensus sequencing, theoretically

60 reducing my observed error rate to that approximating high-coverage sequencing platforms. I then amplify a region of the ribosomal DNA operon spanning three loci commonly used to characterize fungal communities from the marine samples described in Chapter 2, and use them to generate a maximum likelihood phylogeny to estimate the true taxonomic identities of the novel fungal phylotypes observed. Finally, I compare the consistency of taxonomic reference databases in assigning identities to novel fungi using three diﬀerent loci and three diﬀerent curated reference databases.

3.2 Materials and Methods

3.2.1 Community DNA samples

For this study, I examined four microbial communities. The first, a “mock community”, was comprised of seven fungal taxa (Table 3.1) primarily from genera that were commonly observed in marine samples from coastal North Carolina (Picard, 2017), representing four of the seven fungal phyla as defined by Spatafora et al. (2016). Genomic DNA from four reference taxa was acquired directly from the American Type Culture Collection (ATCC): Aspergillus fumigatus ATCC 1022 (As- comycota), Exophiala dermatitidis ATCC 34100 (Ascomycota), Malassezia globosa ATCC 4612 (Basidiomycota), and Trichoderma virens ATCC 9645 (Ascomycota). Freeze-dried tissue from Umbelopsis fusiformis (Mucoromycota) ATCC 60022 and a cryopreserved aliquot of Spizellomyces punctatus (Chytridiomycota) ATCC 48900 in liquid culture were also acquired. U. fusiformis and S. punctatus cultures were maintained on malt extract broth and K-1 media (Atlas, 2010), respectively, on a temperature controlled shaker at 25˝C until sufficient tissue for DNA extraction was present (14–21 days). Genomic DNA for the two cultured taxa was extracted using the DNEasy Plant Mini Kit (QIAGEN) according to the manufacturer’s protocol. Finally, genomic DNA previously extracted from an axenic culture of Rhizidium phy-

61 cophilum (Chytridiomycota) as described by Picard et al.(2009) was also included in the mock community. Two mock community libraries were prepared and sequenced: one in which all reference strains were pooled in an equimolar solution to be used as a template for PCR ampliﬁcation (“pre-pooled”), and a second in which reference taxa were ampliﬁed individually and pooled in an equimolar solution following PCR cleanup (“post-pooled”). The three additional communities included in this study originated from coastal marine habitats in eastern North Carolina (persistent wetland sediments, intertidal sand, and benthic sediments) that were previously characterized using Ion Torrent sequencing [see Chapter2 and Picard(2017)]. For each of these habitats, four libraries were generated, representing seasonal sampling.

3.2.2 Library preparation and PacBio sequencing

Amplicons spanning the full internal transcribed spacer (ITS) region and partial 28s rRNA were generated using rDNA primers ITS5 (5’-GGAAGTAAAAGTCGTAACAAGG-3’) (White et al., 1990) and LR6 (5’-CGCCAGTTCTGCTTACC-3’)(Vilgalys and Hester, 1990) (Figure 3.1). Both forward and reverse primers were tagged with symmet- ric 16-nt barcodes (https://github.com/PacificBiosciences/Bioinformatics- Training/wiki/Barcoding-with-SMRT-Analysis-2.3) to allow for multiplexing within PacBio sequencing runs. For each sample, PCR reactions were performed in quadruplicate and pooled to increase yield and decrease bias. Each 25 µl reaction contained the following components: 1 µl template (1 ng to 10 ng of total gDNA), 1.25 µl of each primer (0.5 µmol final concentration for each), 12.5 µl 2X PlatinumTM SuperFiTM PCR Master Mix (1X final concentration) (Invitrogen), and 9 µlof molecular biology grade water (Fisher Scientific). Amplification conditions included an initial denaturation at 95˝ C for 2 min, followed by 35 cycles of denaturation for 10 s at 95˝ C, annealing for 10 s at 60˝ C, and extension for 1 min at 72˝ C, with a

62 ITS5 LR0R EDF360 LR6 ITS1 ITS2 SSU/18S 5.8S LSU/28S

100 bp ITS region

Figure 3.1: Primer map indicating the relative positions of primers used in this study (blue) to generate full ITS1-5.8S-ITS2 and partial LSU sequences from mock communities and marine samples. Primers used in Chapter2 (LR0R and EDF360, red) are also included to show the diﬀerence in amplicon size.

final extension for 5 min at 72˝ C. For each forward/reverse barcode combination, a negative control containing no DNA template was included to detect contamination. PCR products were visualized on a 0.8% (weight/volume) high-melt agarose gel in 1x TBE buffer stained with SYBR Safe. PCR replicates were pooled and cleaned using the illustra GFXTM PCR DNA and Gel Band Purification Kit (GE Health- care) according to manufacturer’s protocol, but with an added ethanol wash before product elution. DNA concentration for each sample library was quantified using a Qubit R 2.0 fluorometer (Life Technologies). Samples were pooled in an equimolar solution and submitted to the Duke University Genome Sequencing and Analysis Core (Durham, NC), where PacBio SMRTbell adapters were ligated onto barcoded PCR products. Libraries were sequenced on a PacBio RS II SMRT DNA Sequencing System using the P6-C4 chemistry. Mock community libraries were sequenced on a single SMRT cell, while environmental samples were sequenced using two SMRT cells. The same ITS/LSU target region was amplified from the seven reference taxa using non-barcoded ITS5 and LR6 primers and cleaned as described above. Ampli- cons were sequenced in both directions on an ABI 3730xl DNA Analyzer (Applied Biosystems) at the Duke University DNA Analysis Facility using primers ITS5, LR6, and ITS4 (5’-TCCTCCGCTTATTGATATGC-3’)(White et al., 1990).

63 Table 3.1: Strain/voucher information for reference taxa used in mock community analyses

Taxon Strain designations Phylum Source Aspergillus fumigatus Fresenius NRRL 163; CBS 133.61; ATCC 1022 Ascomycota DNA Exophiala dermatitidis (Kano) de Hoog 8656; CBS 525.76; ATCC 34100 Ascomycota DNA Trichoderma virens (Miller et al.) von Arx, anamorph T-1; ATCC 9645 Basidiomycota DNA Malassezia globosa Midgely, E. Guho et J. Guillot CBS 7966; ATCC MYA-4612 Basidiomycota DNA Rhizidium phycophilum Picard KP 013 Chytridiomycota Culturea Spizellomyces punctatus (Koch) Barr DAOM BR117; ATCC 48900 Chytridiomycota Cultureb Umbelopsis fusiformis Yip DAR 51606; ATCC 60022 Mucoromycota Culturec a Maintained on conditioned PmTG liquid media (Picard et al. 2013) b Maintained on Koch’s K-1 liquid media (Atlas 2010) 64 c Maintained on liquid malt extract media (Atlas 2010) 3.2.3 Sequence processing of mock community data

To compare the observed sequencing error rate to the error rate predicted by PacBio and find the best analysis pipeline to balance read accuracy and sequence retention, mock community data was processed largely according to the recommendations of Schloss et al.(2016) and Schlaeppi et al.(2016). Error rates were calculated for the dataset at each filtering step, which included: (1) basic filtering – calculation of circular consensus sequences (CCS) from raw data with a minimum of 3 full passes and default expected accuracy rate (90%); (2) primer filtering – removal of sequences with more than one mismatch to forward and reverse primers and/or more than 2 mismatches to forward and reverse barcodes; and (3) predicted accuracy filtering – removal of sequences that have a predicted accuracy rate ă99%. All error rates were calculated using the seq.error command in mothur v1.36.1 (Schloss et al., 2009), which, in addition to calculating per-base errors, identifies chimeric sequences and their parents. CCS were generated from raw reads using bax2bam v0.08 (Pacific BioSciences) and pbccs v2.0.2 (Pacific BioSciences), as implemented in PipeCraft v1.0 (Anslan et al., 2017). The resulting reads of insert (ROI; consensus sequences meeting user- specified filtering criteria) were demultiplexed in mothur. Initially, sequences were not filtered by minimum and maximum lengths for two reasons: 1) most partial fragments were discarded in basic filtering; and 2) I observed wide variation in the length of the ITS5-LR6 fragment amplified from reference taxa, which ranged from a minimum length of 1,664 bp in Aspergillus fumigatus and a maximum length of 2,412 bp in Spizellomyces punctatus (Table 3.2). Similarly, no limit on the length of homopolymers was imposed because some fungal groups can have long single- nucleotide strings in rDNA regions (Hart et al., 2015). Due to different substitution rates across rDNA regions, which are further com-

65 plicated by variations in rDNA conservation across fungal phyla, choosing a clustering threshold that accurately delineates between molecular operational taxonomic units is difficult. Here, clustering was performed on full ITS5-LR6 fragments using a sequence similarity threshold of 97%. Though 97% similarity is the most widely employed cutoff for MOTU delineation in high-throughput sequencing studies of fungal diversity across disparate loci, it may mask—or, alternatively, inflate—diversity within and among fungal clades (Nilsson et al., 2008). MOTU clustering was carried out using UPARSE (Edgar, 2013), which features built-in de novo chimera detection; all chimeric and singleton MOTU clusters were discarded. The most abundant sequence from each cluster was selected as the representative sequence for that MOTU and used in downstream analyses. Because the primary objective of this study was to generate sequences for uncultured MOTUs for use in curated reference databases, sequence quality was paramount over sequence retention. Thus, sequence data originating from marine samples were filtered using the 99% predicted accuracy threshold and primer/barcode mismatch constraints and then clustered into OTUs at a 97% similarity threshold in UPARSE. During clustering, a minimum sequence length of 1,250 bp was enforced.

3.2.4 Phylogenetic analyses

ITS has been widely adopted as the fungal barcode (Schoch et al., 2012), yet it has limited utility as a phylogenetic marker for inferring deep relationships among the fungi. The ribosomal subunits (i.e., SSU/18S, 5.8S, LSU/28S), however, are suﬃ- ciently conserved for sequence alignment across distantly related fungal groups, and contain variable domains that allow for ﬁner scale resolution. To extract LSU sequences for phylogenetic analyses, MOTU representative sequences were processed with ITSx v.1.0.11 (Bengtsson-Palme et al., 2013), which uses hidden Markov models and reference alignments for major eukaryotic groups to identify the conserved

66 Table 3.2: Sequence lengths of individual rDNA regions for reference taxa used to create a mock community for PacBio sequencing

Amplicon/locus length (bp) Taxon Total SSU ITS1 5.8S ITS2 LSU Ascomycota Aspergillus fumigatus 1664 32 184 158 165 1125 Exophiala dermatitidis 2137 32 199 159 209 1538 Trichoderma virens 1667 32 199 158 167 1111 Basidiomycota Malassezia globosa 1879 32 252 158 288 1149 Chytridiomycota Rhizidium phycophilum 2217 32 430 157 406 1192 Spizellomyces punctatus 2412 32 295 157 234 1694 Mucoromycota Umbelopsis fusiformis 1789 32 169 158 214 1216 loci flanking ITS regions. For each unique input sequence, ITSx output sequences for partial SSU, full ITS1, full 5.8S, full ITS2, partial LSU, and the full ITS region (ITS1+5.8S+ITS2). Representative sequences that were tagged by ITSx as prob- lematic, either for missing a locus or because ITSx could not locate the end of a conserved region, were removed from the dataset. To remove non-fungal taxa from the marine sampling datasets, MOTUs were assigned provisional taxonomies using the RDP Classifier v2.10 trained with LSU fungal training set 11. All MOTUs whose best match was to the fungal kingdom were retained for phylogenetic analyses. Putative fungal sequences were aligned to the core 28S rRNA gene data set from James et al.(2006b) and supplemented with the closest BLASTn hits (minimum of 1,000 bp in length) for each putative fungal MOTU. Sequences were aligned in MAFFT v.7 (Katoh and Standley, 2013) and refined by eye, with ambiguously aligned regions excluded from analysis. Phyloge- netic trees were inferred using maximum likelihood (ML) in RAxML v8.0.0 using the GTRCAT model for nucleotide substitution (Stamatakis, 2014) across 100 heuristic

67 searches. Support for inferred relationships was estimated from 1,000 rapid bootstrap replicates. After preliminary analyses, reference sequences that were not closely aﬃl- iated with any marine MOTUs or their closest BLASTn hits were removed from the dataset to improve visualization (see Table C.2 for accession and taxonomic data). The ﬁnal alignment contained 261 taxa and 677 positions.

3.2.5 Comparison of fungal reference databases

To compare the consistency of taxonomic identities provided by fungal reference databases, ITS1, ITS2, and LSU sequences from each fungal MOTU were assigned taxonomies using at least one of three databases. ITS1 and ITS2 assignments were made off of both the UNITE database (Koljalg et al., 2013) and the Warcup fungal ITS training set 2 (Deshpande et al., 2016). Taxonomic assignments based on LSU sequences were made using the RDP Classifier’s LSU fungal training set 11, as described in the previous section. To remove the confounding effects of using different assignment algorithms (e.g., comparing similarity/nearest-neighbor assignments to probability-based assignments), taxonomic affinities for each locus and database were assigned using the RDP Classifier, which was retrained prior to switching reference databases. Finally, to examine the overlap in MOTUs recovered from PacBio sequencing of the ITS5-LR6 amplicons and those found in Chapter2, the representative sequences for the 770 fungal OTUs recovered by Ion Torrent sequencing the LR0R-EDF360 amplicon were BLASTed against the representative sequences from PacBio-generated MOTUs while enforcing a minimum 100% sequence similarity and minimum e-value threshold of 10-150.

68 3.3 Results

3.3.1 Eﬀect of sequence processing on observed error rate

Mock community sequencing generated 63,593 ROI (58,749 unique) after implement-

ing baseline ﬁltering criteria of ě3 full passes and ě90% predicted sequence accuracy (Table 3.3). The average observed sequencing error rate was 1.35%. Substitutions, deletions, and insertions accounted for 49.5%, 38.2%, and 12.3% of all errors, respectively. Substitution errors were nearly equally likely, though adenines were marginally more likely to be replaced than other bases (27.1% of substitutions). Dele- tions and insertions, however, were not equally distributed across bases. Guanines comprised 36.3% of all deletions, while thymidines represented only 18.6%. Con- versely, thymidines were disproportionately represented among insertions (43.0%). Chimeras comprised 18.5% of unique sequences and were most likely to be the result of recombination between parent sequences from Malassezia globosa, Umbelopsis fusiformis, and Exophiala dermatitidis (Table C.1). Culling sequences that contained more than one primer mismatch or more than two barcode mismatches reduced the average error rate to 0.066%, a decrease of 95%. Chimeras were reduced by 97.9%, to only 232 unique sequences. However, to achieve this error rate, which is still more than three times that of pyrosequencing or MiSeq, over 60% of the full dataset was discarded (Table 3.3). Similarly, requiring a minimum predicted accuracy cutoﬀ resulted in a dramatic reduction in error rate and a concomitant decrease in sequence reads. After removing

consensus sequences with ď99% predicted accuracy, the observed error rate of the remaining 18,616 sequences (29.3%) of the original dataset, was 0.298%. Interestingly, chimeras still comprised 8.5% of the dataset after accuracy ﬁltering (Table C.1), suggesting that even recombinant sequences can exhibit high base quality. When combining the accuracy and primer ﬁlters, only a quarter of the original sequences

69 are retained in the dataset, but the error rate falls to 0.025%. For the taxa included in this mock community, this error rate corresponds to 0.42–0.60 erroneous bases across the entire ITS5-LR6 amplicon. Despite increasingly stringent filtering, no filtering strategy was able to recover only seven reference taxa (Table 3.3). When using default parameters in the absence of cutoffs for primer or barcode mismatches (i.e., basic filtering), 15 MOTUs were recovered. By contrast, filtering the dataset by predicted accuracy results in just one fewer MOTU. Instituting thresholds for primer and barcode mismatches, however, recovers all seven reference taxa and additional two taxa, one a yeast that shares 100% sequence similarity across 900 bp of 28S with two Candida isolates (GenBank accessions KY106739 and KY106732), and another that is most similar to GenBank accessions from various Mucoromycota taxa and environmental samples. Neither OTU was abundant, with each contributing fewer than 10 sequences. The putative Candida MOTU may in fact represent a contaminant of either DNA or culture tissue from ATCC, as both close matches originate from the Centraalbureau voor Schim- melcultures (CSB, the former name of the Westerdijk Fungal Biodiversity Institute). Many ATCC strains are derived from those housed at the CSB, including three of the reference taxa included in this study. Furthermore, the Candida MOTU was only found in the “post-pooled” amplicon library, suggesting that it was a contaminant of either the prepared DNA or culture tissue of another reference taxon and was amplified in concert. By contrast, the Mucoromycota-like ninth MOTU was only recovered in the amplicon pool generated from an equimolar mixture of genomic DNA from all reference taxa (i.e., “pre-pooled” template). Thus, it remains unclear whether this MOTU represents a chimeric sequence, a divergent rDNA operon, or another contaminant.

70 Table 3.3: Error rates, retained reads, and non-singleton MOTUs recovered from PacBio sequencing of mock community samples when implementing various quality ﬁltering steps. The total number of MOTUs recovered should be 7.

Filtering Error rate (%) Reads remaining (%) No. MOTUs Basic 1.325 63593 14 Basic + Primer 0.066 25841 (40.6%) 9 Predicted 0.298 18616 (29.3%) 15 Predicted + Primer 0.025 15798 (24.8%) 9 Basic: ě3 passes, 90% predicted accuracy Primer: ď1 primer mismatch and ď2 barcode mismatches Predicted: ě3 passes, 99% predicted accuracy

3.3.2 Phylogenetic assignment of fungal MOTUs from marine habitats

Phylogenetic analyses revealed that the marine fungal phylotypes recovered in this study represent both close relatives of circumscribed taxa, as observed in several Ascomycete-affiliated MOTUs, and distant relatives of circumscribed species, most clearly demonstrated among the zoosporic fungi. Overall, 46 MOTUs were placed to the Ascomycota (Figures 3.2–3.4), five to the Basidiomycota (Figure 3.5), eight to the Chytridiomycota, sensu lato (Figure 3.6), and one to the Cryptomycota. The final MOTU was placed to a single-taxon branch outside of defined groups, nested between the Cryptomycota and the rest of the Fungi (Figure 3.6). Support for inferred relationships varied widely, thus the true phylogenetic positions of many MOTUs remain in question. Marine phylotypes were not equally distributed within individual phyla. In the Ascomycota, only three classes were represented (Sordariomycetes, Dothideomycetes, and Eurotiomycetes), with just shy of half of all marine MOTUs recovered (30) placing within the Dothideomycetes (Figure 3.3). Ascomycete MOTUs were often allied to representatives from known marine genera such as Aspergillus, Penicillium, Pestalotiopsis, and Phoma, or to genera with known phytopathogenic members, such

71 Hydropisphaera erubescens Fig. 2 Hydropisphaera erubescens AY545726 Nalanthamala squamicola AF373281 NC_4062B_2249 SC_4092D_6173 Fig. 3 Acremonium alternatum FJ176883 NC_4062B_5079 NC_4062B_6226 NC_4062B_677 Isaria farinosa KC510278 Fig. 4 Hypocrea citrina Sedecimiella taiwanensis KP671735 NC_4062C_3008 Eucasphaeria capensis EF110619 NC_4062C_400 Fig. 5 Volutella citrinella HQ843772 Microascus trigonosporus NC_4062C_3514 NC_4062C_2961 Acremonium sp. JX535073 Fig. 6 Fusarium graminearum Fusarium verticillioides XR 001989350 Habitat Haematonectria haematococca Ascomycota: Sediment Core Neurospora crassa Sordariomycetes Intertidal Sand Sordaria fimicola Wetland Sediments Chaetomium globosum Magnaporthe grisea NC_4062B_7369 Barbatosphaeria varioseptata KM492869 NC_4062B_1199 Cytospora diatrypelloidea DQ923537 Diaporthe eres Gnomonia gnomon Daldinia pyrenaica KY610413 NC_4062C_5761 Xylaria hypoxylon Xylaria acuta NC_4062C_3866 To Figure 3 Pestalotiopsis microspora KY366173 NC_4062B_5040 Lulworthia grandispora Lindra thalassiae 0.2 substitutions/site

Figure 3.2: Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the class Sordariomycetes (phylum Ascomy- cota) based on a 28S/LSU rDNA maximum likelihood phylogram. Sequences in red indicate marine MOTUs. Colored circles next to environmental sequences indicate presence (ﬁlled) or absence (open) in sediment core (red), intertidal sand (yellow), and wetland sediment (blue) samples. Thickened branches indicate ML bootstrap support ě70%. Branches marked with an asterisk (*) have been shortened by 75% for clarity.

