I V the Role of Seedling Pathogens in Temperate Forest

The Role of Seedling Pathogens in Temperate Forest Dynamics

Michelle Heather Hersh

University Program in Ecology Duke University

Date:______Approved:

______James S. Clark, Co-Supervisor

______Rytas Vilgalys, Co-Supervisor

______Marc A. Cubeta

______Katharina Koelle

______Daniel D. Richter

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the University Program in Ecology in the Graduate School of Duke University

2009

ABSTRACT

The Role of Seedling Pathogens in Temperate Forest Dynamics

Michelle Heather Hersh

University Program in Ecology Duke University

Date:______Approved:

______James S. Clark, Co-Supervisor

______Rytas Vilgalys, Co-Supervisor

______Marc A. Cubeta

______Katharina Koelle

______Daniel D. Richter

An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the University Program in Ecology in the Graduate School of Duke University

2009

Abstract

Fungal pathogens likely play an important role in regulating populations of tree seedlings and preserving forest diversity, due to their ubiquitous presence and differential effects on survival. Host-specific mortality from natural enemies is one of the most widely tested hypotheses in community ecology to explain the high biodiversity of forests. The effects of fungal pathogens on seedling survival are usually discussed under the framework of the Janzen-Connell (JC) hypothesis, which posits that seedlings are more likely to survive when dispersed far from the parent tree or at low densities due to pressure from host-specific pathogens (Janzen 1970, Connell 1971). One of the key challenges to assessing the importance of JC effects has been to identify and quantify the effects of the large numbers of potential pathogens required to maintain host diversity.

The primary objectives of this research were to (1) characterize the fungi associated with seedling disease and mortality for a number of important southeastern US forest tree species; and (2) determine if these associations are consistent with the Janzen-Connell hypothesis in terms of differential effects on seedling survival.

Culture-based methods and ribosomal DNA (rDNA) sequencing were used to characterize the fungal community in recently dead and live seedlings of thirteen common tree species in a temperate mixed hardwood forest (North Carolina, USA), with the goal of identifying putative seedling pathogens. Cultures were initially classified and grouped into 130 operational taxonomic units (OTUs) using 96% internal transcribed spacer (ITS) sequence similarity; 46% of all OTUs were found only once. Using rarefaction, it was concluded that the richness of the system was not fully sampled and likely included over 200 taxa (based on non-parametric richness estimators). Species richness did not differ between sampling sites or among the five most common hosts

sampled. The large ribosomal subunit (LSU) region of rDNA was then sequenced for representative samples of common OTUs and refined identifications using a constrained maximum likelihood phylogenetic analysis. Phylogenetic placement verified strong

BLAST classifications, and allowed for placement of unknown taxa to the order level, with many of these unknowns placed in the Leotiomycetes and Xylariales

(Sordariomycetes).

Next, a hierarchical Bayesian model was developed to predict the effects of multiple putative fungal pathogens on individual seedling survival, without forcing the effects of multiple fungi to be additive. The process of disease was partitioned into a chain of events including incidence, infection, detection, and survival, and conditional probabilities were used to quantify each component individually, but in the context of one another. The use of this modeling approach was illustrated by examining the effects of two putative fungal pathogens, Colletotrichum acutatum and Cylindrocarpon sp. A, an undescribed species of Cylindrocarpon, on the survival of five seedling hosts in both a maximum likelihood and Bayesian framework.

Finally, the model was used to assess the impacts of these fungi on seedling survival, alone and in combination, using data on five potential fungal pathogens and five hosts. Multi-host fungi had differential effects on seedling survival depending on host identity, and multiple infections may impact survival even when single infections do not. Evaluating these interactions among multiple plant and fungal species generates a set of targeted hypotheses of specific plant-fungal combinations that could help us better understand pathogen-driven diversity maintenance at larger scales than previously possible. Building on these results, some recommendations are provided as

to how the Janzen-Connell hypothesis can be re-evaluated with respect to host specificity, pathogen distribution, and environmental context.

Dedication

This dissertation is dedicated to my father, who always believed in me as a scientist, and to my mother, who always listened with an open heart.

vii

Contents

Abstract...... iv

List of Tables ...... xi

List of Figures ...... xii

Acknowledgements ...... xiii

1. Introduction: Revisiting the Janzen-Connell hypothesis from a microbial perspective. 1

Introduction...... 1

The pathology of seedling disease ...... 3

Pre-emergence pathogens ...... 5

Post-emergence pathogens...... 7

Quantifying Janzen-Connell effects...... 8

Patterns of seedling demography...... 9

Searching for the biotic drivers...... 11

Challenges in identifying the biotic drivers ...... 14

Host specificity ...... 15

Co-infection...... 15

Environmental variation...... 16

Plasticity in fungal lifestyles...... 17

Introduction to thesis research ...... 18

2. Characterizing the fungal associates of tree seedlings in a temperate mixed hardwood forest: Implications for forest diversity...... 20

Introduction...... 20

Methods ...... 24

Study locations ...... 24

Field studies...... 24

viii

Isolation and initial identification of fungi...... 25

Further resolution of fungal taxa using phylogenetics ...... 27

Statistical analyses...... 29

Results ...... 30

Overall fungal richness ...... 30

Phylogenetic placement of fungal taxa...... 33

Correlates of fungal incidence...... 36

Discussion...... 36

Richness and specificity of symbiotic fungi...... 36

Phylogenetic placement of fungal taxa...... 38

Influence of soil moisture and seedling lifespan on infection...... 39

Implications for forest diversity...... 40

3. Causal inference for plant disease: application to fungal associates of tree seedlings . 42

Introduction...... 42

An application: Fungal maintenance of tree diversity...... 45

Inferring cause based on data: the graphical model...... 47

Causal relationships: Fungal incidence, infection status, and survival...... 48

Likelihood: joint distribution of detection and survival...... 51

A traditional analysis: Maximum likelihood inference ...... 51

Multiple hosts, multiple fungi, and covariates ...... 54

Discussion...... 60

4. Impacts of fungal co-infection on temperate forest diversity...... 62

5. Synthesis: Moving the Janzen-Connell hypothesis forward ...... 73

Host specificity and co-infection ...... 74

Defining specificity...... 74

Spatial distribution of fungi...... 75

Environmentally mediated lifestyle switching ...... 76

Conclusions ...... 76

Appendix 1: Seedling survival and sampling...... 78

Appendix 2: Complete list of all fungal taxa...... 79

Appendix 3: Dothideomycete phylogeny ...... 84

Appendix 4: Eurotiomycete phylogeny...... 86

Appendix 5: Leotiomycete phylogeny...... 87

Appendix 6: Sordariomycete phylogeny...... 88

References...... 89

Biography ...... 106

List of Tables

Table 2.1: The 20 most common fungal taxa sampled and identified in this study...... 31

Table 4.1: List of seedling hosts and potential fungal pathogens used in this study...... 63

List of Figures

Figure 1.1: A schematic of the Janzen-Connell hypothesis, following Janzen (1970) ...... 2

Figure 2.1: Rank abundance of non-singleton taxa identified in this study...... 30

Figure 2.2: Rarefaction curve of OTUs identified per sampling effort...... 32

Figure 2.3: Rarefaction curves of fungal OTU richness for the five host species most sampled...... 33

Figure 2.4: Rarefaction curves of fungal OTU richness for the three study sites sampled...... 34

Figure 2.5: Maximum likelihood tree of 67 cultured taxa and 414 reference taxa (Schoch et al. in press) in the Ascomycota, along with backbone constraint tree of named taxa.. . 35

Figure 3.1: The graphical framework of the model of incidence, infection, survival, and detection...... 48

Figure 3.2: Probabilities for infection and survival with and without infection of five tree seedling hosts with two fungi calculated using maximum likelihood...... 53

Figure 3.3. Infection probabilities given incidence of Colletotrichum and Cylindrocarpon on five plant hosts, estimated using a hierarchical Bayesian approach...... 57

Figure 3.4: Estimates of survival of five host plants with and without infection with or incidence of Colletotrichum, Cylindrocarpon, or a combination of the two, estimated using a hierarchical Bayesian approach ...... 59

Figure 4.1: Posterior model probabilities of effects of different combinations of fungi on host survival...... 66

Figure 4.2: Predictive distributions of survival probabilities of uninfected hosts (Pr S|I=0), infected hosts (Pr S|I=1), and infected hosts marginalized over incidence (Pr S|P=1)...... 68

Figure 4.3: Predictive distributions of fungal incidence, Pr(P), at different levels of soil moisture for five fungal species...... 70

Figure 4.4: Predictive distributions of survival probabilities of uninfected plants (Pr S|I=0) and survival probabilities marginalized over infection and incidence (Pr S|I,P) at different levels of soil moisture...... 71

xii

Acknowledgements

I’d first like to thank my advisors, Jim Clark and Rytas Vilgalys, and my committee. Jim’s unwavering enthusiasm about this project has been a constant source of support, along with his patience as I developed skills in coding and modeling. I appreciate Rytas’ generosity in allowing me to slowly become a member of his lab, and his similarly unwavering enthusiasm about all things mycological (and boat-related!).

Thank you, Jim and Rytas, for all of your help through all of these years. Marc Cubeta has provided useful advice about methodology, connections with the plant pathology community, and the occasional much-needed reality check. I’d also like to thank Katia

Koelle and Dan Richter for their help and support in various phases of this project, from

Dan’s enthusiasm as I developed this project to Katia’s thoughtful reviews of my thesis chapters.

This project would not have been possible without the help of a large number of field and lab assistants. Emily Wear, Sarah Rorick, and Amy Hamilton all made significant contributions to this project by sequencing fungal DNA and assisting with culture maintenance. Melissa Burt, Clint Oakley, and Kathie Sun also provided critical support in the mycology lab. Amber Allen assisted greatly with data entry and database maintenance. Along with the folks listed above, Dave Bell, Nathan Buchanan, Ivan

Bukovnik, Maryana Draga, Saida Ismayilova, Noah Lavine, Ryan Littlewood, Allen

McBride, Luke Pangle, Danielle Racke, Carl Salk, Jason Styons, Erica Tsai, Nathan

Welch, and Peter Werrell all assisted with the field component of this project, and I am thankful for all of their contributions.

I’m also grateful to my labmates in both the Clark and Vilgalys labs. Dave Bell, my collaborator at our field sites, has always been willing to help in the field and with

xiii

various data issues. Luke Pangle and Nathan Welch provided organization to the lab when it was needed most and much-appreciated support to me. Mike Dietze, Ines

Ibáñez, Shannon Ladeau, and Mike Wolosin all provided interesting ideas and comments during the early stages of this project. In the mycology lab, Jeri Parrent played an important role in developing the initial ideas of this project. Terri Porter provided useful insight on the bioinformatics components of this work. Greg Bonito,

Jason Jackson, and Tim James provided support in all sorts of ways, large and small, throughout the process. All of the members of the Clark and Vilgalys labs, past and present, have provided interesting ideas and insight throughout the course of this project. Outside of my lab groups, Katherine Whitten Buxton and Gloria Abad at NCSU provided protocols for working with Colletotrichum and oomycetes, respectively. Jolanta

Miadlikowska helped set up the constraint tree in chapter 2. Chicita Culberson provided an unexpected moment of inspiration when I needed it most. Terri Porter, Denis Valle,

Erica Tsai, and Greg Bonito carefully read chapter drafts and provided valuable comments.

This work could not have been accomplished without funding from the National

Science Foundation, the Duke Department of Biology, and Sigma Xi. I would also like to thank Shirley Billings and Katherine Goodman Stern, both of whom sponsored Duke fellowships that I was extremely lucky and grateful to receive.

Many excellent staff members assisted with this project and my progression through graduate school at Duke. Lisa Bukovnik and the IGSP sequencing facility staff have been an incredibly trustworthy and useful resource. Marcia Kirinus, Norm Hill,

Beverly Calhoun, Mel Turner, Todd Smith, Jerome Smith, and all other Phytotron and

Greenhouse staff supported portions of my thesis research. The Duke Biological and

xiv

Environmental Sciences library and the ILL staff helped me find so many obscure plant pathology references. And finally, Susan Gerbeth-Jones, Anne Lacey, Nancy Morgans,

Meg Stephens, Jim Tunney, Andrew Turnier, and all of the administrative staff in the

Biology Department and the Nicholas School continue to do excellent work to support us all.

Finally, I could not have done any of this without the love and support of my friends and family. My parents and sister have supported me through this entire journey, and I appreciate their patience and understanding through all of these years of grad school. My partner, Carl Salk, has given me comfort, love, and support throughout the years we’ve been together. Thank you, Carl, for everything. I owe a big chunk of my sanity to Stevie Jones, an incredible friend and an amazing listener. Claymakers Pottery

Studio, in downtown Durham, has been my little oasis of peace and creativity. And finally, I’ll never forget the wonderful friends that I made here at Duke. Thank you

Meredith Bastian, Sara Chun, Julie DeMeester, Jackson Fox, Paul Gong, Nat Grier, Jerry

Hsu, Suzanne Joneson, Melissa Kenney, Nathalie Nagalingum, Chris Oishi, Jonathan

Perry, Susan Satterthwaite, Ariana Sutton-Grier, Alex Tobler, Erica Tsai, Nathan Welch, and Kim Woolcock, and the other friends who have been such an important part of my life here. Erica, thank you for being my running, coffee, and rant buddy. Melissa and

Julie, thank you for so many fierce Wednesdays. Kim, thank you for bringing perspective and adventure at the same time. I consider myself incredibly lucky to have you all in my life.

1. Introduction: Revisiting the Janzen-Connell hypothesis from a microbial perspective

Introduction

One of the most pressing questions in forest community ecology is what mechanisms maintain the coexistence of large numbers of apparently similar tree species in temperate and tropical forests. An important mechanism often invoked in sustaining coexistence is unequal rates of tree seedling mortality caused by pathogens (Gillett 1962,

Janzen 1970, Connell 1971). Differential interactions between trees and pathogens could alter spatial patterns of seedling recruitment, and subsequently the composition and relative abundance of tree species in a forest stand. Yet the ecology of the pathogens that inhabit seedling roots is still not well understood, nor are the organisms themselves well characterized. This dissertation research focuses on how seedling pathogens affect forest dynamics.

Most discussions of the role of pathogens in forest dynamics are framed by the

Janzen-Connell-hypothesis (Janzen 1970, Connell 1971). The Janzen-Connell (JC) model of seedling recruitment predicts that seedlings are more likely to successfully establish and survive when dispersed far from the parent tree (Figure 1.1). Increased recruitment far from the parent tree, despite the lower seed density, could be due to lower density- dependent predation pressure on seeds or seedlings or release from host-specific pathogens. Adult trees likely act as “reservoirs” of pathogens, maintaining populations of oomycetes, fungi, or other pathogens on their roots, leaves, or other organs that have a negligible effect on adult growth but can mortally damage seedlings (Gilbert 2002).

These pathogens must have limited dispersal capabilities such that there is a greater localized abundance of propagules in close proximity to parents (Gilbert 2002, Adler and

Muller-Landau 2005). 1

Figure 1.1: A schematic of the Janzen-Connell hypothesis, following Janzen (1970) with modifications. The green line represents seed rain, the blue line shows the probability of seedling survival, and the red dashed line indicates the concentration of natural enemies.

The Janzen-Connell hypothesis is consistent with current theory on diversity maintenance and has the potential to explain the high diversity of tree species in tropical and temperate forests. Tree species are all competing for the same set of resources (light, water, several key nutrients), yet despite their fairly similar niches, a diverse assemblage of trees manages to survive. Frequently proposed mechanisms to explain this diversity involve niche differentiation in the form of tradeoffs between plant traits, such as the classic competition-colonization tradeoff or other means of limiting similarity (Pacala and Tilman 1994, Tilman 1994, Rees et al. 2001, Silvertown 2004) facilitated by spatial or temporal environmental heterogeneity (Levine and Rees 2002, Kneitel and Chase 2004,

Questad and Foster 2008). Yet often discussion of these tradeoffs boils down a large amount of variation onto a few trait axes onto which we can map different species, and models quantifying these tradeoffs often fail to achieve coexistence (Condit et al. 2006,

Clark et al. 2007). Higher-dimensional approaches accounting for a broader range of species and individual differences can improve our understanding of diversity

maintenance (Clark et al. 2003, Clark et al. 2004, Clark et al. in prep). Diversity maintenance via Janzen-Connell effects is inherently high-dimensional in that there is no limit to the number of natural enemies that can exist to regulate hosts.

However, the effectiveness of Janzen-Connell depends on two key assumptions.

First, natural enemies must respond to local host density, increasing attack in areas where hosts are concentrated. Second, enemies need to be host specific, such that they will regulate host populations, without simultaneously attacking the co-occurring tree species that could benefit from pathogen attack on their most abundant competitors. A large body of research attempts to identify patterns of abundance and demography consistent with concentrated attack in areas of high conspecific density or closeness to conspecific adults (e.g. Clark and Clark 1984, Connell et al. 1984, Condit et al. 1992, 1994,

Wills et al. 1997, Webb and Peart 1999, Harms et al. 2000, Wright 2002, Webb et al. 2006,

Comita and Hubbell 2009). Evidence for concentration of enemy attacks where host density is high is well-known in agriculture and forestry (Manion 1981, Tainter and

Baker 1996, Agrios 2005).

Despite this large literature, there is limited experimental evidence identifying the specific pathogens involved, their levels of host specificity (especially when potential host diversity is high), the extent to which they could impact germination, growth, and survival, and how their interactions with hosts depend on the environment. This chapter will describe in further detail the current state of knowledge about how tree seedling diseases may regulate plant diversity, along with a discussion of evidence for Janzen-

Connell effects driven by seedling pathogens.

The pathology of seedling disease

Although adult trees can produce up to one million seeds during a typical growing season, few plants survive to germination and fewer still beyond the first year

(Clark et al. 1998, Clark et al. 2004). This enormous seed and seedling mortality can be attributed to a number of factors, including resource limitation (such as light, water, and nutrients), physical damage, or interactions such as herbivory, competition, and pathogen attack (Moles and Westoby 2004). This complexity of potential interactions makes attributing the death of a seedling to a single one of these factors challenging.

Very detailed surveys of fungi have been performed in the Duke Forest, revealing a large number of potential seed and seedling pathogens (O'Brien et al. 2005, see also Chapter 2 of this document). Yet implicating any one fungus as a disease agent is complicated by several factors. First, unlike many diseases of adult trees or crop plants, a diverse group of fungi and oomycetes can cause similar disease symptoms in seedlings, such as stem softening and other traditional so-called “damping-off” symptoms (Vaartaja et al. 1961, Tainter and Baker 1996, Agrios 2005). Many of these necrotrophic fungi and oomycetes attack plants using similar mechanisms, infecting plants at or below the soil line and using pectinases, along with proteolytic and cellulolytic enzymes, to invade and consume plant tissue (Agrios 2005). In some cases, seedlings often die rapidly from damping-off and may simply disappear between sampling dates, precluding any disease diagnosis. In other cases, the primary impacts of disease may be a reduction of vigor that will render the individuals competitively inferior but may not be immediately apparent (Burdon 1987). In addition, given that the identification of pathogens is both time and labor intensive, many ecologists have chosen to compare field-collected soils or soil extracts to bulk sterilized soils or fungicide-treated soils, and interpret differences between these treatments to quantify the net effect of soil biota on plant health (e.g. Olff et al. 2000, Packer and Clay 2000,

Callaway et al. 2004, McCarthy-Neumann and Kobe 2008, Petermann et al. 2008)

Although fungicide studies have provided valuable evidence as to whether or not biotic

factors drive changes in seedling demography, they leave open the question of which biotic factors cause any observed demographic changes. Finally, although a suite of fungi and oomycetes may consistently be present in low levels in the soil or in plants as commensalists, many of these organisms may cause disease symptoms episodically, in concert with environmental or internal cues. This unique set of challenges makes working with seed and seedling pathogens unlike other plant diseases. Given the amount of tree mortality occurring at these tree life stages and the potential importance of pathogens in driving this mortality, it is critical that we develop novel approaches to determine the identities of seed and seedling pathogens.

Pathogens causing seedling disease are traditionally grouped into two classes—

“pre-emergence” (attacking seeds before they emerge from the litter or soil) and “post- emergence” (attacking emerged seedlings). Many of the same fungi and oomycetes are implicated in both pre and post-emergence damping-off. Here we will briefly discuss the effects of both.

Pre-emergence pathogens

Many pathogens can infect viable seeds or new germinants, suppressing tree recruitment. Some of these pathogens can be transmitted vertically, from parent to offspring, while others are transmitted horizontally, infecting seeds when encountering them in the soil or littler layer. Pathogens can also infect viable seeds that have newly germinated, killing seedlings before they emerge from the soil. This pre-emergence damping-off commonly occurs in nurseries of both deciduous and evergreen trees

(Tainter and Baker 1996), but extensive seed loss has also been reported in natural settings (Kirkpatrick and Bazzaz 1979, Crist and Friese 1993, Dalling et al. 1998). Both fungi, such as Rhizoctonia and Fusarium, and oomycetes, such as Pythium and

Phytophthora are often implicated as disease agents causing pre-emergence damping–off

(Wright 1944, Hendrix and Campbell 1973, Tainter and Baker 1996, Agrios 2005).

Although this study does not include identification of pre-emergence pathogens, we will briefly discuss some of the effects they may have on forest communities, as they may also be important drivers of patterns of demography consistent with the Janzen-Connell hypothesis.

It has been widely shown in the literature that fungi are present on seeds, and in many species, are quite common (e.g. Crist and Friese 1993, Dalling et al. 1998, Schafer and Kotanen 2004, Kluger et al. 2008). For example, soil-incubated seeds of four Cecropia species had seventy-three associated fungal species, and the community of fungi had some host affinity and community structure at the crown scale (Gallery et al. 2007). In

Mount Halla, Korea, Cho et al. (2007) found several fungi isolated from natural seedbeds, most importantly Racodium therryanium, could cause significant decreases in germination. Fungicide-based studies have generally shown that treatment of seeds with fungicides can improve germination and survival. For example, treatment of seeds with the fungicide Captan increased the germination of four temperate tree species, increased viability of one species, and had no effect on a sixth (O'Hanlon-Manners and Kotanen

2006). The degree to which seeds are impacted by fungi can be modified by environmental conditions and seed characteristics. Fungicide treatments increased germination of Betula papyrifera (white birch) more strongly in forest understory than in canopy gaps (O'Hanlon-Manners and Kotanen 2004) . In sixteen tropical trees, susceptibility of seeds to pathogens was greater in heavier seeds and seeds from shade- tolerant species, but was unrelated to seed hardness or germination time (Pringle et al.

2007).

Post-emergence pathogens

Establishment and survival of juvenile trees from germinated seeds to first-year seedlings can strongly limit recruitment of individuals into later life stages (Clark et al.

1998). Pathogens can directly cause seedling mortality or depress seedling vigor, making seedlings less likely to effectively compete for limited resources (Burdon 1987). Young seedlings are more likely to be killed by pathogens before roots become woody, but the growth and vigor of older seedlings can also be diminished (Agrios 2005).

“Post-emergence damping-off” describes a common type of disease in which seedlings are infected at the roots and lower stem, become soft at the base, fall over, and rapidly die (Agrios 2005). Like pre-emergence damping-off, it can occur in both seedling nurseries and natural settings. The predominant genera of damping-off pathogens in forests include both oomycetes (e.g. Pythium, Phytophthora) and fungi (e.g. Rhizoctonia,

Fusarium) (Davis 1941, Wright 1944, Tint 1945a, Vaartaja et al. 1961, Hendrix and

Campbell 1973, Tainter and Baker 1996). Most damping-off pathogens are assumed to be host generalists (Agrios 2005) and may compete or interact within a single root. Seedling mortality due to damping-off can vary with temperature, soil moisture, light, soil organic matter, pH, and nutrient conditions (Tint 1945c, Tint 1945b, Vaartaja et al. 1961,

Tainter and Baker 1996, Martin and Loper 1999). Seedling disease is not limited to damping-off; pathogens with more specific symptoms, such as shoot and needle blights, rusts, root rots, and powdery mildews are known to occur in seedling nurseries (Cordell et al. 1989). The detrimental effects of pathogens may be lessened by mutualistic fungi, such as arbuscular mycorrhizae (e.g. Newsham et al. 1995); however, these interactions are beyond the scope of this research. Studies of how post-emergence pathogens can affect recruitment are considered in greater depth as we discuss them in the context of the Janzen-Connell hypothesis.

