Drivers of Fungal Community Composition and Function In

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DRIVERS OF FUNGAL COMMUNITY COMPOSITION AND FUNCTION IN

TEMPERATE FORESTS

A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Matthew D. Gacura

Dissertation written by

Matthew David Gacura

B.S., Youngstown State University, 2007

M.S., Youngstown State University, 2009

Ph.D., Kent State University, 2018

Approved by

Christopher B. Blackwood, Ph.D. , Chair, Doctoral Dissertation Committee

Mark W. Kershner, Ph.D. , Members, Doctoral Dissertation Committee

Xiaozhen Mou, Ph.D.

Mandy J. Munro-Stasiuk, Ph.D.

Abdul Shakoor, Ph.D.

Accepted by

Laura G. Leff, Ph.D. , Chair, Department of Biological Sciences

James L. Blank, Ph.D. , Dean, College of Arts and Sciences

TABLE OF CONTENTS

TABLE OF CONTENTS…………………………………………….…………………………...iii

LIST OF FIGURES…………………………….………………….………………………………v

LIST OF TABLES……………….………………………………………………………………..x

ACKNOWLEDGEMENTS……………………………………………………………………...xii

I. CHAPTER 1: INTRODUCTION………………………..……………………………1

REFERENCES……………………..………………………………………………..20

II. CHAPTER 2: NICHE VS NEUTRAL: FACTORS INFLUENCING THE STRUCTURE OF SAPROTROPHIC FUNGAL COMMUNITIES AT FINE AND LARGE SPATIAL SCALES……………...…………………………………………35

ABSTRACT………………………………………………………………………….35

INTRODUCTION…………………..……………………………………………….36

MATERIALS AND METHODS…………...……………………..…………………40

RESULTS……………………..……………………………………………………..47

DISCUSSION……………..……………………………………………………..…..51

ACKNOWLEDGEMENTS………………………………………………………….60

REFERENCES……..………………………………………………………………..71

III. CHAPTER 3: THE ROLE OF PRIORITY EFFECTS IN SAPROTROPHIC FUNGAL COMMUNITIES IN TEMPERATE FORESTS………………………...……..…….84

ABSTRACT………………………………………………………………………….84

INTRODUCTION…………………..……………………………………………….85

MATERIALS AND METHODS……...………..………………………………...….89

RESULTS……………………………………………..………………………….….95

iii

DISCUSSION……………………………………………..…………………...…….98

ACKNOWLEDGEMENTS……………………………………………………...…104

REFERENCES…………………………………..…………………………………117

IV. CHAPTER 4: COMPARISON OF PECTIN-DEGRADING FUNGAL

COMMUNITIES IN TEMPERATE FORESTS USING GLYCOSYL HYDROLASE

FAMILY 28 PECTINASE PRIMERS TARGETING ASCOMYCETE FUNGI..….130

ABSTRACT………………………………………………………………………...130

INTRODUCTION………..………………………………………………….……..131

MATERIALS AND METHODS…………..……...………………………..………133

RESULTS…………………..………………………………………………...…….137

DISCUSSION…………………..………………………………………….……….139

ACKNOWLEDGEMENTS………………………………………………………...142

REFERENCES………..……………………………………………………………149

V. CHAPTER 5: SYNTHESIS………...……………………………………….……...157

REFERENCES……………………..………………………………………………165

VI. APPENDIX I………………..……………………………...…………….………..171

APPENDIX I REFERENCES………………….………………..………………....176

LIST OF FIGURES

Figure 1. Map of Manistee National Forest MI. Indicated on this map are important glacial landforms and other geologic structures that determine soil characteristics and ecosystem type.

Nine sites were utilized in this study and their spatial locations can be observed from the figure.

These sites are also color coded by ecosystem type as follows: Black = BOWO, Red = SMRO, and Green = SMBW. Map generated using ArcGIS; glacial land systems taken from Michigan

Department of Natural Resources (generated April 2017)……………………………...………...18

Figure 2. Map of Manistee National Forest MI. Indicated on this are the major soil suborders found in Manistee that play a role in ecosystem type. Nine sites were utilized in this study and their spatial locations can be observed from the figure. These sites are also color coded by ecosystem type as follows: Black = BOWO, Red = SMRO, and Green = SMBW. Map generated using ArcGIS; Soil data taken from Michigan Department of Natural Resources (generated April

2017)……………………………………………………………………..………………………19

= SMBW. Map was generated using ArcGIS, geologic features, cities, and other structures were taken from Michigan Department of Natural Resources (generated April 2017)……………..…..61

v number of sample pairs (ranging from 1-50 pairs per step). Error bars are included for each point indicating standard error. Summarized on each plot are the site number and ecosystem. * P <

0.05…………………………………………………………………………………………….…63

Figure 5. RDA ordination plots for hellinger transformed T-RFLPs profiles in A) April and B)

August. Colors represent each site, with centroids labeled with each ecosystem type: BW=BOWO,

SB = SMBW and RS = SMRO. Shapes indicate different leaf species and are indicated by a key.

Percent varience explained by RDAs are indicated for each season is indicated by the bar graph found in panel C…………………………………………………...…………………………..…65

Figure 6. Percent varience explained by RDAs for hellinger transformed T-RFLPs profiles for each season. Each color indicates a separate explanatory factor as indicated by side panel………66

Figure 7. Abundance of taxa found on each leaf type. Classification for each taxa is the highest reliable resolution given for an OTU. Classes of each type of fungi are given with orders found below them also included……………………………………………………………………..….67

Ectomyco=ectomycorrhizal, Arbuscular=arbuscular mycorrhizal, PrimSap=primary saprotroph and Whiterot=white rot saprotroph. Designations for each factor are found on the key on the side…………………………………………...…………………………………………………..68

Figure 9. RDA ordination plot of pyrosequencing OTUs. Colors represent each site, with centroids labeled with each ecosystem type: BW=BOWO, SB = SMBW and RS = SMRO. Shape indicate leaf species as indicated by the key found on the plot………………………………………...…..69

Figure 10. Average percent abundances for each functional group for each leaf type. Functional groups are identified by a key on the side. Various includes all functional groups that were less 1% combined with taxa that were found to be responsible for multiple functions………...... …….70

Figure 11. Map of Manistee National Forest, MI with sites and ecosystem type indicated. Each ecosystem is indicated by a separate color: green indicates SMBW, black indicates BOWO, and red indicates SMRO. An example of the experimental set up for each site is indicated. Three subsites within each site are indicated, with each separated by 500 m. Map was generated utilizing the program ArcGIS……………………………………………………...……………..………106

Figure 12. Percent variance explained (Y-axis), as indicated by Redundancy Analysis, for T-RFLP profiles at 1-Month and 5-Months in the field. The interactions category includes interactions between the factors: leaf type, isolate, and ecosystem………………………….……………….109

Figure 13. Ordination plots of T-RFLP community profiles separated by ecosystem and time point. Shape indicates litter bag leaf type (circles = oak leaves; triangle = maple leaves). Color indicates subsites found within each site (nine subsites across the three sites for each ecosystem at each time point)…………………………………………………………………………………110

Figure 14. Significant factors that influence percent mass loss as indicated by Redundancy

Analysis, for each data set at 1-month and 5-months. Variance explained for each significant factor is indicated on the y axis. The interactions category includes interactions between the factors: leaf type, isolate, and ecosystem…………………………………………………………………….111

Figure 15. Average decomposition of leaf litter, as indicated by percent mass loss (y axis), for treatments at 1-month and 5-months. Treatments are noted by what ecosystem they were placed in

vii and what leaf type litter bags consisted of. Isolated type is indicated by bar color and pattern. Error bars are utilized to indicate standard error…………………………………...………………….112

Figure 16. Variance explained (Y axis) for enzyme profiles per sample and each separate enzyme functional group at 1-month (A) and 5-months (B). Laccase produced at 1 month was very low or absent across all samples. The interactions category includes interactions between the factors: leaf type, isolate, and ecosystem……………………………………………...……………………..113

Figure 17. Ordination plots for enzyme profiles at 1-month and 5-months. Each ecosystem is separated by panel, with three replicate sites found on each. Shape indicates litter bag leaf type, circles indicate oak leaf bags and triangles indicate maple leaf bags. Color indicates isolate type original colonized onto litter bag with: red indicating isolate 1, blue indicating isolate 2, and black indicating isolate 3. Amount of variation explained by RDA vectors is indicated on the X and Y axis…………………………………………………………………...…………………………114

Figure 18. Extracellular enzyme activity after 1-Month. Enzyme activity has been categorized based upon enzyme function: A. phosphatase activity, B. polysaccharide degrading enzyme activity, C. nitrogen obtaining enzyme activity, and D. laccase activity. Isolate type, leaf species, and ecosystem type are included for sample. Error bars indicating standard error are included.………………………………………………………………………...…………...….115

Figure 19. Extracellular enzyme activity after 5-months. Enzyme activity has been categorized based upon enzyme function: A. phosphatase activity, B. polysaccharide degrading enzyme activity, C. nitrogen obtaining enzyme activity, and D. laccase activity. Isolate type, leaf species, and ecosystem type are included for sample. Error bars indicating standard error are included………………………………………………………………………..……………..…116

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Figure 20. RDA ordination of GH28 endopolygalacturonase OTUs. Each point indicates an individual leaf. Ecosystem is indicated by color (yellow=SMBW, black=BOWO). Symbols are different for each forest stand. Leaves from the same forest stand are connected by a line…...... 147

Figure 21. RDA ordination of ITS-OTUs (taxonomic community composition). Each point indicates an individual leaf. Ecosystem is indicated by color (yellow=SMBW, black=BOWO).

Symbols are different for each forest stand. Leaves from the same forest stand are connected by a line……………………………………………………………………………………………....148

LIST OF TABLES

Table 1. Mantel values for testing of T-RFLP data sets within site community composition. Season and ecosystem are listed for each site. Each factor (proximity, leaf type and depth) were analyzed with either Hellinger or Jaccard transformation. Significance is indicated for each value with a

*………………………………………………………………...……………………………..….62

Table 2. Adjusted R-square values for testing within site community composition. Season and ecosystem are listed for each site. Each factor (proximity, leaf type and depth) were analyzed with either Hellinger or Jaccard transformation. Significance is indicated by a * being present in front of the value……………………………………….…………………………………………..…..64

Table 3. Isolate summary information table. Included for each isolate is the original ecosystem, leaf type, and decay stage that they were isolated from along with their molecular identification.

Also included is the physiological traits for each fungal isolate. These physiological traits include: growth rates on several media types, and activities for several different functional groups of enzymes. Cellulase reaction was quantified using the method found in Kasana et al. 2008. Guaiacol color change was used to determine the presence of lignin degrading oxidative enzymes

(Westermark and Eriksson 1974)…………………………………...………………………..…105

Table 4. Summary Table of percent variance explained for factors influencing leaf litter decomposition rates. Results are given for 1-month and 5-month data sets. Only significant explanatory factors are reported with nonsignificant factors indicated by

NS.……………………………………………………………………..…………………...…..107

Table 5. Table summarizing percent variance explained a for factors influencing log-transformed extracellular enzyme activity. Results are presented for each enzyme functional group at 1 month

x and 5 months. Abbreviations are the following: Nitr. = nitrogen obtaining enzymes, Phos. = phosphatase, Poly. = polysaccharide degrading enzymes, and Lacc. = Laccase. Only significant results are indicated with percent variance explained with nonsignificant factors indicated by

NS.………………………………………………………………………………….……...... …108

Table 6. Primer regions and examples of conserved sequences from fungal genomes…….....…143

Table 7. List of fungal isolates used to test GH28 clade F primers, and results from PCR reactions using selected primers……………………………………………………………………….….144

Table 8. Taxonomy of closest matches in the NCBI Genbank database for the twelve GH28-OTUs with the greatest number of sequences (all closest matches were endopolygalacturonase sequences)…………………………………………………………….……………...……...….146

Table 9. Summary of taxonomic names given to OTUs with potential ecological function.

Included in table is: phylogeny, citation(s), degree of confidence, and whether name is present in the data set, or it is used as a reference. Category of confidence is defined as the degree of certainty in functionl designation based upon literature review………………………….……………..…171

Acknowledgements

I would like to take this time to thank and acknowledge everyone who has helped me on my long academic journey. It is with extreme gratitude that I acknowledge my PhD advisor Dr

Chris Blackwood. During my time at Kent State University he has helped me in innumerable aspects of my research and provided me with advice not only on academics, but also for my career. He has been supportive of my ideas and allowed me to progress into the scientist/educator that I am today. Not only has he been an amazing mentor, but he has also become my friend and has never stopped believing in me during my time at Kent State University. Some of my fondest memories of my time at Kent have been our field work experiences in Manistee National Forest, and Jennings Woods. I will always be grateful for the time that I have spent under his guidance.

I would also like to thank my committee members Dr. Mark Kershner, Dr. Xiaozhen

Mou, and Dr. Mandy Munro-Stasiuk for all their support over the years. I would also like to thank the National Science Foundation, Kent State University and the Kent State University

Department of Biological Sciences for providing me with funding and support during my time here. Youngstown State University, my Master’s thesis advisor Dr. Carl Johnston, and members of the Johnston Lab also deserve acknowledgement as they provided me with invaluable experience and guidance which allowed me to finish my research at Kent State University.

I would like to acknowledge the Blackwood lab for their friendship, support, statistical know how, writing advice, and technical expertise. First and foremost, I would like to thank my

xii graduate student mentor’s Dr. Larry Feinstein and Dr. Oscar Valverde for their guidance and inspiration, especially for when I first started at Kent State University. Also, I would like to thank Oscar for all the amazing memories involving music and going to many concerts/music festivals. I would like to thank both Dr Florence Hsu and Mui Clark for their help in keeping me motivated, their advice, and providing me with friendship during my time at Kent. The friendships, conversations, and experiences that I have gained from Suhana Chattopadhyay,

Devinda Hiripitiyage, Eugene Ryee, Amber Horning, Anna Droz, Colleen Cosgrove, Anthony

Minerovic, Kristine Dominguez, Andrew Eager, Scott Vernon, Scott Menicos, Tom Zocolo,

Dean Horton, Alex Delvalle, Eddie Campana, Tiberius Bertea, Garrett Licata-Portentoso, and many others will last me a lifetime and will always bring back fond memories.

Finally, I would like to thank and dedicate this dissertation to my family and friends for all their support over the years. My parents Bill and Barb for always believing in me and always showing support for my time in academia. My older brother Greg for always having my back, providing me with advice, and being one of my biggest role models as I grew up. My best friends

Matt Purdy and Nick Koupal for always being there for me. My aunts, uncles, grandparents, and cousins for their support. A very special thanks goes to my Grandfather Ralph Demarco for giving me my love of fungi and Grandfather Donald Gacura for inspiring me to pursue the sciences. I would also like to thank my muse Jessica Fisher for being there for me no matter the situation and always providing me with emotional support.

Matthew D Gacura 08/17/2018

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Chapter 1:Introduction

Community Assembly and Community Ecology

Communities are complex assemblages of many distinct species that interact and live together in the same environment. These groupings can range from the simplicity of a few organisms found in acid mine drainage to the complexity of a tropical rain forest. While an important part of our environment, their formation and overall importance can be quite cryptic.

Thus, understanding how these communities assemble is a central question in ecology

(HilleRisLambers et al. 2012). Many mechanisms of community assembly have been studied, but they can largely be placed into two broad categories: environmental selection or stochastic mechanisms (Vellend 2010; Nemergut et al. 2013). Understanding of the relative importance of these mechanisms is needed to facilitate conservation and the recovery of various environments.

At the same time, understanding community assembly mechanisms is more important than ever due to anthropogenic processes fundamentally changing many parts of our biosphere (Jackson and Blois 2014; Williams et al. 2015).

Historically, studies that have tried to understand how these complex ecological units assemble have focused more on macroscopic organisms, such as many seminal studies dealing with plant and animal communities (Hubbel 1979; Diamond 1975; MacArthur 1960). However, as time has progressed so has our understanding of the natural world and the realization that communities made up of microorganisms are some of the most dynamic units in ecology and

1 extremely important in driving many ecosystem processes (Konopka 2009; Shade et al. 2012).

In terrestrial systems, microorganisms are largely responsible for many processes in the cycling of nutrients, such as nitrogen fixation and decomposition (Nanniperi et al. 2003). These microbial communities are incredibly complex and diverse, with millions of species and billions of individuals being contained in soil ecosystems (Bardgett and van der Putten 2014). These communities can contain high diversity even in small areas; for instance, it was found that in soil ecosystems, microaggregates of soil contained very distinct assemblages of bacteria and fungi

(Bach et al. 2018) and diverse communities of both bacteria and fungi have been found dwelling on different regions of the human body (Findley et al. 2013).

Fungi and their Importance in Decomposition

Fungi are a diverse group of microorganisms responsible for a wide a range of ecosystem processes. This group of microorganisms is found worldwide with many species possibly having a cosmopolitan distribution (Tedersoo et al. 2014). Members of this group are recognized as both important symbionts and pathogens in a wide range of larger organisms. For instance, fungal plant pathogens attack all groups of plants, use a wide variety of mechanisms of attack, and are major drivers in the distribution and mortality of plant species (Pandey et al. 2016; Maron et al.

2011). At the same time, every major plant group is recognized as having a close association with fungal symbionts, either as endophytes or as mycorrhizal fungi. These symbioses are responsible for a wide range of benefits for their plant host, including drought tolerance, nutrient acquisition, and pathogen defense (Krishnakumar et al. 2013; Porras-Alfaro and Bayman 2011).

Fungi are also highly recognized for their importance in the process of decomposition and thus the recycling of nutrients locked away in dead tissue, such as senesced plant material.

Fungi play key roles in decomposition by driving reactions that catalyze the breakdown of complex compounds into more easily metabolized forms. Decomposition reactions are catalyzed by specialized extracellular enzymes produced by saprotrophic fungi and released into the surrounding environment (Zak et al. 2006). These extracellular enzymes break down specific structural components of plant material, ranging from highly labile compounds, such as pectin, to more recalcitrant compounds such as cellulose and hemicellulose, and finally to highly recalcitrant lignin (Baldrian et al. 2011). Due to the energy intensive nature of enzyme production, their production is likely to be associated with specific members and functional groups found in saprotrophic fungal communities. The succession of saprotrophic microbes on plant material is thought to be highly related to changing litter chemistry. Early colonizing saprotrophs are thought to exploit labile compounds the fastest, while organisms more capable of breaking down recalcitrant tissue colonize later (Moorhead and Sinsabaugh 2006; Rinkes et al.

2014). This breakdown of plant tissue will continue until the most recalcitrant materials are left over. This is the traditional view of succession and community assembly of these complex communities. However, newer methods, more recent research and expansions in knowledge about taxonomy have called this theory into question and indicate other mechanisms may be at work.

Deterministic Community Assembly Processes in Microbial Ecology

As stated previously, factors playing a role in community assembly have been broadly divided into two distinct groups. One group includes environmentally based deterministic processes, in which organisms are favored if the environment matches the species niche, such as plant litter chemistry selecting for specific types of saprotrophic fungi. The other grouping is based upon more stochastic processes that directly affect the spatial and temporal variability of

3 species distributions. Each of these areas of research have a rich history and there is much debate as to which of these processes is more important in structuring communities (Hanson et al. 2012;

Vellend et al. 2010).

There has been a great deal of research on the impacts that the environment has on the assembly of these complex communities. Under this paradigm, community assembly is determined by the species niche, which can be thought of as the range of environmental conditions in which a species can survive and optimally reproduce. This mechanism was thought to be the dominant mechanism in community assembly and can be best summarized with the

Baas-Becking paradigm which states that “Everything is everywhere, but the environment selects” (de Witt and Bouvier 2006). Deterministic community assembly depends upon abiotic environmental conditions, climate, interactions with hosts, and other selective pressures playing a role in the distribution of species. As stated earlier, the composition and succession of saprotrophic fungal communities is thought to be heavily influenced by the changing chemistry of plant material as decomposition progresses (Voriskova and Baldrian 2013; Snajdr et al. 2011).

A great deal of research has focused on the influence that environmental factors have on other types of microbial communities as well (Garbeva et al. 2004 and Aponte et al. 2010). In studies on soil microbial communities, soil pH (Bru et al. 2011), moisture content of the soil, (Lennon et al. 2012) and texture (Moebius-Clune et al. 2013) have all been found to be important in structuring community composition. Anthropogenic alterations to the environment can also have an influence on microbial communities. For example, elevated levels of CO2 and O3 were found to change soil and litter fungal communities after 5 years of exposure (Edwards and Zak 2011).

In addition, it was also found that anthropogenic nitrogen addition can directly lead to decreases in the relative abundance of lignin degrading fungi on woody debris (Entwistle et al. 2018).

However, the overall effect of environmental conditions on community assembly has become more controversial. From a theoretical standpoint, methods of detecting environmental filtering have been criticized as unable to distinguish between filtering and species competition

(Cadotte and Tucker 2017; Kraft et al. 2015). Other studies have found that importance of deterministic processes might vary in dissimilar groups of organisms, with some groups being strongly structured by environmental selection while others being more stochastic in nature

(Powell et al. 2015; Goldmann et al. 2016). While environmental factors have been traditionally seen as major mechanisms in community assembly, other processes may play an important role as well.