72 NC_4062B_5832 Fig. 2 To Figure 2 NC_4062C_4027 Phoma cladoniicola JQ238625 NC_4062C_4813 NC_4062B_1651 NC_4062B_1660 Fig. 3 Phaeosphaeriaceae sp. KY090665 Ophiosphaerella korrae KP690985 NC_4062B_1930 NC_4062C_496 Coniothyrium obiones DQ678054 Fig. 4 NC_4062B_859 Dictyosporium stellatum JF951177 Bipolaris woodii KX452441 NC_4062B_8472 Cochliobolus heterostrophus Fig. 5 Pleospora herbarum Alternaria sp. DQ678068 PVZ_4085_4574 Pyrenophora phaeocomes Fig. 6 Paraphaeosphaeria sporulosa KX359599 Curreya pityophila DQ384102 Habitat NC_4062B_936 Sediment Core NC_4062B_8005 Intertidal Sand NC_4062B_961 Wetland Sediments NC_4062C_3234 Bimuria novae zelandiae AY016356 NC_4062C_3889 Didymellaceae sp. HM595583 NC_4062B_7533 NC_4062C_2980 NC_4062B_693 NC_4062B_2334 Lentithecium unicellulare KX505376 Periconia sp. AB807570 NC_4062C_4181 NC_4062C_1481 Phoma herbarum AY293791 Preussia terricola GQ203725 Teichosporaceae sp. KU848206 NC_4062B_3798 Trematosphaeria heterospora NC_4062C_3373 Preussia intermedia GQ203738 NC_4062B_1964 Preussia minima GQ203744 NC_4062B_70 Arthopyrenia salicis KP671722 Westerdykella cylindrica Ascomycota: Trypethelium unknown Teratosphaeriaceae sp. KP671744 Dothideomycetes Capnodiales sp. GU323223 NC_4062C_1293 Passalora sp. GQ852622 NC_4062C_5165 Capnodium coffeae NC_4062C_1652 Capnodium sp. KU985278 Bryochiton microscopicus EU940149 To Figure 4 PVZ_4085_1255 Cladosporium uredinicola EU019264 NC_4062B_4087 Toxicocladosporium irritans EU040243 0.2 substitutions/site Dothidea sambuci Umbilicaria mammulata Ascomycota: Lecanoromycetes

Figure 3.3: Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the class Dothideomycetes (phylum Ascomy- cota) based on a 28S/LSU rDNA maximum likelihood phylogram. Sequences in red indicate marine MOTUs. Colored circles next to environmental sequences indicate presence (ﬁlled) or absence (open) in sediment core (red), intertidal sand (yellow), and wetland sediment (blue) samples. Thickened branches indicate ML bootstrap support ě70%. Branches marked with an asterisk (*) have been shortened by 75% for clarity. 73 Fig. 2 To Figure 3

Fig. 3 Botryotinia fuckeliana Monilinia fructicola Leotia lubrica Coccomyces dentatus Ascomycota: Dermea acerina Leotiomycetes Fig. 4 Chlorociboria aeruginosa Lachnum virgineum Cudoniella clavus Phacidiopycnis pyri Aspergillus flavus HQ395773 Fig. 5 NC_4062C_344 Aspergillus oryzae KP256849 Aspergillus nidulans Monascus purpureus Fig. 6 Aspergillus fumigatus Penicillium solitum JN642222 PVZ_4085_4075 Habitat Spiromastix warcupii Sediment Core Histoplasma capsulatum Intertidal Sand Coccidioides immitis Wetland Sediments Capronia pilosella Anisomeridium polypori Ascomycota: Exophiala pisciphila Eurotiomycetes Ramichloridium anceps Exophiala dermatitidis Endocarpon cfpusillum Dermatocarpon miniatum Staurothele frustulenta Agonimia sp Pyrenula pseudobufonia Pyrgillus javanicus Morchella esculenta Disciotis venosa Gyromitra californica Helvella compressa Caloscypha fulgens Ascobolus crenulatus Peziza vesiculosa Ascomycota: Peziza proteana Pezizomycetes Cheilymenia stercorea Aleuria aurantia Scutellinia scutellata Sarcoscypha coccinea Pyronema domesticum Orbilia auricolor Orbilia vinosa Ascomycota: Orbiliomycetes Candida glabrata Saccharomyces cerevisiae Kluyveromyces waltii Kluyveromyces lactis Ashbya gossypii Ascomycota: Saccharomyces castellii Saccharomycotina Yarrowia lipolytica Candida albicans Candida tropicalis Candida guilliermondii Debaryomyces hansenii Taphrina wiesneri Protomyces inouyei Ascomycota: Schizosaccharomyces pombe Taphrinomycotina Pneumocystis carinii

0.2 substitutions/site

To Figure 5

Figure 3.4: Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the class Eurotiomycetes (phylum Ascomycota) based on a 28S/LSU rDNA maximum likelihood phylogram. Sequences in red indicate marine MOTUs. Colored circles next to environmental sequences indicate presence (ﬁlled) or absence (open) in sediment core (red), intertidal sand (yellow), and wetland sediment (blue) samples. Thickened branches indicate ML bootstrap support ě70%. Branches marked with an asterisk (*) have been shortened by 75% for clarity. 74 Fig. 2 Boletellus projectellus To Figure 4 Suillus pictus Calostoma cinnabarinum Hygrophoropsis aurantiaca Basidiomycota: Henningsomyces candidus Fig. 3 Coprinopsis cinerea Agaricomycotina Amanita brunnescens Psathyrella maculata GQ249290 NC_4062C_1092 NC_4062C_1864 Fig. 4 Pleurotus ostreatus Lycogalopsis solmsii KF017599 NC_4062C_492 Gautieria otthii Ramaria rubella Fig. 5 NC_4062B_1734 Myriostoma coliforme KC582020 Hydnum albomagnum Endocronartium harknessii Fig. 6 Puccinia graminis Platygloea disciformis Basidiomycota: Habitat Rhodotorula hordea Pucciniomycotina Sediment Core Colacogloea peniophorae Ustilago maydis Intertidal Sand Cintractia sorghi vulgaris Basidiomycota: Wetland Sediments Entyloma holwayi Tilletiaria anomala Ustilagomycotina Malassezia globosa KT310070 PVZ_4085_5783 Cryptococcus neoformans Glomus mosseae Glomus intraradices Scutellospora heterogama Glomeromycota Paraglomus occultum Geosiphon pyriformis Endogone pisiformis Phycomyces blakesleeanus Rhizopus oryzae “Zygomycota” I Umbelopsis ramanniana Spiromyces aspiralis Smittium culisetae Allomyces arbusculus Physoderma maydis Blastocladiomycota Basidiobolus ranarum Piptocephalis corymbifera Olpidium brassicae “Zygomycota” II To Figure 6 Conidiobolus coronatus Entomophthora muscae 0.2 substitutions/site

Figure 3.5: Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the phylum Basidiomycota based on a 28S/LSU rDNA maximum likelihood phylogram. Sequences in red indicate marine MOTUs. Colored circles next to environmental sequences indicate presence (ﬁlled) or absence (open) in sediment core (red), intertidal sand (yellow), and wetland sediment (blue) samples. Thickened branches indicate ML bootstrap support ě70%. Branches marked with an asterisk (*) have been shortened by 75% for clarity. as Bipolaris, Periconia, and Cladosporium. However, many phylotypes derived from marine samples could also be found forming clades among themselves instead of their closest BLASTn hits, suggesting that existing genetic resources for marine Ascomycota are also lacking. Basidiomycete MOTUs were primarily representatives of terrestrial taxa such as

75 Fig. 2 To Figure 5

Rhizophydiales sp. FR670788 Rhizophydiales sp. FR670787 Fig. 3 Rhizophydium sp. DQ273779 NC_4062C_3076 NC_4062B_6581 Rhizophydium macroporosum Rhizophydium brooksianum DQ273770 Fig. 4 NC_4062B_2694 NC_4062B_105 Batrachochytrium dendrobatidis Rhizophlyctis rosea Rhizophlyctis rosea NG 027649 Fig. 5 Catenomyces sp. DQ273789 Chytridiomycota, sensu lato Spizellomyces punctatus Nowakowskiella sp. DQ273798 Endochytrium sp. DQ273816 Fig. 6 NC_4062B_8297 Polychytrium aggregatum Habitat Cladochytrium replicatum Sediment Core Lobulomyces angularis DQ273815 Intertidal Sand NC_4062C_4806 Wetland Sediments NC_4062C_4046 NC_4062C_2039 Rhizoclosmatium sp Hyaloraphidium curvatum Monoblepharella sp Neocallimastix sp NC_4062C_4174 Early Diverging Fungi, incertae sedis NC_4062B_4127 Uncultured fungus KY687859 Rozella allomycis Cryptomycota Rozella sp. DQ273766 Oryza sativa Arabidopsis thaliana Chlamydomonas reinhardtii Outgroups Toxoplasma gondii Cryptosporidium parvum Cyanidioschyzon merolae * Dictyostelium discoideum * Caenorhabditis elegans Homo sapiens * 0.2 substitutions/site Drosophila melanogaster Ciona intestinalis Monosiga brevicollis

Figure 3.6: Phylogenetic placement of environmental marine fungal phylotypes among circumscribed fungal taxa in the phyla Chytridiomycota and Cryptomycota based on a 28S/LSU rDNA maximum likelihood phylogram. Sequences in red indicate marine MOTUs. Colored circles next to environmental sequences indicate presence (filled) or absence (open) in sediment core (red), intertidal sand (yellow), and wetland sediment (blue) samples. Thickened branches indicate ML bootstrap support ě70%. Branches marked with an asterisk (*) have been shortened by 75% for clarity. the puffball fungus Lycogalopsis solmsii, the earthstar Myriostoma coliforme, and the mushroom Psathyrella maculata, and therefore may not represent obligately marine fungi, but rather taxa that can tolerate fluctuations in salinity or input from the scrub-shrub forest separating Town Marsh (persistent wetland sediment sampling site) and Bird Shoal (intertidal sand sampling site). However, one MOTU was sister to a known marine taxon, the ubiquitous opportunistic pathogen Malassezia globosa

76 (Figure 3.5). Within the Chytridiomycota, four marine MOTUs allied to the Rhizophydiales, albeit with middling support (Figure 3.6). Three MOTUs form a well-supported clade with Lobulomyces angularis and may represent the ﬁrst marine members of the Lobulomycetales. One MOTU (NC 4062B 8297) nested among polycentric taxa belonging to the families Cladochytriaceae and Nowakowskiellaceae, but the lack of robust support for that relationship precludes deﬁnitive assignment to either group. One MOTU clustered within the Cryptomycota (Figure 3.6), and it is most closely allied to a novel “singleton” sequence derived from soil samples (Tedersoo et al., 2017). Finally, a novel “singleton” lineage (NC 4062C 4174) was also present in the marine dataset and may itself indicate that there are obligately marine clades of early-diverging fungi awaiting discovery.

3.3.3 Distribution of marine MOTUs

Marine MOTUs were unevenly distributed across sampling habitats, with 45 MO- TUs being found in wetland sediments, 35 in intertidal sand, and seven in benthic sediments from Cape Lookout Bight. Only a single MOTU, sister to the hyperpar- asite Cladosporium uredinicola (Figure 3.3), was recovered from all three habitats. There is considerable overlap between the MOTUs recovered from wetland sediments and intertidal sand, a ﬁnding that echoes diversity and distribution trends seen in Chapter2.

3.3.4 Taxonomic assignment across target loci and reference databases

As observed in Chapter2, the quality of taxonomic assignment was highly dependent upon representation of a phylum within a database (Table C.3). Representatives from the Ascomycota and Basidiomycota were more likely to be correctly identiﬁed, even if bootstrap support for that designation was minimal. Only two out of 46

77 Ascomycete MOTUs received an incorrect phylum designation from at least one database/locus combination, and in both cases, the incorrect assignments were made using the UNITE database coupled with ITS1. Basidiomycete MOTUs were also generally assigned the correct phylum, save for one MOTU (NC 4062C 492) that was also misidentified by the UNITE database when using either ITS1 and ITS2 as a query. Members of the early diverging clades, however, had remarkably inconsistent assignments across all databases, even when using different loci. In comparison to 28S/LSU, ITS1 and ITS2 provided particularly misleading assignments, even for MOTUs identified to the correct phylum by the RDP LSU database (Table C.3). It is important to note that the support values for phylum assignments made to MOTUs in the Chytridiomycota and Cryptomycota contained in Table C.3 are quite low, low enough, in fact, that most researchers would collapse those MOTUs into a catch-all category for “unassigned fungi”. Even using multiple reference databases and comparing taxonomic assignments for congruence, as I did in Chapter2, cannot ensure the correct taxonomic classification, even at the coarsest level. Thirteen MOTUs generated from PacBio sequencing matched identically to MOTUs observed in the survey of marine habitats outlined in Chapter2 (Table C.4). Despite using two different assignment algorithms and databases to identify phylotypes recovered in the Ion Torrent survey, the phylum- level designations attached to each MOTU from a non-Dikarya lineage do not reflect their inferred phylogenetic affinities. As nine of the 13 MOTUs matched are among the 50 most abundant OTUs recovered in the Ion Torrent survey, misidentification at any level, but particularly at phylum, could drastically alter the inferred composition of the community being investigated and any subsequent ecological conclusions.

78 3.4 Discussion

Environmental surveys using ribosomal genes have radically altered our understanding of the evolution, ecology, and diversity of microbial consortia. However, as sequencing technologies have continued to develop, allowing for ever longer read lengths, deeper sequencing coverage, and lower per-base error rates, investigations of microbial diversity and ecology have shifted focus away from individual taxa or clades and toward complex communities. A major consequence of this changing perspec- tive is the decline of low-throughput sequencing methods, such as Sanger sequencing, and with it a reduction in the generation of high-quality reference sequences from axenic cultures. Because locus-specific reference databases, and the full- or nearly full-length sequences that populate them, are vitally important tools in service of microbial ecology surveys, failure to address the yawning chasm between the number of novel MOTUs recovered with each environmental sequencing study and the creation of new reference sequences with which to identify them will serve only to underpin existing gaps in our understanding of microbial diversity—phylogenetic, ecological, or otherwise (Yahr et al., 2016). After all, what good is the discovery of a new organism if there are no tools to put it into an evolutionary or ecological context? Third-generation sequencing platforms such as PacBio and Oxford Nanopore, which allow for long read lengths and are easily scalable, may be promising alter- natives to the highly accurate, but low-throughput technologies of yesterday. In the last few years, several studies have chronicled the improving error profiles of PacBio SMRT when sequencing nearly full length 16S sequences (Mosher et al., 2013, 2014; Schloss et al., 2016; Singer et al., 2016), finding that longer reads improve both phylogenetic and taxonomic resolution and provide sufficient sequence data for probe and primer design. Prior to this study, only one other had investigated the poten-

79 tial of PacBio sequencing in proﬁling fungal communities. Estimating the error rate across successively longer rDNA amplicons generated from arbuscular mycorrhizal fungi, Schlaeppi et al.(2016) concluded that given enough passes (in their case,

ě 5), PacBio reads could successfully be used to characterize low-diversity fungal communities. In contrast to the aims of Schlaeppi et al., the primary purpose of the study presented here was not to exhaustively catalog the fungal communities present in coastal marine sediment samples. Rather, my objective was to first use a synthetic mock community to estimate PacBio’s overall sequencing error rate, and then explore samples that had already been surveyed using a high-throughput, short-read platform to determine if PacBio sequencing could provide high-resolution sequence reads for use in phylogenetic analyses, reference databases, and, perhaps in the future, the development of sequencing primers and probes for targeting poorly known fungal groups. Although the error rate of consensus reads generated using PacBio’s default parameters was high (1.35%), successive rounds of filtering brought it down to a level on par with 454 [0.02%; (Schloss et al., 2011)] and Illumina MiSeq [0.02%; (Kozich et al., 2013)], a finding that is consistent with that of other recent studies (Schloss et al., 2016; Schlaeppi et al., 2016; Singer et al., 2016) assessing the suit- ability of PacBio for rDNA surveys. However, it should be noted that achieving such a low error rate comes at a high cost: following all filtering steps resulted in a loss of 75% of my raw data. While other sequencing platforms have been shown to have high numbers of spurious reads (Kemler et al., 2013), their higher throughput buffers some of the sequence loss. Thus, while PacBio sequencing can be used to generate long rDNA sequences for improved taxonomic resolution of microbial communities, comparatively low throughput may make this sequencing platform un- desirable for surveys requiring more exhaustive sampling, or at least until PacBio’s newly developed SEQUEL platform, with its improved throughput, becomes more

80 widely available. Despite the relatively small fraction of the dataset retained following sequence filtering, the benefits of sequencing a more substantial length of the rDNA operon were manifold. The approximately 1,800 bp rRNA fragment that I amplified, spanning partial SSU, the full ITS region (ITS1-5.8S-ITS2), and partial LSU, provided sufficient phylogenetic signal to infer the likely relationships of MOTUs. Perhaps surprisingly, given historical notions of marine fungal diversity, marine MOTUs were placed to clades beyond the Dikarya, including those at the base of the tree, although the vast majority of MOTUs observed belonged to the Ascomycota. Most of the Ascomycete MOTUs recovered appear to be allied with various pathogenic taxa, primarily of plants, but also included pathogens of insects (Isaria) and, potentially, humans (Aspergillus). Several of the Ascomycete genera allied to marine MOTUs are themselves marine-derived, or have marine representatives, such as: Passalora, which is primarily known as a genus of plant pathogens, but has been described from Hawaiian beach sands (Steele, 1967); Phoma, a broadly distributed marine genus that has been observed as a parasite of plants and animals (Kohlmeyer and Volkmann- Kohlmeyer, 1991), and recovered from deep-sea sediments (Lai et al., 2007); the mangrove-derived Sedecimiella (Pang et al., 2010); and the algal parasite Daldinia (Tarman et al., 2012). Interestingly, half of all MOTUs in the Chytridiomycota were nested within the Rhizophydiales, a primarily freshwater order (Letcher et al., 2006) that has in recent years, with increased sampling, added several marine species to its ranks (Letcher et al., 2015), including the virulent dinoflagellate parasite Dinomyces arenysensis (Lepelletier et al., 2014). Environmental sequencing surveys have also reported MO- TUs binned to the Rhizophydiales comprising significant fractions of the fungal community in marine waters off Australia (Jeffries et al., 2016), the Western English Channel (Taylor and Cunliffe, 2016), and the Arctic circle (Hassett and Gradinger,

81 2016; Comeau et al., 2016), suggesting that Rhizophydialian taxa are widespread in aquatic ecosystems, freshwater and marine alike. The abundance and ubiquity of Rhizophydialian taxa in aquatic systems may suggest that they play an important role in governing nutrient transfer between primary producers and consumers as part of the mycoloop, a conceptual model describing how zoosporic fungi mediate trophic transfer through parasitizing phytoplankton, serving as prey for zooplankton, and degrading refractory materials (Kagami et al., 2014). The recovery of novel phylotypes among the zoosporic fungi was not restricted to the Chytridiomycota; one putative Cryptomycota MOTU and another MOTU that could not be placed to any described phylum were also recovered. The inability to assign any further taxonomic information to MOTUs that fall into poorly understood clades is not uncommon. So few reference sequences exist for the Cryptomycota and other “basal” clades that it can be difficult to link Cryptomycotan MOTUs to any circumscribed taxa (Panzer et al., 2015; Tedersoo et al., 2017). However, despite the uncertainty surrounding the true taxonomic identities of these MOTUs, they offer a tantalizing clue as to the diversity yet to be explored. Furthermore, the consistent recovery of MOTUs from the earliest branches of the fungal tree from marine environments lends credence to a marine origin-story for the fungi (Pirozynski and Malloch, 1975). A final advantage of using PacBio to sequence environmental reads was the ability to compare the performance of individual reference databases queried with different loci originating from the same MOTU. Such comparisons are not possible with short- read technologies due to sequence length limitations. A few studies have compared the taxonomic profiles generated by different loci targeting the same environmental samples, generally finding congruence between the ITS and LSU datasets (Brown et al., 2014; Porras-Alfaro et al., 2014). However, due to inherent primer biases coupled with deficits in each locus-specific database, congruence may only mean

82 that both primer sets found the most easily amplified and best characterized taxa in a sample. By using a single sequence from a single MOTU containing ITS1, ITS2, and LSU, I demonstrated that for Ascomycota and Basidiomycota, both the Warcup and UNITE ITS databases generally succeed in assigning the correct coarse- level taxonomies. However, when you consider taxa outside of the Dikarya, even phylum-level assignments may be unpredictable across databases, as demonstrated in the zoosporic lineages. Ultimately, if one’s objective is to pursue the diversity of non-Dikarya fungi, it is advisable to use 28S in place of either ITS1 or ITS2. Finally, without an organism in hand or long-term sequence data originating from the same habitats, it can be difficult, if not impossible, to assign ecological function and activity to MOTUs. Many of the MOTUs recovered in this study likely represent metabolically active marine fungi, but the nature of fungal propagules and their persistence in soils and sediments can muddy the waters when distinguishing between fungi that live in a given place, and those that happen to be passing through. At present, there is insufficient data to allow for anything other than speculation as to the roles these MOTUs may play in their respective environments. However, merely identifying these fungi and giving them a name (albeit a sequence-specific string of letters and numbers) bring us closer to teasing out their contributions to marine food webs and nutrient cycles.

3.5 Conclusions

High-throughput sequencing has dramatically improved our understanding of fungal diversity. However, the movement away from culturing and reference sequence generation for relatively few taxa in favor of generating millions of short reads for many hundreds of taxa will stymie future eﬀorts to create and manage the reference databases needed to carry out microbial sequencing surveys. Here I demonstrate that PacBio sequencing can produce rDNA sequences that (1) have few to no errors;

83 (2) are long enough to encompass multiple loci that are commonly used in environmental surveys of fungal communities; and (3) can not only be used for phylogenetic inference to elucidate the breadth of genetic diversity in the fungi, but can also serve as reference sequences themselves, whether within curated reference databases or as reference for the development of new sequencing probes and primers.

84 4

Further Insights into the Diversity and Evolution of Early-Diverging and Marine Fungi

In the course of my doctoral studies at Duke, I have been fortunate to work with several collaborators, both here in the United States and abroad. I have made contributions to four studies, as outlined below. While all four studies vary as to their scope, they are broadly focused on zoosporic fungal evolution and/or ecology. Additionally, I contributed to one review article on marine zoosporic fungi (Gleason et al., 2011), which signiﬁcantly inﬂuenced the direction of my doctoral research. Davis et al. (in review, Mycologia) used a multiphasic approach to investigate the morphological and phylogenetic diversity of zoosporic fungi from temporal ponds in Alabama. Stern et al.(2015) demonstrated the utility of a newly-designed water sampler deployed within the Continuous Plankton Recorder (CPR), which is part of a long- standing survey of plankton in global subsurface marine waters. Pyrosequencing of samples collected from across the English Channel revealed that the Cryptomy- cota represent the dominant fraction of the microeukaryotic (ď 200 µm) community

85 present in subsurface waters. Picard et al.(2013) was a continuation and expansion of work started in the course of my Masters research at the University of Alabama under Drs. Martha Pow- ell and Peter Letcher. This study focused on characterizing the interaction between the zoosporic fungus Rhizidium phycophilum and a previously unknown unicellular coccoid green alga. Molecular phylogenetic analyses placed the alga within the genus Bracteacoccus (Sphaeropleales). Fitness experiments demonstrated that the interaction between R. phycophilum and Bracteacoccus sp. was facultatively mutualistic, and electron and brightfield microscopy coupled with dialysis experiments suggested that the interaction involved the exchange of diffusible compounds. This study was the first to report a mutualistic interaction between a zoosporic fungus and a photosynthetic partner. Letcher et al.(2012) amended the taxonomy of the previously described zoosporic fungus Phlyctochytrium aureliae to include morphological and ultrastructural features as determined by brightfield and electron microscopy which confirm the phylogenetic placement of P. aureliae in the Chytridiales, as shown by Velez et al.(2011).

86 Conclusions

Summary

In this dissertation, I examined the diversity and distribution of fungal communities in disparate marine habitats and used emerging sequencing technologies to improve our understanding of the phylogenetic diversity of marine fungi. Fungi are known to play critical roles as parasites, symbionts, and degraders of organic detritus in terrestrial environments, but comparatively little is known about their presence in aquatic habitats and their contributions to nutrient cycles therein. Thus, cataloguing the ecological and genetic diversity of otherwise unknown aquatic fungi is an essential ﬁrst step toward untangling how these taxa contribute to important biogeochemical processes. In Chapter1, I used pyrosequencing to demonstrate that marine protist communities in the English Channel are comprised of complex assemblages of taxa from across the eukaryotic tree, and that individual lineages vary widely in their respective diversity and relative abundance. Through repeated sampling of the planktonic community across seasons and by estimating niche breadth and inferring ecological roles for individual taxa, I was also able to show that these microeukaryotic communities exhibit temporal variation, though there are likely other factors, such as trophic interactions, that govern overall community dynamics. Finally, I found that fungi in the English Channel, though relatively minor contributors to overall taxonomic diversity, may facilitate nutrient transfer between other members of the planktonic

87 community through parasitism, as has been shown in freshwater plankton. Fur- thermore, the fungi observed were only distantly related to circumscribed species, suggesting that the diversity and importance of marine fungi has been historically overlooked. Based on my findings from the English Channel, I chose to focus Chapter2 on coastal habitats, from which most marine fungal taxa have been described. Us- ing high-throughput molecular sequencing, which is able to detect microbial taxa present at minute levels or otherwise unlikely to be observed in morphological studies, I cataloged seasonal diversity of marine fungi from four coastal habitats in North Carolina to determine if classical culturing and microscopy surveys of marine fungi may have underestimated extant diversity in coastal systems. I showed not only that fungal communities in coastal habitats are spatio-temporally structured, but also that there are likely hundreds to thousands of fungal species yet to be characterized from marine environments. Furthermore, by using a combination of different taxonomic assignment algorithms, I was able to show that many marine fungi represent novel lineages at several taxonomic levels—both high (phylum, class) and low (genus, species)—with taxa affiliated with the non-Dikarya lineages proving the most difficult to identify using established genetic and taxonomic resources. To my knowledge, this study represents the first molecular survey of coastal marine fungal diversity focused on identifying putatively novel taxa, particularly among the zoosporic and zygosporic lineages. In light of the difficulty I had assigning provisional taxonomic identities to the fungi I observed in Chapter2, my final chapter (Chapter3) addresses the paucity of genetic resources available for non-Dikarya fungi in general, and marine fungi in particular. By using a mock community approach, I developed a sequence analysis pipeline to generate high-quality rDNA sequences for uncultured marine fungi. I find it is possible to use a high-throughput, long-read sequencing platform to create

88 reference data for uncultured microbiota, and that those sequences can provide improved taxonomic resolution for molecular operational taxonomic units. Moreover, I used phylogenetic analyses of long-read fragments to show that marine fungi detected in coastal North Carolina habitats belong to known groups in the Ascomycota and the Basidiomycota, but also represent potentially unknown classes or phyla among the zoosporic lineages at the base of the fungal tree of life. Finally, using long-read data allowed me to confirm my initial findings from Chapter2 that existing reference databases consistently fail to provide accurate taxonomic identities to taxa outside of the Dikarya, not only across databases, but across target loci. Consequently, studies that rely on these databases to understand fungal diversity and ecology are likely to misrepresent the true diversity of mycobiota and underestimate contributions of non-Dikarya fungi to ecosystem functioning. Collectively, these studies have contributed to a better appreciation of fungal diversity and recapitulated the importance of generating reference material for fungal species, be it in the form of physical cultures/tissue or genetic data for uncultured taxa. I have clearly demonstrated that while the marine realm harbors numerous fungi whose identity and importance remain unknown, we can use existing molecular and bioinformatic tools to assign identities to otherwise invisible fungi, allowing us to address lingering questions about fungal origins, evolution, and ecology, even as mycology shifts away from more traditional taxonomic approaches. Perhaps most importantly, my work has also provided tools and a framework for studying uncultured/unculturable fungi using developing sequencing platforms, and generated sorely needed genetic resources for marine fungi, aiding future efforts to assess the breadth of functional and phylogenetic diversity in the fungal kingdom.