Quantifying Janzen-Connell effects

As discussed earlier, the Janzen-Connell model predicts that seedlings will have a higher probability of survival when dispersed far from conspecific adults, and that these changes in survival will be driven by host-specific pathogens concentrated near conspecific adults (Figure 1.1). These demographic shifts can be broken down into two component parts—a “density effect” and a “distance effect.” The “density effect” posits that seedlings are less likely to survive when the density of conspecific seedlings is high.

High seedling density lessens the dispersal distance required for a pathogen to get from seedling to seedling, and increases the probability of a pathogen finding another host of the same species (Burdon and Chilvers 1982). The “distance effect” postulates that seedlings that disperse further from conspecific adults have increased probabilities of survival due to escape from high concentrations of pathogens directly beneath adults.

Underlying this is the critical assumption that pathogen dispersal is limited and pathogen distribution is patchy throughout the landscape (Adler and Muller-Landau

2005). Plant pathogens can exhibit a range of dispersal abilities based on different modes of transmission; for example, aerial spores from rusts are less dispersal-limited than many soil pathogens which require close contact between individuals or transmission in water for infection (Thrall and Burdon 1997). “Distance” can also be complicated to quantify when reproductive adults are not isolated. Therefore, the “distance effect” has been measured as the absolute distance from an isolated adult (e.g. Packer and Clay

2000) or the distance to the nearest conspecific adult (e.g. Gilbert et al. 1994). It can also be more broadly defined as a “neighborhood” effect, or the number or basal area of conspecific adults within a given distance from a seedling (e.g. Webb and Peart 1999).

The “distance” and “density” effects can be difficult to distinguish observationally as

seed rain, and thus seedling density, tends to be high directly beneath reproductive adults.

Patterns of seedling demography

The Janzen-Connell hypothesis is one of the most frequently invoked hypotheses for tree diversity in both tropical and temperate forests. The theory of pests promoting diversity was originally proposed to explain diversity in tropical forests (Gillett 1962,

Janzen 1970, Connell 1971), and many of the key plant demographic studies were performed in tropical forests. Demographic evidence consistent with Janzen-Connell has been found multiple times in the absence of the identification of some kind of biotic mechanism. Initially, many studies which supported (Clark and Clark 1981, Augspurger

1983a, Clark and Clark 1984, Schupp 1992) or failed to support (Condit et al. 1994)

Janzen-Connell focused on one or a small number of species. As datasets of mapped tropical forest stands became increasingly available, the number of studies incorporating multiple species grew larger, although results were mixed, including many showing a larger number of species in compliance with the JC model (Wills et al. 1997, Webb and

Peart 1999, Wills and Condit 1999, Harms et al. 2000, Peters 2003, Webb et al. 2006) but others showing equivocal or limited evidence for JC effects (Connell et al. 1984, Hubbell et al. 1990, Condit et al. 1992, Dalling et al. 1998, Uriarte et al. 2004). There is not yet a consensus on whether JC applies only to a few species (Connell et al. 1984), only common species (Hubbell et al. 1990, Condit et al. 1992) or if Janzen-Connell effects are widespread (Wills et al. 1997, Harms et al. 2000, Peters 2003). But the bulk of the evidence is on the side of Janzen-Connell mechanisms operating on a significant portion of forest tree species. Several previous studies (Clark and Clark 1984, Connell et al. 1984,

Gilbert 2002, Wright 2002) have provided thorough review of this topic.

In general, the Janzen-Connell hypothesis per se is discussed in terms of its relevance to tropical forests, but discussions about distance and density effects of conspecific adults on seedling growth have been fairly common in the literature on temperate forests. In a study of eight forest communities along a latitudinal gradient, there was no relationship between the proportion of trees experiencing density- dependent mortality and latitude (HilleRisLambers et al. 2002), showing that this phenomenon is not restricted to the tropics. Observations of temperate forest trees inhibiting their own offspring pre-date JC (reviewed in Fox 1977) and theories of pest pressure as a mechanism maintaining temperate forest diversity have a similarly long history. For example, in Hubbard Brook Experimental Forest in New Hampshire, saplings of yellow birch (Betula alleghaniensis) and beech (Fagus grandifolia) were negatively associated with their own overstories and seedlings of sugar maple (Acer saccharum) and beech seedlings were positively associated with the canopies of other species (Forcier 1975). Woods (1979) coined the term “reciprocal replacement” (RR) to describe observations of late-successional temperate trees (also beech and maple, in this case) which fail to recruit under their own canopies but are successful under the canopies of other species (for additional examples, see Fox 1977, Woods and Whittaker

1981). Unlike JC, the theory of reciprocal replacement allows for multiple mechanisms— including pest pressure but also niche partitioning—to drive these recruitment patterns.

Yet the demographic patterns described are consistent, to some extent, with the predictions of JC. Fox (1977) discusses whether a combination of competition and fungal pathogens could drive RR-like patterns, but questions whether the damping-off pathogens attacking seedlings are specific enough to attack some tree seedlings and not others. In addition, RR is far from universal; Runkle (1981), studying gap dynamics in three temperate forest stands, observed RR between beech and sugar maple in canopy

gaps, but found that many other species showed a tendency towards self-replacement when a gap was created.

More recent studies testing demographic effects of seedling density or distance from adults on survival have had mixed results, which supported (Streng et al. 1989,

Jones et al. 1994, Wada and Ribbens 1997, HilleRisLambers and Clark 2003) or failed to support (Houle 1992, 1998, Hiura and Fujiwara 1999) JC- or RR-like predictions. For example, in a mixed hardwood forest in western North Carolina, seedling abundances show a shift consistent with density- and distance from adult from seed to seed bank to seedling stages (HilleRisLambers and Clark 2003). In an east Texas pine-hardwood forest, fewer seedlings of several different tree species tended to be found around beech and magnolia adults (Glitzenstein et al. 1986). However, in a similar system, first-year seedling survival of five small-seeded species was negatively correlated with distance to a conspecific adult (Streng et al. 1989). The relatively low diversity of temperate forests might limit Janzen-Connell effects, due to overlapping seed shadows of conspecific adult trees and effects of large disturbances, such as fire and windstorms, on recruitment patterns (Gilbert 2002). Regardless, more research is clearly needed to assess how this influential model applies to species distributions and turnover in temperate forests.

Searching for the biotic drivers

It has been well-established that plant pathogens can play an important role in shaping plant community structure (Burdon 1987, Reynolds et al. 2003). Pathogens can directly shift community structure through species-specific differences in susceptibility

(van der Putten et al. 1993, Mills and Bever 1998) or by creating large disturbances by killing large adult individuals and creating canopy gaps (Hansen and Goheen 2000).

They could also indirectly promote coexistence through negative feedbacks, in which individual plants “cultivate” soil biota that negatively impact conspecifics growing in

close proximity, thereby preventing competitive dominance by any one species (Bever

1994, 2003). For example, in 24 herbaceous species in Switzerland, the majority of the test species responded negatively to growth in soils ‘cultivated’ by conspecific individuals, and these effects were heightened when grown in competition with heterospecific individuals (Petermann et al. 2008). Soil sterilization, but not fungicide alone, minimized these impacts, indicating that non-fungal soil pathogens were driving these effects in this system. Overall, the magnitude of negative feedbacks in natural settings is still unclear.

To complicate matters, the diversity of not only pathogens, but also all biota living in plant tissue, can be quite significant but is not well characterized. Up to 49 fungal phylotypes (groups of distinct organisms defined solely by DNA sequences) have been found in the roots of a single grass species at one location, most of whose functions are unknown (Vandenkoornhuyse et al. 2002). Approximately 350 fungal phylotypes were identified from the stem, roots, and leaves of a common reed, but the vast majority of these species were rare (Neubert et al. 2006). Additional information about fungi living in plant tissue, especially pathogens, and the nature of the interactions that occur will improve basic knowledge of tree demography and aid in projections of future forest dynamics.

Although many of the studies referenced above specifically discuss pathogens as drivers of demographic patterns consistent with JC (e.g. Wills et al. 1997, Webb and

Peart 1999, Webb et al. 2006), no researchers have yet identified the specific suite of pathogens creating and maintaining these patterns in most cases. Yet it is clear that in order to do so, such a set of pathogens must meet certain critical assumptions about their behavior. First, pathogens must be strongly host-specific, meaning that they must decrease survival of one host species but not co-occurring heterospecific species

(Freckleton and Lewis 2006). In addition, in order for negative impacts of pathogens to be concentrated near conspecific adults, pathogens must have a limited ability to disperse away from an adult host (Adler and Muller-Landau 2005).

Several empirical studies have tested the Janzen-Connell model with explicit consideration of pathogens in natural settings, mostly in the tropics. In Panama, the mortality of seedlings of Platypodium elegans, a shade-tolerant tropical tree, was inversely correlated to distance from parent tree and seedling density (Augspurger 1983b, 1984,

Augspurger and Kelly 1984). Disease symptoms on seedlings were consistent with damping-off (Augspurger 1983b, 1984, Augspurger and Kelly 1984). Thus, average distance between adults and seedlings increased from seedling to the sapling stage

(Augspurger 1983a). Similar effects were seen in another tropical tree, Ocotea whitei, in which stem cankers were correlated with increased distance between adults and living juveniles over time (Gilbert et al. 1994). High seedling density and low light were also positively correlated with pathogen-induced mortality in sixteen of eighteen tropical tree species in greenhouse conditions (Augspurger and Kelly 1984). Bell and colleagues

(2006) used a biocide treatment to show that oomycete pathogens caused greater seedling mortality when seedling density of the neotropical tree Sebastiana longicuspis was high and seedlings were close to conspecific adults. Conversely, seedlings of 21 tropical trees showed variable impacts on survival when treated with microbial soil extracts “cultured” from conspecific individuals, though shade tolerant species tended to respond more negatively than shade intolerant species (McCarthy-Neumann and

Kobe 2008). Although clear symptoms of fungal disease were observed in all of these cases, the causal agents of disease were not definitively identified in any of these studies.

Some studies in temperate forests have also indicated that fungi or oomycetes may be involved in creating distance or density effects. In a temperate forest in Indiana,

Prunus serotina seedlings experienced higher mortality and lower growth due to oomycete pathogens (Pythium sp.) when cultivated in soils collected close to conspecific adults (Packer and Clay 2000, 2003). In its non-native range (northwestern Europe), these inhibitory effects were not found (Reinhart et al. 2003). P. serotina individuals grew in closer proximity to one another, and sterilization of soils collected near conspecific adults did not decrease seedling mortality or increase biomass (Reinhart et al. 2003).

Distance-dependence mortality driven by both damping-off pathogens and the leaf spot

Phaeoisaiopsis pruni-grayane was similarly observed in the Japanese temperate tree Prunus grayana (Seiwa et al. 2008). In a seed-focused study in Ontario, seeds of Tsuga canadensis

(eastern hemlock) benefited more from fungicide treatments under canopies of conspecific adults. However, in Acer saccharum (sugar maple), fungicide treatments were equally beneficial under conspecific and heterospecific adults (Kotanen 2007). All of these studies collectively indicate that some kind of biotic mechanism—in some cases, a specific fungal or oomycete species—are creating JC-like patterns of seedling demography.

Challenges in identifying the biotic drivers

The research described above clearly shows that biotic factors are involved in creating demographic patterns consistent with Janzen-Connell. Yet in all but a few specific cases, the causal agent of seedling mortality is unclear. As the field of disease ecology expands, further complications of the “one host-one pathogen” scenario inherent to Janzen-Connell have begun to emerge. Below we describe four complicating factors that need to be considered when evaluating the role of pathogens in maintaining diversity: host specificity, co-infection, environmental variation, and lifestyle plasticity.

Host specificity

The assumption of strict host specificity in the Janzen-Connell model may be challenging to meet for many plant pathogens. Many common damping-off pathogens

(e.g. Pythium, Phytophthora, Rhizoctonia) have long been classified as generalists (Agrios

2005), and examples of plant pathogens in unmanaged systems with multiple hosts are fairly common (Burdon 1987, Gilbert 2002). Infection ability may vary at levels deeper than species; for example, in a cross-inoculation study of 53 necrotrophic plant pathogens in Panama, most pathogens had multiple hosts, but their ability to infect other species decreased with phylogenetic distance (Gilbert and Webb 2007). Generalist pathogens that can infect multiple hosts may also have unequal impacts on survival of different hosts. Pathogenic Pythium isolates from tropical and temperate soils could infect multiple plant hosts, but had unequal effects on mortality (Augspurger and

Wilkinson 2007). Theoretically, any diversity-maintaining effects will be diluted if pathogens are not fairly host-specific; otherwise, interspecific and intraspecific density- dependence will be equivalent (Freckleton and Lewis 2006). Unequal impacts of a single pathogen on different hosts may even be driven by differences in virulence of pathogen genotypes (Burdon 1987). In this regard, examining pathogen impacts at the species level may mask these strain-specific differences.

Co-infection

Extensive studies of fungal diversity in plant tissue have shown repeatedly that many plants are simultaneously infected by multiple species of fungi (Saikkonen et al.

1998, Vandenkoornhuyse et al. 2002, Fitt et al. 2006, Neubert et al. 2006). Moreover, an individual plant can even be infected by multiple strains of the same fungus (Hood

2003). How does co-infection by multiple pathogens, fungal or otherwise, affect plant health? The literature on dynamics within parasite communities in animal hosts is 15

quickly expanding (Pedersen and Fenton 2007); case studies on interactions among different animal parasites have shown examples of dynamic interactions between pathogens, such as a less virulent symbiont weakening the impacts of more virulent strains (Thomas et al. 2003) or combinations of parasites having stronger negative impacts than either parasite alone (Jolles et al. 2008). In plants, there is also evidence that multiple pathogens may have non-additive impacts on plant communities. In a mesocosm study of four species of Brassica, two pathogens (one bacterial, one fungal) had more positive impacts on plant diversity singly than in combination (Bradley et al.

2008). Other studies show evidence of competition between fungal pathogens (Al-Naimi et al. 2005) and viruses (Power 1996) in plant systems. The extent of localization of infections with different pathogens in a single plant may limit direct interactions, but pathogens may still compete indirectly for resources or via stimulation of plant immune responses (i.e. apparent competition) (Pedersen and Fenton 2007). Since multiple infection appears to be the rule rather than the exception (Cox 2001), dealing with co- infection is unavoidable.

Environmental variation

Changing environmental conditions may alter the nature of Janzen-Connell interactions and also cannot be ignored. Extensive documentation from plant pathology shows strong influences of environmental factors like temperature, moisture, and relative humidity on pathogen infection and aggressiveness in many crop species

(Agrios 2005). In natural settings, multiple studies have documented how shifts in light and moisture can alter interactions between seedlings and pathogens. Dispersal of seedlings into light gap and experimental growth under high light treatments decreased the amount of damping-off in fifteen tropical trees (Augspurger 1983b, Augspurger and

Kelly 1984). Moisture availability is critical during the early stages of fungal disease for

release and dispersal of spores and inoculum and entry into host cells, and in many cases precipitation is positively correlated with disease incidence or severity (Burdon

1987, Agrios 2005). However, drought can also increase some diseases in forest trees, especially canker/dieback diseases, and this is likely mediated by the negative effects of water stress on host physiology (Desprez-Loustau et al. 2006). Although fungi may experience direct negative impacts of water stress, many plant pathogens are often relatively plastic in terms of drought tolerance (Ma et al. 2001, Jonsson et al. 2003,

Desprez-Loustau et al. 2006). Fungi which have once colonized hosts asymptomatically can shift to a more parasitic lifestyle under drought conditions, as is the case of the fungal symbiont Discula quericina on its host Quercus cerris (Moricca and Ragazzi 2008).

Plasticity in fungal lifestyles

Lifestyle switching from parasitism to commensalism to even mutualism is well documented in many symbiotic fungi found in plant tissue. For example, the biotrophic rust pathogen Coleosporium ipomeae can shift from parasitism on Ipomea purpurea to commensalism, depending on microenvironment, damage from other enemies, and growing season length (Kniskern and Rausher 2006). Plant pathogens in the genus

Colletotrichum can also span the symbiotic spectrum, depending on host identity and physiology (Redman et al. 2001, Peres et al. 2005). These fungi can be described as maintaining an “unstable equilibrium” between different lifestyles (Moricca and Ragazzi

2008), transitioning between neutral/beneficial symbionts and opportunistic pathogens depending on environmental conditions and potentially other unknown triggers.

Lifestyle switching complicates efforts to define the function of fungal symbionts based on host and symbiont identity alone.

Introduction to thesis research

My dissertation research focuses on the role of seedling pathogens in maintaining diversity in a temperate forest stand. Specifically, it addresses the following questions:

1) Which fungi and oomycetes may act as seedling pathogens in a temperate hardwood

forest, and are these symbionts host specific?

2) Do these fungi and oomycetes negatively impact seedling survival, and how do

environmental factors, such as light and soil moisture, change these interactions?

Using a combination of field studies, laboratory analyses, and statistical modeling, I attempt to create an integrated picture of the role of symbiotic fungi in seedling recruitment, and determine if the fungi isolated in this study could drive

Janzen-Connell like patterns of seedling recruitment.

In chapter 2, “Characterizing the fungal associates of tree seedlings in a temperate mixed hardwood forest: Implications for forest diversity,” I examine the community structure of the >1000 fungal cultures collected from thirteen seedling hosts in 2007. I discuss the species richness and diversity of the field sites, and whether or not sampling has been sufficient to capture the full range of diversity. 130 operational taxonomic units (OTUs) of fungi are defined using nuclear ribosomal internal transcribed spacer (ITS) sequencing. OTUs belonging to the phylum Ascomycota are then placed using phylogenetic methods based on large subunit (LSU) sequencing. We then test hypotheses regarding the effects of site, host identity, soil moisture, and seedling age on fungal community structure.

Chapter 3, “Causal inference for plant disease: application to fungal associates of tree seedlings,” presents the statistical model we use to determine the impacts of multiple pathogens on multiple hosts in an ecological context, and discusses more

broadly the use of causal models in ecology. The statistical details of the model are published in a separate document (Clark and Hersh 2009). Briefly, the model estimates the impacts of symbiotic fungi on seedling survival singly and in combination by using a

Bayesian model selection framework. Detection is based on multiple sources of information (culture morphology, DNA sequencing).

Finally, in chapter 4, “Impacts of fungal co-infection on temperate forest diversity,” we summarize the major results of the model discussed in chapter 3. Using data on seedling survival and fungal detection, in the context of relevant covariates

(light, soil moisture), we assess the effects of five putative fungal pathogens singly and in combination on five seedling hosts. Most fungal symbionts/potential pathogens found in this system can be isolated from multiple hosts and initially do not appear to meet the assumption of host specificity. Yet model results indicate that some of these generalist pathogens have differential impacts on survival depending on host identity, and that the impacts of combinations of pathogens are non-additive.

In the final synthesis chapter, several recommendations for how to re-evaluate the Janzen-Connell hypothesis are discussed. We discuss in detail the challenges of defining host specificity and the influence of multihost fungi, along with how spatial patterns of fungal incidence and heterogeneous environmental conditions may alter

Janzen-Connell predictions.

2. Characterizing the fungal associates of tree seedlings in a temperate mixed hardwood forest: Implications for forest diversity

Introduction

Seedling disease is an important cause of mortality and influence on plant diversity (Burdon 1987, Gilbert 2002, Burdon et al. 2006, Freckleton and Lewis 2006).

Disease has an unquestionable impact on plant population size (Burdon 1987), productivity (Mitchell 2003), and competition (van der Putten and Peters 1997,

Alexander and Holt 1998). Disease can also change plant community structure through direct effects on survival and fecundity (Dobson and Crawley 1994). Negative feedbacks, in which some plants support microbial communities detrimental to competitors, can strengthen the effects of disease on plant competition (Bever 1994). In seedlings and saplings, the role of disease is often discussed in the context of the Janzen-Connell (JC) hypothesis (Janzen 1970, Connell 1971). Under this hypothesis, seedlings are more likely to survive when dispersed far from the parent tree due to increased pressure from host- specific pathogens when seedlings are growing either close to conspecific adults and/or at high densities. Multiple studies in both tropical (e.g. Augspurger 1984, Harms et al.

2000, Comita and Hubbell 2009) and temperate (e.g. Packer and Clay 2000,

HilleRisLambers and Clark 2003) systems show patterns of plant demography consistent with Janzen-Connell, but relatively few studies have identified the pathogens driving these patterns. In this study, we seek to identify fungal symbionts of tree seedlings that may act as parasites or pathogens, or otherwise reduce seedling survival.

Symbiotic fungi are diverse and ubiquitous in the tissues of almost all forest trees, but their ecological functions are challenging to identify (Sieber 2007). We define the term symbionts broadly, and allow it to include any fungus living in close contact

with its host regardless of the nature of the interaction (de Bary 1879). In recent years, evolving tools to classify previously cryptic symbiotic fungi have made addressing questions about their ecological function a possibility (Horton and Bruns 2001, Peay et al. 2008). An extensive literature on mycorrhizal fungi (e.g. Smith and Read 2008) has greatly improved our understanding of these important mutualists, but the functions of non-mycorrhizal fungi are less apparent. These plant symbionts can range from opportunistic pathogens to neutral or beneficial endophytes, and context-dependency

(host, location, environmental conditions) appears to be the rule rather than the exception (Schulz and Boyle 2005, Arnold 2007, Sieber 2007, Moricca and Ragazzi 2008).

Neither the extent of the diversity of these fungi nor their ecological functions are well resolved. Yet given their ubiquity and potential impacts on plant health, it is critical to understand this important guild of fungi.

The diversity of fungi inhabiting a single plant host can be considerable. Forty- nine fungal phylotypes (groups of distinct organisms defined solely by DNA sequences) have been found in the roots of the grass Arrhenatherum elatius at one location; most of the functions of these taxa are unknown (Vandenkoornhuyse et al. 2002). In the wetland reed Phragmites australis, 345 sequence-based operational taxonomic units (OTUs), including many unknown taxa, were identified from all plant parts, but only 11 of these taxa were abundant (Neubert et al. 2006). Soils from pine-dominated plots in the Duke

Forest (Orange County, NC) contained 412 fungal OTUs, including close matches to fungi known as saprotrophs, mycorrhizae, and pathogens (O'Brien et al. 2005).

Extracting information on fungi that influence plant survival and growth necessitates sorting through this large diversity.

In this study, we seek to identify putative fungal pathogens. Most discussions of seedling disease focus primarily on “damping-off”—a set of symptoms where plants

soften at the base of the stem, fall over, and rapidly die. It is not straightforward to diagnose the casual agent of mortality when seedlings are found with damping-off symptoms. Post-emergence damping-off is generally attributed to both oomycetes

(Pythium, Phytophthora) and fungi (Fusarium, Rhizoctonia), although it can be caused by other species; most of these organisms are thought to be host generalists (Agrios 2005).

Along with damping-off, tree seedlings are known to be susceptible to other kinds of diseases, such as shoot and needle blights, rusts, root rots, and powdery mildews

(Cordell et al. 1989). To complicate matters further, many symbiotic fungi that cause disease in one host may have other lifestyles in different hosts. They may exist as latent pathogens waiting for environmental conditions more favorable to disease, or they may be genotypes or species of pathogens that do not elicit disease symptoms in particular host genotype or species (Schulz and Boyle 2005). For example, species of Colletotrichum that are pathogens in some hosts can act as mutualists in others, increasing plant drought tolerance or protecting against other diseases (Redman et al. 2001). Clearly, assessment of the ecological role of many fungi may be case-dependent.