The Role of Stochastic Mechanisms in Microbial Community Assembly

In addition to environmental selection, mechanisms involving more stochastic processes may also be important in community assembly. These stochastic processes depend upon a wide range of mechanisms including: probabilistic dispersion, random loss of species, and ecological drift (Chase and Myers 2011). These types of mechanisms result in patterns that appear as distance decay and dispersal limitation and may also play an important role in structuring ecological communities. Neutral theory represents an extreme view of community assembly dominated by stochastic processes, in which it is assumed that all species are equivalent in their abilities to survive and reproduce in a given environment (i.e., have identical niches; Chave

2004). Due to this functional equivalence, the main factor regulating community assembly in a habitat patch is dispersal from the surrounding metacommunity (Hubbel 2001). Therefore, one prediction of neutral theory is a pattern of decreasing similarity in community composition with increasing spatial separation, also known as a distance decay pattern. Many communities have been found to display distance decay and dispersal limitation to varying degrees (Soininen et al.

2007). It has been thought that fungi would not be spatially limited because many fungal species disperse through prolific spore production, and that fungal communities would be structured by environmental selection (Peay et al. 2010a). However, it is also possible that, due to the small spatial scales in which these organisms reside and limitations in dispersal mechanisms, many could be spatially limited and demonstrate a distance decay pattern. For instance, Norros et al.

(2012) found that even though wood decay fungi had a potential for much further dispersion, the diluting effect of distance and low chance of spore establishment success combined to make fungi dispersal limited to small distances. Indoors, assemblages of fungal spores have been shown to be limited by dispersal, with the environment outside of the buildings playing the largest role in determining fungal community composition (Adams et al. 2013). In soil, ectomycorrhizal community species richness tends to decrease as the communities increase in isolation, also suggesting that fungal spores have a limited dispersal distance (Glassman et al.

2017; Peay et al. 2010b). Finally, dispersal limitation was found to be one of the driving forces of fungal root community composition found in soil in Australia (Beck et al. 2015).

Besides variation in probabilities of colonization due to distance, stochasticity in assembly history (i.e., order of species colonization) may also impact subsequent community composition. This dynamic process is called "priority effects" (Chase 2003). Several long-term studies have shown that priority effects may play a role in assembly of fungal and invertebrate communities responsible for the decomposition of woody debris (Weslien et al. 2011; Dickie et al. 2012). In wood degrading fungi, it was found that decomposition and community composition was directly influenced by colonization history (Heilmann-Clausen and Boddy 2004; Fukami et al. 2010; Lindner et al. 2011). If endophytic fungi are able to shift to a saprotrophic lifestyle upon leaf senescence, they would be ideally situated to influence the structure of subsequent

6 saprotrophic fungal community assembly through priority effects because they pre-colonize the tissue while it is alive. Hence, differences in the species composition of endophytic fungi may be able to influence future colonization through varying competitive exclusion, production of secondary metabolites, selective use of plant compounds and movement of soil nutrients. The degree to which assembly history impacts community composition is still relatively uncertain, especially when compared to much more studied deterministic mechanisms. Priority effects must be addressed to gain a full understanding of how communities assemble (Fukami et al. 2015).

Communities Assembled by Multiple Mechanisms

While stochastic and deterministic processes may seem quite distinct from one another, it is now thought that these mechanisms work in conjunction with one another to assemble communities. Many community assembly studies now incorporate stochastic mechanisms with environmental selection, and conceptual developments have moved past this dichotomy over the past decade (Zhou and Ning 2017). For example, in a study on stream invertebrate communities it was found that consistency with neutral or niche processes depended on trophic level examined

(Thompson and Townsend 2006). Shipley et al. (2012) found that tree community assembly in

French Guiana followed deterministic processes at large scales (1-ha) and was structured stochastically at smaller scales. In arbuscular mycorrhizal fungi, both dispersal limitation and soil pH were found to have significant effects on community composition (Dumbrell et al. 2010).

Community assembly mechanisms may also vary based on the functional group investigated. Functional groups are sets of organisms with similar ecological roles. For instance, in a global soil survey of fungal sequences, it was found that while climate was very important in community composition, other factors dealing with soil conditions, plant richness, and proximity

7 varied in importance for differing functional groups such as pathogens and saprotrophs

(Tedersoo et al. 2014). In another study performed on desert microbe communities, it was found that microbes found in different trophic and functional groups were influenced by stochastic and niche-based assembly mechanisms to different degrees (Caruso et al. 2011). While it is agreed that these previously mentioned mechanisms (environmental selection, dispersal limitation, community history) have varying degrees of importance in community assembly, few studies have tried to rigorously test many of them at the same time (Weiher et al. 2011). Tying together these various mechanisms will be key in understanding the complexity of community assembly

(Agrawal et al. 2007).

The Influence of Spatial Scale on Understanding Community Assembly

It has also been recognized that spatial scale plays a key role in determining what factors are found to influence microbial community assembly (Nemergut et al. 2013; Ettema and Wardle

2002). Truly deterministic community assembly is essentially spatially independent. However, depending upon the scale of the community in question, influencing factors may become less important or be lost as unexplained variation at differing spatial scales. For example, while studying stream communities, Paavola et al. (2006) discovered that community concordance between aquatic arthropods increased as the spatial scale of inquiry increased from individual streams to entire regions. Another example, at a much smaller spatial scale, focused on saprotrophic fungi found on individual decaying leaves (Feinstein and Blackwood 2013). At the scale of individual leaves, fungal community similarity decreased as distance between leaves increased, while at a larger scale, environmental factors explained a significant amount of variation between communities. A significant taxa-area relationship was also found when analyzing saprotrophic fungal communities on individual leaves in the same area (Feinstein et al.

2012). Both studies have found interesting results at spatial scales not typically utilized.

However, they were performed in a single geographic area at relatively small spatial scales and it would be of interest to see how these factors hold across much larger areas.

Factors Influencing Functional Traits and Ecosystem Processes in Microbial Communities

While most investigations into community assembly have emphasized taxonomic composition, the functional characteristics of a community are key to understanding how community composition will affect ecosystem processes. These functional characteristics are largely determined by functional traits, which are physical, behavioral and chemical characteristics of an organism that impact its fitness and surroundings (Nock et al. 2016). Due to the importance of these functional traits in survival, they are expected to be linked to environmental parameters as they should help organisms acquire resources and deal with environmental conditions. The sum or average value of a functional trait across all the individuals that make up a community is called a community-aggregated functional trait (Shipley et al. 2006). Whether the same processes that impact community composition will also impact community aggregated functional traits and related ecosystem process is central question in ecology (Nemergut et al. 2014; Graham et al. 2016). For instance, Wallenstein et al. (2009) found that, in tundra soils, season influenced the production and temperature sensitivity of extracellular enzymes, which are produced by individual decomposer microorganisms but measured on a community-aggregated basis. Atmospheric nitrogen deposition has been shown to decrease lignin degrading capabilities in saprotrophic fungal communities but had a minimal effect on fungal community structure (Hassett et al. 2009). The same was seen with the introduction of elevated O3 and CO2 concentrations, which resulted in a minimal effect on community composition but an increase in enzymatic activities (Edwards and Zak 2011). Biotic

9 interactions between fungi (Snajdr et al. 2011) and between fungi and surrounding plant communities (Schweitzer et al. 2011) can also influence decomposition rates and enzyme production.

Factors that influence the structure of communities will be especially important in determining community aggregated traits if there is a strong correlation of these traits with specific taxa. This relationship would be evident if changes in microbial community composition would result in changes to community-aggregated functional traits or ecosystem processes themselves. Several studies have demonstrated this link (Bier et al. 2015). For instance, evidence supporting this pattern was observed in experimental manipulations of aquatic bacterial communities that resulted in changes to both broad and specialized community functions

(Delgado-Baquerizo et al. 2016). These patterns have also been demonstrated in soil microbial communities. Philippot et al. (2013) found that reducing the denitrifier diversity in soils resulted in a decrease in potential denitrification rates. It was also shown in several forest and agricultural soil environments that soil microbial community composition is related to ability to utilize carbon substrates (Chodak et al. 2016; Bolscher et al. 2016).

Although there is some evidence for specificity in functional traits, there is also evidence to suggest that community aggregated traits are decoupled from community structure due to functional redundancy in microbial communities. Functional redundancy is thought to be a result of both the extremely high degree of taxonomic diversity found in microbial communities and that most metabolic processes can be performed by a wide range of organisms (Louca et al.

2018). This pattern is thought to be prevalent especially for saprotrophs involved in the decomposition of labile compounds. Rarity of enzymes capable of the breakdown of recalcitrant compounds such as lignin would make this mechanism less functionally redundant as few

10 species are capable of their production (McGuire and Treseder 2010). For instance, even though drastic changes occurred in bacterial and fungal growth and composition along soil pH gradients, levels of carbon mineralization changed very little (Rousk et al. 2009). Wood decomposition rates have been found to be unaffected despite variation in saprotrophic fungal community composition brought on by the inclusion of invasive rats (Peay at al. 2013) and direct experimental manipulation in the field (Dickie et al. 2012). Thus, the importance of functional redundancy in a community may vary based upon communities being analyzed, the function/process in question, and the spatial scale of inquiry (Schimel and Schaeffer 2012). Due to this conflicting evidence, functional redundancy in microbial communities is still a controversial subject, with more research needed to be able to predict the conditions under which it is or is not important in the environment.

Advancements in Microbial Community Ecology

Microbial communities have traditionally been difficult to analyze due to their cryptic nature, inability to be cultured, and high level of biodiversity (Peay et al. 2008). However, with advances in next generation sequencing technology and more robust statistical software, we are more likely than ever to determine the true nature and diversity of microbial communities

(Franzosa et al. 2015). For instance, pyrosequencing has allowed us to expand our ability to sequence DNA without the need of cloning vectors, increasing sequencing rates of PCR amplified fragments from less than 100 to 100,000s (Shokralla et al. 2012). Pyrosequencing has been used to understand not only taxonomic diversity, but also functional diversity and the dynamic nature of microbial communities from environments such as decomposing wood, decomposed leaves, and living leaves (Kazartsev et al. 2018; Purahong et al. 2016; Jumpponen and Jones 2009). Even newer more advanced sequencing methods, such as Illumina MiSeq,

11 allows for sequencing of communities at a fraction of the cost per sequence and allowing for the data sets to now include millions of sequences at once (Taylor et al. 2016; Schmidt et al. 2013).

With these methods many microorganisms that were previously unculturable, and thus undetected, are just now being recognized as extremely abundant in the environment. For example, the newly discovered phylum of basal fungi Rozellomycota has been recognized to be not just in aquatic environments, but much more ubiquitous (Grossart et al. 2016).

Advances in technology are not only allowing us to more effectively view communities taxonomically, but also characterizing the functions of these communities and connecting them to ecosystem processes. Past challenges in microbial ecology have dealt with understanding how microbial communities relate to their functional roles (Rastogi and Sani 2011). However, more advanced biochemical methods and greater amounts of molecular tools, such as functional gene primers, are allowing for an understanding of microbial functional diversity (Treseder et al.

2015; Peay et al. 2008). For instance, the use of fluorescent compound linked substrates (ex. 4- methylumbelliferone or MUB) and micro plate readers have allowed for the linking of microbial communities to ecosystem processes (Burns et al. 2013; Zak et al. 2006). At the same time molecular methods allow for the analysis of community aggregated functional traits by using culture independent methods and searching for the presence of functional genes (Zhou et al.

2015; Hazen et al. 2013). These tools allow the scientific community to better understand taxonomic communities their related functions.

Global Project Hypotheses

This project was performed to explore the influence of multiple assembly mechanisms in saprotrophic fungal communities, and the related ecosystem process of plant litter decomposition. The main goals of this project are to determine how deterministic processes,

12 dispersal limitation, and priority effects influence the assembly of saprotrophic fungal communities across multiple spatial scales. These scales range from discrete habitat patches in which individual fungi are likely to engage in competition (individual leaves, litter bags) to forest stands characterized by broad environmental heterogeneity due to development on different glacial land forms. It is expected that, due to the stochastic nature of microbial communities, there will be stronger impact of dispersal limitation and priority effects on the community composition of fungi. As a secondary goal, this project was performed to determine the degree of functional redundancy in saprotrophic fungal communities. It is expected that, due to the high levels of functional redundancy thought to be found in fungal communities, environmental factors (e.g., ecosystem type, leaf type) will play the strongest role in determining community aggregated functional traits, presence of functional groups, and ecosystem processes.

Observational and experimental projects were set up and performed in Manistee National Forest,

MI to take advantage of the presence of a variety of environmental and spatial factors.

Study Site (Manistee National Forest)

The study site utilized in this project was Manistee National Forest, a large forest in the northwestern part of the Lower Peninsula of Michigan (Figure 1 and 2). As with many forests,

Manistee National Forest has experienced extensive logging in its past, particularly from the late

1800s to the early 1900s. This area has since recovered to a second growth forest covering approximately 1 million acres. The topography and soil parent material in the landscape was largely determined during the previous glaciation of the area, around 9000 years ago (Figure 1).

The glacial retreat has left the entire area with many glacial landforms (moraines, outwash plains, alluvial plains, etc.) that have a key role in ecosystem development (Host and Pregitzer

1992). Ecosystems in this study area can be separated based upon dominant over story tree

13 coverage, previously characterized soil properties, and nutrient dynamics (Host et al. 1988, Zak et al. 1989). The soils that dominate the sites utilized here generally contain a high sand content

(percentages varying from 55 to 72%) and are mainly classified as Typic Udipsaments and Entic

Haplorthods (Host et al. 1988; Zak et al. 1989). Major soil suborders that can be found in the

Manistee area and near experimental plots can be found in Figure 2. The three ecosystems this study will focus on are black oak/white oak (BOWO), sugar maple/ red oak (SMRO) and sugar maple/ basswood (SWBW). The leaf litter generated by these ecosystems is of very different biochemical quality and reflective of other processes aboveground and belowground (Zak et al.

1989, Host et al. 1988). An advantage of working at this study site is the high environmental heterogeneity over small scales and replication of ecosystems over a wide range of spatial scales.

These differences in the environment can be seen in a map of Manistee, along with sites utilized in this study (Figure 1 and 2).

This area is also a convenient location for research due to its already rich history of large scale ecological research dealing with the drivers of microbial community composition, their related ecosystem processes, and the impacts of anthropogenic nutrient additions. For example, by analyzing extracellular enzymes from soil sampled from several ecosystems located in

Manistee, it was found that different environmental factors (soil quality, tree coverage, etc.) played a large role in their activities (Sinsabaugh et al. 2008). Phospholipid fatty acid analysis indicated that stands producing more recalcitrant leaf litter (i.e., BOWO stands) contain higher amounts of fungal biomass (Waldrop et al. 2004, Gallo et al. 2004). In addition, denaturing gradient gel electrophoresis (DGGE) indicated that ecosystems producing more recalcitrant leaf litter (BOWO and SMRO) had a greater abundance of basidiomycete white-rot fungi capable of

14 production of extracellular enzymes that facilitates degradation of lignin-rich leaf litter

(Blackwood et al. 2007).

Dissertation Chapter Summaries

Chapter 2 (Niche vs Neutral: Factors influencing the Structure of Saprotrophic Fungal

Communities at Fine and Large Spatial Scales)

The focus of chapter 2 was to understand the roles of stochastic and deterministic mechanisms in the assembly of saprotrophic fungal communities and the distribution of fungal functional groups at both a small scale (assemblages of adjacent leaves) and large scale (between forest stands). In this chapter we quantified fungal community composition by utilizing two types of molecular methods in the quantification of microbial community composition, terminal restriction fragment length polymorphism (T-RFLP) and pyrosequencing. To understand the impact of various assembly processes at smaller scales, leaves were sampled and treated as discrete habitat patches. The proximity of neighboring leaves was recorded so that the effects of distance decay could be compared to the effects of leaf type on community composition.

Similarly, at large scales we discerned the impact of spatial factors and environmental factors. In addition to community composition we also characterized the likely functional roles of the different OTUs obtained in our molecular analysis and. analyzed the impact that the community assembly mechanisms had on their distribution.

Chapter 3 (The Role of Priority Effects in Assembly of Saprotrophic Fungal Communities)

In chapter 3 we performed an experiment to focus on the role that community assembly history has on microbial communities across the same field sites analyzed in chapter 2. To gain a better understanding of the role of priority effects, we compared its effects to both deterministic

15 environmental factors and dispersal limitation (distance decay). At the same time, we investigated how these distinct community assembly mechanisms influence community aggregated functional traits and ecosystem processes. This was performed by first obtaining fungal isolates from senesced and decomposed leaf litter from Manistee National Forest. Three functionally distinct isolates were then inoculated onto leaf litter to create different starting communities and placed back into the field. Community composition, extracellular enzyme production, and percent mass loss of leaf litter were quantified for each litter bag to understand the relative importance of priority effects and other community assembly mechanisms.

Chapter 4 (Comparison of Pectin-Degrading Fungal Communities in Temperate Forests Using

Ascomycete Specific Glycosyl Hydrolase Family 28 Pectinase Primers)

The primary goal of chapter 4 was to develop primers for the amplification of pectinase genes, facilitating analysis of the distribution of pectinase genes in the environment and how these functional genes relate to microbial community composition. Many ecosystem processes that microbial communities are responsible for are catalyzed by extracellular enzymes. One such group of enzymes are pectinases, which are a diverse group of enzymes utilized by many microbes, including fungi, for breakdown of the polysaccharide pectin that makes up part of plant cell walls. While these enzymes are utilized by saprotrophs, they are also important plant pathogen virulence factors (Zhao et al. 2013). However, even though pectinases are of great importance in understanding many functional groups, no primers have been developed that are able to amplify pectinase encoding genes. In this chapter primers specific for the GH28 family of fungal pectinases were developed using a database of 293 fungal GH28 genes from 40 genomes

(Sprockett et al. 2011). These primers were then tested on both pure culture fungi and

16 environmental samples (leaf litter). This information was then compared to the community composition of fungal saprotrophs found on the leaf litter.

Figure 1. Map of Manistee National Forest MI. Indicated on this map are important glacial landforms and other geologic structures that determine soil characteristics and ecosystem type. Nine sites were utilized in this study and their spatial locations can be observed from the figure. These sites are also color coded by ecosystem type as follows: Black = BOWO, Red = SMRO, and Green = SMBW. Map generated using ArcGIS; glacial land systems taken from Michigan Department of Natural Resources (generated April 2017).

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Chapter 2:Niche vs Neutral: Factors Influencing the Structure of Saprotrophic Fungal

Communities at Fine and Large Spatial Scales.

Abstract

The assembly of microbial communities is an extremely complex process that is influenced by a wide range of mechanisms. These mechanisms can be categorized as environmental factors, dealing with niche partitioning, or more stochastic factors, that rely upon dispersal limitation. At the same time, the importance of these mechanisms can be influenced by the spatial scale in question, with factors varying in importance at large and small scales, adding additional complexity to this process. In this study, we have analyzed factors that influence community assembly at two spatial scales, between and within forest stands. Senesced leaf litter was gathered from 9 forest stands that were from one of three ecosystem types at two time points. To capture within-forest stand variation, leaves were gathered and their degree of contact to one another was recorded. These leaf interactions were assembled into proximity matrices and utilized determine the impact of distance decay on leaf litter communities found on individual leaves. Saprotrophic fungal communities were analyzed on all gathered leaves using T-RFLP, while pyrosequencing was performed on smaller subsets from 6 of the previously mentioned sites. OTUs gathered from pyrosequencing were placed into functional groups, after an intense literature review. Analysis of fungal community profiles indicated that both stochastic and deterministic factors were important in determining community composition, however the

35 importance of these factors varied by spatial scale. At the forest stand scale, there was a strong impact of both ecosystem type and site replicate in determining community composition, with ecosystem type explaining the most variation in both TRFLP and pyrosequencing data sets. It was found that stochastic factors played a large role in explaining variation for within forest stands, with distance decay being particularly important in oak dominated forest stands. When analyzing communities as functional groups, environmental factors, such as ecosystem and leaf type, played the largest role in explaining variation. This pattern was further investigated further when each functional group was analyzed separately, with groups being highly determined by spatial distance and others highly determined by environmental conditions. This study demonstrates that both deterministic and stochastic factors can play varying roles in the assembly of microbial communities. However, their importance varies based upon spatial scale and whether communities are grouped together functionally or taxonomically.

Introduction

Understanding the factors that influence community assembly is one of the primary challenges of ecology. Fungal communities are particularly challenging due to their complexity, high species diversity, and the cryptic nature of many fungal species (Taylor et al. 2014).

However, recent conceptual advances in community ecology and methodological advances in molecular microbial ecology have created the opportunity to test competing theories about microbial community assembly (Peay et al. 2016). Processes that drive community assembly range from selection of species by local environmental conditions to stochastic variation in species colonization (Hanson et al. 2012). Although these hypothesized mechanisms are often presented as alternatives, most communities will not be influenced by only environmental or

36 stochastic factors, leaving community ecologists with the challenge of understanding where a community falls in this spectrum (Chase 2003).

Previous observations of fungal communities have indeed supported both environmental selection and stochastic processes, indicating that they may operate simultaneously or that the strength of each may depend on other factors such as dispersal frequency and ecosystem characteristics. For example, environmental selection has been indicated by spatial patterns in fungal community composition linked to moisture content (Lennon et al. 2012), soil texture

(Moebius-Clune et al. 2013), or surrounding tree species (Barberan et al. 2015). Saprotrophic fungal communities vary between forest stands dominated by differing tree species (Urbanova et al. 2015), in part due to contrasting senesced leaf litter chemistry in differing tree species

(Schneider et al. 2012). This may also occur at the level of individual leaves, with fungal community composition depending upon leaf types (Aneja et al. 2006; Kurbatova et al. 2009).