89 Future Directions

While mycologists have spent a century poking around beaches, scraping mangroves, and sifting through sea foam looking for fungal spores and thalli, high-throughput studies of marine fungi are still in their infancy. Advances in molecular sequencing methods have radically altered our appreciation of both the species richness and ecological significance of microbial consortia across the globe, and I expect that increased focus on marine mycology will provide insight into fundamental questions of fungal evolution. Chief among these long-standing mycological mysteries are the timing and order of the diversification of major fungal lineages and where the earliest fungi emerged. Efforts to reconstruct the complete evolutionary history of the fungal kingdom have been hampered by limited taxonomic sampling across all phyla, but particularly among the microfungi in the zoosporic and other non-Dikarya lineages. Based on the findings from the studies presented here and others (Hassett and Gradinger, 2016; Comeau et al., 2016; Jeffries et al., 2016; Richards et al., 2015), marine habitats harbor taxa that can help to fill in gaps or break up long branches in and among the clades at the base of the fungal tree. For example, in Chapter3, at least one MOTU recovered from coastal North Carolina samples could not be placed to any known phylum, instead falling between the Chytridiomycota and the Cryptomycota—the earliest branch of the fungal tree, comprised primarily of phylotypes recovered from environmental surveys. With increased sampling of disparate marine habitats—and the animals and plants (i.e., potential hosts or symbionts of fungi) within them—I anticipate the discovery of many more clades of fungi at or near the base of the tree. Using amplicon-based surveys and iterative primer development, we can target uncultured fungi from marine habitats to help improve the resolution of phylum- or kingdom-wide phylogenetic analyses.

90 Another long-term goal of this research, which dovetails with efforts to infer fungal relationships, is providing an environmental context for the origin of the fungi. Culturing surveys of marine fungi from the twentieth century found few fungi outside of the Dikarya, and phylogenetic studies showed that marine Ascomycetes are derived from terrestrial ancestors. Coupled with the relatively high diversity of “early diverging fungi” in freshwater and transitional habitats, these findings have led to speculation that the first fungi diversified in freshwater environments before adopt- ing a terrestrial habit. Yet, the results presented in this dissertation directly refute historical notions of marine fungal diversity, showing that fungi from the zoosporic and zygosporic phyla are both found across marine habitats and represent previously unknown lineages near the base of the fungal tree. We may speculate as to what these fungi are doing in their respective habitats based on their known ecological potential in terrestrial and freshwater environments. However, only through continued targeted sampling of “early diverging” fungi from the oceanic realm, using both culture-dependent and culture-independent methods, will we be able to piece together their contributions to today’s marine habitats, and provide insight into the potential roles played by the first fungi. Molecular data generated through long-read sequencing and molecular cloning surveys can aid in probe design for fluorescent in situ hybridization (FISH) studies, which can be used to visualize fungi on or within substrata or hosts, providing critical morphological and life history data to taxa that are difficult, if not impossible, to bring into culture. In time, the application of meta-genomic and -transcriptomic tools to studies of marine fungi will allow us to investigate not just their phylogenetic diversity, but their functional diversity as well, providing insight into the types of trophic modes marine fungi employ and how they compare to the ecological roles played by their terrestrial cousins. As we piece together the trajectory of early fungal evolution, we will be able to investigate whether ecological patterns such as geographical distribution, habitat preference, and host

91 speciﬁcity are phylogenetically structured, potentially painting a composite picture of the ﬁrst fungi.

92 Appendix A

Supplementary Information for Chapter1

93 . Table A.1: Sequence statistics for 30 water samples collected in the English Channel under diﬀerent ﬁltering stringencies (laxa, moderateb,stringentc)

Filtered Reads Non-Metazoan Reads OTUs Read Sample ID L M S L M S L M S WS14 4556 4192 3566 3818 3518 2975 679 495 162 WS15 8306 8073 7651 481 433 315 176 145 42 WS16 4847 4407 3699 2356 2051 1529 723 591 169 WS17 4414 4025 3544 3804 3495 3062 948 767 222 WS18 4475 4185 3885 3242 2966 2619 908 696 229 WS19 3590 3228 2616 3449 3091 2500 867 637 225 WS20 3427 3137 2708 2481 2265 1991 788 627 176 WS21 5005 4737 4452 4183 3944 3624 848 712 203 WS22 1348 1245 1115 1096 999 939 474 392 123 WS23 4681 4454 4309 4671 4435 4304 948 769 230 WS24 4588 4482 4156 1341 1340 1091 141 123 25 WS25 13140 13032 12870 427 336 113 160 130 35 WS26 2468 2164 1887 2250 1959 1702 788 570 206 WS27 2587 2304 1978 2224 1971 1667 785 594 185 WS28 6595 6443 6218 689 605 376 319 230 83 WS29 1979 1649 1395 752 659 499 404 315 111 WS30 1073 914 747 522 442 335 338 266 105 WS31 17464 15971 13942 14392 12929 11186 2509 1800 522 WS32 2874 2845 2823 662 626 591 272 243 80 WS33 14370 14124 13534 1515 1321 809 472 391 104 WS34 1359 1279 1152 428 373 272 213 155 72 WS35 2512 2215 1857 2044 1754 1462 706 525 191 WS36 1944 1731 1507 1758 1551 1335 654 492 194 WS37 2507 2342 2189 1327 1178 1038 544 416 175 WS38 3814 3656 3496 291 241 112 145 112 37 WS39 2679 2224 1939 2205 1781 1528 927 639 251 WS40 6463 5987 4992 6359 5891 4895 457 345 126 WS41 4999 4585 4097 2619 2220 1768 859 605 198 WS42 9207 9183 9106 335 298 105 130 122 38 WS43 1090 1012 847 492 438 380 284 242 82 a Lax – First pass filtering, no denoising, chimeras and singletons retained b Moderate – First pass filtering, pseudo-denoising (preclustering at 99%), chimeras and singletons removed c Stringent dataset – First pass filtering, denoising, chimeras and singletons removed

94 100 Rare Groups (<1%) Unidentified Eukaryotes MAST-3 Stramenopiles Bicosoecida; Cafeteriidae 80 Prymnesiophyceae;Other Prymnesiophyceae; Prymnesiales Haptophyta Prymnesiophyceae; Phaeocystis Cryptomycota; LKM11 Fungi Mamiellophyceae; Ostreococcus 60 Mamiellophyceae; Micromonas Chloroplastida Mamiellophyceae; Bathycoccus Other Protalveolata; Syndiniales Alveolata 40 Dinoflagellata; Dinophyceae Ciliophora; Spirotrichea

Relative abundance (%) Ciliophora; Conthreep

0 Figure A.1: Taxonomic composition of total community observed across all water samples

95 Table A.2: Taxonomy, OTU diversity, read abundance, and functional annotation of OTUs in ecological inference dataset. Trophic mode designations: AUT –autotroph; HET –heterotroph; PAR–parasite; POL–polymorphic

Supergroup Subgroup Genera No. OTUs No. Reads Trophic Mode Reference Colpoda 4 636 HET J¨urgenset al.(1997) Cryptocaryon 5 63 PAR Colorni and Diamant(1993) Cyclidium 6 2765 HET J¨urgenset al.(1997) Laboea 1 15 POL Putt(1990) Metacylis 1 98 HET Dolan and Pierce(2012) Ciliophora Myrionecta 1 4 POL Johnson and Stoecker(2005) Pseudotontonia 1 18 POL Skovgaard and Legrand(2005) Salpingella 2 54 HET Dolan et al.(2009) Strobilidium 1 17 HET Montagnes(1996) Strombidium 1 31 POL

96 Alveolata Akashiwo 1 2 POL Tarutani et al.(2000) Alexandrium 1 6 POL Anderson et al.(2012) Dinophysis 1 3 POL Hansen(1991) Gymnodinium 2 65 POL Yoo et al.(2010) Gyrodinium 26 852 POL Dinoflagellata Karlodinium 6 285 POL Garcéset al.(2006) Neoceratium 3 137 AUT Gomez et al.(2010) Protoperidinium 12 383 HET Jeong and Latz(1994) Spatulodinium 2 91 HET Gomez and Souissi(2007) Woloszynskia 3 342 POL Kang et al.(2011) Amoebophrya 18 323 PAR Syndiniales Guillou et al.(2008) Duboscquella 2 61 PAR Bathycoccus 8 1704 AUT Mamiella 3 18 AUT Mamiellophyceae Micromonas 10 1501 AUT Chloroplastida Not et al.(2012) Ostreococcus 6 769 AUT Pycnococcus 1 17 AUT Prasinophytae Pyramimonas 2 378 AUT Continued on next page Table A.2 – Continued from previous page Supergroup Subgroup Genera No. OTUs No. Reads Trophic Mode Reference Conosa Variosea Flamella 1 81 HET Michel and Smirnov(1999) Cryptophyta Cryptomonadales Geminigera 3 234 AUT van den Hoff and Bell(2015) Discosea Flabellinia Neoparamoeba 1 2 PAR Young et al.(2008) Longamoebia Acanthamoeba 1 27 HET Visvesvara et al.(2007) Dicellomyces 1 3 PAR Ingold(1985) Basidiomycota Fungi Exobasidium 1 12 PAR Punyasiri et al.(2005) Cryptomycota LKM11 6 632 PAR Lara et al.(2010) Chrysochromulina 9 253 AUT Chrysoculter 1 5 AUT Haptophyta Prymnesiophyceae Emiliana 1 58 AUT Not et al.(2012) Imantonia 2 51 AUT Phaeocystis 10 711 AUT Holozoa Choanamonada Diaphanoeca 4 63 HET Boenigk and Arndt(2002) Lobosa Tubulinea Hartmannella 2 17 HET Horn et al.(2000)

97 Allas 1 5 HET Cryothecomonas 2 38 HET Cercozoa Partenskyella 1 43 HET Bass et al.(2009) Rhizaria Protaspa 2 56 HET Pseudopirsonia 1 11 HET Acanthometra 1 74 HET Radiolaria Gilg et al.(2010) Pseudocubus 1 10 HET Bicosoeca 1 19 HET Bicosoecida Boenigk and Arndt(2002) Symbiomonas 8 1471 HET Attheya 2 12 AUT Chaetoceros 3 25 AUT Coscinodiscus 1 4 AUT Diatomea Cyclotella 1 2 AUT Theriot(2010) Stramenopiles Leptocylindrus 6 273 AUT Minutocellus 1 6 AUT Thalassiosira 4 84 AUT Labyrinthulomycota Aplanochytrium 1 25 PAR Raghukumar(2002) Pelagophyceae Aureococcus 2 37 AUT Not et al.(2012) Continued on next page Table A.2 – Continued from previous page Supergroup Subgroup Genera No. OTUs No. Reads Trophic Mode Reference Pirsonia Pirsonia 2 20 HET Skovgaard(2014) Other Telonema 12 430 HET Boenigk and Arndt(2002) 98 Appendix B

Supplementary Information for Chapter2

99 Table B.1: Taxonomic assignments for 770 fungal OTUs estimated by the RDP Classiﬁer and the RDP fungal 28S database. Percentages for each rank assignment indicate bootstrap support for that designation as estimated by the RDP Classi- ﬁer. Keys for phylum/taxonomic designations are: Asc–Ascomycota; Bas–Basidiomycota; Bla–Blastocladiomycota; Chy– Chytridiomycota; Cry–Cryptomycota; Ent–Entomophthoromycota; Glo–Glomeromycota; Neo–Neocallimastigomycota; Zyg–Zygomycota; ‘i.s.’–incertae sedis

OTU Kin % Phy % Class % Order % Family % Genus %

1011 Fun 96% Asc 96% Archaeorhizomycetes 96% Archaeorhizomycetales 95% Archaeorhizomycetales i.s. 95% Archaeorhizomyces 95% 69 Fun 100% Asc 100% Arthoniomycetes 35% Arthoniales 35% Arthoniaceae 34% Arthonia 34% 1503 Fun 100% Asc 100% Ascomycota i.s. 76% Ascomycota i.s. 76% Ascomycota i.s. 76% Antennariella 76% 247 Fun 100% Asc 100% Ascomycota i.s. 18% Ascomycota i.s. 18% Ascomycota i.s. 18% Capnobotryella 11% 737 Fun 100% Asc 100% Ascomycota i.s. 64% Ascomycota i.s. 64% Ascomycota i.s. 64% Capnobotryella 60% 918 Fun 53% Asc 29% Ascomycota i.s. 7% Ascomycota i.s. 7% Ascomycota i.s. 7% Capnobotryella 6% 30 Fun 100% Asc 99% Ascomycota i.s. 14% Ascomycota i.s. 14% Ascomycota i.s. 14% Curvicladium 14% 1481 Fun 100% Asc 100% Ascomycota i.s. 39% Ascomycota i.s. 39% Ascomycota i.s. 39% Dictyosporium 36% 805 Fun 100% Asc 100% Ascomycota i.s. 78% Ascomycota i.s. 78% Ascomycota i.s. 78% Floricola 78% 1251 Fun 100% Asc 100% Ascomycota i.s. 56% Ascomycota i.s. 56% Ascomycota i.s. 56% Lecophagus 53% 463 Fun 100% Asc 100% Ascomycota i.s. 99% Ascomycota i.s. 99% Ascomycota i.s. 99% Phaeomoniella 99% 989 Fun 100% Asc 100% Ascomycota i.s. 99% Ascomycota i.s. 99% Ascomycota i.s. 99% Phaeomoniella 99% 651 Fun 100% Asc 100% Ascomycota i.s. 40% Ascomycota i.s. 40% Ascomycota i.s. 40% Phaeosclera 40% 802 Fun 100% Asc 100% Ascomycota i.s. 63% Ascomycota i.s. 63% Ascomycota i.s. 63% Polyschema 35% 100 1274 Fun 100% Asc 100% Ascomycota i.s. 86% Ascomycota i.s. 86% Ascomycota i.s. 86% Scolecobasidiella 83% 832 Fun 100% Asc 100% Ascomycota i.s. 23% Ascomycota i.s. 23% Ascomycota i.s. 23% Sphaeriothyrium 15% 844 Fun 100% Asc 100% Ascomycota i.s. 40% Ascomycota i.s. 40% Ascomycota i.s. 40% Sphaeriothyrium 40% 853 Fun 100% Asc 100% Ascomycota i.s. 32% Ascomycota i.s. 32% Ascomycota i.s. 32% Sphaeriothyrium 32% 1334 Fun 99% Asc 99% Ascomycota i.s. 16% Ascomycota i.s. 16% Ascomycota i.s. 16% Sphaeriothyrium 8% 1356 Fun 100% Asc 100% Ascomycota i.s. 21% Ascomycota i.s. 21% Ascomycota i.s. 21% Sphaeriothyrium 21% 1431 Fun 100% Asc 100% Ascomycota i.s. 27% Ascomycota i.s. 27% Ascomycota i.s. 27% Sphaeriothyrium 27% 1488 Fun 100% Asc 100% Ascomycota i.s. 31% Ascomycota i.s. 31% Ascomycota i.s. 31% Sphaeriothyrium 12% 1560 Fun 100% Asc 100% Ascomycota i.s. 21% Ascomycota i.s. 21% Ascomycota i.s. 21% Sphaeriothyrium 10% 1606 Fun 98% Asc 91% Ascomycota i.s. 17% Ascomycota i.s. 17% Ascomycota i.s. 17% Trichoconis 5% 27 Fun 100% Asc 100% Ascomycota i.s. 69% Ascomycota i.s. 69% Ascomycota i.s. 69% Trichothecium 69% 1369 Fun 100% Asc 100% Ascomycota i.s. 24% Ascomycota i.s. 24% Ascomycota i.s. 24% Trichothecium 24% 1747 Fun 100% Asc 100% Ascomycota i.s. 5% Ascomycota i.s. 5% Ascomycota i.s. 5% Trichothecium 5% 1447 Fun 100% Asc 100% Ascomycota i.s. 86% Ascomycota i.s. 86% Ascomycota i.s. 86% Troposporella 76% 1612 Fun 100% Asc 100% Ascomycota i.s. 54% Trichosphaeriales 54% Trichosphaeriales i.s. 54% Nigrospora 54% 697 Fun 100% Asc 100% Dothideomycetes 84% Botryosphaeriales 15% Botryosphaeriaceae 15% Melanops 13% 77 Fun 100% Asc 100% Dothideomycetes 100% Botryosphaeriales 46% Botryosphaeriaceae 46% Microdiplodia 46% 695 Fun 100% Asc 100% Dothideomycetes 57% Botryosphaeriales 23% Botryosphaeriaceae 23% Microdiplodia 16% 1116 Fun 100% Asc 100% Dothideomycetes 100% Botryosphaeriales 84% Botryosphaeriaceae 84% Microdiplodia 84% 1379 Fun 98% Asc 92% Dothideomycetes 86% Botryosphaeriales 20% Botryosphaeriaceae 20% Microdiplodia 20% 1698 Fun 100% Asc 100% Dothideomycetes 100% Botryosphaeriales 95% Botryosphaeriaceae 95% Microdiplodia 95% 38 Fun 100% Asc 100% Dothideomycetes 100% Botryosphaeriales 100% Botryosphaeriaceae 100% Saccharata 100% 1397 Fun 100% Asc 100% Dothideomycetes 93% Botryosphaeriales 57% Botryosphaeriaceae 57% Saccharata 52% 1487 Fun 100% Asc 99% Dothideomycetes 43% Botryosphaeriales 17% Botryosphaeriaceae 17% Saccharata 16% 1659 Fun 100% Asc 100% Dothideomycetes 46% Botryosphaeriales 40% Botryosphaeriaceae 40% Saccharata 39% 891 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Capnodiales i.s. 50% Batcheloromyces 50% 1016 Fun 100% Asc 100% Dothideomycetes 86% Capnodiales 76% Capnodiales i.s. 19% Batcheloromyces 13% 1084 Fun 100% Asc 100% Dothideomycetes 98% Capnodiales 98% Capnodiales i.s. 45% Batcheloromyces 31% 1475 Fun 100% Asc 100% Dothideomycetes 99% Capnodiales 99% Capnodiales i.s. 69% Batcheloromyces 61% 1739 Fun 100% Asc 100% Dothideomycetes 99% Capnodiales 99% Capnodiales i.s. 35% Batcheloromyces 23% 1746 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Capnodiales i.s. 41% Batcheloromyces 25% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1683 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 98% Capnodiales i.s. 58% Catenulostroma 26% 913 Fun 100% Asc 100% Dothideomycetes 84% Capnodiales 84% Capnodiales i.s. 73% Devriesia 64% 1262 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Capnodiales i.s. 55% Devriesia 46% 1531 Fun 100% Asc 100% Dothideomycetes 98% Capnodiales 91% Capnodiales i.s. 53% Devriesia 19% 1583 Fun 100% Asc 100% Dothideomycetes 92% Capnodiales 92% Capnodiales i.s. 86% Devriesia 80% 1616 Fun 100% Asc 100% Dothideomycetes 97% Capnodiales 97% Capnodiales i.s. 80% Devriesia 47% 880 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Capnodiales i.s. 93% Rachicladosporium 89% 1221 Fun 100% Asc 100% Dothideomycetes 98% Capnodiales 91% Capnodiales i.s. 62% Ramichloridium 49% 1237 Fun 100% Asc 100% Dothideomycetes 65% Capnodiales 51% Capnodiales i.s. 23% Ramichloridium 15% 1620 Fun 100% Asc 100% Dothideomycetes 99% Capnodiales 99% Capnodiales i.s. 81% Ramichloridium 81% 32 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Davidiellaceae 100% Cladosporium complex 99% 957 Fun 100% Asc 99% Dothideomycetes 99% Capnodiales 99% Davidiellaceae 75% Cladosporium complex 50% 976 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Davidiellaceae 98% Cladosporium complex 53% 1242 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Davidiellaceae 97% Cladosporium complex 74% 1491 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Davidiellaceae 96% Cladosporium complex 79% 604 Fun 100% Asc 100% Dothideomycetes 40% Capnodiales 20% Dissoconiaceae 11% Dissoconium 11% 569 Fun 100% Asc 100% Dothideomycetes 94% Capnodiales 79% Mycosphaerellaceae 51% Cercosporella 10% 956 Fun 100% Asc 100% Dothideomycetes 91% Capnodiales 72% Mycosphaerellaceae 61% Cercosporella 5% 988 Fun 100% Asc 100% Dothideomycetes 97% Capnodiales 90% Mycosphaerellaceae 58% Cercosporella 9% 1218 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Mycosphaerellaceae 71% Cercosporella 38% 1294 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Mycosphaerellaceae 66% Cercosporella 25% 1658 Fun 100% Asc 100% Dothideomycetes 99% Capnodiales 97% Mycosphaerellaceae 64% Cercosporella 38% 1024 Fun 100% Asc 100% Dothideomycetes 94% Capnodiales 47% Mycosphaerellaceae 28% Graphiopsis 27% 995 Fun 99% Asc 98% Dothideomycetes 76% Capnodiales 29% Mycosphaerellaceae 24% Miuraea 8% 1247 Fun 100% Asc 100% Dothideomycetes 66% Capnodiales 42% Mycosphaerellaceae 30% Miuraea 22% 6 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Mycosphaerellaceae 83% Mycosphaerella 31% 101 1836 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 94% Mycosphaerellaceae 63% Mycosphaerella 16% 1259 Fun 100% Asc 100% Dothideomycetes 67% Capnodiales 61% Mycosphaerellaceae 51% Ramulispora 13% 1382 Fun 100% Asc 100% Dothideomycetes 99% Capnodiales 99% Mycosphaerellaceae 68% Ramulispora 26% 1564 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 98% Mycosphaerellaceae 93% Ramulispora 29% 1493 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Mycosphaerellaceae 99% Septoria 58% 673 Fun 100% Asc 100% Dothideomycetes 100% Capnodiales 100% Mycosphaerellaceae 81% Zasmidium 80% 336 Fun 100% Asc 100% Dothideomycetes 100% Dothideales 100% Dothideaceae 83% Coniozyma 57% 175 Fun 100% Asc 100% Dothideomycetes 97% Dothideales 84% Dothideales i.s. 45% Selenophoma 45% 448 Fun 100% Asc 97% Dothideomycetes 94% Dothideomycetes i.s. 21% Dothideomycetes i.s. 21% Asteromella 21% 515 Fun 92% Asc 79% Dothideomycetes 67% Dothideomycetes i.s. 26% Dothideomycetes i.s. 26% Asteromella 26% 1061 Fun 99% Asc 92% Dothideomycetes 92% Dothideomycetes i.s. 64% Dothideomycetes i.s. 64% Asteromella 64% 1395 Fun 100% Asc 100% Dothideomycetes 98% Dothideomycetes i.s. 15% Dothideomycetes i.s. 15% Glonium 15% 1258 Fun 100% Asc 100% Dothideomycetes 100% Dothideomycetes i.s. 100% Dothideomycetes i.s. 100% Repetophragma 100% 1374 Fun 50% Asc 33% Dothideomycetes 21% Dothideomycetes i.s. 18% Micropeltidaceae 18% Stomiopeltis 18% 1351 Fun 100% Asc 100% Dothideomycetes 38% Hysteriales 20% Hysteriaceae 20% Hysteropatella 20% 1060 Fun 100% Asc 100% Dothideomycetes 90% Myriangiales 43% Elsinoaceae 43% Sphaceloma 32% 121 Fun 70% Asc 34% Dothideomycetes 27% Pleosporales 18% Delitschiaceae 8% Delitschia 8% 1344 Fun 100% Asc 100% Dothideomycetes 88% Pleosporales 79% Delitschiaceae 65% Delitschia 65% 1591 Fun 100% Asc 100% Dothideomycetes 84% Pleosporales 84% Didymellaceae 58% Didymella 58% 33 Fun 100% Asc 100% Dothideomycetes 88% Pleosporales 79% Lentitheciaceae 39% Keissleriella 39% 1206 Fun 100% Asc 100% Dothideomycetes 77% Pleosporales 69% Lentitheciaceae 46% Keissleriella 46% 471 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Leptosphaeriaceae 67% Leptosphaeria 67% 593 Fun 95% Asc 95% Dothideomycetes 93% Pleosporales 93% Leptosphaeriaceae 78% Leptosphaeria 78% 599 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Leptosphaeriaceae 97% Leptosphaeria 97% 656 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Leptosphaeriaceae 86% Leptosphaeria 86% 1353 Fun 99% Asc 97% Dothideomycetes 90% Pleosporales 87% Leptosphaeriaceae 60% Leptosphaeria 59% 1427 Fun 99% Asc 99% Dothideomycetes 97% Pleosporales 96% Leptosphaeriaceae 39% Leptosphaeria 39% 1554 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 73% Leptosphaeriaceae 66% Leptosphaeria 66% 1797 Fun 86% Asc 78% Dothideomycetes 77% Pleosporales 74% Leptosphaeriaceae 69% Leptosphaeria 69% 347 Fun 100% Asc 100% Dothideomycetes 91% Pleosporales 78% Lophiostomataceae 45% Herpotrichia 32% 468 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Lophiostomataceae 91% Herpotrichia 91% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1076 Fun 100% Asc 100% Dothideomycetes 96% Pleosporales 92% Lophiostomataceae 28% Lophiostoma 28% 1421 Fun 100% Asc 100% Dothideomycetes 96% Pleosporales 93% Lophiostomataceae 17% Lophiostoma 17% 1485 Fun 100% Asc 99% Dothideomycetes 77% Pleosporales 69% Lophiostomataceae 17% Lophiostoma 17% 342 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 95% Massariaceae 34% Massaria 34% 634 Fun 100% Asc 100% Dothideomycetes 79% Pleosporales 68% Melanommataceae 12% Mycopepon 12% 1255 Fun 100% Asc 99% Dothideomycetes 60% Pleosporales 18% Melanommataceae 10% Mycopepon 10% 1241 Fun 100% Asc 99% Dothideomycetes 99% Pleosporales 88% Montagnulaceae 59% Bimuria 59% 1565 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 82% Montagnulaceae 47% Bimuria 46% 1456 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 75% Montagnulaceae 58% Paraconiothyrium 44% 1700 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 79% Montagnulaceae 59% Paraconiothyrium 40% 842 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 93% Montagnulaceae 87% Paraphaeosphaeria 86% 1687 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 98% Phaeosphaeriaceae 41% Chaetosphaeronema 31% 446 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 65% Phaeosphaeriaceae 41% Loratospora 25% 1333 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Phaeosphaeriaceae 78% Loratospora 38% 16 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Phaeosphaeriaceae 55% Phaeodothis 55% 1771 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 96% Phaeosphaeriaceae 96% Phaeodothis 96% 10 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Phaeosphaeriaceae 71% Phaeosphaeria 66% 800 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 99% Phaeosphaeriaceae 63% Phaeosphaeria 58% 835 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 99% Phaeosphaeriaceae 61% Phaeosphaeria 61% 839 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Phaeosphaeriaceae 94% Phaeosphaeria 92% 856 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Phaeosphaeriaceae 87% Phaeosphaeria 82% 932 Fun 97% Asc 97% Dothideomycetes 91% Pleosporales 90% Phaeosphaeriaceae 70% Phaeosphaeria 47% 1193 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 99% Phaeosphaeriaceae 77% Phaeosphaeria 56% 1201 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Phaeosphaeriaceae 58% Phaeosphaeria 58% 1656 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 97% Phaeosphaeriaceae 69% Phaeosphaeria 63% 1697 Fun 100% Asc 100% Dothideomycetes 98% Pleosporales 97% Phaeosphaeriaceae 63% Phaeosphaeria 48% 102 1805 Fun 100% Asc 100% Dothideomycetes 98% Pleosporales 98% Phaeosphaeriaceae 75% Phaeosphaeria 70% 1030 Fun 100% Asc 100% Dothideomycetes 83% Pleosporales 51% Pleomassariaceae 26% Asteromassaria 26% 1680 Fun 94% Asc 91% Dothideomycetes 31% Pleosporales 21% Pleomassariaceae 19% Dendryphiopsis 19% 1497 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 100% Alternaria 100% 1498 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 98% Alternaria 97% 1790 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 100% Alternaria 100% 983 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 100% Cochliobolus 98% 1450 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 97% Cochliobolus 92% 1484 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 98% Cochliobolus 89% 1556 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 100% Cochliobolus 95% 1624 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 99% Pleosporaceae 99% Cochliobolus 95% 1587 Fun 100% Asc 100% Dothideomycetes 94% Pleosporales 92% Pleosporaceae 25% Pithomyces 18% 373 Fun 100% Asc 100% Dothideomycetes 96% Pleosporales 94% Pleosporaceae 37% Pyrenochaeta 30% 547 Fun 100% Asc 100% Dothideomycetes 97% Pleosporales 97% Pleosporaceae 88% Pyrenochaeta 84% 977 Fun 100% Asc 100% Dothideomycetes 94% Pleosporales 94% Pleosporaceae 28% Pyrenophora 21% 1239 Fun 100% Asc 100% Dothideomycetes 80% Pleosporales 77% Pleosporaceae 30% Pyrenophora 18% 1585 Fun 100% Asc 100% Dothideomycetes 74% Pleosporales 73% Pleosporaceae 22% Pyrenophora 13% 1651 Fun 100% Asc 100% Dothideomycetes 93% Pleosporales 92% Pleosporaceae 20% Pyrenophora 12% 1653 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporaceae 30% Pyrenophora 18% 1655 Fun 100% Asc 100% Dothideomycetes 85% Pleosporales 84% Pleosporaceae 39% Pyrenophora 21% 1754 Fun 100% Asc 100% Dothideomycetes 86% Pleosporales 84% Pleosporaceae 35% Pyrenophora 26% 1236 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 100% Pleosporales i.s. 34% Camarosporium 19% 1671 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 90% Pleosporales i.s. 78% Camarosporium 70% 411 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 98% Pleosporales i.s. 65% Corynespora 65% 1072 Fun 100% Asc 100% Dothideomycetes 94% Pleosporales 88% Pleosporales i.s. 44% Corynespora 40% 1093 Fun 100% Asc 100% Dothideomycetes 98% Pleosporales 94% Pleosporales i.s. 43% Massariosphaeria 43% 996 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 97% Pleosporales i.s. 39% Ochrocladosporium 39% 1282 Fun 100% Asc 100% Dothideomycetes 98% Pleosporales 98% Pleosporales i.s. 37% Ochrocladosporium 31% 1324 Fun 100% Asc 100% Dothideomycetes 68% Pleosporales 64% Pleosporales i.s. 26% Ochrocladosporium 25% 1422 Fun 100% Asc 100% Dothideomycetes 69% Pleosporales 68% Pleosporales i.s. 42% Ochrocladosporium 17% 1570 Fun 100% Asc 100% Dothideomycetes 94% Pleosporales 94% Pleosporales i.s. 74% Ochrocladosporium 73% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