Diverse factors can influence how communities of fungal symbionts in plants are structured. If symbiotic fungi are host-specific, host identity could shape fungal communities. Limited dispersal could create localized patterns of abundance; longer distance dispersal could homogenize populations at the local scale (Burdon et al. 1989).

Environmental factors, such as temperature, moisture, light, and nutrient conditions could also influence various stages of the infection process, including incidence of a fungus in a given plot, entrance of the fungus into the host, and host ability to resist fungal invasion (Agrios 2005). Similarly, selective inhibition or promotion of certain species of fungi by plant defensive compounds or root exudates can alter fungal community structure (Saunders and Kohn 2009). Finally, an additional temporal

component may exist as older seedlings amass more opportunities to accumulate symbionts (Arnold and Herre 2003), but simultaneously develop age-related defense mechanisms against pathogens (Heath 1996).

In this study, we characterized the fungi in tissues of both living and recently dead seedlings of thirteen common Southeastern tree species in three mixed hardwood stands in North Carolina, USA. We selected target hosts with a range of seed sizes, degrees of shade tolerance, and relative abundances within the stand. We used a culture-based method to characterize the fungal community and identified cultured fungi using nuclear ribosomal DNA sequencing. We initially classified the fungi based on NCBI BLAST (blastn) searches, but then refined the classifications, especially of fungi with no clear BLAST matches, using a phylogenetic approach.

We tested three main hypotheses about the nature of the fungal community inhabiting these seedlings. First, we tested the hypothesis that (1) communities of fungal symbionts differ between host species. Although many known seedling pathogens are considered non-host specific (Agrios 2005), the Janzen-Connell hypothesis requires that distinct natural enemies will be found on different hosts (Janzen 1970, Connell 1971).

Second, we predicted that (2) the fungal community would be well mixed among sites, since the three stands shared many tree species. Finally, for several common fungal species, we predicted that (3) infection of seedlings with common fungal taxa would generally increase with soil moisture and seedling age. We expected to find differences reflecting the interplay between developmental features of young seedlings that are susceptible to fungal colonization and the ability of particular fungal pathogens to successfully disperse and colonize seedlings. Ultimately, our goal was to characterize the fungal community in these seedlings to develop a list of candidate pathogens with the potential to influence forest diversity.

Methods

Study locations

All field studies took place at three mixed hardwood stands in the Eno and

Blackwood divisions of the Duke Forest (Chapel Hill, NC), in the North Carolina

Piedmont (36°N, 79°W). Average monthly temperatures range from 19.2-31.7°C in July to 2.3-10.2°C in December (Southeast Regional Climate Center, http://www.dnr.sc.gov/climate/sercc/). Approximately 1,180 mm of precipitation fall annually. March, July, and August are generally the wettest months, while November and December are generally the driest (Southeast Regional Climate Center). Plots in both divisions are fully mapped two hectare stands, in which all trees are tagged, identified, and located on an x, y coordinate system. These stands share many tree species, but differ in parent material, topography, and other biotic and abiotic factors.

Field studies

In May 2006, we planted seeds of thirteen tree species across natural gradients of soil moisture. Focal plants encompass a range of successional types, seed sizes, and relative abundances (Appendix 1), and include the following species: Acer barbatum

(Southern sugar maple), Acer rubrum (red maple), Carya ovata (shagbark hickory),

Diospyros virginiana (persimmon), Fagus grandifolia (American beech), Liquidambar styraciflua (sweetgum), Liriodendron tulipifera (tulip poplar), Nyssa sylvatica (black tupelo),

Pinus taeda (loblolly pine), Quercus alba (white oak), Quercus phellos (willow oak), Quercus rubra (Northern red oak), and Quercus stellata (post oak). All seeds were washed with a mild surfactant and rinsed thoroughly prior to planting. In December 2006, we re- planted a second cohort of seedlings. Existing seedlings were flagged and left in place.

All species were re-planted in equal numbers, with the exception of Q. stellata, for which

seed was not available. Seedlings were collected throughout the 2007 growing season, with an additional collection of 43 live seedlings in October 2008.

Seeds were planted in 1 x 0.9 x 0.46 m herbivore exclosures constructed from 1.27 cm mesh hardware cloth to avoid seed predation and herbivory. Three exclosures were installed at each of 20 locations in the Eno and Blackwood divisions of the Duke Forest.

Planted seeds were randomly arranged. At each location, two cages contained 90 seeds

(five per species, “high density” treatment), and one cage contained 38 (two per species,

“low density” treatment). Since germination varied drastically between sites (data not shown), we did not distinguish between intended “high” and “low” density treatments.

Additional seeds were sown in the same locations for several species with known poor germination rates (Acer barbatum, Liquidambar styraciflua, Liriodendron tulipifera, Pinus taeda). Mortality of planted seedlings was observed weekly; any dead or dying seedlings were collected for fungal identification. Hemispherical canopy photos were taken on

September 2006 at all cages to measure canopy openness; photos were analyzed using

Hemiview (Delta-T Devices, Cambridge, UK; for detailed methods, see Clark et al.

(2003)). Soil moisture was measured monthly at each location using time-domain reflectometry (Tektronic 1502B; Tektronix, Beaverton, Oregon, USA).

Isolation and initial identification of fungi

Shoots and roots of collected seedlings were scored macro-and microscopically for disease symptoms, and images of seedlings with distinct symptoms were taken with a Canon CoolPix 990 digital camera. Seedlings were rinsed under running tap water for one hour to remove soil particles; then half of all samples were treated with 30 seconds in 70% ethanol, rinsed with sterile water, treated for 30 seconds in 10% bleach, then rinsed again and allowed to dry on sterile paper towels. Root, stem, and leaf fragments were then plated onto two kinds of selective media: Pentachloronitrobenzene Ampicillin

Rifampicin Pimaricin (PARP) for Pythium and Phytophthora, and alkaline water agar

(AWA) for Fusarium, Rhizoctonia and other phytopathogenic soil fungi (Singleton et al.

1992; G. Abad and M. Cubeta, pers. comm.). Subcultures were created from isolation media onto corn meal agar (CMA) and diluted potato dextrose agar (PDA30) until pure cultures were established. We ultimately combined data from all treatments, as considerable overlap existed in species composition.

Cultures were classified using both macro-morphology and DNA sequencing and subsequent phylogenetic analyses. We began by amplifying nuclear ribosomal DNA from the internal transcribed spacer (ITS) and 5.8S regions, since these regions contain sufficient variability to identify many groups to the species level, using primers ITS1F

(Gardes and Bruns 1993) and ITS4 (White et al. 1990). Due to the large number of samples processed, we used a direct PCR approach to quickly obtain DNA. Small fragments of mycelia from cultures were collected using a sterile pipette tip, and placed immediately into tubes containing PCR cocktail (4 µL dNTPs, 2.5 µL BSA (bovine serine albumin; 10mg/mL), 2.5 µL 10x PCR buffer, 1.25 µL each of 10 µmol forward and reverse

primers, 0.5 µL MgCl2 (25 mM/mL), 0.175 µL taq polymerase, 12.825 µL autoclaved ultrapure water). Thermal cycler conditions were as follows: 10 minutes at 94°C, 35 cycles of 1 minute at 94°C, 1 minute at 52°C, and 1 minute at 72°C, followed by a 10- minute final elongation at 72°C. PCR products were visualized using gel electrophoresis, and successfully amplified products were then cleaned using the Qiaquick PCR

Purification kit (Qiagen), and sequenced on an ABI 3730 Autosequencer (Applied

Biosystems). ITS sequences were edited and trimmed at the motifs CATTA (5’ end) and

GGAGGAA (3’ end) by hand using the program Sequencher 4.8 (Gene Codes

Corporation). They were then clustered into operational taxonomic units (OTUs) using the percent sequence similarity algorithm in Sequencher 4.8. We set the threshold for

delineating OTUs at 96% similarity, and included the 5.8S region. Names were assigned to OTUs based on results of NCBI BLAST searches of GenBank using the blastn algorithm (Altschul et al. 1990). OTUs were named if the top named fungal sequence had an E-value of 0.0 and Max Identity greater than or equal to 97 percent.

Further resolution of fungal taxa using phylogenetics

The ITS region is suitable for genus to species level classifications; however, the divergent nature of the region makes it unalignable across a phylogenetically diverse range of fungal families (Bruns et al. 1991). Thus, phylogenetic placement was aided by sequencing ~800 base pairs of the 5’ end of the large subunit (LSU). The LSU region contains several divergent domains and is thus is suitable for order to genus level classification, but it also includes several well-conserved regions and can still be aligned across diverse groups of fungi (Bruns et al. 1991). For each unique fungal OTU with isolates from more than five isolates, and a subset of rarer taxa, we also amplified a portion of the large subunit of nuclear ribosomal DNA using primer pair LROR and LR5

(Vilgalys and Hester 1990). LSU sequences were edited and trimmed at the motifs

ACCCGCT (5’ end) and TCGTCAAA (3’ end) in Sequencher. We were able to amplify the LSU region for 67 of 130 ITS-based OTUs, including 31 of the 38 taxa that had been isolated from more than five individuals.

LSU sequences from this study were initially aligned using MUSCLE 3.7 (Edgar

2004). This alignment was then incorporated by hand into an existing alignment of 414 taxa from across the Ascomycota (Schoch et al. in press) available on TREEBASE

(Sanderson et al. 1994). For a large-scale ecological study such as this, creating multi- gene phylogenies may prove to be excessively time-consuming and costly. Instead, we chose to take advantage of existing information on the relationships between the

Ascomycota by using a constraint tree approach following Arnold et al. (2007).

Phylogenetic relationships within the Ascomycota have been relatively well-resolved using multi-gene phylogenies including protein-coding genes that are more phylogenetically informative at deeper levels (e.g. Lutzoni et al. 2004, James et al. 2006,

Spatafora et al. 2006, Schoch et al. in press). In order to include as much existing information as possible, including resolution of deeper nodes, we ran subsequent maximum likelihood (ML) phylogenetic analyses using a constraint tree based on the results of Schoch et al. (in press). Constraint trees, or DNA scaffolds, have been used in other studies in which taxa with a relatively small amount of phylogenetically informative data were incorporated into a phylogeny based on a considerably larger amount of information. This approach has not only been used for fungal associates of plants (Arnold et al. 2007), but also for combining plant or animal data from the fossil record with molecular datasets (Springer et al. 2001, Manos et al. 2007). The constraint tree included the 414 reference taxa from Schoch et al. (in press) with large subunit sequences; we used only nodes with greater than 80% maximum likelihood bootstrap support on the 2G434 tree as constraints. We then incorporated sequences from this study onto the constraint tree in a maximum likelihood-based phylogenetic context using RAxML version 7.0.4 (Stamatakis 2006b) facilitated by the python wrapper

PYRAXML2 beta (Kauff 2006). We determined the best topology, including branch lengths, by selecting the tree with the best log likelihood score from 200 individual runs using the GTRMIX model, which uses the GTRCAT model (Stamatakis 2006a) to search for the best topology, and the GTRGAMMA model to evaluate the likelihood. Support for individual nodes was assessed using 1000 ML bootstrap replicates using the

GTRCAT model (Stamatakis 2006a). We coded the known taxa on this tree for ecological function in Appendices 3-6 following Schoch et al. (in press).

Statistical analyses

The program EstimateS 8.0.0 (Colwell 2005) was used to generate sample-based rarefaction curves and 95% confidence intervals for fungal diversity (Colwell et al. 2004).

Three non-parametric, incidence-based estimators of species richness were used to estimate the total richness in the system—ICE (Incidence-Based Coverage Estimator; Lee and Chao 1994), Chao 2 (Chao 1987), and Jackknife 2 (second-order jackknife; Smith and van Belle 1984). ICE is robust to differences in sample size, population density, and patchiness (Chazdon et al. 1998), while Chao2 and Jacknife2 have been recommended for highly diverse communities based on their ability to reach an asymptote with less sampling effort (Colwell and Coddington 1994, Unterseher et al. 2008). Regardless, all three methods are commonly used for highly diverse microbial communities.

We examined whether seedling lifespan and soil moisture were correlated with incidence of twelve of the most commonly sampled species of fungi (Colletotrichum acutatum, Cylindrocarpon sp. A, Pestalotiopsis sp. A, Phomopsis sp. A, B, and D, Alternaria sp. A, Phialocephala sp. A, Taphrina sp. A, Penicillium sp. A, Unknown 38, and

Cryptosporiopsis sp. A), using generalized linear models (GLMs) with a binomial error distribution and a logit link. This analysis was restricted to dead seedlings of the five most abundant host species collected in 2007. We used the following model:

logit (If) = β0 + β1a + β2m where If is infection of an individual seedling with OTU f, a is seedling age, and m is soil moisture. June soil moisture was used as a representative sample of relative differences in soil moisture between plots. Based on comparisons of AIC scores for different nested models, the model in which all host species were pooled and with no interactions between soil moisture and seedling age had the best fit for the highest proportion of species tested, as opposed to models that contained an interaction term, subdivided the

intercept among host species, or both. All GLMs were performed in the programming language R version 2.8.1 (R Development Core Team 2008).

Figure 2.1: Rank abundance of non-singleton taxa identified in this study, ordered by abundance. For a complete list of OTU names, see Appendix 2.

Results

Overall fungal richness

We sampled 293 tree seedlings for fungal symbionts, and identified 130 fungal taxa based on 96% ITS sequence similarity. Of these taxa, the majority (121) was placed in the Ascomycota, with a small number in the Basidiomycota (2) and basal fungal lineages (7). The majority of the fungi in this system were relatively rare; 94 taxa (72.3%) were isolated from less than five individual seedlings, and 60 (46.1%) were isolated from only one individual. A truncated rank abundance curve, including only non-singleton taxa, is shown in Figure 2.1. Table 2.1 lists the twenty most commonly sampled fungi, the major hosts and sites from which they were isolated, and their phylogenetic placement. All of these common fungi were isolated from at least three different hosts

and most were found at all three sites. Pilidiella sp. A was the only taxon of the twenty most commonly sampled fungi to be found at only one site. A complete list of fungal taxa and their top BLAST hits is provided in Appendix 2. Although we did isolate oomycetes in the genera Pythium and Phytophthora from the same system in 2006 (M.

Hersh unpublished data), none were recovered in this study. Contrary to expectations, two commonly isolated fungal genera known to cause damping-off, Rhizoctonia and

Fusarium, were not found in abundance. We only found one individual infected with

Rhizoctonia solani and detected relatively few individuals (13) infected with one of the five Fusarium taxa.

Table 2.1: The twenty most common fungal taxa identified in this study. Class and order assigned by phylogenetic analysis, except when marked with an asterisk, in which case assigned by BLAST ID. Identification of C. acutatum included visualization of conidia. Taxonomy follows Schoch et al. (in press)

We created a rarefaction curve to determine if the total richness of the system was fully sampled. The rarefaction curve did not reach an asymptote (Figure 2.2), thus it cannot be concluded that all fungi in this system have been sampled. We used three non-parametric estimators (ICE, Chao2, and Jackknife 2) to predict total richness in the 31

system, and found the results of the three estimators to be reasonably congruent (Figure

2.2); however, none reached an asymptote. Based on our OTU definition, there are at least 200 unique fungal taxa in association with the tree species sampled.

Figure 2.2: Rarefaction curve of OTUs identified per sampling effort (black solid line) with 95% confidence intervals of richness estimates (black dotted lines), along with three incidence-based estimators to extrapolate a lower limit for total species richness (colored lines).

For the five tree seedling species best represented in the data (Acer barbatum,

Diospyros virginiana, Liquidambar styraciflua, Nyssa sylvatica, and Pinus taeda), we created separate rarefaction curves to identify hosts with the most species-rich community of symbionts (Figure 2.3). However, 95% confidence intervals for all hosts were clearly overlapping, indicating that the number of fungal taxa identified from these species was not detectably different based on equal sampling effort. We performed a similar analysis in which species were divided by site, but again did not uncover any differences in the number of species identified per unit of sampling effort (Figure 2.4).

Figure 2.3: Rarefaction curves of fungal OTU richness for the five host species best sampled. Dotted lines are 95% confidence intervals around richness estimates.

Phylogenetic placement of fungal taxa

Large subunit sequences were obtained for 67 ascomycete taxa. Fungi were found predominantly in three classes within the Ascomycota: Sordariomycetes (34 taxa),

Dothideomycetes (15 taxa), and Leotiomycetes (14 taxa), with four OTUs in the

Eurotiomycetes (Figure 2.5). Within the Dothideomycetes, Eurotiomycetes, and

Sordariomycetes, all fungi fell within a subset of existing orders with strong maximum likelihood bootstrap support, with the exception of two Colletotrichum taxa. Conversely, only one taxon, Phialocephala sp. A, could be placed in an existing order in the

Leotiomycetes with high bootstrap support. Expanded trees showing all taxa in these four classes are in Appendices 3 (Dothideomycetes), 4 (Eurotiomycetes), 5

(Leotiomycetes), and 6 (Sordariomycetes). Almost all taxa were grouped into clades most closely related to saprobes or plant associates (endophytes or pathogens;

Appendices 3-6). One exception, Unknown 68, was sister to an animal pathogen in the

Eurotiomycetes. No taxa were most closely related to lichenized fungi or mycoparasites. 33

Figure 2.4: Rarefaction curves of fungal OTU richness for the three study sites sampled. Dotted lines are 95% confidence intervals around richness estimates.

Many fungi assigned names based on BLAST searches appear in the expected phylogenetic position (Appendices 3-6). For example, sequences blasting to

Colletotrichum species clustered together, and were grouped with the closest congeneric reference sequence, Glomerella cingulata (anamorph: Colletotrichum gloeosporiodes), with

86% bootstrap support (Figure 2.5, Appendix 6). Unknown taxa were placed in all four orders, with the greatest number in the Leotiomycetes and the Sordariomycetes (9 taxa each), followed by the Dothideomycetes (7 taxa) and the Eurotiomycetes (1 taxon). In the

Leotiomycetes, no unknown could be placed within a named monophyletic clade consistent with the nomenclature of Schoch et al. (in press) with high bootstrap support.

In the Sordariomycetes, two-thirds of the unknown taxa were in the order Xylariales.

OTU definitions set by 96% sequence similarity were generally correlated with phylogenetic clustering, with several notable exceptions: (1) Several cultured sequences from this study (Phoma sp. A, Botryosphaeria sp. A, Phomopsis sp. A) were placed in clades that included reference taxa; (2) One taxon (Cylindrocarpon sp. A), split into three

Figure 2.5: Maximum likelihood tree of the Ascomycota. Complete legend on following page.

Figure 2.5 (continued): Maximum likelihood tree of 67 cultured taxa (red) and 414 reference taxa (black) (Schoch et al. in press) in the Ascomycota, along with backbone constraint tree of named taxa (inset). Numbers above nodes represent percent maximum likelihood bootstrap support for 1000 replicates; only values above 50 are shown. Branches with greater than 70% support are bolded. Orders labeled in red contain OTUs from this study; the number of OTUs is in brackets following the order name. subgroups closely related to one another that formed a clade with Cylindrocarpon sp. D

(Appendix 6).

Correlates of fungal incidence

In general, infection with most fungi was not correlated to seedling age or soil moisture, with several exceptions: (1) Seedling age was positively correlated with

Phomopsis sp. B (residual deviance (rD) = 45.513, p = 0.0003) and Phialocephala sp. A (rD =

38.875, p = 0.00363), and marginally correlated with Phomopsis sp. D (rD = 13.753, p =

0.0580); (2) Soil moisture was positively correlated with Cylindrocarpon sp. A (rD =

135.98, p = 0.026) and marginally correlated with Phomopsis sp. D (rD = 13.753, p =

0.0747). All runs had 154 degrees of freedom. All other relationships between age, moisture, and fungal infection were not statistically significant (p>0.10).

Discussion

Richness and specificity of symbiotic fungi

We found 130 fungal taxa from thirteen species of both recently dead and surviving tree seedlings in a mixed hardwood stand. The shape of the rank abundance curve (Figure 2.1) is typical of other fungal diversity studies in soils (e.g. Porter et al.

2008), plant tissue (e.g. Neubert et al. 2006), and mycorrhizal root tips (e.g. Richard et al.

2005), containing a small subgroup of common taxa and a long tail of rare taxa. Based on both the lack of asymptote on the rarefaction curve, and the estimates of total richness

from three non-parametric estimators, we can conclude that we did not completely sample the richness in the system (Figure 2.2). This may have been partially due to differences in sampling between species; since a higher proportion of large-seeded species survived, sampling of dead seedlings was skewed towards smaller-seeded species (Appendix 1). In addition, we did not use culture-independent methods to sample for fungi that are unculturable or require more specific conditions for growth in culture, including obligate biotrophs. In other systems, using culture-independent methods significantly increased the amount of richness found in the system over sporocarps (Porter et al. 2008) and cultures (Lynch and Thorn 2006, Menkis et al. 2006,

Neubert et al. 2006) alone. None of the estimators for total species richness reached an asymptote, and can thus only be interpreted as a lower boundary for total species richness (Gotelli and Colwell 2001). We did not find differences in species richness between hosts or sites (Figures 2.3 and 2.4). Comparisons between these small-seeded hosts and large-seeded hosts may yield differences in richness between species, but our sample sizes for large-seeded species were insufficient to test for this (Appendix 1).

All common fungi were found in multiple hosts, which was not consistent with our hypothesis that fungal communities would be host specific. However, it is unclear as to whether each fungus serves the same ecological function in different hosts. Many multihost fungi can still have some degree of host preference at a deeper phylogenetic level or serve different ecological roles on different hosts (Gilbert and Webb 2007).

Variation at the genotype level in host or fungus, environmentally-driven lifestyle switching, or a combination of the two are potential mechanisms that could drive differences in ecological function within species. Future empirical testing of effects of fungi on host survival through targeted inoculations will help to elucidate their

ecological functions in as pathogens, mutualists, or commensalists. Koch’s postulate tests would be needed to identify the causal agent(s) of any disease symptoms.

Phylogenetic placement of fungal taxa

The ascomycete taxa we identified were placed in the Sordariomycetes,

Dothideomycetes, Leotiomycetes, and Eurotiomycetes (Figure 2.5), similar to many other studies of fungal plant associates (Sieber 2007). We chose to define our OTUs based on 96% similarity in Sequencher; however, there is no universally accepted cutoff in delineating fungal taxa based on ITS sequencing (Arnold et al. 2007). Most OTUs assigned using this method formed monophyletic clades when multiple LSU sequences were available. No taxa appeared to be anomalous in terms of lifestyle, since almost all cultured taxa clustered with plant associates or saprobes as opposed to lichenized fungi, mycoparasites, or animal pathogens.

For well-studied genera, BLAST searches did an adequate job of identifying unknowns, aside from obvious database errors (e.g. fungal sequences identified as plant

DNA in the GenBank database). However, in many cases BLAST identifications provided little information on fungal identity, especially in the Leotiomycetes. Most unknown taxa were placed in the Leotiomycetes or the Xylariales. Almost all unknown taxa in the Leotiomycetes failed to fit into a well-supported clade. However, phylogenetic analysis using larger regions of rDNA alone is insufficient to delineate orders well within the Leotiomycetes (Wang et al. 2006), so this is not unexpected. The

Leotiomycetes includes many plant associates, including pathogens and endophytes, along with many taxa with poorly understood ecological functions (Spatafora et al.

2006). In the Xylariales, incorporation of unknown sequences into phylogeny with additional taxa from this clade (e.g. Davis et al. 2003) could provide further insight on the identities of unknown taxa. The Xylariales is one of the largest orders in the

Sordariomycetes, and includes many saprobes along with plant pathogens and endophytes (Zhang et al. 2006).