However, stochastic mechanisms, that are independent of environmental factors, may also play an important role in fungal community assembly. "Priority effects" refers to initial colonists affecting the performance of subsequent colonizers and has been demonstrated in fungal community assembly in several environments (Fukami et al. 2010, Kennedy et al. 2009). Neutral theory adopts an extreme form of priority effects, assuming that species are equivalent in their abilities to survive and reproduce across varying environmental conditions, so local community assembly is determined entirely by the order of colonists that disperse from the surrounding metacommunity (Hubbel 2001; Chave 2004). If dispersal ranges are limited, neutral dynamics can result in spatial patterns (patchy distributions) that are independent of environmental conditions. For example, Feinstein and Blackwood (2013) examined individual leaves found together in 20x20 cm patches on the forest floor and discovered a distance-decay pattern

37 independent of leaf type. Spatial patterns consistent with this scenario have also been found, at various spatial scales, in communities of soil asocmycota (Green et al. 2004), ectomycorrhizal fungi (Peay et al. 2010), soil bacteria (Eisenlord et al. 2012), and plant saprotrophic microbes

(Qvit-Raz et al. 2012). However, if colonists are not dispersal limited, strong priority effects could result in both independence from environmental conditions and lack of spatial patterns.

One explanation for patterns in fungal community composition being consistent with multiple community assembly processes is that fungal communities are comprised of multiple functional groups. These groups are responsible for different biological interactions and ecosystem functions (i.e., necrotrophs, biotrophs, mycoparasites, mycorrhizae, endophytes, yeasts, wood degraders, and primary saprotrophs (non-wood plant tissue degraders)) (van der

Heijden et al. 2008). The different functional groups have distinct environmental requirements for growth and survival, which should result in strong environmental niche selection at the levels of whole functional groups (i.e., comparisons among functional groups) (Bahram et al. 2016).

For instance, yeasts are thought to be tolerant of desiccation and nutrient depletion and should therefore have higher relative abundance in environments characterized by these conditions

(Treseder and Lennon 2015). Fungi that can break down recalcitrant compounds (e.g., wood degraders) are expected to be more prevalent on plant material with a higher lignin content

(Rajala et al. 2012). In contrast, stochastic factors may be particularly strong within functional groups because species in the same functional group should have similar niche requirements.

This functional redundancy may allow species within a functional group to vary independently from the environment, depending on their modes of dispersal, even while the overall abundance of the group is constrained. We predict that groups that disperse and spread slowly (i.e. wood rot fungi and yeasts) or that have patchier distributions (mycorrhizal fungi) should be more

38 influenced by dispersal limitation (Belisle et al. 2012; Horn et al. 2014; Peay and Bruns 2014).

Necrotrophic plant pathogens and primary saprotrophs are prolific spore producers and have low environmental specificity (Brown and Hovmoller 2002; van Kan 2006; Halbwachs et al. 2015), and thus may largely be influenced by priority effects without a strong spatial pattern. On the other hand, endophytes and biotrophs should be structured by biological environmental factors, such as leaf type or host, due to their high degree of environmental specificity (Wearn et al.

2012; Spanu and Kamper 2010).

The importance of environmental selection, dispersal limitation, or priority effects in community assembly may also be influenced by spatial scale (Ettema and Wardle 2002; Green and Bohannan 2006; Martiny et al. 2011; Horn et al. 2015). Some mechanisms may not be recognized if they are occurring at a larger or smaller scale than the one under investigation, although they still may play a significant role in assembly processes. Scale dependence in community assembly has been demonstrated several times in soil fungal communities (Green et al. 2004; Pellissier et al. 2014). For instance, Robinson et al. (2009) found that prairie soil fungal communities varied significantly among depths at small scales in the same soil horizon, but not at larger scales (1 meter horizontally). Likewise, Feinstein and Blackwood (2013) found that, at the scale of individual leaves, fungal community similarity decreased as distance between leaves increased, but at the scale of ecosystems, environmental factors were more important.

This study was performed to better understand how deterministic and stochastic factors influence saprotrophic fungal communities at both individual leaf and forest stand spatial scales.

We focused on individual senesced leaves as natural sampling units due to their discrete nature as distinct nutrient patches with independent histories. We tested the following hypotheses: H1.)

At small scales (between adjacent leaves), overall fungal community composition is influenced

39 by both environmental selection (through leaf type) and dispersal limitation/priority effects. H2.)

In contrast, at larger spatial scales (between forest stands), community composition is more influenced by environmental selection (through ecosystem type). H3.) Relative abundances of fungal functional groups will be strongly structured by environmental factors, including ecosystem and leaf type. H4.) In contrast, the importance of factors influencing community composition within functional groups will depend on the dominant dispersal mechanisms in each functional group. To test these hypotheses, leaves were gathered from well characterized ecosystems in the northern lower peninsula of Michigan. Individual leaves with differing chemical properties were collected after mapping their locations on the forest floor. Fungal communities were characterized using two strategies: terminal restriction fragment length polymorphism (T-RFLP) analysis was used to achieve high replication of individual leaf communities, whereas pyrosequencing was used to obtain high resolution profiles and functional characterization of a more limited number of communities.

Materials and Methods

Sites and Sample Collection

Leaves were sampled from Manistee National Forest, in northern lower Michigan, where ecosystems have been previously delineated based on over-story tree species, understory plants, and soil properties (Host et al. 1988; Zak et al. 1986). Leaves were taken from 9 previously studied forest stands, representing 3 sites from each of the following ecosystems: sugar maple/basswood (SMBW), sugar maple/red oak (SMRO) and black oak/white oak (BOWO)

(Figure 3). Sampling sites in each forest stand were selected where there was approximately equal coverage of each dominant leaf type on the forest floor.

In late April and late August of 2010, thirty decayed leaves (which senesced and fell in

Oct/Nov 2009) were collected from a ~20x20 cm area on the forest floor of each forest stand. A digital camera, equipped with a tripod, was used to photograph the forest floor before and after each leaf was removed. Each leaf was collected using ethanol sterilized gloves and placed individually into a bag. Samples were transported to the lab on ice and then freeze dried and stored at -80oC. Leaves were than ground in a Genogrinder (SPEX Certiprep 2000) with sterilized 1 mm steel beads for two minutes at 1000 beats per minute.

Image Analysis and Construction of Geographic Distance Matrices

The "geographic distance" between leaves in the forest floor was quantified by constructing a network of physical leaf adjacencies from their positions on the forest floor, following Feinstein and Blackwood (2013). Pictures of the forest floor before and after sampling each leaf were turned into image stacks using the software ImageJ (Schneider et al. 2012). Leaf images and their locations were used to estimate the percent area overlap between each pair of leaves (0%, 20%, 40%, 60%, 80% or 100%). Network distance matrices were then obtained by finding the shortest path between each pair of leaves in the adjacent leaf network topology using the package RBGL (Carey et al. 2016) and the software Graphviz (Ellson et al. 2000) in the R statistical analysis program (R core team 2014). The distances were measured either in unweighted steps (e.g., leaves that are touching are one step apart, leaves separated by another leaf are two steps apart, etc.) or in steps weighted by the overlap between each pair of leaves.

There were negligible differences in results obtained using these two-distance metrics, so all results shown are from the analysis of unweighted step matrices. Depth of each leaf was also noted and recorded as another explanatory variable.

DNA extraction

A CTAB extraction method was used to extract DNA from freeze dried leaf tissue (Wu et al. 2011). Subsamples were frozen in liquid nitrogen and then ground into a fine powder using a

Genogrinder for two minutes at 1000 strokes per minute. This ground material was then suspended in a CTAB/β-mercaptoethanol buffer and incubated at 60oC for 20 min. The material was centrifuged, and the supernatant was mixed with concentrated chloroform and centrifuged again. The aqueous layer was collected and mixed with ice-cold isopropanol and allowed to sit at

-20oC overnight (~16hrs). The DNA was pelleted by centrifugation and washed one time with cold 70% ethanol. The resulting pellet was air dried and then re-suspended in 50ul of sterile water.

T-RFLP Community Analysis

Terminal restriction fragment length polymorphism (T-RFLP) was used to determine fungal community composition of each leaf as in Feinstein et al. 2013. The fungal internal transcribed spacer region (ITS) was amplified with the primer set NSI1F (5’-

GATTGAATGGCTTAGTGAGG) and NLB4R (5’-GGATTCTCACCCTCTATGAC) (Martin and Rygiewicz 2005). The NSI1F primer was labeled with HEX fluorescent dye. PCRs were performed using a DNA engine Dyad Peltier thermal cycler (Bio-Rad, Hercules, CA, USA) with the following protocol: 95oC for 3 minutes followed by 35 cycles of 94oC at 30 seconds, 60oC for

30 seconds and 72oC for 1 minute 30 seconds. For each PCR, the following reaction mix was utilized: Ammonium buffer (1X) (B-Bridge, Santa Clara, CA, USA), bovine serum albumin (0.5 mg/mL), MgCl2 (3.0 mM), dNTPs (0.2mM each), Taq polymerase (0.03 U/uL) (B-bridge), primers (0.16uM each). Negative controls were used for each set of reactions. PCR products were first purified using the UltraClean PCR Cleanup Kit (MO BIO Laboratories, Carlsbad, CA,

USA). Samples were then digested using 10 units of the restriction enzyme HaeIII (New England

Biolabs, Ipswich, MA, USA) for 16 hrs at 37oC. Digested samples were further cleaned using

E.Z.N.A. DNA Probe Purification kit (Omega Bio-tek, Norcross, GA, USA). The samples were then sent to the Ohio State Plant Microbe Genomics Facility for capillary electrophoresis on a

3730 DNA Analyzer using a LIZ1200 size standard. For each sample, only peaks between 50-

600bp in size and greater than 0.5% of relative peak abundance were included in the analysis.

Pyrosequencing Community Analysis

Pyrosequencing was also performed on a subset of leaves taken from samples used for T-

RFLP analysis. Seven leaves were selected from each of the SMBW and BOWO stands (42 leaves in total). A 350 bp segment of the fungal ITS1 region was amplified using the tag encoded primers ITS1F (5’-CTT GGT CAT TTA GAG GAA GTA A) and ITS2 (5’ GCT GCG TTC TTC

ATC ATC GAT GC) (Buee et al. 2009). PCR amplifications were performed using the same protocol that was used for T-RFLP community analysis except with negative controls being used for each sample-primer combination.

Amplified PCR products were purified using a 1% agarose gel, and excised bands were cleaned with an UltraClean GelSpin DNA Extraction kit (MoBio laboratories, Carlsbad, CA).

DNA samples were then further purified using Agencourt Ampure XP purification beads

(Agencourt Bioscience Corporation, MA, USA). DNA concentrations were quantified using the

PicoGreen dsDNA Assay Kit (Life Technologies, Eugene, OR, USA). All samples were then brought to a concentration of 1,000,000 molecules of DNA and then pooled together in a equimolar concentration. This pooled sample was then amplified with emulsion PCR and then sequenced in a FLX GS Junior 454 Pyrosequencer (454 Life Sciences) following the manufacturer’s instructions.

Sequence data processing

After completion of the pyrosequencing run, raw sequencing data was taken and then further analyzed with the program Quantitative Insights into Microbial Ecology (QIIME)

(Caporaso et al. 2010). In QIIME sequences were demultiplexed by first filtering sequences without an identifying barcode and/or correct primer sequence and any sequences below 100 bp in size and below a quality score of 25 were removed. Sequences were then assigned to their original leaf sample based upon their barcode and then denoised. Sequences were then clustered into 97% similarity OTUs with the UCLUST algorithm in QIIME. Finally, singletons were removed from our data set and it was then exported as a Biological Observation Matrix (biom) table. We searched for matches to a representative sequence, longest sequence, from each OTU using Basic Local Alignment Search Tool (BLAST; Altschul et al. 1990) in both Genbank and

Unite databases. The last common ancestor algorithm was then used to parse BLAST scores and taxonomy of the top 50 similar sequences using the software MEtaGenome Analyzer 5.0

(MEGAN; Huson et al. 2011). Taxonomic identifications from the curated UNITE database were preferred unless the identification from Genbank (which is more comprehensive) agreed with

UNITE and provided a finer level of taxonomic resolution.

Functional Identification

Taxa detected were categorized into functional groups if they were over 1% of the relative abundance for at least one sample (Appendix I Table 9). Assignment to a functional group was made where possible after an in-depth literature review. Our goal was to perform an analysis at the level of broad functional groups, not to provide a definitive functional designation for any particular OTU. However, many families, genera, or species could be confidently designated to a specific functional group. Taxonomic clades, such as orders, were designated to a

44 functional group if it seemed clear that most representatives found in leaf tissue or soil would correspond to a particular functional group based on taxa that have been described in the clade.

For example, Helotiales was designated as necrotrophic plant pathogens because a majority of

Helotiales taxa have this capability (Appendix I Table 9). In contrast, an order such as

Xylariales, which is also made up of fungi in a wide range of functional groups, was categorized as primary saprotrophs because most families in this order are saprophytic in lifestyle (Appendix

I Table 9). Taxa that could not be confidently assigned to a specific functional group because of lack of information or taxonomic resolution were listed as “not available” or “various”, respectively (Appendix ITable 9).

Statistical Analyses

Statistical analyses were performed in the R statistical analysis program (R core team

2014). Packages we utilized included vegan (Oksanen et al. 2013), PCNM (Legendre 2010),

AEM (Blanchet et al. 2015), Packfor (Dray et al. 2016), Graph (Gentleman et al. 2016). T-RFLP profiles and pyrosequencing OTU data were analyzed using both Hellinger and Jaccard distances as two measures of differences in community composition. Hellinger distance utilizes relative abundances and Jaccard distance is based on presence-absence only, and thus places greater weight on low abundance taxa (Legendre et al. 2001).

Analysis of variation within sites.

Mantel tests and redundancy analysis (Legendre and Anderson 1999) were utilized to test the effects that environmental and spatial factors have on community composition. Mantel analysis tests for a significant correlation between spatial distance (number of steps between leaves) and either Hellinger distance or Jaccard distance. A few leaves were collected that were

45 not touching other collected leaves, and these were excluded from this analysis. However, separate networks of leaves collected within the same sampling frame were combined in the

Mantel analysis by removing any pairs of leaves with undefined spatial distances as in Feinstein and Blackwood (2013). Mantel tests were also performed using distance matrices for other explanatory variables (leaf type and depth) to discern if there was an effect of the environment on community composition.

We also investigated how spatial distance and other factors relate to community composition using redundancy analysis. Spatial distance matrices were converted to principal coordinates of neighbor matrix (PCNM) vectors, which are explanatory variables that contain the entire of range of potential spatial structures in data matrices (Borcard et al. 2004). These vectors are then used to compare the effect that spatial proximity has on community composition.

Redundancy analysis was also utilized to determine the amount of variation explained by distance (PCNM vectors), leaf type and depth within each forest stand. Forward selection was utilized to select the minimal number of PCNM vectors to reduce the chance overestimating variance explained and lower the risk of type 1 error (Blanchet et al. 2008). Adjusted R-square values (Peres-Neto et al. 2006) were obtained after each analysis and interpreted as percent variation explained for each explanatory variable. When analyzing within site data using pyrosequencing data, all samples were rarified to the minimum number of sequences per sample per site.

Analysis of variation between sites.

Larger scale effects of environmental and spatial factors were investigated by RDA on communities from all sites combined into a single data set. Environmental explanatory variables included ecosystem type, leaf type (nested within ecosystem type), and depth and the spatial

46 factor investigated at this scale was forest stand replicate (again nested as a factor within ecosystem type). This analysis was performed after singleton leaf types were removed. For

TRFLPs, this analysis was performed either with all leaves included (n=523) or only with dominant trees species leaves from each ecosystem (n=422). As with for within site analysis of pyrosequencing data, all analyses were performed after rarefaction to the minimum number of sequences for each data set.

Analysis of variation in functional groups.

Analyses were repeated on functional group profiles inferred from sequence data.

Analyses were performed between functional groups (i.e., after summing OTUs comprising each functional group) and within functional groups (i.e., considering only OTUs from within a single functional group).

Results

High Replication Analysis of Community Composition using T-RFLP Profiles

We detected a total of 122 TRFs. Across all leaf pairs, the average Jaccard distance was

0.64 (with values ranging from 0.14 and 0.97). Surprisingly, for adjacent leaves, the average

Jaccard distance was also 0.62 (ranging from 0.20 to 0.94), indicating that overlapping leaves that are touching each other shared an average of only 38% of their fungal taxa. However, within-site statistical analysis revealed distance-decay relationships at the scale of individual leaves, partially supporting H1. Mantel tests comparing T-RFLP Jaccard distance to leaf proximity showed a significant distance-decay relationship in all April BOWO sites and one

April SMRO site (Figure 4, Table 1). In contrast, leaf type played a significant role in structuring all April SMBW sites, as well as one SMRO and one BOWO site. Hence, in April, distance

47 decay was more important for BOWO sites than leaf type, and the opposite was true for SMBW sites. In contrast to April, August sites did not demonstrate a clear pattern, with significant distance-decay relationships being more evenly spread out across ecosystems (Table 1). Leaf type was significant in 6 out of 9 sites in August, again including all SMBW sites (Table 1).

Depth did not play a significant role in most of the sites. Partial mantel tests indicated that proximity and leaf type were not confounded with each other. Due to the presence of rare leaves at each site, the analyses were also repeated using only dominant leaves, resulting in leaf type explaining less variation in August sites 3 and 24, losing significance in April site 41, and becoming significant in August site 41. Analysis of Jaccard distances appeared to be more sensitive than analysis of Hellinger distance, resulting in a greater number of significant explanatory relationships (Table 1).

Like the Mantel test, RDA also indicated that spatial proximity had a significant effect on fungal T-RFLP profiles, with PCNM vectors explaining substantial amounts of variation in

Jaccard distances in 6 sites in April and 3 sites in August (Table 2). However, the sites where

PCNM vectors had a significant effect were evenly distributed among the ecosystems (two sites of each ecosystem in April and one of each ecosystem in August). Leaf type was also found to be a significant factor in many of the sites, especially for BOWO and SMBW sites in both April and

August. Depth was not significant in most sites, and when significant did not explain much variation. Just as with Mantel analysis, when only dominant leaves were included in the analysis, leaf type became less important, becoming non-significant for April sites 3, 24, 41, and 58.

In contrast to what was found within sites, explanatory factors accounted for twice as much variation explained between sites when analyzed with Hellinger transformations than with

Jaccard transformation. When leaves (dominant species only) from all sites were analyzed

48 together, 30% of the variation in Hellinger-transformed T-RFLP profiles in April was explained by the RDA model using all factors (site, ecosystem, leaf-type and depth), and 38% of the variation in August was explained (Figure 5 and 6). In support of hypothesis 2, ecosystem was the most important factor, although other factors were significant. Differences among sites of the same ecosystem type accounted for nearly as much variation as ecosystem type, suggesting that both environmental selection and stochastic factors are important at this landscape scale (Figure

5 and 6). Leaf type and depth were significant but explained negligible amounts of variation when compared to site or ecosystem (Figure 6).

High Resolution Analysis of Community Composition using Pyrosequencing

After sequence processing, the removal of singletons and the removal of three due to low sequence numbers, pyrosequencing resulted in 252,087 sequences, which were clustered into 485

OTUs, with a minimum of 422 sequences and a maximum of 66,130 sequences per leaf. Across all leaves and ecosystems, Ascomycota dominated the sequences, averaging from 80% of sequences on BO leaves to 98% on SM leaves. Basidiomycota made up 20% of sequences on

BO leaves, but varied greatly between individual leaves with one leaf having a basidiomycete abundance of 68%. OTUs identified as Glomeromycota, Mortierellales and fungi of uncertain placement were present, but at abundances of less than 1%.

The Ascomycete order Helotiales was the most prevalent order on all leaves followed by

Capnodiales, Xylariales and Hypocreales (Figure 7). In BOWO ecosystems, >10% of sequences came from the Basidiomycete class Tremellomycetes, which was negligible in SMBW leaf samples (Figure 7). Other groups that were more prevalent in BOWO leaves included the classes

Lecanoromycetes, and Agaricomycetes and the order Myriangiales. There were a greater number of Ascomycete sequences that could not be identified to class in SMBW leaves. In addition,

49 compared to BOWO leaves, sugar maple leaves had a greater proportion of Capnodiales and

Xylariales, and basswood leaves were more dominated by Helotiales.

Like T-RFLPs, there was a low amount of community similarity even between overlapping leaves. Across all leaves there was an average Jaccard distance of 0.68, with distances ranging from 0.44 to 0.97. For adjacent leaves there was an average Jaccard distance of

0.62. with values ranging from 0.44 to 0.83. This once again demonstrates a high degree of dissimilarity of leaf litter communities, indicating that adjacent leaves on average shared less than half of their communities. However, in contrast to the T-RFLP results obtained within individual sites, explanatory factors (leaf type, depth, and proximity) did not explain significant variation in OTU composition within any of the sites.