757 Fun 100% Asc 100% Dothideomycetes 94% Pleosporales 94% Pleosporales i.s. 93% Zeloasperisporium 92% 19 Fun 100% Asc 100% Dothideomycetes 100% Pleosporales 99% Sporormiaceae 75% Preussia 70% 1479 Fun 100% Asc 100% Dothideomycetes 89% Pleosporales 84% Sporormiaceae 54% Preussia 25% 1550 Fun 100% Asc 100% Dothideomycetes 89% Pleosporales 84% Sporormiaceae 33% Preussia 15% 1686 Fun 100% Asc 100% Dothideomycetes 99% Pleosporales 99% Sporormiaceae 63% Preussia 51% 1181 Fun 99% Asc 99% Dothideomycetes 90% Pleosporales 66% Sporormiaceae 13% Westerdykella 13% 1184 Fun 100% Asc 100% Dothideomycetes 75% Pleosporales 35% Testudinaceae 11% Lepidosphaeria 11% 740 Fun 100% Asc 100% Eurotiomycetes 99% Chaetothyriales 98% Chaetothyriaceae 97% Ceramothyrium 97% 220 Fun 100% Asc 100% Eurotiomycetes 97% Chaetothyriales 97% Herpotrichiellaceae 97% Cladophialophora 42% 1286 Fun 100% Asc 100% Eurotiomycetes 59% Chaetothyriales 49% Herpotrichiellaceae 49% Cladophialophora 35% 1330 Fun 100% Asc 100% Eurotiomycetes 77% Chaetothyriales 76% Herpotrichiellaceae 76% Cladophialophora 28% 72 Fun 100% Asc 100% Eurotiomycetes 100% Chaetothyriales 100% Herpotrichiellaceae 100% Cyphellophora 91% 485 Fun 100% Asc 100% Eurotiomycetes 91% Chaetothyriales 88% Herpotrichiellaceae 82% Cyphellophora 77% 1477 Fun 100% Asc 100% Eurotiomycetes 94% Chaetothyriales 93% Herpotrichiellaceae 93% Cyphellophora 60% 2 Fun 100% Asc 100% Eurotiomycetes 98% Chaetothyriales 98% Herpotrichiellaceae 98% Exophiala 48% 272 Fun 100% Asc 100% Eurotiomycetes 93% Chaetothyriales 92% Herpotrichiellaceae 92% Exophiala 51% 438 Fun 100% Asc 100% Eurotiomycetes 92% Chaetothyriales 88% Herpotrichiellaceae 88% Exophiala 52% 476 Fun 100% Asc 100% Eurotiomycetes 90% Chaetothyriales 83% Herpotrichiellaceae 83% Exophiala 30% 488 Fun 100% Asc 100% Eurotiomycetes 84% Chaetothyriales 84% Herpotrichiellaceae 84% Exophiala 82% 811 Fun 100% Asc 100% Eurotiomycetes 88% Chaetothyriales 71% Herpotrichiellaceae 71% Exophiala 53% 843 Fun 100% Asc 100% Eurotiomycetes 100% Chaetothyriales 100% Herpotrichiellaceae 100% Exophiala 99% 1250 Fun 100% Asc 100% Eurotiomycetes 83% Chaetothyriales 76% Herpotrichiellaceae 67% Exophiala 42% 1327 Fun 100% Asc 100% Eurotiomycetes 88% Chaetothyriales 88% Herpotrichiellaceae 88% Exophiala 32% 1578 Fun 100% Asc 100% Eurotiomycetes 87% Chaetothyriales 87% Herpotrichiellaceae 87% Sarcinomyces 48% 1677 Fun 100% Asc 100% Eurotiomycetes 87% Chaetothyriales 84% Herpotrichiellaceae 84% Sarcinomyces 54% 1661 Fun 100% Asc 100% Eurotiomycetes 94% Chaetothyriales 86% Herpotrichiellaceae 84% Sorocybe 60% 103 1607 Fun 98% Asc 94% Eurotiomycetes 62% Chaetothyriales 61% Herpotrichiellaceae 61% Thysanorea 22% 125 Fun 100% Asc 100% Eurotiomycetes 100% Chaetothyriales 100% Herpotrichiellaceae 100% Veronaea 70% 169 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 100% Trichocomaceae 100% Aspergillus 69% 206 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 95% Trichocomaceae 95% Aspergillus 81% 837 Fun 100% Asc 100% Eurotiomycetes 99% Eurotiales 95% Trichocomaceae 95% Aspergillus 35% 1027 Fun 100% Asc 100% Eurotiomycetes 97% Eurotiales 96% Trichocomaceae 96% Aspergillus 40% 1284 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 100% Trichocomaceae 100% Aspergillus 100% 1393 Fun 100% Asc 100% Eurotiomycetes 97% Eurotiales 90% Trichocomaceae 90% Aspergillus 55% 876 Fun 100% Asc 100% Eurotiomycetes 95% Eurotiales 94% Trichocomaceae 94% Chromocleista 75% 1521 Fun 99% Asc 97% Eurotiomycetes 85% Eurotiales 76% Trichocomaceae 76% Chromocleista 36% 706 Fun 100% Asc 100% Eurotiomycetes 97% Eurotiales 89% Trichocomaceae 89% Eupenicillium 68% 760 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 100% Trichocomaceae 99% Eupenicillium 86% 1340 Fun 100% Asc 100% Eurotiomycetes 98% Eurotiales 92% Trichocomaceae 92% Eupenicillium 31% 1401 Fun 100% Asc 100% Eurotiomycetes 93% Eurotiales 92% Trichocomaceae 92% Eupenicillium 54% 1534 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 100% Trichocomaceae 100% Eupenicillium 98% 1714 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 100% Trichocomaceae 100% Eupenicillium 76% 60 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 99% Trichocomaceae 99% Eurotium 94% 1289 Fun 100% Asc 100% Eurotiomycetes 99% Eurotiales 97% Trichocomaceae 96% Hamigera 37% 277 Fun 100% Asc 100% Eurotiomycetes 100% Eurotiales 100% Trichocomaceae 100% Penicillium 100% 1199 Fun 100% Asc 100% Eurotiomycetes 100% Onygenales 100% Onygenaceae 100% Spiromastix 100% 1268 Fun 100% Asc 100% Lecanoromycetes 53% Agyriales 53% Agyriaceae 53% Sarea 53% 12 Fun 100% Asc 100% Lecanoromycetes 90% Lecanorales 89% Lecanoraceae 86% Lecanora 81% 1584 Fun 100% Asc 100% Lecanoromycetes 97% Lecanorales 96% Lecanoraceae 83% Lecidella 59% 1245 Fun 100% Asc 100% Lecanoromycetes 91% Lecanorales 90% Physciaceae 88% Amandinea 34% 1513 Fun 100% Asc 100% Lecanoromycetes 57% Lecanorales 55% Physciaceae 49% Amandinea 13% 1519 Fun 100% Asc 100% Lecanoromycetes 89% Lecanorales 89% Physciaceae 86% Amandinea 33% 0 Fun 100% Asc 100% Lecanoromycetes 95% Lecanorales 95% Physciaceae 95% Buellia 55% 630 Fun 100% Asc 100% Lecanoromycetes 97% Lecanorales 96% Physciaceae 96% Buellia 38% 1496 Fun 100% Asc 100% Lecanoromycetes 97% Lecanorales 97% Physciaceae 97% Buellia 55% 1679 Fun 100% Asc 100% Lecanoromycetes 89% Lecanorales 86% Physciaceae 85% Buellia 16% 1813 Fun 100% Asc 100% Lecanoromycetes 80% Lecanorales 80% Physciaceae 77% Buellia 15% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

339 Fun 100% Asc 100% Lecanoromycetes 89% Lecanorales 69% Physciaceae 62% Dirinaria 52% 1001 Fun 100% Asc 100% Lecanoromycetes 88% Lecanorales 86% Physciaceae 86% Phaeophyscia 26% 1304 Fun 100% Asc 100% Lecanoromycetes 33% Ostropales 13% Stictidaceae 12% Acarosporina 12% 1118 Fun 100% Asc 100% Lecanoromycetes 95% Peltigerales 79% Peltigeraceae 78% Peltigera 78% 1508 Fun 100% Asc 100% Lecanoromycetes 100% Peltigerales 100% Peltigeraceae 100% Peltigera 100% 1153 Fun 100% Asc 100% Leotiomycetes 75% Helotiales 70% Helotiaceae 32% Ombrophila 12% 1059 Fun 100% Asc 100% Leotiomycetes 88% Helotiales 76% Helotiales i.s. 32% Hyalodendriella 21% 1073 Fun 100% Asc 100% Leotiomycetes 95% Helotiales 88% Helotiales i.s. 53% Hyalodendriella 43% 1183 Fun 100% Asc 100% Leotiomycetes 95% Helotiales 54% Helotiales i.s. 32% Hyalodendriella 24% 1476 Fun 100% Asc 100% Leotiomycetes 53% Helotiales 29% Helotiales i.s. 12% Hyalodendriella 12% 1553 Fun 100% Asc 100% Leotiomycetes 89% Helotiales 74% Helotiales i.s. 27% Hyalodendriella 12% 964 Fun 100% Asc 100% Leotiomycetes 97% Helotiales 96% Helotiales i.s. 96% Pilidium 92% 284 Fun 100% Asc 100% Leotiomycetes 30% Helotiales 25% Helotiales i.s. 18% Rhizocladosporium 17% 985 Fun 100% Asc 100% Leotiomycetes 91% Helotiales 85% Helotiales i.s. 68% Tetracladium 65% 117 Fun 100% Asc 100% Leotiomycetes 81% Helotiales 68% Hyaloscyphaceae 18% Hyaloscypha 14% 648 Fun 100% Asc 100% Leotiomycetes 75% Helotiales 73% Sclerotiniaceae 26% Botryotinia 16% 701 Fun 97% Asc 86% Leotiomycetes 52% Helotiales 48% Sclerotiniaceae 32% Botryotinia 32% 9 Fun 100% Asc 100% Leotiomycetes 97% Helotiales 94% Sclerotiniaceae 85% Sclerotinia 31% 1512 Fun 100% Asc 100% Leotiomycetes 93% Leotiomycetes i.s. 84% Myxotrichaceae 84% Myxotrichum 83% 269 Fun 100% Asc 100% Leotiomycetes 87% Thelebolales 66% Thelebolaceae 66% Thelebolus 66% 1132 Fun 100% Asc 100% Leotiomycetes 100% Thelebolales 100% Thelebolaceae 100% Thelebolus 100% 453 Fun 100% Asc 100% Pezizomycetes 100% Pezizales 100% Ascobolaceae 100% Saccobolus 100% 320 Fun 100% Asc 100% Pezizomycetes 96% Pezizales 96% Ascodesmidaceae 65% Ascodesmis 63% 1572 Fun 60% Asc 26% Pezizomycetes 18% Pezizales 18% Caloscyphaceae 12% Caloscypha 12% 467 Fun 100% Asc 100% Pezizomycetes 99% Pezizales 99% Sarcosomataceae 99% Pseudoplectania 96% 838 Fun 67% Asc 20% Pneumocystidomycetes 7% Pneumocystidales 7% Pneumocystidaceae 7% Pneumocystis 7% 104 180 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Lipomycetaceae 100% Dipodascopsis 68% 794 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Lipomycetaceae 100% Dipodascopsis 100% 595 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Metschnikowiaceae 100% Clavispora 100% 910 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Metschnikowiaceae 100% Metschnikowia 100% 1209 Fun 96% Asc 87% Saccharomycetes 80% Saccharomycetales 80% Metschnikowiaceae 79% Metschnikowia 79% 144 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Saccharomycetaceae 100% Debaryomyces 100% 1161 Fun 100% Asc 100% Saccharomycetes 99% Saccharomycetales 99% Saccharomycetaceae 78% Debaryomyces 38% 79 Fun 100% Asc 100% Saccharomycetes 98% Saccharomycetales 98% Saccharomycetaceae 94% Kluyveromyces 55% 1228 Fun 95% Asc 86% Saccharomycetes 34% Saccharomycetales 25% Saccharomycetaceae 15% Kodamaea 10% 987 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Saccharomycetaceae 100% Kuraishia 100% 335 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Saccharomycetaceae 100% Lodderomyces 99% 1210 Fun 99% Asc 93% Saccharomycetes 65% Saccharomycetales 64% Saccharomycetaceae 54% Pachysolen 42% 304 Fun 87% Asc 80% Saccharomycetes 48% Saccharomycetales 47% Saccharomycetaceae 46% Pichia 45% 384 Fun 91% Asc 80% Saccharomycetes 62% Saccharomycetales 62% Saccharomycetaceae 62% Pichia 62% 309 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Saccharomycetaceae 100% Saccharomyces 100% 483 Fun 96% Asc 96% Saccharomycetes 88% Saccharomycetales 88% Saccharomycetaceae 86% Saturnispora 84% 1208 Fun 99% Asc 96% Saccharomycetes 57% Saccharomycetales 52% Saccharomycetaceae 41% Williopsis 31% 1279 Fun 100% Asc 100% Saccharomycetes 100% Saccharomycetales 100% Saccharomycetaceae 100% Williopsis 100% 553 Fun 100% Asc 99% Saccharomycetes 98% Saccharomycetales 98% Saccharomycodaceae 80% Wickerhamia 80% 1219 Fun 87% Asc 30% Schizosaccharomycetes 12% Schizosaccharomycetales 12% Schizosaccharomycetaceae 12% Schizosaccharomyces 12% 1362 Fun 56% Asc 29% Schizosaccharomycetes 23% Schizosaccharomycetales 23% Schizosaccharomycetaceae 23% Schizosaccharomyces 23% 865 Fun 100% Asc 100% Sordariomycetes 100% Boliniales 100% Catabotrydaceae 100% Catabotrys 100% 892 Fun 100% Asc 100% Sordariomycetes 100% Calosphaeriales 36% Calosphaeriaceae 36% Phaeoacremonium 36% 1028 Fun 100% Asc 100% Sordariomycetes 100% Chaetosphaeriales 99% Chaetosphaeriaceae 99% Chaetosphaeria 98% 615 Fun 100% Asc 100% Sordariomycetes 100% Chaetosphaeriales 100% Chaetosphaeriaceae 100% Melanopsammella 100% 1507 Fun 98% Asc 98% Sordariomycetes 91% Chaetosphaeriales 33% Chaetosphaeriaceae 29% Melanopsammella 20% 1005 Fun 100% Asc 100% Sordariomycetes 100% Coniochaetales 97% Coniochaetaceae 97% Coniochaeta 82% 473 Fun 100% Asc 100% Sordariomycetes 100% Diaporthales 100% Diaporthales i.s. 100% Harknessia 100% 1370 Fun 100% Asc 100% Sordariomycetes 100% Diaporthales 100% Gnomoniaceae 100% Gnomonia 42% 997 Fun 100% Asc 100% Sordariomycetes 100% Diaporthales 100% Valsaceae 100% Valsa 82% 1504 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 97% Bionectriaceae 52% Hydropisphaera 26% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