Along those lines, sequencing the large subunit alone provided enough phylogenetic information to place many taxa to the order level, but not beyond. Many branch lengths on the trees produced were short (Figure 2.5, Appendices 3-6) indicating few differences between closely related taxa. Incorporating additional genes refines identifications, especially since the large subunit is known to be less phylogenetically informative at deeper nodes than many protein-coding genes, such as RPB1 (Lutzoni et al. 2004, Schoch et al. in press). In addition, detailed phylogenies of more taxonomically constrained groups (families, genera) could strengthen identifications. Using a constraint tree allowed us to use the results of previous studies of fungal systematics to facilitate placement of unknown taxa. An unconstrained analysis using the same dataset placed taxa at the same terminal nodes (data not shown), but increased the computational time by nearly a factor of ten. Using a constraint tree not only allowed us to incorporate existing knowledge on fungal phylogenies, but also significantly reduced the computational burden of analyzing large datasets using maximum likelihood.

Influence of soil moisture and seedling lifespan on infection

Generalized linear models relating detection of twelve common fungi to seedling lifespan and soil moisture did not uncover many significant relationships. We expected that most fungi would respond positively to soil moisture, since many fungi require moisture for nutrition, dispersal, and infection (Agrios 2005). However, there are plant pathogens, such as Fusarium solani, known to cause more disease in soils with low to intermediate moisture (Colhoun 1973). Cylindrocarpon sp. A, a strong BLAST match to isolates of the common soil and root fungus/root pathogen Cylindrocarpon destructans

(Mantiri et al. 2001), was the only fungus to strongly respond to soil moisture.

Interestingly, Phialocephala sp. A and Cryptosporiopsis sp. A, putative root endophytes based on other taxa in those genera (Addy et al. 2005), did not respond to soil moisture.

Similarly, we expected mixed responses to seedling age. Older seedlings have more opportunities for exposure to inoculum (Saikkonen 2007) and become more structurally diverse and thus susceptible to different pathogen types (Burdon 1987). However, host defense may strengthen both structurally (McClure and Robbins 1942, Neher et al. 1987) and biochemically (Ficke et al. 2002, Kus et al. 2002) as seedlings mature. Detection of two species of Phomopsis and Phialocephala sp. A was positively correlated with seedling age, but no others. This response may be scale-dependent; for example, community shifts in foliar endophytes occur at the scale of years (Espinosa-Garcia and Langenheim

1990), but this study was looking instead at weeks to months.

Implications for forest diversity

Based on the results of this study, we can make recommendations of fungal taxa to pursue further as putative seedling pathogens. For a fungus to meet the assumptions of the Janzen-Connell hypothesis, it must be host-specific and have distinct patterns of localized abundance (Gilbert 2002). However, we observed no obvious strict host specialists, but instead a list of common generalist fungi whose ecological roles in multiple hosts need further elucidation. While many fungi are rare, even the most common fungi in the system were not very abundant; C. acutatum, the most commonly isolated fungus, was only found in 22% of individuals sampled. It is unclear as to whether this constitutes relative rarity in the system, detection error, or undersampling.

From the list of common fungi in Table 2.1, we can eliminate several taxa as probable endophytes (Phialocephala, Cryptosporiopsis) or saprobes (Bionectria, Microdiplodia), but are left with a list of “potential pathogens” for further studies, including two fungi poorly identified by BLAST (Unknowns 38 and 70). Although many fungi are important plant

pathogens, the fungi isolated in this study are not the only natural enemies in this system. Further studies could assess the roles of obligately biotrophic fungi (which were not sampled using these methods) and other taxa (oomycetes, bacteria, insect herbivores) in causing significant seedling mortality, thus building a more integrated picture of enemy-induced seedling mortality in this temperate forest.

3. Causal inference for plant disease: application to fungal associates of tree seedlings

Introduction

Disease and parasitism are an important component of ecosystem function.

These interactions regulate population growth (Dobson and Hudson 1986, Burdon 1987), add complexity to food webs (Lafferty et al. 2006), and can have large impacts on biodiversity (Gilbert 2002, Hudson et al. 2006). The broad taxonomic diversity and often- cryptic nature of parasites, coupled with inevitable undersampling, makes estimation of total parasite diversity daunting (Dobson et al. 2008, Bordes and Morand 2009). Yet we know that simultaneous infections with multiple parasites are common (Cox 2001,

Seabloom et al. 2009), and different parasites within a single host can interact directly and indirectly with a range of potential outcomes that makes these interactions unpredictable (Lello et al. 2004, Pedersen and Fenton 2007). Additionally, the environmental conditions surrounding both hosts and parasites can change the nature of disease interactions, strengthening or weakening infection rates, host defense and parasite vigor (e.g. Jarosz and Burdon 1988, Desprez-Loustau et al. 2006, Roberts and

Paul 2006). Like many other multitrophic interactions, the complexity of disease can be high. A deeper understanding of these interactions requires an analytical approach that can accommodate more of the complexity than has been possible in the past. We present here a hierarchical Bayesian model of seedling disease that incorporates interactions between multiple hosts, multiple parasites, and environmental conditions, and discuss why such an approach can improve our understanding of disease ecology. In the case study we present, results show evidence for the host specificity that would be required if

tree diversity is regulated by the Janzen-Connell effect, but only if we consider the impacts of co-infection by multiple pathogens on multiple hosts.

The limitations of null hypothesis testing and reductionist methods have long been acknowledged when addressing complex ecological problems (e.g. Hilborn and

Mangel 1997, Anderson et al. 2000, Stephens et al. 2007). Causal inference can allow for not only the effects of a focal treatment (i.e. a “cause”), but also the effects of other potential alternative “causes”, and thus lends itself to real-world situations, such as epidemiological studies (Rubin 1990). Understanding the factors that might contribute to disease emergence and spread can involve multiple hypotheses of causation and require integration of data from different spatial and temporal scales (Anderson et al. 2004,

Farnsworth et al. 2006, Jewell et al. in press). Plowright et al. (2008) highlight the importance of defining the potential causal pathways through graphical methods, allowing for the full set of interactions to contribute to the understanding of disease emergence. They also discuss the importance of ‘triangulation,’ or combining information from field, laboratory, and modeling studies, along with historical datasets.

Integrating these independent approaches could help to inform and sharpen future hypothesis testing for each approach in light of the results of other approaches

(Plowright et al. 2008). Methods allowing for multiple causal pathways and integration of different forms of evidence are relevant not only for disease, but could have application in all areas of environmental science, including such diverse problems as coral decline (Fabricius and De'Ath 2004) and gene flow across complex landscapes

(Cushman et al. 2006).

The challenge of causal inference comes in the analysis--how does one assimilate the many interactions, identify those that are important, and quantify not only the direct relationships, but also how effects cascade through potentially many interacting species?

Recent efforts rely on traditional tools, such as Akaike information criterion (AIC) comparisons to select from among several competing models for a small number of relationships, or structural equation modeling to quantify correlations. There are several reasons why these approaches have limitations with problems involving species interactions. Structural equation models are linear and Gaussian, whereas most ecological interactions are non-linear and might not be well-described by Gaussian distributions (Clark 2005, Clark 2007). Information criteria are used to compare the fit of two models to the same data set, but they are not helpful for building or selecting among models with many interactions (Clark 2007, Gelman and Hill 2007)

Problems involving multi-species interactions, such as the role of disease in maintaining tree diversity, can be computationally large and inherently complex. A hierarchical Bayesian approach attempts to accommodate complexity and environmental variability and formally account for uncertainty associated with multiple causal relationships (Clark and Gelfand 2006, Cressie et al. 2009). The potential value of causal models that account for complexity extends well beyond disease, having immediate application in all areas of ecology (Dorazio and Royle 2005, Clark 2007,

Cressie et al. 2009). Although the potential for applications is expanding rapidly, the methodology is not well understood. Flexible approaches are not readily available in software, thus requiring some basic distribution theory to describe and analyze relationships among a large number of variables and customized algorithms.

We demonstrate how causal inference can be used to evaluate the weight of evidence for impacts of putative fungal pathogens on seedling survival. We illustrate how the model graph is translated into equations, provide some basic distribution theory needed for analysis, and discuss algorithm development for computation. We begin with a basic causal model, illustrating inference in a traditional setting and why it

provides but limited insight. We follow with application of techniques independently developed for microarray data (Huang et al. 2007, Grzegorczyk et al. 2008, Li et al. 2008), to show that powerful inference is possible and can help to understand complex causal interactions.

An application: Fungal maintenance of tree diversity

Differential mortality of tree seedlings caused by pathogens is thought to play an important role in maintaining forest diversity. The importance of seedling pathogens is often discussed in the context of the Janzen-Connell hypothesis. The Janzen-Connell hypothesis posits that forest diversity is maintained by host-specific natural enemies

(Janzen 1970, Connell 1971), such that seedlings are more likely to survive when dispersed far from the parent tree. Multiple studies have shown patterns of tree recruitment consistent with Janzen-Connell (e.g. Augspurger 1983a, Wills et al. 1997,

Webb and Peart 1999, Harms et al. 2000, Comita and Hubbell 2009) and highlighted the importance of pathogens in creating these demographic patterns (Augspurger 1983b,

1984, Gilbert et al. 1994, Packer and Clay 2000, Bell et al. 2006). However, in many cases, the pathogens involved remain unidentified. Proper identification of the pathogens is complicated by the facts that individual seedlings can support infections by numerous fungi simultaneously (Chapter 2), and a wide range of pathogens can cause similar disease symptoms in seedlings (Agrios 2005). For H hosts and K pathogens, there are H x

2K combinations of hosts and pathogens that could potentially alter seedling growth and mortality, making inference more complex than single host/single pathogen studies.

We examine fungi associated with the tissues of living and dead tree seedlings in a temperate mixed hardwood stand in the Duke Forest, Orange County, NC, USA. We focused on five native tree species—Acer barbatum (Southern sugar maple), Diospyros virginiana (persimmon), Liquidambar styraciflua (sweetgum), Nyssa sylvatica (black gum),

and Pinus taeda (loblolly pine). Seeds were planted in 20 locations, each with three subplots, in May and December 2006. Seedlings were monitored weekly for survival during the 2007 growing season, and both dead and live seedlings were collected for fungal identification. Fungi were cultured from plant tissue, and identified using rDNA- based sequencing and subsequent phylogenetic analysis. Two relevant abiotic covariates, soil moisture and light, were measured using time domain reflectometry and hemispheric canopy photos, respectively. For a detailed description of field and laboratory methods, see Chapter 2 of this document.

To illustrate a causal inference approach, we discuss infection with the two most common symbionts—Colletotrichum acutatum and Cylindrocarpon sp. A, a strong BLAST match to Cylindrocarpon destructans. Colletotrichum acutatum is a cosmopolitan plant pathogen infecting many woody and herbaceous hosts (Peres et al. 2005). It has not been previously observed in most of the tree species we studied, though it has been found in congeneric species (Farr et al. 1989). In adult trees, symptoms occur on reproductive structures, and in some cases, young leaves and twigs. Small plants show symptoms on stems and roots (Peres et al. 2005); the fungus can also reside in both juvenile and adult plants asymptomatically (Freeman et al. 2001, Yoshida et al. 2007). Species in the genus

Colletotrichum can cause disease, confer benefits, or have no measurable effect on hosts.

This plasticity in fungal lifestyle is controlled at least in part by host physiology

(Redman et al. 2001). Colletotrichum is splash dispersed, and sometimes present in the litter layer (Peres et al. 2005). Cylindrocarpon destructans is a known root rot of several coniferous and deciduous tree species (Beyerericson et al. 1991, Hernandez et al. 1998,

Seifert et al. 2003, Alaniz et al. 2007). It may be capable of both pathogenic and endophytic lifestyles (Axelrood et al. 1998), depending on host and environment.

Because both these fungi are known to have multiple lifestyles, we discuss how this

modeling approach can help generate hypotheses about fungal function based on observations of detection in live and dead seedlings.

Inferring cause based on data: the graphical model

Consider a fungus that, if present near the host, could infect the host with some probability. If infected, the risk of mortality may change--if pathogenic, the mortality risk increases. Infection might be detected by one of several techniques. The observables for this problem include the survival and detection status of the host. Based on these observations, we wish to infer whether or not the fungus was present at the site, and if so, whether the host was infected and the impact of infection on host survival. Because there is detection error, we cannot equate lack of detection with absence. Thus, detection error (the false negative rate) becomes part of the model. Although the example is specific, the elements of the problems are shared by other studies of multiple pathogens on multiple hosts, especially when sampling is limited.

Causal inference makes use of the graphical model (Figure 3.1) as basis for evaluation of the unknowns in the model. A fungus may be present in a plot (P = 1) or not (P = 0) with probability p(P = 1) = λ. If present, a seedling can become infected with probability p(I = 1|P = 1) = θ. However, knowledge of infection status is limited by our ability to detect a given fungus in plant tissue; thus, we also fit a detection rate, p(D =

1|I = 1) = φ. An infected individual survives with probability p(S = 1| I = 1) = s1, and a non-infected individual survives with probability p(S = 1| I = 0) = s0. To begin with basic causal inference we infer values for the five parameters λ, θ, φ, s0, and s1, based on the model of causes from Figure 3.1. Inference must be based on the combinations of observations for detection and survival p(D = 0,S = 0), p(D = 1,S = 0), p(D = 0,S = 1), and p(D = 1,S = 1). Individuals of known survival status, but unknown detection, and incorporated into the analysis with missing values for detection. In the next section we 47

illustrate how causal inference is used to move from observations to inference on causal relationships.

Figure 3.1: The graphical framework of the model of incidence, infection, survival, and detection. Note that only detection and survival can be observed directly.

Causal relationships: Fungal incidence, infection status, and survival

The probability of infection can be evaluated given observations of fungal detection (D) and seedling survival (S), when incidence (P) is unknown p(I|D,S) or known p(I|D,S,P). Incidence is unknown when the fungus has not been detected in any host tested at the site. We write this probability as p(I|D,S). It is not conditioned on P, because P is unknown. Incidence is known in the case where infection has been detected in another individual at the same location. When incidence is known, we can determine the probability of infection given that the fungus is present, or p(I|D,S,P = 1). Of course, if the fungus is known to be absent, then infection cannot occur, p(I|D,S,P = 0) = 0.

To arrive at infection probability, consider first the factorization

The first factor on the right hand side of this equation is the joint probability of infection and observations of detection and survival, given presence of a fungus (P). This factored form is useful, because we might know that the fungus exists at the location having found another infected individual at the same location. If there are no observations of survival or detection, then the probability of infection is

If the site is known to support the fungus the infection rate increases to

Observations change the probability of infection. Using Bayes theorem, the probability of infection conditioned on observations is

Substitution gives three possibilities:

detected (1)

not detected, died (2)

not detected, survived (3)

Note that information on infection status comes not only from detection, but also from survival. The greater the difference between survival probability for infected and uninfected individuals, the more information the survival data provide. Thus far, we have relationships that allow us to evaluate infection based on observations. We now

need to evaluate the probabilities for observations themselves, which could include only survival or the multinomial combinations of survival and detection.

The probability of survival based on detection, p(S|D), is obtained from

Note that we do not have p(I|D), but rather p(D|I). So we invert the problem using

Bayes theorem,

Upon substitution

Then for the two cases, survival probability is

(4)

(5)

If no infection is detected, then survival is a weighted average of survival probabilities, the weights being the probability of no infection and infection without detection. If detected, then infection is certain, and we apply the survival probability for infected individuals. If there is detection data, we need to consider the different combinations of survival and detection.

Likelihood: joint distribution of detection and survival

For model fitting, we need probabilities for the observations D and S. Returning to the joint distribution and factoring further, we have

These are all univariate distributions, describing the probability for each step in the

'chain of events' leading to the detection and survival observations (see Figure 3.1). Note the problem simplifies using substitutions p(I,P) = p(I|P) p(P) and p(D,S|I) = p(D|I) p(S|I), the latter justified by the fact that, given I, D and S are conditionally independent, meaning that the probability of host survival does not depend on our ability to detect the fungus, only on the infection status itself. This representation provides the basis for determining the probability of observations (D, S) as

These probabilities are used in the likelihood function, which we will describe below.

A traditional analysis: Maximum likelihood inference

Using relationships derived in the previous sections, the model can be fitted to data. This requires a likelihood for the observations of detection D and survival S. The likelihood can be expressed jointly for observations D and S as the vector of probabilities

, where subscripts designate combinations of observed D and S.

The likelihood function, based on a multinomial distribution, is:

(6)

(6) where n is the total number of seedlings and yD,S is the number of seedlings observed in each of the four (D,S) classes, each having probability pD,S. Assuming the fungus is present at the site, this multinomial likelihood is

(7)

For some individuals, there is no detection information, in which case the probability of survival is

(8)

If we have observations of both types (some including detections and some not including detections), then there are six total types of observations, and the likelihood is

(9)

When incidence is not known, there is an addition source of uncertainty, requiring inference on λ. We can still evaluate the likelihood, incorporating additional terms for incidence. However, we cannot identify λ, because it is closely linked to the unknown value for θ.

We estimated the maximum likelihood (ML) values for the parameters λθ, φ, s0, and s1 using a parametric bootstrap. The multinomial model (equation 9) is fitted to the data, and then is randomly resampled 10,000 times to estimate ML values for the four parameters. The 2.5% and 97.5% quantiles from this resampled data are used to create confidence intervals. We fitted the model using detection and survival data for five hosts

and the potential pathogens Colletotrichum and Cylindrocarpon. Maximum likelihood estimates from the model reveal that mean infection probabilities do not vary much between species of hosts or fungi (Figure 3.2), with the exception of the high probability of Colletotrichum infecting Acer barbatum. Infection with Colletotrichum or Cylindrocarpon tends to decrease survival in three of the five hosts. Although we have no prior information to suggest that fungi are mutualistic, we find that infection is associated with higher survival in two other host species. In addition to these point estimates, confidence intervals on all of these survival probabilities are broad and overlap to such a degree as to provide little insight on infection probability or its consequences.

Figure 3.2: Infection and survival probabilities estimated using maximum likelihood. Complete legend on following page. 53

Figure 3.2 (continued) Probabilities for infection (upper panels) and survival with and without infection (lower panels) of five tree seedling hosts with two fungi calculated using maximum likelihood. Dots represent means; bars are 95% confidence intervals estimated using a parametric bootstrap. Abbreviations for seedling hosts are as follows: acba=Acer barbatum, divi=Diospyros virginiana, list=Liquidambar styraciflua, nysy=Nyssa sylvatica, pita=Pinus taeda.

Despite the value of providing direct solutions for parameters based on data, the inference that results is limited. As previously mentioned, infection and incidence are not uniquely identifiable, so we have to assume that the fungus is present, or that we are actually drawing inference on a 'combined parameter' λθ. Second, estimates of survival probabilities lack precision; the uncertainty is simply too large to provide much insight.

Finally, the majority (over 80%) of the seedlings assayed for potential pathogens were found to be infected by multiple fungal morphotypes, and this may be an underestimate because morphotypes of fungal symbionts were defined macroscopically. This method does not allow for analysis of impacts of multiple infections on survival. To better exploit the causal modeling framework and the data themselves, we discuss how the analysis can be extended through hierarchical structures and computational tools.

Multiple hosts, multiple fungi, and covariates

The foregoing model treats host species and fungi independently, such that parameters for each combination of host and fungus are calculated separately. For at least three reasons, it makes sense to combine all hosts and fungi in a single analysis.

First, the probability of fungal incidence on a plot depends on observations obtained for all species on the plot, because many fungi can infect multiple tree species (see Chapter

2). Second, host survival could depend on the full fungal load, not just on a single fungus. Multiple infecting fungi could directly or indirectly compete, or even benefit one another (Al-Naimi et al. 2005). Non-additive interactions between pathogens within plants have been observed for viruses, bacteria, and fungi (Power 1996, Morris et al.

2007, Bradley et al. 2008). Therefore, we extended the model to host species h, host individual i, location j, and fungus k. Although we will discuss some individual components of the results here (i.e. effects of a certain fungus or combination of fungi on a certain host), as these specific interactions are of particular ecological interest, the output comes from an integrated run which fits separate parameters for all hosts and fungi.

In addition to relationships among co-infecting fungi, both fungal incidence and host response to infection depend on the environment. Changing environmental and resource conditions can impact survival and dispersal of fungi outside of the host, infection rates, and host health, which can in turn affect a host’s ability to survive infection (Agrios 2005, Desprez-Loustau et al. 2006, Roberts and Paul 2006). For example, Discula quericina, a fungus that can reside asymptomatically in its oak tree hosts, can enter a more pathogenic stage under drought conditions (Moricca and

Ragazzi 2008). Here we include light and soil moisture as covariates that can affect fungal incidence and host survival. The covariates affect incidence of fungus k at a location j (soil moisture) and survival of seedling i of host h (light, soil moisture, infection status; Figure 3.1). We extend the modeling approach to incorporate multiple fungi and hosts, with environmental interactions. We discuss these issues broadly here; for a formal treatment, see Clark and Hersh (2009).

For the first step of the process (Figure 3.1), incidence of fungal taxon k at plot j

(Pkj), we model incidence probability λ as a logistic function of soil moisture. Incidence can be informed by detection information on any seedling in a plot, regardless of species, making this component of the model particularly strengthened by using data from all seedling hosts. Although we have little information on how different fungi respond to soil moisture, we do know that many fungi require moisture for resource

acquisition and dispersal (Alexopoulos et al. 1996). Therefore, we constrain the soil moisture parameter for incidence to be non-negative.

Infection probability θhk of host species h with fungus k has a non-informative prior density, reflecting limited knowledge. The detection probabilities for each fungal taxon have a non-informative prior on the central part of the probability scale, but truncated at (0.2, 0.95). We estimated survival probabilities for uninfected (s0) and infected (s1) hosts using a logistic relationship with light, soil moisture, and the full fungal load as covariates.

Assessing interactions between multiple hosts and multiple fungi results in a huge model space, one that cannot be explored with traditional inference tools.

Therefore, we developed a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm to determine which the many combinations of fungi influence survival probability of all hosts (Clark and Hersh 2009). Results from Clark et al. (2004) were used to specify prior densities for the effects of light and soil moisture on seedling survival, but prior densities for fungal effects on survival were flat truncated to describe two different prior densities. Effects of fungi were constrained based on understanding of fungal lifestyle from the literature. A uniform distribution on the logit scale unif(-10,

10) allowed that fungi might have negative or positive effects on survival. A second prior distribution unif(-10, 0) allows that fungi can have negative effects, but not positive. The second case is like a one-tailed hypothesis test for negative survival effects-

-each fungal taxon can be included in the model or not depending whether it has an effect on survival. Both analyses predicted the same combinations fungal taxa have negative effects on survival of particular hosts.

We implemented the full model with the five hosts used in the previous analysis and two fungi using Gibbs sampling. The Gibbs sampler was initially run for 500,000

iterations until convergence was observed graphically in all parameters. This initial set of iterations was discarded as burnin, and the model was run for an additional 500,000

Gibbs steps. Here we discuss selected results to illustrate the differences between this approach and traditional maximum likelihood estimation. Details of the approach are provided in Clark and Hersh (2009).

Figure 3.3. Infection probabilities given incidence of Colletotrichum and Cylindrocarpon on five plant hosts, estimated using a hierarchical Bayesian approach. Points are median parameter estimates, while lines show 95% credible intervals. Abbreviations for hosts follow Figure 3.2.

Figure 3.3 shows infection probabilities given incidence, comparable to the traditional analysis in Figure 3.2a. Among the differences between these approaches are that we can now can estimate the probability of infection, θ, given fungal incidence, a much more specific quantity than the product of incidence and infection shown in

Figure 2a. Information on incidence comes from all host species. Median infection probability estimates are low, generally ranging from 0.2 to 0.4, with the exception of infection of Acer barbatum by Colletotrichum.

We can also estimate the effects of these fungi on seedling survival, given infection.