Considering all leaves and sites together, RDA indicated that 35% of the variation in

Hellinger-transformed OTU abundances was explained using all explanatory factors (ecosystem, site, leaf-type, and depth). While 25% of the variation in Jaccard transformed OTUs abundances were explained. Once again, ecosystem played the most significant role, followed by site, leaf type, and depth (Figure 8). The RDA ordination showed that communities varied much more among SMBW sites than BOWO sites (Figure 9). This was also apparent within sites, with all

BOWO leaves clustered much more closely together than SMBW leaves (Figure 9).

Functional Group Abundances

Approximately 90% of OTUs were tentatively categorized into functional groups based on their taxonomic affiliations (Appendix I Table 9). Redundancy analysis of Hellinger transformed functional group abundances supported hypothesis 3 by indicating that there was a

50 strong effect of both ecosystem and leaf type on the distribution of functional groups, while site was not significant (Figure 8).

All leaf types were dominated by potential necrotrophic plant pathogens, but these sequences were at higher levels on SM leaves (53%) than other leaf types (35-50%) (Figure 10).

Primary saprotrophs (non-wood degrading, non-pathogenic saprotrophs) were much more prevalent in BW leaves (44%) when compared to other leaf types (5%-20%). Mycoparasites had a much higher abundance in BOWO leaves than in SMBW leaves.

H4) Analysis of Variation of OTU Abundances Within Each Functional Group

We found that distinct factors structured OTU composition within the different functional groups, supporting Hypothesis 4. Ecosystem type had a stronger effect than site on mycoparasites/yeasts, necrotrophic plant pathogens, endophytes, and lichens, while the opposite was true for ectomycorrhizal fungi, arbuscular mycorrhizal fungi, primary saprotrophs, and white rot saprotrophs (Figure 8). Depth was found to explain a high amount of variation in arbuscular mycorrhizal fungi and lesser amounts in mycoparasites and white rot fungi. Leaf type explained a significant amount of variation for all groups except endophytes and white rot saprotrophs; however, this variation amounted to <8% in all cases. Analysis of composition within functional groups within each site showed no significant effects of leaf type, spatial proximity, or depth.

Discussion

We determined that both stochastic and environmental factors play a role in community assembly in saprotrophic fungal communities. However, these factors appear to change in importance by spatial scale, with environmental factors playing a significant role in community composition at between-forest stand scales and distance decay patterns being observed at within-

51 forest stand scales. However, these distance decay patterns are clearly demonstrated in some environments, while they are absent in others. At the same time these patterns also change in importance when the community is viewed as a taxonomic community or as functional groups.

However, a great deal of variance is still left unexplained, especially at the within stand scales.

This may indicate the importance of priority effects and other stochastic mechanisms in the assembly of these communities.

Impacts of Deterministic and Stochastic Mechanisms on Community Assembly

In support of hypothesis 1, between adjacent leaves, both dispersal limitation and niche effects influenced fungal community composition, although these factors varied in importance by ecosystem and season. Distance decay relationships were consistently found within oak- dominated forest stands in April, but not in maple-dominated stands. The more recalcitrant leaves found in BOWO stands should result in slower fungal growth rates due to a need for increased investment in extracellular enzyme production, which is necessary for carbon and nutrient acquisition (Gallo et al. 2004; Moorhead and Sinsabaugh 2006; Osono et al. 2007). This type of substrate therefore reduces the benefits of arriving first, because specialized fungi have an inherent advantage in resource-acquisition. Therefore, this type of substrate would be more favored by organisms seen as stronger competitors which are slower to disperse, possibly giving patchier species distributions (Knietel and Chase 2004). Colonization of specialized fungi from neighboring leaves may also be favored because integration of physically separate resources is more important in these environments, leading to patchier distributions and distance decay patterns at the scale of individual leaves. The lack of distance decay seen in the more labile leaves found in SMBW stands may be the result of rapid and stochastic colonization by a diverse array of generalists with high growth rates (Calcagno et al. 2007). Faster growth on labile

52 substrate would result in better ability of initial colonists to exclude fungi that colonize later, resulting in stronger priority effects and thus a more random distribution of species. In contrast to distance decay, leaf type played a much larger role in determining fungal community composition within SMBW ecosystems. Leaf type effect is a classic example of niche partitioning of fungal communities, as different species and different decay stages contain varying levels of resources which will select for differing organisms (Baldrian et al. 2016). The strong leaf type effect found in SMBW ecosystems may be the attributed to the differences in the chemistry of leaf litter, which can vary quite heavily between species (Dighton 2007). This more labile food resource may select for a more distinct community of saprotrophic fungi, when compared to other leaf types. In addition to litter chemistry, this pattern may also be reflective of differences in mycorrhizal type between the focal tree species located in different ecosystems. It has been documented that arbuscular mycorrhizal associated plants have higher decomposability than the more recalcitrant ectomycorrhizal associated plant tissue (Cornelissen et al. 2001).

Despite finding significant effects of ecosystem type, leaf proximity, and leaf type on fungal community composition, a great deal of variation was still left unexplained. For example, the fungal communities of leaves even in direct contact with one another were extremely distinct.

These community differences between leaves were comparable to global and continental scales found in Meiser et al. (2014). This variation may be the result of further stochastic mechanisms, such as the history of individual leaves, and implies the presence of strong, spatially unstructured priority effects (Johnson 2015). Early colonists could positively or negatively affect future fungal colonizers for prolonged periods of time (Weslien et al. 2011) through generation of secondary metabolites, release of nutrients, and niche preemption (Heilmann-Clausen and Boddy 2005;

Fukami et al. 2015). The importance and outcome of these priority effects may also be impacted

53 by season and forest stand, as it has been demonstrated that both temperature and plant tissue biochemistry can affect their outcome (Hiscox et al. 2016). This mechanism could further complicate the assembly of these already complex communities and further analysis is needed to understand them.

Feinstein and Blackwood (2013) found that depth of a leaf in the forest floor explained a significant portion of fungal community variation in 5 out of 6 sites they studied, which included upland, riparian, and vernal pool areas. In contrast, we found that depth accounted for a very small amount of variation in fungal community composition and was usually not significant.

This is surprising given that environmental conditions, especially moisture, vary with depth in leaf litter (Sato et al. 2004). However, among the ecosystem types studied by Feinstein and

Blackwood (2013), the upland sites were the most similar to the Manistee ecosystems studied here. Feinstein and Blackwood (2013) found that depth was only significant in one of two upland sites; thus, it is possible that depth is only sporadically significant in upland forest floor ecosystems. This finding may also clarify the relationship between distance decay and depth found by Feinstein and Blackwood 2013. They found both factors significant in explaining community composition but were unable to disentangle which one of them was the most important.

In support of hypothesis 2, ecosystem type was the most important factor in explaining variation among forest stands for both sampling dates. This may be related to a wide variety of factors that differ between these ecosystem types and which have been found to play a role in structuring fungal communities (Prescott and Grayston 2013; Brockett et al. 2012), including contrasting soil types, moisture availability, and tree community members (Host et al. 1988). Site was also important in structuring communities; however, site was much more important in

54 explaining community composition in August than April. This pattern may indicate communities are becoming more distinct from one another due to dispersal limitation, as site indicates communities that are spatially separated. At the same time the change in the importance of factors may be related to differences in the abundance and diversity of taxa within these communities, as litter decomposing fungi can vary greatly between seasons (Voriskova et al.

2014). Leaf type on the other hand played a small role for between community variation, indicating that the surrounding factors conditions associated with forest stand and ecosystem were much more important in what can colonize leaf litter.

In contrast to findings regarding taxonomic communities and in support of hypothesis 3, relative abundances of functional groups were largely determined by leaf type and ecosystem, but not the spatial factors of forest stand or leaf proximity. In contrast, species composition was strongly influenced by forest stand, but not leaf type. This indicates that communities have some degree of functional redundancy, which is the ability of one species to carry out the same processes as another under similar environmental conditions (Rosenfeld 2002). Functional redundancy is thought to be common in fungal communities due to their high levels of species diversity (Peay et al. 2016) and has been demonstrated in both wood and leaf litter decomposer communities (Purahong et al. 2014; Peay et al. 2012; Hattenschwiler et al. 2005). While many researchers consider this concept of functional redundancy to be controversial, this study has found it to occur to some degree over a wide geographic area and in several distinct fungal communities.

High Resolution Community Analysis

Ascomycete dominance has previously been observed on senesced leaves and can be explained by both the presence of Ascomycete endophytes on leaves and the abundance of labile compounds promoting the growth of fast-growing early colonizing fungi (Urbanova et al. 2015;

Schneider et al. 2012). However, it is surprising that these saprotroph communities were dominated to such as degree so many months after senescence. This is due to ascomycetes traditionally being viewed as early colonizers in decomposition who are slowly replaced by basidiomycetes (Frankland 1998). However, research has shown that some ascomycetes, including endophytes, can produce enzymes capable of breaking down recalcitrant compounds seen in more decomposed litter (Fillat et al. 2017; Osono et al. 2007). Dominance of fungal order

Helotiales on most leaves can be explained by the fact that it is a highly diverse fungal order and includes major groups of necrotrophic plant pathogens, cellulose decomposers, and early colonizers of leaf litter (Lindahl et al. 2007; Purahong et al. 2016). Other prevalent groups of

Ascomycete fungi found include the orders Hypocreales and Capnodiales, both of which include endophytes, plant pathogens, and saprotrophs (Rehner and Samuels 1995; Crous et al. 2009). A sizable portion (17 percent) of the sequences in this dataset were most reliably matched to the fungal species Mycoarthis corallinia (Appendix I Table 9). This species was originally found in aquatic ecosystems (Marvanova et al. 2002; Baschien et al. 2006) and has been more recently been found in soil and decomposing plant material (Jacobsen et al. 2005) and possibly pine forest top soil (Baldrian et al. 2012). Its ecological niche is quite cryptic; however, due to its association with plant tissue, it may be a saprotroph.

Basidiomycetes were more prevalent on recalcitrant leaf litter (BOWO leaves). This was expected as they have been demonstrated to be more prevalent on leaf litter of lower nutrient

56 quality (Voriskova and Baldrian 2013) and due to their ability to break down recalcitrant plant cell wall components such as cellulose and lignin (Baldrian and Valakova 2008; Lundell et al.

2010). However, we were surprised to find that yeasts from the class Tremellomycetes were the most prevalent group of Basidiomycetes, instead of basidiomycete classes containing species known as specialists in degradation of recalcitrant compounds, such as Agaricomycetes. Soil yeasts have resistant dormant stages and can survive in stressful environments such as those with frequent desiccation and low productivity (Treseder and Lennon 2015). Their prevalence in

BOWO ecosystems may therefore be due to the very sandy soil and low moisture availability in this ecosystem.

Functional Group Community Analysis

The dominance of necrotrophic plant pathogens and fungal endophytes in leaves that had been decaying six or ten months indicates that these functional groups likely play an influential role in decomposition as facultative saprotrophs. These findings reinforce the idea that many necrotrophic plant pathogens are non-obligate pathogens (Charkowski 2016; Oliver and Ipcho

2004). Although some of the necrotrophic plant pathogen sequences may be from spores, their clear dominance across leaf types and ecosystems indicates their important role as saprotrophs.

Endophytes have been found to be early saprotrophs on leaves and woody tissue (Song et al.

2017), as they are already present when leaves senesce (Osono et al. 2006). Plant pathogens were much more prevalent on SM leaves than any leaves from ectomycorrhizal trees (BO, WO and

BW). This may be the result of high overall nutrient quality for leaves in AM species compared to trees which ectomycorrhizal (Cornelissen et al. 2001). Unsurprisingly, ectomycorrhizal fungi were found to be more abundant in BOWO ecosystems than in SMBW due to the abundance of ectomycorrhizal species in BOWO ecosystems. The abundance of mycoparasites/yeasts and

57 lichens on BOWO leaves may be due water limitation in this ecosystem and their overall resistance to desiccation (Kranner et al. 2008; Treseder and Lennon 2015).

Community composition within functional groups was associated with differing explanatory factors for differing functional groups, in support of hypothesis 4. Two major categories emerged. Some functional groups were associated with environmental factors

(ecosystem and leaf type), these included: mycoparasites/yeasts, necrotrophic plant pathogens, endophytes, and lichens). Whereas others were associated more with forest stand

(ectomycorrhizal fungi, arbuscular mycorrhizal fungi, white rot saprotrophs) and seemed to be more spatially distributed. Primary saprotrophs were structured by both. Organisms that depend upon a high amount of spore production, such as necrotrophic plant pathogens and primary saprotrophs, were expected to be more randomly distributed. However, this was not the case.

Necrotrophs were instead found to be structured by environmental factors, indicating that they must have a high degree of host specificity. While some plant pathogens do have a broad host range, it has been demonstrated that many are limited to hosts that are phylogenetically related

(Schulze-Lefert and Panstruga 2011; Gilbert and Webb 2007). Primary saprotrophs were structured by both environmental and spatial factors, indicating more niche specificity and dispersal limitation then was expected. This may be due primary saprotrophs consisting of variety of organisms, such as possible endophytes, early colonizing saprotrophs and cellulose/hemicellulose degraders, all with varying dispersal mechanisms. Endophytes exhibit high degrees of species specificity for hosts and this may carry over as facultative saprotrophs

(Wearn et al. 2012; Promputtha et al. 2007). Mycoparasites/yeasts and lichen were found to be highly structured by the environment, which could be explained by their resistance to conditions found in BOWO ecosystems. Both ectomycorrhizal and arbuscular mycorrhizal fungal

58 distributions were mainly explained by forest stand, indicating a strong effect of dispersal limitation at the landscape scale. Each of these groups of mycorrhizal fungi have been demonstrated to be strongly impacted by dispersal limitation (Peay et al. 2012; Peay et al. 2010;

Lekberg et al. 2007). This information clearly demonstrates that separate groups of fungi are difficult to group together in ecological studies, as they are impacted by community assembly mechanisms to varying degrees.

Transformation and Methodology Variation

There was noticeable variation in results based upon different methods and statistical analyses in this study. For instance, at the leaf neighborhood scale, more variation in Jaccard transformed T-RFLP data was explained than in Hellinger transformed T-RFLP data. This may have to do with Jaccard transformation more heavily weighting less abundant taxa compared to

Hellinger transformation, which is based upon relative abundance (Legendre and Gallagher

2001). While T-RFLP analysis is an excellent method for generating rapid and robust data sets, it is still susceptible to PCR bias (Chauhan et al. 2011). This bias can affect peak intensity which can cause Hellinger transformed data to be more susceptible to noise, whereas Jaccard transformation accounts for peak presence/absence only, which has been shown to be less subject to noise and more sensitive to subtle differences in community composition when T-RFLP profiles are equally strong (Blackwood et al. 2003). Thus, small scale variation may be the result of rare taxa being the dominant drivers of this distance decay at small scales. Differences were also demonstrated between T-RFLP and pyrosequencing data sets, most notably regarding within site variation. This may have been the result of a difference in statistical power between the two methods, as seven leaves were used for each site in the pyrosequencing data set (42 in total) and up to 30 were used per site for T-RFLPs (270 in total for each season). However, repeating the

59 analyses using T-RFLP data only from leaves used in pyrosequencing still resulted in significant effects on the T-RFLP profiles for some sites (data not shown). This discrepancy in the results may be the result of T-RFLP data simplifying microbial community composition, as taxa with similar terminal ends can be grouped together as the same peak and thus lead to a loss of resolution (Anderson and Cairney 2004; Dickie and Fitzjohn 2007).

Conclusion

This study has illustrated the high degree of both taxonomic and functional heterogeneity found in saprotrophic microbial communities. This heterogeneity is influenced by a variety of processes, both environmental and stochastic, whose importance is determined by spatial scale.

This uncertainty may be explained by priority effects and the next step in this research will be to determine its influence. At the same it was also demonstrated that analyzing microbial community data from a variety of directions can lead to a better understanding of assembly processes. Understanding these processes will become even more important in the future due to the increasing effect that anthropogenic factors will have on these ecologically important groups.

Acknowledgements

This research was supported by grants from the US National Science Foundation

(DEB0918240 and DEB-0918878) and US Department of Energy (DE-SC0004335). Additional funding was provided by the Kent State Department of Biological Sciences.

Figure 3. Map of Manistee National Forest. Included are locations for each site used, geographic features, locations of largest cities, and a scale bar indicating distances. Sites are separated by between 3 and 56 km. These sites are also color coded for which ecosystem type that they are classified as. Ecosystem classifications are as follows: Black = BOWO, Red = SMRO, and Green = SMBW. Map was generated using ArcGIS, geologic features, cities, and other structures were taken from Michigan Department of Natural Resources (generated April 2017)

Jaccard Hellinger Jac Jac Leaf Jac Hel Hel Leaf Hel Site Season Ecosystem Prox Type Depth Prox Type Depth U Apr BOWO *0.15 0.00 0.02 0.02 -0.01 -0.01 3 Apr BOWO *0.19 -0.04 -0.08 -0.06 -0.14 -0.14 58 Apr BOWO *0.31 *0.11 *0.18 *0.20 *0.16 0.12 6 Apr SMBW -0.06 *0.13 *0.32 0.00 *0.34 0.09 22 Apr SMBW -0.01 *0.12 0.05 0.14 0.09 -0.05 24 Apr SMBW -0.06 *0.28 -0.07 -0.05 *0.34 0.00 7 Apr SMRO 0.08 0.07 0.19 0.09 0.01 0.17 56 Apr SMRO *0.10 0.08 0.01 *0.13 *0.12 *0.19 41 Apr SMRO 0.06 *0.13 -0.05 -0.11 0.12 -0.07 U Aug BOWO -0.02 *0.10 -0.06 -0.01 0.01 0.02 3 Aug BOWO -0.01 *0.15 -0.05 0.00 *0.15 0.01 58 Aug BOWO *0.26 -0.06 -0.13 *0.24 -0.05 -0.04 6 Aug SMBW -0.03 *0.29 0.10 0.02 -0.04 0.18 22 Aug SMBW *0.15 *0.27 -0.02 *0.42 *0.39 -0.02 24 Aug SMBW 0.05 *0.22 -0.03 *0.39 -0.07 -0.16 7 Aug SMRO *0.27 *0.36 *0.40 0.13 *0.33 *0.15 56 Aug SMRO *0.15 0.09 0.10 *0.13 *0.23 0.06 41 Aug SMRO 0.11 -0.05 *0.18 0.13 0.05 0.08

Figure 4. Plots of average Jaccard transformed community distance (based on T-RFLP profiles) at increasing spatial distance (number of steps), for all April sites. Each point indicates an average community distance of all leaves at that distance, with the size of each point proportional to the number of sample pairs (ranging from 1-50 pairs per step). Error bars are included for each point indicating standard error. Summarized on each plot are the site number and ecosystem. * P < 0.05

Jaccard Hellinger Ecosyste Jac Jac Leaf Jac Hel Hel Leaf Hel Site Season m Prox Type Depth Prox Type Depth U Apr BOWO 0.02 0.02 0.01 -0.01 0.01 0.00 3 Apr BOWO *0.08 *0.03 *0.03 *0.07 0.00 *0.05 58 Apr BOWO *0.11 *0.05 *0.05 *0.23 *0.12 0.04 6 Apr SMBW *0.08 0.01 0.02 -0.06 *0.23 -0.01 22 Apr SMBW *0.09 *0.03 0.00 *0.08 0.03 0.01 24 Apr SMBW -0.01 *0.13 -0.03 0.05 *0.16 -0.01 7 Apr SMRO *0.03 0.01 0.01 *0.09 0.00 *0.05 56 Apr SMRO 0.07 0.04 0.00 *0.08 0.02 *0.04 41 Apr SMRO *0.05 *0.05 *0.02 0.02 *0.07 0.00 U Aug BOWO 0.00 *0.03 0.00 0.01 0.01 0.00 3 Aug BOWO 0.04 *0.05 -0.01 0.60 0.04 -0.01 58 Aug BOWO *0.10 0.01 -0.03 0.01 0.05 0.02 6 Aug SMBW -0.05 *0.07 0.00 0.03 *0.06 0.04 22 Aug SMBW *0.15 *0.44 -0.04 *0.46 *0.51 -0.01 24 Aug SMBW -0.02 0.25 0.08 0.05 -0.05 0.06 7 Aug SMRO *0.09 *0.06 *0.04 *0.12 *0.07 0.02 56 Aug SMRO 0.02 0.03 0.01 0.00 *0.11 *0.05 41 Aug SMRO 0.04 -0.01 0.01 0.04 0.04 0.01

A te

B te

Figure 5. RDA ordination plots for hellinger transformed T-RFLPs profiles in A) April and B) August. Colors represent each site, with centroids labeled with each ecosystem type: BW=BOWO, SB = SMBW and RS = SMRO .Shapes indicate different leaf species and are indicated by a key.

Figure 6. Percent varience explained by RDAs for hellinger transformed T-RFLPs profiles for each season. Each color indicates a separate explanatory factor as indicated by side panel.

Figure 8. Amount of variation explained by each factor for all pyrosequencing OTUs, functional groups pooled together and each functional group individually. Functional groups are abbreviated as follows: Mycop./Yeast=mycoparasite/yeast, Necrotroph=nectrophic plant pathogen, Ectomyco=ectomycorrhizal, Arbuscular=arbuscular mycorrhizal, PrimSap=primary saprotroph and Whiterot=white rot saprotroph. Designations for the each factor are found on the key on the side.

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Chapter 3:The Role of Priority Effects in Saprotrophic Fungal Communities in Temperate

Forests.