151 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 100% Bionectriaceae 99% Nectriopsis 88% 1191 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 95% Bionectriaceae 91% Valsonectria 91% 1473 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 100% Clavicipitaceae 90% Chaunopycnis 88% 1555 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 100% Clavicipitaceae 98% Metarhizium 39% 217 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 66% Clavicipitaceae 30% Paecilomyces 10% 115 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 76% Cordycipitaceae 28% Cordyceps 24% 1182 Fun 100% Asc 100% Sordariomycetes 97% Hypocreales 60% Cordycipitaceae 24% Phytocordyceps 24% 1433 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 99% Hypocreaceae 98% Aphysiostroma 87% 676 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 100% Hypocreaceae 100% Hypocrea 80% 1524 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 100% Hypocreaceae 100% Trichoderma 100% 1785 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 98% Hypocreaceae 97% Trichoderma 89% 26 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 32% Nectriaceae 32% Cryptadelphia 29% 55 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 99% Nectriaceae 97% Gibberella 96% 166 Fun 100% Asc 100% Sordariomycetes 96% Hypocreales 90% Nectriaceae 66% Pseudonectria 65% 1231 Fun 100% Asc 100% Sordariomycetes 83% Hypocreales 69% Nectriaceae 53% Pseudonectria 52% 1796 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 78% Nectriaceae 22% Viridispora 15% 114 Fun 100% Asc 100% Sordariomycetes 99% Hypocreales 98% Niessliaceae 70% Emericellopsis 68% 1718 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 94% Niessliaceae 39% Melanopsamma 39% 1229 Fun 100% Asc 100% Sordariomycetes 100% Hypocreales 86% Ophiocordycipitaceae 73% Ophiocordyceps 45% 678 Fun 100% Asc 100% Sordariomycetes 100% Lulworthiales 72% Lulworthiaceae 72% Lulworthia 72% 179 Fun 100% Asc 100% Sordariomycetes 100% Magnaporthales 100% Magnaporthaceae 100% Gaeumannomyces 100% 147 Fun 100% Asc 100% Sordariomycetes 100% Magnaporthales 100% Magnaporthaceae 100% Juncigena 100% 563 Fun 100% Asc 100% Sordariomycetes 100% Magnaporthales 92% Magnaporthaceae 92% Magnaporthe 36% 1804 Fun 100% Asc 100% Sordariomycetes 60% Magnaporthales 54% Magnaporthaceae 54% Phialophora 54% 1203 Fun 100% Asc 100% Sordariomycetes 100% Microascales 74% Halosphaeriaceae 73% Ceriosporopsis 47% 363 Fun 100% Asc 100% Sordariomycetes 100% Microascales 90% Halosphaeriaceae 90% Lanspora 90% 105 1133 Fun 87% Asc 81% Sordariomycetes 41% Microascales 19% Halosphaeriaceae 19% Lanspora 14% 716 Fun 100% Asc 100% Sordariomycetes 100% Microascales 51% Halosphaeriaceae 49% Lignincola 4% 123 Fun 100% Asc 100% Sordariomycetes 100% Microascales 85% Halosphaeriaceae 82% Nimbospora 52% 908 Fun 100% Asc 100% Sordariomycetes 100% Microascales 95% Halosphaeriaceae 94% Nimbospora 85% 1163 Fun 100% Asc 100% Sordariomycetes 100% Microascales 92% Microascaceae 92% Doratomyces 49% 158 Fun 100% Asc 100% Sordariomycetes 100% Microascales 58% Microascaceae 54% Microascus 36% 1253 Fun 100% Asc 100% Sordariomycetes 100% Microascales 98% Microascaceae 98% Petriella 50% 1520 Fun 100% Asc 100% Sordariomycetes 100% Ophiostomatales 100% Ophiostomataceae 100% Ophiostoma 98% 62 Fun 100% Asc 100% Sordariomycetes 100% Phyllachorales 84% Phyllachoraceae 84% Plectosphaerella 84% 947 Fun 100% Asc 100% Sordariomycetes 91% Phyllachorales 40% Phyllachoraceae 40% Plectosphaerella 40% 1119 Fun 100% Asc 100% Sordariomycetes 93% Sordariales 49% Annulatascaceae 49% Annulatascus 49% 53 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 100% Chaetomiaceae 99% Chaetomidium 65% 1336 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 99% Chaetomiaceae 97% Chaetomidium 38% 828 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 100% Lasiosphaeriaceae 99% Apiosordaria 86% 383 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 100% Lasiosphaeriaceae 100% Cercophora 48% 890 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 91% Lasiosphaeriaceae 86% Cercophora 47% 301 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 98% Lasiosphaeriaceae 98% Immersiella 49% 653 Fun 99% Asc 99% Sordariomycetes 96% Sordariales 15% Lasiosphaeriaceae 15% Strattonia 10% 674 Fun 100% Asc 100% Sordariomycetes 99% Sordariales 82% Lasiosphaeriaceae 74% Triangularia 29% 1704 Fun 100% Asc 100% Sordariomycetes 99% Sordariales 56% Lasiosphaeriaceae 34% Triangularia 23% 849 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 100% Sordariaceae 98% Gelasinospora 82% 850 Fun 100% Asc 100% Sordariomycetes 100% Sordariales 99% Sordariaceae 95% Gelasinospora 80% 344 Fun 99% Asc 99% Sordariomycetes 98% Sordariomycetes i.s. 62% Amplistromataceae 60% Amplistroma 60% 986 Fun 100% Asc 100% Sordariomycetes 93% Sordariomycetes i.s. 55% Apiosporaceae 29% Apiospora 29% 406 Fun 100% Asc 100% Sordariomycetes 100% Sordariomycetes i.s. 100% Glomerellaceae 100% Colletotrichum 100% 1067 Fun 100% Asc 100% Sordariomycetes 98% Sordariomycetes i.s. 26% Papulosaceae 21% Papulosa 21% 1174 Fun 100% Asc 100% Sordariomycetes 100% Sordariomycetes i.s. 59% Papulosaceae 57% Papulosa 57% 439 Fun 99% Asc 96% Sordariomycetes 89% Sordariomycetes i.s. 38% Sordariomycetes i.s. 38% Caudatispora 36% 394 Fun 100% Asc 100% Sordariomycetes 100% Sordariomycetes i.s. 100% Sordariomycetes i.s. 100% Myrmecridium 100% 539 Fun 100% Asc 100% Sordariomycetes 85% Sordariomycetes i.s. 46% Sordariomycetes i.s. 35% Myrmecridium 30% 28 Fun 100% Asc 100% Sordariomycetes 100% Xylariales 100% Amphisphaeriaceae 100% Pestalotiopsis 92% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

437 Fun 100% Asc 100% Sordariomycetes 100% Xylariales 49% Diatrypaceae 46% Phaeoisaria 46% 84 Fun 100% Asc 100% Sordariomycetes 100% Xylariales 100% Xylariaceae 98% Anthostomella 93% 1840 Fun 100% Asc 100% Sordariomycetes 100% Xylariales 98% Xylariaceae 93% Hypoxylon 35% 279 Fun 100% Asc 100% Sordariomycetes 100% Xylariales 29% Xylariaceae 26% Induratia 26% 743 Fun 52% Asc 28% Sordariomycetes 15% Xylariales 9% Xylariales i.s. 8% Subramaniomyces 2% 901 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 87% Agaricaceae 54% Coprinus 54% 998 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 96% Cortinariaceae 36% Cortinarius 33% 1015 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Cortinariaceae 93% Gymnopilus 89% 1509 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Crepidotaceae 97% Crepidotus 94% 1186 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Lycoperdaceae 86% Lycoperdon 83% 1684 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Pleurotaceae 100% Hohenbuehelia 54% 127 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Psathyrellaceae 91% Psathyrella 91% 589 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Schizophyllaceae 99% Schizophyllum 65% 961 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 98% Strophariaceae 56% Weraroa 25% 954 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Tricholomataceae 99% Armillaria 97% 1070 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Tricholomataceae 100% Baeospora 100% 102 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 97% Tricholomataceae 90% Clitocybe 49% 399 Fun 100% Bas 100% Agaricomycetes 100% Agaricales 100% Tricholomataceae 72% Rhodotus 26% 210 Fun 100% Bas 100% Agaricomycetes 100% Atheliales 61% Atheliaceae 61% Amphinema 61% 671 Fun 100% Bas 100% Agaricomycetes 100% Atheliales 90% Atheliaceae 90% Athelia 90% 534 Fun 100% Bas 100% Agaricomycetes 100% Atheliales 97% Atheliaceae 97% Tylospora 59% 205 Fun 100% Bas 100% Agaricomycetes 100% Auriculariales 100% Auriculariaceae 93% Auricularia 93% 305 Fun 100% Bas 100% Agaricomycetes 100% Boletales 100% Astraeaceae 80% Astraeus 80% 1635 Fun 100% Bas 100% Agaricomycetes 100% Boletales 98% Boletaceae 43% Boletellus 29% 337 Fun 100% Bas 100% Agaricomycetes 100% Boletales 69% Boletales i.s. 15% uncultured 15% 899 Fun 100% Bas 100% Agaricomycetes 100% Boletales 100% Pisolithaceae 100% Pisolithus 100% 106 118 Fun 100% Bas 100% Agaricomycetes 100% Boletales 100% Rhizopogonaceae 97% Rhizopogon 97% 1750 Fun 100% Bas 100% Agaricomycetes 100% Boletales 100% Tapinellaceae 100% Pseudomerulius 99% 978 Fun 100% Bas 100% Agaricomycetes 100% Cantharellales 99% Botryobasidiaceae 99% Botryobasidium 99% 211 Fun 64% Bas 31% Agaricomycetes 25% Cantharellales 11% Cantharellaceae 7% uncultured 7% 480 Fun 80% Bas 40% Agaricomycetes 36% Cantharellales 26% Cantharellaceae 21% uncultured 21% 631 Fun 100% Bas 94% Agaricomycetes 86% Cantharellales 29% Cantharellaceae 16% uncultured 16% 1075 Fun 100% Bas 100% Agaricomycetes 100% Cantharellales 100% Ceratobasidiaceae 100% Ceratobasidium 97% 1466 Fun 61% Bas 32% Agaricomycetes 19% Geastrales 12% Geastraceae 12% Myriostoma 12% 1223 Fun 100% Bas 99% Agaricomycetes 98% Geastrales 95% Geastraceae 95% Radiigera 43% 598 Fun 100% Bas 100% Agaricomycetes 100% Gomphales 42% Gomphaceae 42% Beenakia 7% 214 Fun 100% Bas 100% Agaricomycetes 94% Gomphales 23% Gomphaceae 23% Ramaricium 14% 1196 Fun 100% Bas 99% Agaricomycetes 91% Gomphales 24% Gomphaceae 24% Ramaricium 11% 1260 Fun 100% Bas 100% Agaricomycetes 100% Hymenochaetales 95% Hymenochaetaceae 94% Hymenochaete 84% 1215 Fun 100% Bas 100% Agaricomycetes 100% Hymenochaetales 82% Hymenochaetaceae 80% Phellinus 53% 870 Fun 100% Bas 100% Agaricomycetes 100% Phallales 100% Phallaceae 100% Phallus 100% 422 Fun 100% Bas 100% Agaricomycetes 100% Phallales 98% Phallaceae 98% Protubera 64% 1008 Fun 100% Bas 95% Agaricomycetes 93% Polyporales 63% Coriolaceae 56% Anomoporia 14% 442 Fun 100% Bas 100% Agaricomycetes 100% Polyporales 87% Coriolaceae 87% Antrodia 86% 759 Fun 100% Bas 100% Agaricomycetes 99% Polyporales 67% Coriolaceae 54% Bjerkandera 12% 246 Fun 100% Bas 100% Agaricomycetes 100% Polyporales 92% Coriolaceae 87% Ceriporiopsis 73% 82 Fun 67% Bas 30% Agaricomycetes 25% Polyporales 16% Coriolaceae 16% Poria 15% 999 Fun 100% Bas 99% Agaricomycetes 99% Polyporales 44% Coriolaceae 39% Rigidoporus 15% 672 Fun 100% Bas 100% Agaricomycetes 100% Polyporales 100% Coriolaceae 100% Wolﬁporia 96% 1580 Fun 100% Bas 92% Agaricomycetes 85% Polyporales 53% Polyporaceae 32% Osmoporus 29% 845 Fun 100% Bas 100% Agaricomycetes 100% Polyporales 81% Polyporaceae 47% Perenniporia 40% 540 Fun 100% Bas 100% Agaricomycetes 100% Polyporales 85% Polyporaceae 72% Trichaptum 72% 412 Fun 100% Bas 100% Agaricomycetes 100% Polyporales 18% Polyporales i.s. 18% Aphyllophoralean 18% 165 Fun 100% Bas 100% Agaricomycetes 100% Russulales 100% Bondarzewiaceae 99% Heterobasidion 99% 1197 Fun 100% Bas 100% Agaricomycetes 100% Russulales 20% Gloeocystidiellaceae 15% Laxitextum 12% 761 Fun 100% Bas 100% Agaricomycetes 100% Russulales 100% Hyphodermataceae 100% Galzinia 100% 831 Fun 100% Bas 99% Agaricomycetes 98% Russulales 44% Hyphodermataceae 43% Galzinia 43% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1041 Fun 100% Bas 99% Agaricomycetes 96% Russulales 46% Podoscyphaceae 33% Cyphellostereum 33% 692 Fun 57% Bas 22% Agaricomycetes 18% Russulales 16% Podoscyphaceae 16% Granulobasidium 16% 720 Fun 100% Bas 100% Agaricomycetes 98% Russulales 44% Stereaceae 37% Acanthobasidium 34% 617 Fun 100% Bas 100% Agaricomycetes 96% Russulales 94% Stereaceae 94% Stereum 39% 444 Fun 99% Bas 95% Agaricomycetes 93% Sebacinales 74% Sebacinaceae 74% uncultured 38% 322 Fun 100% Bas 100% Agaricostilbomycetes 100% Agaricostilbales 100% Agaricostilbaceae 100% Bensingtonia 100% 536 Fun 91% Bas 81% Agaricostilbomycetes 47% Agaricostilbales 47% Agaricostilbaceae 45% Bensingtonia 35% 868 Fun 100% Bas 94% Agaricostilbomycetes 63% Agaricostilbales 62% Agaricostilbaceae 62% Bensingtonia 56% 1177 Fun 95% Bas 85% Agaricostilbomycetes 64% Agaricostilbales 63% Agaricostilbaceae 63% Bensingtonia 62% 1552 Fun 100% Bas 99% Agaricostilbomycetes 99% Agaricostilbales 99% Agaricostilbaceae 99% Bensingtonia 99% 1216 Fun 89% Bas 88% Agaricostilbomycetes 88% Spiculogloeales 88% Spiculogloeaceae 88% Spiculogloea 88% 705 Fun 100% Bas 99% Agaricostilbomycetes 70% Spiculogloeales 40% Spiculogloeales i.s. 40% Mycogloea 40% 719 Fun 71% Bas 50% Agaricostilbomycetes 45% Spiculogloeales 44% Spiculogloeales i.s. 35% Mycogloea 35% 990 Fun 100% Bas 100% Cystobasidiomycetes 98% Cystobasidiales 98% Cystobasidiaceae 98% Cystobasidium 67% 1793 Fun 100% Bas 100% Cystobasidiomycetes 100% Cystobasidiales 100% Cystobasidiaceae 100% Cystobasidium 100% 192 Fun 100% Bas 100% Cystobasidiomycetes 100% Cystobasidiales 99% Cystobasidiaceae 99% Rhodotorula 1 50% 860 Fun 100% Bas 99% Cystobasidiomycetes 45% Cystobasidiales 26% Cystobasidiaceae 26% Rhodotorula 1 17% 1276 Fun 57% Bas 21% Cystobasidiomycetes 2% Cystobasidiales 2% Cystobasidiaceae 2% Rhodotorula 1 1% 112 Fun 100% Bas 99% Cystobasidiomycetes 99% Erythrobasidiales 65% Erythrobasidiaceae 65% Rhodotorula 2 63% 545 Fun 100% Bas 100% Cystobasidiomycetes 100% Erythrobasidiales 48% Erythrobasidiaceae 48% Sporobolomyces 46% 1257 Fun 100% Bas 98% Dacrymycetes 97% Dacrymycetales 97% Dacrymycetaceae 96% Guepiniopsis 42% 1002 Fun 100% Bas 100% Exobasidiomycetes 100% Exobasidiales 100% Exobasidiaceae 99% Exobasidium 99% 686 Fun 100% Bas 97% Exobasidiomycetes 93% Exobasidiales 63% Graphiolaceae 49% Graphiola 49% 7 Fun 100% Bas 100% Exobasidiomycetes 100% Malasseziales 100% Malasseziaceae 100% Malassezia 100% 218 Fun 100% Bas 100% Exobasidiomycetes 100% Malasseziales 100% Malasseziaceae 100% Malassezia 100% 355 Fun 100% Bas 100% Exobasidiomycetes 100% Microstromatales 100% Microstromataceae 46% Microstroma 46% 107 1113 Fun 85% Bas 25% Exobasidiomycetes 7% Tilletiales 7% Tilletiaceae 7% Conidiosporomyces 7% 294 Fun 92% Bas 82% Microbotryomycetes 42% Leucosporidiales 24% Leucosporidiales i.s. 24% Leucosporidium 24% 259 Fun 100% Bas 100% Microbotryomycetes 100% Microbotryales 100% Microbotryaceae 100% Microbotryum 100% 1296 Fun 100% Bas 100% Microbotryomycetes 100% Microbotryomycetes i.s. 87% Microbotryomycetes i.s. 87% Kriegeria 87% 199 Fun 100% Bas 100% Microbotryomycetes 100% Sporidiobolales 100% Sporidiobolales i.s. 100% Rhodotorula 4 100% 1064 Fun 100% Bas 100% Microbotryomycetes 98% Sporidiobolales 68% Sporidiobolales i.s. 68% Sporidiobolus 66% 1190 Fun 86% Bas 34% Microbotryomycetes 19% Sporidiobolales 17% Sporidiobolales i.s. 17% Sporidiobolus 16% 1065 Fun 100% Bas 100% Pucciniomycetes 100% Platygloeales 100% Eocronartiaceae 92% Eocronartium 92% 597 Fun 100% Bas 100% Pucciniomycetes 100% Pucciniales 100% Pucciniaceae 100% Puccinia 100% 1349 Fun 100% Bas 100% Pucciniomycetes 100% Pucciniales 100% Pucciniaceae 100% Uromyces 95% 1664 Fun 100% Bas 100% Pucciniomycetes 100% Pucciniales 100% Pucciniaceae 99% Uromyces 98% 1314 Fun 100% Bas 100% Pucciniomycetes 100% Septobasidiales 99% Septobasidiaceae 99% Septobasidium 95% 736 Fun 100% Bas 99% Tremellomycetes 79% Cystofilobasidiales 79% Cystofilobasidiaceae 79% Mrakia 38% 317 Fun 100% Bas 100% Tremellomycetes 100% Cystofilobasidiales 100% Cystofilobasidiaceae 100% Udeniomyces 100% 801 Fun 98% Bas 98% Tremellomycetes 60% Tremellales 53% Christianseniaceae 29% Christiansenia 29% 1426 Fun 74% Bas 20% Tremellomycetes 17% Tremellales 17% Sirobasidiaceae 11% Fibulobasidium 11% 1569 Fun 100% Bas 100% Tremellomycetes 99% Tremellales 99% Sirobasidiaceae 31% Fibulobasidium 31% 307 Fun 100% Bas 100% Tremellomycetes 100% Tremellales 100% Tremellaceae 92% Asterotremella 87% 519 Fun 100% Bas 100% Tremellomycetes 99% Tremellales 99% Tremellaceae 88% Asterotremella 55% 798 Fun 100% Bas 100% Tremellomycetes 100% Tremellales 100% Tremellaceae 78% Asterotremella 72% 951 Fun 100% Bas 100% Tremellomycetes 99% Tremellales 99% Tremellaceae 81% Asterotremella 51% 469 Fun 100% Bas 100% Tremellomycetes 100% Tremellales 100% Tremellaceae 100% Cryptococcus 100% 1586 Fun 99% Bas 99% Tremellomycetes 96% Tremellales 96% Tremellaceae 90% Cryptococcus 70% 1774 Fun 100% Bas 99% Tremellomycetes 98% Tremellales 98% Tremellaceae 93% Filobasidiella 62% 1470 Fun 100% Bas 100% Tremellomycetes 100% Tremellales 100% Tremellaceae 89% Tremella 52% 29 Fun 100% Bas 100% Ustilaginomycetes 100% Ustilaginales 100% Ustilaginaceae 100% Pseudozyma 49% 541 Fun 97% Bas 89% Wallemiomycetes 87% Wallemiales 87% Wallemiaceae 87% Wallemia 87% 86 Fun 95% Bla 43% Blastocladiomycetes 43% Blastocladiales 43% Catenariaceae 42% Catenomyces 42% 119 Fun 82% Bla 9% Blastocladiomycetes 9% Blastocladiales 9% Catenariaceae 8% Catenomyces 8% 143 Fun 96% Bla 43% Blastocladiomycetes 43% Blastocladiales 43% Catenariaceae 43% Catenomyces 43% 178 Fun 99% Bla 32% Blastocladiomycetes 32% Blastocladiales 32% Catenariaceae 31% Catenomyces 31% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

216 Fun 99% Bla 37% Blastocladiomycetes 37% Blastocladiales 37% Catenariaceae 37% Catenomyces 37% 230 Fun 99% Bla 22% Blastocladiomycetes 22% Blastocladiales 22% Catenariaceae 22% Catenomyces 22% 409 Fun 94% Bla 29% Blastocladiomycetes 29% Blastocladiales 29% Catenariaceae 27% Catenomyces 27% 513 Fun 77% Bla 22% Blastocladiomycetes 22% Blastocladiales 22% Catenariaceae 9% Catenomyces 9% 570 Fun 100% Bla 55% Blastocladiomycetes 55% Blastocladiales 55% Catenariaceae 55% Catenomyces 55% 667 Fun 79% Bla 10% Blastocladiomycetes 10% Blastocladiales 10% Catenariaceae 9% Catenomyces 9% 668 Fun 96% Bla 34% Blastocladiomycetes 34% Blastocladiales 34% Catenariaceae 34% Catenomyces 34% 728 Fun 51% Bla 11% Blastocladiomycetes 11% Blastocladiales 11% Catenariaceae 11% Catenomyces 11% 735 Fun 73% Bla 25% Blastocladiomycetes 25% Blastocladiales 25% Catenariaceae 25% Catenomyces 25% 773 Fun 81% Bla 9% Blastocladiomycetes 9% Blastocladiales 9% Catenariaceae 5% Catenomyces 5% 789 Fun 71% Bla 21% Blastocladiomycetes 21% Blastocladiales 21% Catenariaceae 16% Catenomyces 16% 864 Fun 67% Bla 20% Blastocladiomycetes 20% Blastocladiales 20% Catenariaceae 20% Catenomyces 19% 879 Fun 100% Bla 31% Blastocladiomycetes 31% Blastocladiales 31% Catenariaceae 31% Catenomyces 31% 1102 Fun 53% Bla 5% Blastocladiomycetes 5% Blastocladiales 5% Catenariaceae 4% Catenomyces 4% 1205 Fun 92% Bla 43% Blastocladiomycetes 43% Blastocladiales 43% Catenariaceae 42% Catenomyces 42% 1207 Fun 97% Bla 14% Blastocladiomycetes 14% Blastocladiales 14% Catenariaceae 14% Catenomyces 14% 1263 Fun 99% Bla 42% Blastocladiomycetes 42% Blastocladiales 42% Catenariaceae 42% Catenomyces 42% 1354 Fun 93% Bla 44% Blastocladiomycetes 44% Blastocladiales 44% Catenariaceae 44% Catenomyces 44% 1568 Fun 55% Bla 16% Blastocladiomycetes 16% Blastocladiales 16% Catenariaceae 13% Catenomyces 13% 1729 Fun 53% Bla 21% Blastocladiomycetes 21% Blastocladiales 21% Catenariaceae 13% Catenomyces 13% 1816 Fun 94% Bla 15% Blastocladiomycetes 15% Blastocladiales 15% Catenariaceae 15% Catenomyces 15% 814 Fun 63% Bla 21% Blastocladiomycetes 21% Blastocladiales 21% Catenariaceae 16% Catenophlyctis 16% 89 Fun 73% Bla 27% Blastocladiomycetes 27% Blastocladiales 27% Coelomomycetaceae 24% Coelomomyces 24% 948 Fun 92% Chy 67% Chytridiomycetes 60% Chytridiales 24% Chytridiaceae 23% Chytridium 22% 1056 Fun 96% Chy 47% Chytridiomycetes 40% Chytridiales 26% Chytridiaceae 25% Chytridium 17% 18 Fun 70% Chy 30% Chytridiomycetes 26% Chytridiales 7% Chytridiaceae 7% Karlingiomyces 6% 108 87 Fun 65% Chy 9% Chytridiomycetes 8% Chytridiales 6% Chytridiaceae 3% Karlingiomyces 3% 219 Fun 63% Chy 23% Chytridiomycetes 19% Chytridiales 11% Chytridiaceae 11% Karlingiomyces 11% 282 Fun 62% Chy 36% Chytridiomycetes 34% Chytridiales 24% Chytridiaceae 23% Karlingiomyces 22% 299 Fun 90% Chy 81% Chytridiomycetes 80% Chytridiales 38% Chytridiaceae 38% Karlingiomyces 37% 382 Fun 71% Chy 28% Chytridiomycetes 27% Chytridiales 15% Chytridiaceae 14% Karlingiomyces 13% 386 Fun 69% Chy 5% Chytridiomycetes 5% Chytridiales 5% Chytridiaceae 5% Karlingiomyces 5% 764 Fun 55% Chy 29% Chytridiomycetes 26% Chytridiales 18% Chytridiaceae 10% Karlingiomyces 10% 903 Fun 53% Chy 29% Chytridiomycetes 22% Chytridiales 14% Chytridiaceae 14% Karlingiomyces 14% 1018 Fun 58% Chy 31% Chytridiomycetes 31% Chytridiales 22% Chytridiaceae 22% Karlingiomyces 22% 1441 Fun 72% Chy 31% Chytridiomycetes 31% Chytridiales 23% Chytridiaceae 19% Karlingiomyces 15% 1459 Fun 56% Chy 30% Chytridiomycetes 30% Chytridiales 21% Chytridiaceae 21% Karlingiomyces 20% 54 Fun 97% Chy 66% Chytridiomycetes 45% Chytridiales 22% Chytridiaceae 19% Mesochytrium 18% 80 Fun 80% Chy 34% Chytridiomycetes 26% Chytridiales 24% Chytridiaceae 17% Mesochytrium 17% 95 Fun 97% Chy 64% Chytridiomycetes 63% Chytridiales 51% Chytridiaceae 49% Mesochytrium 17% 116 Fun 96% Chy 93% Chytridiomycetes 93% Chytridiales 92% Chytridiaceae 91% Mesochytrium 91% 120 Fun 89% Chy 68% Chytridiomycetes 67% Chytridiales 54% Chytridiaceae 53% Mesochytrium 53% 331 Fun 86% Chy 51% Chytridiomycetes 50% Chytridiales 29% Chytridiaceae 23% Mesochytrium 23% 727 Fun 96% Chy 88% Chytridiomycetes 87% Chytridiales 71% Chytridiaceae 68% Mesochytrium 67% 1440 Fun 75% Chy 43% Chytridiomycetes 42% Chytridiales 21% Chytridiaceae 20% Mesochytrium 13% 177 Fun 99% Chy 75% Chytridiomycetes 75% Chytridiales 33% Chytridiales i.s. 26% Batrachochytrium 26% 208 Fun 54% Chy 28% Chytridiomycetes 28% Chytridiales 26% Chytridiales i.s. 26% Batrachochytrium 26% 766 Fun 55% Chy 28% Chytridiomycetes 28% Chytridiales 20% Chytridiales i.s. 19% Batrachochytrium 19% 804 Fun 58% Chy 20% Chytridiomycetes 19% Chytridiales 13% Chytridiales i.s. 9% Batrachochytrium 9% 20 Fun 100% Chy 67% Chytridiomycetes 67% Chytridiales 29% Chytridiales - i.s. 29% Entophlyctis 29% 43 Fun 97% Chy 82% Chytridiomycetes 82% Chytridiales 59% Chytridiales - i.s. 59% Entophlyctis 59% 83 Fun 99% Chy 88% Chytridiomycetes 88% Chytridiales 57% Chytridiales - i.s. 57% Entophlyctis 57% 156 Fun 100% Chy 84% Chytridiomycetes 84% Chytridiales 53% Chytridiales - i.s. 53% Entophlyctis 53% 194 Fun 98% Chy 89% Chytridiomycetes 89% Chytridiales 48% Chytridiales - i.s. 48% Entophlyctis 48% 313 Fun 94% Chy 52% Chytridiomycetes 50% Chytridiales 36% Chytridiales - i.s. 36% Entophlyctis 36% 345 Fun 99% Chy 99% Chytridiomycetes 99% Chytridiales 67% Chytridiales - i.s. 67% Entophlyctis 67% 511 Fun 100% Chy 73% Chytridiomycetes 72% Chytridiales 46% Chytridiales - i.s. 46% Entophlyctis 46% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