Figure 3.4a shows estimates of survival probabilities for the five target host seedlings

with Colletotrichum, Cylindrocarpon, or a combination of the two when detections are present. The overall trend for all combinations of hosts and fungi is a tendency for fungi to decrease survival. Estimates of survival without infection (Figure 3.4a, black bars) from the hierarchical model have more precision than estimates from likelihood, although in many cases credible intervals on survival remain fairly broad. In addition, we can also look at the effects of co-infection. In the third panel, estimates of survival given infection (Figure 3.4a, red bars) are shown for the three species in which co- infection with Colletotrichum and Cylindrocarpon was detected more than twice. Although effects of multiple infections on survival appear similar to those of single infections in

Acer barbatum and Pinus taeda, in Nyssa sylvatica, co-infections have stronger negative effects on survival than either fungus alone. This result could not have been obtained using a maximum likelihood approach, which cannot allow for non-additive impacts of multiple fungi. Finally, using the RJMCMC algorithm, we can assess how well different submodels of particular host and fungus combinations fit the data by calculating posterior model probabilities (pM), the fraction of times in which each submodel is selected over a reduced model not including any fungal effects. Submodels with pM >

0.5 are marked with asterisks in Figure 3.4a.

Figure 3.4: Upper panels: Estimates of survival of five host plants with and without infection with Colletotrichum, Cylindrocarpon, or a combination of the two. Red bars represent p(S|I=1) and black bars represent p(S|I=0). Lower panels: estimates of survival given incidence marginalized over infection (red bars; p(S|P=1)), compared to survival of uninfected individuals (black bars; p(S|I=0). Points are median parameter estimates, while lines show 95% credible intervals. Abbreviations for hosts follow Figure 3.2. Stars over certain host/fungus combinations indicates a posterior model probability of greater than 0.5 for that submodel in upper panels.

Breaking apart the process of seedling infection into its component parts using conditional probabilities sheds additional light into this system. Figure 3.4b shows similar estimates of estimates of survival probabilities, but this time survival is conditional only on fungal presence in the plot, but not necessarily infection (Figure

3.4b, red bars). Incorporating the low infection probabilities of many of these plant- fungus combinations (Figure 3.3) makes median estimates of survival probabilities when the fungus is present or absent similar, although the slight tendency for survival given incidence to be lower remains. Without a strong ability to infect potential hosts, fungi

that may otherwise have large effects on survival of individual hosts may not have a substantive effect on populations.

Discussion

Ecological studies of disease in systems with multiple hosts and/or pathogens must allow for a suite of interactions between hosts, pathogens, and the environment.

Like epidemiological studies in humans, research on disease in natural settings must include observational data. It is often impossible to design and execute the

“counterfactual” experimental design (i.e. randomized manipulations) required by a traditional hypothesis testing framework (Plowright et al. 2008). Disease exposure and risk involves multiple factors, not only one host and one pathogen, but also environmental conditions having many direct and indirect effects (Lafferty and Holt

2003) and co-infection (Pedersen and Fenton 2007). Hierarchical modeling for causal inference allows us to quantify multiple potential causal factors and to sort through a large number of variables that could be important in different settings (Clark and Hersh

2009).

Our approach provides new insight on the Janzen-Connell hypothesis, allowing us to reduce a large number of potentially pathogenic interactions to small number associated with infection and mortality risk that can be the subject of targeted studies.

In this system, Colletotrichum and Cylindrocarpon are capable of infecting multiple tree seedling hosts, and they increase mortality risk. Low infection probabilities may limit their impact. The large credible intervals in estimates of survival given infection make assessments of the strength of these negative effects challenging. For Nyssa sylvatica the impacts of multiple infection are estimated to be greater than the impacts of either fungus alone. Most importantly, co-infection impacts introduce the possibility of many more ways to regulate hosts, thus increasing the efficacy of Janzen-Connell effects. .

Using this hierarchical model on observational data sets the stage for future work by creating strong hypotheses of fungal effects on hosts to test empirically, and highlighting critical components of the process for sharpened observational or manipulative studies. In a multi-host, multi-pathogen system such as tree seedlings and their fungal symbionts, empirically testing all combinations of hosts and fungi using field inoculations can be infeasible in terms of effort and time. This approach generates hypotheses about which combinations of hosts and fungi have large effects on seedling survival, such as Nyssa sylvatica and a combination of Colletotrichum and Cylindrocarpon.

Using this information, more targeted empirical tests can be designed, such as experimental inoculations to demonstrate Koch’s postulates under the relevant environmental conditions. This can be an important part of the triangulation approach, which combines observational data, experimental manipulations, and statistical models to better understand disease (Plowright et al. 2008). In addition, Figure 3.4 illustrates the importance of precise estimates of infection probabilities in the process of seedling disease. Given that this has been highlighted as a limiting factor, we would next want to strengthen our assays for fungal symbionts deemed important by the model using fungal-specific primers or quantitative PCR, to ensure that our assessments of infection are as thorough as possible. In a system where multiple infections and lifestyle switching are common, hierarchical modeling allows us to isolate salient information despite complexity.

4. Impacts of fungal co-infection on temperate forest diversity

Ecologists have long hypothesized that diverse tree communities are maintained by a rare-species advantage, with host-specific natural enemies disproportionately attacking dominant tree species when they are abundant (Gillett 1962, Janzen 1970,

Connell 1971). Under this hypothesis, hosts harbor specialist enemies that suppress their own offspring, and negative feedbacks prevent dominance by any one species (Bever

1994). Host specificity is critical to this hypothesis to assure that rare species do not suffer together with their abundant competitors. For this mechanism to explain diversity, each host must have a unique set of natural enemies. The high diversity of the microbial community living in plant tissue (Rodriguez et al. 2009) and soils (Torsvik and

Ovreas 2002, O'Brien et al. 2005) lends credence to this hypothesis, especially given that the ecological function of many of these organisms remains almost entirely unknown.

Spatial relationships between juveniles and conspecific adults (e.g. Augspurger 1984,

Packer and Clay 2000) and the effects of conspecific densities on juvenile survival and recruitment (e.g. Webb and Peart 1999, Harms et al. 2000, Comita and Hubbell 2009) are widely cited in support of this hypothesis.

Determining if host-specific pathogens explain coexistence of tens to hundreds of tree species requires inference on incidence of pathogens, infection risk, and survival in the context of natural environmental variation, because hosts and pathogens respond differently to the environment. This inference is substantially more challenging than has previously been appreciated. First, interactions resulting in significant disease need to be distinguished from more benign associations involving plants and microbes (Schulz and

Boyle 2005). Once pathogens are identified, confirmation that they have host-specific effects is required, along with assessment of how interactions vary across environmental

gradients. Other than notable cases where host-specific pathogens have been identified

(Packer and Clay 2000, Seiwa et al. 2008), most studies on pathogen-driven diversity maintenance attribute mortality to known diseases, such as damping-off (Augspurger

1983b, 1984) or stem cankers (Gilbert et al. 1994, Gilbert et al. 2001), that cannot be attributed to one particular causal agent. Studies involving targeted biocides or sterilization treatments have also demonstrated clear biotic effects on host survival, but again do not identify specific causal agents (Bell et al. 2006, Petermann et al. 2008).

Although much previous work has targeted single pathogen infections in a given host, multiple pathogen infections are in fact common (Fitt et al. 2006, Seabloom et al.

2009) and hosts could be regulated by combinations of pathogens, depending on the interactive effect of co-infection. In other words, the rare species advantage need not derive from one pathogen per host, but rather from one pathogen combination per host.

The model space represented by combinations of pathogens attacking diverse tree communities is large, given by H× 2K for H host species and K pathogens. The inherent complexity requires a synthetic analysis that can address the interactions between communities of fungi and plants, along with environmental conditions that may alter the nature of these interactions.

Table 4.1: List of seedling hosts and potential fungal pathogens used in this study

To determine if biodiversity regulation could result from differential consequences of co-infection, we assayed for potential fungal pathogens on seedlings of five tree species (Table 4.1) in a temperate mixed hardwood forest (Duke Forest, Orange

County, NC) and developed a new modeling approach for analysis of their high-

dimensional interactions (Clark and Hersh 2009). The approach allowed us to test for efficacy of all host-fungus combinations, integrating infection and survival risks and their dependence on environmental variables. Host species were selected to incorporate a range of shade tolerance and relative abundance. Density-dependent effects on survival have been observed on N. sylvatica (HilleRisLambers and Clark 2003), and distance-dependent effects have been observed on L. styraciflua (Streng et al. 1989).

Potentially pathogenic fungi were isolated from experimental seedlings using culture- based methods and identified using DNA sequencing; five of the thirteen most common fungi we identified that are known pathogens or members of genera containing known pathogens were selected for analysis (Chapter 2). Data on detection of fungi are combined with seedling survival data and covariates to predict the full effects of each multi-fungus combination.

We developed a hierarchical Bayesian model of fungal effects on seedling survival that breaks down the process of disease into its component parts: fungal incidence (P), host infection (I), host survival (S), and fungal detection (D). We can only observe survival and detection, and must model infection and incidence as latent states.

The model fits five parameters—incidence, infection, detection, and survival probabilities (with and without infection). All parameters are evaluated on an individual level, with the exception of incidence, which is assessed on the plot level. Environmental conditions are incorporated into the model as covariates on incidence (soil moisture) and survival (light, soil moisture). Combinations of hosts and fungi are evaluated as a network of interactions, using a reversible jump Markov chain Monte Carlo algorithm to reduce the model space to combinations of fungi that have consequential impacts on host survival. We examine different stages of the process jointly using conditional probabilities, such as detection given infection, survival given infection or lack thereof,

and infection given incidence. For a more detailed description of the model, see chapter

3 of this document. The ability of the model to distinguish fungi with deleterious effects on survival from commensalists has been demonstrated with simulation experiments

(Clark and Hersh 2009). We use the model output to re-assess the rare species advantage, determine which fungi or combinations of fungi may affect survival of specific hosts, and determine how those effects may depend on environmental variables.

Figure 4.1 shows posterior model probabilities for combinations of fungi infections (rows) on seedling hosts (columns). We assess which combinations of fungi affect host survival using posterior model probabilities, or the fraction of times in which each submodel is selected over a reduced model not including any pathogen effects.

Posterior model probabilities greater than 0.5 (warm shaded boxes) indicate co- infections that reduce survival. Probabilities less than 0.5 (cool unshaded boxes) indicate no effect. The many empty cells represent co-infection combinations that do not occur in the dataset. Extensive simulation studies demonstrate that false positive and false negative rates are less than 5%, allowing for confidence in these probabilities (Clark and

Hersh 2009).

Figure 4.1: Posterior model probabilities of effects of different combinations of fungi on host survival. Rows are fungi, columns are hosts following Table 4.1. Posterior model probabilities > 0.5 indicate co-infections that reduce survival; probabilities <0.5 indicate no effect. False positive and false negative rates are less than 5%, based on simulations (Clark and Hersh 2009). Empty cells represent co-infection combinations that do not occur in the dataset.

Results demonstrate that although many of these fungi infect multiple hosts, their impacts differ from host to host (Figure 4.1, rows 1-5). For example, infection with

Cylindrocarpon alone affects survival of P. taeda, but not any of the other four species that it can infect. Similarly, Phomopsis sp. B alone affects D. virginiana, but not two of the other four hosts for which there was sufficient detection. These results are consistent 66

with current knowledge that fungal plant symbionts can display multiple lifestyles, depending on host identity (Redman et al. 2001). Even strong pathogens can infect alternate hosts in which effects on survival are weaker, including Cryphonectria parasitica, the causal agent of chestnut blight (Nash and Stambaugh 1987), and Phytophthora ramorum, the causal agent of sudden oak death (Rizzo et al. 2005). Our modeling framework allows us to distinguish between commensalist and parasitic lifestyles within fungal species, and generates a targeted set of hypotheses of which fungi affect which hosts for future empirical testing.

The effects of co-infection can differ from infection by individual fungi. In some hosts, fungi reduce survival only in combination with others (Figure 4.1). None of the five fungi tested affect the survival of N. sylvatica alone, yet the combination of

Colletotrichum and Cylindrocarpon did. Similarly, survival of L. styraciflua was reduced only in combination—Phomopsis B/Cylindrocarpon and Phomopsis A/Phomopsis D.

Alternatively, P. taeda was reduced by both the combination of Colletotrichum and

Cylindrocarpon and by Cylindrocarpon alone. The effects of combined infections may not be additive because of potential interactions between infecting fungi. Multiple infecting fungi could directly or indirectly compete for resources within a host, compete indirectly by stimulating plant immune responses, or even facilitate one another (Adee et al. 1990,

Nolan et al. 1999, Al-Naimi et al. 2005, Pedersen and Fenton 2007). Non-additive interactions between pathogens within plants have been documented in systems with various plant pathogens, including viruses, bacteria, and fungi (Power 1996, Morris et al.

2007, Bradley et al. 2008). Our approach allows us to assess the importance of these combined effects when the number of combinations is large and the nature of these interactions is unknown.

Figure 4.2: Predictive distributions of survival probabilities. Complete legend on following page.

Figure 4.2 (continued): Predictive distributions of survival probabilities of uninfected hosts (Pr(S|I=0), dashed black lines), infected hosts (Pr(S|I=I), dotted colored lines), and the probability of survival given incidence (Pr(S|P=1), solid colored lines). Only combinations of fungi and hosts with a posterior model probability greater than 0.5 are included. Color coding for fungi is as follows: Colletotrichum-red, Cylindrocarpon-yellow, Phomopsis sp. B-blue, and combinations accordingly. The combination of Phomopsis sp. D and A is brown.

In addition to identifying co-infections that reduce survival, this model provides inference for incidence and infection, and it allows us to combine them, to determine effects on different scales (Fig. 4.2). Infection reduces survival, as demonstrated by the differences between probabilities of survival of uninfected individuals, p(S|I = 0), and probabilities of survival given infection, p(S|I = 1) (dashed black vs. solid colored curves in Fig. 4.2). Even if the fungus is present, not all individuals are infected, so the probability of survival given incidence, p(S|P = 1), marginalizes over infection probability (dotted colored curve) and is intermediate. Effects of fungi on survival probabilities can be large, but depend on infection rates, which differ for each host- fungus combination. The predictive intervals in Figure 4.2 marginalize over incidence, infection, and survival. The effects of fungi on survival could be large if these annual rates compound each year, since trees can remain in the seedling stage for years to decades depending on species and environmental conditions (Streng et al. 1989, Marks and Gardescu 1998, Szwagrzyk et al. 2001).

The effects of infection are mediated by the environment, which affects both fungi and hosts. Adequate soil moisture can improve spore dispersal and germination, and invasion into plant tissue, but it can also improve plant growth and survival (Agrios

2005). Figure 4.3 shows predictive distributions of the five fungi at the range of soil moisture levels observed during the time of this study. The probability of incidence for all five fungi is positively associated with soil moisture. The importance of soil moisture in host survival is also illustrated by the predictive distributions of survival probabilities

at low (blue lines) and high (red lines) soil moisture values from our study (Figure 4.4).

We compare survival of uninfected seedlings, Pr(S|I=0) (dashed lines) to survival probabilities marginalized over infection and incidence, Pr(S) (solid lines). When

Colletotrichum can infect A. barbatum (left panel), the magnitude of the difference in survival probabilities when the fungus is present or absent does not change. However, when Phomopsis sp. B can infect D. virginiana (right panel), soil moisture shifts the nature of the interaction. At low soil moisture, survival probability does not change with incidence, but at high soil moisture, the fungus depresses survival. Both host and fungus respond positively to soil moisture, but effects of the fungus negate any survival benefits to the host at high soil moisture.

Figure 4.3: Predictive distributions of fungal incidence probabilities, Pr(P), at the range of soil moisture levels observed in this study for five fungal taxa (abbreviations following Table 4.1). Solid lines represent mean incidence probabilities; dotted lines are 95% credible intervals.

Figure 4.4: Predictive distributions of survival probabilities of uninfected plants (Pr(S|I=0), dashed lines) and survival probabilities marginalized over infection and incidence (Pr(S), solid lines) at different levels of soil moisture. Red lines show survival at low soil moisture (10% quantile of observed values); blue lines show survival at high soil moisture (90% quantile). Fungus and host names are in upper left corner of panels.

By accounting for the interactions among potential fungal pathogens, their hosts, and environmental variation, we show evidence of specialization that is consistent with the rare species advantage. Effects of fungi on survival could be magnified given the range of environmental variation present in ecosystems. A single fungal infection could have differential impacts on the survival of each of its hosts, depending on how it interacts with other fungi at different moisture levels. Each additional fungus could help support the coexistence of multiple hosts, through its 2K potential interactions with other pathogens, each of which can be modified by the environment. The full effects of the fungi analyzed in this study will depend on how they compound over years. Higher levels of soil moisture tend to increase the probability of fungal incidence, but increased opportunity for infection may not translate to stronger effects on survival, depending on host benefits from improved moisture status. Thus, interactions between seedlings, fungi, and the environment must be considered in a unified framework.

A synthetic study that incorporates the effects of multiple types of host-specific enemies (fungi, oomycetes, bacteria, herbivores, seed predators) could provide further insight concerning whether or not a large number of interacting enemies regulate plant biodiversity at a large scale. The diversity of the microbial community within individual plants can be considerable (Vandenkoornhuyse et al. 2002), and the microbial community can vary depending on the spatial distributions of multiple hosts and environmental conditions. The analytical approach presented here helps to manage the complexity of these interactions, thereby focusing hypotheses for further testing. Model- based analyses, such as this one, are an important contribution to a triangulation approach to disease ecology (Plowright et al. 2008), in which field, laboratory, and modeling studies are combined to improve our understanding of the complex web of factors causing disease in unmanaged systems.

5. Synthesis: Moving the Janzen-Connell hypothesis forward

This research provides evidence that both supports and contradicts the Janzen-

Connell hypothesis. In Chapter 2, we confirmed that the diversity of fungi inhabiting both dead and live seedlings is quite high; we identified 130 fungal taxa and estimate that at least 200 fungi inhabit the thirteen tree species sampled. However, all common fungi were isolated from multiple hosts. In Chapter 3, we outlined a new analytical method for assessing the impacts of fungal infection in tree seedlings, breaking down the process into its component parts: fungal incidence, infection, seedling survival, and fungal detection. We discussed how a Bayesian approach to causal inference allows us to directly incorporate the unavoidable complexity of multiple hosts, multiple pathogens, and the environment into our analysis, and used conditional probabilities to draw inference on different parts of this process in the context of one another. In the final chapter, we combined the fungal data from Chapter 2 with the modeling approach in Chapter 3 to assess impacts of selected fungi on seedling survival. We found that although many common fungi could infect multiple hosts, effects on survival vary between hosts. In addition, we found that some fungi only had effects on survival when found in combination with other fungal symbionts. We were able to identify specific combinations of hosts and fungi in which host survival is reduced, and generate targeted hypotheses for future empirical testing.

The Janzen-Connell hypothesis remains a widely discussed mechanism for maintaining forest diversity. However, this hypothesis is in need of updating given the current state of knowledge about fungal seedling pathogens, and their effects on seedling survival. Here we outline several components of the Janzen-Connell hypothesis that can be improved in the context of not only this research, but also other

recent studies: host specificity, spatial distribution of fungal incidence, and environmental context.

Host specificity and co-infection

For a pathogen to be consistent with the mechanisms predicted by Janzen-

Connell, it needs to be host-specific, having far greater impacts on the survival of its primary seedling host than other co-occurring species (Freckleton and Lewis 2006).

Although in some systems there is a clear relationship between one host and one host- specific pathogen (Packer and Clay 2000), we found few fungi that were strict host specialists in this system. However, many of these polyphagous fungi appear to have unequal effects on their different hosts (Chapter 4), consistent with other studies on necrotrophic fungal pathogens (Redman et al. 2001, Gilbert and Webb 2007). In addition, the impacts of combinations of infections may also influence survival, increasing potential opportunities for host specificity. The question remains as to whether these differences in pathogenicity between hosts are large enough to translate into strongly differential mortality in seedlings. Seemingly weak survival impacts in any one year could be compounded if they continue to occur over the entire residence time of a seedling in the seedling bank. Finally, other strict specialists may exist in this system, as we did not sample all the potential natural enemies. Obligate fungal biotrophs, oomycetes, bacteria, viruses, and insect herbivores likely all contribute to seedling mortality. A truly integrated study of Janzen-Connell effects must include a broader suite of enemies.

Defining specificity

Unequal impacts on seedling survival within a single fungal species may be driven by specificity at the strain level (Burdon 1987). Lumping both fungi and seedlings at the species level may mask differences between virulent and non-virulent pathogen 74

genotypes, and susceptible and non-susceptible host genotypes. We also cannot ignore the possibility that the methods used to delineate fungal taxa did not make sufficiently fine divisions. Different methods for designating OTUs and thresholds for making OTU divisions exist throughout the literature, and in fact there may be no universal cutoff or method appropriate for delineating taxa based on DNA sequences for all fungi (Arnold et al. 2007). The groups we designated are generally phylogenetically consistent based on genetic markers we used, but these markers are not precise enough to define strains within species. Using microsatellites, AFLPs, or other fine-scale genetic markers to define strains within existing OTUs and assess strain-level specificity would be a natural extension of this work. Finally, even if pathogens are generalists or affect multiple host species, this does not mean they do not influence plant community structure.

Phytophthora cinnamomi, a generalist pathogen with a broad host range, has large impacts on plant community structure in Australia, changing the understory of eucalypt woodlands from shrubs to sedges (Burdon 1987). Resistance to any generalist pathogen could influence plant communities by acting as another axis in the high-dimensional framework of niche differentiation or trade-offs.

Spatial distribution of fungi

Another important assumption inherent to the Janzen-Connell hypothesis is that natural enemies of a given seedling host have a clumped distribution at the landscape scale, and are clustered near adults (Gilbert 2002). Dispersal of fungi needs to be relatively poor in order to maintain the “distance effect” of Janzen-Connell (Adler and

Muller-Landau 2005). Broadly distributed, multihost fungi seem initially unlikely to meet this criterion. There are several ways in which we may be able to reconcile these differences. First, although a fungus may be well-dispersed throughout the landscape, genotypes or strains that are more virulent to particular hosts may be clumped around

those hosts. Second, fungi with strong impacts on one particular host may have a much greater relative abundance near that host, increasing infection probabilities through increased levels of inoculum. Finally, when combinations of infections are important, areas in which combinations of fungi are present necessary for impacts on survival may be patchily distributed despite individual species having broader distributions. More detailed spatial studies on the patterns of fungal incidence in forests can elucidate whether the spatial signal of Janzen-Connell, in terms of fungal incidence, is a true phenomenon.

Environmentally mediated lifestyle switching

Interactions between hosts and fungi cannot be taken out of the context of the environment. In this study, we found that soil moisture influences incidence of fungi on the landscape, and that these elevated levels of incidence and subsequent infection can negate positive direct effects of soil moisture on seedling survival (Chapter 4). Unequal impacts on survival could also be driven by environmentally induced lifestyle switching, as environmental context can play a large role in determining the nature of interactions between plants and fungi (Desprez-Loustau et al. 2006, Moricca and Ragazzi

2008). For example, dispersal into light gaps reduced the amount of pathogen-induced mortality near parent trees (Augspurger 1984). Clearly, future studies assessing the importance of JC or other negative feedbacks will need to incorporate interactions among hosts, pathogens, and the environment.

Conclusions

The Janzen-Connell hypothesis has played an important role in shaping discussions of how forest diversity is sustained. However, we can refine this hypothesis by incorporating our understanding of seedling disease from a microbial perspective.

Most importantly, we need to reconsider our definition of host specificity, and allow for 76

both the inclusion of multihost pathogens with differential effects and co-infection by multiple pathogens. Future research should focus on not only identifying pathogens, but also determining whether distributions of pathogens at the landscape scale match the predictions of Janzen-Connell. And finally, environmental conditions that may mediate host-pathogen interactions need to be more explicitly considered in future analysis of

Janzen-Connell effects. By addressing this hypothesis not only from the perspective of the hosts, but also from that of the pathogens, we can improve our understanding of this important mechanism for maintaining forest diversity.