Abstract

The mechanisms responsible for the assembly of microbial communities are quite varied and can range from more deterministic environmental factors, to stochastic factors involving dispersal limitation. One such community assembly mechanism that has been traditionally overlooked is the overall impact that community colonization history has on community composition, termed priority effects. In this study, priority effects, deterministic environmental factors, and dispersal limitation were tested for their degree of importance in the assembly of saprotrophic fungal communities and function. This was accomplished by inoculating functionally distinct fungal isolates onto sterilized senesced leaf litter, that was then placed back into the field. After collection, community composition was characterized, while communities were functionally characterized through the quantification of extracellular enzyme activity. It was found that priority effects mostly influenced community aggregated functional traits

(extracellular enzyme activity) and leaf litter mass loss (decomposition). Priority effects were not significant in impacting community composition. In addition, it was found that community composition was heavily influenced by spatial factors such as forest stand replicate and also within-forest stand replicates. Ecosystem processes and community aggregated functional traits were most heavily influenced by environmental factors such as leaf type and ecosystem type.

This study demonstrates the overall complexity of community assembly in saprotrophic fungal communities and indicates the impact that various factors can play in their related ecosystem processes.

Introduction

The question of how ecological communities assemble is one of the key questions in ecology. There have been many theories about how these ecological units assemble, including through deterministic environmental selection and more stochastic mechanisms (Weiher et al.

2011). Both mechanisms depend upon dispersal to varying degrees, where dispersal is not limiting if environmental selection is important, and dispersal is often assumed more limited if stochastic mechanisms are important. However, ecological studies examining spatial patterns in natural communities are often left with a substantial amount of unexplained variation, even when taking these mechanisms into account (Peay et al. 2016). A mechanism that may be able to explain more of this variation is the assembly history of a community, called priority effects.

This process results from strong competitive interactions between organisms, the outcome of which is determined only by the order that organisms colonize a resource patch (Chase et al.

2003; Fukami and Nakajima 2011). It is generated by, and amplifies the signal of, the stochastic dispersal history of an area. While possibly significant, priority effects have been largely overlooked as a mechanism in community assembly, due to its inability to be easily distinguished with spatial patterns (Fukami et al. 2015; Nemergut et al. 2013). For instance, if dispersal distances are long but frequency is stochastic, then priority effects would indistinguishable from noise in spatial analysis.

Saprotrophic fungi are an important group of organisms in nutrient cycling and the breakdown of senesced plant material. However, they are a challenging group in which to evaluate the

85 importance of community assembly processes, due in part to their ubiquity and high biodiversity

(Tedersoo et al. 2014). Adding to this challenge is that these organisms, owing to their microbial nature, have distinct community assembly processes not seen in most macroscopic organisms, such as: high rates of passive dispersal, fast growth rates, dormancy, and high amounts of genetic diversity in small areas (Nemergut et al. 2013; Cordero and Datta 2016). Some of these above characteristics make these communities highly likely to be under the influence of assembly history and priority effects. For instance, recent studies have demonstrated the key role that colonization history can play in the community assembly of nectar yeasts (Peay et al. 2012A), ectomycorrhizal fungi (Kennedy et al. 2009) and arbuscular mycorrhizal fungi (Werner and

Kiers 2014). It has also been suggested that priority effects may be particularly important in saprotrophic fungal communities due to differences among groups in the timing of colonization, such as early colonization of senesced tissue by facultative saprotrophs, (Ottoson et al. 2014;

Hiscox et al. 2015). The effect of assembly history can be due to both positive and negative feedback mechanisms from previous colonists (Fukami 2015; Chase 2003). For instance, in leaf litter communities, initial colonization by white rot fungi can free up nutrients from the lignin- cellulose matrix of the plant that facilitates growth of other functional groups (Osono 2007).

Alternatively, direct combative interactions may occur between species as one tries to colonize an already occupied resource patch (Boddy 2000).

While priority effects have been traditionally overlooked, other community assembly processes have been studied more thoroughly. Fungal communities are often highly structured by environmental factors, suggesting the importance of deterministic processes (Urbanova et al.

2015; Lindahl et al. 2007). For instance, the overall amount of labile and recalcitrant compounds in leaf litter has been found to be of great significance in saprotrophic fungal community

86 composition (Prescott and Grayston 2013; Frankland 1998). This diversity of resources can lead to differences in communities as more labile compounds can be used by a wide range of microorganisms, while breakdown of recalcitrant compounds requires highly specialized organisms and enzymes (Osono 2007). On the other hand, several studies have found that fungal communities exhibit spatial autocorrelation and distance-decay patterns and attributed this to dispersal limitation (Cline and Zak 2014; Bahram et al. 2013; Peay et al. 2010). In addition, even though spores are capable of further dispersal, most establishment of wood decay fungal spores occurs within 10-100m (Norros et al. 2012), and distinct fungal communities are found in logs within the same forest stands (Kubartova et al. 2012). However, evidence suggests that both environmental and spatial factors can play a role in community assembly, as studies on endophytes (David et al. 2016; Vincent et al. 2015), ectomycorrhizal fungi (Peay and Bruns

2014) and saprotrophs (Abrego et al. 2017) have indicated these communities are influenced by several mechanism at varying degrees of importance. While these mechanisms, including priority effects, have been studied separately, few studies have tried to look at all mechanisms at the same time and understand their degree of importance in relation to one another.

Ecosystem processes, such as decomposition, are primarily thought to be driven by environmental factors (Zhang et al. 2008; Berg 2014), but there is evidence that other important factors, such as community composition, may have been overlooked due to common study designs (Bradford et al. 2016). Community composition is thought to affect ecosystem processes through effects on community aggregated functional traits, whose measurement is instrumental in understanding the environmental roles of microbial communities (Fierer et al. 2014). In saprotrophic fungal communities, important community aggregated functional traits include the concentration of extracellular enzymes involved in litter decomposition, or the abundance of

87 functional genes coding for these enzymes. Thus, if community composition affects the distribution of these traits, priority effects could also have important effects on decomposition.

For instance, interactions and competition between fungal species may play a large role in enzyme production (Crowthler et al. 2015). In wood degrading fungal communities, it was found that decomposition rates could be directly influenced by fungal colonization history (Fukami et al. 2010; Lindner et al. 2011). However, saprotrophic microbial communities are often assumed to be highly functionally redundant, resulting in extracellular enzymes that are produced similarly despite differences in community composition (Wardle 2006; Setala and McLean

2004). While these studies do indicate the influence of priority effects on community aggregated functional traits and ecosystem processes, there are still many unknowns. For instance, does colonization history of a resource patch change microbial community composition and thus result in changes to the expression of community aggregated functional traits? Or does the environment play the larger role and will not be impacted by priority effects due to functional redundancy?

The purpose of this study was to test the effects of multiple community assembly mechanisms on saprotrophic fungal communities, their community aggregated functional traits, and related ecosystem processes. The following hypotheses were tested: H1.) In more labile leaves (containing more sugars/starches and less lignin), taxonomic variability is heavily influenced by priority effects. This is due to initial colonizers of labile leaves being able to grow rapidly and occupy more space, thus influencing colonization of later species. On recalcitrant leaves organisms will grow slower, be more limited in their dispersal and thus be more influenced by spatial factors. H2.) In contrast to taxonomic community composition, community aggregated functional traits (extracellular enzyme activities) and ecosystem processes

(decomposition) will be structured more by the environment and less by stochastic processes due to the functional redundancy within microbial communities. To test these hypotheses, saprotrophic fungi were isolated from the field, functionally characterized, and inoculated onto sterilized, senesced leaf litter with differing levels of recalcitrance. Inoculated leaf litter was deployed back into replicate forest stands of differing environmental characteristics using a spatially explicit design. After a period of decomposition, fungal community composition was characterized using terminal restriction fragment length polymorphism (T-RFLP), and extracellular enzyme activity was determined as a measure of community aggregated functional traits related to decomposition of the leaf litter.

Materials and Methods

Experimental sites

The experiment was conducted in replicate forest stands of three previously characterized ecosystems in Manistee National Forest (Host et al. 1988). These ecosystems are as follows: black oak/white oak (BOWO), sugar maple/red oak (SMRO) and sugar maple/ basswood

(SMBW). Each ecosystem is characterized by distinct soil properties, elevation and dominant overstory tree species leading to varied environmental conditions. The geography of the overall area is mainly dominated by glacial landforms typical of this region, such as glacial outwash plains and moraines (Zak et al. 1989). Soils in each of these ecosystems are sandy (percentages varying from 55 to 72%) dominated mainly by Typic Udipsamments and Entic Haplorthods (Zak et al. 1989).

Obtaining isolates and Characterization

Fungal isolates were selected from a micro-fungi culture collection obtained from the ecosystems described above. Isolates were characterized for extracellular enzyme production, growth rates on various media, and spore production. Isolate physiologies were then compared using non-metric multidimensional scaling, and the three most distinct isolates were selected for use in this experiment (Table 3). These isolates included a polysaccharide degrading enzyme producing organism isolated from a Quercus velutina leaf in a BOWO site (Isolate 1), a less prolific enzyme producer isolated from a Tilia americana leaf from a SMBW site (Isolate 2), and a slower growing, laccase producing organism isolated from a Quercus rubra leaf in a SMRO site (Isolate 3). The internal transcribed spacer region (ITS) of each isolate was also sequenced.

Substrate collection and colonization by selected fungi

Red maple (Acer rubrum) and red oak (Quercus rubra) leaf litter was gathered in Fall of

2011, air-dried, and stored for several months prior to use. The leaves were sterilized by autoclaving at 121oC for 30 minutes. Approximately 150 ml of the autoclaved leaf mixture was then placed into pre-weighed 400 ml mason jars, which were then autoclaved for 30 minutes.

Forty ml of distilled autoclaved water containing 0.55g/L of ampicillin and streptomycin was added to each jar. Jars were mixed to distribute moisture evenly throughout the jar. Microcosms were inoculated with three ~1cm agar plugs from a one-week old culture of one of the fungi described above. Microcosms were then incubated at 20oC for six weeks to allow for colonization of leaf litter. Fungal colonization was confirmed through visual observation of fungal growth.

Litter Bag Deployment and Harvest

Litter bags were constructed from 5mm mesh. The bags contained either red oak or sugar maple leaves colonized by one of the three previously characterized isolates. Litter bags were deployed in Manistee National Forest, MI in late spring of 2012. Litter bags were tethered to stakes in the field to facilitate recovery of the bags. The experiment was conducted in three forest stands of the three previously mentioned ecosystem types. Bags were placed in three locations per stand (subsites), separated by 500 m, to enable the testing of spatial effects within forest stands (Figure 11). Duplicate blocks were placed in each location, each block consisting of all leaf and inoculum types. Bags were gathered in early June 2012 (1 month after deployment) and October 2012 (5 months). As bags were gathered surrounding trees were identified and recorded, to characterize the surrounding environment. A total of 324 bags were constructed and deployed in the field (2 leaf types x 3fungal isolates x 3 points x 2 seasons x 9 sites).

After bags were recovered from the field they were placed on ice and brought back to lab for processing. Leaf material was weighed, and then subsamples were taken for determination of moisture content and ash weight and storage at -80°C. Moisture content for litter was determined by drying sub samples of material at 65oC until weight measurements had stabilized. Ash content was also obtained by ashing samples at 400oC until weight had stabilized.

Extracellular Enzyme Analysis (Community Aggregated Functional Traits)

Extracellular enzyme activity was measured using 4-methylumbelliferone (MUB) linked substrates for hydrolytic enzymes and ABTS (2,2’-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid (Fisher Scientific, Waltham, MA, USA) for oxidative enzymes. Substrates used for each enzyme are as follows: 4-MUB alpha-D-glucoside (Fisher Scientific) for alpha glucosidase, 4-

MUB beta-D-glucopyranoside (Sigma Aldrich, St. Louis, MO, USA) for beta glucosidase, 4-

MUB beta-D-xylopyranoside (Sigma Aldrich) for xylopyranosidase, 4-MUB beta-D-cellobioside

(Biosynth, Itasca, IL, USA) for cellobiohydrolase, 4-MUB N-acetyl-beta-D-glucosmanide

(Biosynth) for β-N-acetylglucosaminidase, 4-MUB phosphate (Biosynth) for phosphatase, and

L-leucine-7-amido-4-methylcoumarin hydrochloride (Biosynth) for leucine aminopeptidase .

Enzyme assays were performed on 0.5 g of leaf litter suspended in 125 ml of sodium acetate buffer (pH = 5.0) homogenized with a hand blender (Hamilton Beach, Minneapolis, MN, USA).

Hydrolytic enzymes were detected and quantified using methods modified from Marx et al.

2001. In summary, leaf and buffer suspensions were placed into black 96 well plates containing negative controls and quench wells. Samples were incubated with MUB linked substrates for 1.5 hours at 20oC. Enzyme activity was quantified using a Biotek Synergy 2 plate reader (Biotek,

Winooski, VT, USA) with an excitation wavelength of 365/40 nm and an emission wavelength of 450/40 nm. For oxidative enzymes, leaf/buffer mixtures were placed into clear plates containing negative controls. Samples were incubated with ABTS for 2 hours at 20oC. After incubation oxidative enzymes were quantified by observing total absorbance (420 nm wavelength) upon the oxidation of ABTS using a Biotek Synergy 2 plate reader.

Terminal Restriction Fragment Length Polymorphism (Taxonomic Community Profile)

The CTAB extraction method was used to extract DNA from freeze dried plant material

(Wu et al. 2011). Subsamples were frozen in liquid nitrogen and then ground into a fine powder using a Genogrinder. The ground material was then suspended in a CTAB/β-mercaptoethanol buffer and incubated at 60oC for 20 min. The material was centrifuged for 10 sec. at 4000 rpm, and the supernatant was mixed with chloroform and then centrifuged again for 10min at 4000 rpm. The aqueous layer was collected and mixed with ice cold isopropanol and allowed to sit at -

20oC overnight (~16hrs). The DNA was pelleted by centrifugation and washed with cold 70% ethanol. The resulting pellet was air dried and then re-suspended in 50 ul of sterile water.

Terminal restriction fragment length polymorphism (T-RFLP) was used to determine the fungal community composition of each individual leaf. This method is used to generate a molecular fingerprint of microbial communities. Fungal internal transcribed spacer (ITS) regions were amplified from genomic DNA by PCR using the primers NSI1F (5’-

GATTGAATGGCTTAGTGAGG) and NLB4R (5’-GGATTCTCACCCTCTATGAC) (Martin and Rygiewicz 2005) (Integrated DNA Technologies, Coralville, IA, USA). The primer NSI1F was labeled with the fluorescent dye HEX (hexachloro-6-carboxyfluorescein). To achieve amplification of approximately 50% of samples from June, primer LR3-R (5’-

CCGTGTTTCAAGACGGG) (Integrated DNA Technologies) was used as the reverse primer.

PCRs were carried out on a DNA engine Dyad Peltier thermal cycler (Bio-Rad, Hercules, CA,

USA) using the following program: 95oC for 3 minutes followed by 35 cycles of 94oC at 30 seconds, 60oC for 30 seconds and 72oC for 1 minute 30 seconds. The following mix was used for each PCR reaction: ammonium buffer (1X) (B-Bridge, Santa Clara, CA, USA), bovine serum albumin (0.5 mg/mL), MgCl2 (3.0 mM), dNTPs (0.2mM each), Taq polymerase (0.03 U/uL) (B-

Bridge), and primers (0.16uM each). Each PCR reaction was also performed with a negative control. PCR products were purified using an UltraClean PCR Cleanup Kit (MO BIO

Laboratories, Carlsbad, CA, USA) and then digested using 10 units of the restriction enzyme

HaeIII (New England Biolabs, Ipswich, MA, USA) for 16 hrs at 37oC. The digestion buffer was removed, and samples were resuspended in H2O using the E.Z.N.A. DNA Probe Purification kit

(Omega Bio-tek, Norcross, GA, USA). After purification, samples were sent to the Ohio State

University Plant Microbe Genomics Facility for capillary electrophoresis using a 3730 DNA

Analyzer with a LIZ1200 size standard

The terminal restriction fragment (TRF) profile peaks between 50-600bp in size and with a relative peak abundance of greater than 0.5% were utilized for further analysis. All other peaks outside of these criteria were omitted from further analysis. TRFs were then aligned between samples using the program T-Rex (Culman et al. 2009).

Statistical Analysis

All statistical analyses were performed using the program R version 3.3.2 (R core team

2016). The R packages utilized in this study include: vegan (Oksanen et al. 2013), nlme

(Pinheiro et al. 2016), LMERConvenienceFunctions (Tremblay et al. 2015), and lme4 (Bates et al. 2015).

Analysis of Taxonomic communities

For testing of hypothesis 1, the T-RFLP profiles of each sample were first transformed to

Hellinger distance. This distance utilizes relative abundances and is a commonly used transformation of community profiles as way to give greater weight to more rare species in a community and give less emphasis to more abundant species (Legendre and Gallagher 2001).

Each sampling date was analyzed separately using redundancy analysis (RDA) to determine the significance of three broad categories of explanatory factors: isolate type (priority effects), environmental factors, and spatial factors (site and sub site). Environmental factors included ecosystem type, leaf type, and ecosystem×leaf type interactions. Spatial factors included forest stand (nested within ecosystem type) and subsite (nested within stand). Under hypothesis 1, we expected isolate type and spatial factors to be more important in explaining variation in TRFLP

94 profiles. Furthermore, hypothesis 1 would be supported by a significant isolate×leaf type interaction.

Analysis of Decomposition and Extracellular enzymes

For testing of hypothesis 2, ecosystem processes were quantified by observing the percent mass loss of leaf litter. Percent mass loss was analyzed using a Mixed Model ANOVA to discern which factors significantly impacted mass loss at 1 month and 5 months. The fixed factors analyzed included: isolate type, ecosystem type, and leaf type. Spatial factors (forest stand replicate and subsite) were included as random effects. Hypothesis 2 would be supported if environmental factors (ecosystem and leaf types) explain the most variation.

Further testing of hypothesis 2 involved the analysis of extracellular enzyme activities.

Extracellular enzyme activities were transformed using the function log(activity+1) to reduce the influence of enzymes with greater activity values (McCune and Grace 2002; Romani et al. 2006;

Stone et al. 2014). Enzymatic activity was analyzed in two ways. We first took a multivariate approach using RDAs to analyze enzyme profiles in the same way as for analysis of T-RFLP profiles. In addition, we summed enzymes by function (X and Y for nitrogen acquisition; phosphatase for phosphorus acquisition; A, B, and C for polysaccharide degrading and carbon acquisition; and laccase for lignin degrading). Enzyme functional groups were then analyzed individually using mixed model ANOVAs, as performed in the analysis of mass loss. Hypothesis

2 would be supported if environmental factors are the factors most important in determining variation in extracellular enzyme activities in samples.

Results

Taxonomic Community (T-RFLP Analysis)

A total of 272 TRFs were obtained from both sampling dates after TRF alignment, with individual samples having between 7-111 (average of 25.32+/-13.43) TRFs. When looking at within site community variation, subsite played the largest role in structuring communities at both 1-month and 5-months (Figure 12). Subsite effects appear to become more pronounced at 5- months vs 1-month as demonstrated by a much clearer separation of subsites within each site

(Figure 13). Spatial factors were also significant with site (forest stand) playing a significant role in structuring communities at 5-months (P<0.05) and subsite (within forest stand) playing a significant role at both 1-month (P<0.05) and 5 months (P<0.05) (Figure 12). This overriding importance of spatial factors appears to partially support hypothesis 1. In contrast, no significant effect of isolate type (priority effects) was found at either 1-month or 5-months (Figure 12). This is surprising as each isolate appeared to be distinct physiologically (Table 3). Ecosystem type had a smaller but still significant effect (P<0.05) on community composition at both time points, as did leaf type (P<0.05, Figure 12).

Impact of Priority Effects on Ecosystem Processes (decomposition)

Leaf type had a very strong effect on mass loss at both 1-month (P<0.05) and 5-months

(P<0.05). Oak leaves generally lost much less mass than maple leaves, especially after 1-month in the field. While isolate type (i.e., priority effects) was not a significant main effect on its own, several interactions of isolate type and other factors did play a significant impact on mass loss

(Figure 14). The leaf type×isolate interaction effect was significant at both 1 month and 5- months (P<0.05, Table 4). This interaction of leaf type×isolate is very noticeable at 5-months, with variable isolate effects found on oak and maple leaves (Figure 15). There was also a significant isolate×ecosystem interaction effect (P<0.05) at 1-month (Table 4 and Figure 15). For example, isolate 3 induced large differences in mass loss of maple leaves among the three

96 ecosystems, whereas differences among ecosystems were much smaller for the other isolates.

Interestingly, at 5-months there was a significant leaf type×ecosystem (P<0.05) interaction

(Table 4). This pattern can be observed in the degree of mass loss between maple and oak leaves found in contrasting ecosystems (i.e. much lower mass loss in of oak leaves in SMBW ecosystems).