813 Fun 100% Chy 92% Chytridiomycetes 92% Chytridiales 41% Chytridiales - i.s. 41% Entophlyctis 41% 830 Fun 89% Chy 65% Chytridiomycetes 63% Chytridiales 36% Chytridiales - i.s. 36% Entophlyctis 36% 153 Fun 89% Chy 70% Chytridiomycetes 67% Lobulomycetales 63% Lobulomycetaceae 59% Clydaea 39% 215 Fun 99% Chy 82% Chytridiomycetes 82% Lobulomycetales 50% Lobulomycetaceae 49% Clydaea 21% 893 Fun 95% Chy 45% Chytridiomycetes 41% Lobulomycetales 27% Lobulomycetaceae 27% Clydaea 26% 980 Fun 100% Chy 87% Chytridiomycetes 84% Lobulomycetales 67% Lobulomycetaceae 63% Clydaea 29% 1098 Fun 89% Chy 77% Chytridiomycetes 76% Lobulomycetales 70% Lobulomycetaceae 70% Clydaea 43% 1099 Fun 94% Chy 91% Chytridiomycetes 89% Lobulomycetales 67% Lobulomycetaceae 63% Clydaea 39% 1173 Fun 67% Chy 38% Chytridiomycetes 37% Lobulomycetales 25% Lobulomycetaceae 22% Clydaea 18% 1540 Fun 93% Chy 35% Chytridiomycetes 35% Lobulomycetales 23% Lobulomycetaceae 23% Clydaea 23% 75 Fun 92% Chy 79% Chytridiomycetes 77% Lobulomycetales 60% Lobulomycetaceae 51% Lobulomyces 37% 111 Fun 99% Chy 97% Chytridiomycetes 97% Lobulomycetales 97% Lobulomycetaceae 97% Lobulomyces 72% 452 Fun 98% Chy 90% Chytridiomycetes 90% Lobulomycetales 88% Lobulomycetaceae 85% Lobulomyces 47% 1361 Fun 96% Chy 89% Chytridiomycetes 88% Lobulomycetales 85% Lobulomycetaceae 84% Lobulomyces 50% 101 Fun 92% Chy 78% Chytridiomycetes 75% Lobulomycetales 50% Lobulomycetaceae 38% Maunachytrium 23% 434 Fun 93% Chy 81% Chytridiomycetes 81% Lobulomycetales 52% Lobulomycetaceae 51% Maunachytrium 50% 475 Fun 99% Chy 88% Chytridiomycetes 86% Lobulomycetales 56% Lobulomycetaceae 52% Maunachytrium 20% 525 Fun 98% Chy 79% Chytridiomycetes 75% Lobulomycetales 32% Lobulomycetaceae 31% Maunachytrium 31% 619 Fun 92% Chy 64% Chytridiomycetes 56% Lobulomycetales 45% Lobulomycetaceae 32% Maunachytrium 32% 627 Fun 74% Chy 46% Chytridiomycetes 40% Lobulomycetales 9% Lobulomycetaceae 8% Maunachytrium 8% 629 Fun 100% Chy 86% Chytridiomycetes 83% Lobulomycetales 48% Lobulomycetaceae 46% Maunachytrium 31% 641 Fun 95% Chy 90% Chytridiomycetes 86% Lobulomycetales 57% Lobulomycetaceae 51% Maunachytrium 50% 660 Fun 96% Chy 82% Chytridiomycetes 81% Lobulomycetales 49% Lobulomycetaceae 44% Maunachytrium 44% 677 Fun 83% Chy 69% Chytridiomycetes 69% Lobulomycetales 64% Lobulomycetaceae 62% Maunachytrium 30% 715 Fun 90% Chy 84% Chytridiomycetes 80% Lobulomycetales 44% Lobulomycetaceae 42% Maunachytrium 42% 790 Fun 97% Chy 88% Chytridiomycetes 76% Lobulomycetales 48% Lobulomycetaceae 32% Maunachytrium 32% 109 823 Fun 76% Chy 45% Chytridiomycetes 36% Lobulomycetales 23% Lobulomycetaceae 12% Maunachytrium 11% 826 Fun 81% Chy 64% Chytridiomycetes 61% Lobulomycetales 27% Lobulomycetaceae 23% Maunachytrium 19% 847 Fun 91% Chy 80% Chytridiomycetes 76% Lobulomycetales 43% Lobulomycetaceae 42% Maunachytrium 42% 982 Fun 65% Chy 39% Chytridiomycetes 35% Lobulomycetales 21% Lobulomycetaceae 21% Maunachytrium 21% 1290 Fun 84% Chy 73% Chytridiomycetes 69% Lobulomycetales 61% Lobulomycetaceae 60% Maunachytrium 60% 1462 Fun 78% Chy 48% Chytridiomycetes 47% Lobulomycetales 28% Lobulomycetaceae 25% Maunachytrium 23% 1548 Fun 98% Chy 94% Chytridiomycetes 94% Lobulomycetales 73% Lobulomycetaceae 68% Maunachytrium 68% 1713 Fun 96% Chy 85% Chytridiomycetes 85% Lobulomycetales 57% Lobulomycetaceae 33% Maunachytrium 32% 1800 Fun 93% Chy 81% Chytridiomycetes 81% Lobulomycetales 67% Lobulomycetaceae 64% Maunachytrium 41% 357 Fun 83% Chy 75% Chytridiomycetes 71% Lobulomycetales 46% Lobulomycetales i.s. 41% Lobulomycetales i.s. 41% 172 Fun 100% Chy 96% Chytridiomycetes 96% Rhizophydiales 87% Alphamycetaceae 70% Betamyces 49% 200 Fun 99% Chy 75% Chytridiomycetes 74% Rhizophydiales 58% Alphamycetaceae 42% Betamyces 38% 265 Fun 100% Chy 99% Chytridiomycetes 99% Rhizophydiales 95% Alphamycetaceae 91% Betamyces 91% 696 Fun 100% Chy 81% Chytridiomycetes 80% Rhizophydiales 49% Alphamycetaceae 29% Betamyces 29% 820 Fun 100% Chy 94% Chytridiomycetes 94% Rhizophydiales 66% Alphamycetaceae 56% Betamyces 56% 325 Fun 100% Chy 78% Chytridiomycetes 78% Rhizophydiales 67% Terramycetaceae 54% Boothiomyces 40% 445 Fun 100% Chy 95% Chytridiomycetes 94% Rhizophydiales 57% Terramycetaceae 51% Boothiomyces 50% 1224 Fun 91% Chy 79% Chytridiomycetes 78% Rhizophydiales 29% Terramycetaceae 26% Terramyces 17% 17 Fun 80% Chy 28% Chytridiomycetes 25% Spizellomycetales 21% Olpidiaceae 20% Olpidium 20% 52 Fun 70% Chy 37% Chytridiomycetes 37% Spizellomycetales 29% Olpidiaceae 26% Olpidium 26% 98 Fun 51% Chy 22% Chytridiomycetes 20% Spizellomycetales 12% Olpidiaceae 7% Olpidium 7% 244 Fun 81% Chy 28% Chytridiomycetes 24% Spizellomycetales 23% Olpidiaceae 16% Olpidium 16% 292 Fun 86% Chy 21% Chytridiomycetes 18% Spizellomycetales 17% Olpidiaceae 12% Olpidium 12% 575 Fun 50% Chy 19% Chytridiomycetes 17% Spizellomycetales 15% Olpidiaceae 11% Olpidium 11% 590 Fun 91% Chy 81% Chytridiomycetes 81% Spizellomycetales 80% Olpidiaceae 76% Olpidium 76% 1012 Fun 64% Chy 38% Chytridiomycetes 32% Spizellomycetales 27% Olpidiaceae 23% Olpidium 23% 1085 Fun 69% Chy 31% Chytridiomycetes 28% Spizellomycetales 22% Olpidiaceae 20% Olpidium 20% 1432 Fun 58% Chy 29% Chytridiomycetes 29% Spizellomycetales 21% Olpidiaceae 13% Olpidium 13% 1490 Fun 64% Chy 42% Chytridiomycetes 42% Spizellomycetales 31% Olpidiaceae 24% Olpidium 23% 1500 Fun 88% Chy 77% Chytridiomycetes 77% Spizellomycetales 77% Olpidiaceae 74% Olpidium 74% 1517 Fun 91% Chy 71% Chytridiomycetes 62% Spizellomycetales 57% Olpidiaceae 51% Olpidium 51% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1600 Fun 75% Chy 34% Chytridiomycetes 33% Spizellomycetales 32% Olpidiaceae 18% Olpidium 18% 71 Fun 91% Chy 44% Chytridiomycetes 44% Spizellomycetales 35% Olpidiaceae 21% Powellomyces 19% 224 Fun 55% Chy 39% Chytridiomycetes 39% Spizellomycetales 32% Olpidiaceae 30% Powellomyces 30% 920 Fun 72% Chy 58% Chytridiomycetes 57% Spizellomycetales 21% Olpidiaceae 15% Powellomyces 13% 1091 Fun 100% Chy 100% Chytridiomycetes 100% Spizellomycetales 99% Olpidiaceae 50% Powellomyces 50% 1592 Fun 83% Chy 53% Chytridiomycetes 52% Spizellomycetales 35% Olpidiaceae 17% Powellomyces 17% 37 Fun 98% Chy 62% Chytridiomycetes 62% Spizellomycetales 39% Spizellomycetaceae 34% Gaertneriomyces 21% 250 Fun 66% Chy 35% Chytridiomycetes 32% Spizellomycetales 23% Spizellomycetaceae 16% Gaertneriomyces 5% 895 Fun 98% Chy 61% Chytridiomycetes 60% Spizellomycetales 33% Spizellomycetaceae 33% Gaertneriomyces 15% 1074 Fun 88% Chy 69% Chytridiomycetes 69% Spizellomycetales 62% Spizellomycetaceae 61% Gaertneriomyces 47% 1637 Fun 99% Chy 66% Chytridiomycetes 66% Spizellomycetales 60% Spizellomycetaceae 56% Gaertneriomyces 24% 21 Fun 65% Chy 18% Chytridiomycetes 12% Spizellomycetales 12% Spizellomycetaceae 10% Spizellomyces 9% 23 Fun 90% Chy 73% Chytridiomycetes 69% Spizellomycetales 33% Spizellomycetaceae 30% Spizellomyces 21% 41 Fun 95% Chy 61% Chytridiomycetes 58% Spizellomycetales 40% Spizellomycetaceae 36% Spizellomyces 31% 47 Fun 88% Chy 75% Chytridiomycetes 75% Spizellomycetales 38% Spizellomycetaceae 31% Spizellomyces 10% 110 Fun 92% Chy 79% Chytridiomycetes 77% Spizellomycetales 35% Spizellomycetaceae 35% Spizellomyces 34% 149 Fun 87% Chy 84% Chytridiomycetes 84% Spizellomycetales 47% Spizellomycetaceae 45% Spizellomyces 45% 150 Fun 95% Chy 79% Chytridiomycetes 76% Spizellomycetales 35% Spizellomycetaceae 34% Spizellomyces 19% 167 Fun 87% Chy 67% Chytridiomycetes 63% Spizellomycetales 35% Spizellomycetaceae 31% Spizellomyces 31% 223 Fun 93% Chy 62% Chytridiomycetes 60% Spizellomycetales 37% Spizellomycetaceae 31% Spizellomyces 22% 245 Fun 90% Chy 64% Chytridiomycetes 64% Spizellomycetales 44% Spizellomycetaceae 42% Spizellomyces 32% 288 Fun 67% Chy 21% Chytridiomycetes 17% Spizellomycetales 16% Spizellomycetaceae 12% Spizellomyces 9% 300 Fun 91% Chy 16% Chytridiomycetes 16% Spizellomycetales 15% Spizellomycetaceae 13% Spizellomyces 12% 333 Fun 89% Chy 68% Chytridiomycetes 68% Spizellomycetales 41% Spizellomycetaceae 33% Spizellomyces 26% 356 Fun 83% Chy 27% Chytridiomycetes 27% Spizellomycetales 19% Spizellomycetaceae 13% Spizellomyces 13% 404 Fun 98% Chy 67% Chytridiomycetes 66% Spizellomycetales 32% Spizellomycetaceae 31% Spizellomyces 19% 110 456 Fun 87% Chy 60% Chytridiomycetes 60% Spizellomycetales 40% Spizellomycetaceae 34% Spizellomyces 32% 461 Fun 88% Chy 82% Chytridiomycetes 82% Spizellomycetales 68% Spizellomycetaceae 68% Spizellomyces 65% 472 Fun 68% Chy 49% Chytridiomycetes 49% Spizellomycetales 39% Spizellomycetaceae 29% Spizellomyces 25% 487 Fun 89% Chy 55% Chytridiomycetes 46% Spizellomycetales 23% Spizellomycetaceae 22% Spizellomyces 17% 494 Fun 88% Chy 42% Chytridiomycetes 42% Spizellomycetales 28% Spizellomycetaceae 27% Spizellomyces 26% 518 Fun 61% Chy 20% Chytridiomycetes 19% Spizellomycetales 18% Spizellomycetaceae 9% Spizellomyces 9% 522 Fun 88% Chy 81% Chytridiomycetes 76% Spizellomycetales 51% Spizellomycetaceae 47% Spizellomyces 43% 610 Fun 83% Chy 54% Chytridiomycetes 54% Spizellomycetales 43% Spizellomycetaceae 31% Spizellomyces 17% 628 Fun 96% Chy 60% Chytridiomycetes 60% Spizellomycetales 55% Spizellomycetaceae 46% Spizellomyces 43% 654 Fun 100% Chy 100% Chytridiomycetes 100% Spizellomycetales 100% Spizellomycetaceae 100% Spizellomyces 100% 690 Fun 80% Chy 47% Chytridiomycetes 46% Spizellomycetales 26% Spizellomycetaceae 22% Spizellomyces 18% 703 Fun 89% Chy 32% Chytridiomycetes 32% Spizellomycetales 24% Spizellomycetaceae 23% Spizellomyces 17% 713 Fun 71% Chy 20% Chytridiomycetes 19% Spizellomycetales 18% Spizellomycetaceae 15% Spizellomyces 10% 734 Fun 91% Chy 49% Chytridiomycetes 49% Spizellomycetales 23% Spizellomycetaceae 22% Spizellomyces 17% 767 Fun 93% Chy 68% Chytridiomycetes 66% Spizellomycetales 58% Spizellomycetaceae 31% Spizellomyces 30% 819 Fun 80% Chy 65% Chytridiomycetes 57% Spizellomycetales 17% Spizellomycetaceae 17% Spizellomyces 17% 854 Fun 81% Chy 54% Chytridiomycetes 52% Spizellomycetales 13% Spizellomycetaceae 12% Spizellomyces 12% 881 Fun 81% Chy 69% Chytridiomycetes 62% Spizellomycetales 52% Spizellomycetaceae 51% Spizellomyces 50% 905 Fun 83% Chy 36% Chytridiomycetes 35% Spizellomycetales 13% Spizellomycetaceae 11% Spizellomyces 10% 911 Fun 100% Chy 91% Chytridiomycetes 82% Spizellomycetales 78% Spizellomycetaceae 69% Spizellomyces 57% 931 Fun 68% Chy 41% Chytridiomycetes 40% Spizellomycetales 20% Spizellomycetaceae 16% Spizellomyces 15% 993 Fun 96% Chy 46% Chytridiomycetes 46% Spizellomycetales 44% Spizellomycetaceae 24% Spizellomyces 24% 1037 Fun 95% Chy 66% Chytridiomycetes 50% Spizellomycetales 32% Spizellomycetaceae 24% Spizellomyces 11% 1080 Fun 92% Chy 55% Chytridiomycetes 55% Spizellomycetales 36% Spizellomycetaceae 35% Spizellomyces 31% 1097 Fun 94% Chy 59% Chytridiomycetes 57% Spizellomycetales 55% Spizellomycetaceae 42% Spizellomyces 31% 1145 Fun 96% Chy 63% Chytridiomycetes 63% Spizellomycetales 47% Spizellomycetaceae 41% Spizellomyces 37% 1171 Fun 88% Chy 63% Chytridiomycetes 63% Spizellomycetales 32% Spizellomycetaceae 27% Spizellomyces 21% 1179 Fun 93% Chy 48% Chytridiomycetes 48% Spizellomycetales 28% Spizellomycetaceae 21% Spizellomyces 21% 1200 Fun 75% Chy 49% Chytridiomycetes 48% Spizellomycetales 45% Spizellomycetaceae 39% Spizellomyces 39% 1302 Fun 61% Chy 5% Chytridiomycetes 5% Spizellomycetales 3% Spizellomycetaceae 3% Spizellomyces 3% 1355 Fun 88% Chy 34% Chytridiomycetes 33% Spizellomycetales 28% Spizellomycetaceae 24% Spizellomyces 22% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1371 Fun 79% Chy 57% Chytridiomycetes 56% Spizellomycetales 26% Spizellomycetaceae 19% Spizellomyces 13% 1557 Fun 95% Chy 75% Chytridiomycetes 65% Spizellomycetales 24% Spizellomycetaceae 19% Spizellomyces 17% 1604 Fun 78% Chy 51% Chytridiomycetes 46% Spizellomycetales 36% Spizellomycetaceae 28% Spizellomyces 26% 1638 Fun 81% Chy 28% Chytridiomycetes 24% Spizellomycetales 18% Spizellomycetaceae 13% Spizellomyces 13% 1668 Fun 91% Chy 73% Chytridiomycetes 73% Spizellomycetales 71% Spizellomycetaceae 70% Spizellomyces 68% 1705 Fun 74% Chy 19% Chytridiomycetes 19% Spizellomycetales 17% Spizellomycetaceae 16% Spizellomyces 15% 1717 Fun 72% Chy 23% Chytridiomycetes 23% Spizellomycetales 14% Spizellomycetaceae 12% Spizellomyces 12% 88 Fun 95% Chy 65% Chytridiomycetes 64% Spizellomycetales 38% Spizellomycetaceae 36% Triparticalcar 12% 450 Fun 92% Chy 73% Chytridiomycetes 73% Spizellomycetales 42% Spizellomycetales i.s. 30% Spizellomycete 30% 1040 Fun 75% Chy 35% Chytridiomycetes 34% Spizellomycetales 24% Spizellomycetales i.s. 14% Spizellomycete 14% 1220 Fun 62% Chy 29% Chytridiomycetes 28% Spizellomycetales 17% Spizellomycetales i.s. 12% Spizellomycete 12% 1478 Fun 79% Chy 50% Chytridiomycetes 50% Spizellomycetales 33% Spizellomycetales i.s. 17% Spizellomycete 17% 49 Fun 55% Chy 28% Monoblepharidomycetes 16% Monoblepharidales 16% Monoblepharidales i.s. 16% Hyaloraphidium 16% 1 Fun 85% Chy 52% Monoblepharidomycetes 26% Monoblepharidales 26% Oedogoniomycetaceae 23% Oedogoniomyces 23% 164 Fun 88% Chy 57% Monoblepharidomycetes 32% Monoblepharidales 32% Oedogoniomycetaceae 20% Oedogoniomyces 20% 185 Fun 75% Chy 31% Monoblepharidomycetes 19% Monoblepharidales 19% Oedogoniomycetaceae 19% Oedogoniomyces 19% 256 Fun 99% Chy 92% Monoblepharidomycetes 38% Monoblepharidales 38% Oedogoniomycetaceae 31% Oedogoniomyces 31% 455 Fun 49% Chy 37% Monoblepharidomycetes 23% Monoblepharidales 23% Oedogoniomycetaceae 16% Oedogoniomyces 16% 479 Fun 80% Chy 48% Monoblepharidomycetes 22% Monoblepharidales 22% Oedogoniomycetaceae 17% Oedogoniomyces 17% 493 Fun 62% Chy 14% Monoblepharidomycetes 9% Monoblepharidales 9% Oedogoniomycetaceae 8% Oedogoniomyces 8% 504 Fun 88% Chy 75% Monoblepharidomycetes 55% Monoblepharidales 55% Oedogoniomycetaceae 54% Oedogoniomyces 54% 548 Fun 76% Chy 20% Monoblepharidomycetes 16% Monoblepharidales 16% Oedogoniomycetaceae 13% Oedogoniomyces 13% 796 Fun 60% Chy 25% Monoblepharidomycetes 10% Monoblepharidales 10% Oedogoniomycetaceae 9% Oedogoniomyces 9% 852 Fun 83% Chy 68% Monoblepharidomycetes 14% Monoblepharidales 14% Oedogoniomycetaceae 13% Oedogoniomyces 13% 884 Fun 84% Chy 63% Monoblepharidomycetes 36% Monoblepharidales 36% Oedogoniomycetaceae 24% Oedogoniomyces 24% 1055 Fun 64% Chy 47% Monoblepharidomycetes 30% Monoblepharidales 30% Oedogoniomycetaceae 26% Oedogoniomyces 26% 111 1128 Fun 74% Chy 54% Monoblepharidomycetes 36% Monoblepharidales 36% Oedogoniomycetaceae 35% Oedogoniomyces 35% 1254 Fun 85% Chy 80% Monoblepharidomycetes 61% Monoblepharidales 61% Oedogoniomycetaceae 48% Oedogoniomyces 48% 1312 Fun 73% Chy 40% Monoblepharidomycetes 10% Monoblepharidales 10% Oedogoniomycetaceae 10% Oedogoniomyces 10% 1391 Fun 68% Chy 50% Monoblepharidomycetes 24% Monoblepharidales 24% Oedogoniomycetaceae 16% Oedogoniomyces 16% 1423 Fun 74% Chy 64% Monoblepharidomycetes 40% Monoblepharidales 40% Oedogoniomycetaceae 40% Oedogoniomyces 40% 1425 Fun 62% Chy 46% Monoblepharidomycetes 15% Monoblepharidales 15% Oedogoniomycetaceae 12% Oedogoniomyces 12% 1762 Fun 86% Chy 47% Monoblepharidomycetes 30% Monoblepharidales 30% Oedogoniomycetaceae 30% Oedogoniomyces 30% 430 Fun 72% Cry 35% Cryptomycota 34% Cryptomycota 28% Cryptomycota 19% Rozella 19% 809 Fun 72% Cry 38% Cryptomycota 37% Cryptomycota 31% Cryptomycota 17% Rozella 16% 1010 Fun 86% Cry 47% Cryptomycota 42% Cryptomycota 39% Cryptomycota 35% Rozella 35% 61 Fun 80% Ent 26% Entomophthoromycetes 26% Entomophthorales 1 18% Basidiobolaceae 18% Basidiobolus 18% 93 Fun 72% Ent 33% Entomophthoromycetes 33% Entomophthorales 1 9% Basidiobolaceae 9% Basidiobolus 9% 201 Fun 87% Ent 44% Entomophthoromycetes 44% Entomophthorales 1 11% Basidiobolaceae 11% Basidiobolus 11% 207 Fun 85% Ent 28% Entomophthoromycetes 28% Entomophthorales 1 5% Basidiobolaceae 5% Basidiobolus 5% 248 Fun 62% Ent 15% Entomophthoromycetes 15% Entomophthorales 1 3% Basidiobolaceae 3% Basidiobolus 3% 257 Fun 73% Ent 45% Entomophthoromycetes 45% Entomophthorales 1 36% Basidiobolaceae 36% Basidiobolus 36% 385 Fun 82% Ent 15% Entomophthoromycetes 15% Entomophthorales 1 11% Basidiobolaceae 11% Basidiobolus 11% 440 Fun 79% Ent 40% Entomophthoromycetes 40% Entomophthorales 1 23% Basidiobolaceae 23% Basidiobolus 23% 490 Fun 87% Ent 40% Entomophthoromycetes 40% Entomophthorales 1 18% Basidiobolaceae 18% Basidiobolus 18% 507 Fun 75% Ent 36% Entomophthoromycetes 36% Entomophthorales 1 9% Basidiobolaceae 9% Basidiobolus 9% 509 Fun 67% Ent 26% Entomophthoromycetes 26% Entomophthorales 1 14% Basidiobolaceae 14% Basidiobolus 14% 571 Fun 64% Ent 20% Entomophthoromycetes 20% Entomophthorales 1 6% Basidiobolaceae 6% Basidiobolus 6% 669 Fun 69% Ent 33% Entomophthoromycetes 33% Entomophthorales 1 28% Basidiobolaceae 28% Basidiobolus 28% 731 Fun 77% Ent 13% Entomophthoromycetes 13% Entomophthorales 1 7% Basidiobolaceae 7% Basidiobolus 7% 898 Fun 67% Ent 18% Entomophthoromycetes 18% Entomophthorales 1 4% Basidiobolaceae 4% Basidiobolus 4% 1026 Fun 82% Ent 16% Entomophthoromycetes 16% Entomophthorales 1 9% Basidiobolaceae 9% Basidiobolus 9% 1043 Fun 59% Ent 11% Entomophthoromycetes 11% Entomophthorales 1 8% Basidiobolaceae 8% Basidiobolus 8% 1100 Fun 68% Ent 31% Entomophthoromycetes 31% Entomophthorales 1 21% Basidiobolaceae 21% Basidiobolus 21% 1110 Fun 81% Ent 17% Entomophthoromycetes 17% Entomophthorales 1 3% Basidiobolaceae 3% Basidiobolus 3% 1168 Fun 58% Ent 22% Entomophthoromycetes 22% Entomophthorales 1 15% Basidiobolaceae 15% Basidiobolus 15% 1278 Fun 71% Ent 30% Entomophthoromycetes 30% Entomophthorales 1 9% Basidiobolaceae 9% Basidiobolus 9% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1505 Fun 65% Ent 22% Entomophthoromycetes 22% Entomophthorales 1 11% Basidiobolaceae 11% Basidiobolus 11% 1178 Fun 87% Glo 23% Glomeromycetes 23% Diversisporales 23% Diversisporaceae 23% Otospora 23% 1528 Fun 53% Glo 7% Glomeromycetes 7% Diversisporales 7% Diversisporaceae 7% Otospora 7% 1367 Fun 73% Glo 12% Glomeromycetes 12% Diversisporales 12% Pacisporaceae 11% Pacispora 11% 1453 Fun 58% Glo 18% Glomeromycetes 17% Diversisporales 17% Pacisporaceae 17% Pacispora 17% 586 Fun 100% Neo 100% Neocallimastigomycetes 100% Neocallimastigales 100% Neocallimastigaceae 100% Anaeromyces 81% 31 Fun 64% Neo 20% Neocallimastigomycetes 20% Neocallimastigales 20% Neocallimastigaceae 20% Cyllamyces 15% 50 Fun 75% Neo 19% Neocallimastigomycetes 19% Neocallimastigales 19% Neocallimastigaceae 19% Cyllamyces 19% 66 Fun 91% Neo 46% Neocallimastigomycetes 46% Neocallimastigales 46% Neocallimastigaceae 46% Cyllamyces 46% 67 Fun 68% Neo 23% Neocallimastigomycetes 23% Neocallimastigales 23% Neocallimastigaceae 23% Cyllamyces 22% 74 Fun 57% Neo 9% Neocallimastigomycetes 9% Neocallimastigales 9% Neocallimastigaceae 9% Cyllamyces 9% 81 Fun 82% Neo 26% Neocallimastigomycetes 26% Neocallimastigales 26% Neocallimastigaceae 26% Cyllamyces 26% 136 Fun 83% Neo 28% Neocallimastigomycetes 28% Neocallimastigales 28% Neocallimastigaceae 28% Cyllamyces 24% 145 Fun 61% Neo 15% Neocallimastigomycetes 15% Neocallimastigales 15% Neocallimastigaceae 15% Cyllamyces 15% 159 Fun 89% Neo 36% Neocallimastigomycetes 36% Neocallimastigales 36% Neocallimastigaceae 36% Cyllamyces 35% 191 Fun 76% Neo 19% Neocallimastigomycetes 19% Neocallimastigales 19% Neocallimastigaceae 19% Cyllamyces 19% 228 Fun 79% Neo 45% Neocallimastigomycetes 45% Neocallimastigales 45% Neocallimastigaceae 45% Cyllamyces 44% 237 Fun 53% Neo 28% Neocallimastigomycetes 28% Neocallimastigales 28% Neocallimastigaceae 28% Cyllamyces 27% 324 Fun 77% Neo 27% Neocallimastigomycetes 27% Neocallimastigales 27% Neocallimastigaceae 27% Cyllamyces 26% 359 Fun 89% Neo 12% Neocallimastigomycetes 12% Neocallimastigales 12% Neocallimastigaceae 12% Cyllamyces 9% 376 Fun 88% Neo 32% Neocallimastigomycetes 32% Neocallimastigales 32% Neocallimastigaceae 32% Cyllamyces 31% 389 Fun 83% Neo 25% Neocallimastigomycetes 25% Neocallimastigales 25% Neocallimastigaceae 25% Cyllamyces 19% 397 Fun 57% Neo 11% Neocallimastigomycetes 11% Neocallimastigales 11% Neocallimastigaceae 11% Cyllamyces 9% 491 Fun 50% Neo 14% Neocallimastigomycetes 14% Neocallimastigales 14% Neocallimastigaceae 14% Cyllamyces 14% 544 Fun 67% Neo 16% Neocallimastigomycetes 16% Neocallimastigales 16% Neocallimastigaceae 16% Cyllamyces 14% 592 Fun 63% Neo 21% Neocallimastigomycetes 21% Neocallimastigales 21% Neocallimastigaceae 21% Cyllamyces 21% 112 613 Fun 74% Neo 11% Neocallimastigomycetes 11% Neocallimastigales 11% Neocallimastigaceae 11% Cyllamyces 11% 620 Fun 98% Neo 30% Neocallimastigomycetes 30% Neocallimastigales 30% Neocallimastigaceae 30% Cyllamyces 19% 691 Fun 57% Neo 14% Neocallimastigomycetes 14% Neocallimastigales 14% Neocallimastigaceae 14% Cyllamyces 14% 700 Fun 63% Neo 24% Neocallimastigomycetes 24% Neocallimastigales 24% Neocallimastigaceae 24% Cyllamyces 21% 751 Fun 89% Neo 19% Neocallimastigomycetes 19% Neocallimastigales 19% Neocallimastigaceae 19% Cyllamyces 10% 793 Fun 90% Neo 34% Neocallimastigomycetes 34% Neocallimastigales 34% Neocallimastigaceae 34% Cyllamyces 30% 818 Fun 95% Neo 48% Neocallimastigomycetes 48% Neocallimastigales 48% Neocallimastigaceae 48% Cyllamyces 45% 821 Fun 92% Neo 47% Neocallimastigomycetes 47% Neocallimastigales 47% Neocallimastigaceae 47% Cyllamyces 39% 824 Fun 95% Neo 39% Neocallimastigomycetes 39% Neocallimastigales 39% Neocallimastigaceae 39% Cyllamyces 33% 834 Fun 90% Neo 34% Neocallimastigomycetes 34% Neocallimastigales 34% Neocallimastigaceae 34% Cyllamyces 32% 836 Fun 87% Neo 26% Neocallimastigomycetes 26% Neocallimastigales 26% Neocallimastigaceae 26% Cyllamyces 24% 900 Fun 89% Neo 44% Neocallimastigomycetes 44% Neocallimastigales 44% Neocallimastigaceae 44% Cyllamyces 44% 912 Fun 54% Neo 8% Neocallimastigomycetes 8% Neocallimastigales 8% Neocallimastigaceae 8% Cyllamyces 8% 970 Fun 66% Neo 19% Neocallimastigomycetes 19% Neocallimastigales 19% Neocallimastigaceae 19% Cyllamyces 19% 1175 Fun 83% Neo 15% Neocallimastigomycetes 15% Neocallimastigales 15% Neocallimastigaceae 15% Cyllamyces 14% 1212 Fun 73% Neo 12% Neocallimastigomycetes 12% Neocallimastigales 12% Neocallimastigaceae 12% Cyllamyces 9% 1225 Fun 92% Neo 28% Neocallimastigomycetes 28% Neocallimastigales 28% Neocallimastigaceae 28% Cyllamyces 23% 1265 Fun 81% Neo 10% Neocallimastigomycetes 10% Neocallimastigales 10% Neocallimastigaceae 10% Cyllamyces 10% 1272 Fun 81% Neo 39% Neocallimastigomycetes 39% Neocallimastigales 39% Neocallimastigaceae 39% Cyllamyces 38% 1542 Fun 82% Neo 35% Neocallimastigomycetes 35% Neocallimastigales 35% Neocallimastigaceae 35% Cyllamyces 31% 1601 Fun 95% Neo 21% Neocallimastigomycetes 21% Neocallimastigales 21% Neocallimastigaceae 21% Cyllamyces 19% 1625 Fun 68% Neo 16% Neocallimastigomycetes 16% Neocallimastigales 16% Neocallimastigaceae 16% Cyllamyces 14% 1626 Fun 58% Neo 27% Neocallimastigomycetes 27% Neocallimastigales 27% Neocallimastigaceae 27% Cyllamyces 25% 1299 Fun 63% Zyg 36% Kickxellomycotina 36% Harpellales 28% Harpellaceae 14% Furculomyces 14% 486 Fun 60% Zyg 32% Kickxellomycotina 32% Harpellales 24% Legeriomycetaceae 9% Capniomyces 7% 1529 Fun 59% Zyg 42% Kickxellomycotina 42% Harpellales 23% Legeriomycetaceae 17% Capniomyces 17% 124 Fun 66% Zyg 44% Kickxellomycotina 44% Harpellales 15% Legeriomycetaceae 14% Smittium 12% 319 Fun 56% Zyg 29% Kickxellomycotina 29% Harpellales 20% Legeriomycetaceae 17% Smittium 13% 327 Fun 68% Zyg 27% Kickxellomycotina 27% Harpellales 19% Legeriomycetaceae 17% Smittium 13% 797 Fun 78% Zyg 25% Kickxellomycotina 25% Harpellales 22% Legeriomycetaceae 19% Smittium 17% 859 Fun 94% Zyg 43% Kickxellomycotina 43% Harpellales 27% Legeriomycetaceae 25% Smittium 19% Continued on next page Table B.1 – Continued from previous page OTU Kin % Phy % Class % Order % Family % Genus %