Appendix 1: Seedling survival and sampling

Appendix 1: Total number of tree seedlings, survival data, and sampling amounts. Shade tolerance classifications follow Burns and Honkala (1990).

Seed Shade Total Proportion Sampled Total Tree species size tolerance seedlings survived seedlings sampled died surv Acer barbatum small tolerant 131 0.618 34 15 49 Acer rubrum small tolerant 36 0.361 9 1 10 Carya ovata large intermediate 183 0.940 2 1 3 Diospyros virginiana medium tolerant 51 0.549 14 11 25 Fagus grandifolia large tolerant 23 0.696 6 0 6 Liquidambar styraciflua small intolerant 150 0.347 42 15 57 Liriodendron tulipifera small intolerant 29 0.034 10 0 10 Nyssa sylvatica medium tolerant 116 0.534 39 12 51 Pinus taeda small intermediate 153 0.052 48 7 55 to intolerant Quercus alba large intermediate 107 0.935 3 2 5 Quercus phellos large intermediate 193 0.933 6 2 8 Quercus rubra large intermediate 123 0.878 8 1 9 Quercus stellata large intolerant 77 0.818 5 0 5

Appendix 2: Complete list of all fungal taxa

Appendix 2. Complete list of all 130 fungal taxa identified, and the BLAST results of representative samples from each OTU. Names were assigned to fungi based on NCBI BLAST results if the top named fungal sequence had an E-value of 0.0 and Max Identity greater than 97 percent. Top BLAST hits are reported for all taxa; if a lower hit met the aforementioned criteria and was used for naming, it was also included and preceded by its rank among BLAST hits in parentheses. Class and order were assigned by phylogenetic analysis, except when marked with an asterisk, in which case they were assigned by BLAST ID. Top fungal BLAST hits not concordant with phylogenetic analyses are marked by a pound sign. Tables are divided by phylum.

2a. Phylum: Ascomycota

Class Order OTU label Number Top fungal BLAST hit Accession Bit E- Max isolations score value Ident. Sordariomycetes Incertae sedis Colletotrichum acutatum 65 Glomerella acutata AB443950 1011 0.0 100.00 Sordariomycetes Hypocreales Cylindrocarpon sp. A 52 Neonectria radicicola AJ875329 944 0.0 100.00 Sordariomycetes Xylariales Pestalotiopsis sp. A 25 Pestalotiopsis cocculi EF055194 1050 0.0 100.00 Sordariomycetes Diaporthales Phomopsis sp. B 21 (1) Fungal endophyte MUT 885 AF373046 949 0.0 99.28 (8) Phomopsis sp. VegaE4-74 EU002933 906 0.0 97.23

79 Sordariomycetes Diaporthales Phomopsis sp. D 21 Diaporthe phaseolorum AY577815 910 0.0 97.48

Dothideomycetes Pleosporales Alternaria sp. A 20 Alternaria alternata AB470912 960 0.0 100.00 Leotiomycetes Helotiales 8 Phialocephala sp. A 20 (1)Uncult. ecto.(Phialocephala) FJ803971 975 0.0 100.00 (2)Phialocephala fortinii AY394921 969 0.0 99.81 Sordariomycetes Xylariales Pestalotiopsis sp. C 20 Pestalotiopsis sp. Ht-3 FJ947050 924 0.0 100.00 Taphrinomycetes* NA Taphrina sp. A 18 Taphrina communis AF492088 1014 0.0 99.66 Eurotiomycetes Eurotiales Penicillium sp. A 17 Penicillium sp. CLF-S AB505424 1003 0.0 100.00 Sordariomycetes Xylariales Unknown fungus 38/ 16 Cylindrium elongatum AY853244 710 0.0 94.75 Xylariales sp. A Leotiomycetes Incertae sedis Cryptosporiopsis sp. A 15 Cryptosporiopsis ericae EF114392 940 0.0 99.81 Sordariomycetes Diaporthales Phomopsis sp. A 13 Phomopsis vaccinii EU571096 1000 0.0 99.82 Dothideomycetes Pleosporales Epicoccum sp. A 12 Epicoccum nigrum FM991735 939 0.0 100.00 Dothideomycetes Capnodiales Cladosporium sp. A 11 Cladosporium sp. MH6 FJ950740 951 0.0 100.00 Sordariomycetes Hypocreales Bionectria sp. G 11 (1) Uncultured Bionectriaceae clone EU754945 982 0.0 100.00 (2) Bionectria ochroleuca AB369487 982 0.0 100.00 Leotiomycetes Incertae sedis Unknown fungus 70/ 10 (1) #Basidiomycete sp. OS-S4 AM084751 868 0.0 89.46 Leotiomycete sp. A (2) Lachnum nudipes AF505517 742 0.0 84.43 Sordariomycetes Incertae sedis Colletotrichum sp. B 10 Colletotrichum truncatum AJ301985 985 0.0 99.82 Dothideomycetes Pleosporales Microdiplodia sp. A 9 Microdiplodia sp. G16A EF432267 998 0.0 100.00 Sordariomycetes Diaporthales Pilidiella sp. A 9 Pilidiella sp. B8-71 AB465200 1058 0.0 99.66 Leotiomycetes Incertae sedis Leptodontidium sp. A 8 (1) Uncultured ascomycete clone EU003001 1065 0.0 100.00 (2) Leptodontidium orchidicola AF486133 1058 0.0 99.83

Leotiomycetes Incertae sedis Unknown fungus 27/ 8 Uncult. mycorrhizal fungus PHIALO1 AY656940 919 0.0 99.42 Leotiomycete sp. B Sordariomycetes Xylariales Amphisphaeria sp. A 8 (1) Uncult. Amphisphaeriaceae clone EF619630 942 0.0 99.26 (2)Amphisphaeria sp. Kobayashi 6-(4) AF346545 921 0.0 97.83 Sordariomycetes Xylariales Unknown fungus 6/ 8 Sordariomycete sp. 7423 EU680554 964 0.0 100.00 Xylariales sp. B Sordariomycetes Diaporthales Dicarpella sp. A 7 Dicarpella dryina AY853241 901 0.0 99.81 Dothideomycetes* Dothideales* Aureobasidium sp. A 6 Aureobasidium pullulans DQ680687 982 0.0 99.00 Dothideomycetes Pleosporales Phoma sp. A 6 Phoma herbarum AF218792 895 0.0 98.27 Eurotiomycetes Eurotiales Penicillium sp. C 6 Penicillium soppii AB465208 1007 0.0 100.00 Sordariomycetes Hypocreales Fusarium sp. A 6 Fusarium sp. 08025 EU750678 969 0.0 99.81 Leotiomycetes Incertae sedis Rhexocercosporidium sp. A 5 Rhexocercosporidium sp. Dzf14 EU543257 926 0.0 99.24 Sordariomycetes Diaporthales Discula sp. A 5 Discula quercina FN386280 998 0.0 99.82 Sordariomycetes* Hypocreales* Bionectria sp. AB 5 Bionectria compactiuscula AF210690 911 0.0 99.80 Sordariomycetes Hypocreales Cylindrocarpon sp. D 5 (1) Uncult. endophytic fungus clone FJ524310 940 0.0 100.00 (3) Cylindrocarpon sp. BGS16 AB369414 922 0.0 99.80 Sordariomycetes Hypocreales Pochonia sp. A 5 (1) Uncultured Clavicipitaceae clone FJ553724 975 0.0 99.82 (2) Pochonia bulbillosa EU999952 975 0.0 99.82 Sordariomycetes* Hypocreales* Trichoderma sp. A 5 Trichoderma koningiopsis DQ379015 1041 0.0 99.83 Sordariomycetes* Hypocreales* Verticillium sp. B 5 Verticillium cf. aranearum AF108467 949 0.0 97.28 Dothideomycetes Pleosporales Unknown fungus 24/ 4 #Trichocladium opacum FN386299 937 0.0 99.24 Pleosporales sp. A 80 Dothideomycetes Pleosporales Unknown fungus 8/ 4 Pyrenochaeta sp. CF-2008 EU885415 818 0.0 95.45 Pleosporales sp. B Eurotiomycetes Eurotiales Penicillium sp. E 4 Penicillium daleae AF033442 729 0.0 99.78 Leotiomycetes Incertae sedis Gloeosporium sp. A 4 Gloeosporium sp. DAOM 233252 EF672242 949 0.0 100.00 Leotiomycetes Incertae sedis Unknown fungus 20/ 4 Mycorrhizal asco. of Rhododendron AB089660 921 0.0 99.61 Leotiomycete sp. C Leotiomycetes Incertae sedis Unknown fungus 25/ 4 Uncultured soil fungus clone 9a13 DQ420921 930 0.0 99.05 Leotiomycete sp. D NA NA Unknown fungus 4 4 Uncultured fungus clone A48 EF040841 911 0.0 99.42 Sordariomycetes Hypocreales Unknown fungus 26/ 4 Annulohypoxylon truncatum FJ478107 710 0.0 86.73 Hypocreales sp. A Dothideomycetes Botryosphaeriales Botryosphaeria sp. A 3 Botryosphaeria dothidea EF638749 1005 0.0 100.00 Dothideomycetes Pleosporales Phoma sp. C 3 (1) Uncult. asco. clone BF-OTU277 AM901824 839 0.0 97.59 (2) Phoma cava AY354263 792 0.0 97.27 Leotiomycetes Incertae sedis Unknown fungus 9/ 3 Uncultured fungus isolate H5 EU113213 908 0.0 99.80 Leotiomycete sp. E Leotiomycetes Incertae sedis Unknown fungus 41/ 3 Uncultured ectomy. (Helotiales) clone DQ974826 962 0.0 100.00 Leotiomycete sp. F Sordariomycetes* NA Colletotrichum sp. C 3 (1) Fungal sp. ARIZ L90.p FJ612887 989 0.0 100.00 (2) Colletotrichum crassipes FJ515007 989 0.0 100.00 Sordariomycetes Hypocreales Fusarium sp. B 3 Fusarium sp. AJH20 EU605879 937 0.0 100.00

Dothideomycetes Pleosporales Unknown fungus 23/ 2 Uncult. ascomycete clone C32_A05 EU490095 744 0.0 92.97 Pleosporales sp. C Dothideomycetes Pleosporales Unknown fungus 44/ 2 Uncultured asco. isolate AY969642 1061 0.0 99.67 Pleosporales sp. D dfmo0690_212 Eurotiomycetes* Eurotiales* Penicillium sp. B 2 Penicillium glabrum EU128643 980 0.0 100.00 Eurotiomycetes* Eurotiales* Penicillium sp. F (FG) 2 Penicillium sp. 17-M-1 EU076929 957 0.0 98.73 Leotiomycetes Incertae sedis Unknown fungus 49/ 2 Uncultured ascomycete clone EU003078 810 0.0 93.75 Leotiomycete sp. G NA NA Paecilomyces sp. A 2 Paecilomyces carneus AB258369 1088 0.0 99.35 NA NA Unknown fungus 1 2 Gloeosporium sp. DAOM 233252 EF672242 778 0.0 93.56 NA NA Unknown fungus 2 2 Uncultured ascomycete clone EF619849 931 0.0 99.06 NA NA Unknown fungus 66 2 Uncultured fungus clone FJ820764 695 0.0 93.81 Sordariomycetes Diaporthales Didymella sp. A 2 Didymella clavulata EU254820 890 0.0 100.00 Sordariomycetes Diaporthales Phomopsis sp. E 2 Phomopsis sp. A57 EF564153 958 0.0 99.44 Sordariomycetes* Hypocreales* Fusarium sp. C 2 Fusarium sp. DSCF10 AB470905 910 0.0 99.80 Sordariomycetes Ophiostomatales Sporothrix sp. A 2 Sporothrix sp. 1-CMW9488 AY546720 1005 0.0 98.15 Sordariomycetes Xylariales Unknown fungus 5/ 2 Monochaetia sp. PSH2004I-1032 DQ078307 838 0.0 91.74 Xylariales sp. C Sordariomycetes Xylariales Unknown fungus 53/ 2 Xylariaceae sp. VegaE1-70 EU009957 930 0.0 96.79 Xylariales sp. D Dothideomycetes* Botryosphaeriales* Botryosphaeria sp. B 1 Botryosphaeria corticola AY268421 1002 0.0 100.00 Dothideomycetes* Botryosphaeriales* Botryosphaeria sp. C 1 Botryosphaeria parva EU249466 686 0.0 99.50 81 Dothideomycetes Botryosphaeriales Botryosphaeria sp. D 1 Botryosphaeria sp. GU071005 AB472081 951 0.0 99.64

Dothideomycetes* Pleosporales* Saccharicola sp. A 1 (1) Uncultured fungus, clone AM260897 899 0.0 98.83 (2) Saccharicola bicolor AF455415 892 0.0 98.64 Dothideomycetes Pleosporales Unknown fungus 45/ 1 Uncultured ascomycete clone EU490040 659 0.0 90.02 Pleosporales sp. E Dothideomycetes Pleosporales Unknown fungus 47/ 1 Uncultured ascomycete clone EU489946 1002 0.0 99.64 Pleosporales sp. F Dothideomycetes Pleosporales Unknown fungus 69/ 1 Uncultured asco. isolate AY969370 980 0.0 100.00 Pleosporales sp. G dfmo0688_072 Dothideomycetes* Xylariales* Anthostomella sp. A 1 Anthostomella sepelibilis AY908989 760 0.0 98.24 Eurotiomycetes Chaetothyriales Unknown fungus 68/ 1 Uncultured ascomycete isolate AY970158 944 0.0 98.24 Chaetothyriales sp. A dfmo1059_160 Eurotiomycetes* Eurotiales* Penicillium sp. H 1 Penicillium canescens DQ658168 957 0.0 97.83 Leotiomycetes Incertae sedis Unknown fungus 22/ 1 Uncultured soil fungus clone DQ420918 857 0.0 96.74 Leotiomycete sp. I Leotiomycetes Incertae sedis Unknown fungus 34/ 1 Pseudoclathrosphaerina spiralis EF029231 818 0.0 96.04 Leotiomycete sp. H Leotiomycetes* NA Botryotinia sp. A 1 Botryotinia porri FJ169672 888 0.0 98.44 Leotiomycetes* NA Cadophora sp. A 1 Cadophora luteo-olivacea FJ430742 1052 0.0 97.87 Leotiomycetes* NA Oidiodendron sp. A 1 Oidiodendron citrinum AF307762 805 0.0 99.15 NA NA Unknown fungus 7 1 Uncultured Entrophospora clone EU516767 814 0.0 90.78

NA NA Unknown fungus 10 1 Ascomycete sp. DAR 73142 AY568066 682 0.0 94.70 NA NA Unknown fungus 11 1 Uncultured fungus, clone YJ4-61 AJ877190 446 4.00E 84.47 -122 NA NA Unknown fungus 12 1 Uncultured asco. clone 3S1.10.F05 EF619871 874 0.0 100.00 NA NA Unknown fungus 13 1 Pleosporales sp. MH179 FM995624 821 0.0 99.58 NA NA Unknown fungus 16 1 Pochonia chlamydosporia EU733637 807 0.0 95.20 NA NA Unknown fungus 17 1 Fungal sp. KF-4 EU051626 668 0.0 99.73 NA NA Unknown fungus 19 1 Uncult. asco isolate dfmo0688_130 AY969421 737 0.0 99.52 NA NA Unknown fungus 28 1 Uncultured fungus clone IVP4-17 EU516846 803 0.0 96.72 NA NA Unknown fungus 29 1 Fungal endophyte, isolate 3487 FN392306 1065 0.0 99.34 NA NA Unknown fungus 30 1 Uncultured Pleosporales clone EF619741 868 0.0 99.19 NA NA Unknown fungus 31 1 Uncultured fungus clone OTU19 EF521221 818 0.0 98.13 NA NA Unknown fungus 32 1 Uncultured Dothideomycetes clone EU726287 928 0.0 98.34 NA NA Unknown fungus 33 1 Pseudorobillarda texana FJ825372 488 1.00E 81.82 -134 NA NA Unknown fungus 35 1 Hypocreales sp. HLS104 FJ770062 946 0.0 98.89 NA NA Unknown fungus 37 1 Fungal endophyte, isolate 3351 FN392307 818 0.0 99.56 NA NA Unknown fungus 39 1 Alternaria sp. KL-2008a strain Tax-4 EU442281 466 3.00E 92.97 -128 NA NA Unknown fungus 40 1 Uncultured Sarcosomataceae clone EU516860 610 2.00E 89.29 -171 NA NA Unknown fungus 42 1 Uncultured ascomycete clone EU490073 967 0.0 99.63 82 NA NA Unknown fungus 57 1 Soil fungal sp. 14-M-1 EU076922 816 0.0 99.56

NA NA Unknown fungus 58 1 Uncultured fungus isolate Ba12 EU113206 879 0.0 99.80 NA NA Unknown fungus 59 1 Fungal sp. ARIZ B364 FJ612995 782 0.0 93.86 NA NA Unknown fungus 60 1 Dictyochaeta fertilis AF178540 769 0.0 95.83 NA NA Unknown fungus 61 1 Acremonium sp. CCF3791 FJ430713 580 3.00E 92.46 -162 NA NA Unknown fungus 62 1 Xylariales sp. JP150 AB255288 821 0.0 96.99 NA NA Unknown fungus 63 1 Leptosphaeriaceae sp. HKC16 DQ092530 874 0.0 98.82 NA NA Unknown fungus 64 1 Lophiostoma sp. CBS 109932 EF042107 744 0.0 95.91 NA NA Unknown fungus 65 1 Paecilomyces lilacinus EU553303 520 2.00E 93.39 -144 NA NA Unknown fungus 71 1 Gliocephalotrichum simplex DQ366704 886 0.0 96.98 Sordariomycetes Chaetospheriales Chloridium sp. A 1 (1) Fungal sp. R38 AY699679 946 0.0 100.00 (2) Chloridium virescens EF029220 926 0.0 98.87 Sordariomycetes Coniochaetales Unknown fungus 52/ 1 (1) Fungal sp. R52 AY699696 957 0.0 100.00 Coniochaetales sp. A (2) Lecythophora luteoviridis DQ404354 931 0.0 98.01 Sordariomycetes Diaporthales Cryptodiaporthe sp. A 1 (1) Diaporthales sp. MosID14 AY961405 930 0.0 98.01 (2) Cryptodiaporthe cf. hystrix EU255020 919 0.0 100.00 Sordariomycetes Diaporthales Phomopsis sp. F 1 Phomopsis sp. GFP1 AB302248 982 0.0 99.28 Sordariomycetes Diaporthales Unknown fungus 48/ 1 Uncultured sordariomycete clone EF619767 964 0.0 99.81 Diaporthales sp. A

Sordariomycetes* Hypocreales* Acremonium sp. A 1 Acremonium alternatum AB470879 944 0.0 99.81 Sordariomycetes* Hypocreales* Fusarium sp. D 1 (1) Sordariomycete sp. 7670B EU680539 897 0.0 100.00 (2) Gibberella moniliformis EU364864 897 0.0 100.00 Sordariomycetes Hypocreales Fusarium sp.F 1 Fusarium lateritium FJ459977 960 0.0 100.00 Sordariomycetes* Hypocreales* Nectria sp. A 1 Nectria sp. olrim133 AY590785 774 0.0 97.59 Sordariomycetes Xylariales Unknown fungus 55/ 1 Polyscytalum fecundissimum EU035441 810 0.0 90.59 Xylariales sp. E Sordariomycetes Xylariales Unknown fungus 67/ 1 Seiridium ceratosporum AY687314 845 0.0 94.65 Xylariales sp. F Sordariomycetes Xylariales Xylaria sp. A 1 (1) Sordariomycete sp. 7653 EU680529 1002 0.0 100.00 (3) Xylaria sp. ntu33 FJ205466 944 0.0 97.85

2b. Phylum: Basidiomycota

Class Order OTU label Number Top fungal BLAST hit Accession Bit E- Max isolations score value Ident. Agaricomycetes* Cantharellales* Thanatephorus cucumeris 1 (1) Uncultured Ceratobasidiaceae EF619649 335 0.0 99.81 (3)Thanatephorus cucumeris AG6-HGI AF354102 870 0.0 97.67 NA NA Unknown fungus B2 1 Uncultured ecto. (Ceratobasidiaceae) AY634121 1047 0.0 95.57

2c. Basal fungal lineages (formerly Zygomycota)

Class Order OTU label Number Top fungal BLAST hit Accession Bit E- Max isolations score value Ident. NA NA Mortierella sp. B 8 Mortierella sp. dd08032 FJ810149 1068 0.0 100.00 NA NA Unknown fungus Z2 7 Uncultured fungus clone TF4_OTU158 EF434088 1003 0.0 94.60 NA NA Unknown fungus Z4 3 Fungal sp. GD9C EF031104 1063 0.0 99.84 Basal fungi Mortierellales* Mortierella sp. A 2 Uncultured Mortierella clone EU517054 1182 0.0 99.42 Basal fungi Mucorales* Umbelopsis sp. A 2 (1) Uncultured fungus isolate E2-4 EU113211 1038 0.0 99.66 (6) Umbelopsis ramanniana AB193533 1025 0.0 98.50 NA NA Unknown fungus Z3 1 Gonapodya sp. JEL183 AY349112 895 0.0 96.80 NA NA Unknown fungus Z6 1 Rhizomucor sp. Rm-33 EF583634 690 0.0 86.78

Appendix 3: Dothideomycete phylogeny

Appendix 3: Dothideomycete phylogeny. Complete legend on following page. 84

Appendix 3 (continued):Maximum likelihood tree of the Dothideomycetes, and constraint tree used in building phylogeny (inset). Cultures from this study are in red; the number of sequences for each taxon is in brackets following the taxon name. Numbers above nodes represent percent bootstrap support for 1000 replicates; only values above 50 are shown. Branches with greater than 70% support are bolded. Colored dots after named taxa represent nutritional mode following Schoch et al. (in press), and are labeled as follows: brown-saprobes, blue-plant associates, yellow- animal pathogens, purple-mycoparasites, green-lichen-forming fungi.

Appendix 4: Eurotiomycete phylogeny

Appendix 4: Maximum likelihood tree of the Eurotiomycetes, and constraint tree used in building phylogeny (inset). Labeling follows Appendix 3.

Appendix 5: Leotiomycete phylogeny

Appendix 5: Maximum likelihood tree of the Leotiomycetes, and constraint tree used in building phylogeny (inset). Labeling follows Appendix 3.

Appendix 6: Sordariomycete phylogeny

Appendix 6: Maximum likelihood tree of the Sordariomycetes, and constraint tree used in building phylogeny (inset). Labeling follows Appendix 3. 88

References

Addy, H. D., M. M. Piercey, and R. S. Currah. 2005. Microfungal endophytes in roots. Canadian Journal Of Botany 83:1-13.

Adee, S. R., W. F. Pfender, and D. C. Hartnett. 1990. Competition between Pyrenophora tritici-repentis and Septoria-nodorum in the wheat leaf as measured with de Wit replacement series. Phytopathology 80:1177-1182.

Adler, F. R. and H. C. Muller-Landau. 2005. When do localized natural enemies increase species richness? Ecology Letters 8:438-447.

Agrios, G. 2005. Plant Pathology. Fifth edition. Elsevier Academic Press, San Diego.

Al-Naimi, F. A., K. A. Garrett, and W. W. Bockus. 2005. Competition, facilitation, and niche differentiation in two foliar pathogens. Oecologia 143:449-457.

Alaniz, S., M. Leon, A. Vicent, J. Garcia-Jimenez, P. Abad-Campos, and J. Armengol. 2007. Characterization of Cylindrocarpon species associated with black foot disease of grapevine in Spain. Plant Disease 91:1187-1193.

Alexander, H. M. and R. D. Holt. 1998. The interaction between plant competition and disease. Perspectives in Plant Ecology, Evolution and Systematics 1:206-220.