Impact of Priority Effects on Community Aggregated Functional Traits (Extracellular Enzymes)

In the analysis of enzyme profiles by RDA, there was a significant effect of fungal isolate at 1-month (P<0.05), but not at 5-months (Figure 16A and Figure 16B). This separation between isolate types at 1-month can clearly be observed in both SMRO and BOWO ecosystems (Figure

17). Enzymatic profiles at both time points were strongly influenced by environmental factors

(ecosystem and leaf type), as well as, to a lesser extent, spatial factors (site). This impact of environmental factors on extracellular enzyme activity profiles at both time points lends support to hypothesis 2. A clear separation of enzyme profiles between oak and maple leaves can be observed at both time points, with this pattern especially noticeable in BOWO ecosystems after

5-months (Figure 17). There was a significant impact of interactions between factors at both time points. These observations are especially evident as an isolate effect at 1-month and a leaf type×ecosystem interaction at 5-months (Figure 16A and Figure 16B). The isolate×leaf interaction was noticeable especially in SMRO and BOWO ecosystems as clear separation existing between maple leaves colonized with isolate 2 from other leaves (Figure 17).

Unique patterns were found when enzyme functional groups were analyzed separately

(Table 5, Figure 16A and Figure 16B). In general, enzyme activities were higher at 5-months

97 than 1-month, with laccase activity largely being largely undetected at 1-month (Figure 18 and

19). Isolate type (priority effects) had a larger effect after 1-month than 5-months, with significant main effects on phosphatase (P<0.05) and polysaccharide-degrading enzyme

(P<0.05) activities in 1-month samples (Figure 18). Also, at 1-month, there was a significant isolate×leaf type interaction on phosphatase (P<0.05), polysaccharide-degrading enzymes

P<0.05, and nitrogen acquisition enzymes P<0.05, with clear differences in enzyme activity being observed with isolate type and leaf litter type (Figure 18). After 5-months, isolate type was significant as an interaction with leaf type for phosphatase (P<0.05) and polysaccharide degrading enzymes (P<0.05) (Figure 6B). Environmental factors (leaf type and ecosystem type) significantly affected the distributions of enzymes groups at both 1-month and 5-months (Figure

6A and 6B). This pattern was particularly strong regarding laccase activity, which also had a strong ecosystem×leaf type interaction at 5-months (P<0.05). This is reflected in the much higher laccase activity that can be found on oak leaves and in oak leaves located in BOWO ecosystems (Figure 19D). The importance of environmental factors in explaining variation in each extracellular enzyme functional group gives further support to hypothesis 2.

Discussion

Fungal communities are complex ecological units that can be highly dynamic and be influenced by a wide range of distinct assembly processes (Nemurgut et al. 2013; Hanson et al.

2012). Our study demonstrates this by determining the relative importance of multiple assembly mechanisms in communities of litter degrading fungi. Community history was found not to play a significant role in the community composition of leaf litter degrading fungal communities, with spatial factors playing the largest role. This, combined with the environment playing a small role in community composition, clearly demonstrates the stochastic nature that these communities

98 potentially possess. In contrast, the distribution of extracellular enzymes and decomposition of leaf litter were influenced by both the environment and colonization history, indicating potential plasticity of functional traits and adding another layer of complexity to the concept of functional redundancy. Finally, a large amount of variation is still left unexplained, especially regarding taxonomic community composition, indicating that other community assembly mechanisms/processes must be at work.

Fungal community composition was heavily influenced by spatial factors, but not by colonization history, providing partial support for hypothesis 1. The fact that spatial factors were the most important in structuring community composition reinforces dispersal limitation as being a major factor in fungal community assembly (Hanson et al. 2012). However, low importance of environmental factors (ecosystem type, leaf litter species) contrasts with other studies that have found they often work in conjunction with spatial factors in structuring fungal communities

(Stegen et al. 2012; Tedersoo et al. 2014; Gacura et al. 2016). Our findings may have been influenced by our communities starting off in a simple state, with communities in a more transitional or starting state being more likely to be dominated by stochastic processes and less by the environmental (Dini-Andreote et al. 2015). More time may be needed before the environment can play a decisive role in determining what types of saprotrophs will be able to colonize these resource patches.

Our findings regarding colonization history and community composition contrasts with studies that have found strong priority effects in the assembly of saprotrophic fungal communities (Hiscox et al. 2015 and Weslien et al. 2011), ectomycorrhizal fungi (Kennedy et al.

2009), and nectar yeasts (Peay et al. 2012A). However, this study utilizes leaf litter, which is far more labile and dynamic, than the woody substrates and other environments utilized in previous

99 priority effects research. This discrepancy may be reflective of the more rapid turnover of leaf litter and the diverse array of organisms capable of utilizing more labile substrates that comprise leaf litter (Pietsch et al. 2014; Voriskova and Baldrian 2013). These dynamic communities of saprotrophs may be under different environmental pressures then those found in other environments (including wood), due to decomposition processes changing the overall nutrient quality of leaf litter very quickly over short-time scales and leading to rapid successional changes in these microorganisms (Purahong et al. 2016; Tlaskal et al. 2016; Voriskova and Baldrian

2013). Rapid changes in environmental conditions and high amounts of temporal variability in conditions could lead to low impact of priority effects, by limiting the growth rate of colonizing organisms (Fukami et al. 2015; Tucker and Fukami 2014). Also, there may be differences in the types of organisms found in these communities as well as organisms belonging to different functional groups (Bani et al. 2018).

The importance of colonization history in community aggregated functional traits and ecosystem processes is surprising, but some evidence has been obtained in previous studies that demonstrate this in saprotrophic fungal communities. For instance, the decomposition rates of woody material have been shown to be influenced by colonization history (Dickie et al. 2012;

Fukami et al. 2010). Wood decomposition studies have also shown that pre-colonization with varying saprotrophs can alter mass loss rates (Oliver et al. 2010; Osono and Hirose 2003). This difference in enzyme production and decomposition may be the direct result of interactions between the inoculated fungi and the colonizing community of saprotrophs (Gessner et al. 2010).

It is also possible that the fungal isolates utilized in this study have fundamentally changed leaf litter biochemical composition leading to changes in decomposition rates (van der Wal et al.

2012; Allison 2012) and/or that the addition of fungal necromass of varying levels of

100 recalcitrance has changed nutrient composition (Fernandez et al. 2016). However, the impact of priority effects was only short lived and had largely diminished by 5 months. This may be due to the quick succession of saprotrophic microbes that occur on leaf litter as it decomposes over several months (Voriskova and Baldrian 2013). It may also be related to the loss of more labile compounds during this time period, and a shift in relative abundance to more recalcitrant nutrients (Snajdr et al. 2011; Moorhead and Sinsabaugh 2006).

The observation that ecosystem and leaf type played the most significant role in both mass loss and extracellular enzyme production supports hypothesis 2, suggesting that decomposition rates and related processes are strongly influenced by environmental conditions

(Allison et al. 2013; Prescott et al. 2010). For instance, it has been observed that soil extracellular enzyme activity can be highly impacted by overall moisture, soil pH, and climate (Kivlin and

Treseder 2014; Sinsabaugh et al. 2008). Leaf litter decomposition rates were strongly impacted by leaf type (oak leaves vs maple leaves), which is similar to other leaf decomposition studies

(Bargali et al 2015; Blair 1988). The leaf type effect on decomposition is most likely a result of differences in the overall composition (amounts of labile and recalcitrant compounds) found in different tree species (Schneider et al. 2012). Interestingly, these factors are decoupled from what was observed with taxonomic communities, indicating a strong effect of environmental conditions and isolate modification of the leaf litter. This provides evidence to support the concept of functional redundancy, which involves the assumption that due to the high amount of biodiversity found in microbial communities, many organisms can perform similar ecosystem functions. This is coupled with the core idea that multiple organisms can perform the same function. Functional redundancy should result in community structure having low importance on community aggregated functional traits and ecosystem processes, compared to environmental

101 conditions. While functional redundancy is still a controversial subject (Allison and Martiny

2008) it has been demonstrated in many ecosystems, including saprotrophic fungal communities

(Banerjee et al. 2016; Peay et al. 2012B).

In addition to enzyme profiles, enzyme functional groups were influenced by varying mechanisms, which may be reflective of the degree in which they are expressed and utilized by different fungal groups (Talbot et al. 2013; Talbot et al. 2015). The strong impact of site and subsite on polysaccharide degrading enzymes after 1-month, but un-seen in other enzyme groups, may indicate some element of stochasticity in the types of saprotrophs that can first colonize a resource patch. This effect was noticed mainly in the enzyme group of Beta- glucosidase after 1-month (data not shown) and may indicate colonization by ectomycorrhizal fungi, which can produce this group of enzymes and have been found to be structured spatially

(Burke et al. 2011; Peay et al. 2010). Both ecosystem and leaf type were highly significant in laccase production, with activity being the highest on oak leaves found in BOWO and SMRO ecosystems. This indicates that organisms capable of decomposing lignified tissue are more likely to be found in environments containing more recalcitrant compounds, which corresponds with findings indicating that the abundance of genes coding for laccase production and laccase enzyme activity were higher in oak dominated forest stands (Blackwood et al. 2007; Theuerl and

Buscot 2010). It was also noticed that, laccase production was only detected substationally after

5 months, reinforcing the idea that fungi degrading recalcitrant compounds may take longer to colonize resource patches and that laccase production is repressed until more labile compounds are exhausted (Osono 2007).

Interactions between ecosystem and leaf type observed in our experiment indicate that litter is more efficiently decomposed in ecosystems dominated by similar species. This suggests

102 that the native saprotroph communities present in ecosystems are more efficient at breaking down leaf litter native to a specific forest stand. Previous studies have also indicated the occurrence of this pattern, termed homefield advantage (Gholz et al. 2000). It is hypothesized to the be the result the specialization of saprotrophic microbial and invertebrate detritivore communities (Austin et al. 2014) and the overall recalcitrance (i.e. nutrient quality) of leaf litter

(Veen et al. 2015). Homefield advantage has been demonstrated in a wide range of ecosystems around the world and has been suggested to help explain variation in litter decomposition experiments (Ayres et al. 2009). It will be of great interest to explore this mechanism further in future experiments with a wider array of ecosystems, inclusion/exclusion of certain decomposer species, and leaf litter from a more diverse array of tree species.

Conclusion

This is the first experiment to our knowledge to test the effects of so many community assembly mechanisms (environmental selection, dispersal, and col onization history) on not only the taxonomic composition of communities, but also on the ecosystem processes that they carry out. Our findings have demonstrated that the impact of colonization history on microbial communities is not as clear as originally thought and may be dependent on a wide variety of factors. Community composition was clearly impacted by spatial factors and not by colonization history, indicating that communities are highly impacted by dispersal limitation. This importance of dispersal limitation reinforces that these types of communities are strongly limited spatially

(Hanson et al. 2012). Through the analysis of extracellular enzymes and decomposition we have demonstrated evidence for functional redundancy, functional trait plasticity, and homefield advantage. Further research should be applied to different resource patches of varying levels of lability and on creating more complex starting communities as these may impact the importance

103 of priority effects (Fukami et al. 2015). However, this study does indicate the complexity of the assembly and function of these communities. This information is of great importance as fungi have been recognized as important drivers in the storage and supply of soil nutrients in soils and will be influenced by future anthropogenic changes (Treseder and Lennon 2015; Brabcova et al.

2018).

Acknowledgements

This research was supported by grants from the US National Science Foundation

(DEB0918240 and DEB-0918878) and US Department of Energy (DE-SC0004335). Additional funding was provided by the Kent State Department of Biological Sciences.

104

Table 3. Isolate summary information table. Included for each isolate is the original ecosystem, leaf type, and decay stage that they were isolated from along with their molecular identification. Also included is the physiological traits for each fungal isolate. These physiological traits include: growth rates on several media types, and activities for several different functional groups of enzymes. Cellulase reaction was quantified using the method found in Kasana et al. 2008. Guaiacol color change was used to determine the presence of lignin degrading oxidative enzymes (Westermark and Eriksson 1974).

Isolate ID Number 1 2 3 Colletotrichum Nectriaceae sp. Sarocladium sp. Isolate Taxonomic ID sp. Original Leaf Species Quercus velutina Tilia americana Quercus rubra Original Ecosystem Type BOWO SMBW SMRO Original Decay Stage Living Decayed Senesced Growth Rate GYM Media (cm/24hr) 3.73 3.14 2.82 Growth Rate Cellulose (cm/24hr) 2.43 1.08 2.50 Growth Rate Lignin (cm/24hr) 3.71 0.54 2.34 Guiaicol Color Reaction After 24hrs Yes Yes No Cellulase Reaction After 24hrs Yes Yes Yes Nitrogen Obtaining Enzyme Activity (nmol/h/g) 7629.60 84.75 5448.58 Phosphotase Activity (nmol/h/g) 8915.24 0.00 2018.67 Polysaccharide Degrading Enzyme Activity (nmol/h/g) 10715.53 2651.83 3156.26 Laccase Activity (nmol-1/h-1/g-1) 0.00 0.00 118.40

105

106

Table 4. Summary Table of percent variance explained for factors relating to leaf litter decomposition rates. Results are given for 1-month and 5-month data sets. Only significant explanatory factors are reported, with nonsignificant factors indicated by NS. % Variance (1 % Variance (5 Explanatory Factor Month) Month) leaf 15.00 9.74 ecosystem NS NS isolate NS NS leaf: ecosystem NS 7.63 leaf: isolate 3.92 6.76 ecosystem: isolate 5.78 NS site NS NS subsite NS 14.31

107

Table 5. Table summarizing percent variance explained for factors influencing log-transformed extracellular enzyme activity. Results are presented for each enzyme functional group at 1 month and 5 months. Abbreviations are the following: Nitr. = nitrogen obtaining enzymes, Phos. = phosphatase, Poly. = polysaccharide degrading enzymes, and Lacc. = Laccase. Only significant results are indicated with percent variance explained with nonsignificant factors indicated by NS.

1-Month 5-Months Explanatory Factor Nitr. Phos. Poly. Lacc. Nitr. Phos. Poly. Lacc. leaf NS 4.85 16.97 NS NS NS NS 21.19 ecosystem 6.32 10.67 NS NS NS 7.88 NS 11.25 isolate NS 3.99 9.22 NS NS NS NS NS leaf: ecosystem 5.33 NS NS NS NS NS 6.97 8.25 leaf: isolate NS 3.40 5.25 NS NS 3.81 3.67 NS ecosystem: isolate 6.14 7.39 NS NS NS NS NS NS leaf: ecosystem: isolate NS NS NS NS NS NS NS NS site NS NS 4.75 NS NS NS NS NS subsite NS NS 10.92 NS NS NS NS NS

108

Figure 12. Percent variance explained (Y-axis), as indicated by Redundancy Analysis, for T- RFLP profiles at 1-Month and 5-Months in the field. The interactions category includes interactions between the factors: leaf type, isolate, and ecosystem.

109

110

Figure 14. Significant factors that influence percent mass loss as indicated by redundancy analysis, for each data set at 1-month and 5-months. Variance explained for each significant factor is indicated on the y axis. The interactions category includes interactions between the factors: leaf type, isolate, and ecosystem.

111

Figure 15. Average decomposition of leaf litter, as indicated by percent mass loss (y axis), for treatments at 1-month and 5-months. Treatments are noted by what ecosystem they were placed in and what leaf type litter bags consisted of. Isolated type is indicated by bar color and pattern. Error bars are utilized to indicate standard error.

112

A te

B te

113

114

Figure 18. Extracellular enzyme activity after 1-Month. Enzyme activity has been broken up based upon enzyme function: A. phosphatase activity, B. polysaccharide degrading enzyme activity, C. nitrogen obtaining enzyme activity, and D. laccase activity. Isolate type, leaf species, and ecosystem type are included for sample. Error bars indicating standard error are included.

115

Figure 19. Extracellular enzyme activity after 5-months. Enzyme activity has been broken up based upon enzyme function: A. phosphatase activity, B. polysaccharide degrading enzyme activity, C. nitrogen obtaining enzyme activity, and D. laccase activity. Isolate type, leaf species, and ecosystem type are included for sample. Error bars indicating standard error are included.

116

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Chapter 4: Comparison of Pectin-Degrading Fungal Communities in Temperate Forests

Using Glycosyl Hydrolase Family 28 Pectinase Primers Targeting Ascomycete Fungi.

(This chapter was originally published in the Journal of Microbiological Methods in April 2016)

Citation

Gacura M.D., Sprockett D.D., Heidenreich B. and Blackwood C.B. 2016. Comparison of pectin- degrading fungal communities in temperate forests using glycosyl hydrolase family 28 pectinase primers targeting Ascomycete fungi. Journal of Microbiological Methods, Vol. 123: 108-113.

Preface

Matthew Gacura was the primary researcher for the pure culture/environmental testing of the primers and the primary author of the publication. Daniel Sprockett and Bess Heidenreich were responsible for initial primer sequence development and preliminary pure culture testing of the primers. Dr. Chris Blackwood helped conceive the procedures and data analysis in the project.

Abstract

Fungi have developed a wide assortment of enzymes to break down pectin, a prevalent polymer in plant cell walls that is important in plant defense and structure. One enzyme family used to degrade pectin is the glycosyl hydrolase family 28 (GH28). In this study we developed primers for the amplification of GH28 coding genes from a database of 293 GH28 sequences from 40 fungal genomes. The primers were used to successfully amplify GH28 pectinases from all Ascomycota cultures tested, but only three out of seven Basidiomycota cultures. In addition,

130 we further tested the primers in PCRs on metagenomic DNA extracted from senesced tree leaves from different forest ecosystems, followed by cloning and sequencing. Taxonomic specificity for

Ascomycota GH28 genes was tested by comparing GH28 composition in leaves to internal transcribed spacer (ITS) amplicon composition using pyrosequencing. All sequences obtained from GH28 primers were classified as Ascomycota; in contrast, ITS sequences indicated that fungal communities were up to 39% Basidiomycetes. Analysis of leaf samples indicated that both forest stand and ecosystem type were important in structuring fungal communities.

However, site played the prominent role in explaining GH28 composition, whereas ecosystem type was more important for ITS composition, indicating possible genetic drift between populations of fungi. Overall, these primers will have utility in understanding relationships between fungal community composition and ecosystem processes, as well as detection of potentially pathogenic Ascomycetes.

Introduction

Pectin is a complex polysaccharide comprising as much as 35% of plant primary cell walls (Voragen et al. 2009). It is a polymer of up to 17 monosaccharides, but is commonly defined by a backbone of 1,4 linked α-D-galactosyluronic acid monomers (Mohnen 2008). Due to its prevalence and structural diversity, microorganisms have evolved many extracellular enzymes to aid in pectin decomposition (Prade et al. 1999, A. Manucharova 2009 and Glass et al.

2013). The gene family glycosyl hydrolase 28 (GH28) codes for a diverse array of hydrolytic enzymes involved in pectin depolymerization, including polygalacturonases, rhamnogalacturonases and xylogalacturonases (Abbott and Boraston 2007 and Markovic and

Janecek 2001). Because of its role in decomposition and plant pathogenicity, fungal GH28 gene abundance and diversity is of particular importance in ecosystem functioning (Kjoller and

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Struwe 2002, Herron et al. 2000 and Reignault et al. 2008). However, effective assays for examining the distribution of GH28 genes in natural communities have not been developed.

Development of primers targeting functional genes with key biogeochemical roles provides a unique opportunity to reveal linkages between microbial communities and ecosystem processes (Zak et al. 2006, Peay et al. 2008). For example, PCR based assays have been used to show that gene diversity and abundance are linked to nitrogen fixation, nitrification, and denitrification (Levy-Booth et al. 2014). Primers have also been developed for the analysis of several extracellular enzymes involved in decomposition, such as laccases, cellulases, and chitinases (Williamson et al. 2000, Luis et al. 2004, Edwards et al. 2008 and Takaya et al. 1998).

Subsequently, functional gene primers have been used to study how environmental perturbations such as nitrogen deposition (Hofmockel et al. 2007), agricultural management (Fan et al. 2012), and hydrocarbon contamination (Cebron et al. 2015) influence ecosystem processes through altering distributions of microbial functional genes. Because of the important role played by the distribution of functional genes in constraining ecosystem processes, it is critical to continue development of new primers that can be used in a community context.

In addition to contributing to our understanding of decomposition responses to environmental factors through microbial communities, a pectinase PCR assay may also reveal important insights into the distribution of plant pathogens. Glycosyl hydrolases, including members of the GH28 family, are important virulence factors in plant pathogens (Zhao et al.

2013). The number of GH28 gene homologues in fungal genomes has been found to be reflective of species ecological niches, with biotrophic pathogens having fewer GH28 genes than necrotrophic pathogens or saprotrophic fungi (Sprockett et al. 2011). Pectinase gene distribution, including GH28, can also vary among genomes based upon plant host, with increased pectinase

132 gene numbers in organisms that attack hosts and tissues enriched in pectin (de Wit et al. 2012,

King et al. 2011).

The purpose of this project was to develop fungi-specific PCR primers for GH28 genes.

We focus on a single clade within GH28 specific for endopolygalacturonases (clade F sensu

Sprockett et al. 2011). Clade F genes are found in a wide array of dikarya fungi, including many types of plant pathogens and saprotrophs, and are closely related to GH28 genes found in pathogenic Phytophthora species (Sprockett et al. 2011). After successful testing using cultured organisms, we further tested the primers by examining GH28 diversity in a limited number of leaf samples, allowing us to obtain a preliminary understanding of the role that environmental factors have on pectinase gene distribution. We tested the hypotheses that ecosystem type or leaf type influenced GH28 gene community composition and diversity. Finally, pyrosequencing of fungal internal transcribed spacer region (ITS) genes was performed on the leaf samples to determine the taxonomic specificity of the selected primers.