1050 Fun 85% Zyg 43% Kickxellomycotina 43% Harpellales 38% Legeriomycetaceae 37% Smittium 34% 1627 Fun 77% Zyg 45% Kickxellomycotina 45% Harpellales 31% Legeriomycetaceae 28% Smittium 27% 1654 Fun 80% Zyg 20% Kickxellomycotina 20% Harpellales 18% Legeriomycetaceae 16% Smittium 15% 664 Fun 84% Zyg 28% Kickxellomycotina 28% Kickxellales 13% Kickxellaceae 12% Coemansia 7% 810 Fun 57% Zyg 34% Kickxellomycotina 34% Kickxellales 17% Kickxellaceae 17% Coemansia 17% 1019 Fun 58% Zyg 27% Kickxellomycotina 27% Kickxellales 13% Kickxellaceae 12% Coemansia 12% 1530 Fun 53% Zyg 23% Kickxellomycotina 23% Kickxellales 14% Kickxellaceae 12% Coemansia 12% 1326 Fun 100% Zyg 99% Mortierellomycotina 99% Mortierellales 98% Mortierellaceae 98% Dissophora 76% 1533 Fun 70% Zyg 39% Mortierellomycotina 39% Mortierellales 22% Mortierellaceae 22% Dissophora 19% 126 Fun 60% Zyg 41% Mortierellomycotina 41% Mortierellales 33% Mortierellaceae 33% Gamsiella 23% 1390 Fun 64% Zyg 30% Mortierellomycotina 30% Mortierellales 11% Mortierellaceae 11% Gamsiella 6% 1434 Fun 91% Zyg 43% Mortierellomycotina 43% Mortierellales 21% Mortierellaceae 21% Gamsiella 10% 1782 Fun 100% Zyg 100% Mortierellomycotina 100% Mortierellales 100% Mortierellaceae 100% Mortierella 72% 11 Fun 100% Zyg 48% Mucoromycotina 48% Endogonales 41% Endogonaceae 41% Endogone 41% 137 Fun 97% Zyg 60% Mucoromycotina 60% Endogonales 23% Endogonaceae 23% Endogone 23% 858 Fun 97% Zyg 47% Mucoromycotina 47% Endogonales 20% Endogonaceae 20% Endogone 20% 1404 Fun 99% Zyg 62% Mucoromycotina 62% Endogonales 24% Endogonaceae 24% Endogone 24% 1551 Fun 97% Zyg 45% Mucoromycotina 45% Endogonales 30% Endogonaceae 30% Endogone 30% 1264 Fun 100% Zyg 100% Mucoromycotina 100% Mucorales 1 100% Mucorales 1 i.s. 100% Umbelopsis 100% 1089 Fun 100% Zyg 100% Mucoromycotina 100% Mucorales 2 100% Choanephoraceae 100% Choanephora 100% 1165 Fun 100% Zyg 100% Mucoromycotina 100% Mucorales 2 100% Mucoraceae 100% Apophysomyces 100% 702 Fun 54% Zyg 28% Mucoromycotina 28% Mucorales 2 17% Mucoraceae 15% Phycomyces 15% 1581 Fun 94% Zyg 51% Mucoromycotina 51% Mucorales 2 48% Mucoraceae 29% Phycomyces 26% 1669 Fun 95% Zyg 67% Mucoromycotina 67% Mucorales 2 46% Mucoraceae 34% Phycomyces 32% 1737 Fun 100% Zyg 100% Mucoromycotina 100% Mucorales 2 100% Mucoraceae 74% Rhizopus 56% 1495 Fun 100% Zyg 100% Mucoromycotina 100% Mucorales 2 100% Pilobolaceae 49% Pilobolus 49% 113 135 Fun 70% Zyg 44% Mucoromycotina 44% Mucorales 2 37% Thamnidiaceae 26% Cokeromyces 26% 447 Fun 54% Zyg 22% Mucoromycotina 22% Mucorales 2 10% Thamnidiaceae 6% Cokeromyces 6% 1310 Fun 68% Zyg 42% Mucoromycotina 42% Mucorales 2 28% Thamnidiaceae 19% Cokeromyces 19% 196 Fun 100% Zyg 100% Mucoromycotina 100% Mucorales 2 100% Thamnidiaceae 25% Thamnidium 20% 133 Fun 69% Zyg 19% Zoopagomycotina 19% Zoopagales 12% Piptocephalidaceae 12% Kuzuhaea 11% 233 Fun 57% Zyg 25% Zoopagomycotina 25% Zoopagales 14% Piptocephalidaceae 14% Kuzuhaea 13% 311 Fun 71% Zyg 11% Zoopagomycotina 11% Zoopagales 10% Piptocephalidaceae 10% Kuzuhaea 10% 391 Fun 70% Zyg 30% Zoopagomycotina 30% Zoopagales 14% Piptocephalidaceae 14% Kuzuhaea 9% 423 Fun 57% Zyg 25% Zoopagomycotina 25% Zoopagales 14% Piptocephalidaceae 14% Kuzuhaea 14% 460 Fun 59% Zyg 28% Zoopagomycotina 28% Zoopagales 8% Piptocephalidaceae 7% Kuzuhaea 7% 591 Fun 68% Zyg 22% Zoopagomycotina 22% Zoopagales 8% Piptocephalidaceae 8% Kuzuhaea 7% 777 Fun 74% Zyg 11% Zoopagomycotina 11% Zoopagales 10% Piptocephalidaceae 9% Kuzuhaea 6% 1042 Fun 85% Zyg 38% Zoopagomycotina 38% Zoopagales 23% Piptocephalidaceae 23% Kuzuhaea 23% 1213 Fun 64% Zyg 19% Zoopagomycotina 19% Zoopagales 8% Piptocephalidaceae 5% Kuzuhaea 2% 1416 Fun 65% Zyg 35% Zoopagomycotina 35% Zoopagales 28% Piptocephalidaceae 28% Kuzuhaea 27% Table B.2: Per-site relative abundances of the most abundant fungal classes observed in plankton (PT), estuarine wetland sediments (WS), intertidal sand (IS), and sediment core (SC) samples. Class assignments are based on results of the ‘best-match’ RDP analysis.

Fungal Class PT WS IS SC Agaricomycetes 0.9% 1.2% 3.0% 6.2% Ascomycota incertae sedis 0.6% 2.8% 3.2% 2.9% Blastocladiomycetes 0.1% 1.3% 0.7% 4.0% Chytridiomycetes 2.1% 12.8% 12.6% 38.4% Dothideomycetes 16.3% 43.2% 37.1% 12.0% Entomophthoromycetes 0.2% 0.8% 0.6% 2.5% Eurotiomycetes 20.2% 1.9% 2.2% 3.5% Exobasidiomycetes 1.5% 4.8% 5.4% 7.1% Lecanoromycetes 49.8% 0.1% 0.1% 0.2% Monoblepharidomycetes 0.2% 19.8% 13.8% 1.3% Mucoromycetes 0.2% 3.6% 5.2% 1.3% Neocallimastigomycetes 0.6% 1.6% 3.4% 6.6% Sordariomycetes 2.3% 3.9% 7.9% 8.5%

114 Table B.3: Per-site relative abundances of the most abundant fungal orders observed in plankton (PT), estuarine wetland sediments (WS), intertidal sand (IS), and sediment core (SC) samples. Class assignments are based on results of the ‘best-match’ RDP analysis.

Fungal Order PT WS IS SC Agaricales 0.2% 0.4% 1.4% 2.4% Ascomycota incertae sedis 0.6% 2.8% 3.2% 2.9% Blastocladiales 0.1% 1.3% 0.7% 4.0% Botryosphaeriales 1.4% 1.4% 2.0% 2.7% Capnodiales 9.3% 18.0% 3.5% 3.1% Chaetothyriales 19.5% 0.4% 0.7% 0.9% Chytridiales 0.4% 3.2% 4.4% 15.7% Endogonales 0.1% 2.9% 4.9% 0.9% Entomophthorales 0.2% 0.8% 0.6% 2.5% Eurotiales 0.7% 1.5% 1.5% 2.6% Helotiales 4.2% 0.1% 0.4% 1.2% Hypocreales 0.3% 1.8% 3.0% 1.2% Lecanorales 49.8% 0.0% 0.1% 0.1% Lobulomycetales 0.2% 1.6% 1.4% 2.2% Malasseziales 1.5% 4.7% 5.4% 7.0% Monoblepharidales 0.2% 19.8% 13.8% 1.3% Neocallimastigales 0.6% 1.6% 3.4% 6.6% Pleosporales 5.3% 23.7% 31.5% 5.9% Saccharomycetales 0.2% 0.1% 0.8% 1.1% Sordariales 0.2% 0.9% 1.1% 3.4% Spizellomycetales 1.3% 7.5% 6.5% 19.8% Xylariales 0.7% 0.7% 2.2% 0.8%

115 Table B.4: Phylum-level taxonomic composition of the total fungal community observed in plankton (PT), estuarine wetland sediments (WS), intertidal sand (IS), and sediment core (SC) samples. Values indicate the number of OTUs assigned to each phylum. Phylum assignments are based on results of the ‘best-match’ RDP analysis.

Phylum PT WS IS SC Ascomycota 225 177 199 146 Basidiomycota 73 56 68 56 Blastocladiomycota 6 19 13 12 Chytridiomycota 54 87 88 106 Cryptomycota 0 0 2 1 Entomophthoromycota 6 11 14 12 Glomeromycota 1 1 1 2 Neocallimastigomycota 17 20 26 26 “Zygomycota” 17 29 29 15

Table B.5: Phylum-level taxonomic composition of the total fungal community observed in coastal marine sites across seasons. Values indicate the number of OTUs assigned to each phylum. Phylum assignments arebased on results of the ‘best-match’ RDP analysis.