Alexopoulos, C. J., C. W. Mims, and M. Blackwell. 1996. Introductory Mycology, 4th edition. John Wiley and Sons, New York, USA.

Altschul, S. F., W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. 1990. Basic Local Alignment Search Tool. Journal of Molecular Biology 215:403-410.

Anderson, D. R., K. P. Burnham, and W. L. Thompson. 2000. Null hypothesis testing: Problems, prevalence, and an alternative. Journal of Wildlife Management 64:912-923.

Anderson, P. K., A. A. Cunningham, N. G. Patel, F. J. Morales, P. R. Epstein, and P. Daszak. 2004. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends in Ecology & Evolution 19:535-544.

Arnold, A. E. 2007. Understanding the diversity of foliar endophytic fungi: progress, challenges, and frontiers. Fungal Biology Reviews 21:51-66.

Arnold, A. E., D. A. Henk, R. L. Eells, F. Lutzoni, and R. Vilgalys. 2007. Diversity and phylogenetic affinities of foliar fungal endophytes in loblolly pine inferred by culturing and environmental PCR. Mycologia 99:185-206.

Arnold, A. E. and E. A. Herre. 2003. Canopy cover and leaf age affect colonization by tropical fungal endophytes: Ecological pattern and process in Theobroma cacao (Malvaceae). Mycologia 95:388-398.

Augspurger, C. K. 1983a. Offspring recruitment around tropical trees - changes in cohort distance with time. Oikos 40:189-196.

Augspurger, C. K. 1983b. Seed dispersal of the tropical tree, Platypodium elegans, and the escape of its seedlings from fungal pathogens. Journal of Ecology 71:759-771.

Augspurger, C. K. 1984. Seedling survival of tropical tree species - interactions of dispersal distance, light-gaps, and pathogens. Ecology 65:1705-1712.

Augspurger, C. K. and C. K. Kelly. 1984. Pathogen mortality of tropical tree seedlings - experimental studies of the effects of dispersal distance, seedling density, and light conditions. Oecologia 61:211-217.

Augspurger, C. K. and H. T. Wilkinson. 2007. Host specificity of pathogenic Pythium species: Implications for tree species diversity. Biotropica 39:702-708.

Axelrood, P. E., W. K. Chapman, K. A. Seifert, D. B. Trotter, and G. Shrimpton. 1998. Cylindrocarpon and Fusarium root colonization of Douglas-fir seedlings from British Columbia reforestation sites. Canadian Journal Of Forest Research 28:1198-1206.

Bell, T., R. P. Freckleton, and O. T. Lewis. 2006. Plant pathogens drive density- dependent seedling mortality in a tropical tree. Ecology Letters 9:569-574.

Bever, J. D. 1994. Feedback between plants and their soil communities in an old field community. Ecology 75:1965-1977.

Bever, J. D. 2003. Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. New Phytologist 157:465-473.

Beyerericson, L., E. Damm, and T. Unestam. 1991. An overview of root dieback and its causes in Swedish forest nurseries. European Journal of Forest Pathology 21:439- 443.

Bordes, F. and S. Morand. 2009. Parasite diversity: an overlooked metric of parasite pressures? Oikos 118:801-806.

Bradley, D. J., G. S. Gilbert, and J. B. H. Martiny. 2008. Pathogens promote plant diversity through a compensatory response. Ecology Letters 11:461-469.

Bruns, T. D., T. J. White, and J. W. Taylor. 1991. Fungal Molecular Systematics. Annual Review Of Ecology And Systematics 22:525-564.

Burdon, J. J. 1987. Diseases and plant population biology. Cambridge University Press, Cambridge, UK.

Burdon, J. J. and G. A. Chilvers. 1982. Host density as a factor in plant disease ecology. Annual Review of Phytopathology 20:143-166.

Burdon, J. J., A. M. Jarosz, and G. C. Kirby. 1989. Pattern and patchiness in plant- pathogen interactions--causes and consequences. Annual Review Of Ecology And Systematics 20:119-136.

Burdon, J. J., P. H. Thrall, and L. Ericson. 2006. The current and future dynamics of disease in plant communities. Annual Review of Phytopathology 44:19-39.

Burns, R. M. and B. H. Honkala. 1990. Silvics of North America. USDA Forest Service Agriculture Handbook 654, Washington, D.C.

Callaway, R. M., G. C. Thelen, A. Rodriguez, and W. E. Holben. 2004. Soil biota and exotic plant invasion. Nature 427:731-733.

Chao, A. 1987. Estimating the Population Size for Capture-Recapture Data with Unequal Catchability. Biometrics 43:783-791.

Chazdon, R. L., R. K. Colwell, J. S. Denslow, and M. R. Guariguata. 1998. Statistical methods for estimating species richness of woody regenration in primary and secondary rain forests of NE Costa Rica. Pages 285-309 in F. Dallmeier and J. A. Comiskey, editors. Forest biodiversity research, monitoring, and modeling: Conceptual background and Old World case studies. Parthenon Publishing, Paris.

Cho, H. Y., T. Miyamoto, K. Takahashi, S. G. Hong, and J. J. Kim. 2007. Damage to Abies koreana seeds by soil-borne fungi on Mount Halla, Korea. Canadian Journal of Forest Research 37:371-382.

Clark, D. A. and D. B. Clark. 1981. Effects of seed dispersal by animals on the regeneration of Bursera graveolens (Burseraceae) on Santa Fe Island, Galapagos. Oecologia 49:73-75.

Clark, D. A. and D. B. Clark. 1984. Spacing dynamics of a tropical rain-forest tree - evaluation of the Janzen-Connell model. American Naturalist 124:769-788.

Clark, J. S. 2005. Why environmental scientists are becoming Bayesians. Ecology Letters 8:2-14.

Clark, J. S. 2007. Models for Ecological Data. Princeton University Press.

Clark, J. S., M. Dietze, S. Chakraborty, P. K. Agarwal, I. Ibanez, S. LaDeau, and M. Wolosin. 2007. Resolving the biodiversity paradox. Ecology Letters 10:647-659.

Clark, J. S. and A. E. Gelfand, editors. 2006. Hierarchical Modelling for the Environmental Sciences. Oxford University Press.

Clark, J. S. and M. H. Hersh. 2009. Inference in incidence, infection, and impact: Co- infection of multiple hosts by multiple pathogens. Bayesian Analysis 4:337-366.

Clark, J. S., S. LaDeau, and I. Ibanez. 2004. Fecundity of trees and the colonization- competition hypothesis. Ecological Monographs 74:415-442.

Clark, J. S., E. Macklin, and L. Wood. 1998. Stages and spatial scales of recruitment limitation in southern Appalachian forests. Ecological Monographs 68:213-235.

Clark, J. S., J. Mohan, M. Dietze, and I. Ibanez. 2003. Coexistence: How to identify trophic trade-offs. Ecology 84:17-31.

Colhoun, J. 1973. Effects of Environmental Factors on Plant Disease. Annual Review of Phytopathology 11:343-364.

Colwell, R. K. 2005. EstimateS: Statistical estimation of species richness and shared species from samples. . Page User's Guide and application published at: http://purl.oclc.org/estimates.

Colwell, R. K. and J. A. Coddington. 1994. Estimating Terrestrial Biodiversity through Extrapolation. Philosophical Transactions: Biological Sciences 345:101-118.

Colwell, R. K., C. X. Mao, and J. Chang. 2004. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85:2717-2727.

Comita, L. S. and S. P. Hubbell. 2009. Local neighborhood and species' shade tolerance influence survival in a diverse seedling bank. Ecology 90:328-334.

Condit, R., P. Ashton, S. Bunyavejchewin, H. S. Dattaraja, S. Davies, S. Esufali, C. Ewango, R. Foster, I. Gunatilleke, C. V. S. Gunatilleke, P. Hall, K. E. Harms, T. Hart, C. Hernandez, S. Hubbell, A. Itoh, S. Kiratiprayoon, J. LaFrankie, S. L. de Lao, J. R. Makana, M. N. S. Noor, A. R. Kassim, S. Russo, R. Sukumar, C. Samper, H. S. Suresh, S. Tan, S. Thomas, R. Valencia, M. Vallejo, G. Villa, and T. Zillio. 2006. The importance of demographic niches to tree diversity. Science 313:98-101.

Condit, R., S. P. Hubbell, and R. B. Foster. 1992. Recruitment Near Conspecific Adults And The Maintenance Of Tree And Shrub Diversity In A Neotropical Forest. American Naturalist 140:261-286.

Condit, R., S. P. Hubbell, and R. B. Foster. 1994. Density-Dependence In 2 Understory Tree Species In A Neotropical Forest. Ecology 75:671-680.

Connell, J. H. 1971. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. Pages 298-312 in P. J. Boer and G. R. Graadwell, editors. Dynamics of Numbers in Populations Centre for Agricultural Publishing and Documentation, Wageningen, The Netherlands.

Connell, J. H., J. G. Tracey, and L. J. Webb. 1984. Compensatory recruitment, growth, and mortality as factors maintaining rain-forest tree diversity. Ecological Monographs 54:141-164.

Cordell, C. E., R. L. Anderson, W. H. Hoffard, T. D. Landis, R. S. J. Smith, and H. V. Toko. 1989. Forest Nursery Pests. USDA Forest Service, Agricultural Handbook No. 680. 184p.

Cox, F. E. G. 2001. Concomitant infections, parasites and immune responses. Parasitology 122:S23-S38.

Cressie, N., C. A. Calder, J. S. Clark, J. M. V. Hoef, and C. K. Wikle. 2009. Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecological Applications 19:553-570.

Crist, T. O. and C. F. Friese. 1993. The impact of fungi on soil seeds - implications for plants and granivores in a semiarid shrub-steppe. Ecology 74:2231-2239.

Cushman, S. A., K. S. McKelvey, J. Hayden, and M. K. Schwartz. 2006. Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. The American Naturalist 168:486-499.

Dalling, J. W., M. D. Swaine, and N. C. Garwood. 1998. Dispersal patterns and seed bank dynamics of pioneer trees in moist tropical forest. Ecology 79:564-578.

Davis, E. C., J. B. Franklin, A. J. Shaw, and R. Vilgalys. 2003. Endophytic Xylaria (Xylariaceae) among liverworts and angiosperms: Phylogenetics, distribution, and symbiosis. American Journal of Botany 90:1661-1667.

Davis, W. C. 1941. Damping-off of Longleaf Pine. Phytopathology 11:1011-1016. de Bary, H. A. 1879. Die Erscheinung der Symbiose in L. Shernoff, editor. The Origins of Symbiosis. Mushroom, the Journal of Wild Mushrooming, Chicago, IL. .

Desprez-Loustau, M. L., B. Marcais, L. M. Nageleisen, D. Piou, and A. Vannini. 2006. Interactive effects of drought and pathogens in forest trees. Annals of Forest Science 63:597-612.

Dobson, A. and M. Crawley. 1994. Pathogens and the structure of plant communities. Trends in Ecology & Evolution 9:393-398.

Dobson, A., K. D. Lafferty, A. M. Kuris, R. F. Hechinger, and W. Jetz. 2008. Homage to Linnaeus: How many parasites? How many hosts? Proceedings of the National Academy of Sciences of the United States of America 105:11482-11489.

Dobson, A. P. and P. J. Hudson. 1986. Parasites, disease and the structure of ecological communities. Trends in Ecology & Evolution 1:11-15.

Dorazio, R. M. and J. A. Royle. 2005. Estimating size and composition of biological communities by modeling the occurrence of species. Journal of the American Statistical Association 100:389-398.

Edgar, R. C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32:1792-1797.

Espinosa-Garcia, F. J. and J. H. Langenheim. 1990. The endophytic fungal community in leaves of a coastal redwood population - diversity and spatial patterns. New Phytologist 116:89-97.

Fabricius, K. E. and G. De'Ath. 2004. Identifying ecological change and its causes: A case study on coral reefs. Ecological Applications 14:1448-1465.

Farnsworth, M. L., J. A. Hoeting, N. T. Hobbs, and M. W. Miller. 2006. Linking chronic wasting disease to mule deer movement scales: A hierarchical Bayesian approach. Ecological Applications 16:1026-1036.

Farr, D. F., G. F. Bills, G. P. Chamuris, and A. Y. Rossman. 1989. Fungi on Plants and Plant Products in the United States. APS Press, St. Paul.

Ficke, A., D. M. Gadoury, and R. C. Seem. 2002. Ontogenic resistance and plant disease management: a case study of grape powdery mildew. Phytopathology 92:671- 675.

Fitt, B. D. L., Y. J. Huang, F. van den Bosch, and J. S. West. 2006. Coexistence of related pathogen species on arable crops in space and time. Annual Review of Phytopathology 44:163-182.

Forcier, L. K. 1975. Reproductive strategies and co-occurrence of climax tree species. Science 189:808-810.

Fox, J. F. 1977. Alternation and coexistence of tree species. American Naturalist 111:69- 89.

Freckleton, R. P. and O. T. Lewis. 2006. Pathogens, density dependence and the coexistence of tropical trees. Proceedings of the Royal Society B-Biological Sciences 273:2909-2916.

Freeman, S., S. Horowitz, and A. Sharon. 2001. Pathogenic and nonpathogenic lifestyles in Colletotrichum acutatum from strawberry and other plants. Phytopathology 91:986-992.

Gallery, R. E., J. W. Dalling, and A. E. Arnold. 2007. Diversity, host affinity, and distribution of seed-infecting fungi: A case study with Cecropia. Ecology 88:582- 588.

Gelman, A. and J. Hill. 2007. Data Analysis Using Regression and Multilevel/ Hierarchical Models. Cambridge University Press, New York.

Gilbert, G. S. 2002. Evolutionary ecology of plant diseases in natural ecosystems. Annual Review of Phytopathology 40:13-43.

Gilbert, G. S., K. E. Harms, D. N. Hamill, and S. P. Hubbell. 2001. Effects of seedling size, El Nino drought, seedling density, and distance to nearest conspecific adult on 6- year survival of Ocotea whitei seedlings in Panama. Oecologia 127:509-516.

Gilbert, G. S., S. P. Hubbell, and R. B. Foster. 1994. Density and distance-to-adult effects of a canker disease of trees in a moist tropical forest. Oecologia 98:100-108.

Gilbert, G. S. and C. O. Webb. 2007. Phylogenetic signal in plant pathogen-host range. Proceedings of the National Academy of Sciences of the United States of America 104:4979-4983.

Gillett, J. B. 1962. Pest pressure, an underestimated factor in evolution. Systematics Association Publication 4:37-46.

Glitzenstein, J. S., P. A. Harcombe, and D. R. Streng. 1986. Disturbance, succession, and maintenance of species-diversity in an east Texas forest. Ecological Monographs 56:243-258.

Gotelli, N. J. and R. K. Colwell. 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4:379-391.

Grzegorczyk, M., D. Husmeier, K. D. Edwards, P. Ghazal, and A. J. Millar. 2008. Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinformatics 24:2071-2078.

Hansen, E. M. and E. M. Goheen. 2000. Phellinus weirii and other native root pathogens as determinants of forest structure and process in western North America. Annual Review of Phytopathology 38:515-539.

Harms, K. E., S. J. Wright, O. Calderon, A. Hernandez, and E. A. Herre. 2000. Pervasive density-dependent recruitment enhances seedling diversity in a tropical forest. Nature 404:493-495.

Heath, M. C. 1996. Plant resistance to fungi. Canadian Journal Of Plant Pathology 18:469-475.

Hendrix, F. F. and W. A. Campbell. 1973. Pythiums As Plant Pathogens. Annual Review of Phytopathology 11:77-98.

Hernandez, M. E. S., A. R. Davila, A. P. de Algaba, M. A. B. Lopez, and A. T. Casas. 1998. Occurrence and etiology of death of young olive trees in southern Spain. European Journal of Plant Pathology 104:347-357.

Hilborn, R. and M. Mangel. 1997. The Ecological Detective: Confronting Models with Data. Princeton University Press, Prinecton, NJ, USA.

HilleRisLambers, J. and J. S. Clark. 2003. Effects of dispersal, shrubs, and density- dependent mortality on seed and seedling distributions in temperate forests. Canadian Journal Of Forest Research 33:783-795.

HilleRisLambers, J., J. S. Clark, and B. Beckage. 2002. Density-dependent mortality and the latitudinal gradient in species diversity. Nature 417:732-735.

Hiura, T. and K. Fujiwara. 1999. Density-dependence and co-existence of conifer and broad-leaved trees in a Japanese northern mixed forest. Journal of Vegetation Science 10:843-850.

Hood, M. E. 2003. Dynamics of multiple infection and within-host competition by the anther-smut pathogen. American Naturalist 162:122-133.

Horton, T. R. and T. D. Bruns. 2001. The molecular revolution in ectomycorrhizal ecology: peeking into the black-box. Molecular Ecology 10:1855-1871.

Houle, G. 1992. Spatial relationship between seed and seedling abundance and mortality in a deciduous forest of north-eastern North America. Journal of Ecology 80:99- 108.

Houle, G. 1998. Seed dispersal and seedling recruitment of Betula alleghaniensis: Spatial inconsistency in time. Ecology 79:807-818.

Huang, Y., J. Wang, J. Zhang, M. Sanchez, and Y. Wang. 2007. Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks. Journal of Mulitmedia 2:1796-2048.

Hubbell, S. P., R. Condit, and R. B. Foster. 1990. Presence and absence of density dependence in a neotropical tree community. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 330:269-281.

Hudson, P. J., A. P. Dobson, and K. D. Lafferty. 2006. Is a healthy ecosystem one that is rich in parasites? Trends in Ecology & Evolution 21:381-385.

James, T. Y., F. Kauff, C. L. Schoch, P. B. Matheny, V. Hofstetter, C. J. Cox, G. Celio, C. Gueidan, E. Fraker, J. Miadlikowska, H. T. Lumbsch, A. Rauhut, V. Reeb, A. E. Arnold, A. Amtoft, J. E. Stajich, K. Hosaka, G. H. Sung, D. Johnson, B. O'Rourke, M. Crockett, M. Binder, J. M. Curtis, J. C. Slot, Z. Wang, A. W. Wilson, A. Schussler, J. E. Longcore, K. O'Donnell, S. Mozley-Standridge, D. Porter, P. M. Letcher, M. J. Powell, J. W. Taylor, M. M. White, G. W. Griffith, D. R. Davies, R. A. Humber, J. B. Morton, J. Sugiyama, A. Y. Rossman, J. D. Rogers, D. H. Pfister, D. Hewitt, K. Hansen, S. Hambleton, R. A. Shoemaker, J. Kohlmeyer, B. Volkmann-Kohlmeyer, R. A. Spotts, M. Serdani, P. W. Crous, K. W. Hughes, K. Matsuura, E. Langer, G. Langer, W. A. Untereiner, R. Lucking, B. Budel, D. M. Geiser, A. Aptroot, P. Diederich, I. Schmitt, M. Schultz, R. Yahr, D. S. Hibbett, F. Lutzoni, D. J. McLaughlin, J. W. Spatafora, and R. Vilgalys. 2006. Reconstructing the early evolution of Fungi using a six-gene phylogeny. Nature 443:818-822.

Janzen, D. H. 1970. Herbivores and the number of tree species in tropical forests. American Naturalist 104:501-527.

Jarosz, A. M. and J. J. Burdon. 1988. The effect of small-scale environmental-changes on disease incidence and severity in a natural plant-pathogen interaction. Oecologia 75:278-281.

Jewell, C. P., T. Kypraios, P. Neal, and G. O. Roberts. in press. Bayesian Analysis for Emerging Infectious Diseases. Bayesian Analysis.

Jolles, A. E., V. O. Ezenwa, R. S. Etienne, W. C. Turner, and H. Olff. 2008. Interactions between macroparasites and microparasites drive infection patterns in free- ranging African buffalo. Ecology 89:2239-2250.

Jones, R. H., R. R. Sharitz, P. M. Dixon, D. S. Segal, and R. L. Schneider. 1994. Woody plant regeneration in four floodplain forests Ecological Monographs 64:345-367.

Jonsson, U., T. Jung, U. Rosengren, B. Nihlgard, and K. Sonesson. 2003. Pathogenicity of Swedish isolates of Phytophthora quercina to Quercus robur in two different soils. New Phytologist 158:355-364.

Kauff, F. 2006. Pyraxml2, a python wrapper for RAxML. Available at www.lutzonilab.net/downloads.

Kirkpatrick, B. L. and F. A. Bazzaz. 1979. Influence of certain fungi on seed-germination and seedling survival of 4 colonizing annuals. Journal of Applied Ecology 16:515- 527.

Kluger, C. G., J. W. Dalling, R. E. Gallery, E. Sanchez, C. Weeks-Galindo, and A. E. Arnold. 2008. Host generalists dominate fungal communities associated with seeds of four neotropical pioneer species. Journal of Tropical Ecology 24:351-354.

Kneitel, J. M. and J. M. Chase. 2004. Trade-offs in community ecology: linking spatial scales and species coexistence. Ecology Letters 7:69-80.

Kniskern, J. M. and M. D. Rausher. 2006. Environmental variation mediates the deleterious effects of Coleosporium ipomoeae on Ipomoea purpurea. Ecology 87:675- 685.

Kotanen, P. M. 2007. Effects of fungal seed pathogens under conspecific and heterospecific trees in a temperate forest. Canadian Journal Of Botany 85:918-925.

Kus, J. V., K. Zaton, R. Sarkar, and R. K. Cameron. 2002. Age-related resistance in Arabidopsis Is a developmentally regulated defense response to Pseudomonas syringae. Plant Cell 14:479-490.

Lafferty, K. D., A. P. Dobson, and A. M. Kuris. 2006. Parasites dominate food web links. Proceedings of the National Academy of Sciences of the United States of America 103:11211-11216.

Lafferty, K. D. and R. D. Holt. 2003. How should environmental stress affect the population dynamics of disease? Ecology Letters 6:654-664.

Lee, S.-M. and A. Chao. 1994. Estimating Population Size Via Sample Coverage for Closed Capture-Recapture Models. Biometrics 50:88-97.

Lello, J., B. Boag, A. Fenton, I. R. Stevenson, and P. J. Hudson. 2004. Competition and mutualism among the gut helminths of a mammalian host. Nature 428:840-844.

Levine, J. M. and M. Rees. 2002. Coexistence and relative abundance in annual plant assemblages: the roles of competition and colonization. The American Naturalist 160:452-467.

Li, S. M., J. Wakefield, and S. Self. 2008. A transdimensional Bayesian model for pattern recognition in DNA sequences. Biostatistics 9:668-685.

Lutzoni, F., F. Kauff, C. J. Cox, D. McLaughlin, G. Celio, B. Dentinger, M. Padamsee, D. Hibbett, T. Y. James, E. Baloch, M. Grube, V. Reeb, V. Hofstetter, C. Schoch, A. E. Arnold, J. Miadlikowska, J. Spatafora, D. Johnson, S. Hambleton, M. Crockett, R. Shoemaker, S. Hambleton, M. Crockett, R. Shoemaker, G. H. Sung, R. Lucking, T. Lumbsch, K. O'Donnell, M. Binder, P. Diederich, D. Ertz, C. Gueidan, K. Hansen, R. C. Harris, K. Hosaka, Y. W. Lim, B. Matheny, H. Nishida, D. Pfister, J. Rogers, A. Rossman, I. Schmitt, H. Sipman, J. Stone, J. Sugiyama, R. Yahr, and R. Vilgalys. 2004. Assembling the fungal tree of life: Progress, classification and evolution of subcellular traits. American Journal of Botany 91:1446-1480.

Lynch, M. D. J. and R. G. Thorn. 2006. Diversity of Basidiomycetes in Michigan agricultural soils. Applied and Environmental Microbiology 72:7050-7056.

Ma, Z., D. P. Morgan, and T. J. Michailides. 2001. Effects of water stress on Botryosphaeria blight of pistachio caused by Botryosphaeria dothidea. Plant Disease 85:745.