Materials and Methods

Primer Development

Primer design was conducted using a database of 293 fungal GH28 DNA sequences from

40 genome sequences (Sprockett et al. 2011). We focused primer design on GH28 phylogenetic clade F because of a high level of sequence conservation and good representation of major fungal lineages (Sprockett et al. 2011). Primers matching the maximum possible number of sequences were designed to have as few degeneracies as possible, avoid self-priming and primer- dimers, and other appropriate physiochemical properties (e.g., annealing temperatures) (Table 6).

Primer Testing on Isolates

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Initial primer testing was performed on Aspergillus niger strain NRRL 3 and

Phanerochaete chrysosporium strain NRRL 6361 (ARSEF Culture Collection, Ithaca, NY).

Genomic DNA was extracted from fungal isolates using a modified CTAB method (Griffiths et al. 2004). Samples were frozen in liquid nitrogen, ground and then treated with CTAB buffer and

β-mercapthoethanol. This was followed by removal of co-extractants with chloroform and DNA precipitation using isopropanol.

Four different primer pair combinations were tested in PCR reactions. Primers were ordered from Integrated DNA Technologies (IDT; Coralville, IA). PCR was performed under a variety of conditions to determine an optimal protocol for use in all subsequent tests. Reagent concentrations in the optimal PCR protocol were 0.025 U/µl DNA Taq DNA polymerase (B-

Bridge, Santa Clara, CA, USA ), 1× standard buffer (B-Bridge), 1.5 mM MgCl2, 0.16 mM each deoxynucleotide triphosphate, 0.1µg/µl bovine serum albumin (New England BioLabs, Ipswich,

MA), 0.1 µM of each of the forward and reverse primers, and 0.3 ng/ul of template genomic

DNA. Optimal PCR conditions were: 95ºC denaturation for 3 min, followed by 35 cycles of

94ºC for 30 s, 64ºC for 30 s, 72ºC for 1 min 30 s, and final extension at 72ºC for 7 min. PCR products were checked on a 1.5% agarose gel stained with ethidium bromide.

PCR products from A. niger NRRL3 and P. chrysosporium NRRL 6361 were purified using an UltraClean PCR Cleanup Kit (MoBio Laboratories, Carlsbad, CA), quantified using

Picogreen fluorescent DNA stain (Life Technologies, Eugene, OR, USA), and ligated into the pGEM-T easy vector (Promega, Madison, WI) following instructions from the manufacturers.

Plasmids were then used to transform Escherichia coli JM109. PCR was performed on colonies using M13 primers to amplify cloned inserts. Clones with different amplicon insert sizes were grown overnight in 4 ml LB broth with ampicillin and glycerol, and plasmids were isolated using

134 an UltraClean Plasmid Prep Kit (MoBio Laboratories). Sanger sequencing was performed at

Ohio University Genomics Facility.

Following these initial tests, one primer pair was selected for further analysis: GH28F-

1786F (5’-TRB TGG GAY GGH NWR GG-3’) and GH28F-2089R (5’-GCV ABR CAR TCR

TCY TGR TT-3’) (Table 6). Amplification and analysis was then performed on genomic DNA extracted from additional well-characterized fungi shown in Table 7. The one or two most intense bands were gel-purified using an UltraClean GelSpin DNA Extraction kit (MoBio

Laboratories) and sequenced at Ohio University.

Sequences from cultured organisms were trimmed for quality using the program

Sequencher® version 5.4.1 sequence analysis software (Gene Codes Corporation, Ann Arbor,

MI, USA). The program BLAST (Altschul 1990) was used to find the closest matches in the

NCBI nucleotide database. All hits with the best bit scores and e-values were examined to look for consistency in their functional and taxonomic annotation. Results were further confirmed by classifying sequences in the Protein family database (Pfam) (Bateman et al. 2004).

Testing Primers on Metagenomic DNA from Environmental Leaf Samples

Senesced leaf material was gathered from three forest stand replicates (sites) of each of two ecosystems in Manistee National Forest in Northwest Michigan: sugar maple/basswood

(SMBW) and black oak/white oak (BOWO) (Zak et al. 1989). These ecosystems differ based upon their dominant overstory tree coverage, topography and soil characteristics. Sites were separated by varying distances, with a maximum distance of 56 km. At each site, leaf samples were collected for each of the two dominant tree species (e.g., black oak and white oak from a

BOWO site). Leaves were freeze dried, homogenized in a Genogrinder 2000 (SPEX Certiprep;

Metuchen, NJ, USA) for two minutes at 1000 beats per minute, and then stored at -80oC.

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Genomic DNA was extracted from the microbial community of each leaf using the CTAB DNA extraction following Wu et al. (2011). PCRs were performed on metagenomic DNA and cloned into E. coli JM109 as described above. Inserts were sequenced at the University of Kentucky

Advanced Genetic Technologies Center. Sequences were processed and taxonomically and functionally identified as described above. Sequences were then clustered into operational taxonomic units (GH28-OTUs) with cut offs from 70% to 90%-similarity using the software CD-

Hit (Huang et al. 2010). Non-GH28 clusters (generated from non-specific amplification) were removed from the data set before further community analysis. No differences in results were observed with different cut offs, so only results using the 90% cut off are reported here.

Pyrosequencing Fungal ITS Region from Leaf Samples

To determine phylogenetic composition of fungal communities on leaves used for GH28 primer testing, the fungal ITS region was amplified using barcode-labeled primers ITS1F and

ITS4, as described in Chattopadhyay et al. (2014). Amplicons were gel purified using the

UltraClean GelSpin DNA Extraction kit, followed by Agencourt Ampure XP (Beckman Coulter,

Brea, CA, USA) purification. Afterwards, DNA concentrations were quantified using the

PicoGreen dsDNA Assay Kit. Samples were sequenced in a GS Junior 454 Pyrosequencer

(Roche) as in Chattopadhyay et al. (2014). The program QIIME (Caporaso et al. 2010) was used for demultiplexing, denoising and clustering into 97% similarity ITS-OTUs. Phylogenetic identification was performed using BLAST of sequences representing each OTU against the databases Unite and Genbank. BLAST results were then parsed using the last common ancestor algorithm using the software MEtaGenome ANalyzer (MEGAN) 5.0 (Huson and Mitra 2012).

Unite (Kõjalg et al. 2005) and Genbank results were then consolidated by comparing the taxonomic strings output from MEGAN. We used the finest taxonomic assignment (down to

136 genus) that did not result in higher level disagreement between databases. In cases of disagreements, we used the assignment from the curated UNITE database.

Statistical Analysis of Leaf Fungal GH28 and ITS Composition

Statistical analyses were performed in the R statistical analysis program (R core team

2014) using the vegan package (Oksanen et al. 2013). OTU frequencies were rarefied to equal sampling depth, converted to relative abundance, and Hellinger transformed (Legendre and

Gallagher 2001) before analysis. Rarefaction and analyses were repeated 10 times to account for variation among simulations of even sampling effort. Redundancy analysis (RDA) (Legendre and Alexander 1999) was utilized to test for effects of ecosystem, site, and leaf type. Adjusted

R-squared values (Peres-Neto et al. 2006) were obtained from each RDA and interpreted as variation explained by each factor.

Sequence Availability

Sequences obtained from GH28 primers have been submitted to GenBank under accession numbers KU664125 - KU664184.

Results

Primer Testing on Isolates

Four conserved regions of GH28-clade F were chosen as potential primer sites based on different physicochemical properties. Amplification with primers GH28F-1786f/GH28F-2089r resulted in the most consistent and distinct visible product by gel electrophoresis. These primers were selected for testing on a collection of fungal cultures from a variety of different taxa (Table

7). Clear bands were produced by all of the isolates except Rhodotorula graminis. Amplification from six Ascomycota isolates resulted in sequences that matched endopolygalacturonases in

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Genbank and Pfam databases (Table 7). However, inconsistent results were found in

Basidiomycota, with GH28 sequences obtained from three of seven isolates (five of nine bands sequenced). GH28 sequences were variable in length (~300-500bp) due to the presence of introns.

Pectinase gene diversity found on decomposing leaves

A total of 901 clones were sequenced from GH28 PCRs of 12 leaves. These were clustered at 90% similarity into 456 GH28-OTUs. After deletion of singleton OTUs, it was found that 77% of the sequences matched fungal endopolygalacturonases (GH28 clade F sequences) in

NCBI. The majority (61%) of GH28 sequences were located in the twelve largest GH28-OTUs

(Table 8). Representative sequences from these GH28-OTUs matched endopolygalacturonases found in a variety of fungal taxa, representing saprotrophs and major plant pathogens. All GH28 sequences obtained from leaves had best matches to database GH28 genes obtained from

Ascomycota (Table 8).

Several leaves were dominated by very few GH28-OTUs; for example, OTU 1 made up

60% of the clone library from sample BO-U and OTU 3 made up 40% of the clone library for sample BW-6. However, OTU richness, Shannon diversity and Simpson diversity were not significantly impacted by ecosystem or leaf type. Some GH28-OTUs were found only in single ecosystems or sites (Table 8). This is reflected in results of RDAs performed on GH28-OTU relative abundances, with OTU composition significantly affected by both ecosystem (Adj. R- square= 0.075, P-value= 0.023) and site (Adj. R-square=0.142, P-value= 0.035). The larger role of site in structuring GH28-OTU composition, instead of ecosystem, can be seen in the RDA ordination in Figure 20. Other factors such as leaf species, leaf size, and location in the litter layer were found to be non-significant.

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Taxonomic Structure of Fungal Communities

Pyrosequencing was performed to determine if leaf communities were indeed dominated by Ascomycota, as indicated by GH28 clone libraries, or if primers were biased against

Basidiomycota. A total of 56,134 fungal ITS sequences were obtained from the leaf samples described above. These sequences were clustered, with a 97% cut off, into 251 ITS-OTUs. For individual leaves, Basidiomycota abundance varied from 0% to 39% of ITS sequences.

Communities were dominated by Ascomycota, with an average relative abundance of 90%

Ascomycota sequences in BOWO leaves and 98% in SMBW leaves. Redundancy analysis on

ITS-OTU profiles indicated that ecosystem was the most important factor explaining variation in fungal communities (Adj. R-Square= 0.167, P-value= 0.011), with site not having a significant effect (Adj. R-square= 0.08, P-value= 0.101). Ecosystem and site together explained more variation in taxonomic community composition than in GH28-OTU distribution (Figure 21). The ordination plot indicates a very strong separation of communities between ecosystem types

(Figure 21). Fungal communities in BOWO ecosystems were grouped more closely together in the RDA plot than the SMBW communities. As in GH28 OTUs, other factors such as leaf species, leaf size, and location in the litter layer were found to be non-significant in ITS sequences.

Discussion

This study, to our knowledge, is the first report of general primers for fungal pectinase genes, providing a valuable tool to link community composition to decomposition and plant pathogenicity. Testing of pure culture organisms confirmed that these primers will amplify

GH28 clade F genes from several different taxonomic and functional groups of fungi, but are particularly useful for Ascomycete fungi. Also, the primers amplified Ascomycete GH28 genes

139 from environmental samples, which will be useful for determining major environmental factors that drive GH28 gene distribution.

On senesced leaves there is a rich community of potentially pathogenic and saprotrophic fungi (Gessner et al. 2010 and Hattenscheweiler 2011). Using our GH28 clade F primers, we detected endopolygalacturonases from Ascomycota fungi on all leaves tested. Although we were able to successfully quantify GH28 composition and diversity, fine taxonomic assignments (e.g., genus-level) are likely unreliable because sequence matches generally had low confidence values and fungal GHs in Genbank are not representative of the fungal diversity in leaves. Novel fungi may also be present, as fungal diversity is high in temperate ecosystems (Schmit and Mueller et al. 2007) and many ITS sequences could not be identified past the order level.

The dominance of Ascomycota sequences in leaf samples was unexpected because

Basidiomycota are thought to be common decomposers of plant tissue in forests (Heilmann-

Clausen et al. 2001, Osono 2007 and Voriskova and Baldrian 2013). However, analyses of ITS sequences as well as GH28 sequences clearly indicated fungal communities on leaves from our sites were dominated by Ascomycota. Ascomycota fungi include diverse, highly prevalent saprotrophic, pathogenic, and endophytic organisms, and it is thus perhaps not surprising that they dominate communities on senesced leaves (Schoch et al. 2009). Leaves that came from tree species with more lignified tissue (i.e. oaks) did have a higher percentage of Basidiomycota (up to 39% of sequences), as found previously in forests dominated by oak species (Blackwood et al.

2007). The absence of Basidiomycete GH28 sequences even from oak leaves confirms the primer bias for Ascomycota GH28 genes.

There is a high degree of variation in dominant tree species and soil properties between different ecosystem types in Manistee National Forest (Host et al. 1988). This environmental

140 variation appears to have had a strong influence on the structure of saprotrophic fungal communities as indicated by ITS sequences. This could be the result of differences in plant tissue chemistry between the two ecosystems, which has been shown to play a large role in structuring fungal communities (Schneider et al. 2012 and Hanson et al. 2008). In contrast, GH28 sequences were mainly influenced by site-to-site heterogeneity, whereas ecosystem type played a negligible role. The high level of taxonomic similarity but lower level of functional gene similarity among sites of the same ecosystem type is unexpected under the dominant paradigm that microbial communities harbor a high level of functional redundancy (Wardle et al. 2006 and Nielson et al.

2011). One explanation is that genetic drift between sub-populations of fungi in separate forest stands could cause divergence in GH28 composition. Necrotrophic plant pathogens have a high degree of genomic plasticity for genes coding for virulence factors as a result of transposable elements, translocation and duplication events, and mutations arising from compensatory variation in host defense genes (Raffaele and Kamoun 2012). Sexual reproduction within a species will also result in new combinations of GH28 alleles, after which new GH28 combinations can become prevalent in a site through asexual reproduction (McDonald and Linde

2002). Differences between GH28 and ITS datasets may also have arisen from different numbers of sequences generated by different sequencing methods (cloning and Sanger sequencing for

GH28, pyrosequencing for ITS). It is possible that differences in GH28 composition between ecosystems could have been masked by increased error due to smaller number of sequences.

Primers developed here will be suitable for amplification of GH28 genes in Ascomycota fungi, but not Basidiomycota. The primers should be useful because Ascomycota appear to dominate decomposer communities in leaf tissue, and many of the most prevalent plant pathogens are Ascomycota that depend on GH28 genes (Klosterman et al. 2011). GH28 genes

141 are particularly prevalent in necrotrophic plant pathogen genomes (Floudas et al. 2012, Sprockett et al. 2011) and are important virulence factors in necrotrophic plant pathogens (van Kan 2006,

Hematy et al. 2009 and Zhao et al. 2013). Our results indicate that these primers will be most useful when used in sequencing based assays because non-GH28 sequences can be removed from datasets before further analysis. Direct sequencing methods have been used for rapid screening of fungal isolates for functional genes (Lee et al. 2001 and Pointing et al. 2005) and for detecting the presence of pathogens in plant hosts (McCartney et al. 2003). Hence, these primers should be a valuable new molecular tool to complement other methods for both detection of pathogens and better understanding the roles that fungi play in ecosystem processes.

Acknowledgements

This research was supported by grants from the US National Science Foundation (DEB-

0918240 and DEB-0918878) and US Department of Energy (DE-SC0004335).

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Table 6. Primer regions and examples of conserved sequences from fungal genomes.

Genbank assembly accession GH28F-1786f region GH28F-2089r number Primers TRBTGGGAYGGHNWRGG AAYCARGAYGAYTGYVTBGC Phanerochaete chrysosporium TACTGGGATGGCCAAGG AACCAGGACGACTGCCTCGC GCA_000167175.1 Aspergillus niger TACTGGGACGGCGAGGG AACCAGGACGACTGTGTTGC AM270980 – AM270998 A. niger TGGTGGGATGGAGAGGG AACCAAGATGACTGCGTTGC A. niger TGGTGGGATGGCGAGGG AACCAAGATGACTGTGTGGC A. niger TGGTGGGATGGCAAGGG AACCAGGATGACTGTCTTGC A. niger TGGTGGGACGGTGAGGG AACCAGGACGACTGCGTCGC Sclerotinia sclerotiorum TATTGGAATGGGTATGG AATCAGGATGACTGCGTAGC GCA_000146945.1 Pyrenophora tritici-repentis TGGTGGGATGGACTCGG AACCAAGATGATTGCGTTGC GCA_000149985.1 Laccaria bicolor TATTGGGATGGTACGGG AATCAAGACGATTGCCTTGC GCA_000143565.1 Botrytis cinereal TATTGGGATGGTCTTGG AATCAGGATGATTGCGTCGC GCA_000143535.1

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Table 7. List of fungal isolates used to test GH28 clade F primers, and results from PCR reactions using selected primers.

Species Name Phylum1 Ecological Niche GH28 Amplify Band Size GH28 GH28 Copy (bp) (NCBI)3 Pfam4 Number2 Aspergillus niger A Pathogen 23 Yes 1 300 Yes Yes (ATCC1015 and FGSC A1513) 2 350 Yes Yes Cenococcum A Mycorrhizal 4 Yes 1 350 Yes Yes geophilum (ATCC 52041) 2 450 Yes Yes Neurospora crassa A Saprotroph (not wood) 2 Yes 1 300 Yes Yes (OR74A) Saccharomyces A Saprotroph (not wood) Not Yes 1 350 Yes Yes paradoxus (NRRL Available 17217) Talaromyces stipitatus A Saprotroph (not wood) 8 Yes 1 350 Yes Yes (ATCC 10500) Trichoderma reesei A Saprotroph (not wood) 4 Yes 1 350 Yes Yes (QM6a) Agaricus bisporus (var. B Saprotroph (not wood) 6 Yes 1 300 No No burnettii JB137-S8) Coprinopsis cinerea B White Rot Saprotroph 3 Yes 1 350 No No (FGSC 9003) Laccaria bicolor (ATCC B Mycorrhizal 6 Yes 1 300 Yes Yes 4686) 2 500 No No Mycena galopus (ATCC B White Rot Saprotroph Not Yes 1 350 Yes No 62051) Available 2 500 Yes Yes

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Phanerochaete B White Rot Saprotroph 4 Yes 1 350 No No chrysosporium (RP-78) Rhodotorula graminis B Saprotroph (not wood) 1 No N/A N/A N/A N/A (FGSC 10291) Schizophyllum B White Rot Saprotroph 3 Yes 1 350 Yes Yes commun e (H4-8) 2 450 Yes No

1 A indicates Ascomycota and B indicates Basidiomycota. 2 GH28 copy number indicates the number of genes present in an organism’s genome (if data is currently available) (Sprockett et al. 2011 and Floudas et al. 2012). 3 Indicates whether sequences were successfully matched to a GH28 sequence in BLAST analysis of the NCBI database. 4 Indicates whether sequences were successfully matched to a GH28 sequence in the PFAM database.

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Table 8. Taxonomy of closest matches in the NCBI Genbank database for the twelve GH28-OTUs with the greatest number of sequences (all closest matches were endopolygalacturonase sequences).

OTU # Sequences Ecosystem1 Leaf Species2 Organism (Closest Match) BLASTn e-value Genbank Score Accession OTU1 48 BOWO and SMBW SM and BO Cochliobolus carbonum 62.6 0.000001 KU664125 OTU2 43 SMBW SM and BW Aspergillus fumigatus 62.6 0.000002 KU664126 OTU3 33 SMBW SM and BW Colletotrichum lindemuthianum 163 6.00E-37 KU664127 OTU4 28 BOWO BO and WO Botryotinia calthae 170 4.00E-39 KU664128 OTU5 19 BOWO BO Colletotrichum lupine 95.1 3.00E-16 KU664129 OTU6 16 BOWO and SMBW SM, BO and WO Botryotinia fuckeliana 167 5.00E-38 KU664130 OTU7 15 SMBW SM and BW Aspergillus niger 156 9.00E-35 KU664131 OTU8 14 SMBW SM and BW Thielavia terrestris 118 3.00E-23 KU664132 OTU9 13 BOWO BO and WO Aspergillus oryzae 176 1.00E-40 KU664133 OTU10 12 SMBW SM and BW Pyrenophora teres 57.2 0.00009 KU664151 OTU11 12 SMBW SM and BW Botryotinia convolute 210 5.00E-51 KU664145 OTU12 10 BOWO and SMBW SM, BW, BO and WO Botryotinia fuckeliana 192 1.00E-41 KU664165

1 BOWO indicates black oak/ white oak and SMBW indicates sugar maple/basswood forest stands 2 For leaf species SM indicates sugar maple, BW indicates basswood, BO indicates black oak

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Figure 21. RDA ordination of ITS-OTUs (taxonomic community composition). Each point indicates an individual leaf. Ecosystem is indicated by color (yellow=SMBW, black=BOWO). Symbols are different for each forest stand. Leaves from the same forest stand are connected by a line.

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Chapter 5:Synthesis

This dissertation set out to better understand the various assembly mechanisms impacting microbial communities. There is still much conjecture about which of these mechanisms

(stochastic vs deterministic) plays the most critical role in the construction of these ecological units (Nemergut et al. 2013; Hanson et al. 2012). This dissertation was able to show that the relative importance of distinct community assembly mechanisms depends upon the spatial scale analyzed and resource type availability (Chapters 2 and 3). The effect of spatial scale has been explored previously but was much more limited in several key aspects. (Feinstein and

Blackwood 2013). Research in this dissertation was also focused upon how community composition relates to community aggregated functional traits and their related ecosystem processes. I was able to show that environmental conditions play a large role in the distribution of functional traits, saprotrophic microbial communities display functional redundancy at the ecosystem scale (Chapters 2 and 3), and that community assembly mechanisms change in importance depending upon microbial functional groups (Chapters 2, 3, and 4). These aforementioned findings and how they relate to the literature will be discussed in the remainder of this synthesis section.