Phylum Winter Spring Summer Fall Ascomycota 249 86 162 235 Basidiomycota 81 30 52 79 Blastocladiomycota 20 11 10 13 Chytridiomycota 103 92 91 100 Cryptomycota 0 1 0 2 Entomophthoromycota 12 11 12 12 Glomeromycota 2 1 2 1 Neocallimastigomycota 26 27 16 21 “Zygomycota” 31 16 17 30

116 OTUs Observed iueB.1 Figure aeato uvsfralekroi sequences. eukaryotic all for curves Rarefaction : Sequences persample 117 SC -Fall SC -Summer SC -Spring SC -Winter IS -Fall IS -Summer IS -Spring IS -Winter WS -Fall WS -Summer WS -Spring WS -Winter PT -Fall PT -Summer PT -Spring PT -Winter a. Town Marsh (WS)

67 (8.7%) Cape Lookout (SC) 74 24 (9.6%) (3.1%) 73 27 (9.5%) 42 23 (3.5%) (5.5%) (3%)

28 132 (3.6%) (17.1%) 23 28 (3%) (3.6%) 27 49 (3.5%) (6.4%)

Piver’s Island (PT) 103 50 (13.4%) (6.5%) Bird Shoal (IS)

b. Spring

34 (4.4%) Fall 107 25 (13.9%) (3.2%) 105 17 (13.6%) (2.2%) 82 38 (10.6%) (4.9%)

97 121 (12.6%) (15.7%) 25 8 (3.2%) 39 15 (1.0%) (5.1%) (1.9%)

Winter 40 17 (5.2%) (2.2%) Summer

Figure B.2: Edwards Venn diagrams of shared fungal OTUs across coastal marine sites (a.) and seasons (b.). Values in parentheses indicate percentage of total fungal OTUs. 118 Appendix C

Supplementary Information for Chapter3

119 Table C.1: Statistics for chimeric sequences observed for data ﬁltering steps.

No. Chimeras - Parent 1 No. Chimeras - Parent 2 Taxon Basic B+Primer Predicted Pred+Primer Basic B+Primer Predicted Pred+Primer Aspergillus fumigatus 731 1 17 0 589 2 13 3 Exophiala dermatitidis 773 59 110 40 2923 3 1049 4 Malassezia globosa 4091 20 1203 12 2368 203 182 112 Rhizidium phycophilum 618 0 53 0 287 1 7 0 Spizellomyces punctatus 715 111 61 58 1007 4 65 3 Trichoderma virens 873 5 43 3 1254 5 154 4 Umbelopsis fusiformis 3081 36 97 22 2454 14 114 9 120 Table C.2: Taxonomy and GenBank accessions for 28S rDNA sequences for reference taxa used in phylogenetic analyses in Chapter3. Phylum designations are based on the revised taxonomy presented from Spatafora et al.(2016).

Phylum Species GenBank Accession Ingroup taxa Ascomycota Acremonium alternatum FJ176883 Ascomycota Acremonium sp. JX535073 Ascomycota Agonimia sp. DQ782913 Ascomycota Aleuria aurantia AY544654 Ascomycota Alternaria sp. DQ678068 Ascomycota Anisomeridium polypori DQ782906 Ascomycota Arthopyrenia salicis KP671722 Ascomycota Ascobolus crenulatus AY544678 Ascomycota Ashbya gossypii AF113137 Ascomycota Aspergillus ﬂavus HQ395773 Ascomycota Aspergillus fumigatus AY660917 Ascomycota Aspergillus nidulans AF454167 Ascomycota Aspergillus oryzae KP256849 Ascomycota Barbatosphaeria varioseptata KM492869 Ascomycota Bimuria novae-zelandiae AY016356 Ascomycota Bipolaris woodii KX452441 Ascomycota Botryotinia fuckeliana AY544651 Ascomycota Bryochiton microscopicus EU940149 Ascomycota Caloscypha fulgens DQ247799 Ascomycota Candida albicans AACQ01000290 Ascomycota Candida glabrata AY198398 Ascomycota Candida guilliermondii AAFM01000051 Ascomycota Candida tropicalis AAFN01000124 Ascomycota Capnodiales sp. GU323223 Ascomycota Capnodium coﬀeae DQ247800 Ascomycota Capnodium sp. KU985278 Ascomycota Capronia pilosella DQ823099 Ascomycota Chaetomium globosum AY545729 Ascomycota Cheilymenia stercorea AY544661 Ascomycota Chlorociboria aeruginosa AY544669 Ascomycota Cladosporium uredinicola EU019264 Ascomycota Coccidioides immitis AY176713 Ascomycota Coccomyces dentatus AY544657 Ascomycota Cochliobolus heterostrophus AY544645 Ascomycota Coniothyrium obiones DQ678054 Continued on next page

121 Table C.2 – Continued from previous page Phylum Species GenBank Accession Ascomycota Cudoniella clavus DQ470944 Ascomycota Curreya pityophila DQ384102 Ascomycota Cytospora diatrypelloidea DQ923537 Ascomycota Daldinia pyrenaica KY610413 Ascomycota Debaryomyces hansenii AF485980 Ascomycota Dermatocarpon miniatum AY584644 Ascomycota Dermea acerina DQ247801 Ascomycota Diaporthe eres AF408350 Ascomycota Dictyosporium stellatum JF951177 Ascomycota Didymellaceae sp. HM595583 Ascomycota Disciotis venosa AY544667 Ascomycota Dothidea sambuci AY544681 Ascomycota Endocarpon cf pusillum DQ823097 Ascomycota Eucasphaeria capensis EF110619 Ascomycota Exophiala dermatitidis DQ823100 Ascomycota Exophiala pisciphila DQ823101 Ascomycota Fusarium graminearum AY188924 Ascomycota Fusarium verticillioides XR001989350 Ascomycota Gnomonia gnomon AF408361 Ascomycota Gyromitra californica AY544673 Ascomycota Helvella compressa AY544655 Ascomycota Histoplasma capsulatum Genome Ascomycota Hydropisphaera erubescens AY545726 Ascomycota Hydropisphaera erubescens AY545726 Ascomycota Hypocrea citrina AY544649 Ascomycota Isaria farinosa KC510278 Ascomycota Kluyveromyces lactis NC006040 Ascomycota Kluyveromyces waltii AADM01000465 Ascomycota Lachnum virgineum AY544646 Ascomycota Lentithecium unicellulare KX505376 Ascomycota Leotia lubrica AY544644 Ascomycota Lindra thalassiae DQ470947 Ascomycota Lulworthia grandispora DQ522856 Ascomycota Magnaporthe grisea AB026819 Ascomycota Microascus trigonosporus DQ470958 Ascomycota Monascus purpureus DQ782908 Ascomycota Monilinia fructicola AY544670 Ascomycota Morchella esculenta AY544664 Ascomycota Nalanthamala squamicola AF373281 Continued on next page

122 Table C.2 – Continued from previous page Phylum Species GenBank Accession Ascomycota Nectria haematococca AY489729 Ascomycota Neurospora crassa AF286411 Ascomycota Ophiosphaerella korrae KP690985 Ascomycota Orbilia auricolor DQ470953 Ascomycota Orbilia vinosa DQ470952 Ascomycota Paraphaeosphaeria sporulosa KX359599 Ascomycota Passalora sp. GQ852622 Ascomycota Penicillium solitum JN642222 Ascomycota Periconia sp. AB807570 Ascomycota Pestalotiopsis microspora KY366173 Ascomycota Peziza proteana AY544659 Ascomycota Peziza vesiculosa DQ470948 Ascomycota Phaeosphaeriaceae sp. KY090665 Ascomycota Phoma cladoniicola JQ238625 Ascomycota Phoma herbarum AY293791 Ascomycota Pleospora herbarum DQ247804 Ascomycota Pneumocystis carinii AF047831 Ascomycota Potebniamyces pyri DQ470949 Ascomycota Preussia intermedia GQ203738 Ascomycota Preussia minima GQ203744 Ascomycota Preussia terricola GQ203725 Ascomycota Protomyces inouyei AY548294 Ascomycota Pyrenophora phaeocomes DQ499596 Ascomycota Pyrenula pseudobufonia AY640962 Ascomycota Pyrgillus javanicus DQ823103 Ascomycota Pyronema domesticum DQ247805 Ascomycota Ramichloridium anceps DQ823102 Ascomycota Saccharomyces castellii AACF01000279 Ascomycota Saccharomyces cerevisiae U53879 Ascomycota Sarcoscypha coccinea AY544647 Ascomycota Schizosaccharomyces pombe Z19136 Ascomycota Scutellinia scutellata DQ247806 Ascomycota Sedecimiella taiwanensis KP671735 Ascomycota Sordaria ﬁmicola AY545728 Ascomycota Spiromastix warcupii DQ782909 Ascomycota Staurothele frustulenta DQ823098 Ascomycota Taphrina wiesneri AY548292 Ascomycota Teichosporaceae sp. KU848206 Ascomycota Teratosphaeriaceae sp. KP671744 Continued on next page

123 Table C.2 – Continued from previous page Phylum Species GenBank Accession Ascomycota Toxicocladosporium irritans EU040243 Ascomycota Trematosphaeria heterospora AY016369 Ascomycota Trypethelium sp.. AY584652 Ascomycota Volutella citrinella HQ843772 Ascomycota Westerdykella cylindrica AY779322 Ascomycota Xylaria acuta AY544676 Ascomycota Xylaria hypoxylon AY544648 Ascomycota Yarrowia lipolytica AJ616903 Basidiomycota Amanita brunnescens AY631902 Basidiomycota Boletellus projectellus AY684158 Basidiomycota Calostoma cinnabarinum AY645054 Basidiomycota Cintractia sorghi AY745726 Basidiomycota Colacogloea peniophorae AY629313 Basidiomycota Coprinopsis cinerea AF041494 Basidiomycota Cryptococcus neoformans AE017342 Basidiomycota Endocronartium harknessii AY700193 Basidiomycota Entyloma holwayi AY745721 Basidiomycota Gautieria otthii AF393058 Basidiomycota Henningsomyces candidus AF287864 Basidiomycota Hydnum albomagnum AY700199 Basidiomycota Hygrophoropsis aurantiaca AY684156 Basidiomycota Lycogalopsis solmsii KF017599 Basidiomycota Malassezia globosa KT310070 Basidiomycota Myriostoma coliforme KC582020 Basidiomycota Platygloea disciformis AY629314 Basidiomycota Pleurotus ostreatus AY645052 Basidiomycota Psathyrella maculata GQ249290 Basidiomycota Puccinia graminis AF522177 Basidiomycota Ramaria rubella AY645057 Basidiomycota Rhodotorula hordea AY631901 Basidiomycota Suillus pictus AY684154 Basidiomycota Tilletiaria anomala AY745715 Basidiomycota Ustilago maydis AF453938 Blastocladiomycota Allomces arbusculus AY552525 Blastocladiomycota Catenomyces sp. DQ273789 Blastocladiomycota Physoderma maydis DQ273767 Chytridiomycota Batrachochytrium dendrobatidis AY546693 Chytridiomycota Chytriomyces angularis DQ273815 Chytridiomycota Cladochytrium replicatum AY546688 Continued on next page

124 Table C.2 – Continued from previous page Phylum Species GenBank Accession Chytridiomycota Endochytrium sp. DQ273816 Chytridiomycota Hyaloraphidium curvatum DQ273771 Chytridiomycota Monoblepharella sp. AY546687 Chytridiomycota Neocallimastix sp. DQ273822 Chytridiomycota Nowakowskiella sp. DQ273798 Chytridiomycota Polychytrium aggregatum AY546686 Chytridiomycota Rhizoclosmatium sp. DQ273769 Chytridiomycota Rhizophlyctis rosea NG27649 Chytridiomycota Rhizophlyctis rosea DQ273787 Chytridiomycota Rhizophydiales sp. FR670788 Chytridiomycota Rhizophydiales sp. FR670787 Chytridiomycota Rhizophydium brooksianum DQ273770 Chytridiomycota Rhizophydium macroporosum DQ273823 Chytridiomycota Rhizophydium sp. DQ273779 Chytridiomycota Spizellomyces punctatus AY546692 Cryptomycota Rozella allomycis DQ273803 Cryptomycota Rozella sp. DQ273766 Cryptomycota Uncultured fungus KY687859 incertae sedis Olpidium brassicae DQ273818 Mucoromycota Endogone pisiformis DQ273811 Mucoromycota Geosiphon pyriformis AM183920 Mucoromycota Glomus intraradices DQ273828 Mucoromycota Glomus mosseae DQ273793 Mucoromycota Mortierella verticillata DQ273794 Mucoromycota Paraglomus occultum DQ273827 Mucoromycota Phycomyces blakesleeanus DQ273800 Mucoromycota Rhizopus oryzae AY213626 Mucoromycota Scutellospora heterogama DQ273792 Mucoromycota Umbelopsis ramanniana DQ273797 Zoopagomycota Basidiobolus ranarum DQ273807 Zoopagomycota Conidiobolus coronatus AF113418 Zoopagomycota Entomophthora muscae DQ273772 Zoopagomycota Piptocephalis corymbifera AY546690 Zoopagomycota Smittium culisetae DQ273773 Zoopagomycota Spiromyces aspiralis DQ273801 Outgroup taxa Animalia Caenorhabditis elegans X03680 Animalia Ciona intestinalis AF212177 Animalia Drosophila melanogaster M21017 Continued on next page

125 Table C.2 – Continued from previous page Phylum Species GenBank Accession Animalia Homo sapiens U13369 Apicomplexa Cryptosporidium parvum AF040725 Apicomplexa Toxoplasma gondii X75429 Chlorophyta Chlamydomonas reinhardtii AF183463 Choanaﬂagellida Monosiga brevicollis AY026374 Mycetozoa Dictyostelium discoideum X00601.1 Plantae Arabidopsis thaliana X52322 Plantae Oryza sativa M11585 Rhodophyta Cyanidioschyzon merolae AB158485

126 Table C.3: Comparison of taxonomic assignments made with three fungal reference databases interrogating three target rDNA loci from marine MOTU sequences. Keys for phylum designations are: ASC–Ascomycota; BAS–Basidiomycota; BLA–Blastocladiomycota; CHY–Chytridiomycota; CRYPTO–Cryptomycota; EDF i.s.–Early Diverging Fungus, incertae sedis; FUN UNID–Fungi, unidentiﬁed; GLO–Glomeromycota; ZYG–Zygomycota. Keys for column headers are: MOTU ID–Molecular Operational Taxonomic Unit ID; 28S PHY–Estimated phylum placement based on Maximum Likelihood phylogeny inferred from 28S/LSU sequences; ITS*WC–Taxonomic assignments from WARCUP database using ITS1/2 sequence queries; ITS*UN–Taxonomic assignments from UNITE database using ITS1/2 sequence queries; RDPLSU– Taxonomic assignments from the RDP LSU (28S) reference database using 28S sequence queries. Percentages for each assignment indicate bootstrap support for that phylum designation as estimated by the RDP Classiﬁer.

28S ITS1 ITS1 ITS1 ITS1 ITS2 ITS2 ITS2 ITS2 RDP RDP MOTU ID PHY WC WC% UN UN% WC WC% UN UN% LSU LSU% NC 4062B 1199 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% 127 NC 4062B 1651 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 1660 ASC ASC 100% ASC 100% ASC 100% ASC 99% ASC 100% NC 4062B 1930 ASC ASC 76% ASC 62% ASC 70% ASC 58% ASC 100% NC 4062B 1964 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 2249 ASC ASC 96% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 2334 ASC ASC 77% FUN UNID 20% ASC 97% ASC 100% ASC 100% NC 4062B 3798 ASC ASC 90% ASC 79% ASC 95% ASC 99% ASC 100% NC 4062B 4087 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 5040 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 5079 ASC ASC 95% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 5832 ASC ASC 85% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 6226 ASC ASC 98% ASC 100% ASC 98% ASC 100% ASC 100% NC 4062B 677 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 693 ASC ASC 88% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 70 ASC ASC 77% ASC 90% ASC 79% ASC 99% ASC 100% Continued on next page Table C.3 – Continued from previous page 28S ITS1 ITS1 ITS1 ITS1 ITS2 ITS2 ITS2 ITS2 RDP RDP MOTU ID PHY WC WC% UN UN% WC WC% UN UN% LSU LSU% NC 4062B 7369 ASC ASC 94% ASC 100% ASC 98% ASC 100% ASC 100% NC 4062B 7533 ASC ASC 90% ASC 95% ASC 100% ASC 99% ASC 100% NC 4062B 8005 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 8472 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 859 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 936 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062B 961 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 1293 ASC ASC 100% ASC 99% ASC 84% ASC 87% ASC 100% NC 4062C 1481 ASC ASC 100% ASC 89% ASC 100% ASC 100% ASC 100% NC 4062C 1652 ASC ASC 99% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 2961 ASC ASC 82% FUN UNID 71% ASC 88% ASC 88% ASC 100%

128 NC 4062C 2980 ASC ASC 88% ASC 95% ASC 100% ASC 99% ASC 100% NC 4062C 3008 ASC ASC 97% ASC 100% ASC 100% ASC 61% ASC 100% NC 4062C 3234 ASC ASC 81% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 3373 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 344 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 3514 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 3866 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 3889 ASC ASC 93% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 400 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 4027 ASC ASC 85% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 4181 ASC ASC 80% ASC 100% ASC 100% ASC 100% ASC 100% NC 4062C 4813 ASC ASC 95% ASC 89% ASC 100% ASC 100% ASC 100% NC 4062C 496 ASC ASC 84% ASC 100% ASC 99% ASC 100% ASC 100% NC 4062C 5165 ASC ASC 100% ASC 99% ASC 95% ASC 82% ASC 100% NC 4062C 5761 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% PVZ 4085 1255 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% Continued on next page Table C.3 – Continued from previous page 28S ITS1 ITS1 ITS1 ITS1 ITS2 ITS2 ITS2 ITS2 RDP RDP MOTU ID PHY WC WC% UN UN% WC WC% UN UN% LSU LSU% PVZ 4085 4075 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% PVZ 4085 4574 ASC ASC 100% ASC 100% ASC 100% ASC 100% ASC 100% SC 4092D 6173 ASC ASC 100% ASC 91% ASC 100% ASC 87% ASC 100% NC 4062B 1734 BAS BAS 78% BAS 73% BAS 82% BAS 80% BAS 100% NC 4062C 1092 BAS BAS 100% BAS 100% BAS 100% BAS 100% BAS 100% NC 4062C 1864 BAS BAS 100% BAS 90% BAS 100% BAS 70% BAS 100% NC 4062C 492 BAS BAS 64% FUN UNID 90% BAS 47% FUN UNID 100% BAS 99% PVZ 4085 5783 BAS BAS 100% BAS 100% BAS 100% BAS 100% BAS 100% NC 4062B 105 CHY ASC 27% BAS 54% BAS 68% BAS 47% CHY 75% NC 4062B 2694 CHY CHY 8% GLO 16% CHY 18% BAS 58% BLA 26% NC 4062B 6581 CHY ASC 70% ASC 56% ASC 36% ASC 44% BLA 46%

129 NC 4062B 8297 CHY CHY 16% BAS 38% BAS 45% GLO 20% CHY 56% NC 4062C 2039 CHY BAS 37% GLO 21% ASC 48% GLO 14% CHY 90% NC 4062C 3076 CHY BAS 47% ASC 46% ASC 66% ASC 61% BLA 45% NC 4062C 4046 CHY ASC 41% FUN UNID 10% BAS 45% CHY 10% CHY 83% NC 4062C 4806 CHY CHY 21% CHY 100% BAS 49% CHY 96% CHY 90% NC 4062B 4127 CRYPTO GLO 7% GLO 6% BAS 77% BAS 70% CHY 46% NC 4062C 4174 EDF i.s. ASC 48% CHY 8% ZYG 15% BAS 62% CHY 53% Table C.4: Marine MOTUs recovered in both PacBio and Ion Torrent surveys. PHY – Phylogenetic placement; Matched IT OTU – Corresponding Ion Torrent Operational Taxonomic Unit; IT Taxonomy – Taxonomic assignment from Ion Torrent survey; Top 50 – one of 50 most abundant OTUs in Ion Torrent survey. Keys for phylum designations are: ASC–Ascomycota; BAS–Basidiomycota; BLA–Blastocladiomycota; CHY–Chytridiomycota; ZYG–Zygomycota.

MOTU ID PHY Matched IT OTU IT Taxonomy Top 50 NC 4062B 1651 ASC OTU 10 ASC Y NC 4062C 2039 CHY OTU 11 ZYG Y NC 4062C 1092 BAS OTU 127 BAS Y NC 4062B 1964 ASC OTU 19 ASC Y NC 4062B 105 CHY OTU 216 BLA SC 4092D 6173 ASC OTU 27 ASC Y NC 4062B 5040 ASC OTU 28 ASC Y NC 4062C 2961 ASC OTU 30 ASC Y NC 4062C 3076 CHY OTU 325 CHY NC 4062C 2980 ASC OTU 33 ASC Y NC 4062B 6581 CHY OTU 409 BLA NC 4062C 1293 ASC OTU 6 ASC NC 4062C 3234 ASC OTU 77 ASC Y

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151 Biography

Kathryn Therese Picard was born on November 20th, 1984 in New Orleans, Louisiana, U.S.A. She attended the University of Alabama in Tuscaloosa, Alabama, where she earned a B.S. in Biology and Philosophy, magna cum laude, in May 2007. As an undergraduate, Kathryn conducted research in reptilian digestive morphology before transitioning to fungal systematics. She pursued a M.S. in Biology (Systematics and Evolution) at Alabama under the direction of Drs. Martha J. Powell and Peter M. Letcher. She completed her M.S. thesis entitled “Ultrastructure, phylogenetic, and biodiversity studies of the Rhizophydiales (Chytridiomycota)” in August 2009. Later that same month, Kathryn began her doctoral studies at Duke University in the Department of Biology. Her peer-reviewed publications are:

1. Davis WJ, Picard KT, Antonetti J, Edmonds J, Fults J, Letcher PM, Powell MJ. In review. Inventory of chytrid diversity in two temporary forest ponds using a multiphasic approach. Mycologia.

2. Picard KT. 2017 Coastal marine habitats harbor novel early-diverging fungal diversity. Fungal Ecology. 25: 1-13.

3. Stern RF, Picard KT, Hamilton KM, Walne A, Tarran GA, McQuatters- Gollop A, Edwards M. 2015. Novel lineage patterns from an automated water sampler to probe marine microbial biodiversity with Ships of Opportunity. Progress in Oceanography. 137: 409-420.

152 4. Picard KT, Letcher PM, Powell MJ. 2013. Evidence for a facultative mutualist nutritional relationship between the green coccoid alga Bracteacoccus sp. (Chlorophyceae) and the zoosporic fungus Rhizidium phycophilum (Chytrid- iomycota). Fungal Biology 117: 319-328.

5. Letcher PM , Powell MJ, Picard KT. 2012. Zoospore ultrastructure and phylogenetic position of Phlyctochytrium aureliae Ajello is revealed (Chytridiaceae, Chytridiales, Chytridiomycota). Mycologia 104: 410-418

6. Gleason FH , K¨upper FC, Amon JP, Picard KT, Gachon CM, Sime-Ngando T, Marano AV, Lilje O. 2011. Zoosporic true fungi in marine ecosystems. Marine and Freshwater Research 62: 1-11.

7. Picard KT, Letcher PM, Powell MJ. 2009. Rhizidium phycophilum, a new species in Chytridiales. Mycologia 101: 696-706.

8. Letcher PM, Powell MJ, Barr DJS, Churchill PF, Wakeﬁeld WS, Picard KT. 2008. Rhizophlyctidales-a new order in Chytridiomycota. Mycological Research 112: 1031-1048.

Kathryn has received numerous grants, fellowships and awards during her graduate education, including: Katherine Goodman Stern Dissertation Fellowship, Duke University (2015); Bass Instructional Fellowship, Duke University (2015); Confer- ence Travel Awards, Graduate School, Duke University (3 times, 2012, 2014–2015); National Science Foundation (NSF) Doctoral Dissertation Improvement Grant, co- PI with K.M. Pryer (2013); Department of Biology Grant-in-Aid, Duke University (2 times, 2012–2013); Mycological Society of America Graduate Fellowship (2012); Mycological Society of America Graduate Student Research Prize for Best Oral Pre- sentation (2012); Mycological Society of America R.L. Gilbertson Mentor Travel

153 Award (2012); FESIN Fungal Metagenomics Scholarship (2010); Mycological So- ciety of America H.M. Fitzpatrick Travel Award (2009); NSF Graduate Research Fellowship (2009); Ford Foundation Pre-Doctoral Fellowship (2009); Deans Gradu- ate Fellowship, Duke University (2009); Inge & Ilouise Hill Research Fellowship for Excellence in Teaching, University of Alabama (2009); NSF Bridge to the Doctorate Fellowship (2007).

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