Manion, P. D. 1981. Tree Disease Concepts. Prentice-Hall, Inc., Englewood Cliffs, NJ.

Manos, P. S., P. S. Soltis, D. E. Soltis, S. R. Manchester, O. Sang-Hun, C. D. Bell, D. L. Dilcher, and D. E. Stone. 2007. Phylogeny of Extant and Fossil Juglandaceae Inferred from the Integration of Molecular and Morphological Data Sets. Systematic Biology 56:412-430.

Mantiri, F. R., G. J. Samuels, J. E. Rahe, and B. M. Honda. 2001. Phylogenetic relationships in Neonectria species having Cylindrocarpon anamorphs inferred from mitochondrial ribosomal DNA sequences. Canadian Journal Of Botany 79:334-340.

Marks, P. L. and S. Gardescu. 1998. A case study of sugar maple (Acer saccharum) as a forest seedling bank species. Journal of the Torrey Botanical Society 125:287-296.

Martin, F. N. and J. E. Loper. 1999. Soilborne plant diseases caused by Pythium spp: Ecology, epidemiology, and prospects for biological control. Critical Reviews In Plant Sciences 18:111-181.

McCarthy-Neumann, S. and R. K. Kobe. 2008. Tolerance of soil pathogens co-varies with shade tolerance across species of tropical tree seedlings. Ecology 89:1883-1892.

McClure, T. T. and W. R. Robbins. 1942. Resistance of cucumber seedlings to damping- off as related to age, season of year, and level of nitrogen nutrition. Botanical Gazette 103:684-697.

Menkis, A., R. Vasiliauskas, A. F. S. Taylor, E. Stenstrom, J. Stenlid, and R. Finlay. 2006. Fungi in decayed roots of conifer seedlings in forest nurseries, afforested clear- cuts and abandoned farmland. Plant Pathology 55:117-129.

Mills, K. E. and J. D. Bever. 1998. Maintenance of diversity within plant communities: Soil pathogens as agents of negative feedback. Ecology 79:1595-1601.

Mitchell, C. E. 2003. Trophic control of grassland production and biomass by pathogens. Ecology Letters 6:147-155.

Moles, A. T. and M. Westoby. 2004. What do seedlings die from and what are the implications for evolution of seed size? Oikos 106:193-199.

Moricca, S. and A. Ragazzi. 2008. Fungal endophytes in Mediterranean oak forests: A lesson from Discula quercina. Phytopathology 98:380-386.

Morris, W. F., R. A. Hufbauer, A. A. Agrawal, J. D. Bever, V. A. Borowicz, G. S. Gilbert, J. L. Maron, C. E. Mitchell, I. M. Parker, A. G. Power, M. E. Torchin, and D. P. Vazquez. 2007. Direct and interactive effects of enemies and mutualists on plant performance: A meta-analysis. Ecology 88:1021-1029.

Nash, B. L. and W. J. Stambaugh. 1987. Lack of host specificity in North Carolina isolates of Endothia parasitica. Phytopathology 77:414-417.

Neher, D. A., C. K. Augspurger, and H. T. Wilkinson. 1987. Influence of age structure of plant populations on damping-off epidemics. Oecologia 74:419-424.

Neubert, K., K. Mendgen, H. Brinkmann, and S. G. R. Wirsel. 2006. Only a few fungal species dominate highly diverse mycofloras associated with the common reed. Applied and Environmental Microbiology 72:1118-1128.

Newsham, K. K., A. H. Fitter, and A. R. Watkinson. 1995. Arbuscular mycorrhiza protect an annual grass from root pathogenic fungi in the field. Journal of Ecology 83:991-1000.

Nolan, S., B. M. Cooke, and F. J. Monahan. 1999. Studies on the interaction between Septoria tritici and Stagonospora nodorum in wheat. European Journal of Plant Pathology 105:917-925.

O'Brien, H. E., J. L. Parrent, J. A. Jackson, J. M. Moncalvo, and R. Vilgalys. 2005. Fungal community analysis by large-scale sequencing of environmental samples. Applied and Environmental Microbiology 71:5544-5550.

O'Hanlon-Manners, D. L. and P. M. Kotanen. 2004. Evidence that fungal pathogens inhibit recruitment of a shade-intolerant tree, white birch (Betula papyrifera), in understory habitats. Oecologia 140:650-653.

O'Hanlon-Manners, D. L. and P. M. Kotanen. 2006. Losses of seeds of temperate trees to soil fungi: effects of habitat and host ecology. Plant Ecology 187:49-58.

Olff, H., B. Hoorens, R. G. M. de Goede, W. H. van der Putten, and J. M. Gleichman. 2000. Small-scale shifting mosaics of two dominant grassland species: the possible role of soil-borne pathogens. Oecologia 125:45-54.

Pacala, S. W. and D. Tilman. 1994. Limiting Similarity in Mechanistic and Spatial Models of Plant Competition in Heterogeneous Environments. The American Naturalist 143:222-257.

Packer, A. and K. Clay. 2000. Soil pathogens and spatial patterns of seedling mortality in a temperate tree. Nature 404:278-281.

Packer, A. and K. Clay. 2003. Soil pathogens and Prunus serotina seedling and sapling growth near conspecific trees. Ecology 84:108-119.

Peay, K. G., P. G. Kennedy, and T. D. Bruns. 2008. Fungal Community Ecology: A Hybrid Beast with a Molecular Master. Bioscience 58:799-810.

Pedersen, A. B. and A. Fenton. 2007. Emphasizing the ecology in parasite community ecology. Trends in Ecology & Evolution 22:133-139.

Peres, N. A., L. W. Timmer, J. E. Adaskaveg, and J. C. Correll. 2005. Lifestyles of Colletotrichum acutatum. Plant Disease 89:784-796.

Petermann, J. S., A. J. F. Fergus, L. A. Turnbull, and B. Schmid. 2008. Janzen-Connell effects are widespread and strong enough to maintain diversity in grasslands. Ecology 89:2399-2406.

Peters, H. A. 2003. Neighbour-regulated mortality: the influence of positive and negative density dependence on tree populations in species-rich tropical forests. Ecology Letters 6:757-765.

Plowright, R. K., S. H. Sokolow, M. E. Gorman, P. Daszak, and J. E. Foley. 2008. Causal inference in disease ecology: investigating ecological drivers of disease emergence. Frontiers in Ecology and the Environment 6:420-429.

Porter, T. M., J. E. Skillman, and J.-M. Moncalvo. 2008. Fruiting body and soil rDNA sampling detects complementary assemblage of Agaricomycotina (Basidiomycota, Fungi) in a hemlock-dominated forest plot in southern Ontario. Molecular Ecology 17:3037-3050.

Power, A. G. 1996. Competition between viruses in a complex plant-pathogen. Ecology 77:1004-1010.

Pringle, E. G., P. Alvarez-Loayza, and J. Terborgh. 2007. Seed characteristics and susceptibility to pathogen attack in tree seeds of the Peruvian Amazon. Plant Ecology 193:211-222.

Questad, E. J. and B. L. Foster. 2008. Coexistence through spatio-temporal heterogeneity and species sorting in grassland plant communities. Ecology Letters 11:717-726.

100

R Development Core Team. 2008. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Redman, R. S., D. D. Dunigan, and R. J. Rodriguez. 2001. Fungal symbiosis from mutualism to parasitism: who controls the outcome, host or invader? New Phytologist 151:705-716.

Rees, M., R. Condit, M. Crawley, S. Pacala, and D. Tilman. 2001. Long-term studies of vegetation dynamics. Science 293:650-655.

Reinhart, K. O., A. Packer, W. H. Van der Putten, and K. Clay. 2003. Plant-soil biota interactions and spatial distribution of black cherry in its native and invasive ranges. Ecology Letters 6:1046-1050.

Reynolds, H. L., A. Packer, J. D. Bever, and K. Clay. 2003. Grassroots ecology: Plant- microbe-soil interactions as drivers of plant community structure and dynamics. Ecology 84:2281-2291.

Richard, F., S. Millot, M. Gardes, and M. A. Selosse. 2005. Diversity and specificity of ectomycorrhizal fungi retrieved from an old-growth Mediterranean forest dominated by Quercus ilex. New Phytologist 166:1011-1023.

Rizzo, D. M., M. Garbelotto, and E. A. Hansen. 2005. Phytophthora ramorum: Integrative research and management of an emerging pathogen in California and Oregon forests. Annual Review of Phytopathology 43:309-335.

Roberts, M. R. and N. D. Paul. 2006. Seduced by the dark side: integrating molecular and ecological perspectives on the inflence of light on plant defence against pests and pathogens. New Phytologist 170:677-699.

Rodriguez, R. J., J. J. F. White, A. E. Arnold, and R. S. Redman. 2009. Fungal endophytes: diversity and functional roles. New Phytologist 182:314-330.

Rubin, D. B. 1990. Formal mode of statistical inference for causal effects. Journal of Statistical Planning and Inference 25:279-292.

Runkle, J. R. 1981. Gap regeneration in some old-growth forests of the Eastern United States. Ecology 62:1041-1051.

Saikkonen, K. 2007. Forest structure and fungal endophytes. Fungal Biology Reviews 21:67-74.

Saikkonen, K., S. H. Faeth, M. Helander, and T. J. Sullivan. 1998. Fungal endophytes: A continuum of interactions with host plants. Annual Review Of Ecology And Systematics 29:319-343.

Sanderson, M. J., M. J. Donoghue, W. Piel, and T. Eriksson. 1994. TreeBASE: a prototype database of phylogenetic analyses and an interactive tool for browsing the phylogeny of life. American Journal of Botany 81:183.

101

Saunders, M. and L. M. Kohn. 2009. Evidence for alteration of fungal endophyte community assembly by host defense compounds. New Phytologist 182:229-238.

Schafer, M. and P. M. Kotanen. 2004. Impacts of naturally-occurring soil fungi on seeds of meadow plants. Plant Ecology 175:19-35.

Schoch, C. L., G.-H. Sung, F. Lopez-Giraldez, J. P. Townsend, J. Miadlikowska, V. Hofstetter, B. Robbertse, P. B. Matheny, F. Kauff, Z. Wang, C. Gueidan, R. M. Andrie, K. Trippe, L. M. Ciufetti, A. Wynns, E. Fraker, B. P. Hodkinson, G. Bonito, J. Z. Groenewald, M. Arzanlou, G. Sybren de Hoog, P. W. Crous, D. Hewitt, D. H. Pfister, K. Peterson, M. Gryzenhout, M. J. Wingfield, A. Aptroot, S.-O. Suh, M. Blackwell, D. M. Hillis, G. W. Griffith, L. A. Castlebury, A. Y. Rossman, H. T. Lumbsch, R. Lucking, B. Budel, A. Rauhut, P. Diederich, D. Ertz, D. M. Geiser, K. Hosaka, P. Inderbitzin, J. Kohlmeyer, B. Volkmann-Kohlmeyer, L. Mostert, O. D. Kerry, H. Sipman, J. D. Rogers, R. A. Shoemaker, J. Sugiyama, R. C. Summerbell, W. Untereiner, P. R. Johnston, S. Stenroos, A. Zuccaro, P. S. Dyer, P. D. Crittenden, M. S. Cole, K. Hansen, J. M. Trappe, R. Yahr, F. Lutzoni, and J. W. Spatafora. in press. The Ascomycota Tree of Life: A Phylum-wide Phylogeny Clarifies the Origin and Evolution of Fundamental Reproductive and Ecological Traits. Systematic Biology:in press.

Schulz, B. and C. Boyle. 2005. The endophytic continuum. Mycological Research 109:661- 686.

Schupp, E. W. 1992. The Janzen-Connell model for tropical tree diversity - population implications and the importance of spatial scale. American Naturalist 140:526- 530.

Seabloom, E. W., P. R. Hosseini, A. G. Power, and E. T. Borer. 2009. Diversity and composition of viral communities: Coinfection of barley and cereal yellow dwarf viruses in California grasslands. The American Naturalist 173:E79-E98.

Seifert, K. A., C. R. McMullen, D. Yee, R. D. Reeleder, and K. F. Dobinson. 2003. Molecular differentiation and detection of ginseng-adapted isolates of the root rot fungus Cylindrocarpon destructans. Phytopathology 93:1533-1542.

Seiwa, K., Y. Miwa, N. Sahashi, H. Kanno, M. Tomita, N. Ueno, and M. Yamazaki. 2008. Pathogen attack and spatial patterns of juvenile mortality and growth in a temperate tree, Prunus grayana. Canadian Journal of Forest Research 38:2445- 2454.

Sieber, T. N. 2007. Endophytic fungi in forest trees: are they mutualists? Fungal Biology Reviews 21:75-89.

Silvertown, J. 2004. Plant coexistence and the niche. Trends in Ecology & Evolution 19:605-611.

Singleton, L. L., J. D. Mihail, and C. M. Rush, editors. 1992. Methods for Research on Soilborne Phytopathogenic Fungi. APS Press, St. Paul.

102

Smith, E. P. and G. van Belle. 1984. Nonparametric estimation of species richness. Biometrics 40:119-129.

Smith, S. E. and D. J. Read. 2008. Mycorrhizal Symbiosis. 3rd edition. Academic Press.

Spatafora, J. W., G.-H. Sung, D. Johnson, C. Hesse, B. O'Rourke, M. Serdani, R. Spotts, F. Lutzoni, V. Hofstetter, J. Miadlikowska, V. Reeb, C. Gueidan, E. Fraker, T. Lumbsch, R. Lucking, I. Schmitt, K. Hosaka, A. Aptroot, C. Roux, A. N. Miller, D. M. Geiser, J. Hafellner, G. Hestmark, A. E. Arnold, B. Budel, A. Rauhut, D. Hewitt, W. A. Untereiner, M. S. Cole, C. Scheidegger, M. Schultz, H. Sipman, and C. L. Schoch. 2006. A five-gene phylogeny of Pezizomycotina. Mycologia 98:1018- 1028.

Springer, M. S., E. C. Teeling, O. Madsen, M. J. Stanhope, and W. W. de Jong. 2001. Integrated fossil and molecular data reconstruct bat echolocation. Proceedings of the National Academy of Sciences of the United States of America 98:6241-6246.

Stamatakis, A. 2006a. Phylogenetic models of rate heterogeneity: A high performance computing perspective.in Proc. of IPDPS2006, Rhodos, Greece.

Stamatakis, A. 2006b. RAxML-VI-HPC: Maximum Likelihood-based Phylogenetic Analyses with Thousands of Taxa and Mixed Models. Bioinformatics 22: 2688– 2690.

Stephens, P. A., S. W. Buskirk, and C. M. del Rio. 2007. Inference in ecology and evolution. Trends in Ecology & Evolution 22:192-197.

Streng, D. R., J. S. Glitzenstein, and P. A. Harcombe. 1989. Woody seedling dynamics in an East Texas floodplain forest. Ecological Monographs 59:177-204.

Szwagrzyk, J., J. Szewczyk, and J. Bodziarczyk. 2001. Dynamics of seedling banks in beech forest: results of a 10-year study on germination, growth and survival. Forest Ecology and Management 141:237-250.

Tainter, F. H. and F. A. Baker. 1996. Principles of Forest Pathology. First edition. John Wiley & Sons, New York.

Thomas, M. B., E. L. Watson, and P. Valverde-Garcia. 2003. Mixed infections and insect- pathogen interactions. Ecology Letters 6:183-188.

Thrall, P. H. and J. J. Burdon. 1997. Host-pathogen dynamics in a metapopulation context: the ecological and evolutionary consequences of being spatial. Journal of Ecology 85:743-753.

Tilman, D. 1994. Competition and biodiversity in spatially structured habitats. Ecology 75:2-16.

Tint, H. 1945a. Studies in the Fusarium damping-off of conifers. I. The comparative virulence of certain Fusaria. Phytopathology 35:421-439.

103

Tint, H. 1945b. Studies in the Fusarium damping-off of conifers. II. Relation of age of host, pH, and some nutritional factors to the pathogenicity of the Fusarium. Phytopathology 35:440-457.

Tint, H. 1945c. Sudies in the Fusarium damping-off of conifers. III. Relation of temperature and sunlight to the pathogenicity of Fusarium. Phytopathology 35:498-510.

Torsvik, V. and L. Ovreas. 2002. Microbial diversity and function in soil: from genes to ecosystems. Current Opinion in Microbiology 5:240-245.

Unterseher, M., M. Schnittler, C. Dormann, and A. Sickert. 2008. Application of species richness estimators for the assessment of fungal diversity. FEMS Microbiology Letters 282:205-213.

Uriarte, M., R. Condit, C. D. Canham, and S. P. Hubbell. 2004. A spatially explicit model of sapling growth in a tropical forest: does the identity of neighbours matter? Journal of Ecology 92:348-360.

Vaartaja, O., W. H. Cram, and G. A. Morgan. 1961. Damping-off etiology especially in forest nurseries. Phytopathology 51:35-42. van der Putten, W. H. and B. A. M. Peters. 1997. How soil-borne pathogens may affect plant competition. Ecology 78:1785-1795. van der Putten, W. H., C. van Dijk, and B. A. M. Peters. 1993. Plant-specific soil-borne diseases contribute to succession in foredune vegetation. Nature 362:53-56.

Vandenkoornhuyse, P., S. L. Baldauf, C. Leyval, J. Straczek, and J. P. W. Young. 2002. Extensive fungal diversity in plant roots. Science 295:2051-2051.

Vilgalys, R. and M. Hester. 1990. Rapid genetic identification and mapping of enzymatically amplified ribosomal DNA from several Cryptococcus species. Journal Of Bacteriology 172:4238-4246.

Wada, N. and E. Ribbens. 1997. Japanese maple (Acer palmatum var. Matsumurae, Aceraceae) recruitment patterns: Seeds, seedlings, and saplings in relation to conspecific adult neighbors. American Journal of Botany 84:1294-1300.

Wang, Z., P. R. Johnston, S. Takamatsu, J. W. Spatafora, and D. S. Hibbett. 2006. Toward a phylogenetic classification of the Leotiomycetes based on rDNA data. Mycologia 98:1065-1075.

Webb, C. O., G. S. Gilbert, and M. J. Donoghue. 2006. Phylodiversity-dependent seedling mortality, size structure, and disease in a bornean rain forest. Ecology 87:S123- S131.

Webb, C. O. and D. R. Peart. 1999. Seedling density dependence promotes coexistence of Bornean rain forest trees. Ecology 80:2006-2017.

104

White, T. J., T. D. Bruns, S. Lee, and J. W. Taylor. 1990. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. . Pages 315-322 in M. A. Innis, D. H. Gelfand, J. J. Sninsky, and T. J. White, editors. PCR Protocols: A Guide to Methods and Applications. Academic Press, New York.

Wills, C. and R. Condit. 1999. Similar non-random processes maintain diversity in two tropical rainforests. Proceedings of the Royal Society of London Series B- Biological Sciences 266:1445-1452.

Wills, C., R. Condit, R. B. Foster, and S. P. Hubbell. 1997. Strong density- and diversity- related effects help to maintain tree species diversity in a neotropical forest. Proceedings of the National Academy of Sciences of the United States of America 94:1252-1257.

Woods, K. D. 1979. Reciprocal replacement and the maintenance of co-dominance in a beech-maple forest. Oikos 33:31-39.

Woods, K. D. and R. H. Whittaker. 1981. Canopy-understory interaction and the internal dynamics of mature hardwood and hemlock-hardwood forests. Pages 305-322 in D. C. West, H. H. Shugart, and D. B. Botkin, editors. Forest Succession: Concepts and Application. Springer-Verlag, New York.

Wright, E. 1944. Damping-off in broadleaf nurseries of the Great Plains region. Journal of Agricultrual Research 69:77-94.

Wright, S. J. 2002. Plant diversity in tropical forests: a review of mechanisms of species coexistence. Oecologia 130:1-14.

Yoshida, S., T. Tsukiboshi, H. Shinohara, M. Koitabashi, and S. Tsushima. 2007. Occurrence and development of Colletotrichum acutatum on symptomless blueberry bushes. Plant Pathology 56:871-877.

Zhang, N., L. A. Castlebury, A. N. Miller, S. M. Huhndorf, C. L. Schoch, K. A. Seifert, A. Y. Rossman, J. D. Rogers, J. Kohlmeyer, B. Volkmann-Kohlmeyer, and G. H. Sung. 2006. An overview of the systematics of the Sordariomycetes based on a four-gene phylogeny. Mycologia 98:1076-1087.

105

Biography: Michelle Heather Hersh

Date of birth: February 18, 1978; Toledo, Ohio, USA

Education

• Duke University, University Program in Ecology, Durham, NC Ph.D., September 2009, Ecology; Minor: Microbiology

• Bryn Mawr College, Bryn Mawr, PA A.B. in Biology, Environmental Science concentration, May 2000 Senior Thesis: Elevated Carbon Dioxide and Neotropical Forests: Individual and Ecosystem Responses, Global Implications. Honors: Magna cum laude, Honors in Biology

• Organization for Tropical Studies/Duke University, Costa Rica Undergraduate Semester Abroad Program in Tropical Biology, Fall 1998

Publications

Clark, J.S. and M.H. Hersh. 2009. Inference in incidence, infection, and impact: When multiple pathogens affect multiple hosts. Bayesian Analysis 4(2): 337-366.

McMahon, S., M.C. Dietze, M.H. Hersh, E.V. Moran, and J.S. Clark. 2009. A predictive framework to understand forest responses to global change. In The Year in Ecology and Conservation Biology 2009, eds. R.S. Ostfeld and W.H. Schlesinger. Annals of the New York Academy of Science 1162: 221–236.

Clark, J.S, D.M. Bell, M.C. Dietze, M.H. Hersh, I. Ibáñez, S.L. LaDeau, S.M. McMahon, C.J.E. Metcalf, E.V. Moran, L. Pangle, and M.S. Wolosin. Models for demography of plant populations. The Handbook of Applied Bayesian Analysis, eds. T. O’Hagan and M.West, Oxford University Press. In press.

Ibáñez, I., J.S. Clark, M.C. Dietze, K. Feeley, M.H. Hersh, S.L. LaDeau, A.C. McBride, N.E. Welch, and M.S. Wolosin. 2006. Predicting biodiversity change: outside the climate envelope, beyond the species-area curve. Ecology 87 (8): 1896-1906.

Grants and Awards

• Mycological Society of America Graduate Student Research Prize—Best Oral Presentation, 2009 annual meeting. • Fungal Environmental Sampling and Informatics Network travel award ($1000), 2009 • Stern Dissertation Fellowship, Duke University, (nine month stipend) 2008-2009 • Mycological Society of America Mentor Student Travel Award ($500), 2008 • NSF Doctoral Dissertation Improvement Grant ($11,554), 2007 • Sigma Xi Grant-In-Aid of Research ($400), 2007 • Duke Biology Department Grant-in-Aid ($1000), 2006 106

• Duke Biology Department Grant-in-Aid ($1000), 2004 • National Science Foundation Graduate Research Fellowship, 2003-2007 • James B. Duke Fellowship, Duke University, 2002-2006 • W.D. Billings Fellowship, Department of Biology, Duke University, 2002-2003 • Pew Grant for Summer Internship in Environmental Science, Bryn Mawr College, 1998

Teaching And Professional Experience

• Teaching Assistant, Department of Biology, Duke University o Microbiology (Rytas Vilgalys, Xinnian Dong): Fall 2004, Spring 2008 o Ecology (Chantal Reid): Spring 2005, Fall 2007

• Union of Concerned Scientists, Cambridge, MA o Scientist Outreach Assistant: Jan-May 2002 o Gulf Coast Climate Impacts Program Assistant: Aug-Dec 2001 o Sound Science Initiative Intern: Sep 2000–Jul 2001

• Intern, Smithsonian Environmental Research Center, Edgewater, MD o Global change ecology lab (Bert Drake): Summer 1999

Professional Affiliations

• Ecological Society of America • Mycological Society of America

107