Importance of Stochastic and Deterministic Assembly Mechanisms at Multiple Spatial Scale.

This dissertation confirms that saprotrophic fungal communities are complex units of many species that are structured by both environmental deterministic processes and stochastic 157

dispersal-based mechanisms. However, it appeared that environmental selection (i.e. ecosystem type and leaf litter species) was more important in the assembly of saprotrophic fungal communities, with dispersal-based mechanisms having a more secondary role (Chapters 2 and

4). This supports other studies indicating the importance of environmental conditions in determining saprotrophic fungal community composition when compared to other mechanisms

(Bani et al. 2018). The high amount of variation explained by ecosystem in community composition is most likely related to the fact that the studied ecosystems in Manistee National

Forest are very distinct in characteristics including: soil properties, litter chemistry of plant tissue, and mycorrhizal associations of dominant tree species (Zak et al. 1989; Host et al. 1988).

Stochastic mechanisms also played a role in community assembly, as evidenced by between-site variation explaining a substantial portion of variation in community composition.

This may be related to dispersal limitation, which has been found in several groups of fungi including: flower yeasts, mycorrhizal fungi and wood decomposing fungi (Belisle et al. 2012;

Glassman et al. 2017a; Peay and Brun 2014). Variation among sites was noticed to be especially important in structuring of communities that are starting in a much simpler state (Chapter 3).

This may result from my study design, with simple communities being quickly filled by a random assortment of organisms capable of rapid dispersion. Communities in newly disturbed environments have been found to be more likely influenced by stochastic mechanisms due to empty niche space being colonized by a random assortment of taxa (Dini-Andreote et al. 2015).

Spatial scale played an important role in determining the relative importance of deterministic and stochastic assembly mechanisms in community composition. Community assembly mechanisms have been found to operate at different spatial scales and by analyzing

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systems at fine spatial scales, distinct assembly mechanisms can be recognized in microbial communities (Glassman et al. 2017b; Shi et al. 2015; Bahram et al. 2014). In this dissertation I was able to demonstrate varying assembly patterns when sites were analyzed at smaller scales

(between leaf) and that at these scales, stochastic mechanisms were more or equally as important in the assembly of fungal communities as environmental factors such as leaf litter species. For instance, my sampling scheme allowed the determination of distance decay patterns occurring at small scales in several sites, especially in more recalcitrant forest stands. This importance of stochasticity at small scales in litter communities may be reflective of saprotrophic fungal communities being colonized by the local soil fungal communities upon leaf senescence

(Vorikova and Baldrian 2013). The distance decay pattern observed in BOWO ecosystems may also indicate slower growth and dispersal of saprotrophs in the more nutrient poor and recalcitrant compound dominated ecosystems. I also found that variability in community composition was higher in SMBW ecosystems, possibly due to a wider assortment of fungi being capable of utilizing the more labile resources in this ecosystem, while more specialized and less prevalent saprotrophs can utilize recalcitrant substrates (Purahong et al. 2016; van der Wal et al.

2012). This information suggests that the scale should be considered during studies focused on the assembly of communities and that it will be of great importance in deciphering community assembly mechanisms.

Factors Controlling Community Aggregated Functional Traits and Ecosystem Functions

Both community aggregated functional traits and ecosystem functions contrasted with what was detected for community composition, as stochastic mechanisms played an overall insignificant role in their distribution. This was found to be especially true at the leaf scale, as

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leaf type had no major impact on community composition, but played a large role in enzyme production, decomposition and functional group distributions. The discrepancy between community composition and function may indicate the presence of functional redundancy, which is a mechanism in which certain environmental functions and processes are carried out by a wide range of microbial species (Louca et al. 2018). This is thought to arise in saprotrophic communities due to the high degree of microbial diversity and the relatively limited number biochemical pathways involved in the decomposition of some compounds (McGuire and

Treseder 2010). While other studies have found evidence for this mechanism being at work in fungal communities (Banerjee et al. 2016; Rineau and Courty 2011; Rousk et al. 2010), this dissertation shows a clear relationship between environmental conditions (particularly leaf type) and community aggregated functional traits with a high degree of replication and at different spatial scales. However, it is still worth noting that these environmental factors are not explaining most of the variation in functional traits or groups, so other mechanisms must be at play in determining the distribution of functional traits. This may involve the plasticity of functional traits brought about by: competition between species, other types of environmental variation, and micro-invertebrate grazing pressure (Bradford et al. 2016).

An unexpected finding regarding decomposition was that leaf litter tended to decompose more efficiently (i.e. more mass loss) in the native ecosystem of leaf litter, indicating the possible impact of homefield advantage. This pattern is thought to be the result of microbial communities being specialized to decompose the plant material that dominates the area they are also located in

(Ayres et al. 2009). Homefield advantage has been demonstrated in a variety of environments, however there is still uncertainty about whether local environmental conditions are more responsible for this mechanism (Palozzi and Lindo 2018). My findings give credence to this 160

mechanism occurring, based upon a high degree of replication of ecosystems, leaf material and sites/subsites. As this is an ecosystem level mechanism, it appears that at much larger scales there are limits to functional redundancy and it may be much more prevalent at the leaf scale. It will be of great interest to further test these mechanisms by using more geographically separate sites and dissimilar plant species to fully investigate the complexities of this process. This will give further understanding of the mechanisms involved in the decomposition of plant material.

Distribution of Fungal Functional Groups.

Of great interest in microbial ecology is the relationship between microbial species and community composition to actual functional groups and functional roles in the environment

(Antwis et al. 2017). With this dissertation it was my intention to connect OTU taxonomic identifications to their functional roles and to investigate the prevalence of varying functional groups in the environment. Distributions of these functional groups were found to be heavily influenced by environmental conditions just as what was seen in community aggregated functional traits and ecosystem processes. This strong impact of environmental selection on functional groups and non-significance of stochastic mechanisms reinforces our findings regarding functional redundancy being present in communities of saprotrophic fungi.

A functionally diverse array of fungi colonizes leaf litter during decomposition with high amounts of primary saprotrophs and necrotrophic plant pathogens on many samples. The high abundance of necrotrophic plant pathogens and endophytes appears to confirm studies that indicate there are many facultative/ opportunistic members of these groups and that they play an important part in decomposition (Charkowski 2016; Szink et al. 2016). Another group that was unexpectedly found to be prevalent, especially in recalcitrant leaf litter from BOWO ecosystems,

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were basidiomycete yeasts. While surprising, this may be the result of the more desiccated and nutrient poor environment of BOWO ecosystems, which yeasts may be better able to survive

(Treseder and Lennon 2015). These findings indicate further dimensions to community composition and will be very useful in determining how these communities form in the environment. However, further analysis is needed as only one time point was analyzed using sequencing and, as litter chemistry changes and environmental conditions begin to vary, functional group composition may change as well (Treseder et al. 2014; Persoh et al. 2013).

Assembly Mechanisms of Individual Functional Group Patterns

The connection between species names and function also allowed for the determination of the relative importance of different community assembly mechanisms in distinct functional groups. I found that functional groups of organisms are impacted by different community assembly mechanisms to different degrees. Fungal communities which have been viewed as largely unlimited in dispersal are now recognized as displaying many spatial patterns (Martiny et al. 2006). However, individual functional groups are distinct in their lifestyles, physiology and dispersal mechanisms. This was found to be the case with the composition of different functional groups influenced by different assembly mechanisms, with deterministic and stochastic mechanisms playing varying roles. This difference may be reflective of the varying ways in which these organisms can disperse, which has been found to differ by functional group and lifestyle (Baldrian 2017; Beck et al. 2015; Kivlin et al. 2014).

The use of fungal-specific GH28 primers allowed for the opportunity to determine the distribution of a single functional trait, genes coding for pectinases, within communities of saprotrophic fungi. These genes are prevalent in necrotrophic fungal pathogens of plants, with

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many of the most destructive plant pathogens having high copy numbers within their genomes

(Kubicek et al. 2014; Zhou et al. 2013; Sprockett et al. 2011). My research indicated that there was a stronger impact of spatial proximity on the distribution of GH28 genes in fungal communities than environmental factors (leaf type and ecosystem), which contrasts to what was found in functional groups, decomposition, and extracellular enzymes. This indicates that the distribution of functional genes may also be impacted by varying mechanisms. It also demonstrates the possible plasticity of fungal pathogen virulence factors, which has been found to be large and under evolutionary pressures brought about by host immune responses

(Stukenbrock 2013; Raffaele and Kamoun 2012). Further testing of these primers on other major types of fungal plant pathogens will be needed and they will also need to be utilized on more distinct environments (other types of plant tissue, soil samples, etc.). The use of these primers and others like them, will be very important in gaining an understanding of the distribution of fungal plant pathogens in an agricultural setting. This information regarding fungal functional groups will allow for a much better understanding of the roles fungal communities play in the environment and the overall complexity of their assembly.

Conclusions and Implications

The research and findings documented in this dissertation will help explain how complex saprotrophic communities assemble and interact with their environment. Understanding the roles that these factors play provides a greater understanding of the ecological patterns and processes that these communities are responsible for. This information will also give a much better idea of how these important communities will be impacted by anthropogenic changes such as pollution and climate change. There is evidence that these communities (and their related processes) may

163

be directly or indirectly changed by varying conditions (such as temperature and moisture) that are associated with climate change (Classen et al. 2015). These communities are also gaining recognition as important sources of the greenhouse gases responsible for climate change. The decomposition of plant material, such as leaf litter, is seen as an important source of these greenhouse gases (Lehmann and Kleber 2015). At the same time understanding these communities and composition will be extremely important, as they have been recognized as important drivers in the storage of soil carbon, with its removal from the atmosphere being important in medigating the impacts of anthropogenic climate change (Malik et al. 2016).

164

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Appendix I

Table 9. Summary of taxonomic names given to OTUs with potential ecological function. Included in table is: phylogeny, citation(s), degree of confidence, and whether name is present in the data set, or if it is used as a reference. Category of confidence is defined as the degree of certainty in functionl designation based upon literature review.

Upper Phylogeny Order Family Genus and Species Function Citation Confidence Present Various publications (per class and Ascomycota Various Excellent Present order) Taphrinomycotina Reference Taphrinomycetes Taphrinales Taphrinaceae Taphrina flavorubra Biotroph Tsai et al. 2014 Good Present Pezizomycotina Various Kirk and Cannon 2008 Good Present Sordariomycetes Various Kirk and Cannon 2008 Good Present Polyscytalum algarvense Necrotroph Cheewangkoon et al. 2009 Excellent Present

Xylariales Prim Sapro Various publications (per family) Good Present

Cannon and Kirk 2007; Kirk and Amphisphaeriaceae Prim Sapro Good Reference Cannon 2008 Adisciso kaki Necrotroph Yamamoto et al. 2012 Excellent Present Phlogicylindrium Necrotroph Summerell et al. 2006 Poor Present Cannon and Kirk 2007; Kang et al. Cainiaceae Prim Sapro Good Reference 1999 Cannon and Kirk 2007; Kang et al. Clypeosphaeriaceae Prim Sapro Good Reference 1999 Cannon and Kirk 2007; Acero et al. Diatrypaceae Prim Sapro Good Reference 2004 Cannon and Kirk 2007; Pirozynski Graphostromataceae Prim Sapro Fair Reference 1974 Cannon and Kirk 2007; Wang and Hyponectriaceae Prim Sapro Good Present Hyde 1999 Cannon and Kirk 2007; Tang et al. Xylariaceae Prim Sapro Fair Reference 2009 Anthostomella Necrotroph Lu et al. 1999 Excellent Present Rosellinia abscondita Necrotroph Petrini and Petrini 2005 Good Present 171

Coniochaetales Coniochaetaceae Endophyte Davey et al. 2010 Poor Present Hypocreales Nectriaceae Nectria Necrotroph Booth 1959 Good Present Cylindriumsp.2ICMP18843 Endophyte Johnston et al. 2012 Good Present Hypocreaceae Hypomyces australis Mycoparasite Poldmaa et al. 2011 Good Present Phialemonium Sordariales Cephalothecaceae Prim Sapro Rivero et al. 2009; Gams et al. 1983 Fair Present dimorphosporum Dothideomycetes Various Kirk and Cannon 2008 Good Present Capnodiales Mycosphaerellaceae Necrotroph Braun et al. 2013 Good Present Passalora Necrotroph Beilharz et al. 2004 Good Present Mycosphaerella nyssicola Necrotroph Horst 2013 Excellent Present Myriangiales Necrotroph Various publications (per family) Excellent Present Cannon and Kirk 2007; Jayawardena Elsinoaceae Necrotroph Excellent Reference et al. 2014 Cannon and Kirk 2007; Dissanayake Myriangiaceae Necrotroph Good Reference et al. 2014 Mytilinidiales Gloniaceae Cenococcum geophilum Ectomycorrhizal Pigott 1982 Excellent Present Pleosporales Prim Sapro Zhang et al. 2009 Excellent Present Niranjan and Sarma 2018; Hyde et Aigialaceae Prim Sapro Fair Reference al. 2013. Shearer et al 2009.; Zhang et al. Amniculicolaceae Prim Sapro Fair Reference 2012 Delitschiaceae Prim Sapro Hyde et al. 2013; Kruys et al. 2006 Excellent Reference Hyde et al. 2013; Cannon and Kirk Didymellaceae Necrotroph Good Reference 2007; Avescamp et al. 2010 Cannon and Kirk 2007; Hyde et al. Didymosphaeriaceae Prim Sapro Good Reference 2013 Hypsostromataceae Prim Sapro Cannon and Kirk 2007 Good Reference Lentitheciaceae Prim Sapro Hyde et al. 2013 Good Reference Leptosphaeriaceae Prim Sapro Cannon and Kirk 2007 Good Reference Lindgomycetaceae Prim Sapro Hyde et al. 2013; Raja et al. 2013 Poor Reference Lophiostomataceae Prim Sapro Cannon and Kirk 2007 Good Reference Cannon et al. 2007; Hirayama and Lophiostoma Prim Sapro Good Present Tanaka 2011 Massarinaceae Prim Sapro Cannon and Kirk 2007 Good Reference Melanommataceae Prim Sapro Cannon and Kirk 2007 Good Reference

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Montagnulaceae Prim Sapro Hyde et al. 2013 Good Reference Morosphaeriaceae Prim Sapro Hyde et al. 2013 Good Reference Phaeosphaeriaceae Necrotroph Cannon and Kirk 2007 Good Reference Mycopappus Biotroph Suto et al. 2000 Good Present Pleomassariaceae Prim Sapro Cannon and Kirk 2007 Good Present Pleosporaceae Necrotroph Cannon and Kirk 2007 Good Reference Kruys and Wedin 2009; Cannon and Sporormiaceae Prim Sapro Excellent Reference Kirk 2007 Tetraplosphaeriaceae Prim Sapro Hyde et al. 2013; Tanaka et al. 2009 Fair Reference Hyde et al. 2013; Suetrong et al. Trematosphaeriaceae Prim Sapro Fair Reference 2011 Botryosphaeriales Botryosphaeriaceae Ectomycorrhizal Slippers et al. 2007 Excellent Present Eurotiomycetes Chaetothyriales Herpotrichiellaceae Cladophialophora Prim Sapro Badali et al. 2008 Fair Present Leotiomycetes Various Kirk and Cannon 2008 Good Present Sieber 2007; Various other Helotiales Necrotroph Good Present publications (per family) Pilidium acerinum Necrotroph Rossman et al. 2004; Crous 1991 Excellent Present Ascocorticiaceae Prim Sapro Julich and Vries Fair Reference Cannon and Kirk 2007; Nauta and Dermateaceae Necrotroph Fair Reference Spooner 2000 Kowalski et al. 1995 and Sankaran Cryptosporiopsis Necrotroph Good Present et al. 1995 Pezicula Necrotroph Kehr 1991 Good Present Diplocarpon mespili Necrotroph Park et al. 2011 Good Present Kirk and Cannon 2008; Gross et al. Helotiaceae Necrotroph Fair Reference 2014; Davey et al. 2006 Crocicreas Necrotroph Iturriaga et al. 1999 Poor Present Bisporella citrina Prim Sapro Pavlidis et al. 2005 Fair Present Hymenoscyphus Prim Sapro Wang et al. 2006; Gross et al. 2013 Poor Present Hymenoscyphus caudatus Prim Sapro Kitbrough and Atkinson 1972 Poor Present Hymenoscyphus ginkgonis Prim Sapro Han et al. 2014 Poor Present Hymenoscyphus immutabilis Prim Sapro Uzun et al 2014 Poor Present Hymenoscyphus waikaia Prim Sapro Zheng and Zhuang 2016 Poor Present Hemiphacidiaceae Necrotroph Wang et al. 2006; Korf 1962 Fair Reference Hyaloscyphaceae Prim Sapro Han et al.; Kirk and Cannon Good Reference 173

Incrucipulum Endophyte Leenum et al. 2000 Poor Present Cistella Necrotroph Yamanobe and Oribe 2006 Good Present Pseudaegerita viridis Endophyte Abdullah and Webster 1983 Excellent Present Polydesmia pruinosa Mycoparasite Huhtinen and Santesson 1997 Excellent Present Calycina Prim Sapro Kirk and Cannon 2008; Seaver 1934 Fair Present Lachnum virgineum Endophyte Sharples et al. 2000 Excellent Present Marvanova et al. 2002; Jacobsen et Mycoarthris corallina Prim Sapro al. 2005; Baldrian et al. 2012; Poor Present Yamamoto et al. 2015 Loramycetaceae Prim Sapro Digby and Goos 1987 Good Reference Phacidiaceae Necrotroph Crous et al 2014.: Cannon Fair Reference Phacidium lacerum Necrotroph Wiseman et al. 2016 Good Present Zhao et al 2014.; Kirk and Cannon Rutstroemiaceae Prim Sapro Fair Reference 2008 Cannon and 2007; Kirk and Cannon Sclerotinaceae Necrotroph Good Reference 2008 Kirk and Cannon 2008; Cannon and Vibrisseaceae Prim Sapro Fair Reference Kirk 2007 Leotiales Leotiaceae Prim Sapro Cannon and Kirk 2007 Poor Present Flagellospora Prim Sapro Gongalves et al. 2014 Poor Present Rhytismatales Rhytismataceae Necrotroph Lantz et al. 2011 Good Present Coccomyces Necrotroph Sherwood 1980 Good Present Lecanoromycetes Lichen Kirk and Cannon 2008 Excellent Present Lecanorales Tephromelataceae Tephromelacf.atraLM-2014 Lichen Muggia et al 2008 Excellent Present Various publications (per class and Basidiomycota Various Excellent Present order) Pucciniomycotina Reference

Prim Sapro Aime et al. 2006 Fair Present Microbotryomycetes Libkind et al 2005; Cannon and Kirk Sporidiobolales Prim Sapro Excellent Present 2008 Libkind et al 2005; Cannon and Kirk Sporidiobolaceae Prim Sapro Excellent Reference 2008 Agaricomycotina Reference Kirk and Cannon 2008; Millanes et Tremellomycetes Mycoparasite Excellent Present al 2011

174

Kirk and Cannon 2008; Millanes et Cryptococcus Mycoparasite Poor Present al 2011 Filobasidiales Cryptococcus wieringae Prim Sapro Janisiewicz et al. 2010 Poor Present Cryptococcus Prim Sapro Findley et al. 2009 Good Present Zamora 2011; Milanes 2011; Tremellales Mycoparasite Good Present Lindgren et al. 2015 Kirk and Cannon 2008; Pippola and Tremellaceae Tremella Mycoparasite Excellent Present Kotiranta 2008 Kirk and Cannon 2008; Milanes et al Cryptococcus sp.TSN-649 Mycoparasite Good Present 2011 Zhou et al.. 2013; Kirk and Cannon Agaricomyecetes Auriculariales White Rot Excellent Present 2008 Zhou et al. 2013; Kirk and Cannon Auriculariaceae White Rot Good Reference 2008 Boletales Ectomycorrhizal Binder et al. 2006 Excellent Present Cantharellales Ectomycorrhizal Martin et al. 2016; Tedersoo 2010 Good Present Agaricales Cortinariaceae Ectomycorrhizal Horton et al. 2017 Good Present Tricholomatceae Baeospora myriadophylla White Rot Hibbet and Binder 2002 Good Present Atheliales Atheliaceae White Rot Matheny et al. 2006 Good Present Sebacinales Sebacinaceae Ectomycorrhizal Weiss 2004 Good Present Russulales Gloeocystidiellaceae Gloeocystidiellum White Rot Nakesone et al. 1982 Fair Present Russulaceae Russula Ectomycorrhizal Eberhardt 2002; Kernaghan 2003 Excellent Present Corticiales Corticiaceae Sistotrema Ectomycorrhizal Marino et al 2008 Good Present Glomeromycota Reference Glomeromycetes Paraglomerales Arbuscular Kirk and Cannon 2008 Excellent Present Diversisporales Diversisporaceae Diversispora Arbuscular Kirk and Cannon 2008 Excellent Present

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